esa-sceva/satcom-chunk-collection huggingface dataset:
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## 2 Problem Statements
### Effect of Room Reflections  
Loudspeaker-based reproduction systems are installed in non-anechoic conditions in most practical cases, and consequently unintended room reflections are included in the transfer function of the loudspeakers as follows,  
\[H^{(l)}\left(\overset{\rightarrow}{r}\right|\overset{\rightarrow}{r}_{s}^{ (l)}\right)=H_{f}^{(l)}\left(\overset{\rightarrow}{r}\right|\overset{ \rightarrow}{r}_{s}^{(l)}\right)+H_{\mathit{ref}}^{(l)}\left(\overset{ \rightarrow}{r}\right|\overset{\rightarrow}{r}_{s}^{(l)}\right), \tag{5}\]  
Figure 1: A typical sound field reproduction system in a listening room.  
where \(H_{ref}^{(l)}\bigg{(}\overset{\rightarrow}{r}\bigg{|}\overset{\rightarrow}{r}_{s} ^{(l)}\bigg{)}\) is the transfer function for the reverberant sound field. The reproduced field also includes the room reflections \(P_{ref}\bigg{(}\overset{\rightarrow}{r}\bigg{)}\), which increase the spatial error:  
\[\overline{e}^{2}=\frac{\int_{V_{c}}\bigg{|}\widetilde{P}_{f}\bigg{(}\overset{ \rightarrow}{r}\bigg{)}-P_{f}\bigg{(}\overset{\rightarrow}{r}\bigg{)}-P_{ ref}\bigg{(}\overset{\rightarrow}{r}\bigg{)}\bigg{|}^{2}dV}{\int_{V_{c}} \bigg{|}\widetilde{P}_{f}\bigg{(}\overset{\rightarrow}{r}\bigg{)}\bigg{|}^{2} dV}. \tag{6}\]  
On the contrary, room reflections are known to affect human localization of the sound source to a lesser degree according to law of the first wave front [35]. Late reflections hardly affect human localization of the sound source, and early reflections up to 10 ms can smear or shift the image of the perceived source location [35], but do not significantly change the localized direction. By removing the late reflections with a time window [41; 42], the effect of reflections on the spatial error can be suppressed to some extent. Yet, some early reflections cannot be perfectly removed due to overlap, and increase the spatial error.  
Hence, in non-anechoic conditions, the spatial error gives little explanations on the reproduced field [29]. This means that the spatial error cannot show whether or not there are experimental errors, such as positioning errors of microphones and loudspeakers, or difference between loudspeaker models and actual loudspeaker responses.
## 3 Optimization
### Optimized Workflow Characteristics
#### 3.2.1 Neural Network Structure  
The structure of the neural network influences the classification accuracy. In this evaluation, three common network structures were considered, and they were selected according to their compatibility and computational efficiency with the low-cost hardware. The following network structures were evaluated:  
* MobileNetV2 [37] (trained for five and twenty epochs)
* ResNet-50 [38] (trained for five epochs)
* VGG16 [30] (trained for five epochs)  
MobilenNetV2 is built on a very simple network structure made up of 53 layers, focusing on mobile applications. It uses comparatively few parameters (3.4 million) to achieve good classification performance at low computational costs [37]. The inference is about 50% faster than with RestNet-50 or VGG16.  
RestNet-50 consists of 50 layers with 25.6 million parameters. With the introduction of residual learning, network optimization for a specific application became easier, even for networks with increased numbers of layers [38].  
VGG16 is the biggest of all the tested networks, with a total of 138.4 million parameters in 16 convolutional layers [30]. However, when deploying it on the proposed hardware, including the TPU, it classifies about 3-4 frames per second. All the network structures are presented in detail in the corresponding articles.  
All the networks were trained for five epochs on the test set for a varying number of training images per part. MobileNetV2 was also trained for 20 epochs because it was found that training for 5 epochs achieved relatively low part classification accuracy.  
Figure 8 shows the influence of the neural network structure on the classification accuracy for different numbers of training images per part. Here, the accuracy distribution of MobileNetV2 is shown in blue (five epochs) and red (twenty epochs), ResNet-50 is shown in yellow, and VGG-16 is shown in purple.  
Figure 7: Renderings of test parts for the evaluation of the proposed workflow.  
As can be seen, the VGG-16 network achieved the highest average part classification accuracy of 93.17% on the test set. The influence of the network structure was particularly evident at low numbers of training images per part, where ResNet-50 reached average accuracies that were close to 0%, and the lowest accuracy achieved by the VGG16 network was 76.20%. With high numbers of training images, the difference between the network structures was reduced but was still evident. As in previous experiments, VGG-16 reached a maximum accuracy before dropping again with 400 training images per part. It was found that increasing the number of training epochs appeared to benefit MobileNetV2. However, this also resulted in a 400% increase in the network training effort, and the maximum accuracy was still less than that obtained by the VGG-16 network. Considering these results, VGG-16 was selected as the network structure for the proposed workflow.
# Smart Hospitals and IoT Sensors: Why Is QoS Essential Here?
## 5 Discussion
### Lesson 3: Tailor Your Technology to the Target Environment  
Here, we define what is necessary to bring such technology to modern hospitals. We can divide it into two main steps: deploy the architecture and design the applications. As mentioned before, the first step requires installing some equipment dedicated to running the middleware architecture. The hardware capacity will depend on the type of sensors. For instance, more robust sensors, such as Microsoft Kinect v2, impose specific requirements on the Data Gateway nodes. In particular, USB 3.0 interfaces and the Microsoft Kinect API will be required so that the Gateway can extract image frames from the Kinect. In this case, gateways must be compatible with the demands of computer vision techniques.  
On the other hand, as Sewio RTLS, some lightweight sensors only require the node to be capable of running its virtual machine. Some sensors even only require the compatibility of the node with some specific language to run their APIs. Besides the nodes, the network must be private. The technology and performance should suit the amount of data to be transmitted. All nodes running the Data Gateway instances and the Core server should connect to this private network. The server requires at least two network interfaces: one for the private network and another to connect to the hospital network. Therefore, hospital applications can process data streams from the server in any hospital area, including remote locations.  
Concerning the application's design, nowadays, there are already many well-known publish-subscribe platforms that provide easy-to-use APIs for real-time data. Our middleware is designed to be compatible with such technologies. Thus, applications are only required to use our middleware wrapper, which adds QoS arguments to the subscription calls. The applications do not need to implement any additional code but only add their parameters. As the middleware has default delay and jitter limits, the application will still be compatible if the user does not inform them.
## 2 Model and Methods
### Power Shifting to Access Attractors or Windows
#### 2.3.2 Power-Shift Base Attractor Optimization  
In this investigation we use the power-shift method to collect the statistics associated with the successful access of a desired attractor. We initialize the system on the base attractor for 1 control parameter value \(Y_{0}\) and power shift to \(Y_{PS}\) while at different locations on the limit cycle uniformly distributed in time. For convenience, we set 1 point on the limit cycle as the reference point. An obvious choice for the reference point is the time associated with the attractor's peak-output power. Next, select any location on the limit cycle as the time after the peak (TAP). We observe to which attractor each TAP evolves. Finally, select a new \(Y_{0}\) usually in increments of \(\Delta Y_{0}=\) 0.01 and repeat the investigation for the same power shift, \(Y_{PS}\). The results are tabulated across the domain of the base attractor and evaluated for the largest tranche (or time intervals) of TAPs that accesses the desired attractor, or it might be said, for the most efficient \(Y_{0}\) of the base attractor domain.  
Figure 3: LEs are graphed as a function \(Y\). Lyapunov spectra are plotted in the forward (black) and backward (red) scans for (**a**) WI and (**b**) WII. Note, the regions where red and black dots do not override each other.  
An experimentalist might choose to time the power shift at the middle of the tranche, because it gives the largest allowable margin of error for a successful power shift. In the following discussion we include noise and ramp the injected signal.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 3 Proposed Methodology
### Soft Nearest Neighbor Loss (SNNL)  
The primary limitation of conventional GAN-based augmentation lies in its inability to explicitly optimize the feature similarity between synthetic and real samples. As a result, classifiers trained on GAN-augmented datasets may still struggle with the minority-class detection due to the feature space discrepancy. To address this, we introduce Soft Nearest Neighbor Loss (SNNL) [23] into the GAN training process, ensuring that generated samples not only resemble real data but also align well in the feature space.  
Mathematically, given a dataset with feature embeddings \(X=\{x_{1},x_{2},\ldots,x_{N}\}\) and corresponding class labels \(Y=\{y_{1},y_{2},\ldots,y_{N}\}\), SNNL is defined as  
\[SNNL(X,Y,T)=-\frac{1}{N}\sum\limits_{i=1}^{N}\log\left(\frac{\sum\limits_{ \begin{subarray}{c}i=1\\ j\
eq i,y_{i}=y_{j}\end{subarray}}^{N}e^{-\frac{\|x_{i}-x_{j}\|^{2}}{t}}}{\sum \limits_{\begin{subarray}{c}k=1\\ k\
eq i\end{subarray}}^{N}e^{-\frac{\|y_{i}-x_{k}\|^{2}}{t}}}\right) \tag{1}\]  
where \(\|x_{i}-x_{j}\|^{2}\) represents the squared Euclidean distance between feature vectors, and \(T\) is a temperature parameter that controls the weighting of distances. Lower values of \(T\) emphasize closer neighbors, reinforcing tighter intra-class clustering, whereas higher values allow for broader variations in feature space.  
By minimizing SNNL, the generator is encouraged to produce synthetic samples that exhibit stronger intra-class cohesion while maintaining clear inter-class separability. This ensures that the generated samples not only resemble real data but also preserve the semantic structure of network traffic in the feature space. Furthermore, SNNL helps mitigate mode collapse by enforcing sample diversity, thereby improving the generalizability of the trained IDS classifier.
# A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment
## 6 Conclusions  
In this paper, a novel approach has been proposed to address the problem of high communication overhead in the two-layer federated learning architecture. By incorporating edge computing into federated learning, the frequency of direct interaction between the client and the central server is reduced, which leads to a decrease in communication costs and delays in the federated learning training process. Furthermore, to enhance the training efficiency of the hierarchical federated learning architecture, a time-effective hierarchical aggregation algorithm has been designed, which utilizes a semi-asynchronous aggregation strategy at the edge layer and synchronous aggregation at the service layer. The simulation results validate the effectiveness and reliability of the proposed algorithm. The FedEdge algorithm proposed in this paper is suitable for edge computing scenarios, such as the IoT, and has high application value in data domains organized in a hierarchical structure, such as healthcare and transportation.  
However, it is worth noting that the current study only conducted stand-alone simulation experiments on MNIST and CIFAR-10 image datasets without exploring the algorithm's effectiveness in other application domains. Moreover, the heterogeneity of data and devices simulated in the experimental environment is not representative of actual edge nodes. Thus, the feasibility and efficiency of the algorithm have only been theoretically proven. Hence, future research should focus on modeling complex scenarios in practical applications and addressing communication issues in mobile networks, such as channel interference or congestion, poor network conditions, and limited client resources. Additionally, investigating the applicability of the hierarchical federated learning architecture to real networks should be considered in further studies.  
Conceptualization, W.Z. and Y.Z.; methodology, F.L.; software, F.L. and Y.Z.; validation, W.Z., Y.Z. and F.L.; formal analysis, F.L.; investigation, F.L.; resources, W.Z.; data curation, F.L.; writing--original draft preparation, Y.Z.; writing--review and editing, H.Z.; visualization, Y.Z.; supervision, H.Z.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset used in this study can be obtained from PyTorch.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Comprehensive Performance  
Considering both heat transfer performance and flow resistance characteristic, the comprehensive thermal performance factor (_TPF_) is adopted to evaluate the heat exchanger. _TPF_ is calculated using the following equation [34]:  
\[TPF=\frac{(j_{o}/f_{o}^{1/3})_{nf}}{(j_{o}/f_{o}^{1/3})_{bf}} \tag{18}\]  
\[j_{o}=\frac{Nu_{o}}{Re_{o}\cdot Pr_{o}^{1/3}} \tag{19}\]  
where subscripts _nf_ and _bf_ represent nanofluids and base fluid, respectively.  
Figure 12: Euler number versus Reynolds number.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Correlation** & \(a\) & \(b\) & \(c\) & **Maximum Deviation** \\ \hline Equation (15) & 0.0284 & 0.8692 & 0.3081 & 9.62\% \\ Equation (16) & 4.8456 & \(-\)0.4650 & 0.4484 & 4.04\% \\ Equation (17) & 310.17 & \(-\)0.4694 & 0.4364 & 4.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Predicted values and deviations of the correlations.  
If the value of _TPF_ is higher than 1.0, the integrative performance of the nanofluid is enhanced in comparison with the base fluid by the addition of nanoparticle, and higher value is preferred. Figure 13 shows the variation trend of the thermal performance factor versus volume flow rate. As can be seen from Figure 13, the values of nanofluid with different mass fraction are all higher than 1.0. Compared with the base fluid, the comprehensive performances of the 0.2 wt %, 0.5 wt % and 1.0 wt % nanofluid are enhanced significantly, by 3%-5%, 15%-17% and 24%-26%, respectively.
## 2 Results
Therefore, by making the formal change \((0\to k)\Leftrightarrow(0_{a}\to 1_{a})\) in Equations (6)-(8), one directly obtains the desired result:  
\[\tilde{D}_{0k}=\mathbf{m}_{0k}\cdot\mathbf{m}_{k0}=\left(D_{0k},0\right)\quad\text{ electric dipole strength};\]8 \[\tilde{D}_{0k}=\mathbf{m}_{0k}\cdot\mathbf{m}_{k0}=\left(D_{0k},0\right)\quad\text{ electric dipole strength};\]9  
where \(\hbar\omega_{a}\) is the vibrational quantum of the \(a\)-th normal mode). Moreover, the matrix elements of the electric and magnetic dipole over one-quantum vibrational transitions can be evaluated according to the following expressions [46; 47], based on electrical harmonicity assumption:  
\[\tilde{G}_{0k}=\mathbf{m}_{0k}\cdot\mathbf{m}_{k0}=\left(G_{0k},0\right)\quad\text{ magnetic dipole strength};\]0 \[\tilde{G}_{0k}=\mathbf{m}_{0k}\cdot\mathbf{m}_{k0}=\left(G_{0k},0\right)\quad\text{ magnetic dipole strength};\]1  
In Equation (14), \(L_{ia}=\partial x_{i}/\partial q_{a}=\partial\dot{x}_{i}/\partial\dot{q}_{a}\) is the transformation matrix relating the Cartesian nuclear displacements \(x_{i}\) to the normal coordinate \(q_{a}\). [PERSON]'s \(\mathbf{L}\) matrix [48] is often named \(\mathbf{S}\) in VCD literature, and individual indexes are adopted to label the atom \(\lambda\) and the \(i\)th Cartesian component of the nuclear displacement (\(i=1,2,3\)). This double index notation is cumbersome when writing loops in programs. For this reason, we prefer to merely list the Cartesian nuclear displacements along a vector with \(3N\) components: \(\mathbf{x}=\left(x_{1x},x_{1y},x_{1z},x_{2x},x_{2y},x_{2z},...,x_{Nx},x_{Ny},x_{Nz}\right)\). The real vectors \(\partial\mathbf{\mu}/\partial x_{i}=P_{i}\) collect the Atomic Polar Tensors (APTs) [49; 50; 51]. The \(P_{ik}\) matrix element represents the change of the \(k\)th Cartesian component of the electric dipole caused by a change in nuclear position along the \(x_{i}\) coordinate (\(i=1.3N\)). The real vectors \(M_{i}=\partial\mathbf{m}/\partial\dot{x}_{i}\) collect the Atomic Axial Tensors (AATs) [49; 50; 51]. The \(M_{ik}\) matrix element represents the change in the \(k\)th Cartesian component of the magnetic dipole caused by a change in the nuclear velocity \(\dot{x}_{i}\). By making use of Equation (14), one obtains the following expressions for the dipole and rotatory strengths of the \(0_{a}\to 1_{a}\) vibrational transition:  
\[\tilde{G}_{0k}=\mathbf{m}_{0k}\cdot\mathbf{m}_{k0}=\left(G_{0k},0\right)\quad\text{ magnetic dipole strength};\]2 \[\tilde{G}_{0k}=\mathbf{m}_{0k}\cdot\mathbf{m}_{k0}=\left(G_{0k},0\right)\quad\text{ magnetic dipole strength};\]3  
Further details about the evaluation of Equation (13) are given in Appendix B. The relation of molecular polarizabilities with Lambert-Beer's law and the concept of cross section is given in Appendix C.
## 3 Results and Discussion
### NIRS/NIRS-Based Hyperscanning Research
#### 3.2.3 Motor Synchronization  
Applications in motor synchronization were relatively common and generally had participants complete synchronous physical motions as an experimental task in the lab e.g., [139]. These tasks included a cooperative button press task [140] where participants interact either side-by-side e.g., [141] or face-to-face e.g., [142], computer games [143; 144; 145; 146; 147], joint-tapping tasks [144; 32; 40], and synchronization tasks [145; 146; 147]. One study utilized a finger-tapping task to record between-brain hemodynamics [40]. A unique study examined cooperation-related inter-brain synchrony between mothers and their children [147].  
_Setting._ All of the twelve studies in applications in motor synchronization were conducted in a lab setting such as in [32; 41; 139]. For example, [PERSON] et al. [140] tested the correlation between fNIRS signals and time intervals of button pressing after hearing auditory cues. They were able to manipulate the auditory stimulations and sync with the fNIRS equipment in a lab setting.  
_Orientation._ Applications in motor synchronization adopted various orientation methods e.g., [139; 140; 141; 142; 143; 144; 145; 146; 147]. For example, Six studies had participants oriented side-by-side [139; 144; 145; 133; 146], Two studies had participants oriented back-to-back [144; 32], and four of the studies had participants interact face-to-face [140; 145; 147].  
Group SizeOne group organized participants into groups greater than two [139], but the majority (11) organized participants into pairs. One study selected only females for a coordination task [145], while the remaining studies incorporated both males and females.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.2 Effect of the Size of Spatial Query Ranges  
In this simulation, we varied the side length of spatial query ranges from 500 m to 5000 m to examine how the size of spatial query ranges affects the performances of SR, MQM, QRT, BQRT, and GQRT.  
As shown in Figure 10, GQRT performs much better and are less sensitive to this parameter than SR, MQM, QRT, and BQRT for _Uniform_ and _Skewed_. As the side length of each spatial query range becomes longer (_i.e._, the size of each spatial query range becomes larger), the excessive overlap among spatial query ranges reduces the size of the safe region assigned to each moving object \(o\), and thus the server in SR should frequently determine \(o\)'s new safe region. The excessive overlap among spatial query ranges also increases the number of node split of the BP-tree, QR-tree, and BQR-tree, which incurs huge amount of computation time. In addition, the increment of node splits accelerates height growth of the BP-tree, QR-tree, and BQR-tree, which leads the server to assign smaller resident domains to the moving objects, and thus the server in MQM, QRT, and BQRT frequently searches new resident domains for the moving objects that exit their small resident domains. On the other hand, GQRT is nearly not affected by the side length of spatial query ranges due to the third advantage of the GQRT over MQM, QRT, and BQRT mentioned in Section 5. As compared to SR, MQM, QRT, and BQRT, GQRT takes \(23.5\%\), \(34.1\%\), \(58.9\%\), and \(67.8\%\), respectively, of the server workload for _Uniform_. On the other hand, GQRT takes \(39.9\%\), \(40.6\%\), \(62.3\%\), and \(77.1\%\) of the server workload, as compared to SR, MQM, QRT, and BQRT, respectively, for _Skewed_.  
Figure 11 shows the effect of the side length of spatial query ranges (_i.e._, the size of spatial query ranges) on the total number of messages. As shown in the figure, BQRT and GQRT perform better than SR, MQM, and QRT for _Uniform_ and _Skewed_ due to the same reason mentioned in the first simulation. On the other hand, SR performs the worst for _Uniform_, whereas MQM performs the worst for _Skewed_.  
Figure 10: CPU-time _vs._ size of spatial query ranges. (**a**) _Uniform_; (**b**) _Skewed_.  
This is because longer side length of spatial query ranges more negatively affects the performances of SR and MQM than the performance of QRT. In all cases, GQRT achieves the best performance for _Uniform_ and _Skewed_. As compared to SR, MQM, QRT, and BQRT, GQRT incurs \(7.3\%\), \(10.6\%\), \(17.9\%\), and \(68.8\%\), respectively, of the communication cost for _Uniform_. On the other hand, GQRT incurs \(12.1\%\), \(11.1\%\), \(16.8\%\), and \(66.2\%\) of the communication cost as compared to SR, MQM, QRT, and BQRT, respectively, for _Skewed_.
## 2 Discussion on Water Pressure Reduction Coefficient of Sub-Sea Tunnel Lining
### Proposal of Water Pressure Reduction Coefficient  
This paper puts forward the selection method of water pressure reduction coefficient of sub-sea tunnel lining, mainly considering three factors: surrounding rock permeability coefficient \(\mathbf{k}_{\mathrm{r}}\), tunnel grouting ring thickness \(\mathbf{t}_{2}\) and grouting effect \(\mathbf{k}_{\mathrm{r}}/\mathbf{k}_{\mathrm{g}}\). According to the permeability coefficient, the surrounding rock is divided into class A surrounding rock (\(\mathbf{k}_{\mathrm{r}}\leq 0.2\) m/d), class B surrounding rock (\(0.2\) m/d \(<\mathbf{k}_{\mathrm{r}}\leq 0.8\) m/d), and class C surrounding rock (\(0.8\) m/d \(<\mathbf{k}_{\mathrm{r}}\leq 1\) m/d). The water pressure reduction coefficient selection under various surrounding rock conditions is shown in Tables 4-6. It should be noted that these reduction coefficients can only be used for horseshoe tunnels and have previously defined tunnel support thickness and permeability, and water level values. In addition, the recommended values in the following tables are based on the above discussion and a large number of numerical calculations. The water pressure reduction coefficients are directly given here, which is only a recommended value. Although a few reduction coefficients are verified above, more importantly, more similar projects need to be verified in the future.  
Figure 6: The comparison diagram of external water pressure of initial support of the sub-sea tunnel.