esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials  
The properties of the soils used in the laboratory work are shown in Table 1. The granular materials used in this study were obtained commercially. These soils are poorly graded and have a uniform soil size of 95%, with a mean diameter ranging from 0.5 to 6 mm. The basic properties and soil water characteristic curves of the granular soils were obtained from previous research by [PERSON] [36] in an experimental study on the drainage of sports fields. The clayey sand includes 26% fine content and is classified as clayey sand according to the Unified Soil Classification System (USCS). The plasticity index of the clayey sand soil is 8.5, and the clay friction (<0.002 mm) is about 10%, which affects the soil's hydraulic conductivity considerably.  
Figure 1: Design and mechanism of the three-layer cover with capillary barrier.  
The soil water characteristic curve of the clayey sand was determined by conducting a pressure plate test following ASTM (D6836-16) guidelines at various void ratios. The data obtained from these tests, including volumetric water content and matrix suction, were then analyzed and fitted using [PERSON]'s equation [37] (Figure 2a) to obtain comprehensive curves across the entire range.  
\[\theta(\Psi)=\theta_{\mathrm{r}}+\frac{\theta_{\mathrm{s}}-\theta_{\mathrm{r}}}{ \left[1+(\alpha\Psi)^{\mathrm{n}}\right]^{\mathrm{m}}} \tag{1}\]  
where \(\theta(\psi)\) is the volumetric water content at a given metric potential \(\psi\), \(\theta_{\mathrm{s}}\) is the volumetric water content at saturation, \(\theta_{\mathrm{r}}\) is the residual volumetric water content, and \(\alpha\), \(\mathrm{n}\), and \(\mathrm{m}\) are the van Genuchten fitting parameters. The curve fitting parameters obtained are presented in Table 2. A falling head permeability test, in accordance with ASTM (D5856-15) standards, was conducted on the clayey sand to determine the permeability coefficient.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Type** & **Clayey Sand** & **Fine Sand** & **Medium Sand** & **Coarse Sand** & **Fine Gravel** \\ \hline Soil Classification (USC) & SC & SP & SP & SP \\ Gravel (\%) & 4 & - & - & - & 100 [1] \\ Sand (\%) & 70 & 100 [1] & 100 [1] & 100 [1] & - \\ Fine (\%) & 26 & - & - & - & - \\ Mean diameter, D50 (mm) & 0.45 & 0.5 [1] & 1.0 [1] & 1.19 [1] & 6.0 [1] \\ Coefficient of uniformity, Cu & 440 & 1.47 [1] & 1.57 [1] & 1.90 [1] & 1.40 [1] \\ Coefficient of curvature, Cc & 5.2 & 1.05 [1] & 1.13 [1] & 1.54 [1] & 0.96 [1] \\ Liquid limit, LL (\%) & 28.5 & - & - & - & - \\ Plastic limit, PL (\%) & 20 & - & - & - & - \\ Specific gravity, Gs & 2.7 & 2.65 [1] & 2.72 [1] & 2.66 [1] & 2.65 [1] \\ Saturated permeability, \(k_{\mathrm{sat}}\) (m/s) & \(2.54\times 10^{-7}\) & \(8.27\times 10^{-4}\)[1] & \(2.60\times 10^{-3}\)[1] & \(4.9\times 10^{-3}\)[1] & \(1.07\times 10^{-2}\)[1] \\ \hline \end{tabular} \({}^{1}\) Kesgin [36].  
\end{table}
Table 1: Properties of the soils.  
Figure 2: SWCCs of soils, (**a**) clayey sand and (**b**) granular soils [36].
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.8 Real-World Deployment Considerations  
Despite the significant theoretical advancements in IMI strategies, their practical deployment in real-world B5G environments faces numerous challenges. First, many advanced IM techniques, such as CoMP and DL-based scheduling, require precise synchronization and low-latency backhaul connectivity between BSs, which are often unavailable in rural or infrastructure-limited deployments [221]. Hardware heterogeneity, including variations in processing capabilities and memory at edge nodes, further complicates consistent IM implementation across network tiers [203]. In AI-based IMI frameworks, model complexity and inference latency hinder RT decision-making, especially when deployed on constrained edge devices such as IoT gateways and mobile UEs [203]. Additionally, cooperative techniques like joint transmission, centralized scheduling, and coordinated BF introduce significant signaling overhead, which can degrade performance under mobility and limited backhaul conditions [238]. Finally, achieving interoperability across multi-vendor RAN components and aligning with standardization efforts from 3 GPP and the open RAN alliance remain essential for large-scale, reliable deployment [221]. Addressing these practical constraints is crucial to translating simulation-based performance gains into scalable and robust deployment outcomes.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 4 Experiments
### Robustness Experiment
#### 4.2.3 Geometric Attacks  
The geometric attack types mainly contain anticlockwise rotation attacks with rotation angles \(\theta\) of 5, 15, 30, and 45, random cropping attacks with cropping scales \(q\) of 0.125, 0.25, 0.5, and 0.7, and scaling attacks with scaling factors \(r\) of 0.25, 0.5, 0.75, 1.25, 1.5, and 2. The scaling attack can be rescaled to the cover image size by interpolation after scaling to be used as an input to the watermark extraction network \(N_{dec}\). The above attacks were applied to the slice images in each gradient direction of the DWI, and the watermark extraction BER values are shown in Table 4. It can be seen that almost all values of BER are equal to 0 after the images are subjected to various geometric attacks, indicating that our proposed algorithm has excellent robustness to geometric attacks.  
Figure 12: Visualization results of the recovered images of six slice images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack Type** & **Parameter** & **BER/bit** \\ \hline median filtering & K = 3 & 0.000004 \\ median filtering & K = 5 & 0.000004 \\ median filtering & K = 7 & 0.000016 \\ Gaussian filtering & K = 3 & 0.000005 \\ Gaussian filtering & K = 5 & 0.00006 \\ Gaussian filtering & K = 7 & 0.000081 \\ SPN & \(p=0.01\) & 0.000004 \\ SPN & \(p=0.02\) & 0.000006 \\ SPN & \(p=0.03\) & 0.000006 \\ Gaussian noise & v = 0.01 & 0.000004 \\ Gaussian noise & v = 0.02 & 0.000385 \\ Gaussian noise & v = 0.03 & 0.000013 \\ JPEG compression & Q = 90 & 0.000004 \\ JPEG compression & Q = 70 & 0.000011 \\ JPEG compression & Q = 30 & 0.000005 \\ JPEG compression & Q = 10 & 0.002077 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BER values under common attacks.
## Materials and Methods
### Landsat Datasets and Image Processing  
Data were available and processed with the GEE platform. Landsat Surface Reflectance Tier 1 collections for Landsat 4 Enhanced Thematic Mapper (ETM), Landsat 5 ETM, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) were used in the analysis. Calibration between sensors has been done, so they are suitable for time-series analysis. Correspondent ImageCollection IDs in GEE are: 'LANDSAT/LT04/C01/T1_SR'; 'LANDSAT/LT05/C01/T1_SR'; 'LANDSAT/LE07/C01/T1_SR' and 'LANDSAT/LC08/C01/T1_SR'. The collections correct illumination/viewing geometry and atmospheric effects using LEDAPS in ETM sensors and LaSRC in OLI/TIRS sensors. [30; 31]. We filtered the collection for all images available during the period 1 January 1984 to 31 December 2017. Path and row for all images were 15/53.  
For the removal of clouds, we used the C Function of Mask CFMask algorithm [32]. After the removal of cloudy pixels in the area we obtain clear and masked cloudy pixels for every image. We used GEE to count the highest number of contiguous clear pixels inside our AOI, for the highest number of available images. This reduced the initial area to a limited number of pixels (\(n=1228\)) (Figure 3). Finally, these pixels were cloud-free for 39 images (Figure 4). We inspected every individual image visually to search for cloud or haze remnants.  
Two common VI were calculated for every image NDVI [2] (Equation (1)) and EVI [1] (Equation (2)) and added as new bands.  
\[\text{NDVI}=(\text{NIR}-\text{RED})/(\text{NIR}+\text{RED}) \tag{1}\]  
\[\text{EVI}=\text{G}^{*}((\text{NIR}-\text{RED})/(\text{NIR}+\text{C}_{1}^{*} \text{RED}-\text{C}_{2}^{*}\text{BLUE}+\text{L})) \tag{2}\]  
where NIR, RED and BLUE are Near-infrared; red and blue bands for Landsat images. G (2.5) is a scaling factor; L (1.0) is the canopy background adjustment factor; and C\({}_{1}\) (6.0) and C\({}_{2}\) (7.5) are the coefficients of the aerosol resistance term [33].  
Figure 3: Landsat pixels available (\(30\times 30\) m), \(n=1228\) for the 39 selected images in the AOI after cloud masking, and screening.
## 3 System Setup
### Tx/Rx Dsp
#### 3.1.1 FDMA TX/RX DSP  
The offline DSP blocks of the FDMA-based waveform aggregation/deaggregation scheme used on the transmitter (TX) and receiver (RX) sides are shown in Figure 2. The K point FFT/IFFT(Inverse FFT) size is 16, 32, 64, and 128 for SCs type 0, 1, 2, and 3, respectively. At the transmitter side, a pre-emphasis/pre-compensation block is used to equalize the performance of the FDMA aggregated channels over frequency. The techniques used in the pre-emphasis method involve multiplying each waveform (channel) by an amplitude scaling factor (a set of globally optimized coefficients), which in turn de-emphasizes (minimizes) the channel's power at a lower frequency and pre-emphasizes (maximizes) the channel's power at the higher frequency end. Hence, the EVM along the whole set of channels is set to the same value. For a detailed study of frequency domain pre-emphasis, please refer to our previous investigation [13]. The waveforms are then mapped in the way of a Hermitian symmetry feed to the half point of the M point IFFT set to 4096 input, so that real valued output is clipped and upsampled in frequency to fit the DAC sampling rate [14]. At the receiver side, the received digital signal from the ADC output shown in Figure 1 is down sampled in frequency to the original aggregated channel's sampling frequency. The de-aggregated channels after the K point IFFT are equalized using one tap equalization for the channel impairment compensation, and the average root-mean-square (RMS) error vector (EVM) is estimated as the difference between the ideal transmitted symbol and the received symbols after equalization as RMS. The EVM per channel is obtained as the average over all OFDM subcarriers.  
Figure 2: Frequency division multiple access (FDMA) TX/RX DSP block scheme. NR—new radio.
## 3 Results and Discussion
### IR Leak Light Detection
The determination of their origin is not entirely clear because the diameter of the infrared beam is already several mm at the position of the Channeltron and therefore illuminates various parts of this compact measurement device, potentially causing noise in the measured signal. It also cannot entirely be excluded that the reflected IR signal ionizes impurities in the surrounding of the Channeltron, which causes the charged particle to be detected with some delay. Measurements without the XUV beam showed these peaks, too. Saturation effects of the detection electronics can therefore be excluded. Furthermore, almost no counts appear at a delay of 720 ns, which proves the solar blindness of the Channeltron. Whatever the exact origin of the additional delayed signal may be, due to the higher IR reflectivity of the uncoated surface, the number of counts for this surface is larger (Figure 8b) than that of the CsI-coated surface (Figure 8a). We have always observed the pronounced peak for different experimental conditions. In principle, it should be possible to quantify the IR content by measuring the ratio of the signals at the additional peak and compare it with the ratio at the main peak. In the case of the uncoated stainless steel surface (Figure 8b), the additional count rate from the increased IR signal is in the order of 1% of the total counts. This rather low change in count rate only allows for a qualitative statement, i.e., whether IR light is present or not. This is also in agreement with the evaluation presented in Figure 7d, where the ratio of the calculated and measured reflectivity is different. Whether this will be a positive or negative discrepancy between calculation and measurement depends on the geometry of the detection device, its electronic readout, the distance to the reflecting surface, and the divergence of the XUV and IR beam, respectively. For our temporally resolved measurement of the Channeltron signal, the XUV reflectivity ratio is decreased because the contribution between 780 ns and 800 ns is neglected and the XUV peak is increased by IR photons in the case where uncoated stainless steel have a higher IR reflectivity. Thus, its temporal resolved signal can be directly used for optimization of the spectral filtering and minimization of the IR leak light, respectively. In Figure 8b, we observe a substantial difference in count rate depending on the amount of IR leak light.
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 4 Conclusions  
To cope with rail surface defects, an inspection approach based on UAV images is proposed in this study. The proposed LWLC algorithm can highlight not only defects and homogenized backgrounds of UAV-based rail images, but also eliminates the adverse effects of non-uniform illumination. Furthermore, we put forward the GSME method for defects segmentation, which reduces irregular points and obtains excellent segmentation effects. The integrated LWLC+GSME method further illustrates great flexibility and effectiveness in detecting discrete defects.  
Finally, this study compared LWLC and LWLC+GSME with related methods, and the results of experiments show the significance of the proposed method. The quantitative experimental results show that the proposed method achieves a recall of 93.75% for T-I defects and of 94.26% for T-II defects, and that it is efficient and feasible to detect rail surface defects based on UAV images. It was verified that the proposed model can obtain excellent results.  
In future, our research work will focus on the following two aspects: firstly, we will explore new models for rail defect classification based on UAV images and assess the health of rails; secondly, based on the development of high UAV photography, fast detection models in complex environments will be developed to increase detection efficiency.  
[PERSON] collected and analyzed the data, made charts and diagrams, conceived and performed the experiments and wrote the paper; [PERSON] conceived the structure and provided guidance; [PERSON] provided the UAV equipment and searched the literature; [PERSON] modified the manuscript.  
This research is supported by the National Key R&D Program of China (No. 2016 YFB1200203), National Natural Science Foundation of China (No. 91738303) and State Key Laboratory of Rail Traffic Control and Safety (Contract Nos. RCS2016 ZQ003 and RCS2016 ZT018)  
This research is also supported by National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit.  
The authors declare no conflict of interest.
# Current Context and Research Trends in Linear DC-DC Converters
## 4 Dominant Characteristics of LDOs
### Small Package Size  
Compared with SMPS, where external components such as inductors need to be connected, LDOs are normally available as a single integrated component which makes them a perfect solution for portable devices.  
Table 3 shows some of the LDO ICs in the market specifically designed for particular applications based on their dominant LDO characteristic.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dropout** & **Input Voltage** **(mV)** & **Output Voltage** **Range (V)** & **Current** **Range (V)** & **Maximum** **Rating (mA)** & **Commercially** **Efficiency (\%)** & **Available LDOs** \\ \hline
200 & 1.5–5.5 & \(-0.3\pm 0.3\) & 200 & 96.36 & LDBL20 [13] \\ \hline
65–125 & 0.8–5.5 & 0.8–3.6 & 1500 & 97.72 & LD59150 [14] \\ \hline
80 & 1.5–5.5 & 0.8–3.3 & 100 & 98.55 & LD39015 [15] \\ \hline
175 & 1.1–6.5 & 0.8–5.15 & 4000 & 97.31 & TPS7A54 [16] \\ \hline
225 & 1.5–6 & 0.55–5.5 & 1000 & 96.25 & TLV752 [17] \\ \hline \end{tabular}
\end{table}
Table 2: Commercial ultra-low-dropout regulators.
## Appendix B Case Study DBN Formation
### Creating the CPTs  
The conditional probability tables for each node were created by measuring the frequency of different data combinations with respect to the different node states and supplementing the available data with expert opinions when there was no information available. For example, for the dynamic table for the inlet temperature, the value \(P(\mathit{InletTemperature}|\mathit{ScramState},\mathit{InletTemperature}(t-1))\) was calculated for the different values of inlet temperatures that were associated with each of the different scram/trip states and previous values. Each state was then normalized over the same parent node conditions.  
The manner of providing expert opinion for the CPTs depended on the nature of the CPTs as well as the location of the node:  
* **Accident State (Scram State):** The expert opinion used to construct the CPT for the \"Scram State\" node was based on probabilities taken from the PRISM preliminary safety information document (PSID) and listed in Table 12[34]:* **System Information/Sensor Data (Inlet Temperature, Outlet Temperature, Power-to-Flow Ratio, Pressure near Inlet):** The initial distribution of each measurement was assumed to be within the normal operating baseline of the variable; therefore, the initial measurement for each system sensor started in the middle bin marked \"Medium/Normal\". For the dynamic distributions, the conditional probabilities were dependent on the sensor values in the previous time period; as a result, CPTs that were missing a parent node condition would substitute the blank column with one of the following listed in Table 10:
* **System Prognostics (Failure: Fuel Relocation, Failure: Clad Melting):** Because of the limited number of scenarios that result in an overall system failure from the two outcomes specified in the case study, the prognostics CPTs are the most incomplete in the model. To fill the CPTs with values that would not skew the outcome, it was assumed that the empty parent condition cases would result in a 1:1000 chance of having either system failure occur;
* **System Diagnostics (Diagnostics):** For this model, it is assumed that if a failure occurs, then the system is not healthy. As such, the CPT for the diagnostics node \"Diagnostics\" is constructed using the conditional probability table shown in Table 11:
## 6 Data Transmission
### Success Rate of Data Transmission
#### 6.3.1 Scenario I: Without Retransmission  
The success rate of data transmission can be calculated according to the results in Column 10 of Table 11. If retransmission is not considered, the data will only be transmitted once. The rate of successfully sending a packet from a sensor in grid \((i,j)\) to the next hop can be expressed as  
\[S_{non}(i,j)=1-A. \tag{26}\]
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 2 Related Work
### UAV-Based Routing  
In recent years, research on UAV-based routing has witnessed significant growth, driven by the expanding applications of UAVs in various fields, including surveillance, mapping, and delivery. [PERSON] et al. [35] provided a formal definition of the UAV routing and trajectory optimization problem (UAV RTOP) and introduced a taxonomy. Some studies have concentrated on coverage and exploration path planning for UAVs. For instance, [PERSON] et al. [36] proposed an enhanced exact cellular decomposition method for UAV coverage path planning in polygon areas, reducing the convex polygon coverage problem to width calculation and demonstrating the feasibility of this method. [PERSON] et al. [37] presented a complete coverage neural network (CCNN) algorithm for UAV path planning, which simplifies neural activity calculations to reduce calculation time and improve coverage efficiency. Regarding UAV-based delivery systems, [PERSON] et al. [38] surveyed emerging drone routing algorithms, highlighting three major aspects: trajectory planning, charging, and security. [PERSON] et al. [39] introduced the Public Transport-Assisted Multi-UAV Parcel Delivery (PDD) algorithm, a new routing and scheduling method that iteratively combines segments of existing routes, integrates public transportation schedules, and jointly optimizes the distance and time costs of UAVs.  
In addition, [PERSON] et al. [40] introduced a vehicle-assisted multi-UAV inspection joint route and scheduling algorithm (VAMU) that enables a single vehicle and multiple UAVs to work in parallel to serve multiple targets, thereby optimizing the efficiency of large-scale inspection tasks and reducing time consumption. [PERSON] et al. [41] conducted a survey on bio-inspired optimization-based path planning algorithms in UAVs, analyzing their characteristics, principles, advantages, and limitations. In the area of multi-UAV formation, [PERSON] et al. [42] reviewed UAV formation trajectory planning algorithms, proposed a framework, classified existing algorithms, and summarized the challenges and future directions.  
However, most existing studies assume constant speeds for UAVs and vehicles, treating the task environment as static. These approaches often overlook dynamic factors, such as weather conditions, that affect the speeds of UAVs and vehicles. Consequently, task execution efficiency is significantly impacted by these dynamic factors, which have not been adequately addressed in previous work. In contrast, our research integrates dynamic environmental changes, such as fluctuating weather conditions, into the path planning process, resulting in a more realistic and adaptive optimization of task execution. This enhancement allows for improved real-time decision-making and overall task performance, addressing the limitations of static models commonly used in existing studies.
## 6 Experimental Evaluation
### Experimental Results: Performance of Single-Channel Clean Speech PSD Estimators  
Next, we evaluate different clean speech PSD estimators when combined with Wiener postfiltering (Equation (64)) applied to the MVDR beamformer output. Table 6 shows a comparison between the estimator based on the power difference (WF-Ps) of Equation (74), the one based on the distortionless constraint of MVDR (WF-Ms) of Equation (75) and a standalone MVDR beamformer (i.e., with no postfiltering) as a reference, all of them with the best SAP configurations determined above. This comparison is done in terms of speech quality and intelligibility.  
Results show that WF-Ms performs better than WF-Ps, obtaining slightly better results in CT conditions and clearly outperforming it in FT conditions. The WF-Ms estimation does not make any assumptions about the noise power similarity between microphones as the other approach does. This makes the WF-Ms estimation procedure, which also exploits the cross-correlation elements of the noisy speech and noise SCMs, more robust. Moreover, the power difference assumption is no longer valid in FT conditions, leading the WF-Ps approach to degrade the performance in this scenario, particularly at higher SNRs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Place.**} & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**SD (\%)**} \\ \cline{3-6}  & & & \multicolumn{3}{c}{**Environment**} \\  & & **A** & **B** & **C** & **D** \\ \hline CT & EVD-PLDn & 1.16 & 1.97 & 1.73 & 2.17 \\  & CW-PLDn & 1.08 & 1.87 & 1.59 & 2.08 \\  & eKF-PLDn & **0.59** & **1.24** & **0.87** & **1.09** \\  & _OracleC-PLDn_ & _0.03_ & _0.39_ & _0.17_ & _0.36_ \\ \hline FT & EVD-P\&C & 3.62 & 7.29 & 5.54 & 8.13 \\  & CW-P\&C & 3.62 & 7.78 & 5.55 & 8.26 \\  & eKF-P\&C & **2.92** & **5.12** & **4.32** & **6.13** \\  & _OracleC-P\&C_ & _0.45_ & _1.80_ & _1.06_ & _2.30_ \\ \hline \hline \end{tabular}
\end{table}
Table 5: SD results for different RTF estimators when used for MVDR beamforming. Results are broken down by both reverberation environment and device placement. The noise environments are grouped in terms of the reverberant environment as in Table 1: A (Car, Street, Pedestrian street), B (Bus, Cafe), C (Bus station, Babble) and D (Mall).