esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To the best of our knowledge, there are no works in the literature simultaneously addressing stochastic and fuzzy approaches to model demand uncertainty in a flexible-size LRP. This is a realistic scenario, since many companies might have historical data on trustworthy customers and not enough data on new or unreliable ones. Hence, the main contributions of this paper are two-fold: on the one hand, a new variant of the location routing problem is studied, where facility sizing decisions and hybrid fuzzy-stochastic demands are simultaneously considered. On the other hand, this paper proposes a competitive solution approach based on the hybridization of a metaheuristic algorithm with both simulation and fuzzy logic, i.e., a so-called fuzzy subheuristic, to solve the aforementioned problem. Indeed, simheuristics have been traditionally proposed to deal with stochastic issues in hard combinatorial optimization problems [24]. However, their hybridization with fuzzy logic has been rarely studied. The remainder of this paper is organized as follows: Section 2 describes previous works on the topic. Section 3 presents a description of our addressed problem. Section 4 explains the fuzzy simheuristic approach used to solve the problem. Section 5 describes a series of computational experiments. Section 6 analyzes our obtained results. Finally, Section 7 draws some conclusions and future research perspectives.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 4 Results
### Simulated Point Targets  
Fourteen scattering points are set up in the imaging area. Figure 3 shows the beamforming responses. Beamforming responses of the DAS are the results after delay-and-sum, which the 32 nd element generates ultrasonic and all elements receive echo signal [24]. Figure 4 shows the lateral variation of the beamforming responses at two different depths, z = 41 mm and 45.8 mm, respectively. Figure 4 indicates the peak sidelobe level (PSL) and the full width at half maximum (FWHM) after each beamforming.  
Figure 4: Lateral variation after each beamforming, (**a**) z = 41 mm; (**b**) z = 45.8 mm.  
Figure 3: Beamforming responses of the 14 scattering point targets. (**a**) delay-and-sum (DAS); (**b**) synthetic aperture (SA); (**c**) generalized sidelobe canceler (GSC); (**d**) Broadband-GSC.  
Taking depth z = 41 mm as reference, the PSL and FWHM after each beamforming are shown in Table 1. Broadband-GSC has almost the same FWHM as GSC, while the FWHM is increased by 43.2% and 58.0% compared with SA and DAS, respectively. The observation index PSL is used to indicate the inhibition of sidelobe energy. Broadband-GSC reduces the PSL by 11.6 dB and 26.4 dB compared with SA and DAS, respectively. Sidelobe energy represents the interference and noise signal, consequently, the lower PSL the better [25]. Beamforming responses indicate that Broadband-GSC can obviously improve the PSL and FWHM compared with DAS and SA.  
Comparing GSC with Broadband-GSC, GSC needs both to receive and to transmit focused ultrasonic echo data. Therefore, all elements are ordinal excited and N's receiving focused echo imaging has to be obtained. Broadband-GSC achieves an increase in lateral resolution and PSL with only one single emission, which reduces the system's hardware complexity. Consequently, Broadband-GSC reduces the beamforming process complexity, introduces the possibility for high frame-rate imaging with improved imaging quality.
## 5 Experiments
### Experimental Results
#### 5.4.1 Comparison Experiments with Different Overlap Rates
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE(R)** & **RMSE(t)** & **MAE(R)** & **MAE(t)** & **Times(s)** \\ \hline GDC [53] & 0.012 & 0.083 & 0.006 & 0.046 & 19.4 \\ GAT [44] & 0.040 & 0.281 & 0.010 & 0.073 & 165.0 \\ GCN [21] & 0.014 & 0.100 & 0.007 & 0.057 & 19.1 \\ GraphKan [50] & 0.005 & 0.046 & 0.003 & 0.023 & 27.2 \\ SGC [54] & 0.086 & 0.592 & 0.031 & 0.237 & 28.6 \\ NIGCN [55] & 0.030 & 0.239 & 0.017 & 0.144 & 33.9 \\ Ours & 0.002 & 0.013 & 0.001 & 0.006 & 19.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Registration performance at 60% overlap.
## 5 Performance Evaluation
### Result Analysis and Discussion
#### 5.2.1 Throughput Analysis  
Figure 11a, b illustrates the throughput analysis with the variations in the number of nodes and sink speeds. As compared to the other protocols, our proposed approach acquires the maximum effective packets. However, when the number of nodes increased, there was a slight decrease as a result of the increased control overhead and interference level that occurred with an increase in the node number. Again, compared to other protocols, the modified ring routing outperforms, which infers the use of the ring node for the sink advertisements, although it considers some overhead, but after the path is established, the data packets delivered are less prone to interference and packet loss. In Figure 11b, the variation of the sink speed from the static level up to 15 km/h, shows an increase in the throughput level. The proposed ring routing also performs better in the throughput level than other protocols under the mobile sink. In LBDD and MSRP, the mobility of the sink occurs, which involves the data flow in a line or within a specified area, due to which even if the sink moves, the data is not received properly at the destination. However, in the ring routing, the construction itself consumes more time decreasing the throughput level even if sink is mobile. Therefore, the modified ring builds much faster the ring construction that helps in the frequent sink advertisement only to the CH, which involves the data retrieval and the dissemination at the CH in parallel, making the effective data delivery with the highly mobile sink node towards the CH.
## 5 Experimental Testing and Analysis
### Experimental Results  
By considering path optimality, safety, and feasibility constraints, specific weights are assigned to each cost function to determine the overall objective function value for the problem.  
\[F(X_{i})=\sum_{k=1}^{4}b_{k}F_{k}(X_{i}) \tag{25}\]  
The variables \(b_{1}\sim b_{4}\) represent the weight coefficients, while \(F_{1}(X_{i})\) to \(F_{4}(X_{i})\) denote the values of the objective function corresponding to the aforementioned constraints. In the experiment, the parameters were configured as b\({}_{1}\) = 5, b\({}_{2}\) = 1, b\({}_{3}\) = 1, and b\({}_{4}\) = 5, while the remaining parameters are given in Section 2.  
In this study, the DRSTASA, STASA, SNS, and PSO algorithms were each executed 30 times within the hardware system environment previously described, using a population size of 500 and a maximum of 2000 iterations. Table 7 presents the optimal, worst, and average fitness values obtained from these 30 runs. Figure 6 shows the three-dimensional view, top view, and side view of the optimal path planning results achieved by the algorithms, while Figure 7 illustrates the fitness iteration curves.  
From Table 7, it is clear that this algorithm demonstrates relatively high stability across multiple runs, with both the optimal and worst fitness values being lower than those achieved by other algorithms. This suggests a strong capability for escaping local optima. Figure 5 shows that the optimal path generated by the algorithm successfully avoids obstacles, ensuring a smooth transition from the starting point to the destination. Compared to the other three algorithms, it identifies a safer and faster flight route. Figure 6 illustrates the rapid convergence of the algorithm during iterations, allowing it to find the optimal solution efficiently.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Optimal Fitness** & **Worst Fitness** & **Average Fitness** \\ \hline DRSTASA & 4673.5174 & 5195.8919 & 4737.651 \\ \hline STASA & 4793.8431 & 5485.7753 & 4902.3325 \\ \hline SNS & 4811.4688 & 5934.1597 & 5005.4432 \\ \hline PSO & 4966.2653 & 5857.1326 & 5211.4123 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Algorithm running results.  
Figure 6: Optimal path planning diagram.  
Figure 7: Fitness iteration curve.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 2 Analysis of Traditional Interconnections in Space-Borne Microwave Systems
### Core Wire Winding Soldering  
For core wire winding soldering, the core wire is half wound along the axis perpendicular to the transmission direction, and then forms an \"\(\Omega\)\" shaped interconnection structure in the transmission direction, as shown in the Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Tin–Lead** &
\begin{tabular}{c} **Aluminium** \\ **Alloy** \\ \end{tabular} & **Ceramic** & **Kovar** & **Gold** \\ \hline Thermal expansion & 25 & 22.3 & 7 & 6 & 14 \\ coefficient (ppm/\({}^{\circ}\)C) & 45 & 245 & 300 & 520 & 100 \\ Tensile strength (MPa) & 45 & 70 & 300 & 138 & 77 \\ Young’s ratio & 0.4 & 0.33 & 0.3 & 0.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of materials.  
Figure 2: Photos of cracks in directly soldered structure.  
When core wire winding soldering is adopted, the length of inner core is determined by the packaging structure, and the drop between the inner core and the substrate is strictly controlled during assembly with small change. The solder joint stress is about 32 Mpa with high reliability. The corresponding 3D electromagnetic simulation results are as follows, whose reflection loss is already worse than \(-8\) dB at 5 GHz, so the RF working bandwidth is limited to S-band and below.
## 4 Discussion
### Effect of Crop Stage, Environmental Variables on RE  
The primary cause of seasonal change in RE was changes in the above-ground biomass with crop growth. The rate of RE during the pre-transplanting and pre-sowing periods in rice and wheat was mainly due to soil and weed respiration (Table 1). The RE increased in both rice and wheat until heading and then declined at the ripening stage following the senescence of the green leaf area and old roots [30]. [PERSON] [50] observed higher RE values during the ripening stage in wheat due to the differences in canopy's radiative temperature after the heading stage subsequent to normalizing the LAI, which led to higher levels of canopy respiration. The respiration rate at the different growth stages in rice was relatively higher than in wheat, despite the higher biomass in wheat, due to higher air and soil temperature (ST) during rice. The higher value of RE during the pre-transplanting period in rice was because of the higher ST (31 \({}^{\circ}\)C) which enhanced the soil respiration and also due to the growth of weeds supported by rainfall and an increased number of sunshine hours. Our rice field was frequently irrigated and due to availability of dissolved oxygen in the irrigation water, there was microbial decomposition of soil organic matter leading to higher soil respiration levels in the frequently irrigated rice [34]. [PERSON] and [PERSON] [51] observed that soil respiration increased as the soil water content decreased, but under very dry conditions the CO\({}_{2}\) efflux decreased due to low root and microbial activity. In our study, there was no water stress at any stage in both the crops.  
The RE was significantly positively correlated with ST in rice (0.543, \(p\) < 0.01) (Table 2). The diurnal range of ST was smaller in rice on account of standing water which acted as a barrier to ST changes [52]. The ST during most of the rice crop was nearly constant, except at the maturity stage, while it showed comparatively larger variation in the case of wheat and was influenced by changes in net solar radiation.  
The RE was negatively correlated with soil moisture (SM) in both rice and wheat, however, it was significant only in wheat (\(-\)0.322, \(p\) < 0.01). The RE was positively correlated with RH in rice and negatively correlated in wheat; however, it was not significant in both the crops. The RE was positively correlated with AT in both rice and wheat, however, the correlation was significant only in wheat (0.294, \(p\) < 0.01) (Table 2). Increased AT stimulated respiration and photorespiration in plants and a decline in photosynthetic activity occurred as leaf temperatures increased [53]. The SHF influenced the soil temperature and transferred the energy to or from the soil for maintaining the optimum conditions for soil microbial respiration [54], however, the RE was not significantly correlated with SHF in both rice and wheat (Table 2).
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 5 Security Analysis of WoT
### WoT Attacks
#### 5.3.9 A9: Firmware Injection  
Firmware injection involves inserting malicious code into the firmware of things during the update process. This attack exploits vulnerabilities in firmware update mechanisms that lack proper security checks. One very common example of this is the malware injection attack. Once the malicious firmware is installed, it can give the attacker persistent control over the device, allowing for ongoing data exfiltration or participation in botnet activities. For example, an attacker might inject malware into the firmware of a smart thermostat to control it remotely and disrupt its normal functions [49; 52].
# SDMw: Secure Dynamic Middleware for Defeating Port and OS Scanning
## 5 SDMw: The Secure Dynamic Middleware Architecture
### The Performance of the SDMw System
For a connection-oriented TCP service, the latency depends on whether the incoming segment is a TCP-SYN or a DATA/ACK/FIN segment. For a new connection, SDMw must create a new ISN depending on the OS fingerprint in use, and create a mapping between the incoming ISN and the new ISN in the translational table. DATA/ACK/FIN segments are a part of an ongoing TCP connection. In this case, based on the socket information, SDMw searches the table finds the <\(ISN_{o}\), \(ISN_{n}\)> mapping. Based on this mapping, the Sequence number field in the TCP segment must be updated. For DATA/ACK/FIN segments, SDMw introduces table-lookup latency along with Sequence number updating latency. We collected statistics for 200 ICMP packets (100 from each host) and 200 TCP-DATA/ACK/FIN segments. We also collected the latency associated with TCP-SYN segments associated with the data connection. The performance graph is presented in Figure 4. As it can be seen from the performance graph, the SDMw system did not introduce considerable latency due to the header rewriting process. For TCP-DATA/ACK/FIN segments, the average latency is about 2.2 ms. For the associated TCP-SYN segments, the latency is 2.3 ms. For connectionless ICMP packets, the latency is 1.1 ms. The latency can be improved if we run the SDMw algorithm in an application-specific integrated circuit (ASIC) processor with highspeed content addressable memory.
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### The Application of Remote Sensing and Machine Learning Technique into Weed Detection  
Choosing remote sensing (RS) and machine learning algorithms for SSWM can improve precision agriculture (PA). This situation has resulted in integrating remote sensing and machine learning becoming critical, as the need for RGB, multispectral, and hyperspectral processing systems has developed. Numerous researchers who tested the RS technique successfully produced an accurate weed map with promising implications for weed detection and management. Since the weed management using RS technique application in paddy is still in its early stage, Table 4 lists more studies on weed detection and mapping in various crops that apply remote sensing techniques with acceptable accuracy, for further reviews.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensors** & **Crops** & **Weed Type** & **Technique** & **Accuracy (\%)** & **Implications** & **Year** & **Reference** \\ \hline \multirow{4}{*}{RGB*} & \multirow{4}{*}{Carrots: Autumn} & \multirow{4}{*}{Grass and broad-leaved} & \multirow{4}{*}{Auto-associative neural network} & \multirow{4}{*}{\(>\)75\%} & \multirow{4}{*}{
\begin{tabular}{c} Neural network-based \\ allows the system to learn \\ and discriminate between \\ species without \\ predefined plant \\ descriptions \\ \end{tabular} } & \multirow{4}{*}{20  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Even though numerous platforms for data collection are accessible, a UAV is the best for identifying weeds in paddy because of its availability, high-quality data delivery, and convenience. On the other hand, the review discovered that deep learning (DL) is suitable for classifying grass weeds in paddy and producing high accuracy weed maps. However, when referring to other crops, it might differ for sedge and broad-leaved weeds. Nevertheless, this method necessitates a large amount of training data, resulting in vast agricultural datasets. In the future, to optimize the use of the RS technique, we must know what types of weeds we are dealing with in the paddy fields to choose the best technique for our research. Therefore, to classify weeds, a sophisticated method might not be necessary.
# Data Matrix Based Low Cost Autonomous Detection of Medicine Packages
## 2 Proposed Methodology  
Nowadays, the introduction of medicine packages into the authentication database is performed through the manual scan of their unique Data matrix, one by one by the user, becoming a repetitive and time-consuming process. To improve the users' ergonomy as well as to redirect them to other tasks, the proposed methodology addresses the simultaneous and autonomous acquisition of multiple codes. The present system is initiated by the user that presses a button and, after, the remaining process will automatically be executed. An image of the working area is acquired and the system can, autonomously, identify, extract the information of all the Data matrix codes contained in that area, and send it to the verification system database.
## 3 Key Technology
### Multi-Stage Fault Detection, Isolation and Recovery Strategy
The recovery judgment of the isolated sensors is dependent on the calculated weights and the system framework and prior information function will also be optimized accordingly. The second stage detection function continuously works in the system. We judge whether the sensor errors have a significant impact on absolute location or IMU results.
## 3 Results
### Image Segmentation Result
The maximum value of 1 indicates that the structurally segmented image is similar to the initial one, and inversely, the minimum of 0 is no structure, as described in Table 3.  
\[SSIM(\mathbf{x},\mathbf{y})=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+ C_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)} \tag{13}\]  
where \(\mathbf{x}\) is the original image and \(\mathbf{y}\) denotes the segmented image; \(\mu_{x}\) and \(\mu_{y}\) denote the mean intensity for the reference image \(\mathbf{x}\) and image \(\mathbf{y}\), respectively. In addition, \(\sigma_{\text{x}}^{2}=\
icefrac{{1}}{{N}}-\
icefrac{{1}}{{N}}\sum_{i=1}^{N} \left(x_{i}-\
icefrac{{1}}{{N}}\sum_{i=1}^{N}x_{i}\right)^{2}\) and \(\sigma_{\text{y}}^{2}=\
icefrac{{1}}{{N}}-\
icefrac{{1}}{{N}}\sum_{i=1}^{N} \left(y_{i}-\
icefrac{{1}}{{N}}\sum_{i=1}^{N}y_{i}\right)^{2}\) describe the standard deviation of the original and segmented images, and \(\sigma_{xy}=\
icefrac{{1}}{{N}}-\
icefrac{{1}}{{N}}\sum_{i=1}^{N}(x_{i}- \
icefrac{{1}}{{N}}\sum_{i=1}^{N}x_{i})(y_{i}-\
icefrac{{1}}{{N}}\sum_{i=1}^{ N}y_{i})\) refers to the covariance between the original and segmented images. \(C_{1}=(K_{1}L)^{2}\), and \(C_{2}=(K_{2}L)^{2}\) are constants, in which \(K_{1}\) and \(K_{2}\) should be less than 1 and \(L\) is the number of gray levels of a grayscale image or an image in three channels, such as the RGB image. Figure 8: Five segmented field tomato leaf images: (**a**) The field images of bacterial spot disease; (**b**) Early blight disease; (**c**) Late blight disease; (**d**) Septoria leaf spot disease; (**e**) Healthy leaf; the segmented field images of (**f**) Bacterial spot disease; (**g**) Early blight disease; (**h**) Late blight disease; (**i**) Septoria leaf spot disease; (**j**) Healthy leaf. \begin{table}
\begin{tabular}{l l l} \hline \hline
**No.** & **Image Name** & **SSIM** \\ \hline
1 & Bacterial spot disease & 0.914 \\
2 & Early blight disease & 0.905 \\
3 & Late blight disease & 0.929 \\
4 & Leaf mold disease & 0.908 \\
5 & Septoria leaf spot disease & 0.912 \\
6 & Two-spotted spider mites & 0.946 \\
7 & Target spot disease & 0.924 \\
8 & Yellow leaf curl virus disease & 0.917 \\
9 & Mosaic virus disease & 0.922 \\
10 & Healthy leaf & 0.924 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the SSIM values between 10 segmented leaf images and the original leaf ones.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 2 Channel Model
### Descriptions of 3D GBSM for HAP-MIMO Channel
The delay increment of each multipath relative to the straight path is described using an exponential distribution, and its probability density function can be written as  
\[P_{\text{exp}}(\Delta\tau_{k}(t))=\frac{1}{b}\text{e}^{-\frac{\Delta\tau_{k}( t)}{b}}, \tag{8}\]  
where \(b\) denotes the mean delay increment of each path. The amplitude of the \(k\)-th path is represented by \(a_{k}(t)\). Its probability density function can be expressed as  
\[P(a_{k}(t))=\frac{a_{k}(t)}{\sigma_{k}{}^{2}}\exp\biggl{(}-\frac{a_{k}(t)}{2 \sigma_{k}{}^{2}}\biggr{)}, \tag{9}\]  
where \(2\sigma_{k}{}^{2}(t)\) represents the average power of the Rayleigh distribution.
## 2 Concept of Vertical Seismic Isolation Device  
### Configurations and Dimensions  
The 3D seismic isolator being developed in this study is intended to be applied to individual facilities of nuclear power plants where severe vertical earthquake response amplification is expected. To this end, the horizontal seismic isolator uses a lead-inserted small LRB with proven stability and seismic isolation performance [16; 17], and a spring-damper-based vertical seismic isolator is mounted on the top of the LRB.  
Figure 1 presents the conceptual configuration of the horizontal-vertical integrated 3D seismic isolator being developed in this study. As shown in the figure, the LRB with relatively high vertical stiffness supports vertical springs and dampers of the vertical seismic isolation device.  
The housing in Figure 1 has a function to transmit the horizontal seismic load, simultaneously guiding the vertical seismic motion of the spring due to the superstructure. Table 1 shows the specifications of the 3D seismic isolator based on the lead-inserted LRB used in this study.  
As shown in Table 1, the design vertical load of the 3D seismic isolator considered in this study is 10 kN, the total height of the LRB is 34 mm, and the design horizontal seismic frequency is 2.3 Hz. The vertical seismic isolation device mounted on the top of the LRB will maintain the vertical stiffness by a disc spring or a helical spring and dissipate the vertical seismic energy by steel dampers.  
In the design of the vertical seismic isolation device, the vertical spring mainly controls the VIF, and the vertical damper controls the vertical displacement of the spring. The higher the vertical stiffness, the higher the vertical seismic isolation frequency, and the lower the vertical seismic isolation performance. The lower the vertical stiffness, the better the vertical seismic isolation performance, but the displacement of the vertical spring will significantly increase so that it may not meet the design concept of integral 3D seismic isolator presented in Figure 1. Therefore, a vertical spring design having an appropriate design VIF and a damping device design capable of properly suppressing the vertical spring displacement are required in a design stage.