esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Comparison of Result of ML Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Difference** & **Test** & **F Value** & \(p\)**-Value** & \
\begin{tabular}{c} **Significance** \\ **of Differences** \\ \end{tabular} & **Interpretation** \\ \hline E\({}_{\text{E,R}}\) vs. E\({}_{\text{E,T}}\) & F-test & 0.28 & 6.9 \(\times\) 10\({}^{-11}\) & Yes & The tree has much greater variance of errors than regression \\ \hline E\({}_{\text{E,R}}\) vs. E\({}_{\text{E,RF}}\) & [PERSON] & 2.97 & 0.086 & No &
\begin{tabular}{c} The difference in errors is not statistically significant \\ **of Differences** \\ \end{tabular} \\ \hline E\({}_{\text{E,R}}\) vs. E\({}_{\text{E,ANN}}\) & [PERSON] & 62.65 & 1.2 \(\times\) 10\({}^{-13}\) & Yes & Regression has much greater variance of errors than ANN \\ \hline E\({}_{\text{E,T}}\) vs. E\({}_{\text{E,RF}}\) & [PERSON] & 55.12 & 2.4 \(\times\) 10\({}^{-12}\) & Yes & The tree has much greater variance of errors than random forest \\ \hline E\({}_{\text{E,T}}\) vs. E\({}_{\text{E,ANN}}\) & [PERSON] & 132.45 & \(<\)10\({}^{-16}\) & Yes & The tree has extremely greater variance of errors than ANN \\ \hline E\({}_{\text{E,RF}}\) vs. E\({}_{\text{E,ANN}}\) & [PERSON] & 38.05 & 3 \(\times\) 10\({}^{-9}\) & Yes & Random forest has much greater variance of errors than ANN \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Residual variance Testing (\(\alpha=0.05\)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Rezidual** & \
\begin{tabular}{c} **Label** \\ **of Residual** \\ \end{tabular} & **Mean** & **Median** & **Variance** & **Standard Deviation** & **Max-Min** &
\begin{tabular}{c} **Shapiro–Wilk Test** \\ _(p-Value)_ \\ \end{tabular} \\ \hline F\({}_{\text{E}}-{\text{F}}_{\text{R}}\) & E\({}_{\text{E,R}}\) & 0.389 & 0.392 & 2.679 & 1.637 & 8.652 & 0.531 \\ \hline F\({}_{\text{E}}-{\text{F}}_{\text{T}}\) & E\({}_{\text{E,T}}\) & 0.132 & 0.099 & 9.643 & 3.015 & 13.612 & 0.506 \\ \hline F\({}_{\text{E}}-{\text{F}}_{\text{RF}}\) & E\({}_{\text{E,RF}}\) & 0.099 & 0.015 & 1.993 & 1.412 & 9.427 & 0.029 \\ \hline F\({}_{\text{E}}-{\text{F}}_{\text{ANN}}\) & E\({}_{\text{E,ANN}}\) & 0.056 & 0.037 & 0.452 & 0.672 & 6.144 & 2 \(\times\) 10\({}^{-11}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Characteristics of residuals. The ANN model is the best model in terms of residual variance, followed by the random forest model, and the tree model is the worst. The F-test (or Levene test) statistically confirms that these differences are significant and are not due to chance.
## 1 Introduction
Some of these satellites do not provide SM directly and require the application of various techniques to convert backscatter coefficients, incident angles, polarization, temporal changes in backscatter, and interferometric coherence into SM estimates. Various methods have been developed to estimate SM using passive and active microwave techniques. Passive microwave techniques provide promising SM estimation results over bare soil surfaces with high temporal resolution and are unaffected by cloud cover or diurnal conditions. On the other hand, active microwave techniques offer finer spatial resolution and are also unaffected by clouds and diurnal conditions. Nonetheless, they exhibit coarse temporal resolution, and the accuracy of SM estimation is affected by vegetation cover and surface roughness. Detailed discussions of SM estimation methods using passive and active microwaves, along with their advantages and disadvantages, can be found in the reviews conducted by [PERSON] et al. [34] and [PERSON] et al. [56]. The commonly used microwave satellite for SM estimation is illustrated in Figure 2.  
Figure 2: Commonly used microwave (active and passive) satellite missions for soil moisture monitoring from 1975 to 2024. Purple bars represent sensors operating in the L-band (0.5–1.5 GHz) using longer wavelengths capable of deeper SM penetration. Blue bars represent sensors in the C- or X-band (4–12 GHz) using shorter wavelengths that offer lower penetration capabilities. Green arrows indicate ongoing missions, and thick red lines indicate mission ending dates. SMMR: Scanning Multichannel Microwave Radiometer, SSM/I: Special Sensor Microwave/Imager, TMI: TRMM Microwave Imager, AMSR-E: Advanced Microwave Scanning Radiometer for EOS, GPM: Global Precipitation Measurement, FY-3 (A, B, C, & D)—Fengyun-3 (A, B, C, & D), AMSR-2: Advanced Microwave Scanning Radiometer 2, SMOS: Soil Moisture and Ocean Salinity, SMAP: Soil Moisture Active Passive, ERS: European Remote Sensing Satellite, ASCAT: Advanced Scatterometer. Although RS offers significant opportunities for SM monitoring, SM retrieval methods still face several challenges and limitations. The aforementioned methods are complex, requiring consideration of local meteorological conditions, a detailed understanding of microwave interactions with both bare and vegetated soils, and numerous ground parameters. Consequently, there is an evident need for a shift towards a more simplistic method using fewer inputs and less parameterization. Machine learning (ML) algorithms offer promising solutions to the challenges listed above. This review thoroughly examines the role of ML and RS in estimating SM, detailing the progress, challenges, and opportunities in this field.
# Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery
## 3 Materials and Methods
### Second Stage--VRP  
In the second stage, a combinatorial GA was employed that operated on categorical strings. Each permutation of N represented the genotype of an individual, where mN denotes the number of branch warehouses required to solve the VRP. In this context, each individual corresponded to a solution--a route between branch warehouses. This approach helped to avoid instances of city repetition within routes, as the problem stipulated that a truck could visit each city only once.  
However, a limitation arose when considering multiple trucks, as representing the VRP solutions as single permutations failed to capture the logistics of scenarios that required multiple trucks. To address this, instances where more than one truck was needed were preemptively managed by dispatching fully loaded trucks to the required cities (referred to as full-truck-load delivery, or FTL). This strategy ensured that the complexity of multi-truck logistics was appropriately accounted for.  
The input for the combinatorial GA consisted of a configuration of warehouses, which comprised a list of cities selected in the first stage. The dispatch city was excluded from this list, as it always served as the starting point of the route. These selected warehouses were assigned numerical categories that typically ranged from 1 to N, which correlated to the number of selected warehouses.  
For example, consider a scenario where five branch warehouses, labeled A, B, C, D, and E, are selected. Figure 6a illustrates examples of genotypes for individuals, representing the routes between these warehouses.  
The order of operations remains the same. The algorithm starts with initialization of the initial population. It will occur randomly by obtaining a given number of permutations of the transferred warehouses.  
Next, the value of the fitness function for the entire population is determined. The fitness function is a special module that will calculate the transportation budget. This module is different for each specific problem since it is possible to count the cost of trucking in different ways. The most popular case is multiplication of the route distance by the tariff per unit length of the route. If more than one vehicle is required to organize the transportation of goods along a given route, the route is divided into a number of separate routes, for each of which the budget is calculated. The final value of fitness will be the sum of budgets of all trucking on these routes.  
Selection does not depend on the way an individual is represented, so it is a more universal operator that is suitable for both classical and combinatorial GAs. At the same time, the crossover and mutation operators of the combinatorial GA differ from the classical version because of the peculiarities of genotype encoding.
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 5 Discussion
### Effects of Stem Surface Finish on Subsidence  
On the other hand, migration of the femoral component stem within the medullary canal has been used as evidence of the early failure of artificial hip replacement [54]. The subsidence of the femoral stem is recognized as being related to stem shape design and surface finish [55]. However, different surface finishes of stems can have different mechanical effects on the cement at the cement-bone interface, thus resulting in the progress of subsidence in different post implantation period [56]. [PERSON] et al. [55] and [PERSON] et al. [57], respectively, employed physical cemented composite constructions to assess the effects of stem design in terms of geometry and surface finish on migration of the femoral component under cyclic joint loads. Although the migrations were measured at different ranges, their results indicated that polished stems showed a greater migration rate than roughened stems of identical shape and size. It is obvious that the findings regarding stem migration in the cement mantle are also in agreement with the finite element predictions of the present study. Considered the bonding ability to resist migration of the cement-stem interface, the stem with the roughened surface was superior to the smooth stem. Nevertheless, the results of the present study showed that a roughened stem was associated with a higher probability of failure of the cement mantles and at the cement-stem interfaces, which would increase the subsidence of subsequent gait loadings [4; 23] and initial loss of fixation of the implant [45]. [PERSON] et al. [58] also reported that stem-cement interface friction was a factor affecting the stem subsidence; however, as compared with rough stems, the polished stems have a greater potential to stabilize the debonded stem due to taper locking effect from the increasing cement-stem interface.  
Consequently, the analysis results clearly suggest that a femoral stem with a roughened surface is unfavorable for stabilizing the mechanical performance of the hip prosthesis when compared to a smooth stem. By means of the experimental and finite element approaches, this study has demonstrated the difference in the mechanical performances of cemented hip implants with different surface finish. As a quantification analysis, the cemented stem with polished surface (Ra = 0.15 \(\upmu\)m) and blasting surface (Ra = 5.56 \(\upmu\)m) were found to show different failure rates at the bonding interface and cement mantles, respectively.
## 1 Introduction
SCiOT is a handheld NIR spectrometer designed and manufactured by Consumer Physics Inc. (Tel Aviv, Israel). The device, controlled via Bluetooth from a mobile device such as a smartphone, can scan samples in the region between 740 and 1070 nm, also known as the region of the third overtone in the NIR spectrum [3], in diffuse reflectance mode. Once samples have been scanned, a software allows the user to build a training spectral library that can be processed and analysed through machine learning methods to develop a chemometric model tailored to the final analysis the user aims to perform. These models can then be implemented in a mobile app so the end user can easily and quickly visualise the results of a new scan on their mobile device. This instrument has already been tested in a wide range of scientific fields [23; 24; 25; 26; 27] including the field of forensic science [8]. In this study, five corrosive substances (commonly used in corrosives attacks) have been selected and analysed. These corrosive substances have been scanned through commercially available bottles made of Polyethylene terephthalate (PET) and High-Density Polyethylene (HDPE) using SciOT to assess whether these substances can be correctly identified through the container walls, therefore preventing any exposure to the corrosive in question. Furthermore, the methodology explored during this study was restricted exclusively to the tools available within the SciOT and its associated software to assess the potential implementation of such commercial instruments among the intended end users, e.g., police forces. This is, to the best of the authors' knowledge, the first study to assess the use of NIR, and specifically portable NIR, for the qualitative analysis of corrosive substances in criminal investigations.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Security Performance  
As defined in Section 3, we employ several requirements for the routing process. In the proposed method, all routing information packets are signed by the node that generates the packet, which prevents attackers from impersonating other nodes and can detect attacks such as falsification or forgery of packets via signature verification. Therefore, the proposed method satisfies the first and second requirements defined in Section 3 (i.e., spoofing and falsification). Furthermore, the proposed method assumes that a unique ID and the paired secret key are distributed to each node by a server in advance to utilize the ID-based signature. This feature implies that only pre-authorized nodes can join the network, thereby preventing attacks from unknown nodes such as packet injection attacks. Finally, based on the RERR, the proposed method can mitigate the effects of packet-dropping or delaying attacks by discarding low-quality communication routes.  
All of the compared methods satisfy these requirements. However, they cannot satisfy the fifth requirement (i.e., that the intermediate node that generates the RREP on behalf of the destination node cannot prove to a third party that the route from this node to the destination exists and is correct). Instead, SAODV determines an option wherein the relay node generates an RREP using another signature called a _double signature_, which is received from another node. However, an attacker may continue to use a signature received in the past, even if the attacker does not hold the route to the destination, implying that the attacker can generate an RREP illegally.  
In contrast, with the proposed protocol, the intermediate node can securely generate an RREP because it discloses the RREP packet that was received in the past from the destination node. In this way, the routing efficiency can be significantly improved while maintaining the security performance.
# Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
## 4 The Path-Tracking Controller Design
### State Error Compensator  
The state error compensator is to achieve the state error tending to zero, that is, it makes the lateral deviation and heading error of the vehicle tend to zero. Therefore, the problem of path tracking can be converted to an error minimization problem. In the classic state error compensator in ADRC, there are two kinds, linear and nonlinear; however, their parameter tuning process is more complicated, and the parameter tuning result directly affects the control effect of the controller. Therefore, in this paper, the LQR method is introduced as the error state compensator, the process of parameter tuning is optimized, and the cost function is defined as  
\[J=\frac{1}{2}{\int_{0}^{t_{f}}}\Big{(}x^{T}Qx+u^{T}Ru\Big{)}dt \tag{30}\]  
where \(x=\big{[}y_{e},\dot{y}_{e^{\prime}},\varphi_{e},\dot{\varphi}_{e}\big{]}^{T}\); \(Q\) is a semipositive definite matrix that denotes the weight matrix of the controlled variable; and \(R\) is a positive definite matrix that denotes the weight matrix of the control input variables.  
According to LQR theory, in order to guarantee that the performance index function \(J\) is minimized, the control inputs should be as follows:  
\[u=-R^{-1}B_{0}^{T}Px \tag{31}\]  
where \(P\) can be obtained by solving the Riccati equation, as follows:  
\[A^{T}P+PA+Q-PB_{0}R^{-1}B_{0}^{T}P=0 \tag{32}\]  
As a result, the state error compensator outputs the initial front-wheel steering angle and the initial external yaw moment as  
\[\big{[}\begin{array}{cc}\delta_{f0}&\Delta M_{20}\end{array}\big{]}^{T}=-R^{ -1}B_{0}^{T}Px \tag{33}\]  
By compensating the initial output of the state error compensator according to ADRC theory, the final front-wheel steering angle and external yaw moment required for the vehicle path-tracking control are obtained as  
\[\big{[}\begin{array}{cc}\delta_{f}&\Delta M_{z}\end{array}\big{]}^{T}=-R_{0 }{}^{-1}B_{1}^{T}P_{1}x-\widetilde{b}\tilde{f}(\cdot) \tag{34}\]  
where \(\widetilde{b}=\left[\begin{array}{cc}1/b_{1}&0\\ 0&1/b_{2}\end{array}\right]\).
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 4 Counter-UAS Simulation Results
From there, the drone will try to make a direct approach at maximum speed following the direction depicted as a black arrow in Figure 3). This scenario (i.e., drone trajectory, sensors' location) has been easily represented and executed in real time using the simulation platform. After running it, the plots generated by each of the sensors were retrieved for analysis. These plots are shown in Figure 4, where the plots corresponding to actual drones and the false alarms are represented. To represent the plots from the microphone sensor (where only angular information is available), the actual distance of the drone is used. False alarms are filtered out in Figures 5 and 6 for each of the four sensors to facilitate the analysis. There, the alert distance provided by each of the sensors can be compared. As expected, radar-based sensors have a greater range than the microphone-based one, which only detects the drone in very close proximity. Within the radar-based sensors, active radar provides consistent detections from the beginning of the trajectory, whereas passive and MIMO sensors provide consistent results from a range of 3 and 4 km, respectively (as can be derived from plot density in Figure 7). It can also be checked that detection probability (related to the number of detections) increases as the distance to the sensor decreases. This result is the expected one, as detection probability increases with the SNR, which also increases as the distance to the sensor is reduced. The positioning errors are depicted in Figure 7 for the angle error and Figure 8 for the distance error (microphone sensor is not included here). It can be observed that the magnitude of both type of errors decreases as the drone approaches the sensor receiving location. This can be explained, once again, by the dependency between the computed error and the SNR. More detailed performance measures could be obtained, both in terms of detection and accuracy (i.e., PD vs. range or angle/range error standard deviations vs. range). In the case of monostatic radars/RF sensors and acoustic sensors, it is possible to derive this relation by considering PD/accuracy dependency with SNR, which, in turn, depends directly on range. However, for multistatic sensors, passive radars and distributed sensors in  
Figure 3: Graphical representation of the proposed scenario.
## 4 Experimental Result
This approach ensures that the results highlight the practical effectiveness of capacitor placement within relevant high-frequency operating conditions. This study also highlights the practical advantages of this approach in real-world applications. By enabling post-production integration of capacitors, this method allows for system optimization without the need for redesigning or re-fabricating the circuit board. Such flexibility is particularly beneficial in iterative design environments and high-frequency communication systems, where reducing crosstalk is critical for maintaining signal integrity. This research presents test results for cases where the ground plane between transmission lines is three times the width of the transmission line (3W) and five times the width (5W). In the experiments, capacitors located at the starting, middle, and ending points of the sections where the transmission lines progress together were evaluated. These positions are indicated by the codes F (First), M (Middle), and E (End). Capacitor values of 1 pF, 15 pF, and 82 pF were tested separately for each position and compared with the baseline condition (no capacitors). The test results are presented in graphical format, with the baseline condition shown in black for clarity. To facilitate interpretation, color coding was applied to the graphs, indicating the specific capacitor values and positions. The color codes and corresponding scenarios are detailed in Figure 5.  
This study provides valuable insights into the effects of various ground plane widths and capacitor placements on the capacitive properties of transmission lines in circuit boards fabricated with FR4 dielectric material. Crosstalk measurements obtained using a VNA are presented graphically to illustrate the impact of capacitors on crosstalk reduction under different configurations. The test results include scenarios where capacitors are located at the beginning, midpoint, and endpoint of the transmission line segments. Additionally, the influence of line length was systematically evaluated by comparing results for transmission line lengths of 100 mm and 200 mm, where the lines run parallel. These findings represent a significant step towards optimizing high-frequency transmission line designs by effectively reducing crosstalk and enhancing signal integrity. Figure 5: The capacitor values corresponding to the color-coding on the graph based on the capacitor values and positions.
## 2 BDS-3 SAIM Monitoring Method
### Enhanced Ephemeris Monitoring Methods
#### 2.2.3 Inter-Satellite Ranging
The time interval for imputation is 3 s. Thus, it can be calculated that the reduction error of 3 s is less than 0.0003 ns, which is negligible for judging the ephemeris error. \(\rho_{AB}(t_{0})\) can be measured simultaneously in Formula (2) before they are added together and the clock error parameters are eliminated to obtain the geometric distance. The measured value is used to find the difference in the geometric distance calculated by using the broadcast ephemeris to obtain the distance between two satellites and ultimately obtain the inter-satellite link's ranging residual, which can be used to judge whether the ephemeris of the two satellites is normal. If the ephemeris of Satellite A or Satellite B is abnormal, the ranging residual will increase. Since it is impossible to determine whether the faulty satellite is Satellite A or Satellite B, it is necessary to use the ranging residuals of at least two other satellites for a comprehensive judgment to identify the faulty satellite.