esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A
This capability will be a key for enabling high-frequency spectrum radio access. It will allow for the best delivery of services and to speed up the creation of massive-scale services and applications with flexibility, including ubiquitous ultra-broadband network infrastructure, mass-scale cloud architectures, ultra-dense radio networking with self-backhauling, M2M and D2D communications, and dynamic radio access infrastructure sharing [280]. * Programmable/flexible air interfaces will be enabled by various advanced waveform technologies combined with advances in modulation and coding as well as advances in multiple access schemes such as filtered OFDM (Orthogonal Frequency Division Multiplexing), filter bank multi carrier, pattern division multiple access, sparse code multiple access, interleave division multiple access and beam division multiple access (BDMA) [270; 271; 274; 282]. These schemes are essential to achieve continuing improvements in spectral efficiency, which correspondingly increases the capacity of the system. Moreover, flexible uplink/downlink resource allocation such as TDD-FDD joint operation and dynamic TDD (FDD/TDD = Frequency/Time Division Duplex) will address the growing traffic demand and allow more efficient and flexible use of radio resources. This could also be attained by advanced RF-domain processing, e.g., using single-frequency full-duplex radio technologies, where simultaneous transmission and reception on the same frequency with self-interference cancellation could increase spectrum efficiency significantly. Improvements in all these areas will drive overall network costs down while achieving improved energy efficiency [270; 274; 280; 281]. Moreover, smart antenna and new reconfigurable hardware concepts, in particular at higher frequencies are required for programmable/flexible air interfaces.
## 3 Development of Virtual Sensor
### Virtual Sensor for Fault Detecting in Physical Sensor  
#### 3.2.1 Virtual Sensor Based on LSTM-Autoencoder  
The LSTM-autoencoder was used to develop the virtual sensor for fault detection in an SCWT sensor and consisted of an LSTM-based encoder and decoder. The process for detecting faults in a physical sensor using the virtual sensor proposed in this study is shown in Figure 4.  
The first step is normal time-series data training (Figure 4a). The LSTM-autoencoder compresses the normal time-series data using the LSTM encoder and then reconstructs the data using the LSTM decoder. During this process, the LSTM-autoencoder learns only the characteristics of normal time-series data. The second step is the input of unlabeled time-series data (Figure 4b). Here, the LSTM-autoencoder outputs a high reconstruction error when the input data contains faults. The third step is sensor fault detection (Figure 4c). Here, the reconstruction error generated as faulty time-series data is used as input. The virtual sensor determines that the physical sensor is faulty if the reconstruction error exceeds the threshold value.  
Figure 3: Generic structure of autoencoder.  
Figure 4: Fault detection using the proposed virtual sensor. (**a**) Step 1: normal time-series data training; (**b**) step 2: input of unlabeled time-series data; and (**c**) step 3: fault detection.
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 2 Related Work  
The issue of the detection of ambulance vehicles has recently attracted the attention of many researchers. For instance [21], presents a system that uses two models. YOLOv3 is used as a filtering input for distinguishing whether it is a truck or a car, and then the output goes to the CNN model to detect whether the truck is an ambulance or not. This conventional method has more than several major problems that we can overcome. Firstly, the two-step detection methodology suffers from the YOLOv3's tendency to skip actual inputs of ambulance vehicles, and in that respect, the CNN is left helpless to do anything. Second, the system has significant input lag because it filters the data in more than one step. Third, the system is inefficient in processing the trucks because the ratio of ambulance vehicles to trucks is very low, causing the system to waste time filtering and processing the data, resulting in a large waste of data and time. In solving the first matter, we will not use a hybrid model because the YOLOv8 model has achieved outstanding speed and accuracy. For the second matter, we would not need to filter or have input lag because we are not using a hybrid model. Eventually, our system will be very efficient due to the optimization of the YOLOv8 model. In [22], the authors used ResNet-50, whereas we used YOLOv8, which is superior; they trained on 100 images, whereas we trained on over 2000 images with around 1000 background images to reduce the error rate. We used a vision model from there and were able to identify where the ambulance came from the cameras at the traffic light using a First In First Out (FIFO) algorithm. In [23], the authors used an audio-model to detect the ambulance from the siren, but this approach does not work at the traffic light because there are roads in more than one direction at the traffic light. In [24], the authors have chosen to employ the monolithic approach in their system design, whereas we chose the microservice development strategy because it is more adaptable and flexible while theirs is more expensive, demanding, and incompatible with other systems. In [25], the authors used color and blob detection on OpenCV, an extremely outdated conventional method that lacks precision, while we employed the most cutting-edge model. In [26], a different approach was employed, using a keypad mounted in the ambulance that the driver used to activate the traffic light. However, this method lacks the automated system for controlling the traffic light that we implemented by utilizing YOLOv8 to automatically detect the ambulance vehicles and send the signal to the traffic light. In [27], it is crucial to the proposed system to detect objects from different angles; however, our system can detect objects from any angle even if the camera was flipped. Furthermore, to the best of our knowledge, we are the first to use YOLOv8 to detect the ambulance to allow the traffic signals to turn green to speed up the ambulance's estimated arrival time to the hospital.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
###### Abstract
Although no formal preliminary benchmarking was conducted in this specific area prior to full modeling, the diversity of the algorithms allowed us to compare their performance and robustness under the same conditions. While machine learning models are widely used in landslide prediction, their accuracy and effectiveness are significantly affected by the modeling parameters, data quality, and regional characteristics [24]. Many prior studies have employed single-model approaches; however, comparatively few have systematically optimized and evaluated multiple algorithms within the context of a specific, hazard-prone area. Additionally, the influence of classification strategies on the interpretation of LSI values remains underexplored, despite its critical role in final zoning outputs. To address these gaps, the present study was designed to: (a) conduct a comparative evaluation of four machine learning models (MLP, NB, CDT, and RF) for landslide susceptibility mapping in Mei County; (b) optimize each model's parameters to improve predictive performance; and (c) assess the effectiveness of four LSI classification techniques to identify the most reliable zoning method. The novelty of this research lies not only in its integrated comparative framework but also in its context-specific application to a mountainous, data-limited region. By combining model calibration, certainty factor analysis, and comprehensive validation using ROC curves and statistical indicators, this work presents a refined and transferable methodology for landslide susceptibility assessment. The results are intended to support regional disaster preparedness strategies and inform spatial planning in similarly vulnerable environments.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 5 Discussion

Table 4: Confusion matrix for the comparison between rice areas derived from Ndikumana Agricultural Land Cover and rice areas from RPG-2017 data. used in any agricultural area with similar rice cropping practices and land cover. However, the metrics and the threshold used in this study should be adapted for a good application of this method in other geographical contexts. The method uses the SAR temporal signature of rice plots during its growing season to classify the rice areas. The temporal behavior of the SAR backscattering for rice plots allows us to extract metrics that best describe the rice cultivation, regardless of the surrounding agricultural land cover. Thus, the suggested classification could be applied over different study sites. Moreover, unlike other approaches that directly implement the entire time series to classify a crop type, this method concentrates on first describing a rice cultivation using the SAR time series and introducing relevant metrics that allow us to accurately detect rice crops in a classification technique. Future work should concentrate on the implementation of this approach over other study sites to ensure the transferability of the method. The method could be adapted to other study sites with other forms of land cover and crop types. Moreover, it could be adjusted to work over sites with more than one cropping cycle of rice per year.
# Research of Calibration Method for Industrial Robot Based on Error Model of Position
## 2 Robot Calibration Modeling
### Robot Kinematics Modeling  
The most common kinematics model of industrial robots is the four-parameter DH model (short for Denavit-Hartenberg model) proposed by [PERSON] [20] et al. in 1955. This model has good generality and is currently the most widely used. However, in practice, it is found that when two adjacent axes are nominally parallel, the small deviation of their mutual joint will lead to the numerical distortion in the process of parameter identification based on DH model that means the parameters are discontinuous. To avoid this problem, [PERSON] [21] proposes the MDH model--a modified DH model, which adds a rotational parameter to the \(Y\) axis and becomes a five-parameter model with two translations and three rotations.  
\[\begin{array}{lcl}i_{-1}^{\ \ }T&=&Rot(z_{i-1},\theta_{i})\cdot Tran(z_{i-1},d_{i}) \cdot Tran(x_{i},a_{i})\cdot Rot(x_{i},a_{i})\cdot Rot(y_{i},\beta_{i})\\ &=&\left(\begin{array}{cccc}c\phi_{i}c\beta_{i}-s\theta_{i}s\alpha_{i}\beta _{i}&-s\theta_{i}c\alpha_{i}&c\theta_{i}s\beta_{i}+s\theta_{i}s\alpha_{i}c \beta_{i}&a_{i}c\theta_{i}\\ s\theta_{i}c\beta_{i}-c\theta_{i}s\alpha_{i}s\beta_{i}&c\theta_{i}c\alpha_{i}&s \theta_{i}s\beta_{i}-c\theta_{i}s\alpha_{i}c\beta_{i}&a_{i}s\theta_{i}\\ -c\alpha_{i}s\beta_{i}&s\alpha_{i}&c\alpha_{i}c\beta_{i}&d_{i}\\ 0&0&0&1\end{array}\right)\end{array} \tag{1}\]  
Figure 1: Detection Center Point (DCP) coordinate system position model.  
In the equation, \(\theta_{i}\), \(d_{i}\), \(a_{i}\) and \(\alpha_{i}\) respectively represent the joint Angle, joint bias, length, and torsion angle of link \(i\). In the case that joint \(\beta_{i}\) exists only when adjacent axes are parallel, in other cases \(\beta_{i}=0\) is directly substituted into Equation (1).  
According to Equation (1), the kinematic model of a general N degrees of freedom robot can be obtained as  
\[\begin{array}{c}{}_{0}^{N}T={}_{0}^{1}T_{1}^{2}T\cdots{}_{N-1}^{N}T=\left( \begin{array}{cccc}n_{x}&o_{x}&a_{x}&p_{x}\\ n_{y}&o_{y}&a_{y}&p_{y}\\ n_{z}&o_{z}&a_{z}&p_{z}\\ 0&0&0&1\end{array}\right).\end{array} \tag{2}\]
## 3 Results  
### Moisture, pH, and Titratable Acidity  
Provolone del Monaco's typical shape is shown in Figure 1; the chemical composition at different ripening times and sampled at different portions is reported in Table 1.  
Figure 1: Provolone del Monaco.  
The cheese at time 0 showed a water content ranging from 43.80 to 47.05%. In particular, the moisture percentage was 46.20% and 47.05% in the core (C) portions and 43.80 and 45.76% in the portion under the rind (S) portions, in the 3-kg and 5-kg sized cheeses, respectively. At 90 days of ripening, the highest loss of moisture was observed. In fact, the values decreased to 40.03% and 42.93% in C portions and to 31.63% and 36.89% in S portions in the 3-kg and 5 kg-sized cheeses, respectively. After 90 days of ripening, the loss of water was less marked, probably due to the definitive rind formation. The S portions of both sizes had lower moisture contents than the C portions because the S portion was located closer to the external surface of the cheese in comparison with the C portions, producing a faster loss of water.  
The pH values detected in the analyzed samples ranged from 5.03 to 6.02 in the C portions and from 5.23 to 5.77 in the S portions in the 3-kg and 5 kg-sized chees, respectively. The increase of pH in cheese during ripening has been associated with the proteolytic process that released large amounts of nitrogenated alkaline compounds [16]. After 270 days of ripening, the 5-kg sized cheese showed slightly higher pH values than the 3-kg sized cheese, probably because of the more abundant water content in the 5-kg sized cheese [17].  
With reference to the titratable acidity, the data obtained showed values ranging from 0.17 to 0.22 of lactic acid/100 g of cheese in the C portions and from 0.19 to 0.21 g of lactic acid/100 g of cheese in the S portions during 270 days of ripening in the 3-kg and 5-kg sized cheeses, respectively. In particular, during the first 90 days of storage, all the samples presented a decrease of titratable acidity in comparison with the data observed in the first stage of the maturation period. A significant increase in this parameter was observed at 270 days, possibly due to lactic acid formation from residual lactose still present in the cheese [18].
# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 4 Experiments and Result Analysis
### Dataset  
We used four datasets to train our proposed 3D-SE-Net and validated the performance of our proposed model. The datasets we used for network training are described in the following. Some examples in the dataset are shown in Figure 7.  
ZJU Dataset [27]: The first dataset contains videos of twenty individuals with four clips each, containing a total of 80 videos of subjects in four categories: frontal without glasses and frontal with light-colored glasses, frontal with dark-colored glasses and supine without glasses, with the main subjects in the dataset being of Asian origin. [PERSON] et al. [9] performed geometric transformations, Gaussian blurring and other graphical transformations on the ZJU dataset to reduce the resolution of the images. Their strategy was adopted in our approach.  
NTHU dataset [28]: The second dataset contains driving recordings of 36 subjects of different races in five different scenes (BareFace, Glasses, Night_BareFace, Night_Glasses, Sunglasses), including normal driving, yawning, slow blinking, falling asleep, etc. The scenes contain both day and night lighting states, the video resolution is 640 \(\times\) 480 in AVI format and we converted the video to a picture of size 48 \(\ast\) 48, containing the state of glasses and mouth.  
YawDD dataset [29]: The third dataset is video data recorded by in-car cameras, recording various facial features of drivers in a real car (male and female, with or without  
Figure 6: Schematic diagram of EAR and MAR.  
glasses/sunglasses, different ethnicities), talking, singing, remaining silent and yawning. We transformed all 351 videos into picture frames of size \(48*48\), including 5435 pictures of the mouth state and 6120 pictures of the eyes state, which were divided to scale for model training and validation.  
FDF dataset: The fourth dataset was designed and collected fully in-house and we have named it the FDF dataset. This dataset contains a total of 16 people, both with and without glasses, and the test environment includes both daytime and nighttime scenarios. To ensure the objectivity of the data, our dataset was collected in a real driving environment. The subjects' driving states included 'Normal', 'Talking', 'Sleepy-nodding' and 'Yawning', and we eventually acquired 5360 images of the driver's facial state from the video frames.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Key Concept and Applications
#### 4.5.3 Digital Twins in Vertical Farming  
[PERSON] et al. [99] introduced the ACWA (AI and Cyber for Water and Agriculture) testbed, an advanced system addressing challenges in water resource management and agriculture, including vertical farming water and soil relationships. Integrating AI, ACWA facilitates innovative experimentation. The testbed features modular topologies, sensors, computational clusters, pumps, tanks, and smart water devices, enabling dynamic simulations of water distribution and soil scenarios. It generates datasets for AI model development,water quality analysis, and leakage detection. With high-frequency data collection, ACWA supports detailed analysis and data-driven decision-making, offering an open-access repository to foster collaboration and advancements in sustainable practice.
## 5 Results and Discussion
At the same time, since the lower limit of the detectable stress zone is very low at high stress levels close to the yield strength, the measurable range of stress decreases sharply with a decrease in residual stress, and finally disappears when the lowest detectable limit of stress is reached. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Hardness** & **Measured Residual** & **Predicted Residual** & **Percentage** \\  & **(HV)** & **Stress (MPa)** & **Stress (MPa)** & **Difference** \\ \hline
1 & 443 (\(\pm\)2.85) & 212 (\(\pm\)3.61) & 153.67 & 28.5\% \\
2 & 445 (\(\pm\)2.76) & 237 (\(\pm\)8.54) & 256.12 & 8.1\% \\
3 & 448 (\(\pm\)3.47) & 354 (\(\pm\)6.24) & 409.79 & 15.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The hardness test values, measured residual stress values and predicted residual stress values. As mentioned above, [PERSON] et al. [23] proposed a method to calculate the state and magnitude of residual stress through the energy method in the Vickers indentation cycle. The energy contribution to residual stress can be calculated from the indentation approximation with applied equiaxial residual stresses:  
\[U_{r}=\frac{2}{3}(\tan^{2}\theta)h_{r}^{3}\sigma_{r} \tag{12}\]  
where \(U_{r}\) is the energy contribution to residual stresses on machined surfaces during indentation cycles, \(\theta\) is the residual cone half-angle after stabilization of the indentation after complete unloading of the applied load, \(h_{r}\) is the residual displacement after complete unloading in the load-displacement curve and \(\sigma_{r}\) is the residual stress. The common advantage of the research prediction method proposed in this paper and [PERSON]'s method is that they do not need to consider the solution of the relevant physical quantities of the stress-free state curve of the material. But [PERSON]'s method needed to use 10 physical quantities and was only applicable to a surface with a plasma spray coating. Compared with the method developed in this paper, the calculation of physical quantities was less, with higher precision and wider applicability. At the same time, [PERSON]'s prediction method has an accuracy of less than 75%, which is not as accurate as the precision measurement in this article. Therefore, it is fully proven that the residual stress prediction formula obtained in this paper can achieve more accurate prediction of residual stress by measuring the micro-hardness of the machined surface. Finally, it should be noted that this method only applies to elastoplastic materials according to traditional continuum mechanics. In an actual indentation experiment, the hardening of the material will also have a great impact on the residual stress of the indentation. In addition, indentations with sub-micro depths can be influenced by various microscopic mechanisms, such as strain gradients, surface effects or grain dislocations. These effects are also the cause of the error between the residual stress prediction model and the actual test values. These effects were ignored in this study but will be considered in future research.
## 4 Network Model
### Communication Model  
In this work we consider only large scale propagation effects where the pathloss is characterized by an exponential factor to the transmission distance as \(D^{\eta}\) where \(D\) is the communication range and \(\eta\) is the pathloss exponent. Further, we consider that energy is spend by the transmitter and receiver electronics to process the message, \(e_{t}\) and \(e_{r}\) respectively and energy is required by the power amplifier to transmit the message over the wireless channel, \(e_{d}\). Also for all links the transmit data rate is \(B\) (bps) and the maximum transmit range is \(R\) meters.  
Let \(\bar{v}_{ij}\) be the relative velocity between two communicating nodes, \(i,\ j\). Then function \(g(D_{ij},t)\) determines the absolute distance at time \(t\) between the transceiver pair \(i,j\) during transmission, expressed as follows:  
\[g(D_{ij},t)=|D_{ij}-\bar{v}_{ij}t| \tag{1}\]  
where \(D_{ij}\) be the initial distance between the transceiver pair \(i,j\) at the start of the transmission. Moreover, the total energy consumption between nodes \(i\) and \(j\) can be expressed as follows:  
\[f_{ij}=(e_{r}+e_{t})B\tau+Be_{d}\int_{0}^{\tau}g(D_{ij},t)^{\eta}dt \tag{2}\]  
where \(\tau\) is the transmission duration and function \(g(D_{ij},t)\) accounts for the changing transmission distance as described by (1).