esa-sceva/satcom-chunk-collection huggingface dataset:
# Auxiliary Information-Enhanced Recommendations
## 3 The Proposed SRS Algorithm
### Multi-GRU Layer  
Given a sequence of items interacted by a user, \(s=v_{1},v_{2},\ldots,v_{|s|}\), we first extract the multi-modal features for each item in \(s\). Particularly, for \(v_{t}\in s\), we extract its ID embedding \(\mathbf{m}^{t}\in\mathbf{R}^{d_{n}}\), its image feature embedding \(\mathbf{f}^{t}\in\mathbf{R}^{d/}\) and text feature embedding \(\mathbf{g}^{t}\in\mathbf{R}^{d_{g}}\). Item ID embedding is obtained from a learnable ID-embedding matrix. Item image embedding is extracted via a 16-layer convolutional neural network (CNN) named VGGNet (shortened to VGG-16) [24] that was pre-trained on ImageNet [25]. The text feature embedding is obtained via the commonly used word-embedding algorithm called GloVe [26]. We first use the pre-trained GloVe to obtain the word-embedding vector of each word in the item description texts, and then we introduce the commonly used TF-IDF algorithm to calculate a weight for each word in the item's text. The final item text embedding with a dimension of 100 is calculated as a weighted sum of the embeddings of words in the text. The dimensions of item ID embedding and image embedding are 25 and 1000, respectively. After the embeddings in three modalities are ready for each item in a sequence, they are imported into the modal-specific GRU-based RNN to model the sequential dependencies within each modality.  
Given a sequence \(s\), the embedding vectors from each modality of all the items can form a modal-specific embedding sequence. For the \(t\)th item \(v_{t}\) in \(s\), its ID embedding \(\mathbf{m}^{t}\), image feature embedding \(\mathbf{f}^{t}\) and text feature embedding \(\mathbf{g}^{t}\) are taken as the input of the  
Figure 1: The framework of the proposed MFN4 Rec algorithm.  
GRU of the \(t\)th step in the corresponding GRU-based RNN to output the corresponding hidden state. Accordingly, the multi-GRU layer conducts the following operations in the \(t\)th step:  
\[\mathbf{h}_{m}^{t}=GRU_{m}(\mathbf{m}^{t},\mathbf{h}_{m}^{t-1}) \tag{1}\]  
\[\mathbf{h}_{f}^{t}=GRU_{f}(\mathbf{f}^{t},\mathbf{h}_{f}^{t-1}) \tag{2}\]  
\[\mathbf{h}_{g}^{t}=GRU_{g}(\mathbf{g}^{t},\mathbf{h}_{g}^{t-1}) \tag{3}\]  
where \(GRU\) indicates the operations in a normal GRU cell [27]. \(GRU_{m}\), \(GRU_{f}\) and \(GRU_{g}\) are the corresponding modal-specific GRU cells. \(\mathbf{h}_{m}^{t}\), \(\mathbf{h}_{f}^{t}\) and \(\mathbf{h}_{g}^{t}\) are the corresponding hidden state of the current step and they keep the modal-specific sequential information in the sequence \(s\). The dimension of the hidden state in each modal is equal to the dimension of the corresponding input in the same modality.
## 2 Background and Related Work
### Semantic Technologies  
Semantic technologies allow the formal description of data and support the semantic processing of data by machines, _i.e.,_ the interpretation of electronically stored pieces of information with regard to their content and meaning. The formal, explicit representation of knowledge forms the cornerstone of the Semantic Web and includes both the modeling of knowledge and the definition of formal logics, which provide rules to draw inferences over the modeled knowledge base.  
While several semantic modeling approaches for the representation of knowledge with different expressional power exist (e.g., taxonomies, thesauri, topic maps), ontologies depict the most popular and the most powerful approach of explicit knowledge representation. Ontologies, often defined as \"an explicit specification of a conceptualization\" [8], enable the modeling of information as an independent knowledge base consisting of three basic structures, namely classes, relations and instances.  
Ontologies facilitate the structured exchange of information among heterogeneous systems, resulting in a semantic interoperability. To this end, special description languages are needed. The Web Ontology Language (OWL) is the ontology description language of the Semantic Web initiative standardized by the W3C [9]. OWL provides formal semantics and an RDF/XML-based syntax. The Resource Description Framework (RDF) is a formal description language for meta data, which describes information by means of so called RDF triples (subject-predicate-object) [10]. RDFS (RDF Schema) extends RDF by properties, domain and range constructs and hierarchical structures. It is already possible to model simple taxonomies using RDFS. However, OWL is the most expressive of these description languages. It provides additional features to describe complex logical expressions, cardinalities and axioms (e.g., disjunction) _etc_.
# Analysis of the Aggregate Effect on the Compressive Strength of Concrete Using Dune Sand
## 3 Results and Discussion
### Compressive Strength  
Figure 6 shows the compressive strength measurements according to age. Figure 7 shows the mean compressive strengths and standard deviations of the concrete mixtures with different DS/FA ratios. Overall, the compressive strength increased as the DS/FA ratio increased to 20%. This can be attributed to the high compaction effect of the aggregates [9]. Furthermore, the compressive strength decreased when the DS/FA ratio exceeded 20%, which can be attributed to the decrease in bond strength between the cement paste and DS due to the round and smooth surfaces of the DS particles [19]. In addition, the bond strength reduction effect increased with an increase in the amount of DS used.  
Figure 6: Compressive strengths of the different concrete mixtures according to age; (**a**) W170, (**b**) W160, and (**c**) W150.  
Figure 8 shows the mean compressive strengths and standard deviations of concrete mixtures by unit water content. The compressive strength slightly increased when the unit water content decreased, although the water-cement ratio was same. The lower unit water content can lead to lower porosity in the hardened concrete, and can also lead to a higher compressive strength of concrete.  
The standard deviations in Figure 7 can be interpreted as the variations in the results by their unit water contents. The standard deviations in Figure 8 can be interpreted as the variations in the results by their DS/FA ratios. A comparison of the results shows that the standard deviations in Figure 8 are larger than those in Figure 7, indicating that the effect of the DS/FA ratio on compressive strength may be greater than the effect of the unit water content on compressive strength. Therefore, in terms of concrete strength using DS and CS, we recommend that the DS/FA ratio should be considered more important than the unit water content in the mixture.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 1 Introduction
Other works apply voice activity detection, like [17] where the authors propose to separate the singing voice from the instrumental accompaniment using vocal activity information derived with robust principal component analysis. Spectral information has also been used, as in [18] where Long Short-Term Memory (LSTM) Networks are applied to separate singing from the musical accompaniment in an online way. Deep neural networks are also considered in [19] where a Bidirectional Long Short-Term Memory (BLSTM) Network is applied to enhanced vocal and music components separated by Harmonic/Percussive Source Separation [20]. However, these techniques usually exploit the differences between vocal signals and signals produced by music instruments. In general, they are not well suited for speech and singing separation [11; 12], which is the problem we want to address in this work. We are interested in singing and speech segmentation because it is a basic tool necessary to deal with recordings from bertsolarism. Bertsolarism (bertsolaritza in Basque) is the art of live improvising sung poems typical in the Basque Country. The host of the bertsolarism show proposes the topic for the improvised verses and introduces the singer. Then, the singer has to create a rhymed verse about the proposed topic and sing it with a melody that fits the defined metre. The singers are called bertsolari and they have to perform a capella, without the support of any musical instrument. The main goal is to produce good quality verses and not to sing on tune, so most bertsolaris are not professional singers. Bertsolarism associations provide access to many recorded live sessions of bertsolaritza together with the corresponding transcriptions and metadata. The recordings of the live shows include speech from the host, sung verses and overlapped applauses and, consequently, a good segmentation system is required to isolate the segments of interest. In this paper, we propose a new technique to segment speech and singing using only two parameters derived from the pitch curve. The technique is applied to a Bertsolaritza database and a database of popular English songs and compared with other techniques proposed in the literature. Experiments show that the proposed segmentation algorithm is fast, robust and obtains good results. A novel algorithm to automatically label notes in the audio file is also proposed. The rest of the paper is organized as follows. Section 2 presents the datasets and describes the proposed segmentation system, including the algorithm developed to assign a musical note label to each audio frame. Section 3 describes the experiments and results of both algorithms and compares them to other techniques for note labelling and speech/singing voice classification. Finally, Section 4 discusses our findings.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 5 Conclusions  
This study presents the uplink duty cycles and related parameters determined from in situ measurements near a 4G and 5G base-station antenna in Leuven, Belgium. Six use cases were considered for 4G LTE. These use cases were repeated for 5G NR except for the use case 'Call', which was VoLTE.  
The duty cycles remained relatively stable during each of the use cases. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.  
The 95 th percentile of the PUSCH transmit power ranged between 6% and 63% of the maximum value of 23 dBm. We also investigated the impact of the MCS and RSRP on the duty cycle and PUSCH transmit power per resource block: for 5G NR, the duty cycle tended to decrease for increasing MCS values, as expected, whereas for 4G, no clear impact of the MCS on the duty cycle was observed. Finally, the PUSCH transmit power decreased with increasing RSRP values and showed no impact on the duty cycle.  
Conceptualization: [PERSON], L.V. and S.A.; methodology: L.V. and S.A.; formal analysis: L.V., S.A. and G.V.; investigation: W.J.; writing--original draft preparation: G.V., L.V. and S.A.; writing--review and editing: L.M. and W.J.; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Mobile & Wireless Forum (MWF).  
Not applicable.  
Dataset available on request from the authors.  
The authors would like to thank [PERSON] and [PERSON] for their support in the course of this study.  
The authors declare that the design of this study was agreed upon in cooperation with the funders. The progress of the study, including the analyses and interpretation of the data, was presented to and discussed with the funders at regular intervals. The draft of the manuscript was shared with the funders before its submission.
## 5 Temporal Evolution of \(b\)-Value before the \(M_{5}\) 6.8 Luding Earthquake  
We selected the earthquake catalog within a 50 km radius centered on the Luding \(M_{5}\) 6.8 earthquake (the abnormal region identified in Figure 6) from 2009 to 31 May 2022. Using a statistical window of 300 events and an increment of 1 event, we calculated the temporal evolution of \(b\)-values before the Luding \(M_{5}\) 6.8 earthquake using the maximum likelihood method with \(M_{C}=M_{L}\) 2.0. The results showed that there had been a decline from the second half of 2012 to the end of 2021, from 1.3 in 2012 to 1.0 in 2022 (Figure 8a). Considering that the \(b\)-value error is around 0.1, we believe that this decline is significant. The main period of decrease in the \(b\)-value was from 2013 to 2018, during which more earthquakes with \(M_{L}\geq 3.0\) occurred compared to other periods (Figure 8b). Since the end of 2021, there had been a slight rebound of \(b\)-values for about four months, with a magnitude of 0.05, corresponding to a small-magnitude earthquake cluster near the future mainshock (red rectangle in Figure 6c). This small-scale, short-term phenomenon has occurred multiple times in the past, but there has been no subsequent strong earthquake, so it is difficult to say that it was a precursor to the Luding \(M_{5}\) 6.8 earthquake. The long-term decrease in \(b\)-values reflects the continuous accumulation of stress in the region; similar phenomena have occurred before many strong earthquakes [8; 9].  
Figure 8: (**a**) The temporal \(b\)-value curve in the Luding area from 2009 to May 2022. The red line indicate the \(b\)-value, while the gray lines mark the errors of the \(b\)-value. The gray box marks the data  
accumulation period. (**b**) The M-t diagram with \(M_{L}\geq\) 2.0. The dashed box indicates a cluster (illustrated in Figure 5c, marked by a red rectangle) between December 2021 and March 2022, before the \(M_{\rm S}\) 6.8 Luding earthquake, and a rise of \(b\)-value at the same time.  
To validate the observed phenomena, this study plotted the Gutenberg-Richter (G-R) relationship, fitting graphs for two different periods. Specifically, data from 2010 to 2013 are represented by red dots in Figure 9, while data from 2018 to 2021 are shown with blue dots. Comparison of Figure 9 clearly shows that, despite the identical duration and similar levels of seismic activity (in terms of both frequency and intensity) between the two periods, the later period recorded more earthquakes of magnitude 3.5 and above (4 compared to 3 in the earlier period), and the maximum magnitude increased from 4.0 in the earlier period to 4.9 in the later period. Consequently, the \(b\)-value for the later period dropped to 0.91, below the earlier period's 1.18. This discrepancy is clearly reflected in the \(b\)-value temporal evolution curve, thereby supporting the reliability of the observed decrease in \(b\)-values over time.
# Review of Radiation-Induced Effects in Polyimide
## 3 Results
### Change in Dielectric Properties  
As reported by [PERSON] et al. [118], the dielectric constant and dielectric loss of PI exibit changes under electron irradiation. Irradiation with 1 MeV electrons to a maximum fluence of \(\sim\)10\({}^{15}\) electrons/cm\({}^{2}\) resulted in a decrease of dielectric constant from 3.1 to 2.4, with increasing electron fluence. This is explained by the irradiaton-induced reduction of PI's density and formation of \(\pi\)-electron clouds with their polarization aligned in the direction of the molecular chains. The dielectric loss, \(\varepsilon^{\prime\prime}\), increases with increasing electron fluence, as is typical for most polymer dielectrics [119]. This is attributed to the increased concentration of charged carriers and available hopping sites.  
The dielectric strength of PI irradiated with 21-800 MeV protons up to a maximum fluence of \(2.0\times 10^{16}\) protons/cm\({}^{2}\) was studied by [PERSON] et al. [120]. Insignificant degradation, within 90% of the value of pristine PI, was observed for the proton-irradiated material. A factor of \(\sim\)2 change in dielectric loss and dielectric constant over a wide range of frequencies, attributed to irradiation-induced polymer chain scission, was reported by [PERSON] et al. [121].  
Despite chemical changes induced by VUV exposure of PI films, no significant alterations of the dielectric properties were reported. For example, a small (\(\sim\)8%) decrease of the index of refraction over the 300 nm-1750 nm wavelength range of the UV-exposed PI film was observed [122].
## 3 Proposed Methods
### Overview of Suggested Method
During the training process, feature reconstruction starts at shallow layers and progressively advances to deeper layers. The intermediate feature maps of the teacher models serve as supervisory signals, with an L2 loss function applied to minimize the discrepancies between student and teacher features. Once a layer's reconstruction is complete, its parameters are frozen, thereby enforcing hierarchical learning from low-level textures to high-level semantic features. After the layer-wise training phase, all parameters of the student model are unfrozen and globally fine-tuned using the original age labels. This final step removes residual noise introduced during the distillation process and ensures the alignment of reconstructed features with the ground-truth characteristics. As detailed in Algorithm 1, the process involves layer-wise distillation with feature alignment and subsequent fine-tuning. ```
0: Teacher weights \(W_{T}\), Student weights \(W_{S}\), Number of blocks \(g\)
0: Optimized student weights \(W_{S}^{*}\)
1:for each block \(l=1\) to \(g\)do
2: Extract features: \(\mathbf{F}_{T}^{l}\) (teacher) and \(\mathbf{F}_{S}^{l}\) (student)
3: Align student features: \(\mathbf{F}_{S}^{l}\leftarrow\mathbf{F}_{S}^{l}+\text{Conv}(\text{Concat}(\mathbf{ F}_{S}^{l},\mathbf{F}_{T}^{l}))\)
4: Minimize \(\|\mathbf{F}_{T}^{l}-\mathbf{F}_{S}^{l}\|_{2}^{2}\) w.r.t. \(W_{S}^{l}\)
5: Freeze \(\{W_{S}^{1},\dots,W_{S}^{l}\}\)
6:endfor
7: Unfreeze \(W_{S}^{*}\leftarrow\{W_{S}^{1},\dots,W_{S}^{g}\}\)
8: Fine-tune \(W_{S}^{*}\) using age labels
```  
**Algorithm 1** Layer-Wise Distillation with Feature Alignment and Fine-TuningAs shown in Figure 2, during inference, a binary classification convolutional neural network is deployed to detect occlusions (e.g., sunglasses or masks). If occlusion is detected, the input is routed to the student model for feature reconstruction; otherwise, it is directly processed by the teacher model. The final features from either model are then fed into the age estimation module for prediction.
# The Effect of Speed Humps on Instantaneous Traffic Emissions
## 3 Traffic Flow and Instantaneous Emission Models
### The Modified NaSch Model  
We use the model presented in [47] that extends the NaSch model by modifying the second rule (deceleration) to capture the effect of speed humps on traffic flow. This model considers the distance to the front vehicle and the distance to the closest speed hump at front to compute the new velocity of each vehicle. Additionally, if the vehicle is currently passing over the speed hump then it reduces its velocity to its minimum or stops if there is a vehicle immediately at front.  
The rules of the modified NaSch model used in this study are as follow:  
1. _Acceleration_ If \(v_{t}<V_{\text{max}}\) then increase the speed of the vehicle \[v_{t+1}\leftarrow\min(v_{t}+1,V_{\text{max}})\] (1)
2. _Deceleration_ If the speed hump is close enough then reduce the speed; if the vehicle is on the speed hump then reduce its velocity to its minimum. Otherwise the new velocity is given by the original deceleration rule of the NaSch model. For all cases also consider the distance to the front vehicle. \[v_{t+1}=\begin{cases}\min(D_{d},d_{n})\text{ if }D_{d}<v_{t+1}\\ \min(1,d_{n})\text{ if }D_{d}=0\\ \min(v_{t+1},d_{n})\text{ otherwise}\end{cases}\] (2)
3. _Randomization_ Decrease the velocity of the vehicle with brake probability \(P\) \[v_{t+1}\leftarrow\max(v_{t+1}-1,0)\] (3)
4. _Vehicle movement_ Update the position of the vehicle \[x_{t+1}\gets x_{t}+v_{t+1}\] (4)  
where \(v_{t}\) represents the velocity of the current vehicle at time \(t\); \(V_{\text{max}}\) is the maximum allowed velocity on the lane (the maximum number of cells that a vehicle can move forward per simulation step); \(D_{d}\) represents the number of empty cells between the current vehicle and the closest speed hump ahead, \(d_{n}\) is the _spatial-headway_, the number of empty spaces between the front vehicle and the current one [56]; and \(x_{t}\) is the position on the lane of the current vehicle at time \(t\).
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 6 Fault Detection System  
This chapter presents the fault detection system for detecting tube temperature sensor faults. Here, the fault is represented by the control system designed in Section 5.2, shown in Figure 8. The purpose of fault detection in this study is to switch the control loop to the fault-signal-compensating control system described in Section 5.3 in the event of the fault. The goal is to detect the temperature sensor fault in the tube represented by \(\mathbf{y}_{f}\) in Figure 8. The using fault detection system is a combination of ChangeFinder and One-Class SVM. One-Class SVM is described in Appendix A and ChangeFinder is described in Appendix B. If the change-points detected by ChangeFinder are considered as anomalies, the system has the advantage of reliably detecting change-points even for minor faults. On the other hand, if the reference input changes during control, it is also detected as a change-point. This has the disadvantage that an abnormality cannot be detected during the transient period until it is tracked again. Abnormality detection by One-Class SVM alone has the advantage that detection is effective even for unknown anomalies. It has the disadvantage that it is difficult to detect small faults; for example, when the distance between the normal data during training and the observed anomaly data is small. Therefore, we aim to construct a system that complements each other's weak points by adding the output of ChangeFinder to the input of One-Class SVM. The following two processes are required for anomaly detection.  
1. Anomaly classification modeling with One-Class SVM. An anomaly discrimination model is constructed by preparing training data and having One-Class SVM learn them. The explanation is given in Sections 6.1 and 6.2.
2. Anomaly detection by anomaly discrimination model. Anomaly detection of acquired real-time data is performed using a discriminant model created in advance. The explanation is given in Section 6.3.  
Figure 8: Representation of temperature sensor faults in control systems.  
Figure 7: Overall control system.
## 3 Proposed Scheme
### Encryption Algorithm
To encrypt the text \"Applied Sciences\", each character of the plaintext is first converted into its corresponding ASCII value. The sum of these ASCII values is then calculated, and the modulo \(94\) of the result is obtained. In this case, the remainder \(s\) is found to be \(44\). The next step involves adding \(s\) to each ASCII value of the plaintext, resulting in diffused ASCII values. For the encryption procedure, a specific elliptic curve, denoted as \(E_{227,1}\), is chosen, along with a point \(G=(201,49)\) on this curve, having an order of \(228\). By utilizing the group law operations, a sequence of points \(1G,2G,\ldots,220G\) is generated. Each diffused ASCII value is mapped to an elliptic curve point on the selected curve. Subsequently, the \(y\) coordinates of these points are extracted, representing the original signal \(u_{0}(i)\), where \(1\leq i\leq 16\), for the Type IVa MP wavelet. The approximation signal \(u_{1}(i)\) and the detail signal \(v_{1,1}(i)\) for Type IVa MP-wavelet are then computed using Equations (1) and (2), respectively, for \(1\leq i\leq 16\). The detail signal \(v_{1,1}(i)\) is converted into binary form, where negative values are assigned \(1\) and positive values are assigned \(0\). Additionally, both the approximation signal and the absolute value of the detail signal are transformed into the form \(94q+r\). The remainder \(r\) is increased by \(32\), yielding the ASCII codes of the ciphertext. Finally, these ASCII codes are converted back into text, resulting in the ciphertext \"$FOU-O5 aJB3 TI3Q2\".