esa-sceva/satcom-chunk-collection huggingface dataset:
# Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information
## 4 Experimental Results and Analysis
### Analysis  
Table 1 presents the performance on word similarity and word analogy. The suggested word embeddings achieved the highest average score in both domains. The CN-GCN exhibited higher performance than the other models except for FastText in all word similarity tasks. FastText outperformed the CN-GCN in WS353, WS353R, RW, and MEN3K. These results demonstrate the advantages of the subwords used by FastText for solving the OOV problem. BERT, which uses subwords as a contextual representation model, exhibited low performance in the word embedding benchmarks when the dataset did not include sentences as the input.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Word Similarity**} & \multicolumn{8}{c}{**Word Analogy**} \\ \cline{2-11}  & **WS353** & **WS3535** & **WS353R** & **SimLex999** & **RW** & **MEN3K** & **Avg** & **MSR** & **SemEval12** & **Avg** \\ \hline SG & 61.0 & 68.9 & 53.7 & 34.9 & 34.5 & 67.0 & 53.3 & 30.6 & 20.5 & 25.6 \\ CBOW & 62.7 & 70.7 & 53.9 & 38.0 & 30.0 & 68.6 & 54.0 & 44.0 & 18.9 & 31.5 \\ GloVe & 54.2 & 64.3 & 50.2 & 31.6 & 29.9 & 68.3 & 49.8 & **61.4** & 16.9 & 39.2 \\ FastText & **68.3** & 74.6 & **61.6** & 38.2 & **37.3** & **74.8** & 59.1 & 53.2 & 19.5 & 36.4 \\ Deep & 60.6 & 73.1 & 46.8 & 39.6 & 33.0 & 60.5 & 52.3 & 40.3 & 22.9 & 31.6 \\ BERT-base & 51.8 & 60.1 & 37.3 & 48.1 & 26.7 & 42.8 & 44.5 & 52.7 & 20.8 & 36.8 \\ BERT-large & 57.8 & 65.8 & 46.4 & 47.5 & 27.6 & 51.8 & 49.5 & 57.6 & 20.7 & 39.2 \\ \hline OurModel(CN-GCN) & 65.9 & **78.8** & 54.7 & **57.0** & 36.5 & 71.0 & **60.7** & 54.6 & **24.4** & **39.5** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Task performance in the word similarity and word analogy domains on word embedding benchmarks. The overall performance for each domain is also presented as an average. The highest performance among the models is indicated in bold.  
Our word embeddings achieved a higher average score than that of GloVe, but lower performance in the MSR of word analogy tasks. This demonstrates the limitation in understanding the relationships between words as the word co-occurrence probability was not reflected.  
Table 2 displays the experimental results of the use of a symbolic graph. The TG-GCN, which learned a new graph representation in combination with the SRL graph, exhibited higher performance than the SynGCN. Furthermore, the CN-GCN achieved higher performance than the SemGCN, which was trained only on four relations of WordNet.
## 2 Methodology, Fundamentals, and Materials
### Manufacturing Process Simulation and Digital Twins  
In order to face demands for higher complexity, a more detailed planning of the design and manufacturing of the products by using simulations and other IT tools is key to fulfill the demand requirements. Thus, a holistic manufacturing simulation represents an abstraction of the essential characteristics in a digital model that enable to derive statements concerning practices and properties of systems. The use of digital models in production processes is described by the term of virtual production, which specifies a \"mainstreaming, experimental planning, evaluation and controlling of production processes and plant by means of digital models\" [9]. Virtualization means that organizations are able to monitor their physical processes by collecting sensor data linked to virtual plant models and simulation models. Thus, a virtual copy of the physical world is created [18]. However, nowadays, a continuous flow of information is available only with the application of customized architectures to overcome the problem of heterogeneity involving high costs, and this is the main reason why small and medium-sized enterprises (SMEs) usually have no integration of all available data into a system [9]. In this regard, there are several software applications throughout the product lifecycle, including design, analysis, simulation, process planning, etc. In addition, with a cloud structure, software is easily maintained and utilized on a cloud server, enabling version updating, maintaining, and integrating remotely [19].  
These are digital representations based on semantic data models that allow running simulations in different disciplines. Research on digital twins is still at the beginning, and there is a need for future research works on relevant industrial applications to investigate and demonstrate the wide range of applications and benefits where the DT could express their potential [8]. At operational level, digital twins (DT) have been proven to be useful in supporting the evaluation and control of manufacturing entities. DT can be used to digitally represent products, single-stage machines or processes, or, less often, multi-stage manufacturing systems, being particularly relevant when DT is integrated into in-process control loops, as presented in for process quality improvement. Using DT for decision support, together with predictive engineering, leads to the capability of proactively addressing changes by exploring possible future scenarios and choosing the best available option that optimizes target objectives [5].
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 5 Results and Discussion
### Data Characterization
#### 5.1.1 Taxi Data  
The main datasets used in this work are the Yellow and Green Taxis datasets from New York ([[https://www1.nyc.gov/](https://www1.nyc.gov/)]([https://www1.nyc.gov/](https://www1.nyc.gov/)), accessed on 14 May 2021), which were taken from the NYC Open Data portal, from 2016 and 2017. Part of the period is anonymized, i.e., they do not contain geographical coordinates of the trips' pickups and drop-offs, but, instead, the IDs of regions where they started/ended. These are the two main taxi services in New York: the Yellow Taxis cover mainly the center of Manhattan Island and airports, and the Green Taxis usually cover the periphery of the city. The Yellow and Green Taxis datasets have 113.4 and 11.7 million trips, respectively. We removed invalid data from these datasets, such as trips starting and ending at the same point, vehicles traveling over 100 km/h, or trips over 6 h. The remaining trips were clustered into flows and evaluated.
## 2 Related Work  
Underwater is a less explored area and the research community has been addressing it for the last decade. In UWSNs, nodes' attention and secure communication are vital parts. The security of communication is a critical issue in UWSNs, and several key management frameworks have been proposed in the literature to address this challenge. Key management is an essential component of UWSNs, as it enables secure communication between sensor nodes and protects the network from various attacks.
# Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things
## 3 Results
### SII Evaluation  
Focusing on SII, we have developed the algorithm following the norm ANSI S3.5-1997. We sampled at 44.1 KHz and from the total audible spectrum of 30 bands in 1/3 octave, we selected the 18 bands specified by the ANSI standard. These bands, as we can see in Figure 6, cover on the frequency content of the human voice and are between 160 and 8000 Hz. The results shown in this Figure describe two examples with high and low SII, taking into account the intelligibility scale from Table 2.  
Figure 5(a) corresponds to a recording with SII around 0.79 and an excellent perception and Figure 5(b) shows the frequency bands of an audio corresponding to a room with SII around 0.48 and a bad intelligibility. Figure 6 also shows the Equivalent hearing threshold levels \(T_{i}\) for normal speech used in the SII calculation. The lower the received audio SPL levels are below these levels, the worse will be the speech intelligibility.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SII Value** & **Intelligibility** \\ \hline
0.00–0.30 & Very bad \\
0.30–0.45 & Bad \\
0.45–0.60 & Acceptable \\
0.60–0.75 & Good \\
0.75–1.00 & Excellent \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intelligibility scale according to SII parameter.  
Figure 5: Block diagrams of the SII calculation algorithm defined in ANSI S3.5-1997.  
As far as we can see in this figure, for the computation of SII, not only the Sound Pressure Levels (SPL) per band are important, but also their distribution in the spectrum, since each band is given a specific weight of importance according to the ANSI standard (the so-called \"One-third octave frequency importance functions\").
# A Polishing Processes Optimization Method for Ring-Pendulum Double-Sided Polisher
## 3 RDP Radial-Feed System Control Structure
### \(\varepsilon\)-Approximation of the Single-Tube Terminal Constraint Set  
This section presents the computation of the single-tube terminal constraint set and approximates it by utilizing the \(\varepsilon\)-approximation method.  
We define the single-tube constraint variable as \(z(k)\). According to Equations (14)-(17), we obtain the calculation formulas of single-tube constraint reduction:  
\[z(k+1)=Fz(k)+d(k),d(k)\in D(k) \tag{20}\]  
\[F=\begin{pmatrix}A+LC&0\\ -LC&A+BK\end{pmatrix} \tag{21}\]  
\[d(k)=\begin{bmatrix}I&L\\ 0&-L\end{bmatrix}\begin{bmatrix}w(k)\\ v(k)\end{bmatrix} \tag{22}\]  
\[\mathbb{Z}(k+1)=F\mathbb{Z}(k)\oplus D(k) \tag{23}\]  
In order to extend the allowable range of system disturbances, the \(\varepsilon\)-approximation is designed for the single-tube terminal constraint set. We can utilize the following formula to calculate the single-tube constraint set \(\mathbb{Z}_{\infty}\).  
\[\mathbb{Z}_{\infty}=\mathop{\oplus}\limits_{i=0}^{\infty}F^{i}D \tag{24}\]  
For scalars \(\alpha\in[0,1)\) and \(\varepsilon>0\), there exists a positive integer \(s\) that satisfies the condition \(F^{s}=aI\), which further satisfies \(\mathbb{Z}_{\infty}=(1-\alpha)^{-1}\mathbb{Z}_{s}\). Then, we define \(\mathbb{Z}_{s}\) as the \(\varepsilon\)-approximation of \(\mathbb{Z}_{\infty}\).  
\[\mathbb{Z}(\alpha,s)=(1-\alpha)^{-1}\mathbb{Z}_{s} \tag{25}\]  
When \(0\in\text{int}(D)\), Equation (22) and \(F^{s}\)\(W\subseteq\alpha W\) hold, so that \(0\in\text{int}(\mathbb{Z}(\alpha,s)\ )\) and \(\mathbb{Z}_{\infty}\subseteq\mathbb{Z}(\alpha,s)\). Based on Equations (24) and (25), \(\mathbb{Z}(\alpha,s)\) can approach \(\mathbb{Z}_{\infty}\), when we choose appropriate \(s\) or \(\alpha\).  
\[\alpha(1-\alpha)^{-1}\mathbb{Z}_{s}\subseteq B_{p}^{n}(\varepsilon),\ \ \ \alpha\in[0,1)\ \ \ \varepsilon>0 \tag{26}\]  
Equation (26) shows that when condition \(\varepsilon\geq\alpha(1-\alpha)^{-1}\underset{x\in F_{s}}{\max}\|z\|_{p}= \alpha(1-\alpha)^{-1}\underset{\varepsilon}{\min}\)  
\(\left\{\mathbb{Z}_{s}\subseteq B_{p}^{n}(\varepsilon)\right\}\) holds, \(\mathbb{Z}_{\infty}\subseteq\mathbb{Z}(\alpha,s)\subseteq\mathbb{Z}_{\infty} \oplus B_{p}^{n}(\varepsilon)\), and where \(B_{p}^{n}(\varepsilon)=\left\{z\in R^{n}|\ \|z\|\ _{p}\leq\varepsilon\ \right\}\) represents a p-norm ball in \(R^{n}\), then \(\mathbb{Z}(\alpha,s)\) is an \(\varepsilon\)-approximation of the minimal robust invariant set \(\mathbb{Z}_{\infty}\).  
Table 1 presents an overview of the single-tube RMPC structure, which involves an offline computation phase and online computation phase. Steps 1 and 2 involve the former, while steps 3 and 4 are related to the latter. Step 2 computes \(\mathbb{Z}(\alpha,s)\), which is utilized to constrain the system state. The control quantity \(u(k)\) in step 4 facilitates the system state gradual convergence towards zero. For a more comprehensive understanding of the STRMPC control process, Figure 4 provides a visual representation.
# DIFshilling: A Diffusion Model for Shilling Attacks
## 4 The Design of DIFshilling
### Complexity Analysis of DIFshilling  
Evaluating the computational complexity of DIFshilling is essential for assessing its feasibility in the real world. Since diffusion-based models often introduce additional computational overhead, we analyze both the time and space complexity of the proposed approach, focusing on the noising and denoising processes.  
**Time Complexity:** The noising process consists of \(T\) steps, where each step adds Gaussian noise to the user-item interaction matrix \(x_{t}\). The primary computational cost at each step arises from matrix operations, including element-wise multiplications and additions, which have a time complexity of \(\mathcal{O}(nm)\). Here, \(n\) and \(m\) denote the number of users and items, respectively. Consequently, the total complexity of the noising process is \(\mathcal{O}(Tnm)\). Similarly, the denoising process follows the same iterative structure with \(T\) steps. At each step, a neural network predicts the noise term \(\hat{Z}_{t}\), requiring a forward pass through a deep model. Assuming the neural network has \(L\) layers, with each layer containing at most \(h\) hidden units, the complexity of a single forward pass is \(\mathcal{O}(h^{2})\). Given that noise estimation occurs at each of the \(T\) steps, the total complexity of the denoising process is \(\mathcal{O}(Th^{2}+Tnm)\). Thus, the overall time complexity of DIFshilling is as follows:  
\[\mathcal{O}(T(nm+h^{2})). \tag{16}\]  
**Space Complexity:** The primary memory consumption in DIFshilling arises from storing the user-item interaction matrix and neural network parameters. The matrix \(x_{t}\) requires \(\mathcal{O}(nm)\) space, while the neural network parameters occupy \(\mathcal{O}(Lh^{2})\) space, assuming fully connected layers. Additionally, intermediate variables for backpropagation introduce an extra storage overhead proportional to the batch size \(B\), yielding a total space complexity of the following:  
\[\mathcal{O}(nm+Lh^{2}+Bh^{2}). \tag{17}\]  
Compared to traditional GAN-based shilling attacks, DIFshilling incorporates a diffusion-based mechanism that necessitates an iterative refinement process. While this increases computational cost due to multiple inference steps, it enhances attack stealthiness and flexibility. Optimizations such as reducing the number of noise steps \(T\) or employing model compression techniques can alleviate computational overhead while preserving attack efficacy.
# Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking
## 2 Related Work
### Name-Resolution System  
In existing ICN architectures, name resolution can be mainly divided into two categories according to the coupling relationship with content routing: the name-based routing approach and the standalone name-resolution approach [12; 25; 26]. We do not focus on name-based routing approaches, such as Content-Centric Network (CCN) [27] and Named-Data Network (NDN) [28], because name resolution and content routing are coupled in this approach, and there is no need for a separate name-resolution service. ICN architectures using standalone name resolution include Data-Oriented Network Architecture (DONA) [29], Publish/Subscribe for Internet Routing Paradigm (PSIRP) [30], MobilityFirst [16], and so on. In this approach, name resolution and content routing are decoupled into two steps: the identifier is used to lookup associated locators, and then data is routed by the locators.  
Organization and deployment of NRS are crucial in standalone name-resolution approaches, as they directly affect the efficiency of name resolution and the subsequent routing. MobilityFirst employs a method called Globel Name Resolution Service (GNRS) with realization in the cloud, which leads to long resolution latency [16]. Aiming to enhance scalability, several DHT-based schemas have been proposed and studied, such as Multi-level Distributed Hash Table (MDHT) [17], Hierarchical Distributed Hash Table (HDHT) [18], Scalable Multi-level Virtual Distributed Hash Table (SVMDHT) [19], and Hierarchical Pastry (H-Pastry) [31]. Despite achieving good scalability and robustness, the DHT mechanism has an inherent disadvantage that it is not topology aware. Overlay networks built by DHT do not consider the nearness between nodes, and the name-resolution latency is hard to achieve. Compared with DHT methods, tree-based methods have a more definite query path while taking scalability into account. [PERSON] et al. proposed Griffin, which is based on a tree structure and provides an efficient and scalable name-resolution service. However, its structure is built statically and it cannot adapt to the dynamic environment in the 5G-IoT scenario [32]. Free, developed by [PERSON] et al. [33], stores name mapping in leaf nodes to ensure the nearest response is achieved, but it does not distinguish among different latency demands, and its name-resolution latency still does not promise an upper bound.
time, so we have to select some representative points for optimization. There are generally two options for picking points: one is from the overlapping bands [23], and the other requires layout homonymy points on the ground [24].  
On the one hand, it is impossible to find a one-to-one correspondence on the ground from the rough point cloud that has not been checked and calibrated; on the other hand, measuring and averaging all points in an area is too time-consuming. Given these two factors, we decided to calibrate the system using data from overlapping bands.  
However, the whole set of data from a flight test is too large to be processed in a given period of time. Thus, we need to match the same regions for different point clouds using target features and average them as control points to check parameters.  
The second step is solving all installation errors using the points we selected. The most classic algorithm is the least square method (LSM) for this problem [25]. Unfortunately, this method did not work for our airborne STIL. We have to use nonlinear optimization with global search capabilities.  
Particle swarm optimization (PSO) is one of the best-known global search algorithms [26]; it is widely used in many fields, such as path planning [27] and neural network training [28].  
This algorithm, based on particle competition, is insensitive to initial values and monotonicity and thus has a strong global solution search capability. These features make it ideal for parametric calibration [29; 30; 31; 32; 33].  
Based on PSO, we finally obtained multiple sets of checking parameters, reducing the elevation error from more than 1 m to 0.3 m.
## 4 Results
### River Profile Analysis and Channel/Valley Modifications  
Multitemporal analysis of longitudinal river profiles has been performed using the elevation data derived from the output DEMs for each scenario. The perturbation induced by the dam removal promoted a significant modification of the river profiles (Figure 8), with a complex spatial and temporal pattern of geographic adjustments. Moreover, our simulation also highlights the occurrence of 2-D channel modifications, which are mainly related to a lateral shift of the main channels in the higher altitude sectors surrounding the reservoir infill (Figure 8).  
After the dam removal, the longitudinal profile evolution is mainly featured by a pronounced channel incision, which mainly occurred upward the flat area of the reservoir (Figure 8). In fact, the fast response of main channels to the base-level lowering is a lengthening of the longitudinal profiles, which promoted incision upstream of the former reservoir area (Figure 8, see the yellow curves and the black ones). Channel 2 adjusts its longitudinal profiles forming a major convex knickzone downstream of the removed dam, which retreats of about 180-200 m during the 20-year simulation period. A minor knickpoint can be observed in the three channels at an altitude of about 955-960 m, showing a lower rate of retreat (i.e., about 1 m/yr).  
Cross profiles of Figure 9 furnished additional information about the landscape modification resulting from our simulation. Spatial and temporal evolution of valley/channel modification is complex, with alternating stages of incision and aggradation. Profiles located in the higher altitude sectors of the catchment are featured by meter-scale incision (see for example profile a-a', d-d' and e-e'); in this sector, the highest amount of deepening (i.e., about 3 m) can be observed at profile a-a'. Topographic profiles crossing the reservoir deposits shows a more complex response to dam removal due to a general tendency of channels to cut the infill in the southernmost sectors (Figure 9, see profile b-b' and f-f') and the occurrence of aggradation upward the removed dam (see for example the profile g-g', Figure 9). Pronounced incision phenomena occurred downstream of the dam, with a progressive deepening of the main channels of about 3 m over the 20-year simulation period (Figure 9, profile i-i' and l-l').  
Figure 8: Map showing the planimetric changes of the main channels from the present-day landscape (initial DEM, Pre-DR-T0 in Table 3) to the final stage of the post-dam removal scenario. To the bottom: comparison of longitudinal river profiles of the three main channels (channel 1, 2 and 3 in the map) deriving from the simulation scenarios. River profile analysis highlights the amount of incision related to the base-level fall as well as the development of pronounced knickpoints in the upper and lower reaches of the main channels. Higher rates of knickpoint retreat are associated with the lower reach of the channel 2.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 3 Wavelet Transformation for Non-Stationary Time-Series: State-of-the-Art
### Application of WT in Engineering  
The wavelet analysis has been usefully applied to a variety of pertinent problems in engineering, such as vibration modes, the condition monitoring of rotating machinery, machine diagnostics, etc. For example, [PERSON] and [PERSON] [42] presented a review about the different applications of WT in machine fault diagnostics. The WT applications over the last ten years were divided into six categories, namely time-frequency analysis of signals, fault features extraction, singularity detection, denoising and extraction of weak signals, vibration signal compression, as well as system and parameter identification. [PERSON] et al. [41] used the WPD approach (presented in Section 2.4) to decompose the cutting strain in the tool holder and motor current. To do so, several mother wavelets, such as Daubechies, Symlet and Coiflets, were employed. Finally, the obtained features were analyzed using the radial basis neural network and back-propagation neural networks for the prediction of machine performance. Moreover, [PERSON] and [PERSON] [43] developed a wavelet-based frequency response function (FRF). The main objective was to analyze the vibration system with time-varying parameters. The Morlet wavelet was applied to present the data in the time-frequency domain. Finally, to extract and visualize data from the wavelet, the wavelet ridge algorithm was exploited.  
Two years later, the WT was presented by [PERSON] et al. [44] to detect the bearing faults. The simulated data experienced by the induction machine using the motor current signature analysis were used. At first, the noise was removed using an adaptive Wiener filter. Then, the spectral analysis was performed using Daubechies DWT. The fault severity was estimated using the standard deviation and energy of wavelet coefficients. In addition, the power spectral density of the wavelet was calculated to elevate the fault index parameter. Recently, [PERSON] et al. [45] have applied WT to predict the timing and class of a fluctuation event. For this reason, the frequency TS has been first decomposed using the Morlet wavelet. Then, the data have been divided into regions according to high and low wavelet coefficients. After that, the Kruskal-Wallis ANOVA test has been applied at the 5% significant level to measure the variations for the different regions.