esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, the added value of measurement data in the application of an ANN was demonstrated. For this purpose, a procedure for the extraction and analysis of features from a dynamic measurement data series was presented. Conclusions pertaining to the extracted measurement data characteristics and the weld quality were discussed for each geometry individually. The added value of the procedure was validated, and the following conclusions can be drawn:  
* ANNs are only as good as the data set provided. It is essential that sufficient information is available to describe the target values adequately. Previous work has always used process parameters as input data. In the present work, this was sufficient for predicting the quality category of weld beads. With increasing complexity of the geometry, the process parameters became less and less significant due to a lack of information about the welding process.
* Measurement data provide essential information about the welding process. To use measurement data for an ANN, the focus must be put on data preparation. This includes a suitable feature extraction method to convert dynamic measurement series into static characteristic values.
* A variety of tools are available for feature extraction, depending on the application. In the present case, the Python package tsfresh, univariate analyses and correlation matrices were successfully used. In this context, it must be considered that the feature analysis is dependent on geometry, as well as on the target value.
* The comparison of the ANN with and without the consideration of measurement data shows a clear added value regarding the integration use of sensors. This way, especially complex additive manufactured components can be evaluated significantly more effectively.
* The described feature extraction and analysis method is suitable to reliably identify relevant features.  
Conceptualization, A.M. and S.B.; methodology, A.M.; software, S.B.; validation, A.M., S.B. and J.R.; formal analysis, A.M.; investigation, A.M., S.B. and J.R.; resources, A.M.; data curation, S.B.; writing--original draft preparation, A.M. and S.B.; writing--review and editing, J.R., M.B. and M.R.; visualization, A.M.; supervision, M.B. and M.R.; project administration, A.M.; funding acquisition, A.M., M.B. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry for Economic Airs and Energy (BMWi) through the Industrial Cooperative Research Association (AiF) and the German Welding Association (DVS) under the project entitled \"Certify as you build--Quality assurance for the directed energy deposition\", with the grant number IGF 20.612 N. The authors would like to thank the BMWi, AiF and DVS for their support.  
Not applicable.  
The authors declare no conflict of interest.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol
#### 5.1.2 Key Confirmation  
Key confirmation prevents MITM attacks, ensuring both parties derive the same shared secret.  
Mechanism  
After decapsulation, parties derive a shared secret using a KDF. To confirm the shared secret, each party computes and exchanges HMACs (using HMAC-SHA-256) over a pre-defined data structure including nonces or session identifiers. The MACs are verified to ensure that no MITM attacker has replaced either party's public key.  
Security Assumption  
The security relies on the following:  
1. The collision resistance of SHA-256.
2. The pseudorandomness and unforgeability properties of HMAC.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple
### The Optimization of the Rotor Iron Core by Adopting a Rotor Pole Arc Offset
#### 3.1.3 Cogging Torque and Torque Ripple  
The cogging torque and the electromagnetic torque under different eccentricities are shown in Figures 5-7. The peak value of cogging torque was 20.3 Nm when the offset distance was 0, which is 2.4% of the rated output torque. Peak values were 15.2 Nm and 17.9 Nm when the offset distances were 20 mm and 40 mm, respectively, which is 11.8% and 25.1% lower than that when the offset distance was 0. When the air-gap was uniform, the average value of output electromagnetic torque is 912.4 Nm, the peak value of torque fluctuation was 176.7 Nm, and the fluctuation coefficient was 13.8%. Employing an eccentric rotor pole arc would reduce the cogging torque; however, the average torque output would also be reduced with the increase of the offset distance. As can be found in Figure 6, when the offset distance was 40 mm, the average output electromagnetic torque decreased to 876.5 Nm, which is 3.9% lower than the case of non-offset, and the torque ripple was 74.8 Nm, which is 57.7% lower than the case of non-offset.  
Figure 7 shows the comprehensive comparison of the cogging torque, average torque and torque ripple under different eccentricities. Firstly, the cogging torque varied nonlinearly with offset distance. The cogging torque achieved its greatest and smallest values, i.e., 33.4 Nm and 6.33 Nm, when the offset distances were 5 mm and 15 mm, respectively. Secondly, the average torque output decreased with the increase of offset distance; and the average torque achieved the lowest value when the offset  
Figure 4: The influence of rotor surface arc offset distance on back electro-motive-force.  
distance was 40 mm. In the next part, the topology of the permanent magnets and the iron bridge were optimized to minimize the torque ripple.
## 3 The Construction Model of Virtual Power Plant
### Objective Function
Otherwise, when the output is greater than the planned output, the DG cannot be fully utilized. The objective function is:  
\[\left\{ \begin{array}{l}\max\!f_{3}=\sum\limits_{k=1}^{n}[a\sum\limits_{i=1}^{m }P_{k0}+(1-a)\sum\limits_{i=1}^{m}P_{ki}^{DG}X_{i}^{DG}]\\ a=\left\{\begin{array}{l}1,\sum\limits_{i=1}^{m}P_{ki}^{DG}X_{i}^{DG}-P_{ki}^ {ES}X_{i}^{ES}+P_{ki}^{DR}X_{i}^{DR}-P_{k0}\geq 0\\ 0,\sum\limits_{i=1}^{m}P_{ki}^{DG}X_{i}^{DG}-P_{ki}^{ES}X_{i}^{ES}+P_{ki}^{DR}X_{ i}^{DR}-P_{k0}<0\end{array}\right.\end{array} \right. \tag{14}\]  
where \(a\) is the coefficient, which represents whether the DG is fully utilized; if satisfied, the value is 0, and vice versa. It is for 0, and vice versa 1. Through the construction of VPP, the DG resources in the area can be utilized to the greatest extent, but if all the DG are selected to construct, these resources will cause a lot of waste, so we should make a reasonable choice to make the highest degree of DG consumption. 4 Highest Degree of Regional Polymerization  
When selecting resources for every area, we need to consider the spatial location and load density of the area. The spatial location attribute and the load density attribute of each area are regarded as the attribute data of the spatial elements, and the correlation calculation of the degree of regional aggregation is carried out by using the fusion distance between the two kinds of attributes. The definition of fusion space distance is:  
\[d(i,j)=\omega_{p}\|p_{i}-p_{j}\|+\omega_{\rho}\|\rho_{i}-\rho_{j}\| \tag{15}\]  
where \(\omega_{p}\) and \(\omega_{\rho}\) are the weights of spatial location and load density attributes, respectively, and the sum of them is 1; \(p_{i}\) and \(p_{j}\) are the position coordinates of area \(i\) and area \(j\); \(\rho_{i}\) and \(\rho_{j}\) are the load densities of area \(i\) and area \(j\), respectively. In this paper, the degree of regional polymerization is measured by the sum of the maximum fusion space distance of \(m\) areas. The objective function is:  
\[\left\{ \begin{array}{l}\min\!f_{4}=\sum\limits_{i=1}^{m}\max\{X_{i}X_{h}d(i,h)\}\\ h=1,2,\ldots,m\end{array} \right. \tag{16}\]  
where \(X_{i}\) and \(X_{h}\) indicate whether there is any resources involved in the construction of the VPP in areas \(i\) and \(h\), respectively, and if satisfied, the value is 1, and vice versa 0; \(d(i,h)\) represents the fusion space distance between areas \(i\) and \(h\). To sum up, the optimal objective function set of VPP is as follows:  
\[F=(\min\!f_{1},\min\!f_{2},\max\!f_{3},\min\!f_{4}) \tag{17}\]  
The objective function \(F\) represents the minimum daily average cost, the optimal daily load characteristics, the highest degree of DG consumption and the highest degree of regional polymerization.
## 2 Methodology
### Spatiotemporal Adaptive Prediction Network  
#### 2.2.1 LSTM Network  
To effectively extract the temporal features of assembly steel strut axial force data, this study constructs an LSTM model. The model is designed to capture the dynamic temporal variations in historical axial force data, providing high-quality temporal feature representations for subsequent prediction tasks. LSTM networks, known for their significant advantage in handling long-term dependencies in time-series data, are widely used in dynamic trend prediction applications.  
As shown in Figure 2, the historical axial force data for each assembly steel strut are processed independently through separate LSTM modules. Leveraging the coordinated operation of the input, forget, and output gates, the LSTM effectively retains key features, enabling it to capture the temporal patterns and regularities of axial force variations. After processing through the LSTM network, the output temporal feature representations encapsulate the critical variation trends of assembly steel strut axial forces across different time steps. The computation process of the LSTM network can be described as follows:  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{1}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\tilde{C}_{t}=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{3}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{6}\]  
where \(x_{t}\) is the current input vector, \(h_{t-1}\) and \(h_{t}\) denote the previous and current hidden states, respectively, and \([h_{t-1},x_{t}]\) denotes their concatenation for computing gate activations and candidate states; \(W_{f}\), \(W_{i}\), \(W_{C}\), and \(W_{o}\) together with \(b_{f}\), \(b_{i}\), \(b_{C}\), and \(b_{o}\) are the weight  
Figure 2: Overall structure of LSTM.  
matrices and bias vectors for the forget, input, candidate, and output gates; \(\sigma\) and tanh are the sigmoid and hyperbolic tangent functions, respectively; \(f_{t}\), \(i_{t}\), and \(o_{t}\) are the outputs of the forget, input, and output gates; and \(\hat{C}_{t}\) is the candidate cell state used to update and form the new cell state \(C_{t}\).
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 5 Experiment and Evaluation
### Evaluation
#### 5.4.1 Short-Term Forecasting  
First, we set up a short-term prediction experiment with a forecast length of 16 days. Due to the sufficient number of samples, we were able to compare our model with traditional time series methods that lack few-shot learning capabilities. The experimental results demonstrate that our model achieved the best performance across three key metrics--MAE, MAPE, and RMSE--with average values of 0.0056, 1.1144, and 0.0067, respectively, significantly outperforming all benchmark models. Figure 5 presents a comparative analysis of the average performance of different models on the short-term forecasting task. Specifically, compared to TimesFM, our model reduces MAE, MAPE, and RMSE by approximately 77.6%, 83.7%, and 76.0%, respectively; compared to Informer, Dlinear, and PatchTST, our model achieves reductions of over 99% on all metrics.  
Additionally, Table 1 provides detailed prediction results for two representative resource pools, further validating the superior performance of our model across different resource environments.
# Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
## 4 Performance Evaluation
As the number of active source-destination pairs increased, the link losses caused by packet collisions increased. If the node on the shortest path from the source node to the destination node failed to send the DIO control packet to the intermediate nodes, the node retransmitted the DIO control packet in the next interval, thus, resulting in increased control packet overhead. Collision Avoidance Geographic P2P-RPL achieved average 13.0%, 54.8%, and 80.7% less control packet overhead compared with Location-Aided P2P-RPL, ER-RPL, and P2P-RPL, respectively. The decrease in network convergence time means that P2P-DRO packets sent by the destination node finished the P2P route discovery procedure much faster. This reduced the number of P2P-DIO control packets for the amount of reduced network convergence time. The network convergence time difference of 114 ms between Location-Aided P2P-RPL and Collision Avoidance Geographic P2P-RPL led to additional DIO control packet overhead. Figure 13: The network convergence time versus the number of active source-destination pairs.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 2 System Model
### System Architecture
occur more than once, representing a subset of the total number of apartments per building (i.e., the whole sample space) as follows, and are shown in Figure 2.  
* A subset of apartments deployed with neither a SBS nor a CGC station (i.e., \(\{\mathbf{S_{SBS}}=\varnothing,\mathbf{S_{CGC}}=\varnothing\}\));
* A subset of apartments deployed with only a SBS (i.e., \(\{\mathbf{S_{SBS}},\mathbf{S_{CGC}}=\varnothing\}\));
* A subset of apartments deployed with only a CGC station (i.e., \(\{\mathbf{S_{SBS}}=\varnothing,\mathbf{S_{CGC}}\}\)); and
* A subset of apartments deployed with both a SBS and a CGC station (i.e., \(\{\mathbf{S_{SBS}},\mathbf{S_{CGC}}\}\)). All these above subsets constitute the whole sample space \(S\). Since we limited our focus to estimating only the capacity of aTMS, the deployment options 2 and 4 (i.e., the subset of apartments corresponding to \(\{\mathbf{S_{SBS}},\mathbf{S_{CGC}}=\varnothing\}\) and \(\{\mathbf{S_{SBS}},\mathbf{S_{CGC}}\}\)) in the sample space for the deployment are necessary to consider. Note that if we assume that the deployment of either a SBS or a CGC station in an apartment map is the real value 1 and no deployment of either of them maps to 0. Hence, following all the possible deployment options of a SBS and a CGC station in an apartment, the sample space can be represented as \(S=\{00,10,01,11\}\), where each occurs with an equal probability of \(\frac{1}{4}\). **Remark 1**. : _There are two possible equally likely deployment outcomes for both a SBS and a CGC station so that each outcome occurs with a probability of \(\frac{1}{2}\). Hence, an event concerning the outcome of a SBS and a CGC station occurs with the joint probability of \((\frac{1}{2}\times\frac{1}{2})=\frac{1}{4}\)._
## 3 Results and Analysis
### Microstructure of Rare Earth Boron-Chromium Co-Infiltration  
In our previous studies, it was found that when the CeO\({}_{2}\) rare earth content was 4%, the boronizing layer had better combination and performance [18]. Now, we studied the effect of different Cr\({}_{2}\)O\({}_{3}\) content on rare earth boron-chromium co-infiltration. Figure 4 shows the microstructure of boronizing layer with Cr\({}_{2}\)O\({}_{3}\) content of 1%, 2%, 3%and 4% respectively at boronizing temperature of 950 \({}^{\circ}\)C, time of 4 h, and rare earth content of 4%. As can be seen from Figure 4, with the increase of Cr\({}_{2}\)O\({}_{3}\) content, the microstructure of boronizing layer is gradually changing. When the content is 1%, the microstructure of boronizing layer is relatively sparse, and a large number of holes appear in boronizing layer. At 2%, the boronizing microstructure is compact and there are few holes; at 3%, 4%, the microstructure of boronizing layer shows a few holes. This shows that too much chromium oxide (Cr\({}_{2}\)O\({}_{3}\)) is added to the boronizing agent, which may cause chromium atoms to accumulate on the surface of the boronizing layer, thereby inhibiting the growth of the boronizing layer and reducing the thickness of the boronizing layer. The experiment also shows that the boronizing layer has better microstructure when the content of Cr\({}_{2}\)O\({}_{3}\) is 2%.  
Figure 5 shows the structure of the rare-earth boron-chromium co-diffusion layer at different temperatures when boronizing time is 4 h, rare earth CeO\({}_{2}\) content is 4% and Cr\({}_{2}\)O\({}_{3}\) content is 2%. The figure shows that as the boronizing temperature increases from 850 \({}^{\circ}\)C to 950 \({}^{\circ}\)C, the boronizing layer structure becomes continuous and compact, However, when the boronizing temperature increased from 950 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C, holes began to appear in the structure of the boronizing layer. When the boronizing temperature exceeded 980 \({}^{\circ}\)C, holes appeared in the outermost layer of the boronizing layer. This is due to the fact that the temperature rises too high, accelerates the diffusion of boron atoms, and it is difficult for boron atoms to form a continuous boronizing layer with the surface Fe atoms. The increase in temperature also causes more gas to escape from the infiltrated layer, resulting in holes and sparseness on the surface of the boronizing layer. The results show that the  
Figure 3: Relationship between Ln\(G\) and 1/T under different rare earth contents (straight line).  
boronizing temperature of 950 \({}^{\circ}\)C is a reasonable boronizing temperature for the rare-earth boron-chromium co-diffusion.  
Figure 4: Microstructure of boronizing layer with Cr\({}_{2}\)O\({}_{3}\) content of 1%, 2%, 3%, and 4%, respectively. (a) 1% Cr\({}_{2}\)O\({}_{3}\); (b) 2% Cr\({}_{2}\)O\({}_{3}\); (c) 3% Cr\({}_{2}\)O\({}_{3}\); and (d) 4% Cr\({}_{2}\)O\({}_{3}\).
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization  
Channel equalization techniques generally estimate the transfer function of communication channels and try to remove the channel distortion by an adaptive filter [48]. However, the common equalizers with linear adaptive algorithms become powerless in the field of high-speed VLC, because of the intrinsically limited modulation bandwidth of LEDs [49] and nonlinear distortion introduced by photoelectric devices and VLC channels. Recently, ML-based equalizers, such as artificial neural networks (ANN) [50], etc., have been developed for VLC systems. ML-based equalizers have shown outstanding equalizing performance, especially on modeling nonlinear phenomena, by adopting neural-network-based algorithms. Despite this, challenges such as massive computational complexity, slow convergence speed, and relatively poor generalization still prevent the further practical application of ML-based equalizers for VLC systems. Therefore, researchers have developed many variants, as presented next, to overcome those challenges.
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
## 2 System Model  
### System Description  
We considered an indoor SWIPT network for short-distance downlink communication, as shown in Figure 1. We had a base station with antennas of Nt \(>\) 1 and receivers of K equipped with one antenna. Both the transmitter and receivers used dipole antennas sharing the same spectrum. The transmitter provided SWIPT services to the receivers. In this system, the transmitter transfers information to one receiver, in which the information receiver divides 50% of the received signal for the harvested power, and the other receivers use all of the received signal as the harvested power. We formulated the object function for energy efficiency (EE) with the BER constraint for data communication. We adjusted the excited current and phase for each transmitter antenna, and maximized the wireless power transfer efficiency using the self-adaptive dynamic differential evolution (SADDE) algorithm and asynchronous particle swarm optimization (APSO).
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 3 Related Work
### Related Work on Traffic Prediction  
Traffic prediction is one of the major issues in measurement-based network control. An accurate prediction of the traffic load in the next time interval helps to provide a high quality of service (QoS) for multimedia applications [21] to improve resource allocation for cognitive radio systems [22] and to assist in capacity planning for Wi-Fi networks [23]. The traffic-related time series prediction problem has been extensively explored in both Ethernet and WiFi networks with different applications [23; 24].  
Traffic prediction algorithms are highly dependent on the traffic patterns and the statistical characteristics of traffics. As it is affected by the traffic aggregation and sampling (or smoothing) [25], the network traffic has a mix of self-similarity, short range, and long range dependencies [22]. If the network traffic is stable [26; 27], we can model it as an autoregressive moving average (ARMA) process. However, if the network traffic is dynamic or its statistical properties change over time, ARMA cannot describe those variations very well. In this case, using an adaptive filter or a neural network [24] to predict the network traffic is a wise choice since they can continuously track the network changes by adjusting the weight of the filter (or neural network) in accordance with the instant traffic load. The neural network approach which can capture the non-linear nature of network traffic, however, is complicated to implement. The over-fitting problem in neural network forecasting also limits its applications to online prediction scenarios.  
Traffic load in an arbitrary node of an underwater network, however, is quite different from the large-scale traffic of the Internet, which exhibits aggregation, self-similarity, and long-range dependence features. The traffic at the link layer tends to be more random and dynamic under the effect of all protocols at the upper layers. The packet generation pattern at the application layer and the control flow and reliability control at the transport layer as well as the paths selected at the network layer all affect the traffic pattern at the link layer. For this reason, we estimate the link layer traffic using statistical methods in this paper.