esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Corner joints Studies
The Minifix bolts, which are 34 and 24 mm in size, show a nuanced difference in strength, with the longer bolt slightly outperforming the shorter bolt, although the difference is not significant. As a supplement to previous research, [PERSON] et al. [31] found that particleboard demonstrated lower moment-carrying capacities than other wood and wood-based materials, indicating a relatively weaker performance in load-bearing applications. When applied to particleboards, the Minifix fasteners produced the lowest moment-carrying capacity values, suggesting that the choice of fastener type is crucial for enhancing the structural integrity of the corner joints. In addition, the Clamex P fasteners exhibited high-resistance characteristics in diagonal compression and withdrawal tests, which could imply a potential for better performance in joints under specific conditions. However, the inherent material properties of particleboard, such as its susceptibility to breakage owing to excessive channel widths, can negatively impact the overall performance and durability of the joint. This analysis underscores the importance of selecting an appropriate fastener type based on the specific load requirements and structural considerations of furniture design. Miter corner joints, studied in [38], when compared to conventional cam-connecting fittings and screw joints, exhibit a superior load-bearing capacity, enhancing the overall strength of the furniture by 8.1 and 2.6 times, respectively. This is attributed to the efficient utilization of the mechanical characteristics of the particleboard, particularly when the joints are bonded with the PVAC adhesive, and the surface is veneered. Veneering the particleboard not only increases the aesthetic appeal but also strengthens the outer layer, thereby improving the joint's capacity to carry loads by 19.5%. The application of thin veneer overlays on particleboards reduces creep potential, indirectly mitigating warping over time [47]. The application of adhesive joints in furniture construction contributes to a longer exploitation duration and higher load tolerance than those of dismantlable joints. However, despite the enhanced strength and aesthetic benefits of miter joints, their complexity in manufacturing and assembly limits their widespread application in furniture with large dimensions and complex construction. The strength of furniture corner joints is primarily derived from edge gluing of the face and butt members, emphasizing the importance of joint tightness for durability [20]. The polyurethane adhesive provides a higher bending strength for the corner joints than the PVAC adhesive, indicating that superior glue quality enhances joint durability [43]. The strength and durability assessment of various corner joint types in panel furniture, which is crucial for ensuring the structural integrity and longevity of furniture designs, is presented in Table 5.
# A Data-Driven Approach to Discovering Process Choreography
## 4 Materials and Methods
### Process Choreography Discovery
#### 4.3.4 Method 10: Generation of Inter-Organizational Event Log  
The inter-organizational event log is constructed by merging the event logs \(L\) and \(L^{\prime}\) and the output data from the previous methods, applying the following procedure.  
* Consider the event logs \(L\) and \(L^{\prime}\), the set of pairs of the cases \((T,T^{\prime})\in SCP\), and the set of pairs of the events considered message-type tasks \((a,b)\in PME\).
* A new \(Lf\) event log is created, which will contain the values of the merged event log.
* For each pair of cases \((T,T^{\prime})\in SCP\):
* In the case of \(T\), the message-type tasks found in case \(T^{\prime}\) are added. Therefore, \(T\gets E\cap L^{\prime}\in T^{\prime}\) as long as \((a,b)\in PME|E^{\prime}\in b\).
* In the case of \(T^{\prime}\), the message-type tasks found in case \(T\) are added. Therefore, \(T^{\prime}\gets E^{\prime}\in T^{\prime}\cup E\in T\) as long as \((a,b)\in PME|E\in a\).
* Cases \(T\) and \(T^{\prime}\) are added to the event log \(Lf\gets T\) and \(Lf\gets T^{\prime}\).
* Cases \(Tl\in L\) not found in \(SCP(Tl\
otin SCP)\) are added to the event log \(Lf\gets Tl\).  
\begin{table}
\begin{tabular}{l} \hline \hline
**Description** \\ \hline
**Rule 5**: Given the models _BPMNI_, _BPMNR_; the events \(a\) and \(b\), if the _name_ of the event \(a\) is compound of one of the elements included in the set \(R=\{\)_Receive_, _Accept_, _Status_, _Arrival_, _Notice_, _Admit_\(\}\), then event \(a\) will be a message-type task and subtype _receive_ (\(a.subtype=receive\)), and the task \(b\) will be message-type task and subtype _send_ (\(b.subtype=send\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._* Cases \(Tl^{\prime}\in L^{\prime}\) not found in \(SCP(Tl^{\prime}\
otin SCP)\) are added to the event \(\log Lf\gets Tl^{\prime}\).  
The resulting event \(\log Lf\) (merged) allows you to visualize the IOBP model, including all participants and their interactions. In this log, the event attributes that compound the cases contain information about the interaction through messages from the participants involved in the inter-organizational collaboration.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
## 2 Related Work
### Pronunciation Detection
The result of the GOP showed a high accuracy from 87% to 100%, and the false rejection was zero to less than 10%. HMM for automatic speech recognition system has been proposed to help improve the pronunciation for Malaysian teachers of the Arabic language [18]. The aim is to develop a computer system for standard Arabic pronunciation learning by estimating the pronunciation score based on the HMM log-likelihood probability model. The system is designed to extract feature vectors from speech utterances using the MFCC technique; then, the Baum Welch Algorithm is applied to train the system and build the HMMs set. The pronunciation scoring system uses HMMs with the test speech features to perform classification of the speech utterances by applying the Viterbi Algorithm to calculate word pronunciation score. The dataset consisted of 200 words recorded by 20 native Arabic speakers and 10 non-native speakers. The accuracy performance of the proposed system was 89.69%.
# Flexible Antennas: A Review
## 2 Materials for Flexible Antennas
### Substrates
The recent flexible substrates being introduced for wearable/flexible antennas include the likes of Kapton, PET, paper, liquid crystal polymer, different fabrics, and paper due to their unique physical, electrical, and mechanical properties. Figure 4: Fabricated antenna prototypes on: (**a**) polyethylene terephthalate (PET) [67], (**b**) polyethylene naphthalate (PEN) [70], (**c**) Polyimide [59], (**d**) liquid crystal polymer (LCP) [82], and (**e**) paper [80] substrates.
# Product Demand Prediction with Spatial Graph Neural Networks
## 2 Related Works
### E-Commerce Demand Prediction
#### 2.1.2 Demand Prediction Methods  
Demand prediction in the context of e-commerce is a relatively novel area of research.  
Traditional Statistical Methods. Early efforts in demand prediction for online advertising predominantly employed traditional statistical methods. [PERSON] et al. present a systematic optimization-based approach for customer demand prediction using support vector regression (SVR) [4]. [PERSON] et al. also explore SVR for demand prediction, using a similar three-step algorithm involving nonlinear and linear programming, and a recursive step to adapt to historical sales data for accurate predictions [5]. These traditional statistical models have been widely used due to their simplicity and interoperability.  
Machine Learning Techniques. Machine learning techniques offer enhanced flexibility and accuracy in demand prediction for e-commerce. [PERSON] et al. propose a novel approach that considers market dynamics with multiple sellers offering the same product at different prices [6]. They apply various regression algorithms and a stacked generalization (ensemble learning) technique, showing that the latter yields superior results.  
Deep Learning Model. Deep learning models, particularly neural networks, have shown exceptional capability in processing vast amounts of data and identifying complex patterns. [PERSON] et al. use Long Short-Term Memory (LSTM) networks to exploit nonlinear demand relationships in an e-commerce product hierarchy [7]. In domains beyond online advertising, [PERSON] et al. synthesize research on artificial neural networks (ANNs) in predictions, providing insights into modeling issues and future directions [8]. [PERSON] et al. compare neural networks with traditional models, highlighting their advantages in handling non-linear relationships [9]. [PERSON] et al. propose an LSTM framework for predicting network traffic, demonstrating its effectiveness on real-world data [10].  
Ensemble models. Ensemble models combine different techniques to leverage their respective strengths for improved predicting accuracy. [PERSON] et al. present a hybrid system combining ARIMA models and neural networks for demand prediction, improving accuracy and inventory management in a Chilean supermarket [11]. [PERSON] et al. investigate various classifiers and their combinations, showing that ensemble models outperform single classifiers and simple combinations in predicting accuracy [12].
# On Performance Analysis of Protective Jamming Schemes in Wireless Sensor Networks
## 4 Analysis on Eavesdropping Probability  
We first present the analytical results on the eavesdropping probability of Non-Jam scheme in Section 4.1 and then present the results on the eavesdropping probability of Fri-Jam schemes in Section 4.2.
## 4 Results
### Performance of Wireless Link Lifetime Prediction and One-Hop Channel Delay Prediction Using DNNs
Therefore, as the DNN-based approach has only an RMSE of 0.000331 and much lower computational complexity compared to the optimization-based approach, DNN can be replaced with the computationally inefficient optimization-based approach for wireless link lifetime prediction.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 3 Preliminaries and Dataset  
### Deepfake  
Deepfakes are synthesized media generated using deep learning methods, such as a generative adversarial network or autoencoder. In real-world applications, it is often used in a form of face-swapping. Face-swapping involves an encoder and a decoder that representatively learn target and base face as well as transforms. Deepfake voices are generally divided into two categories: text-to-speech (TTS) generation and voice conversion. TTS generation only trains the model for audio generation using a text transcript as input, while voice conversion is voice-to-voice conversion, similar to face-swapping. Currently, both generation methods show good performance, so lay viewers are usually unable to distinguish between the real and deepfake media intuitively.
## 3 Results
### Numerical Results: Point Scatterer
#### 3.1.1 Receive Aperture
With an AW, a higher contrast can be expected [40], helping the  
Figure 5: **(a)** Distribution of clutter level measured within the red rectangular region shown in Figure 4a. The same area was used in other images. On each box, the central mark indicates the median of the clutter level inside the corresponding region. The bottom and top edges of the box indicate the 25 th and 75 th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers. Outliers were plotted individually using the ‘+’ symbol. **(b)** The level of grating lobes, measured within the black rectangular regions indicated in Figure 4.  
Figure 6: Full-width-half-maximum (FWHM) measurements for different Nr and Ns; a full-aperture transmission was used. motion estimation methods used to estimate the wall velocities in CPWI [8]. Thus, we use AW technique in the reconstruction through rest of the paper. As seen in Figure 4c,d, grating lobes are not well-suppressed for \(\mathrm{Nr=1}\) (see the red circles). For \(\mathrm{Ns=3}\), these effects reduce about 6 dB from \(\mathrm{Nr=1}\) to \(\mathrm{Nr=5}\) and then reduce with a lower rate along with oscillation (see Figures 4c and 5b). Almost the same pattern happens for \(\mathrm{Ns=4}\) from \(\mathrm{Nr=1}\) to \(\mathrm{Nr=5}\). We also noticed the same trend happening at depths of 15 and 20 mm (depths of interest for CPWI [8])--results are not provided here. Based on the lower grating lobes for \(\mathrm{Ns=3}\) than \(\mathrm{Ns}\) = 4 (Figure 5b), the reduction of grating lobes from \(\mathrm{Nr}\) of 1 to 5 for \(\mathrm{Ns=3}\) (Figure 5b), and the same resolution in \(\mathrm{Nr=5}\) for \(\mathrm{Ns}\) of 3 and 4 (see zoomed version in Figure 6), we proceed with \(\mathrm{Ns=3}\) and \(\mathrm{Nr=5}\). It is true that \(\mathrm{Nr=5}\) increases the FWHM more than a smaller \(\mathrm{Nr}\) (see Figure 6), but lower grating lobes are more beneficial/needed in CPWI than a higher resolution due to motion estimation algorithms used in CPWI [8,13]. Although even lower grating lobes can be achieved by a \(\mathrm{Nr}\) larger than 5 (Figure 5b), the rate of FWHM increment is much larger than the rate of grating lobes decrement (see Figure 6) when \(\mathrm{Nr}\) increases. It is expected to have 7 dB higher signal-to-noise ratio (SNR) with \(\mathrm{Nr=5}\) than \(\mathrm{Nr=1}\). Moreover, with \(\mathrm{Ns=3}\) and \(\mathrm{Nr=5}\), an effective covered aperture of 9.3 mm can be obtained, which is large enough to cover the carotid artery in all ages [37]. We conducted the same assessment for scatterers positioned at depths of 15 and 20 mm (results not shown). For different \(\mathrm{Ns}\), almost the same difference in the mean of the level of grating lobes (red lines in Figure 6a) is achieved. The same effects indicated by the red circles Figure 4c,d occur, but with lower power. Following the same procedure \(\mathrm{Ns=3}\) and \(\mathrm{Nr=5}\) can be selected for receive aperture in these depths as well.
## 3 System Functional Modules
### Base Station Module Hardware and Software Design
#### 3.3.4 Implementation of Data Processing Tasks  
The base station module initializes the node management list prior to the implementation of the task, and then waits for the node to upload sample data; the base station sends the correct sampled data to the server via GPRS, while the data are displayed on the screen of the base station; then it is determined whether the sampled data exceeds the proposed alarm conditions, if an alarm condition is satisfied, the base station will send alarm information via GSM SMS to the designated mobile phones; and the data are expected to be uploaded by the next node. This task will be implemented by a loop (Figure 13).  
Figure 12: Interfacing implementation of LPC2138 and JN5139 flow.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 3 Domain Adaptation Tasks for EO Mission  
In this section, we describe two domain adaptation tasks--offline (Section 3.3.1) and online adaptation (Section 3.3.2)--for satellite-borne machine learning applications. The significance of our formulations derives from framing the formulations in the context of a concrete EO mission that has successfully demonstrated an onboard machine learning task. We thus begin by describing the mission context (Section 3.1) before defining the domain adaptation tasks.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 5 Quality of Experience in 6G Networks
### Capturing QoE through Affect  
A human's affect is more commonly referred to as the user's emotional state--an expression of the human's feelings or impression. In day-to-day interactions, a human may discern others' affective states through various \"tells\" or social cues. These include facial expression, tone, language, gesture, and gait. These indicators may also extend to more subtle aspects, such as tolerance to sound, light, or action.  
Recently, there has been a rise in the use of affective sensing or emotional AI in various contexts. In communications, instances such as the one in [69] exploit emotional sensing to enhance the mobile computing experience. Meanwhile, the authors in [70] utilized a proprietary library to estimate the user's MOS and to match the estimates with QoS measurement to enhance QoE estimates.  
These tools hold great promise in terms of automating the capture of the user's affective state. However, there are definite challenges that need to be addressed. For example, the notion of affect and affect discretization remains problematic and has largely relied on empirical consideration [71]. Standardization and cross-culture/context appropriation are similarly problematic [72]. There are also considerable ethical concerns in using these technologies that require careful consideration [73].  
Despite these challenges, and considering the rising demand for explainable intelligence, it is expected that these challenges will be addressed over the next decade. This will avail capable affective sensing technologies for 6G networks.
## 3 Results and Discussion
### Binding Sites and Identification of Binding Sites on HSA  
For static quenching, the number of binding sites can be determined according to the double-logarithmic equation (Equation (3)) [37; 38; 39]:  
\[\log[(F_{0}-F)/F]=\log K_{\rm b}+n\lg[{\rm Q}] \tag{3}\]  
where \(F_{0}\) and \(F\) are the fluorescence intensities in the absence and presence, respectively, of the ligand. The binding constant \(K_{\rm b}\) is obtained from the y-intercept of the plot of \(\log[(F_{0}-F)/F]\) versus \(\log[{\rm Q}]\), \(n\) is not only the number of binding sites per protein, but also the plot slope. The related data were handled by this equation; a good linear relationship with a value of \(n\) of approximately one was obtained, which suggested that PDQL binds to HSA at a molar ratio of 1:1 (Table 2).  
Three well-characterized high-affinity drug binding sites I, II and III, which are located in the hydrophobic cavities of subdomains IIA, IIIA and IB, respectively, of HSA [40], facilitate the effective transport of the drugs in the blood circulation. To confirm the binding site of PDQL on HSA, site-marker competitive-displacement experiments were conducted using phenylbutazone (PB, site I), flufenamic acid (FA, site II) and digitoxin (Dig, site III) as specific markers [41]. The binding constants of PDQL with HSA remarkably decreased after the addition of FA, whereas the addition of PB and Dig caused relatively small changes (Table 3). As discussed above, these results indicated that PDQL could be replaced by FA; thus, the predominant binding site of PDQL is in subdomain IIIA (site II).  
Figure 4: Modified Stern-Volmer plots of PDQL-HSA systems.
# Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance
## 4 Bringing Forth Current R&D Challenges
### Vision-Based Modes  
Here is a brief summary of technologies and their evaluations used for the vision-based modes (see Section 3):  
[Mode#1] Our object detection subsystem is based on a Faster RCNN algorithm at this link: [[https://arxiv.org/pdf/1506.01497.pdf](https://arxiv.org/pdf/1506.01497.pdf)]([https://arxiv.org/pdf/1506.01497.pdf](https://arxiv.org/pdf/1506.01497.pdf)) (accessed on 23 October 2021) Object detectors usually are evaluated in terms of mean average precision (mAP). Faster RCNN with Resnet-101 backbone achieves 48.4% mAP on MSCOCO validation set [28] and is able to process VGA-resolution images in near-real-time (~10 Hz on Nvidia 2080 Ti GPU). Although these are not SOTA characteristics at the moment, we selected Faster RCNN due to satisfactory empirical performance when tested on our prototype and efficient, publicly available implementations.  
[Mode#2] Specific (or a few-shot) object detection subsystem is based on Neurotechnology's SentiBotics Navigation SDK 2.0 software (Neurotechnology, Vilnius, Lithuania). In our set up we use the BRIEF descriptor, which according to the tested data set, provides an accuracy of 84.93% [29]. After tuning thresholds, the SentiBotics algorithm allowed practically eliminate false positive recognitions, while providing fast matching and rapid learning of user-specified objects.  
[Mode#3] Scene captioning subsystem relies on the [28] algorithm. In terms of the BLEU-4 score, its accuracy is 27.7-32.1 when tested on the MSCOCO data set.  
[Mode#4] Face recognition subsystem is based on Neurotechnology's Verilook 12.2 SDK (Neurotechnology, Vilnius, Lithuania). It was evaluated using three publicly available data sets (MEDS-II, LFW, NIR-VIS 2.0). According to obtained estimates, corresponding equal error rate (EER) values:  
MEDS: 0.0455% 0.4149%  
LFW: 0.0080% 0.0147%  
NIR_VIS 2.0: 0.0367% 0.0662%  
It shows that algorithm is capable of high accuracy face recognition.  
[Mode#5] OCR subsystem relies on Google Cloud Platform. According to [30] experiments, the average accuracy on the tested data set of 1227 images from 15 categories (after preprocessing) was 81%.  
[Mode#6] We employed the PointCloud Library ([[https://pointclouds.org/](https://pointclouds.org/)]([https://pointclouds.org/](https://pointclouds.org/)), accessed on 23 October 2021), which is based upon [[https://pcl.readthedocs.io/en/latest/cluster_extraction.html](https://pcl.readthedocs.io/en/latest/cluster_extraction.html)]([https://pcl.readthedocs.io/en/latest/cluster_extraction.html](https://pcl.readthedocs.io/en/latest/cluster_extraction.html)) (accessed on 23 October 2021) and 3D depth camera images. We used Faster RCNN resnet101/resnet 50 or ssd mobilenet neural network architectures. It is worth noting that the Ligh backbone is much faster.
## 2 Materials and Methods
### Dynamic Rheological Measurements  
Small-amplitude oscillatory rheology (SAOR) tests were carried out using a Paar Physica UDS200 MCR rheometer. The rheometer was set up with a parallel-plate geometry (10 mm plate radius and 1 mm gap setting). All samples were gently stirred with a spoon for 30 s prior to the measurement to mix the potential free whey with the resulting gel. Each sample was loaded into the rheometer and allowed to relax and equilibrate at the measurement temperature (25 \(\pm\) 0.1 \({}^{\circ}\)C) for 2 min prior to testing. The temperature of the samples inside the rheometer was maintained using a circulating cooling system. All measurements were conducted at 0.01 strain in the linear viscoelastic range previously determined by the strain sweep. The rheological aspects of all samples were evaluated by carrying out stress amplitude sweep tests. The sweeping amplitude from 1.5 \(\times\) 10\({}^{-2}\) to 1.5 \(\times\) 10\({}^{-1}\) mNm at 0.25 Hz was used and 25 measuring points were performed through the sweeping range. The storage (G\({}^{\prime}\)) and loss (G\({}^{\prime\prime}\)) moduli were recorded. Two replications were made for each sample. Finally, the loss tangent (tan \(\delta\)), i.e., the ratio of G\({}^{\prime\prime}\) to G\({}^{\prime}\), was also calculated. Graphs were created using OriginPro70 software (OriginLab, Northampton, MA, USA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample No.** & **Storage Temperature** (\({}^{\circ}\)**C**) & **Storage Time (Weeks)** \\ \hline
1 & 5 & 2 \\ \hline
2 & 20 & 2 \\ \hline
3 & 5 & 8 \\ \hline
4 & 20 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Combinations of storage temperature and storage time levels considered in the experimental design.