esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Overview  
FedDeep is customized based on historical \(PM_{2.5}\) concentrations and external factors (e.g., weather conditions, temperature, and wind speed). In particular, the data collected by the air quality monitoring and meteorological sites are transmitted to the ECS through wireless links.  
A system schematic of FedDeep is shown in Figure 1. An ECS deployed in each urban region is responsible for data processing and computing offloading. Each ECS offers computing resources for ESTNets to be trained using \(PM_{2.5}\) and external factors from corresponding urban regions. For instance, Figure 1 indicates that the entire Jiangsu Province can be regarded as 13 urban regions; that is, divided according to the boundaries of prefecture-level cities. Note that the study area and data in this section are consistent with that of the subsequent experimental simulations. The ECSs are defined as \(S=(S_{1},S_{2},\ldots,S_{M})\), where \(M=13\) denotes the total number of ECSs. \(D=(D_{1},D_{2},\ldots,D_{M})\) is assumed as the set of local dataset. \(D_{i}\) serves as the local dataset \(i\) originated from urban region \(i\), which is transmitted to the \(i^{th}\) ECS. Let \(\text{ESTNet}=(\text{ ESTNet }_{1},\text{ ESTNet }_{2},\ldots,\text{ ESTNet }_{M})\) represent the set of local \(ESTNet\). Specifically, the local \(ESTNet_{i}\) is deployed on ECS \(i\) for training using the local dataset \(D_{i}\), and then the trained gradients are uploaded to the central cloud to update the parameters of the global \(ESTNet\). In this manner, the computational pressure of the central cloud can be decreased through deploying \(ESTNet\) in a distributed way. In addition, the central cloud does not have to store all the data, which decreases the risk of data leakage.  
In summary, realization of multi-urban \(PM_{2.5}\) forecasting can be organized into four steps: In the first step, a ECS receives \(PM_{2.5}\) and external factors collected by sensors through wireless links. In the following step, the ECS processes these data for training the local ESTNet. Prior to model training, the model parameters in the ECS need to be  
Figure 1: The overview of FedDeep.  
encrypted and shared with models in other ECSs. In view of this, an ECS is required to encrypt these parameters before sending, and then the other ECSs need to decrypt them after receiving. In the last step, ECSs train corresponding ESTNets through local datasets, and then upload the trained gradient to the central cloud to update the parameters of the global ESTNet.
## 3 Prism Pair CEP Shifter
### Theory
#### 3.2.1 General Configuration
In order to obtain analytical results, we rewrite Equation 2 as:  
\[\Lambda_{op}=l\cos\rho+A_{\mathrm{2}}A_{\mathrm{3}} \tag{6}\]4  
By derivation with respect to \(\omega\), one gets:  
\[\Lambda_{op}=l\cos\rho+A_{\mathrm{2}}A_{\mathrm{3}} \tag{6}\]5  
with the following relations:  
\[\Lambda_{op}=l\cos\rho+A_{\mathrm{2}}A_{\mathrm{3}} \tag{6}\]6 \[\Lambda_{op}=l\cos\rho+A_{\mathrm{2}}A_{\mathrm{3}} \tag{6}\]7 (15b) \[\Lambda_{op}=l\cos\rho+A_{\mathrm{2}}A_{\mathrm{3}} \tag{6}\]8 (15c) \[\Lambda_{op}=l\cos\rho+A_{\mathrm{2}}A_{\mathrm{3}} \tag{6}\]9 (15d) \[\Lambda_{op}=l\cos\big{(}\varphi-\theta_{4}\big{)}+A_{\mathrm{2}}A_{\mathrm{3 }}=l\cos\varphi\cos\theta_{4}+l\sin\varphi\sin\theta_{4}+A_{\mathrm{2}}A_{ \mathrm{3}} \tag{7}\]0  
Using for the refractive index, a first order Taylor expansion as a function of the static electric field, \(E\), the isochronous condition leads to the following relation (Appendix I) between \(a\) and \(b\):  
\[\Lambda_{op}=l\cos\big{(}\varphi-\theta_{4}\big{)}+A_{\mathrm{2}}A_{\mathrm{3 }}=l\cos\varphi\cos\theta_{4}+l\sin\varphi\sin\theta_{4}+A_{\mathrm{2}}A_{ \mathrm{3}} \tag{7}\]1  
where \(\theta_{20}\) and \(\theta_{40}\) are respectively the values of \(\theta_{2}\) (internal refraction angle in first prism) and \(\theta_{4}\) (external refraction angle in air at the exit of first prism) for \(E=0\) (Figure 5) and \(\beta\) is the apex angle of the prisms. Similarly, generation of a group delay without variation of the CEP leads to the condition:  
\[\Lambda_{op}=l\cos\big{(}\varphi-\theta_{4}\big{)}+A_{\mathrm{2}}A_{\mathrm{3 }}=l\cos\varphi\cos\theta_{4}+l\sin\varphi\sin\theta_{4}+A_{\mathrm{2}}A_{ \mathrm{3}} \tag{7}\]2  
This situation, that will be called \"Pure Group Delay\" (PGD) generation in the rest of this paper, implies that \(a\) and \(b\) are such that (Appendix II):  
\[\Lambda_{op}=l\cos\big{(}\varphi-\theta_{4}\big{)}+A_{\mathrm{2}}A_{\mathrm{3 }}=l\cos\varphi\cos\theta_{4}+l\sin\varphi\sin\theta_{4}+A_{\mathrm{2}}A_{ \mathrm{3}} \tag{7}\]3  
Finally, the iso-dispersive condition being:  
\[\Lambda_{op}=l\cos\big{(}\varphi-\theta_{4}\big{)}+A_{\mathrm{2}}A_{\mathrm{3 }}=l\cos\varphi\cos\theta_{4}+l\sin\varphi\sin\theta_{4}+A_{\mathrm{2}}A_{ \mathrm{3}} \tag{7}\]4  
This leads again to a similar relation. In this case, however, the analytical result is more complex and is not given here for conciseness. As a first conclusion, we see that for a particular set of the ratio, \(b/a\), that is to say, a specific geometry, it is possible to make the system behave like an isochronous CEP shifter, a PGD generator or an iso-dispersive CEP shifter.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 4 Dosimetric Models
### Multilayered Sphere  
The first model used in this paper is a multilayered sphere. The advantage of this model is that it is possible to define an analytical solution taken as reference to compare the numerical results. Figure 3 shows the voxel-based mesh and the tissue type assigned to each layer. Table 1 reports the details about the geometry and the tissue properties.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Tissue** & **Radius (mm)** & **Conductivity (S/m)** \\ \hline
1 & Skin & 80 & 0.0002 \\
2 & Fat & 76 & 0.043 \\
3 & Muscle & 74 & 0.34 \\
4 & Skull & 72 & 0.02 \\
5 & Muscle & 68 & 0.34 \\
6 & Cerebrospinal fluid & 66 & 2.0 \\
7 & Brain & 64 & 0.11 \\
8 & Cerebrospinal fluid & 42 & 2.0 \\
9 & Brain & 38 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 1: Multilayered sphere structure.  
Figure 2: Tetrahedron with the inradius \(r\) and the circumradius \(R\), respectively, drawn in orange and blue.
# Mechanical Performance of Built-Up Columns
## 1 Introduction
Furthermore, the thickness of the connectors should be the same as that of the tubes. As the novel built-up section is mainly used in the wall and floor in CFS systems, its cross-sectional width is generally between 90-240 mm. However, it is not appropriate to apply the research results of open sections governed by local buckling directly to the novel built-up section. In addition, the study of the multi-tube built-up columns including more than two components is very limited [26]. Most of the widely used multi-tube built-up columns are made of hot rolled steel, whose joints are welded together or bolted with high strength [27]. The instability behaviour of the built-up section made of hot rolled steel connected by welding or bolting is different from that of the built-up section composed of a cold-formed thin-walled square tube assembled through screws. Furthermore, the force on each components of multi-tube columns may not be the same. This structural characteristic determines that the multi-tube built-up column including more than two components is affected more seriously by shear force than a double-chord built-up column. Therefore, it is questionable to apply the research results of a double-chord built-up column directly to a multi-tube built-up column. This study aims to investigate the mechanical performance of the novel built-up column and to explore some structural parameters that can improve the performance. The performance of the novel built-up columns was firstly experimentally investigated. The failure load, failure deformation, load-displacement curve and load-strain curve were measured. The influence parameters, including spacing between tubes, connectors' type and transverse diaphragm, were taken into account in tests. Then, a series of numerical models were established and verified by experimental results. The influence of the numbers of connectors, web height of connector and adding connectors at column ends were analyzed by those numerical models. Finally, the numerical results were compared with the North American Specification.
## 3 The Proposed Protocol
### Login Phase  
In the login phase, the user needs to be verified via the PDA. Upon the PDA's authentication of the user identity's legitimacy, the user can log in via the PDA successfully; then, the PDA generates an authentication request for some specific BASN. Furthermore, the PDA transmits this authentication request to the GWN. The login phase includes three steps from L1 to L3:  
L1: Firstly, \(U_{i}\) enters the identity and password \(\left(ID_{i}^{*},PW_{i}^{*}\right)\). Secondly, PDA uses the physically unclonable function \(PUF_{sum}(\cdot)\) to verify the user's identity. Specifically, PDA computes \(V_{ii}^{*}=PUF_{sum}(PW_{i}^{*}),V_{i}^{*}\parallel S_{M}^{U_{i}*}\parallel k _{ii}^{*}=h\left(ID_{i}^{*}\parallel V_{ii}^{*}\right)\oplus A_{1},k_{i}^{*}= FE.REP(V_{ii}^{*},k_{ii}^{*})\)and \(A_{2}^{*}=h\left(V_{i}^{*}\parallel k_{i}^{*}\parallel S_{M}^{U_{i}*}\right)\bmod n_{0}\). Thirdly, PDA compares whether \(A_{2}{}^{*}\) is equal to \(A_{2}\); if \(A_{2}{}^{*}\)=\(A_{2}\), the user's identity can be verified. Then, it proceeds to step L2 to continue the execution; otherwise, \(PUF_{sum}(\cdot)\) will add 1 to the value of \(sum\) automatically when the user tries to enter another \(\left(ID_{i}^{*},PW_{i}^{*}\right)\) again for the login. If the value exceeds the preset maximum value, the session will be terminated, and the user's account will be frozen until \(U_{i}\) re-registers.  
L2: PDA runs a 1024-bit RSA cryptosystem to generate public key \(e_{i}\) and private key \(d_{i}\) for \(U_{i}\), and PDA keeps \(d_{i}\) secret. Then, the PDA selects a random number \(r_{u}\in\left[1,n-1\right]\); chooses the identity of \(MS_{j}\), which the user wants to acquire; extracts the timestamp \(T_{1}\); and calculates the following parameters: \(B_{1}=h(V_{i})\oplus\left(h(r_{u}\parallel T_{1})\parallel e_{i}\parallel S_ {M}^{U_{i}}\right)\) and \(B_{2}=h\left(PID_{i}\parallel MIS_{j}\parallel h(r_{u}\parallel T_{1}) \parallel e_{i}\parallel S_{M}^{U_{i}}\right)\), where \(e_{i}\) is the public key of \(U_{i}\).  
L3: \(U_{i}\to GWN:\left\{F_{U_{i}}(t),PID_{i},MIS_{j},B_{1},B_{2},T_{1}\right\}\). PDA sends the requested information to \(GWN\) in a public channel.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 5 Simulation Results and Discussion
### Training Performance Evaluation  
For the RBF-NN adopted in the proposed VLC transceiver system, evaluating its training performance was essential to prove its feasibility and superiority compared with traditional DNNs. In addition, the training time of the RBF-NN is critical for implementing it through a hardware design for real-time signal processing. Long training time causes extra delay time for signal transmission and even leads to the failure of compensating for signal loss and distortions. Therefore, decreasing the training time is important for building a real-time signal-processing baseband.  
The convergence curves of training the RBF-NN with the 2.7 Gbps and 3.6 Gbps datasets are presented in Figure 18. It can be noticed that the trained model fixed well with the expectation and owned an error less than \(10^{-8}\) after 1000 epochs, which proved the feasibility of choosing the RBF-NN as the post-equalizer. The training times of different types of NNs vary since the complexity of their structures and training methods determine the time cost. Taking into consideration the implementation of NNs to systems-on-chip as a real-time equalization method, the training time should be as short as possible. The training times of both the RBF-NN and a traditional DNN at different communication data rates were simulated, and the simulated results are presented in Figure 19. It is obvious that, with an increased data rate, the training time for the NN also increased. This proportional relationship reflects the fact that the training resources were dramatically consumed when the transmitted signal deviated hugely from the original. Moreover, compared with the traditional DNN, the RBF-NN consumed much less training time due to its superior characteristic of local approximation. With the same data rate achieved, the training time of the equalization scheme utilizing the RBF-NN could be reduced by 86.7%. Since the RBF-NN was a local approximate network, only part of the weights of all the neurons needed to be trained during each iteration, which could speed up the training process of the RBF-NN. The DNN required a much longer training time since it was a global approximation network, which meant that the weights of all the neurons should be adjusted during each iteration. Therefore, utilizing the RBF-NN contributed to saving computing resources.  
Figure 18: Training convergence curves of RBF-NN: (a) 2.7 Gbps data transmission; (b) 3.6 Gbps data transmission.
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## 5 Conclusions  
In order to realize intelligent SPC, this paper proposes a pattern recognition method based on multilayer Bi-LSTM for quality control, which uses feature learning to realize end-to-end HPR and CCPR. After experimental study, the following conclusions can be drawn. First of all, the convergence speed, recognition accuracy and over fitting degree of the network are related to the optimization algorithm, batch size and network layer number, and these parameters should be properly selected. Secondly, after learning, the network can extract the optimal feature set adaptively from the raw data, which has higher quality than the traditional manual expert feature set. Finally, with the raw data as input, the recognition rate of multilayer Bi-LSTM is 99.89% for HPs and 99.26% for CCPs. The recognition accuracy of this model is significantly better than that of traditional methods and other deep learning methods.  
To sum up, the proposed multilayer Bi-LSTM method reduces the trouble of manual feature extraction, is competent for HPR and CCPR with high accuracy, and can effectively improve the level of intelligence and automation of SPC. It will likely become an integral part of industry 4.0 technology.  
There is still a problem in this study. The length of the control chart and the number of histogram groups input into the network must be fixed, for example, the length of the data in this study is 25. However, in practical application, it may be necessary to adjust the data length to adapt to the production process. The current method must retrain the network, which will cause inconvenience. In the future, we will solve this problem, so that the network can adapt to different data lengths.  
Conceptualization, T.Z. and Z.L.; software, T.Z. and Z.L.; validation, M.W., X.G.; resources, Z.S. and D.C.; writing--original draft preparation, Z.L.; writing--review and editing, T.Z., M.W. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research is supported by China Scholarship Council, grant number 201806545032, and National Natural Science Foundation of China, grant number 51575014, 51875008, and 51975020.  
The production data used in this study are from Tongyu Heavy Industry Co., Ltd.  
The authors declare no conflict of interest.
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 3 Application of Synthetic Data
### Imaging Result for Undulating Terrain  
Figure 6 depicts the imaging result obtained using the improved DEXP method with Model A. The extracted section is taken through the center of Model A at \(y\) = 60 km. To evaluate the validity and reliability of the improved DEXP method, a test was conducted using steep and undulating topography on the observed surface, ranging from 2 km to 10 km. The extreme points obtained from the method provide a satisfactory indication of the location of Model A, accurately capturing its features. Figure 7 presents a plot of the estimated depths from Figure 6 and calculates the error ratio. The statistical data demonstrate that the imaging results can achieve a highly reliable estimation of depth when  
Figure 5: The forward Bouguer gravity anomaly of Model B1, B2, and B3 and imaging results of the main profile (\(y\) = 60 km) through the combined model. **(a)** The forward Bouguer gravity anomaly observed for flat terrain. **(b)** The imaging result of the DEXP method. **(c)** The imaging result of the improved DEXP method.  
the undulating terrain is below 5 km, which is five times the actual depth. However, as the topography becomes more pronounced, the accuracy diminishes. When the undulating terrain reaches 10 km, which is ten times the actual depth, the error ratio reaches 0.2.  
Figure 8 showcases the imaging results obtained using the improved DEXP method applied to the combined models, including Models B1, B2, and B3. The extracted section is taken through the centers at \(y=60\) km. The topography of the observed surface ranges from 1 km to 8 km. For Model B1, even as the degree of the terrain becomes stronger, the extreme points still possess the ability to identify the theoretical depth. The inverted horizontal location and depth are both in accordance with the theoretical parameters. When the observed surface is stronger than 6 km, the accuracy of the inversion decreases slightly, but the extreme points still provide a reliable indicator for the model. The method performs well in providing valuable imaging results for Model B2, which has the strongest anomaly. The recognition is obvious; however, the accuracy decreases as the undulating terrain becomes stronger, reaching 8 km on the observed surface. The signal becomes weaker, and  
Figure 6: Improved DEXP method imaging results of Model A for different undulating terrain (2–10 km).  
there is some migration in the location of depth. In the case of Model B3, which has the medium-strong anomaly, the extreme point can still provide a referenceable location when the topography is below 2 km. However, the reliability of imaging results declines rapidly as the undulating terrain becomes stronger.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 5 Simulation Results
### Network Residual Energy  
In SEEC, two mobile sinks are used only in sparse regions while clustering is done in the top four dense regions. In EEDBR, the nodes with high residual energy and low depth are selected to forward data to sinks, which results in fast energy drainage of these nodes. In DBR, the selection criteria of the forwarder node are based on lowest depth only, which results in much energy consumption. In the proposed routing protocol, two sinks are restricted to left and right regions. Moreover, if the number of sparse regions are very lesser than dense regions; the movement of mobile sinks enhances the energy consumption. In spare regions, the nodes are far from each other, while, in dense regions, nodes are closer to each other. Total network residual energy consumption of SEEC, EEDBR, DBR and the proposed scheme is shown in Figure 4.  
Figure 3: Network lifetime and stability period. SEEC: sparsity aware energy efficient clustering routing protocol; DBR: depth base routing scheme; EEDBR: energy efficient depth based routing protocol.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 3 Results
### Influence of Time Interval on the Micro-Capsule Networks  
To investigate the influence of individual sample size on the epileptic model during the identification of epilepsy EEG signals, the epilepsy state can be identified in a short time, which can save the identification time and detect epilepsy symptoms as early as possible. Epilepsy EEG signals corresponding to different time intervals are used to detect epilepsy and evaluate the performance of the micro-capsule networks. To obtain the best time interval, the time intervals are separately set to 0.6 s, 0.8 s, 1.0 s, 1.2 s, and 1.4 s, and the correct rate of epilepsy at different time intervals can be obtained through the micro-capsule networks. Taking binary classifications as an example, the changing trend of recognition rate is shown in Figure 4.  
With the increase in the time interval, the accuracy of epileptic recognition by the micro-capsule networks is gradually improved. That is, when the time intervals are between 0.6 s and 1.4 s, increasing at 0.2 s intervals, the correct rate of epileptic recognition increases steadily. However, after the time intervals are set to greater than 1.0 s, the curve of epilepsy identification correct rate does not increase significantly. Even if the subsequent time length increases, the epilepsy recognition rate only increases by about 0.35%. To recognize epileptic symptoms using a micro-capsule network model and in the shortest possible time, the input time length of epileptic EEG signals is set to 1.0 s, and the training iteration and accuracy of the micro-capsule network model are obtained for different dataset cases, which are AB vs. E, C or D vs. E and A vs. C vs. E., as shown in Figure 5. The experimental results show that the time interval parameter not only ensures the recognition ability of the proposed model but also reduces the epileptic recognition time.  
Figure 4: The change trend of recognition rate for EEG segment over different time intervals.
## 3 Proposed Method
### Albedo Recovery
#### 3.2.2 Loss Functions  
To train the albedo recovery model, we minimize the error between the reconstructed value and the ground truth. However, the ground truth of unseen regions in real face is unavailable. To address the issue, we flip the reconstructed texture horizontally and make it as similar as possible to the input texture image. The loss function for reconstructed texture is defined as  
\[\begin{array}{ll}L_{recon}&=\frac{1}{t}\sum\limits_{p}\left(\left|UV_{M}[p] \left(UV_{T}^{*}[p]-U\hat{V}_{T}[p]\right)\right)\\ &+\frac{\lambda_{f}}{t}\sum\limits_{p}\left(\left|UV_{M}[p]\left(UV_{T}^{*}[p] -U\hat{V}_{T_{flip}}[p]\right)\right),\end{array} \tag{3}\]  
where \(UV_{M}\) is the visibility mask, \([p]\) denotes the pixel spatial location, \(t\) is the number of visible pixels, \(UV_{T_{flip}}\) means horizontally flipping the reconstructed texture \(UV_{T}\), \(\lambda_{f}\) denotes the weight of the reconstruction loss component associated with the horizontally-flipped reconstructed texture with respect to that associated with the original reconstructed texture, and the symbols \"\" and \"\" indicate the ground truth and reconstructed values, respectively.  
The loss functions for SH illumination coefficients, albedo, and normal are formulated, respectively, as  
\[L_{l}=\parallel SHL^{*}-S\hat{H}L\parallel_{2}^{2}, \tag{4}\]  
\[L_{a}=\left|UV_{A}^{*}-U\hat{V}_{A}\right|, \tag{5}\]  
\[L_{n}=\left|UV_{N}^{*}-U\hat{V}_{N}\right|. \tag{6}\]  
The total loss is defined as  
\[L_{total}=\lambda_{recon}L_{recon}+\lambda_{l}L_{l}+\lambda_{a}L_{a}+\lambda_{ n}L_{n}, \tag{7}\]  
where \(\lambda_{recon}\), \(\lambda_{l}\), \(\lambda_{a}\), and \(\lambda_{n}\) are the weights to balance different losses.  
Figure 2: Pipeline of albedo recovery.