esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 8 Simulation
### Trusted Wireless Network
#### 8.2.5 Cluster Head T/1/235/0/96  
CH T/1/235/0/96 plays a critical role in this network. According to the initial design, it connects to two remote nodes. The first, T/1/657/0/96, already has access to the central node through T/1/257/0/96. The second remote node is the secondary tank, which serves as a link between the two networks, considering the distance between the secondary tank and the CH, and as the secondary tank does not have any other route to connect to the main tank, the directional antenna was used.  
The results in Figure 25 showed that the connection between the CH and well T/1/657/0/96 is above the margin at 10 dBm. However, the connection between the secondary tank and the cluster head was poor, with the margin not reached even with the directional antenna at full TX power, which is due to the earth's curvature obstructing the Frensel zone, increasing the path loss and affecting the RSSI.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sector/Link**} & **Distance** & **Antenna Type** & **TX Power (dBm)** & **Antenna Height (m)** & **RSSI Margin (dB)** & **Fresnel** & \multirow{2}{*}{**Status**} \\ \cline{1-1} \cline{5-7} Cluster Head & & & & & & & \\ T/1/257/0/96 & & Omnidirectional & & & & & \\ \hline T/1/657/0/96 & 0.86 & RAD-ISM-2400-ANT-OMNI-9 & 11 & 5 & 15.2 & \(\sim\)80 & Feasible \\ \hline T/1/678/0/96 & 0.96 & RAD-ISM-2400-ANT-OMNI-9 & 12 & 5 & 15.1 & \(\sim\)80 & Feasible \\ \hline T/1/656/0/96 & 1 & RAD-ISM-2400-ANT-OMNI-9 & 13 & 5 & 15.4 & \(\sim\)80 & Feasible \\ \hline \hline \end{tabular}
\end{table}
Table 13: Link Parameters for Cluster Head T/1/257/0/96.  
Figure 24: Transmit Power vs. RSSI plot for CH T/1/257/0/96.  
Since the antenna height at cluster head T/1/235/0/96 was previously increased to 8.5 m to connect it with the BS, it would be beneficial to increase the height of the directional antenna at the secondary to clear the Fresnel Zone from obstructions and reduce path loss.  
Figure 26 shows that increasing the height up to 11 m made a significant improvement to RSSI. Testing this height at different TX powers showed promising results, as the margin was reached at 15 dBm. The final link parameters are summarized in Table 14.  
Figure 26: RSSI margin and path loss values at different heights.  
Figure 25: Transmit Power vs. RSSI plot for CH T/1/235/0/96.
## 2 Proposed Method
### CID and AoD Estimation Method
When \(w_{v,i}\) has an average power of \(\sigma^{2}\), the SNR after the correlation operation in Equation (5) becomes \(N_{Tx}/\sigma^{2}\) (total Tx power is 1). Thus, the SNR gain that can be achieved by the proposed beam weight generation method is given by \(N_{Tx}\) due to the processing gain obtained by the correlation operation for CID and AoD estimation. This SNR gain is the same as the beamforming gain of ULA with the conventional DFT-based beamforming  
Figure 2: The procedure of the proposed CID and AoD estimation. weight. In addition to the SNR gain, the periodic correlation between \(\mathbf{y}_{0}^{\gamma-1}\) and \(\tilde{\mathbf{z}}_{0}^{\widetilde{\gamma}}\) with a root index \(\widetilde{\gamma}\) is given by  
\[\mathbf{\mu}^{\widetilde{\gamma},\gamma^{\prime}}=(\tilde{\mathbf{Z}}_{0}^{ \widetilde{\gamma}})^{H}\mathbf{r}^{\gamma-1}/N_{Tx}=(\tilde{\mathbf{Z}}_{0}^{ \widetilde{\gamma}})^{H}\mathbf{F}^{\phi^{\gamma-1}}\mathbf{z}_{0}^{\gamma^{ \prime}}/N_{Tx} \tag{12}\]  
where \([\tilde{\mathbf{Z}}_{0}^{\widetilde{\gamma}}]_{k}^{\widetilde{\delta}}=[\tilde {\mathbf{z}}_{0}^{\gamma}]_{k}=[\tilde{\mathbf{z}}_{0}^{\gamma}]_{(k-\delta) _{\gamma=N_{0}}}\). When \(\phi^{\gamma-1}\) is 0 (broadside), the absolute value of \([\mathbf{\mu}^{\widetilde{\gamma},\gamma}]_{k}\) in Equation (12) becomes \(\sqrt{N_{Tx}}\) using the correlation property of the ZC sequence [19].
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
###### Abstract  
Recent advancements in printing technologies have greatly improved the fabrication efficiency of flexible and wearable electronics. Electronic textiles (E-textiles) garner particular interest because of their innate and desirable properties (i.e., conformability, breathability, fabric hand), which make them the ideal platform for creating wireless body area networks (WBANs) for wearable healthcare applications. However, current WBANs are limited in use due to a lack of flexible antennas that can provide effective wireless communication and data transfer. In this work, we detail a novel fabrication process for flexible textile-based multifunctional antennas with enhanced dielectric properties. Our fabrication process relies on direct-write printing of a dielectric ink consisting of ultraviolet (UV)-curable acrylates and urethane as well as 4 wt.% 200 nm barium titanate (BT) nanoparticles to enhance the dielectric properties of the naturally porous textile architecture. By controlling the spray-coating process parameters of BT dielectric ink on knit fabrics, the dielectric constant is enhanced from 1.43 to 1.61, while preserving the flexibility and air permeability of the fabric. The novel combination textile substrate shows great flexibility, as only 2 N is required for a 30 mm deformation. The final textile antenna is multifunctional in the sense that it is capable of operating in a full-duplex mode while presenting a relatively high gain of 9.12 dB at 2.3 GHz and a bandwidth of 79 MHz (2.260-2.339 GHz) for each port. Our proposed manufacturing process shows the potential to simplify the assembly of traditionally complex E-textile systems.  
flexible textile antenna; screen printing; direct-write spray coating; microstrip patch antenna +
Footnote †: journal: materials
## 3 Results and Discussion
### Peroxide Value of CSO  
Similarly, the actual peroxide value of the CSO differed from the predicted values of center point STDs 9, 10, and 11 (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(>\) 1.4 mm), and 20, 21, and 22 (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(<\) 1.4 mm). The lowest actual peroxide value (0.93 mEq/Kg of oil) was observed in STD 11 (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(>\) 1.4 mm), but the lowest predicted peroxide value (1.07 mEq/Kg of oil) was noted in STDs 6 and 17 (restriction die: 0.8 cm, temperature: 60 \({}^{\circ}\)C, and sieve size \(<\) 1.4 mm) (Table 2).  
Analysis of the variance of the reduced cubic model for the peroxide value showed that the model was significant (\(p\) = 0.0110) with adjusted R\({}^{2}\) (0.4430), predicted R\({}^{2}\) (0.2474), and non-significant lack of fit (\(p\) = 0.8405) values (Table 3). The results indicated that the reduced cubic model equation was acceptable for predicting the peroxide values of the CSO samples. The CCD-generated reduced cubic equation for peroxide values (mEq/Kg of oil) was as follows (Equation (9)):  
\[\begin{split}\text{Peroxide value (mEq per kg of oil)}=\\ \text{$\overset{\text{o\gg}}{\sqrt{-20.1043+33.3301\text{A}\ +0.7606\text{B}-1.2121\text{AB}\ -0.0061\text{B}^{2}+0.0099\text{AB}^{2}}$}}+0.6\end{split} \tag{9}\]  
where A = restriction die (0.4-0.8 mm) and B = temperature (40-80 \({}^{\circ}\)C).  
Sieve size does not have a significant effect on peroxide value prediction. The restriction dies and temperature significantly influenced the peroxide value of CSO. Increased die size and temperature produced higher peroxide values (Figure 3).  
Figure 2: Response surface plot showing the effect of temperature and restriction die size on the acid value of CSO. (**a**) Sieve size: \(>\)1.4 mm, (**b**) Sieve size: \(<\)1.4 mm.
## 5 Results Analysis
### Benchmark Datasets Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Approach** & \multicolumn{2}{c}{**Performance (\%)**} & & & & \\  & & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Kappa** \\ \hline UCSD Ped1 & CLBP [39] & 60.76 & 70.35 & 37.20 & 48.67 & 0.2238 \\  & MSCNN [28] & 97.48 & 98.70 & 96.22 & 97.45 & 0.9341 \\  & densenet121 [1] & 94.32 & 97.20 & 91.27 & 94.15 & 0.8673 \\  & Efficientnetv2 [2] & 97.03 & 98.55 & 95.46 & 96.98 & 0.9195 \\  & Our (MCMS-BCNN-Attention+densenet121) & 98.28 & 99.31 & **97.24** & 98.26 & **0.9646** \\  & Our (MCMS-BCNN-Attention+Efficientnetv2) & **98.62** & **99.51** & 97.21 & **98.61** & 0.9576 \\ \hline UCSD Ped2 & CLBP [39] & 62.19 & 73.80 & 37.78 & 49.98 & 0.2819 \\  & MSCNN [28] & 98.39 & **99.67** & 97.10 & 98.37 & **0.9834** \\  & densenet121 [1] & 92.88 & 96.96 & 88.55 & 92.56 & 0.8656 \\  & Efficientnetv2 [2] & 96.37 & 98.34 & 94.33 & 96.30 & 0.9231 \\  & Our (MCMS-BCNN-Attention+densenet121) & **98.95** & 99.54 & **98.36** & **98.95** & 0.9794 \\  & Our (MCMS-BCNN-Attention+Efficientnetv2) & 98.08 & 99.23 & 96.91 & 98.05 & 0.9629 \\ \hline PETS2009 & CLBP [39] & 86.80 & 95.37 & 77.33 & 85.42 & 0.8091 \\  & MSCNN [28] & 95.43 & 98.47 & 92.29 & 95.28 & 0.9222 \\  & densenet121 [1] & 93.17 & 97.02 & 89.09 & 92.88 & 0.8581 \\  & Efficientnetv2 [2] & 96.55 & 98.71 & 94.32 & 96.47 & 0.9327 \\  & Our (MCMS-BCNN-Attention+densenet121) & 96.38 & 98.64 & 94.06 & 96.30 & 0.9273 \\  & Our (MCMS-BCNN-Attention+Efficientnetv2) & **96.97** & **98.81** & **95.08** & **96.91** & **0.9353** \\ \hline UMN Plaza1 & CLBP [39] & 81.86 & 92.18 & 69.62 & 79.33 & 0.6857 \\  & MSCNN [28] & 97.79 & 99.24 & 96.31 & 97.76 & 0.9563 \\  & densenet121 [11] & 96.85 & 98.78 & 94.86 & 96.78 & 0.9404 \\  & Efficientnetv2 [2] & 98.50 & **99.41** & 97.57 & 98.48 & **0.9723** \\  & Our (MCMS-BCNN-Attention+densenet121) & 98.50 & 99.33 & 97.67 & 98.49 & 0.9640 \\  & Our (MCMS-BCNN-Attention+Efficientnetv2) & **99.10** & **99.41** & **98.78** & **99.09** & 0.9642 \\ \hline UMN Plaza2 & CLBP [39] & 60.36 & 70.18 & 36.01 & 47.60 & 0.2681 \\  & MSCNN [28] & 88.11 & 94.44 & 80.98 & 87.19 & 0.7841 \\  & densenet121 [11] & 94.11 & 97.34 & 90.70 & 93.90 & 0.8843 \\  & Efficientnetv2 [2] & 97.33 & 98.82 & 95.79 & 97.28 & 0.9402 \\  & Our (MCMS-BCNN-Attention+densenet121) & **98.38** & **99.12** & **97.63** & **98.37** & **0.9560** \\  & Our (MCMS-BCNN-Attention+Efficientnetv2) & 91.36 & 96.20 & 86.13 & 90.89 & 0.8525 \\ \hline \hline \end{tabular} Bold colour indicates the highest values.  
\end{table} 
Table 6: Performance comparison with existing approaches. Figure 13: Confusion matrix heat map of UMN Plaza 2 for **(a)** MCMS-BCNN-Attention+densenet121 and **(b)** MCMS-BCNN-Attention+Efficientnetv2.
## 3 The Proposed Approach
### Data Pre-Processing  
The data pre-processing phase aims to convert the raw source code into a format that CodeTransFix can process efficiently.  
CodeTransFix has two separate inputs: the bug line and its local context. In this work, we define the \"context\" as the immediate surrounding code within the same method or code block where the buggy line resides. To obtain these inputs, the bug lines and the surrounding contexts are first extracted from the bug project. Specifically, as shown in Figure 2, the third line in Figure 2a is the bug line that needs to be fixed, whereas the processed context is illustrated in Figure 2b, where the bug line is replaced by the special placeholder <BUG>. During the data pre-processing phase, the data are prepared for two different purposes: the bug lines are used directly for subsequent model training, in particular, to learn the transitions from bugs to fixes, whereas the processed context information is used to fine-tune the pre-trained model to enable the model to learn the context information of the bug code.  
Figure 1: An overview of the CodeTransFix architecture.  
Following the method used in a previous study [3; 13], CodeTransFix employs a subword-level tokenizer, known as a byte-pair encoding (BPE) [32], to segment error lines, context lines, and repair lines into subword token sequences. This tokenizer mitigates vocabulary overflow by decomposing rare tokens into frequent subword units through iterative symbol merging. Specifically, The byte-pair encoding (BPE) algorithm iteratively learns subword units: starting with individual characters, it merges frequent character pairs to build a vocabulary that preserves common words (e.g., segmenting 'ValueMarker' into [\"Value',\"Marker\"]) while decomposing rare ones. This frequency-driven merging creates combinatorial subword combinations, enabling effective handling of unseen words without predefined mappings, thus reducing out-of-vocabulary issues compared to word-level tokenization.
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 5 Conclusions  
This paper has proposed a discrete non-stationary MIMO channel model, which is suitable to realize on the FPGA-based platform. A tailored hardware architecture of channel emulator with flexible size and parameters has also been developed. In addition, the hardware implementation of key modules have been illustrated in details and applied in a single FPGA chip. Finally, the PDP and other statistical properties of proposed channel emulator have been tested. The measured results have shown that the output PDP, DPSD, PDF, and CCF are consistent well with the corresponding theoretical ones. Therefore, the proposed non-stationary channel emulator can be applied to evaluate and validate the performance of MIMO communication devices in the future.  
Conceptualization, Q.Z. and W.H.; methodology, Q.Z. and K.M.; software, Z.Z.; validation, W.H. and Z.Z.; writing--original draft preparation, Q.Z. and W.H.; writing--review and editing, all authors; supervision, W.Z. and B.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fundamental Research Funds for the Central Universities (No. NS2020026 and No. NS2020063), in part by the Aeronautical Science Foundation of China (No. 201901052001), and in part by the National Key Scientific Instrument and Equipment Development Project under Grant (No. 61827801). The authors declare no conflict of interest.
# Multi-Input Logic-in-Memory for Ultra-Low Power Non-Von Neumann Computing
## 3 Results and Discussion  
### Multi-Input Operations: \"n-SIMPLY\"  
In traditional static complementary CMOS logic gates, the computation of more complex operations (e.g., large fan-in operations), involves the use of additional transistors, therefore an increase in the required circuit area and complexity. In IMPLY-based architectures, the computation of complex operations instead translates to an increased number of steps of IMPLY and FALSE operations. Therefore, in these architectures, minimizing the number of steps is critical to improve the circuit performance. As suggested by theoretical works, the IMPLY logic can be extended to multi-input (\(>\)2) operations [32; 35] by simultaneously applying V\({}_{\text{SET}}\) to one device (Q) and V\({}_{\text{COND}}\) to many devices (P, S, T,... ). It is only recently that the three input IMPLY operation (i.e., equivalent to the \(OR(Q,\ NOR(P,\ S))\) has been demonstrated by performing circuit simulations of a conventional IMPLY architecture using a physics-based compact model calibrated on a Pt/TaO\({}_{\text{x}}\)/Ta RRAM technology [21] and was shown to reduce the number of steps required for implementing a full adder. However, the reliability of the traditional IMPLY architecture worsens when the number of inputs of the IMPLY operation is increased [21]. Nevertheless, generalizing the IMPLY operation to more than three inputs allows further reducing the number of computing steps, by executing in a single step the more complex function \(OR(Q,\ AND(\overline{P},\ \overline{S},\ \overline{T},\ldots))=OR(Q,\ NOR(P,S,T,\ldots))\), hereafter called n-IMPLY (n being the number of inputs) generalizing the IMPLY with 2 inputs (henceforth 2-IMPLY). Just like in 2-IMPLY, also in n-IMPLY Q must change its state to logic 1 only if all inputs are concurrently 0, that is exactly the condition detected by SIMPLY. So, n-IMPLY can be implemented in SIMPLY (henceforth n-SIMPLY) by applying V\({}_{\text{READ}}\) to all n devices simultaneously with no architectural changes. We simulated the n-SIMPLY (with n = 2, 3, 4) for the 4 RRAM technologies explored in this work using the Cadence(r) Virtuoso software and verified its correct functionality by comparing the circuit parameters and energy per operation (see Table 3), including variability and RTN. While the minimum and maximum energy per n-SIMPLY operation do not change considerably for increasing values of n, the RM decreases when the number of RRAMs read in parallel increases. Nevertheless, the RM with n = 2, 3, 4 was verified to be always large enough, experimentally on technology  
Figure 6: SIMPLY crossbar implementation. More than one comparator can be used to increase the computation parallelism.  
4 (see Figure 7a), and by using simulations on the other three technologies, as shown in Figure 7b. Moreover, in all cases, no logic state degradation is observed (see red curves in Figure 5).
## 3 Evaluation of the Energy Conversion Subsystem and Energy Storage Subsystem
### Conversion Process
#### 3.1.3 Ammonia  
Ammonia (NH\({}_{3}\)) is also a clean energy carrier in the sense that it does not release carbon dioxide at the point of use as an energy source. It can be used to generate energy with conventional technologies with modifications (e.g., ammonia can be reformed to yield hydrogen and nitrogen, which can be used in internal combustion engines [22], gas turbines, and alkaline fuel cells without further purification [23]). It has half the energy density of gasoline and is easier to store than liquid or gaseous hydrogen. Although toxic and corrosive, the transmission and storage technologies are mature. The Haber-Bosch process is considered for synthesis, and the nitrogen (N\({}_{2}\)) needed for this can be separated from air using pressure swing absorption (PSA). The process of electrochemical synthesis of ammonia is not explored since the yield rates and efficiencies are still low and below commercially viable levels. Furthermore, the Haber-Bosch process is not flexible--it is optimised for continuous operation. If the process is run at uneven intervals, there are risks of lower catalyst and equipment lifetime due to thermal cycling and reduced production efficiency. A hydrogen and nitrogen buffer would be required in the SailREC System to take care of renewable energy intermittency. Another approach might be to make the process vary its output just as with energy by plant design and operation techniques. This capability to turn down could be a better solution if possible [23; 24; 25; 26].
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Prototype Design
#### 3.2.2 Software  
The software programmed in the microcontroller can be divided into three parts:  
1. Initialization of the microcontroller peripherals: It is called just once at the beginning, and all parameters are initialized.
2. Continuous loop: The microcontroller continuously tests two things: (i) If valid information has been received from the GNSS receivers; in this case, it calls the attitude computation function. (ii) If any of the data buffers of UARTs 1 and 2 have information; in this case, the information is processed.
3. Interruptions in the data reception of UARTs (DMA).  
Each one of these code groups has protections to prevent blocks, for example, to control that buffers do not exceed their capacity, avoid zero division, etc.  
The configuration of the microcontroller and the GNSS receivers is described in Appendix A.
## 3 Proposed Congestion Management Approach
### MOPSO Algorithm  
The optimization model of P-VSCOPF involves highly complicated implementations of both the objective and constraint equation differentials. To overcome the major restrictions faced by many traditional methods, evolutionary computation algorithms (e.g., PSO or differential evolution) can be used as reliable alternatives in many complex engineering optimization applications [17]. In this paper, the MOPSO based on the Newton-Raphson method is used as the optimization algorithm for the P-VSCOPF problem. The proposed MOPSO is suitable for solving constrained, nonlinear optimization problems. This technique can be managed easily, and it provides good convergence characteristics with high computing efficiencies compared to conventional PSO. Moreover, it obtains good starting values for the initial population before the PSO process begins by using the Newton-Raphson method. Thus, it offers a better performance in finding the optimal Pareto front.  
The velocity and position vectors of a particle in an \(n\)-dimensional space are given by  
\[X_{a} = (x_{a1},\cdots,x_{an}) \tag{33}\] \[Z_{a} = (z_{a1},\cdots,z_{an}) \tag{34}\]  
The best position calculated by a particle is  
\[Pbest_{a}=(x_{a1}{}^{best},\cdots,x_{an}{}^{best}) \tag{35}\]  
The particle among all of the particles in the population that has the best position can be represented as  
\[Gbest_{g}=(x_{g1}{}^{best},\cdots,x_{gn}{}^{best}) \tag{36}\]  
The position and velocity of each particle, updated after \((k+1)\), steps is calculated by  
\[X_{a}{}^{(k+1)}=X_{a}^{k}+Z_{a}^{k+1} \tag{37}\]  
The velocity of the \(i\)th individual during the \((k+1)\)th iteration can be computed by  
\[z_{an}^{t+1}=Wz_{an}^{t}+c_{1}r_{1}(Pbest_{an}^{t}-x_{an}^{t})+c_{2}r_{2}(Gbest_ {gn}^{t}-x_{an}^{t}) \tag{38}\]  
Finally, the inertia weight parameter \(W\) can be expressed as  
\[W=(W_{i}-W_{f})\times\frac{(iter_{max}-iter)}{iter_{max}}+W_{f} \tag{39}\]  
Figure 3 shows the MOPSO process. This application is used to check the feasibility of the non-dominated solutions.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 4 Conflict Resolution Algorithm Based on DNMPC
### Controller Design of DNMPC  
Figure 7 is the solution framework for DNMPC conflict resolution controller. \(k\) is the current moment, and \(N\) is the prediction time domain and the control time domain length. \([\mathbf{x_{i}}(k+1|k),\ldots,\mathbf{x_{i}}(k+N|k)]\) is the predicted state vector of UAV \(i\), \([\mathbf{x_{i}}(k+1|k),\ldots,\mathbf{x_{i}}(k+N|k)]\) is the reference state vector for UAV \(i\), \([\mathbf{U_{i}}(k|k),\ldots,\mathbf{U_{i}}(k+N-1|k)]\) is the input sequence of control vector. Each UAV solves its own optimization problem independently and communicates with other UAVs only when necessary; thus, this framework describes a distributed controller [14]. Meanwhile, the analysis and justification of closed-loop stability can be referred to in Refs. [23; 24].