esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Evaluation Metrics  
To assess the accuracy of the models' predictions, various metrics were employed, like the mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and Theil's inequality coefficient (TIC). These metrics serve as indicators of predictive error, and lower values signify improved predictive accuracy [2; 11; 38]. The formulas for each of these indicators are provided below.  
\[MAE=\frac{1}{m}\sum\limits_{i=1}^{m}\left|k_{i}-\overset{\wedge}{k_{i}}\right| \tag{12}\]  
\[MAPE=\frac{1}{m}\sum\limits_{i=1}^{m}\left|(k_{i}-\overset{\wedge}{k_{i}})/k_ {i}\right|\times 100\% \tag{13}\]  
\[MSE=\frac{1}{m}\sum\limits_{i=1}^{m}\left(k_{i}-\overset{\wedge}{k_{i}}\right) ^{2} \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{m}\sum\limits_{i=1}^{m}\left(k_{i}-\overset{\wedge}{k_{i} }\right)^{2}} \tag{15}\]\[TIC=\frac{\sqrt{\frac{1}{m}\sum\limits_{i=1}^{m}{(k_{i}-\overset{\wedge}{k}_{i})^{2 }}}}{\sqrt{\frac{1}{m}\sum\limits_{i=1}^{m}{k_{i}}^{2}+\sqrt{\frac{1}{m}\sum \limits_{i=1}^{m}{\overset{\wedge}{k}_{i}^{2}}}}} \tag{16}\]  
In these equations, \(k_{i}\) is the actual value at time \(i\), \(\overset{\wedge}{k}_{i}\) is the predicted value at time \(i\), and \(m\) is the number of predicted data points.
# Study on the Deployment of Ultra-Wideband Positioning Base Stations in Pig Farms
## 4 Experimental Analyses
### Simulation and Analysis
In scenario a, when the equilateral triangle, the DOP value is the largest, and when the aspect ratio of rectangles is the inverse of each other, the DOP values are the same. The conclusion can be drawn that when the base stations are deployed in a square configuration, the DOP value is minimized, resulting in optimal positioning accuracy. In comparison, deploying the base stations in a rectangular arrangement yields slightly higher DOP values for the longer side than for the shorter side, indicating relatively higher accuracy along the longer side. Figure 5: Coordinates of base stations when shape is different. Figure 6: Mesh and contour plot of HDOP, VDOP, and PDOP. Note: In this figure, the mesh plot of the DOP, the X and Y coordinates are the location of the base stations; the Z coordinate is the value of the DOP.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 2 LoRaWAN
### LoRaWAN Keys  
The security features as defined in LoRaWAN standards rely heavily on the above-described secret keys. Firstly, the preprogrammed **AppKey** (and **NwkKey**, as in LoRaWAN v1.1) takes care of the secure authentication of LoRaWAN devices during the join procedure. Secondly, the derived session keys enable end-to-end encryption between an LoRaWAN device and the application server. For the encryption algorithm, the well-known AES-128 is defined. A further feature, which protects the integrity of the LoRaWAN application data, is the frame counter. By synchronizing to the up-link counter values, the network server can detect potential spoofing attempts. Thirdly, the integrity of the LoRaWAN messages is guaranteed with the MIC, which, effectively, is a cryptographic signature calculated with the AES-CMAC algorithm.
# Effects of Velocity Profiles on Measuring Accuracy of Transit-Time Ultrasonic Flowmeter
## 2 Methodology
### Turbulent Flow  
For the turbulent flow in a pipe, the velocity profile will be  
\[u=u_{m}(1-\frac{r}{R})^{1/n}=u_{m}(1-s)^{1/n} \tag{19}\]  
where, the exponent n depends on Reynolds number, as shown in Table 1[19; 26].  
Substitute Equation (19) into (9), we get  
\[\Delta T=\frac{2R}{u_{m}\sin\theta\cos\theta}\int_{0}^{1}\frac{2(1-s)^{1/n}}{K ^{2}-\left(1-s\right)^{2/n}}ds \tag{20}\]  
Because \(K^{2}>>\left(1-s\right)^{2/n}\), we finally obtain  
\[\Delta T=\frac{n}{n+1}\frac{4 Ru_{m}\cos\theta}{c^{2}\sin\theta} \tag{21}\]  
The flowrate for turbulent flow is  
\[Q=\frac{n\pi Rc^{2}\tan\theta}{4n+2}\cdot\Delta T \tag{22}\]  
If the effect of velocity distribution is neglected, the velocity u in Equation (9) is considered to be a constant, \(u=u_{m}\), then  
\[\Delta T=\frac{2R}{u_{m}\sin\theta\cos\theta}\int_{0}^{1}\big{[}\frac{1}{K-1} -\frac{1}{K+1}\big{]}ds=\frac{2R}{u_{m}\sin\theta\cos\theta}\cdot\frac{2}{K^{ 2}-1} \tag{23}\]  
Because \(K^{2}>>1\), we finally obtain  
\[\Delta T=\frac{4R}{K^{2}u_{m}\sin\theta\cos\theta}=\frac{4 Ru_{m}\cos\theta}{c ^{2}\sin\theta} \tag{24}\]  
Therefore, the flowrate will be  
\[Q_{0}=\pi R^{2}u=\pi R^{2}u_{m}=\frac{\pi Rc^{2}}{4}\tan\theta\cdot\Delta T \tag{25}\]  
Comparing Equation (22) with (25), it is found that  
\[Q=\frac{2n}{2n+1}\cdot Q_{0} \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Re & \(4\times 10^{3}\) & \(2.56\times 10^{4}\) & \(1.05\times 10^{5}\) & \(2.06\times 10^{5}\) & \(3.2\times 10^{5}\) & \(3.84\times 10^{5}\) & \(4.28\times 10^{5}\) \\ \hline n & 6.0 & 7.0 & 7.3 & 8.0 & 8.3 & 8.5 & 8.6 \\ \hline \end{tabular}
\end{table}
Table 1: Relationship between Re and n.  
Since the exponent \(n\) depends on Reynolds number, the effect of turbulent velocity profile will also depends on the Reynolds number.
## 4 Equivalent Consumption Minimization Strategy (ECMS)  
In this section, the ECMS algorithm will be presented. It is based on the powertrain models presented in Section 2 and the optimization methods presented before. Figure 5 shows the algorithm of the ECMS. The input variables are the current state of charge \(SoC_{k}\), the vehicle speed \(v_{\text{veh},k}\), and the vehicle acceleration \(a_{\text{veh},k}\). Considering an implementation adapted to the real vehicle operation, \(SoC_{k}\) and \(v_{\text{veh},k}\) are measured values, and \(a_{\text{veh},k}\) is a request from the driver. However, for the intended analysis, \(a_{\text{veh},k}\) as well as \(v_{\text{veh},k}\) were obtained from a driving cycle and the \(SoC_{k}\) from a powertrain model. The ECMS algorithm is based on PMP and carries out a local optimization of the Hamilton function (Equation (19)) in each time step. In order to evaluate the Hamilton function, the powertrain model (Equations (1)-(10)) was used to determine the fuel consumption and battery current for a given control variable \(\mathbf{u}_{k}\). Furthermore, the Lagrange multiplier \(\lambda_{k}\) was required, representing a function of the current state of charge \(SoC_{k}\) and position of the vehicle \(z_{\text{veh},k}\). This function was obtained from a prediction of the future driving behavior, which had to be carried out in advance. For the optimization of the Hamilton function, the control variable \(\mathbf{u}_{k}\) was quantized by means of the grid vectors \(\mathbf{s}_{\text{m}}^{\text{g}}\) and \(\mathbf{u}_{\text{ED}}^{\text{g}}\) (see Section 3).  
Figure 5: Implemented ECMS algorithm based on PMP with a prediction of the future driving behavior and the evaluation of the overall fuel consumption and the state of charge.  
This enabled finding the maximum by testing all possible combinations given by the grid vectors. In general, it is possible to apply other methods to obtain the optimum, but it must be ensured that the correct solution is found and the computation time of the ECMS algorithm is still adequate. Once the control variable \(\mathbf{u}_{k}^{*}\) was known, it was used to evaluate the torques and angular velocities of the ICE and electric drive by means of the transmission model (Equations (4)-(6)). For real vehicle operation, these values as well as the transmission control variable \(\mathbf{s}_{\mathbf{m}}^{*}\) were the setpoints for the controllers of the corresponding powertrain components. Here, these values were used to determine the fuel consumption \(m_{t,k+1}\) and state of charge \(SoC_{k+1}\) by means of the same powertrain model, which was already applied to the optimization.  
Crucial for obtaining good results with the ECMS is a well-chosen Lagrange multiplier \(\lambda_{k}\) matching the real driving behavior as well as possible. Since the real driving behavior was unknown in advance, \(\lambda_{k}\) was obtained by means of a prediction. For this purpose, two already known methods from the literature were considered in this contribution. These methods are presented in the following subsections.
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 5 Experimental Methodology, Results, and Discussion
### Scenario 2: PCC vs. BBR-PCC  
Similar to CUBIC and BBR, PCC and BBR exhibited competitive behavior when contending for bandwidth under specific network conditions. Before the switch, Client 1 (PCC) generally outperformed Client 2 (BBR) in terms of throughput by observing throughout fairness \(Th_{ratio}\). PCC's adaptive nature effectively adjusts to bandwidth and latency variations and contributed to its superior performance. Conversely, BBR demonstrated challenges in maintaining fairness, particularly in networks with varying RTTs.  
In Scenario 2, we investigated the impact of Client 2 switching from BBR to PCC at 25 s. Client 2 using BBR initially averaged throughput of 12.32 Mbps, but this surged to 40.40 Mbps after the switch to PCC in Figure 8(a),b. This suggests that PCC offered a more efficient way to utilize bandwidth under these specific network conditions. BBR's congestion control strategy might not have been ideal, leading to underutilized resources.  
Nevertheless, significant improvements were observed in latency and data loss. Client 2 (BBR) initially experienced high variations in RTT (Figure 9(a)). This variability suggests that BBR struggled to maintain consistent latency. Switching to PCC led to a decrease in these variations, with both clients maintaining RTTs below 20 ms. Similarly, jitter (variation in RTT) for Client 2 also decreased post-switch, as seen in Figure 11. These improvements suggest PCC's approach resulted in smoother data flow and lower latency for Client 2, possibly due to better congestion management.  
Figure 10: RTT variation over time: Client 1 (PCC) peaks at 102 ms at 3 s, dropping to 6 ms; Client 2 (BBR->PCC) ranges from 155 ms to 4 ms. Both clients maintained RTTs below 20 ms (**a**). Post-switch to Client 2 (PCC), RTT variation and median values decreased (**b**).  
Figure 9: Comparison of the throughput of BBR and PCC, focusing on Client 2’s switch to PCC at 25 s for understanding throughout fairness \(Th_{ratio}\). Before switching, Client 2 (BBR) averaged 12.32 Mbps, which surged to 40.40 Mbps afterward (**a**). The distribution plot (**b**) shows Client 2’s median throughput increasing from below 15 Mbps to above 35 Mbps.  
Packet loss measurements also saw a dramatic improvement. Client 2 (BBR) displayed a high initial packet loss ratio (48.97%) (Figure 12a,b). This indicates significant data loss. Interestingly, Client 1 (PCC) also experienced a high initial packet loss. However, after the switch, the packet loss ratio for both clients dropped significantly. Client 2's median packet loss dropped to nearly 10%, while Client 1's (PCC) also improved to 7%. This suggests PCC's congestion control strategy effectively mitigated packet loss for both clients, likely by optimizing resource allocation and reducing network congestion.
## 6 Discussion
### Effect of the Excitation Pressure Amplitude  
The excitation pressure was kept constant over the frequency range in the previous studies [39; 40]. While the present study results demonstrated in Figure 6c and Figure 7 show that the calculated modulus of elasticity is independent of the excitation pressure amplitude variations, it can be concluded that the excitation pressure and the generated displacement amplitude have a linear relation at a fixed frequency and the pipette radius diversity cannot affect the linear relationship.
## 3 Results and Discussion
### Cracking Behavior  
The width of cracks serves as a crucial factor in the determination of the integrity of concrete beams. In the case of pre-cracked beams, the initial cracks undergo complete closure subsequent to the application of prestress. During the loading process, cracks consistently form and gradually propagate in an upward direction. Once the rebar undergoes yielding, the pure flexural region at mid-span does not exhibit any more crack formations. While the new cracks begin appearing in the shear span region, they persist until the beam ultimately reaches failure. Throughout the experimental procedure, a crack width gauge was employed to measure the maximum crack width at different levels of applied moment. Figure 6 presents the recorded images depicting the maximum crack width at the mid-span for specimen S-C-50-0.2. The measurements were taken under three different moments: 90 kN-m, 138 kN-m, and 147 kN-m, which correspond to spots A, B, and C in Figure 6(a). Figure 6 depicts the maximum crack width observed throughout the loading process. It is evident that the application of external prestress results in a significant decrease in crack width. With the application of CFRP tendons for reinforcement, there is a deceleration in the rate of crack width growth once cracking occurs. This trend exhibits a positive correlation with the level of prestress applied to CFRP tendons.  
The results shown in Figure 6 and Table 3 indicate that the application of external prestressing on the test beams resulted in a noticeable increase in the cracking moment. The limited development of cracks in concrete beams can be attributed to the compressive stress generated at the bottom of the beam when external reinforcement is applied. Cracks will only continue to grow when the external moment surpasses the counteracting moment. The analysis of beams S-C-30-0, S-C-30-0.2, and S-C-50-0.2 reveals that the width of cracks is influenced by the initial cracks and the magnitude of prestressing level, when subjected to  
Figure 6: Measured crack diagram of the specimen S-C-50-0.2: (**a**) 90 kN-m; (**b**) 138 kN-m; (c) 147 kN-m.  
identical levels of external prestressing, the presence of initial cracks leads to the occurrence of cracks at an earlier stage and with wider width. Figure 7b illustrates a comparative analysis of the maximum crack width observed in simply supported and continuous beams under identical conditions. The comparison curves for both beam types exhibit similar trends, suggesting that the application of external prestressed CFRP tendon reinforcement effectively mitigates crack propagation in both simply supported and continuous beams.
## 3 Model for Security Resource Scheduling Based on MHALSTM-MPC
### Multi-Head Attention Long Short-Term Memory Network (MHALSTM)
2 Multi-Head Attention Layer  
An additional multi-head attention layer is added on top of the LSTM's output in order to use the attention mechanism to weight features. This allows for more refined adjustments to how the model focuses on different parts of the time series. 1 Linear transformations:  
\[Q=h_{t}W^{Q},K=h_{t}W^{K},V=h_{t}W^{V} \tag{7}\]  
The output sequence from the LSTM layer goes through three different linear transformations to produce the query (\(Q\)), key (\(K\)), and value (\(V\)) matrices. These transformations are completed using the weight matrices \(W^{Q}\), \(W^{K}\) and \(W^{V}\), which are model parameters learned through training. 2 Calculating attention scores:  
\[Attention(Q,K,V)=softmax\Big{(}\frac{QK^{T}}{\sqrt{d_{k}}}\Big{)}V \tag{8}\]Attention scores are calculated through the dot product of queries and keys, and are then scaled by \(d_{k}\) (dimension of keys) to stabilize the gradients. After scaling the dot-product attention scores, a softmax function is applied to obtain the final attention weights. Parallel computation across heads:  
\[MultiHead(Q,K,V)=Concat(head_{1},head_{2},\dots,head_{h})W^{O} \tag{9}\]  
In multi-head attention, the process mentioned above is replicated several times (once per head). Each head can learn different features from the sequence. Finally, the outputs from all heads are concatenated and passed through another linear transformation \(W^{O}\) to produce the final output. This step helps to merge information captured by different heads, enhancing the model's representational capability. Each head is calculated as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]0  
Feed-Forward Network Layer:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]1  
Each multi-head attention layer is followed by a feed-forward network. This network consists of two linear transformations and a non-linear activation function. Specifically, \(x_{MHA}\) represents the output from the multi-head attention layer, \(W_{1}\) and \(W_{2}\) are the weight matrices, \(b_{1}\) and \(b_{2}\) are the bias terms, and ReLU(max(0, x)) is the activation function. This network layer enhances the model's capability to process non-linearities, thereby enabling it to capture more complex patterns. Linear output layer:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]2  
In the MHALSTM model, after moving through a series of LSTM and multi-head attention layers, the data are finally passed through a linear layer to generate the final prediction output. This layer transforms the high-dimensional features into the target prediction, which is the passenger flow prediction value.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 5 Improved Modulations  
The use of the continuous spectrum for NFDM systems was originally suggested in [3; 4]. The first modulation technique that has been proposed to exploit this idea is the NIS modulation, in which the QAM signal (or its standard Fourier transform) is directly mapped on the nonlinear spectrum \(\rho(\lambda)\) according to Equation (1), and the optical signal is then obtained by a BNFT [4]. We refer to this mapping as \"standard\" modulation, which is sketched in Figure 3a. The main advantages of the NIS modulation Equation (1) are that (i) it can be seen as a natural nonlinear extension of a conventional linear modulation, to which it reduces at low power, and (ii) under certain circumstances, it can exploit the causality property reported in Section 4.1. An example of a signal encoding \(N_{b}=16\) symbols is shown in Figure 4a, where it is evident that the signals before and after the NFDM mapping are very similar to the first symbols, while they differ on the last symbols, where the latter loses power and acquires a tail. Both the power decay and the tail growth become more relevant as the signal power and duration increase.  
On the other hand, the NIS modulation has some limitations. In particular, as shown in Section 3, it has an unfavorable behavior in terms of minimum Euclidean distance between the generated  
Figure 4: Normalized power of the quadrature amplitude modulation (QAM) signal \(s(t)\) and of the corresponding optical waveform \(q(t)\) for \(N_{b}=16\) symbols and (**a**) NIS modulation; (**b**) b-modulation; (**c**) bExp-modulation. The waveform power is 4 dBm.  
Figure 3: Details of modulations (**a**) NIS modulation; (**b**) b-modulation; (**c**) bExp-modulation; (**d**) NIS + tilt modulation.  
waveforms, which is basically the reason why even the performance of the best detection strategy considered in Section 4 decays with the rate efficiency. After NIS, other techniques for modulating the continuous spectrum have been proposed [17; 18; 31; 32]. In the following, we describe two such techniques and propose a novel power-tilt strategy to improve the NIS modulation.
## 4 Conclusions  
In conclusion, we have designed a dynamically tunable broadband THz meta-material absorber utilizing VO2. The absorber achieves an absorption greater than 90% in the frequency range of 2.4 THz to 11 THz with an absorption bandwidth of up to 8.6 THz and a relative bandwidth of 128.4% when VO2 is in the metallic phase. Perfect absorption of 99.5%, 99.6%, 99.5%, and 99.9% were obtained at the resonance frequencies 3.0 THz, 7.1 THz, 8.7 Thz, and 10.6 Thz, respectively. When VO2 is in the insulating phase, the absorber's absorption in the corresponding wide frequency band decreases to 2%, while the reflection increases to above 98%; thus, the function of the absorber switches between absorption and reflection. By adjusting the conductivity of VO2, the absorption and reflection of the absorber can be dynamically controlled. Additionally, due to the symmetry of the absorber structure, the absorber exhibits polarization-insensitive characteristics when THz waves are vertically incident. Furthermore, the absorber also features wide-angle incidence characteristics, maintaining good absorption performance for TE and TM waves within an incident angle range of 60\({}^{\circ}\). Thus, the proposed dynamically adjustable THz absorber has many potential applications in THz imaging, switches, mobile communications, medical sensors, and other fields.  
Conceptualization, E.L. and M.W.; methodology, E.L. and T.L.; software, E.L. and T.L.; validation, E.L. and M.W.; formal analysis, E.L.; investigation, E.L. and M.W.; writing--original draft preparation, E.L. and T.L.; writing--review and editing, M.W.; supervision, M.W.; project administration, M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Absorption spectrum under different conditions. (**a**) Different angles of polarization; (**b**) TE waves, different incidence angles; (**c**) TM waves, different incidence angles.  
**Funding:** This work was supported by the National Natural Science Foundation of China (11375081).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 3 Description of the Proposed Method
### Evolutionary Operations  
Evolutionary operation is an important mechanism in evolutionary algorithm. Therefore, we use crossover operation and mutation operation to find out better individuals. As the chromosome encoding of S-WTA problem is similar to the WTA problem, the evolutionary operations used in the WTA problem can be also suitable for the S-WTA problem. In this paper, we use the EX operator [13] as the crossover operation. Meanwhile, we use the mutation operation to randomly change a gene. All of the above are commonly used evolutionary operations in WTA problems. However, as the chromosome encoding of S-WTA is different from WTA, the EX operator should be adaptively improved so it can be appropriately used in S-WTA. The improved EX operator is described as follows.  
In the traditional EX operator, the so-called \"good\" genes contained in both parents are preserved. Therefore, for the S-WTA problem, the \"good\" genes represent the most appropriate assignment scheme from Rules 1 and 2. In addition, since the chromosome encoding of S-WTA is composed by two parts, the EX operator should be performed in the two parts, respectively. It is illustrated in Figure 7.  
As shown in Figure 7, suppose that there are 4 targets, 5 sensors, and 6 weapons. We generate two individuals, A and B. First, genes 2, 5, 7, 9, and 10, which have the same value in both A and B, are selected to be evaluated. Assuming that the genes of 2, 5, 7, and 9 are the \"good\" genes. Then, these \"good\" genes are preserved, which means that they do not change during the crossover operation. Afterwards, two genes are randomly selected in the STA part and exchanged to generate the new individual. Similarly, the other two genes are randomly selected in the WTA part and exchanged.  
Figure 7: The illustration of the improved EX operator.