esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of the Statistical Analysis of DARP
### Statistical Analysis of the Energy
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Sum of Squares** & **Df** & **Mean Square** & **F-Ratio** & **\(p\)-Value** \\ \hline \multicolumn{5}{c}{MAIN EFFECTS} \\ \hline A: Baselevel Threshold (TH\_B) & \(2.30\times 10^{5}\) & 2 & 1.15\(\times 10^{5}\) & 1.18 & 0.3080 \\ B: Role Threshold (TH\_R) & \(3.46\times 10^{5}\) & 2 & 1.73 \(\times 10^{5}\) & 1.77 & 0.1702 \\ C: ACK Timer (T\_ACK) * & \(1.35\times 10^{8}\) & 2 & 6.79 \(\times 10^{7}\) & 694 & 0.0000 \\ D: Reconnection Timer (T\_REC) * & 6.39 \(\times 10^{10}\) & 2 & 3.19 \(\times 10^{10}\) & 3.27 \(\times 10^{5}\) & 0.0000 \\ E: Down Timer (T\_DOWN) & \(1.62\times 10^{5}\) & 2 & 8.12 \(\times 10^{4}\) & 0.83 & 0.4358 \\ F: Link Timer (T\_LINK) * & \(1.05\times 10^{8}\) & 2 & 5.27 \(\times 10^{7}\) & 539 & 0.0000 \\ \hline \multicolumn{5}{c}{INTERACTIONS} \\ \hline AD * & \(8.24\times 10^{6}\) & 4 & \(2.06\times 10^{6}\) & 21 & 0.0000 \\ BC * & \(2.60\times 10^{6}\) & 4 & \(6.50\times 10^{5}\) & 6.66 & 0.0000 \\ CD * & \(7.25\times 10^{7}\) & 4 & \(1.81\times 10^{7}\) & 185 & 0.0000 \\ CF * & \(9.88\times 10^{5}\) & 4 & \(2.47\times 10^{5}\) & 2.53 & 0.0386 \\ CG * & \(3.32\times 10^{6}\) & 4 & \(8.31\times 10^{5}\) & 8.51 & 0.0000 \\ DE * & \(1.16\times 10^{6}\) & 4 & \(2.92\times 10^{4}\) & 2.99 & 0.0177 \\ DF * & \(1.89\times 10^{7}\) & 4 & \(4.74\times 10^{6}\) & 48 & 0.0000 \\ DG * & \(2.90\times 10^{8}\) & 4 & \(7.27\times 10^{7}\) & 744 & 0.0000 \\ \hline RESIDUAL & \(1.98\times 10^{9}\) & 20,314 & \(9.77\times 10^{4}\) & & \\ TOTAL (CORRECTED) & \(6.78\times 10^{10}\) & 20,412 & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of the ANOVA table, which decomposes the variability Set-Up Energy (mWs) into contributions due to several factors. The factors and interactions marked with * are statistically significant. \begin{table}
\begin{tabular}{c c c c} \hline
**Reconnection Timer (T\_REC)** & **LS Mean** & **LS Sigma** & **Homogeneous Groups** \\ \hline T\_REC1 & 1013 & 3.81 & X & \\ T\_REC2 & 1557 & 3.87 & X & \\ T\_REC3 & 5031 & 3.82 & & X \\ \hline \end{tabular} Limit to establish significant differences: 10.6  
\end{table} 
Table 7: Results of the Multiple Range Tests for the factor Reconnection Timer (T_REC), using as dependent variable the Set-Up Energy (mWs). \begin{table}
\begin{tabular}{c c c c c} \hline
**ACK Timer (T\_ACK)** & **LS Mean** & **LS Sigma** & **Homogeneous Groups** \\ \hline T\_ACK2 & 2460 & 3.86 & X & \\ T\_ACK3 & 2493 & 3.86 & X & \\ T\_ACK1 & 2648 & 3.78 & & X \\ \hline \end{tabular} Limit to establish significant differences: 10.6  
\end{table} 
Table 8: Results of the Multiple Range Tests for the factor ACK Timer (T_ACK), using as dependent variable the Set-Up Energy (mWs).
## 4 Experiments
### Comparison
#### 4.6.2 Results of CityPersons  
We train the proposed detector on the training set of CityPersons, and compare its performance with the state-of-the-art detectors on the dataset. Table 5 shows the comparison results of the CityPersons validation set. It clearly demonstrates that MA-NMS achieves the best performance on the \(R\), \(P\), and \(B\) subsets, with the strong and weak suppression tokens. In particular, our detector shows a significant 3.5% \(MR^{-2}\) improvement compared to NOH NMS [40]. This improvement can be attributed to the adaptive application of strong and weak suppression in MA-NMS, which imposes a stronger penalty on duplicate proposals and better preserves numerous occluded pedestrians in the \(H\) subset. By additionally applying suppression factors, MA-NMS further enhances its performance on these four subsets, and outperforms CSP [13], with an 1.0% and 0.4% \(MR^{-2}\) improvement on the \(R\) and \(H\) subsets. This can be attributed to the adaptive adjustment of suppression intensity in MA-NMS, which is determined based on the density and count attributes of pedestrians, leading to further suppression of duplicate proposals, and reducing sensitivity to false positives. Moreover, MA-NMS gain a 5.5% and 2% \(MR^{-2}\) improvement compared to TLL [47] and ALFNet [48] due to the comprehensive consideration of pedestrian density and count attributes in MA-NMS, which enables more accurate suppression compared to methods that rely solely on bounding box and confidence score information. The results reported in Table 6 validate the superior performance of MA-NMS for pedestrian detection in slightly crowded scenes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **AP** & **Recall** & **MR\({}^{-2}\)** \\ \hline PBM + R2 NMS [48] & ResNet-50 & 89.3 & 93.3 & 43.4 \\ NOH-NMS [40] & ResNet-50 & 89.0 & 92.9 & 43.9 \\ RepLoss [39] & ResNet-50 & 85.6 & 88.4 & 45.7 \\ AutoFedestrian [50] & ResNet-50 & 87.7 & 93.0 & 46.9 \\ LLA.FCOS [51] & ResNet-50 & 88.1 & 93.4 & 47.9 \\ JointDet [18] & DarkNet-53 & 88.8 & - & 43.4 \\ IDADA [49] & ResNet-50 & 88.0 & 93.6 & 45.3 \\ CouLoss [52] & ResNet-50 & 89.8 & 91.0 & 42.4 \\ \hline MA-NMS (\(w\)) & ResNet-50 & 89.1 & 94.2 & 43.7 \\ MA-NMS (\(w\)) & ResNet-50 & **90.2** & **94.6** & **42.0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with the state-of-the-art methods on CrowdHuman validation set. \(w\) (\(w\)/o) represents with (without) suppression factors.
## 2 Research Methodology
### Transformer Model  
The Transformer model is primarily used in natural language processing and exhibits excellent performance in various tasks such as machine translation, sentence generation, and question-answering [14; 15]. It is based on the attention mechanism and has a structure that can handle sequence data without relying on recurrent neural networks (RNNs). This feature resolves the issue of long-term dependencies while significantly improving computational speed. The Transformer model consists of two main components: an encoder and the decoder. The encoder embeds the input sequence into a multidimensional space and performs the role of encoding information. It is composed of multiple encoder layers, each having the same structure. The encoder layer consists of some key components: the self-attention mechanism and feed-forward neural network. The decoder, relying on the output of the encoder, generates the output sequence. Similarly to the encoder, it is composed of multiple decoder layers with the same structure. The decoder layer consists of the self-attention mechanism, encoder-decoder attention mechanism, and the feed-forward neural network. To preserve the relative positional information between the input and output sequences, the transformer model employs positional encoding. During model training, the cross-entropy loss function is primarily used, and a backpropagation algorithm and optimizer are utilized, with the Adam optimizer being the most common choice. The Transformer model demonstrates exceptional performance in natural language processing tasks, particularly translation tasks. It allows parallel processing, effectively addresses long-term dependency issues, and exhibits a flexible structure that can handle tasks with different input and output lengths.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 2 Related Work
### AR Positioning  
AR positioning techniques have become increasingly diverse in recent years. For instance, Apple's ARKit and Google's ARCore technologies negate the use of many of the visual labels previously required for indoor navigation. Even the installation of additional positioning hardware is no longer needed to achieve precise indoor positioning [16]. Due to the gradual advancement of these technologies and the ubiquitous usage of smartphones, AR applications have found extensive application in indoor navigation [17]. In the context of advanced AR applications for indoor environments, the development of AR-assisted UAV path planning and control highlights a novel use of AR for the precise and automated navigation of drones within indoor spaces, enhancing the capabilities of AR in complex settings [18].  
However, existing studies on the topic have focused on the user perspective of positioning and navigation services. Scant research has analyzed the benefits of these services or their effect on subsequent behaviors. Thus, the spatial positioning and behavior analysis system developed by [PERSON] et al. [6] was designed to aid researchers in understanding and analyzing visitor paths. During the process of AR platform development, it was found that environmental factors could result in positioning shifts with ARCore and ARKit [19], particularly in poorly lit and poorly textured environments. At Google I/O 2018, Google announced its visual positioning system (VPS) [20], which increases the precision of outdoor positioning using cameras, GPS, and the environmental database of Google Maps. This technology is not adaptable for indoor use [21]. The ARCore Depth API introduced by Google in 2019, however, only needs a single camera lens to create depth, thereby enhancing positioning accuracy in indoor venues and improving virtual-physical integration. Also, in 2019, Google released the Persistent Cloud Anchor, which allows virtual objects to be positioned in the real world and their coordinates stored in a cloud. Different users can then see the same AR objects, which gives AR a sharing function [22]. [PERSON] and [PERSON] (2018) used visual-inertial odometry (VIO) in ARKit and image recognition technology to record user paths indoors, which lays the foundation for developments focused on improving positioning stability. [PERSON], [PERSON], [PERSON], and [PERSON] [23] consequently used image recognition and geomagnetic filtering to develop vision-based indoor positioning. PTC launched Area Targets for their Vuforia AR kit in 2020 [24]; this technology uses 3D scans of the surrounding space to establish spatial-point cloud data and allows AR objects to be positioned in the virtual and real space. This makes AR positioning even more accurate and will greatly benefit indoor navigation quality.
# IoT-Based Smart Surveillance System for High-Security Areas
## 1 Introduction
### Research Motivation  
The popularity of the Internet of Things (IoT) has brought revolutionary change in every field of life [3; 5; 6; 7]. The detection of an object behind a wall or obstruction is an interesting research domain for many applications. Researchers have employed different approaches to detect objects, such as surveillance via camera based on a given vicinity. In these methods, Line-of-Sight (LoS) attributes and other privacy issues, such as people's actions regarding legal problems, must be accounted for. The radio-frequency-signal-based monitoring of moving objects also requires experience working with wireless channels, which is a tricky job that requires a license. Therefore, to overcome the discussed issues, the development of low-cost devices that function using microwave signals is helpful from a human perspective as such devices allow users to see moving objects behind obstructions. To address this issue, in this study, an IoT-based low-cost SS-HSA system was developed based on the principle of microwave signals. The proposed system also generates SMS and call alerts to alert a user upon the detection of an object, allowing them to take necessary actions in a timely manner, ultimately contributing to reducing the chances of crimes such as larceny/theft and burglary. The proposed system employs a GMS module that uses a doppler effect method to accurately detect moving objects using microwave signals. No previous studies have employed this technique to efficiently detect objects behind a wall. The proposed SS-HSA provides a cost-effective solution that can be used to revolutionize human life in different ways such as with respect to home automation, gaming, healthcare, homeland or personal security, rescue services, and law enforcement. In the above-mentioned areas, SS-HAS can play a vital role in assessing any level of threat behind a wall in a hostage situation or rescue mission.
# The Influence of Electrode Impedance on the Parameters of Visual Evoked Potentials
## 3 Results
Figure 5: Variation analysis of the VEP latencies for twenty new electrodes of each type: (**a**) gold (Au); (**b**) silver (Ag); (**c**) silver/silver chloride (Ag/AgCl), where N1, N2, P1, P2 are the VEP components. Within the box, a vertical red line means the median value of the data set, the bottom and top blue lines of the box indicate the 25 th and 75 th percentiles, the whiskers present min and max, and the outliers are marked with the “+” symbol. Moreover, the differences between the amplitudes and latencies after the first and twentieth examinations are presented using the coefficient \(\Delta\). The presented analyses confirm the existence of a relationship between the number of performed examinations with the same set of reusable cup electrodes and the values of VEP  
Figure 6: The dependence of the mean values of VEP amplitudes i.e., N1-P1, N1-P2, and N2-P2 on the number of performed examinations recorded by twenty electrodes of each type—gold (Au), silver (Ag), silver/silver chloride (Ag/AgCl)—after the first, fifth, tenth, fifteenth, and twentieth VEP examinations. The coefficient \(\Delta\) represents the differences between the amplitudes after the first and twentieth tests. Figure 7: The dependence of the mean values of VEP latencies i.e., N1, P1, N2, and P2 on the number of performed examinations recorded using twenty electrodes of each type—gold (Au), silver (Ag), silver/silver chloride (Ag/AgCl)—after the first, fifth, tenth, fifteenth, and twentieth VEP examinations. The coefficient \(\Delta\) represents the differences between the latencies after the first and twentieth tests. parameters. Each successive use of a reusable cup electrode in a VEP examination affected the values of the latency, causing it to lengthen. In addition, an increase in amplitude values was noted. Despite obtaining similar values for the VEP parameters after the first registration, each subsequent test showed significant differences in the values of the VEP parameters for all electrodes. Comparing the values of the VEP parameters (e.g., N1-P1 and N1) recorded by the electrodes after the first and the twentieth examinations for the Au electrodes showed that there was an increase in the values of the amplitudes of individual components by an average of 61.14% and a prolongation of the latency by 30.09%. In contrast, for the Ag and Ag/AgCl electrodes, the values of the changes in amplitude and latency were, respectively, 52.93%, 23.5%, 49.56%, and 15.29%. The differences between the amplitudes and latencies of the rest of the VEP components were also considerable.
# Vibration Fatigue Analysis of a Simply Supported Cracked Beam Subjected to a Typical Load
## 2 Modal Analysis  
### Model of Cracked Beam  
As shown in Figure 1, the research object is a rectangular cross-section beam with an initial transverse crack. The length, height and thickness of the beam are \(L\), \(h\) and \(b\), respectively, and the position and depth of the crack are \(x_{c}\) and \(a_{0}\), respectively.  
Based on the crack, the beam can be transformed into a two-segment elastic beam with no mass torsion spring. For simplicity, the torsion spring coefficient \(K_{T}\) is considered only. The torsion spring coefficient [18] of the crack can be equivalent, as follows:  
\[K_{T}=\frac{E*h^{3}}{72(1-v^{2})F(r)} \tag{1}\]  
where \(v\) is the Poisson ratio; \(E^{*}\) is the complex modulus of elasticity [19], and \(E^{*}=E(1+\mathrm{i}\gamma)\); \(\gamma\) is the material damping loss factor; \(r\) is the relative depth of the crack, \(r=a_{0}/h\); and \(F(r)\) is the flexibility correction function [20] obtained through the strain energy density function:  
\[\begin{array}{l}F(r)=1.98r^{2}-3.277r^{3}+14.43r^{4}-31.26r^{5}+63.56r^{6}\\ -103.36r^{7}+147.52r^{8}-127.69r^{9}+61.5r^{10}\end{array} \tag{2}\]
# Comparison of Temperature Dependent Carrier Transport in FinFET and Gate-All-Around Nanowire FET
## 4 Discussion
At strong \(N_{inv}\), on the other hand, the \(\mu_{eff}\) of GAA NW-FET is further degraded for a rising temperature than FinFET. This is another evidence that FinFETs are suffering from the SR scattering than GAA NW-FETs (\(-0.162\) for GAA NW-FET and \(-0.016\) cm\({}^{2}\)/V-s\({}^{\prime}\)C for FinFET). At strong \(N_{inv}\), the \(d\mu_{eff}\)/\(dT\) is moderated approximately 18% compared to the weak \(N_{inv}\) region for GAA NW-FET, but it is dramatically changed (90%) for FinFET. At 25 \({}^{\circ}\)C, the \(\mu_{eff}\) of GAA NW-FET decreases by 23.4% between weak \(N_{inv}\) and strong \(N_{inv}\), whereas FinFET's decrease is more pronounced (56.1%). At \(V_{OV}=0.5\) V, the smaller \(g_{m}\) for FinFET (= 127.1 at 25 \({}^{\circ}\)C, but 188.2 \(\upmu\)S/\(\upmu\)m in the case of GAA NW-FET) is consistent with such different degrees of SR scattering for both devices. The reduced SR scattering for GAA NW-FET could be assisted by a round-shaped NW channel formation process using a vapor HCl etch. The surface roughness of GAA NW-FET channel is softened  
Figure 5: Effective mobility (\(\mu_{eff}\)) behavior comparison between GAA NW-FET and FinFET with \(L_{G}=70\) nm; (**a**) \(\mu_{eff}\)-\(N_{inv}\). Temperature dependence of \(\mu_{eff}\) for (**b**) GAA NW-FET and (**c**) FinFET. by the vapor HCl etching process. Thus, at a large \(V_{OV}\), the relatively higher value and slower degradation of \(g_{m}\) (in Figure 3c) and \(\mu_{eff}\) (in Figure 5a) in GAA NW-FET could be from the suppression of SR scattering in the channel compared to FinFET.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 3 Case Study  
### Single-Bus Transmission System  
The single-line diagram of the power system used in this study is shown in Figure 2. The generator is represented by an equivalent potential source. The transmission line is represented using the Bergeron line model in PSCAD/EMTDC. The load is represented by an ohmic-inductive load [59]. The accuracy of classification depends on the number of data used for training and testing. Therefore, it is necessary to prepare large numbers of data for different faults and system conditions. The PSCAD software (PSCAD V5 FREE) allows the creation of large sets of data using the multi-run component. In PSCAD, data are obtained for different situations by using the fault resistance and the fault onset angle. These data are presented to the user in the form of an output file from PSCAD [60].
## 3 Method Implementation and Application
### Definition of a Landscape Subdivision Criterion and Identification of Symbols or Shapes
#### 3.2.4. Subdivision by Vegetation  
The predominant natural element of Abruzzo region is made up of the numerous centuries-old woods scattered throughout the region. For this reason, we considered the tree distribution as a potential landscape subdivision factor. In this sense, three main areas can be identified:  
* The Adriatic coast of Abruzzo (Region-of-Origin 1 (RoO1) 1 in Figure 6), which extends from north to south along the entire coast and is bordered to the west by the slopes of Majella and Gran Sasso d'Italia. This first area includes woods with a prevalence of Mediterranean plants such as oak trees.
* The second area (RoO2 in Figure 6, divided into three subzones 2a, 2b, 2c) includes the coastal strip at higher altitudes (east sides of Gran Sasso and Majella) and insinuates itself into the center of the region including the Fucino plain and the Aquila hinterland. This area is characterized mainly by beech trees.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Requirement** & **Score** & **Judgement** \\ \hline Number of identified areas & 4 & Adequate \\ Shape suitability & Low & Not adequate \\ Shape/area consistency & 6.8 & Generally not adequate except for L’Aquila \\ \hline \hline \end{tabular}
\end{table}
Table 4: Assessment of the criterion: subdivision by provinces.  
Figure 5: Subdivision by geographical elements.  
* The third area (RoO3 in Figure 6) is the most fragmented geographically and includes the major mountain ranges of the Region (Gran Sasso, Majella, Sirente--Velino, Marsicano Mountains). Again, the beech woods are the forest populations that extend most: they cover more than 50% of the total area.  
The criterion produced three symbols (see Table 2). Again, the criterion results are unsuitable, as they do not fully respond to all the requirements (Table 6).
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 4 Experimental Results
### Baseline Models  
To evaluate the effectiveness of our proposed method, we conducted comparisons with the following well-established methods:  
* Most-Popular: This is one of the most commonly used recommendation methods, which suggests the item that appears most frequently in each session.
* Item-kNN [12]: A prevalent recommendation method that relies on the scores generated by users' ratings of items. It employs cosine similarity to recommend items with similar attributes.
* II-RNN [16]: This model is a session-aware model which leverages the architecture of inner and outer GRU as its primary framework for model learning.
* CAII [17]: This session-aware model is an extension of the method outlined in [16]. CAII incorporates context information, such as images and prices, into the model input. For comparison, we use the CAII-P strategy, which has shown the best performance in its experimental results.
* SASRec [23]: The method is based on the Transformer architecture. SASRec models the entire user sequence using a casual attention mask to consider item IDs for recommendations.
* BERT4 Rec [24]: A recommendation method based on the BERT architecture, which employs bidirectional self-attention to model user behavior sequences. Through the Cloze task [25], also known as the masked language model [26], it learns and predicts recommendations.
* BERT4 Rec\({}_{\text{+ST+TSA}}\)[26]: This is the most recent session-aware method, building upon the foundation set by [24], enhanced with ST (session token) and TSA (temporal self-attention) strategies.
* MCLRec [27]: One of the latest studies of session recommendation based on contrastive learning.