esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Water is fundamental to life on Earth. Knowing the locations and extents of rain and snowfall is vital to understanding how weather and climate impact our environment and Earth's water and energy cycles, including effects on agriculture, fresh water availability, and response to natural disasters. Since rainfall and snowfall vary greatly on a small scale and over time, satellites can provide a better picture of rain and snow distribution around the globe than the ground instruments, particularly in areas where surface measurements are lacking. Nonetheless, evaluation of satellite precipitation estimates is essential prior to operational use. This is why many previous studies are devoted to the validation of satellite estimation [1; 2; 3].  
The primary requirement in precipitation measurement is to know where and how much precipitation is falling at any given time. Such precipitation characteristics may be determined through traditional ground-based rain-gauges and/or advanced satellite precipitation products. Another important aspect deals with the duration of precipitation. In this study, accumulation products have been evaluated daily (derived from accumulation of half-hourly and 3-hourly basis for IMERG, TMPA-3B42 respectively), monthly and seasonally as well.  
Radar rainfall may also be used to provide an indirect measurement of rainfall, but then the radar systems need to cover large areas and have appropriate radar rainfall relationships according to the type of precipitation. For most developing countries and even more so for lesser developed countries, radar systems remain too expensive and difficult to maintain and, thus, are not a feasible option for this purpose [4]. Furthermore, radar has limited coverage over mountainous areas of terrain, and shading problems as well. Satellite based estimates of rainfall are not as accurate as gauges or radar rainfall, but have the advantage of high temporal resolution and global spatial coverage over oceans and land, particularly mountainous regions and sparsely populated areas. In areas where a sufficiently dense gauge network and radar system is not available, satellite-derived rainfall can be a \"critical tool for identifying hazards from smaller-scale rainfall and flood events\" [5].
# 5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
## 3 Fake Base Stations  
In this chapter, we delve into the security implications of fake base stations in the context of 5G-based smart healthcare and mobile networks. Base stations play a pivotal role in mobile networks, acting as the termination point for encryption and integrity protection.  
Despite their significance, the security of base stations is often overlooked compared to the UE and core network. Nevertheless, securing base stations is critical for safeguarding networks from unauthorized access and potential malicious activities. Base stations act as the first line of defense, enforcing authentication and encryption protocols, monitoring network traffic, and mitigating potential threats. With 5G's advent and increased demand for devices, the density of base stations has substantially risen, necessitating a focus on the recent cases of fake base station attacks.
# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 2 Materials and Methods
### Artificial Neural Network  
Artificial neural networks (ANN) emulate the computational processes performed by neurons in the human brain, using mathematical models. At the core of an ANN is the fundamental building block known as a simple perception and its structure is shown in Figure 3a.  
A simple perceptron is a mathematical unit that represents a neuron. It takes in a vector input \(x\) of size \(m\) and performs a series of operations. First, the input vector is multiplied by a weight vector \(w\), and the products are then summed together with a bias coefficient \(b\). The resulting value is then passed through an activation function \(f(\cdot)\), such as binary step, linear, sigmoid, or hyperbolic tangent, to produce the output \(y\). An Artificial Neural Network (ANN) consists of a collection of interconnected basic perceptions. The most common is the multi-layer perceptron (MLP), which is a network where all the neurons in the input, hidden, and output layers are interconnected. This configuration is shown in Figure 3b.  
ANNs find utility in classification, learning, and pattern recognition tasks and are extensively employed across various scientific domains. For instance, some of their recent uses are the early diagnosis of breast cancer [47] and the combination with bio-inspired algorithms to improve the ANN training process [48].  
Specifically, MLP possesses favorable attributes such as straightforward implementation, efficient training time, and the ability to generate high-quality models [49]. Additionally, the hardware implementation of MLP is relatively simple compared to other neural network architectures. Consequently, this work proposes the utilization of MLP based on these advantageous characteristics.  
Figure 3: (**a**) General diagram of a simple perceptron. (**b**) Simplified diagram of an artificial neural network, the connections between layers represent a multi-layer perception network.
## 2 Materials and Methods
### Substrate Sampling
#### 2.2.2 Wetland Bottom Sediment  
In situ pH and oxidation-reduction potential (ORP) measurements were conducted using a calibrated Vio70 Series handheld meter ([PERSON], Carpi, Italy) to assess the redox conditions within the wetland bottom sediment. The ORP is a crucial parameter for determining the sediment's oxidative or reductive state, which significantly influences the mobility and bioavailability of contaminants. Including ORP measurements is essential for understanding how redox conditions affect biogeochemical processes within the wetland and their subsequent impact on pollutant dynamics and overall ecosystem health. pH assessments were also performed to provide a comprehensive view of the sediment's chemical environment.  
Wetland bottom sediment core samples were collected using pre-cleaned, open-ended plastic cylinders (8 cm \(\times\) 8 cm), adhering to [43]. Triplicate samples (n = 3) were collected for each analysis to ensure accuracy and reliability. The samples were securely sealed with  
\begin{table}
\begin{tabular}{c c c} \hline
**Wetland Substrate** & **Assessment** & **Analytical Method** \\ \hline Surface water & Physical and chemical characteristics &
\begin{tabular}{c} Electrometric, conductimertic, inductively \\ coupled plasma (ICP) spectrometry [44; 45] \\ \end{tabular} \\ \hline Bottom sediment & Elemental composition & X-ray fluorescence (XRF) [46] \\ \hline  & Sediment texture analysis & Pipette method for sediment texture analysis [47] \\ \cline{2-3}  & Microbiological diversity & 16s RNA sequencing [48] \\ \hline Macrophyte roots and periphyton & Metal adsorption and absorption & Inductively coupled plasma optical emission spectroscopy (ICP-OES) [49] \\ \hline Unflooded soil precipitate & Mineralogy & X-ray fluorescence (XRF) [50] \\ \hline \end{tabular}
\end{table}
Table 1: A summary of the sampled substrates and the corresponding analytical methods used for each assessment.  
plastic wrap and wooden blocks and transported in a cool, dry container. For analysis, the samples were sent to three SANAS-accredited laboratories:  
* Elemental Composition: Analyzed using X-ray fluorescence (XRF), which identifies and quantifies the elemental composition by measuring the fluorescent X-rays emitted from the sample [46].
* Sediment Texture Analysis: Performed using the pipette method, which separates and classifies sediment particles by size to determine the texture [47].
* Microbiological Diversity: Assessed through 16s RNA sequencing, a technique used to identify and characterize bacterial communities by sequencing the ribosomal RNA genes [48].
## 3 Coordinates Transformation  
To express the system in normal form, the coordinates transformation must be carried out, as follows:  
\[\left.\begin{aligned} z_{1}&=h(\mathbf{x})=x_{3}= \Omega_{h}\\ z_{2}&=L_{f}h(\mathbf{x})=\frac{\partial h(\mathbf{x })}{\partial x}f(\mathbf{x})=\xi_{1}v_{w}^{2}+\xi_{2}v_{w}x_{3}+\xi_{3}x_{3}^{ 2}-\xi_{4}x_{2}\\ z_{3}&=a_{3}\frac{x_{1}}{x_{2}}\end{aligned}\right\} \tag{11}\]  
where \(\xi_{1}=\frac{d_{1}}{\int_{h}g}\), \(\xi_{2}=\frac{d_{2}}{\int_{h}\xi_{T}^{2}}\), \(\xi_{3}=\frac{d_{3}}{\int_{h}\xi_{T}^{3}}\) and \(\xi_{4}=d_{4}=p\Phi_{m}\).  
Since, the system under consideration possesses a relative degree, \(r<n\) (system order), where \(r=2\) and \(n=3\), it implies that the system is only partially linearizable [20; 21]. Moreover, the input-output form (normal form) can be expressed as follows:  
\[\left.\begin{aligned} \dot{z}_{1}&=z_{2}\\ \dot{z}_{2}&=L_{f}^{2}h(\mathbf{x})+L_{g}L_{f}h( \mathbf{x})u\\ \dot{z}_{3}&=L_{f}^{3}h(\mathbf{x})+L_{g}L_{f}^{2}h( \mathbf{x})\dot{u}\end{aligned}\right\} \tag{12}\]  
where one of the transformed system states, i.e., \(z_{3}\) represents the internal (or zero) dynamic state. Now, the Lie derivatives are given as follows:  
\[\left.\begin{aligned} L_{f}^{2}h(\mathbf{x})&=-d_{4} (b_{2}x_{1}+b_{3}x_{1}x_{3}+b_{4}x_{3})+(d_{2}v_{w}+2d_{3}x_{3})(\xi_{1}v_{w}^{ 2}+\xi_{2}v_{w}x_{3}+\xi_{3}x_{3}^{2}-\xi_{4}x_{2})\\ L_{g}L_{f}h(\mathbf{x})&=-\frac{d_{4}x_{2}}{a_{1}} \
eq 0\end{aligned}\right\} \tag{13}\]
## 4 Calibration and Compensation of Gyro Bias
### Elimination of the Gyro Bias's Influence in a One-Axis RINS  
We consider a typical rotational situation in which the IMU rotates along the azimuth direction. The gyro along the z-axis coincides with the direction of the azimuth direction, and x- and y-direction gyros were modulated. We assume that the rotation coordinate system is the s-frame, as shown in Figure 7. In a rotational cycle, the gyro bias transformation between the b-frame and s-frame could be expressed as the following equation, whereby the rotation process could modulate the process.  
\[\begin{array}{c}\varepsilon^{s}=C_{b}^{s}\varepsilon^{b}\Rightarrow\begin{bmatrix} \varepsilon_{x}^{s}\\ \varepsilon_{y}^{s}\\ \varepsilon_{z}^{s}\end{bmatrix}=\begin{bmatrix}\cos(\omega_{1}t)&-\sin( \omega_{1}t)&0\\ \sin(\omega_{1}t)&\cos(\omega_{1}t)&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}\varepsilon_{x}^{b}\\ \varepsilon_{y}^{b}\\ \varepsilon_{z}^{b}\end{bmatrix}=\begin{bmatrix}\cos(\omega_{1}t)\varepsilon _{x}^{b}-\sin(\omega_{1}t)\varepsilon_{y}^{b}\\ \varepsilon_{y}^{b}\\ \varepsilon_{z}^{b}\end{bmatrix}\end{array} \tag{20}\]  
Here, \(\varepsilon^{b}\) is the gyro bias of individual sensor; \(\varepsilon^{s}\) is the equivalent bias in the rotational frame. \(C_{b}^{s}\) is the transformation matrix between the s- and b-frame. \(\omega_{1}\) is the rotational speed of the RINS.  
From the above expression, the gyro drift along the x- and y-axis (roll and yaw directions) could be modulated as the IMU rotates by integrating the equation in one rotation period. The gyro bias along the x and y direction would average to zero. Under this condition, the drift error on the RINS mainly comes from the bias along the azimuth direction.
## 3 Proposed Methods
### Adaptive Pre-Distortion System Using FSS  
In practical VLC environments, Rician fading is expected to occur. However, this effect can be mitigated using FSS [18]. The FSS process follows the structure illustrated in Figure 6. FSS is a technique in which each subcarrier is randomly spread, as illustrated in Figure 6, and then restored at the receiver, thereby equalizing the SNR across all subcarriers of the received signal. The integrated communication system is depicted in Figure 7. At the transmitter, the modulated signal \(d(m,n)\) with \(|d(m,n)|=1\) for each subcarrier is spread using a pseudonoise (PN) sequence \(C_{PN}\) and then summed. In this case, the m-th subcarrier of the n-th OFDM symbol, denoted as \(X(m,n)\), can be expressed as follows:  
\[X(m,n)=\sum_{m=1}^{N_{c}-1}\sum_{v=1}^{N_{c}-1}\sum_{w=1}^{N_{c}-1}C_{PN}(v,w)d (m,n). \tag{12}\]  
Through the spreading process of FSS, the impact of frequency-selective fading such as Rician fading is mitigated by equalizing the SNR per subcarrier for \(d(m,n)\).  
At the receiver, after channel estimation, Minimum Mean Square Error Combining (MMSEC) is performed using the following equation to preserve the orthogonality of the PN sequence \(C_{PN}\) applied during transmission:  
\[Y(k,n)=r(k,n)\omega_{MMSEC}(k,n). \tag{13}\]  
Here, \(r\) is the received signal and the MMSEC weighting \(\omega_{MMSEC}\) is expressed using the estimated channel \(\hat{H}\) and the estimated noise power per subcarrier \(\hat{\sigma}\) in the following equation:  
\[\omega_{MMSEC}=\frac{\sqrt{\frac{2S}{N_{c}}}\hat{H}}{|\sqrt{\frac{2S}{N_{c}}} \hat{H}|^{2}+2\hat{\sigma}^{2}}. \tag{14}\]  
Here, \(S\) is the average transmitted power. In FSS, despreading is performed, and the detected signal \(\hat{X}(m,n)\) is expressed in the following equation:  
\[\hat{X}(m,n)=\sum_{m=1}^{N_{c}-1}\sum_{v=1}^{N_{c}-1}\sum_{w=1}^{N_{c}-1}C_{PN }^{*}(v,w)Y(m,n). \tag{15}\]  
Furthermore, by demapping the detected signal and reconstructing the transmitted signal \(\hat{x}_{n}\), the nonlinear coefficient \(b\) can be estimated by inputting both \(\hat{x}_{n}\) and the distorted signal \(y_{n}\) into the nonlinear coefficient estimator.
###### Abstract  
Anshan-Benxi area in Liaoning province is an important banded iron formations (BIFs) ore-mining district in China. Chlorite is widely distributed in this area, which is related to BIFs and high-grade iron ore, respectively. A fast and convenient method to identify the type and spatial distribution of different chlorites is crucial to the evaluation of high-grade iron ore in this area. Qidashan iron mine is a typical BIFs deposit, and its BIFs-related high-grade iron ore reserves are the second largest in the area. In this paper, the laboratory-based HySpex-320m hyperspectral imaging was used to study the wall rock in Qidashan iron mine. A hyperspectral imaging processing model was established for mineral identification, mineralogy mapping, and chlorite spectral features extraction. The results show that the wavelength positions of OH, Fe-OH, and Mg-OH absorptions of chlorite in the altered wall rock of high-grade iron ore are between 1400 and 1410, 2260 and 2265, and 2360 and 2370 nm, respectively, which are longer than those around BIFs. The relationship between cations in the octahedral layer of chlorite and the wavelengths of OH, Fe-OH, and Mg-OH indicates that Mg and Mg/(Mg + Fe) are inversely related to these wavelengths, whereas Fe is positively related. The wavelengths appear to be weakly influenced by AlVI. Since the bandpass of hyperspectral imaging systems is usually less than 10 nm, these chlorite wavelength differences can be used as a favorable tool for the high-grade iron ore exploration and the iron resources evaluation in the Anshan-Benxi area.  
laboratory-based hyperspectral imaging; HySpex; spectral features extraction; chlorite; Qidashan Iron Deposit 2020, 10, 7444; doi:10.3390/app10217444
## 4 In-Network Computing via ICN
### Reactive Executor Selection Strategies  
Network nodes, particularly those at the edge, operate with constrained resources, such as limited computational power, memory, and energy capacity [64]. As a result, selecting the optimal executor for in-network processing tasks becomes a critical decision to ensure efficient resource utilization and meet both service-level and network-level objectives. The selection process must balance multiple factors and adapt dynamically to varying conditions within the network.  
From the perspective of the network administrator, a range of objectives can be prioritized to optimize the performance, including minimizing traffic congestion by strategically distributing computational workloads across the network to prevent bottlenecks, reducing overall energy consumption to ensure sustainable operations, and achieving load balancing by avoiding overburdening specific nodes while underutilizing others.  
From the client's perspective, requirements are often driven by the specific nature of the service being executed. For latency-sensitive applications, such as real-time video analytical or autonomous systems, the selection process prioritizes nodes capable of providing minimal delay. For applications requiring high reliability, such as industrial automation or healthcare systems, executor selection must ensure robust fault tolerance and consistent availability of processing resources.  
Consequently, research has extensively explored mechanisms to perform efficient service executor selection within the ICN forwarding fabric, as summarized in Table 4. We distinguish distributed approaches, where the decision is directly taken by network nodes, and centralized approaches, where the decision is taken by a centralized entity overseeing the network domain.
## 2 Materials and Methods
### NIR Spectroscopy  
#### 2.4.1 Spectra Acquisition  
The homogenized sausage samples were subjected to NIR spectroscopy using a Spectra Star 2400-D spectrophotometer (Unity Scientific, Milford, MA, USA). The samples were placed in a rotating circular sample cup, set in stepped mode at 24 scans, and scanned at 2 nm intervals over the 1200-2400 nm spectral range. The spectrum was automatically recorded as log (1/Transmittance). The samples were prepared in duplicate, and two spectra were collected per individual sample, i.e., a total of four spectra per sausage.
# Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
## 2 Data Acquisition and Processing
### Moving Average and Moving Differential Method  
Optical loss is inevitable. When light with a wavelength of 1330 nm or 1550 nm propagates in a G652 single-mode fiber, the average losses per kilometer are 0.35 dB and 0.25 dB, respectively. The splice loss caused by fiber core fusion is about 0.05 dB/piece. These losses directly lead to Rayleigh scattered light being strong at the near and weak at the far end. At the same time, due to this light's high sensitivity, it is very easily disturbed by noise in the surrounding environment, such as construction noise along the line or traffic noise in parallel sections of highways and railways. The collected signals are mixed with many unknown noises, which may affect the positioning accuracy of DAS equipment.  
To reduce noise and increase frequency responses, the moving average and moving differential method [13; 14] was used in our detection system. This method consists of acquiring a certain number \(N\) of Rayleigh backscattering traces and choosing a number of the acquired traces \(M\) to be averaged. The averaged traces set is \(R=\{R_{1},R_{2},\cdots,R_{K}\}\), where \(K=N-M+1\), and  
\[R_{i}=\frac{1}{M}\sum_{l=i}^{l=i+M-1}r_{l},l\in[1,N],i\in[1,N-M+1] \tag{1}\]  
Considering the pulse duration time and the decay time of track vibration, \(R_{r}=R_{int(i/2M)\times M+1}\) is used as the moving reference. Then, the differential traces can be obtained by the following equation  
\[\triangle R=\left\{\Delta R_{1},\Delta R_{2},\cdots,\Delta R_{J}\right\} \tag{2}\]  
where \(\Delta R_{i}=R_{i}-R_{r},J=K-1=N-M\). Thus, \(N-M+1\) subsets of averaged traces can be obtained with the noise power in a measurement reduced by a factor of \(1/M\). We performed filtering of the vibration signals generated by the train running at different times and obtained different results, as shown in Figure 5. The \(Y\)-axis of the difference signal represents the amplitude, where the sampling points with larger values are the disturbance caused by the train passing.  
\begin{table}
\begin{tabular}{c c c} \hline Name & Value & Unit \\ \hline Spatial resolution & 6.4 & m \\ Pulse duration & 64 & ns \\ Sampling rate & 2564 & Hz \\ \hline \end{tabular}
\end{table}
Table 1: The key parameters of equipment.  
Figure 4: Original signal.
## 4 Results and Discussions
### Parametric Effects Discussions
#### 4.2.3 Slope Geometry Parametric Analysis  
The influence of the slope geometry parameters on slope safety at different seismic classes is illustrated in Figure 10. It can be observed that the height coefficient \(\xi_{1}\) and the factor of stage width \(\lambda_{a}\) are favorably proportional to FS, while \(\beta_{1}\) and \(\beta_{2}\) are inversely proportional to FS. And it is noteworthy that the design pattern for the two-stage slope could significantly withstand the adverse effects of seismicity on slope safety, as shown in Figure 10a, and its enhancement on FS is more obvious when the seismicity is at a smaller level. Moreover, the location of the additional step on the middle and lower portions of the slope can better improve the stability of the slope, as demonstrated in Figure 10b. It is important to note that, at the same range of 35\({}^{\circ}\)-60\({}^{\circ}\), the FS fluctuation magnitude relating to \(\beta_{2}\) is greater than that of \(\beta_{1}\), which implies that \(\beta_{2}\) has a stronger impact on slope safety. And the above findings have some reference to the design for the two-stage rock slope.  
Figure 8: Effects of pseudo-dynamic parameters: (**a**) the horizontal seismicity factor \(k_{h}\); (**b**) velocity of transverse wave propagating \(V_{s}\); (**c**) seismic period \(T\); (**d**) damping ratio \(D_{r}\).  
Figure 10: Effects of slope geometry parameters: (**a**) the factor of stage width \(\lambda_{a}\); (**b**) the height factor \(\xi_{1}\); (**c**) inclined angle of upper stage \(\beta_{1}\); (**d**) inclined angle of lower step \(\beta_{2}\).  
Figure 9: The interactions between the seismic pseudo-dynamic parameters of the safety of 3D two-stage rock slopes: (**a**) the reciprocal actions between \(V_{s}\) and \(k_{h}\); (**b**) the reciprocal actions between \(T\) and \(k_{h}\); (**c**) the reciprocal actions between \(D_{r}\) and \(k_{h}\).
## 3 Results
### Volatile Compounds in Chicken Meat of Trial 1  
In total, 210 volatile compounds were detected in chicken breast meat produced with the three different diets in Trial 1. The results of the Kruskal-Wallis test, presented in Table 1, indicate that 16 compounds showed significant difference among the treatments, 6 of which were detected in trace amounts (i.e., \(<\)0.30 ng/g). The main groups of compounds affected by diet were alcohols (4), aldehydes (6), ketones (1), hydrocarbons (2), lactones (1), while 2 compounds were not identified (Retention time (RT): 17.96 min and RT = 28.62 min). Two alcohols, 1-pentanol and 1-heptanol, were detected in lower levels in chicken breast meat produced with the HI diet compared to meat from the other two diets (\(p<0.05\)), which did not differ from each other (\(p>0.05\)). Levels of 1-hexanol, 1-octen-3-ol, hexanal
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Future Application Scenario
#### 4.1.2 Relay Node Positioning and Beam Alignment  
The ISTNs are based on terrestrial networks and integrate non-terrestrial networks to jointly build a multi-access converged network architecture across space-air-ground areas. In the envisioned future architecture of ISTNs, the space base is mainly composed of satellites, the air base consists of various types of aircraft, the ground base is made up of land-based cellular mobile communication networks, and the sea base includes various types of oceanic underwater wireless communications, offshore coastal wireless networks, ocean-going vessels, etc. [95]. The relative positions between the nodes are not often stationary, which brings certain challenges to the communication signal transmission process. As a hub between satellites and the ocean, UAVs play a crucial role in the space-air-ground-sea network [96]. In [97], a new integration of UAVs and DFRC systems is explored to assist data collection in ocean monitoring scenarios. In the future, by introducing ISAC in the ISTNs, the position of the receiver will be sensed using integrated signals, and a directional beam will be used for sending signals. Hence, we can improve the quality of communication while reducing the power loss at the transmitter.