esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Internet of Things (IoT) is an upcoming technology that is increasingly being used for monitoring and analysing environmental parameters and supports the progress of farm machinery. Agriculture is the main source of living for many people, including, for instance, farmers, agronomists and transporters. It can raise incomes, improve food security and benefit the environment. However, food systems are responsible for many environmental problems. While the use of IoT in agriculture and environment is widely deployed in many developed countries, it is underdeveloped in Africa, particularly in West Africa. This paper aims to provide a systematic review on this technology adoption for agriculture and environment in West African countries. To achieve this goal, the analysis of scientific contributions is performed by performing first a bibliometric study, focusing on the selected articles obtained using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, and second a qualitative study. The PRISMA analysis was performed based on 226 publications recorded from one database: Web Of Science (WoS). It has been demonstrated that the annual scientific production significantly increased during this last decade. Our conclusions highlight promising directions where IoT could significantly progress sustainability.  
agriculture; environment; IoT; monitoring; sensors; PRISMA
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 2 Methodology
### Information Fusion Algorithm  
#### 2.2.1 Bayesian Network  
In aero engine fault diagnosis, features from gas path, vibration, and lubrication subsystems often differ in scale, structure, and diagnostic uncertainty. These differences pose challenges for direct fusion. Bayesian networks provide an effective solution by modeling the conditional dependencies between subsystem features and component-level faults. Their graphical structure enables clear representation of causality, while the probabilistic framework supports uncertainty reasoning and prior knowledge integration [25].  
A Bayesian network, structured as a directed acyclic graph (DAG), consists of nodes representing random variables, including potential fault hypotheses and measurable di agnostic features. Directed edges between nodes indicate conditional dependencies, with arrows pointing from parent nodes to child nodes. Each node has a conditional probability table (CPT). The conditional probability table can be described by \(p(V_{i}\mid Pa(V_{i}))\), which is used to describe the occurrence probability of the event of this node when other node events are in different states. For any node without parent nodes in a Bayesian network, its conditional probability is reduced to a prior probability. The network determines the conditional probability distribution of child nodes by propagating evidence through the network using the probability chain rule:  
\[p(V_{1},V_{2},\cdots,V_{k})=\prod\limits_{i=1}^{k}p(V_{i}\mid Pa(V_{i})) \tag{2}\]  
where \(Pa(V_{i})\) indicates the parent node of \(V_{i}\). The diagnostic capability of Bayesian network-based information fusion can be significantly improved by incorporating prior knowledge into the network architecture [26]. Reliable prior knowledge can come from expert experience, physical models, statistical information, etc. [27]. The fusion diagnosis results based on Bayesian network can be optimized by relying on prior knowledge, and the difficulty of obtaining prior knowledge is also a factor restricting Bayesian network.
## 4 Challenges in Rainfall Estimation by CMLs
### WAA Quantification  
After the estimation of the baseline, another important source of error is the quantification of WAA. Undercompensation of WAA can lead to a systematic overestimation of rainfall. This phenomenon is especially exacerbated by higher WAA relative to rain-induced attenuation under short CML and light rain. Although a variety of empirical or semi-empirical WAA models have been developed, most of these studies have been derived from a few CMLs with limited frequency. In fact, WAA is highly correlated with the attributes of CML antennas, leading to the possibility that the properties of WAA found from different CMLs may be completely different: [PERSON] et al. [29] reported antenna wetting times of up to 36 min, but other studies [46; 73] have found wetting dynamics to be negligible. Additionally, contrary to common sense, hydrophobic antennas tend to form water beads on the antenna surface, which are more resistant to evaporation than the water film formed by hydrophilic antennas, prolonging the duration of the WAA's impact after rainfall events end [159]. Therefore, the existing empirical WAA models may not be as portable for CMLs with different characteristics.  
Another source of error is the way that WAA is calculated. In general, WAA values are obtained by measured attenuation minus the baseline and rain-induced attenuation, which is converted from rain rates measured by rain sensors near CMLs. However, as mentioned in Section 4.2, there is uncertainty in the estimation of the baseline, in which case the model may no longer simply compensate for WAA but include corrections for WAA and other unknown sources of bias. Furthermore, the number of antennas wetted during rainfall is also an important unknown factor, especially in the case of long CMLs and small-area rainfall.  
Finally, in addition to rainfall, phenomena such as dew and fog may likewise wet the radome and produce WAA, which needs to be filtered out [126]. However, dew- and fog-induced WAA has still not been specifically studied for the properties of its dynamics and magnitude.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Terrain Model Generation
#### 2.3.2 Airborne Optical Data  
For very high resolution airborne cameras the work [95] reports on a method developed for UltraCam images [96; 97]. They perform a classification of the ortho images into 15 classes, both using texture and height information. Then the classification is employed within the DTM generation process. The main difference to spaceborne data is that the underlying DSMs are of significantly higher resolution and of higher accuracy such that image classification works very robust--which would not be the case for spaceborne data. A variational approach which uses a DSM as single input is presented in Reference [98]. The basic concept is to extract a very smooth surface using a strong regularization weight within a variational formulation. This over-smoothed surface is used to determine potential points the on ground (called detection mask) which are then used to interpolate a DTM by employing the same variational formulation as before. However, problems occur on large buildings that are detected as belonging to the ground. Thus, the authors proposed to add a segmentation based on maximally stable extremal regions and a manual interaction step.
## 2 Materials and Methods  
There are two parts to the CMAC workflow. Part 1 generates a grayscale raster of the atmospheric effect across the image using extracted spectral statistics. In Part 2, the grayscale brightness is used as a spatial scalar to adjust the degree of correction to be imposed for SR retrieval from the input TOAR rasters of each band. To apply CMAC, all sensor bands to be corrected must undergo calibration in a step that determines coefficients for translation of the atmospheric effect, grayscale brightness, into two dynamic parameters that guide the retrieval for any level of atmospheric effect. The CMAC formulation captures the regular mathematical structure of changes to reflectance driven by atmospheric effects in the form of families of curves. The description of the CMAC method here draws from greater detail provided in paper 1.  
Sentinel 2 (S2) data were selected as the testbed for CMAC R&D, capitalizing on their cadence, resolution, and high quality. Paper 1 tested the precision and accuracy of the provisional CMAC version 1.1 software for S2 compared to Sen2 Cor software version 2.11. The AC analysis in paper 1 was applied to seven areas of interest (AOIs), across 46 images and thousands of extracted statistics. CMAC output was found to be more precise and accurate than Sen2 Cor over a greater range of atmospheric effects. It required \(\sim\)91 s for 4-band VNIR AC on a desktop PC (64 GB RAM), a short time span made possible through the use of scene statistics and a closed-form solution.  
The present paper expands CMAC evaluation through:  
1. Customizing a Landsat 8 and 9 (L8/9) calibration by reference to the S2 calibration in a test of a rapid automated method, \"master-proxy calibration\" (MPC).
2. Measuring the precision and accuracy of CMAC L8/9 SR retrieval generated from MPC.
3. Evaluating whether CMAC SR retrieval matched LaSRC output, thus verifying CMAC accuracy and stability.
4. Comparing the stability of CMAC and LaSRC as Atm-I of the images increased.  
Test correction and comparison of indices derived from CMAC and LaSRC-corrected images as an indicator of CMAC statistical stability to support machine analysis and AI under moderate-to-extreme levels of haze.
## 4 Results
### Support Evaluation  
Following the DRM (Section 3), the evaluation of support (method and software prototype) was divided into three aspects: Support, application and success evaluation. Support evaluation takes place parallel to the development of support (prescriptive study) and tests whether the support elements work as intended individually and in combination with each other. In this context, this was about the ability to assess the risk factors and the overall change risk. Application evaluation investigates whether the support can be used for the task it is intended for. Therefore, it was focused on whether the support is able to provide sufficient information about risk of requirement changes with an application effort appropriate for industrial application. Finally, the success evaluation identifies whether the support indeed contributes to more efficient management of requirement changes. An overview of the evaluation approach is illustrated in Figure 11.  
For support evaluation the development of new elements of support (i.e., software functionality) was followed by internal tests on functionality in an iterative manner. Tests were performed based on a set of tests procedures created beforehand. After basic functionalities were developed and integrated, an initial software prototype was provided for external testing to project partners. First experiences  
Figure 11: Evaluation approach based on Design Research Methodology (DRM) [47].  
Figure 10: Software prototype for semi-automated application of the method for systematic assessment of requirement change risk.  
were discussed in a project workshop with multiple attendees from all partners. Focus of this workshop is to ensure correctness and completeness of functionality, identify leverages to reduce application effort and raise undetected needs from practice. The results were integrated for the second prototype release (for instance, usage of risk portfolio and import/export via Microsoft Excel data format). After the second release, the prototype was tested regarding usability and applicability in the three case study projects. In preparation for this, all partners used the generic database and adapted it to their individual company and project characteristics. Findings from external testing were discussed in periodic meetings with the development teams and subsequently integrated into the prototype. After two more release and testing cycles, the prototype was considered as sufficiently useable and applicable in industrial practice.
## 2 System Model
### Multiple Access
In case of a very high number of users, instead of \(\{-1,0,1\}\), \(\{-2,-1,0,1,2\}\) can be used and each element of the complex spreading code is found in this set \(\{-2,-2i,-2+2i,-2-i,\)  
Another idea might be to build complex spreading codes starting from PN codes (cPN) whose correlation is very low, lower than the ones obtained from MUSA codes. In this case, the algorithm is simple since all codes have the same intercorrelation values, therefore it is not necessary to sort the codes. The steps followed to obtain the cPN are:  
1. Establish the degree of the polynomial depending on the number of users;
2. Calculate the length of the polynomial based on its degree;
3. Choose the generating polynomial;
4. Define a matrix containing all possible initial states of the shift register that generates the PN codes;
5. Build the set of complex PN codes that has the real and imaginary parts of two PN codes generated with the same polynomial but different initial conditions from which the users can choose randomly;
6. Verify the intercorrelations between the codes from the set of codes;
7. Assign the complex codes to the users.
## 3 Results
### Devices Comparison
#### 3.2.3 ICC and Cronbach's Alpha (\(\alpha\)) Coefficient  
The ICC parameter is used to evaluate the consistency or concordance of degree between measurements performed in samples with a common characteristic. This correlation coefficient appears to have a high value when there is a reduced result disparity, being equal to 1 in the situations that present total agreement between values. The \(\alpha\) coefficient is a measure of internal consistency used to assess a scale's reliability. High internal consistency is observed when this parameter takes values close to 1, implying that the test's analyzed data is homogeneous and likely measures the same construct. However, it is essential to note that those high values can indicate redundancy among items, suggesting that some items might provide repetitive information.  
The par ICC and Cronbach's \(\alpha\) coefficient were calculated for each comparison par: mean MPP Physiosensing(r) versus mean MPP Pedar(r) and mean contact area Physiosensing(r) versus mean contact area Pedar(r). Table 6 displays the values of the ICC and Cronbach's \(\alpha\) values for the MPP and contact area measurements performed by the Pedar(r) and Physiosensing platforms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Point Estimate**} & \multicolumn{2}{c}{**95\% Confidence Interval of**} \\  & & & \multicolumn{2}{c}{**the Difference**} \\ \cline{3-6}  & & & **Inferior** & **Superior** \\ \hline \multirow{3}{*}{Pair 1} & Pedar® MPP [kPa] versus & Cohen’s d & 1.565 & 0.788 & 2.317 \\ \cline{2-6}  & Physiosensing® MPP [kPa] & Hedges Correction & 1.479 & 0.744 & 2.191 \\ \hline \multirow{3}{*}{Pair 2} & Pedar® Contact Area [cm\({}^{2}\)] & Cohen’s d & \(-\)3.306 & \(-\)4.610 & \(-\)1.985 \\ \cline{1-1} \cline{2-6}  & versus Physiosensing® Contact Area [cm\({}^{2}\)] & Hedges Correction & \(-\)3.126 & \(-\)4.358 & \(-\)1.877 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect sizes for paired samples.  
The MPP analysis revealed an ICC of 0.276, indicating a low consistency between the measurements. Values lower than 0.5 reflect poor reliability, which implies inconsistent measurements between the two devices. The [PERSON]'s \(\alpha\) score was 0.562, which displays moderate reliability. Nevertheless, it is still below the ideal value of \(\geq\)0.7, considered the lowest value required to ensure good internal consistency. The analysis of the contact area parameter revealed an ICC of 0.231, indicating an even lower consistency than the MPP, reflecting weak inter-platform reliability. So, it is reinforced that the two systems diverge significantly in terms of collecting contact area data. On the other hand, [PERSON]'s alpha was 0.781, indicating a good internal consistency for the measurements within each system. Despite the low ICC value, it highlights that while individual measurements are consistent within each platform, there are still prominent differences in how the devices acquired the contact area. This discrepancy can be explained by the difference in sensor sensibility between platforms.
## 2 Materials and Methods
### Materials and Measurement Setup  
In previous studies, we have developed the transmission Mueller matrix microscope for measuring thin tissue slices and backscattering Mueller matrix imaging set-ups for measuring bulk tissue samples [19; 31; 32; 33; 34; 35]. The measurement schemes are based on the dual-rotating retarder method. For this method, the PSG (polarization state generator) and PSA (polarization state analyzer) consist of a pair of polarizers fixed in the horizontal direction, which is also parallel to the source-sample-camera triangle plane for the backscattering imaging, and two retarders rotating with a fixed ratio of angles. It should be noted that the source, sample and camera are colinear in transmission imaging geometry. Then the 16 Mueller matrix elements can be calculated by using the Fourier coefficients \(\alpha_{n}\) and \(\beta_{n}\) shown as Equation (19) [36].  
\[I=\alpha_{0}+\sum_{n=1}^{12}\left(\alpha_{n}\cos 2n\theta_{1}+\beta_{n}\sin 2n \theta_{1}\right) \tag{19}\]  
where \(I\) is the intensity, \(\theta_{1}\) is the rotation angle of the retarder 1 for the PSG. The two retarders (retarder 1 for PSG, retarder 2 for PSA) rotate with a fixed rate \(\theta_{1}=5\theta_{2}\). The Mueller matrix measurement setups used in this study were calibrated to ensure that the maximum errors of the measured Mueller matrix elements are less than 1%. More details on this Mueller matrix imaging method and calibration process can be found in [32; 37; 38].  
Here, we prepared two types of tissue samples (\(n=149\)) for the comparative analysis: thin pathological tissue slices for transmission measurement and bulk tissue samples for backscattering measurement. The unstained thin tissue samples include human colorectal, intestinal tuberculosis (ITB), liver, Crohn's disease (CD), bladder, breast, adenoma tissue slices whose thickness are 12 \(\upmu\)m, while the bulk tissue samples include human breast, rat-skin, porcine intestine, porcine stomach, and porcine liver tissues. The H-E stained 4-\(\upmu\)m-thick tissue slices of the samples were also prepared for pathological observations. More details of the samples used in this study are shown in Table 1. It should be noted that the signal-to-noise ratio for the camera is related to the energy obtained from the source, which depends on both the power of LED and the exposure of the camera. As the power of LED for our Mueller matrix imaging is commonly within 1-3 W [24; 25; 34], we have selected an appropriate exposure time for the camera to achieve a good signal-to-noise ratio. Our previous studies showed that the LED with the power of 1 W did not generate a lot of heat to the thick sample when an appropriate exposure time was selected [24; 25; 34]. In this work, the human tissue samples are provided by the Department of Gastroenterology, Zhujiang Hospital, Southern Medical University, and Shenzhen Sixth People's (Nanshan) Hospital. This work was approved by the Ethics Committee of the Shenzhen International Graduate School, Tsinghua University.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## Materials and Methods  
The full workflow of individual forest tree delineation from raw airborne laser scanning data is presented in Figure 1. First, the original point cloud is pre-processed to prepare for the segmentation. Ground points are classified, then the aboveground points are normalized to avoid influence from terrain relief during the segmentation step. In addition, points below 1 m are considered as noise, and thus filtered out. Next, a point-based segmentation method, mean shift, is used to segment the whole point cloud into individual trees. This paper will focus on the assessment of tree segmentation using mean shift, which is an important step that affects the following tree parameter extraction. Other representative methods, such as marker-controlled watershed segmentation [15], are also implemented for comparison. Then for each segment, tree parameters are extracted, such as the location (x, y), height (h), longest crown spread (l) and longest crown cross-spread (l'). Finally, the extractions are validated against field measurements so that the accuracies of the variants of the mean shift algorithm are evaluated.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 3 System Model
### End-to-End Hops in LSN
#### 3.2.1 Inter-Plane Hops \(H_{h}\)  
The hops moving laterally between inter-planes are determined by the longitude difference \(\Delta L_{0}\) of the ascending point of the initial orbit. Given the source and destination satellites, \(Sat_{1}\) and \(Sat_{2}\), as shown in Figure 4,  
\[\Delta L_{0}=(L_{2}-L_{1})\mod 2\pi\in[0,2\pi], \tag{1}\]  
where \(L_{1}\) and \(L_{2}\) denote the ascending meridians of \(Sat_{1}\) and \(Sat_{2}\), respectively. If the destination satellite is on the west side, the longitude difference is \(2\pi-\Delta L_{0}\). Since  
Figure 4: Trajectory of substellar points of LEO satellite constellation.  
Figure 3: Topology model of the LEO satellite network.  
the longitude difference of the ascending points of the adjacent orbits is constant, i.e., \(\Delta\Omega=2\pi/n\), hops moving in the east and west directions can be, respectively, expressed by:  
\[H_{h}^{\leftarrow}=\left\langle\frac{2\pi-\Delta L_{0}}{\Delta\Omega}\right\rangle \!\!,H_{h}^{\rightarrow}=\left\langle\left|\frac{\Delta L_{0}}{\Delta\Omega} \right|\right\rangle\!\!, \tag{2}\]  
where \(\left\langle x\right\rangle=\text{sgn}(x)\left\lfloor x+0.5\right\rfloor\) indicates that \(x\) is rounded to the nearest integer. \(H_{h}^{\leftarrow}\) is the hops count between westward orbits and \(H_{h}^{\rightarrow}\) is the hops count between eastward orbits.