esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 AZTRM-D Simulated Real-World Scenario
### Initial Scenario  
To illustrate the practical implementation of the AZTRM-D methodology, let us consider a hypothetical scenario involving Company A, a global financial services provider. The company is entrusted with safeguarding a vast amount of sensitive data, includingcustomer Personally Identifiable Information (PII), transactional records, and proprietary investment models. Given its complex operations and stringent regulatory requirements (e.g., NIST 800-53, PCI DSS, SOX, GDPR), Company A requires a security strategy that not only enforces ZT principles [49; 55] but also seamlessly integrates RMF methodologies [33] and DevSecOps automation [19] into its software development life cycle.  
Company A operates a hybrid infrastructure that spans on-premises data centers and multi-cloud platforms, each presenting unique security challenges. By adopting AZTRM-D, the company ensures that security, risk management, and compliance enforcement are embedded directly into its DevSecOps framework, enabling a proactive, automated, and adaptive security posture.
## 1 Introduction
### Purpose  
This protocol was designed to achieve two primary goals: (1) gather in situ soundscape assessments from the public, which can be further analyzed and utilized in designing a soundscape index; (2) conduct recordings needed to reproduce the audio-visual environment of a location in a laboratory setting for conducting controlled experiments on soundscape. These two goals represent two levels of data required for developing a general soundscape model. The first enables large scale data collection, resulting in a database with thousands of perceptual responses and their corresponding quantitative data which can be statistically analyzed on a large scale, or used for training in machine learning modeling. In situ assessments also represent the most holistic assessment, ensuring all factors that influence the soundscape are present, including those which cannot be reproduced elsewhere.  
However, there are questions that cannot be practically addressed in situ, such as soundscape assessment of less- or un-populated areas, the influence of mismatched acoustic and visual cues, physiological and neural responses to various soundscapes, and so on [31]. Laboratory experiments with controlled environments are required to address these aspects. Toward the development of a coherent SSID, however, it is important that these two forms of data are collected simultaneously and with compatible methods, such that the results of the two approaches can be confidently combined and compared. In addition, since this protocol is intended to be used for the creation of a large-scale international database with additions carried out by several different and remote teams, it has been designed for efficiency, scalability, and information redundancy.
# Maximizing the Capacity of Edge Networks with Multicasting
## 6 Optimal Number of Copies
### Optimal Number of Copies in the Complete K-Ary Multicast  
In the complete \(k\)-ary multicast, the optimization objective becomes  
\[min\sum_{i=1}^{m}\frac{Q_{2}p_{i}}{(m-s)C_{i}}. \tag{11}\]  
_s.t._  
\[\left\{\begin{array}{l}C_{i}\leq n\\ \sum\limits_{i=1}^{m}C_{i}\leq\ nm\end{array}\right. \tag{12}\]  
**Theorem 4**.: _In the proposed multicast cooperation model, the optimal solution of Equation (11) is_  
\[C_{i}=\begin{cases}0;&p_{i}\leq 0\\ \sqrt{\frac{Q_{2}p_{i}}{\beta(m-s)}};&0<p_{i}<\frac{n^{2}\beta(m-s)}{Q_{2}}. \\ n;&p_{i}\geq\frac{n^{2}\beta(m-s)}{Q_{2}}\end{cases} \tag{13}\]  
The proof of Theorem 4 is placed in Appendix D.  
Theorems 3 and 4 reflect the relationship between the number of copies and the content's popularity. When the number of nodes, the number of files and the cache size remain constant, the number of the \(i\)th content copies is mainly decided by its popularity. The more popular the content, the bigger the copy number. The next step is to distribute these copies throughout the edge caching network.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 4 Experiments
### Experiments on Small-Scale Datasets  
In this section, we evaluate the performance of the proposed approach on three fine-grained image classfication benchmarks. Caltech-UCSD Birds-200-2011 (Birds) [25] includes 5994 images for training dataset and 5794 images for test dataset from 200 bird species. FGVC-Aircraft Benchmark (Aircrafts) [66] consists of 100 aircraft categories, each of which has 67 training images and 33 test images. Stanford Cars (Cars) [67] provides 16,185 images and 196 classes of cars. The Cars dataset has a rough 50-50 split per class, resulting in 8144 training images and 8041 test images. Table 1 presents classification accuracy results and inference speed for both our approach and counterparts. The accuracy results for counterparts are all reported from the studied papers, except for DropCov, whose results are absent in its paper. Our approach achieves good balance between running speed and classification performance. When the backbone is VGG-16, our approach significantly improves the performance of BCNN by 3.7% even though the two works share the same deep network architecture. Similarly, average accuracy increases by 6.9% for ResNet-50, where our network is identical to CBP. iSICE shows very competitive accuracy results, especially for VGG-16 and ResNet-50. On average, its accuracy for VGG-16 is superior to ours. However, iSICE adopts [PERSON]'s iteration, which eventually greatly decelerates the whole network. Even worse, to avoid high computation, iSICE reduces the feature dimension \(D\) to 256. The reduction causes information loss from the original features and impacts negatively on the accuracy. This phenomenon is more obvious for networks ResNet-50, ResNet-101, ConvNext-T [68], Swin  
T [69], and Swin-B [69], whose original \(D\) is large. Compared to iSICE, RegCov saves time from the normalization part while using the original features. The most relevant counterpart to our approach is DropCov. Its network architecture during inference is identical to ours, so running speed is always the same. However, for classification performance, ours is superior to DropCov by a clear margin. This validates our claim that due to the lack of training samples, the network branch in DropCov is not trained sufficiently and has difficulty predicting appropriate dropout rates. On the contrary, at the first training stage of our approach, [PERSON]'s iteration is adopted as the square root normalization approach. Unlike DropCov, [PERSON]'s iteration is not data-driven, so it is naturally robust to small datasets. Thanks to the strong baseline, our networks are trained more effectively at the second stage where the normalization is omitted.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 5 Experiment Setup and Evaluation
### Evaluation Metrics  
In order to determine the detection accuracy rate of the proposed model using IoTR-DS, it is evaluated using well-known performance metrics used in machine learning, which are: accuracy, recall, precision, and F1 score. These metrics are calculated using four parameters: True-Positive (\(T_{P}\)), True-Negative (\(T_{N}\)), False-Positive (\(F_{P}\)) and False-Negative (\(F_{N}\)). \(T_{P}\) determines the amount of malicious traffic that is correctly classified as malicious traffic by the model, where \(T_{N}\) indicates the amount of normal traffic that is correctly classified as normal by the model. On the other hand, \(F_{P}\) determines the amount of normal traffic that is incorrectly classified as malicious traffic by the model, where \(F_{N}\) defines the amount of malicious traffic that is incorrectly classified as normal traffic by the model.  
The accuracy metric measures the correct prediction ratio by dividing the total number of correct predictions (\(T_{P}\) and \(T_{N}\)) by the sum of all predictions, as illustrated in Equation (13).  
\[Accuracy=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}} \tag{13}\]  
Precision (Equation (14)) estimates the ratio of correctly classified attack traffic to all of the predicted attack traffic, whereas recall (Equation (15)) indicates the ratio of correctly classified attack traffic to the total attack traffic. The f1 score (Equation (16)) can be defined as the weighted average of precision and recall.  
\[Precision=\frac{T_{P}}{T_{P}+F_{P}} \tag{14}\]  
\[Recall=\frac{T_{P}}{T_{P}+F_{N}} \tag{15}\]  
\[F_{1}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{16}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Encoder & [6; 3] \\ Decoder & [3; 6] \\ Dropout & 0.5 \\ Activation function in hidden layers & Tanh, ReLU \\ Optimizer & Adamax \\ Loss function & Mean squared error \\ Learning rate & 0.001 \\ Number of epochs & 300 \\ Batch Size & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: DAE Hyperparameter Configuration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Binary Classification** & **Multi-Class** \\  & & **Classification** \\ \hline Number of hidden layers & 3 & 3 \\ Number of neurons in the hidden layer & 32 & 32 \\ Dropout & 0.5 & 0.5 \\ Activation function in the hidden layers & ReLU & ReLU \\ Activation function in the output layer & sigmoid & softmax \\ Optimizer & adam & adam \\ Loss function & BinaryCrossentropy & Categorical\_crossentropy \\ Learning rate & 0.001 & 0.001 \\ Number of epochs & 1000 & 1000 \\ Batch Size & 120 & 120 \\ Early\_stopping & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: DANN Hyperparameter Configuration.
## 4 Conclusions  
An analysis on the dispersion of energy in angular domain and characterization of SOFS of 38 GHz and 28 GHz outdoor and indoor radio propagation channels has been conducted. Plain AoA measurement results conducted for four different scenarios at these frequency bands in outdoor and indoor radio propagation environments have been first extracted from the open literature, and then extended to characterize azimuthal spread and SOFS of the radio channel. The considered quantifiers for SOFS are LCR, AFD, CD, and spatial auto-covariance. A detailed analysis on the impact of under-observation amplitude threshold-level, maximum Doppler frequency, and different envelope distributions on the fading statistics of the radio channel has been conducted. The conducted analysis provides various useful insights into the behavior of mmWave outdoor and indoor radio propagation mechanism. The proposed analysis can further be extended to study the optimal antenna elements separation in multi-antenna systems, modulation schemes, interleaving methods, interference mitigation methods, and channel estimation. Moreover, similar studies for other frequency bands and propagation scenarios of mmWave channels can be conducted to establish a comprehensive and generalized understanding of the fading statistics.  
Figure 11: Spatial auto-covariance and CD for different scenarios of indoor mmWave (Case 3; 28 GHz) radio propagation environments in (**a**,**b**), respectively. (\(m=4\) for LoS Scenario and \(m=1\) for NLoS Scenarios, \(f_{d}=10\) Hz).  
Figure 10: LCR and AFD plotted against different values of threshold levels in (**a**,**b**), respectively; for different scenarios of indoor mmWave (Case 3; 28 GHz) radio propagation environments. (\(m=4\) for LoS Scenario and \(m=1\) for NLoS Scenarios, \(f_{d}=1\) Hz).  
**Author Contributions:** All authors discussed and agreed on the idea and scientific content of the manuscript. [PERSON] and [PERSON] conducted the mathematical modeling and computer simulations. [PERSON] and [PERSON] contributed in writing and formatting of the manuscript. [PERSON] and [PERSON] contributed in discussions and analysis on the obtained results.  
**Funding:** A part of this work was funded by the EU ATOM-690750 research project approved under the call H2020-MSCA-RISE-2015.  
**Acknowledgments:** The authors would like to acknowledge the efforts of teams involved in designing and conducting the measurement campaigns for the results openly published in [4; 6; 7].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Model Validation  
In order to verify the reliability of the numerical model built in this paper, corresponding monitoring points are set up to monitor the surface subsidence in the process of shield tunneling. Seven monitoring points are arranged in the middle section of this area, and the layout of the monitoring points is shown in Figure 5. The surface subsidence of this section is measured and data are collected by a Trimble DiNi03 leveling instrument, and the monitoring frequency is one time for each day. In this paper, the average value of the measured data of each monitoring point during the monitoring week is selected as the verification model data. After the shield construction is completed, the measured results and numerical simulation results of the surface settlement are shown in Figure 6, and the error calculation of the cross section monitoring data and the numerical simulation results are shown in Table 2.  
Figure 4: Schematic diagram of the formation loss of the over-excavated part on the inner side of the curve.  
It can be seen from Figure 6 that the settlement trends of the ground surface are similar for the numerical simulation results and the measured results, the position of the maximum value of surface settlement is relatively close, and the shape of the surface settlement curve is relatively close. It can be seen from Table 2 that the measured data at each section monitoring point have a certain deviation. This is due to the measurement error of the instrument and the incomplete settlement of the ground surface, but its deviation is small, and the maximum standard deviation is 0.26 mm. The measured data can meet the accuracy requirements. However, the maximum error between the numerical simulation and the measured results is 7.3%, and the results of the two are relatively consistent, which verifies the accuracy of the numerical simulation results and the rationality of the recommended values of the shield construction parameters in this paper to a certain extent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Monitoring Points**} & \multicolumn{2}{c}{**Numerical**} & \multicolumn{2}{c}{**Measured Data/Standard**} & \multirow{2}{*}{**Relative Error**} \\  & **Simulation (mm)** & & **Deviation (mm)** & \\ \hline
1 & \(-\)13.84 & \(-\)13.20/0.10 & 4.9\% \\
2 & \(-\)16.25 & \(-\)16.72/0.21 & 2.8\% \\
3 & \(-\)18.34 & \(-\)18.92/0.19 & 3.1\% \\
4 & \(-\)19.98 & \(-\)20.13/0.11 & 0.8\% \\
5 & \(-\)19.40 & \(-\)20.23/0.17 & 4.1\% \\
6 & \(-\)17.76 & \(-\)16.55/0.26 & 7.3\% \\
7 & \(-\)15.34 & \(-\)16.01/0.11 & 4.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of measured and numerical simulation results at three sections.  
Figure 5: Layout of monitoring points.  
Figure 6: Comparison of measured surface settlement and numerical simulation results.
## 1 Introduction
### Modeling of Cell Separation/Partitioning  
While utilizing simplified analytical analysis (such as Stokes flow) and trial and error-driven experimental approaches are prone to many unresolved issues, numerical simulation techniques are becoming a routine interrogation tool to avoid such problems. More importantly, numerical analyses are vital to unravel underlying physics, to test different hydrodynamic effects, and to optimize process conditions and proposed designs, which can be very expensive and require a long series of experiments or almost impossible experimentally with equipment at hand regardless of the scale of the experiments [50; 51; 52; 53]. These approaches characterize microfluidic cell separation devices at two-particle and continuum levels. Formerly, numerical simulations were carried out to identify particle-level interactions using statistical mechanics over individual particle mechanics force field. In the latter, continuum-level simulations based on conservation laws are used to address microfluid system attributes. One can refer to lattice Boltzmann methods (LBM) as the main molecular level numerical simulation, and computational fluid dynamics (CFD) based on Navier-Stokes (NS) equations as the prominent continuum level simulation techniques utilized for cell separation applications (Figure 2). In the following, fundamentals of these two numerical methods are briefly reviewed to convey the essential knowledge needed for the remainder of the work.
# Neural Graph Similarity Computation with Contrastive Learning
## 4 Experiments and Results
### Further Analysis
#### 4.3.3 On Loss Function  
Furthermore, we analyze the hyperparameter \(\beta\) in Equation (5), which controls the contribution of supervised similarity prediction loss and the unsupervised contrastive loss. As shown in Figure 3c, generally speaking, a lower \(\beta\) is preferred, since the contrastive learning module mainly helps to learn more expressive embeddings and does not directly influence the similarity computation. Hence, importance should still be placed on the similarity prediction. Nevertheless, setting \(\beta\) to 0.25 brings higher mse compared with 0.5 on IMDB, showing that contrastive learning still plays a significant role in the overall loss function.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & AIDS & LINUX & IMDB & **Methods** & AIDS & LINUX & IMDB \\ \hline SimCNN & 5.3 & 5 & 6.6 & A\({}^{*}\) & 1324.7 & 896.3 & / \\ GMN & 7 & 7.1 & 9.1 & Beam & 13.2 & 9.5 & 119.4 \\ GraphSim & 7.5 & 7.5 & 8.1 & Hungarian & 8.2 & 6.8 & 105.6 \\ MGMN & 5.2 & 5.6 & 7.1 & V.J & 9 & 7.4 & 109.1 \\ H\({}^{2}\)MN & 0.9 & 0.8 & 1.2 & Noah & \textgreater{}10.0 & / & \textgreater{}100.0 \\ Conga & 0.3 & 0.3 & 0.3 & GENN-A\({}^{*}\) & \textgreater{}10.0 & \textgreater{}10.0 & / \\ w/o CL & **0.2** & **0.2** & **0.2** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency comparison. The values denote average time consumption on one pair of graphs in _milliseconds_. / represents that the results cannot be obtained due to excessive running time or are not provided in the original work (and cannot be reproduced).
# Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics
## 4 Future Challenges
### Open Research Questions in Object Handover  
Several recent works have already developed partial solutions regarding the human-robot object handover procedure [132; 133; 134], some of which are inspired by human strategies [135; 136; 83; 103]. However, none of them provides a generic solution for a human adaptive system, where the exchange of objects occurs fluently, similarly to what happens between two humans, for the robot to become a much more reliable and useful machine. For this purpose, several issues should be explored and developed in the future.  
To pass an object to its human coworker, most of the collaborative robots have a fixed 3D spacial handover point, or pre-plan this handover point, in accordance to human body perception (size, position, orientation). This leads the human to adapt to the movements of the robot, which limits the aimed fluency. The robot should be capable of delivering an object in the hand of its coworker even if the coworker is not looking. This kind of handover occurs frequently between humans, especially in tasks that require an absolute concentration with any sight miss, such as, for example, performing surgeries. For this, the robot should execute a continuous perception of its surroundings and the human, in order to constantly re-plan the handover point until reaching the contact moment.  
Furthermore, by the moment when both agents are simultaneously in contact with the object, the robot has to understand in real time who has possession of that object. For this purpose, payload changes and momentum sensibility must be taken into consideration to develop contact interfaces where the robot and the operator can communicate. The development of this issue will be useful for both situations where the robot plays the given or the receiver role. When contact is mutual, there is not a given and a receiver, since human intention can change at any instant. The machine should be prepared for contexts where the human may no longer want to receive that object that the robot is offering in that precise moment, or where the human reconsiders giving an object to the manipulator.  
Object grasp strategies are also underdeveloped when compared to efficient human-human handover. Apart from the generic object shape gripper adaption, the robot should analyse which part of the object will the human need to grab in order to perform its task without the need for adjusting the hand for grabbing the tool. This requires a deepest understating of each object composition, sectioning the parts where the human will wants or not to receive that same tool.
# An Optical Encoder Chip with Area Compensation
## 3 Theoretical Analysis of Light Intensity and Compensation Design
### Chip Implementation  
The chip has four photodiode arrays corresponding to four code tracks. Each M-code photodiode array has 11 photodiodes that read out 11-bit M-codes in parallel to provide  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Photodiode** & **Height (\(\mu\))** & **Top Length** & **Bottom Length** & **Photodiode** \\  & & **(\(\mu\)m)** & **(\(\mu\)m)** & **Area (\(\mu\)m\({}^{2}\))** \\ \hline A1/A11 & 810.9478 & 303.9871 & 313.4860 & 250,369.2260 \\ A2/A10 & 734.4633 & 304.8830 & 313.4860 & 227,084.6682 \\ A3/A9 &  
absolute position information. The photodiodes corresponding to the two incremental codes adopt an array structure. Each incremental photodiode array is divided into four groups of photodiodes, which output four sinusoidal signals with a 90\({}^{\circ}\) difference. One incremental array is used to assist in the readout of the M-code, and the other incremental array is used to achieve the subdivision of the angle. The chip is designed based on the 0.35 \(\upmu\)m CMOS process and has been taped and packaged. The area of the chip is 5.5 mm \(\times\) 3.9 mm. Figure 6 shows the physical photo of the chip, in which 1 and 4 are the photodiode arrays M1 and M2 corresponding to the M-code, respectively, and 2 is the photodiode array corresponding to the incremental code used to realize the angle subdivision. Finally, 3 is the photodiode array corresponding to the incremental code used to assist in reading the M-code signals.