esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## Appendix A Appendix A Appendix A Appendix A.1 Data Sources
### Break and Trend Detection with BFAST Algorithms  
EVI raster data were cleaned prior to running BFAST's algorithms following the cleaning steps proposed by [PERSON] et al. [41]. Time series for the period between 2003 and 2022 were then derived for each pixel and used as inputs to bfast and bfast01 functions. The magnitude, type (or direction), and time of breaks (including 95% confidence intervals, CIs) were derived from bfast. The magnitude of breaks was used as the response variable in boosted regression tree models as described below. The bfast01 function provided inputs tothe bfastclassify function, which classified each time series into one of eight trend types [57]. The outputs of bfast and bfastclassify were then converted back to raster files. A version of the R code used here can be found on GitHub ([[https://github.com/georod/forc_trends](https://github.com/georod/forc_trends)]([https://github.com/georod/forc_trends](https://github.com/georod/forc_trends))) (accessed on 24 June 2024).  
Apart from the sensitivity analyses shown in Tables 15 and 16, where the values of bfast's \(h\) parameter varied, all other analyses presented here used \(h\) = 0.05. This value of \(h\) is equivalent to 23 observations or 1 year of data given the temporal resolution (16 days) and length (20 years, 2003-2022) of the EVI time series.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(h=0.025\) & \(h=0.05\) & \(h=0.1\) & \(h=0.15\) \\  & **6 Months** & **(1 Year)** & **(2 Years)** & **(3 Years)** \\ \hline Total breaks & 1477 & 15,904 & 11,799 & 10,701 \\ Unique pixels & 590 & 12,348 & 10,146 & 9662 \\ Breaks per pixel & 2.50 & 1.29 & 1.16 & 1.11 \\ \hline Negative breaks (\%) & 822 (56) & 11,969 (75) & 9372 (79) & 8466 (79) \\ Unique pixels & 440 & 9720 & 8276 & 7792 \\ Breaks per pixel & 1.87 & 1.23 & 1.13 & 1.09 \\ \hline Positive breaks (\%) & 655 (44) & 3935 (25) & 2427 (21) & 2235 (21) \\ Unique pixels & 308 & 3033 & 2115 & 2057 \\ Breaks per pixel & 2.13 & 1.30 & 1.15 & 1.09 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sensitivity analysis of bfast’s \(h\) parameter. This parameter determines the temporal window size (or minimal segment size) between potential breaks. The temporal equivalent of \(h\) is shown in brackets along the top row. For example, \(h=0.05\) is equivalent to one year of observations given the 16-day temporal resolution and length of the time series used here (20 years, 2003–2022). The number of breaks, for both positive and negative breaks, changed with different values of \(h\). The highest number of breaks was produced when \(h\) was 0.05. However, the highest value of breaks per pixel (2.50) and the highest proportion of positive breaks (44%) were produced when \(h\) was 0.025 (6 months). A given value of \(h\) will be good for detecting some types of disturbance but not all ([PERSON] et al. [60]). For the main analyses, \(h=0.05\) was chosen as this allowed joining breaks with yearly disturbance data and the production of annual summaries.
## 2 Reliability Analysis Method
### ISSA-Kriging-MCS Model  
#### 2.2.1 Kriging Model  
The Kriging model is adept at handling complex nonlinear problems with high approximation ability and estimation characteristics for predicting uncertainty. It is widely used in various fields, such as geostatistics, engineering optimization, and spatial analysis [24; 25]. In the process of constructing the Kriging model, the Gaussian process function is selected for analysis and calculation. This model requires \(n\) initial sample points, and \(X=\left[x_{1},x_{2},\cdots x_{n}\right]^{T}\). The \(i\)-th sample point is \(x_{i}\in R^{n}(i=1,2,\cdots,n)\), with the response  
The solution for \(\theta_{p}\) can be obtained by estimating the optimal solution of the relevant parameters under maximum likelihood:  
\[\underset{\theta_{p}>0}{\text{max}}\varphi(\theta)=max\left\{-\frac{ln|R|+nln \hat{\sigma}^{2}}{2}\right\} \tag{9}\]  
The mean and variance of the predicted value of any point \(x\) are  
\[\hat{G}(x)=f(x)^{T}\hat{\beta}+r(x)^{T}R^{-1}\big{(}G-F\hat{\beta}\big{)} \tag{10}\]  
\[\hat{\sigma}_{\hat{G}}^{2}(x)=\hat{\sigma}^{2}\big{(}(1+\mu(x)^{T}(F^{T}R^{-1}F )^{-1}\mu(x)-r(x)^{T}R^{-1}r(x) \tag{11}\]  
where \(r(x)=[R(x,x_{1}),R(x,x_{2}),\cdots R(x,x_{n})]^{T}\) are the correlation vectors between the predicted point and all \(n\) samples. The sampling points represent the random variables in the research problem, and the response values represent the performance indexes of the structure, such as failure probability \(P_{f}\) and reliability index \(\beta\).
# An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks
## 5 Cyberattacks and Analysis
### Overview of Selected Cyberattacks
#### 5.1.3 Ransomware  
The following root causes and preventive methods are derived from [10, 77, 91, 97, 98].  
Root Causes:  
1. Downloading email attachments.  
2. Downloading files from untrusted websites.  
3. Allowing downloaded code to run automatically.  
4. Attacker accessing OS APIs.  
Attacker exploiting hardware vulnerabilities (TPM: Trusted Platform Model, BIOS, firmware).
* Freely available educational resources on cybersecurity vulnerabilities and attack techniques. Preventive Mechanisms:
* Deploy IDS (Intrusion Detection System) and IPS (Intrusion Prevention System).
* Utilize TPM. (Note: it was also found that TPM has security vulnerabilities).
* Maintain regular backups.
* Implement blacklist-based detection: block known malicious domains and IP addresses.
* Employ advanced ransomware detection: Use rule-based, statistical, machine learning-based, or hybrid techniques.
* Change the file extensions randomly.
* Provide consistent training and reviews.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### Channel Allocations
#### 3.5.2 Static Channel Assignment (SCA)  
In order to evaluate (later in Section 4) the performance of the proposed RL-CAA, we compare it to the SCA even selection algorithm [35]. The algorithm has been modified in order to comply with our application domain. The operation of the algorithm is as follows. The gateway monitors the available  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RL-CAA Parameters** & **Values** \\ \hline RL Evaluation Steps & 500 \\ RL Convergence Steps & 50 \\ \(\alpha\) (RL learning rate) & 100 \\ \(\beta\) (reward memory factor) & 0.01 \\ \(\sigma\) (probability bias) & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RL-CAA simulations parameters.  
channels. The channels are assigned in an increasing order based on the gateway ID, irrespective of the channel conditions, e.g., SNR, interference, etc. For example, if Gateway 1 needs to allocate a channel and if Channel 1 is currently assigned, then Channel 2 will be assigned to Gateway 1 and Gateway 2 will take Channel 3 and so on.
# An Empirical Study on Ensemble of Segmentation Approaches
## 2 Materials and Methods
### Data Augmentation
#### 2.3.2 Contrast and Motion Blur  
Another technique for data augmentation that allows one to derive new samples from an original dataset is based on the combination of contrast and motion blur. The first one increases or decreases the original contrast, and the second one simulates the movement of the camera taking the image. We developed two different contrast functions, and each time we choose one of them at random.  
The first function is defined as follows:  
\[y=\frac{\Bigl{(}x-\frac{1}{2}\Bigr{)}\sqrt{1-\frac{k}{4}}}{\sqrt{1-k(x-\frac{ 1}{2})^{2}}}+0.5,\ k\leq 4 \tag{16}\]  
The parameter \(k\) controls the contrast. Specifically: T\(\backslash\)hte contrast increases when \(k<0\); it is decreased when \(0<k\leq 4\); the image is unchanged when \(k=0\).  
The value of the parameter is drawn at random in the following range:  
* \(\mathcal{U}(2.8,\ 3.8)\rightarrow\) Hard decrease in contrast;
* \(\mathcal{U}(1.5,\ 2.5)\rightarrow\) Soft decrease in contrast;
* \(\mathcal{U}(-2,-1)\rightarrow\) Soft increase in contrast;
* \(\mathcal{U}(-5,-3)\rightarrow\) Hard increase in contrast.  
The second function is defined as follows:  
\[y=\begin{cases}\frac{1}{2}\big{(}\frac{x}{0.5}\big{)}\alpha\ 0\leq x<\frac{1}{2}\\ \\ 1-\frac{1}{2}\big{(}\frac{1-x}{0.5}\big{)}\alpha\ \frac{1}{2}\leq x\leq 1\end{cases} \tag{17}\]  
The parameter \(\alpha\) controls the contrast. In particular, the contrast is increased when \(\alpha>1\); it is decreased when \(0<\alpha<1\); if \(\alpha=1\), then the image is left unchanged.  
The parameter is chosen randomly from four possible ranges:  
* \(\mathcal{U}(0.25,\ 0.5)\rightarrow\) Hard decrease in contrast;
* \(\mathcal{U}(0.6,\ 0.9)\rightarrow\) Soft decrease in contrast;
* \(\mathcal{U}(1.2,\ 1.7)\rightarrow\) Soft increase in contrast;
* \(\mathcal{U}(1.8,\ 2.3)\rightarrow\) Hard increase in contrast;  
The blurring effect that mimics the movement of the camera is applied right after the contrast adjustment. We use the MATLAB function _fspecial('motion', len, theta)_.
# Implementation of Autonomous Mobile Robot in SmartFactory
## 1 Introduction
Contribution
* This work presents a method of communication of the production line with an autonomous mobile robot MiR100, which is performed wirelessly using an input/output card. * The autonomous mobile robot MiR100 navigates to the final position using precise object markers. The maximum repeated accuracy of approaching the position is \(\pm 3\) mm. * The work brings a comparison of different types of precise marker objects in relation to the repeated accuracy of the approach of an autonomous mobile robot in position. It turns out that not all types of marker objects show the same results. * The work also describes basic information about industrial mobile robots, its applications and current research.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 3 Performance Evaluation  
A model of detections' fusion has been given, and the valuation of the belief function has been proposed in the above section. In order to evaluate the performance of the method, simulations based on the fusion of two spoofing detections are provided in this section.
## 3 Computational Methodology
### Building Modeling  
To carry out the energy analysis, a 3D model of the medical center was built aided by the simulation software Open-Studio(v. 3.4) and Energy-plus(v. 9,4). These are based on BIM methodology, specialized in predicting the indoor environment and energetic behavior of the building through heat transfer calculations and assumptions. They are designed for energy improvement, from advanced envelope strategies to developing air conditioning systems. For this purpose, the software calculates the thermal loads of the built environment and the thermal/electrical load of operation systems by using a transient heat balance [25].  
For simulation, the software requires data of the building materials' thermophysical properties, schedule operation, characteristics of HVAC and lighting systems, and the weather information of the location. The model of the medical center was developed using the envelope materials properties reported in Table 1. Regarding electrical devices, it was given the information collected by energetic audits. Similarly, based on the flow of people and patients served by the medical center (described in Section 2.2), information on the occupancy percentages and hours of activity of the building linked to energy loads was programmed, as is presented in Table 3. The configuration of the HVAC systems was adjusted to a set point of 22 \({}^{\circ}\)C under current regulations in hospitals and health centers [26]. Finally, the climatic file used in the study comprises hourly meteorological data for 2022 obtained through the software METEONORM (v. 8.1.1) [27].
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 1 Introduction
### Background
The research presented in [22] proposed a novel approach to address disaster management issues utilizing a UAV-assisted SWIPT-enabled NOMA-based D2D network. They formulated a nonlinear power allocation optimization problem that maximized the system's energy efficiency performance and solved it using the Dinkelbach approach. Simulation results show that the advanced NOMA system outperforms the ordinary NOMA scheme. Alternately, ref. [23] has investigated a sequential optimization problem for resource allocation and communication mode selection in a UAV-assisted D2D cellular network to improve energy efficiency and ensure satisfactory transmission rates for all ground UEs. They proposed a reinforcement learning-based scheme to solve this problem, which has been shown to be effective through simulated results.
## 3 Tarifit Language Background
### Linguistic Features and Computational Challenges  
Tarifit exhibits the rich agglutinative morphology characteristic of Berber languages, with complex verbal inflection systems that mark person, number, gender, tense, aspect, and mood through prefixes, suffixes, and internal vowel alternations. This morphological complexity, combined with relatively free word order and extensive use of clitics, presents substantial challenges for automated parsing and translation. Traditional rule-based approaches struggle with the combinatorial complexity of morphological variations, while neural approaches require large training corpora that are unavailable for Tarifit.  
The language demonstrates significant lexical borrowing from Arabic due to centuries of contact, creating cognates and shared vocabulary that may facilitate cross-lingual transfer in machine translation contexts, particularly for the Tarifit-Arabic language pair. However, this lexical overlap also introduces false friends and semantic shifts that can mislead automated systems. Additionally, the limited availability of digital corpora and standardized linguistic resources severely constrains the development of traditional data-driven NLP applications, making in-context learning approaches particularly valuable for this language community. The sparse digital presence and inconsistent writing practices documented in recent studies [29] further underscore the need for robust few-shot learning methodologies that can operate effectively with minimal training data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Script** & **Text** & **Usage Context** \\ \hline Tifinagh & eXSH, CH& G\(\circ\)? \\ \hline Latin & Azul, mlih cha? & Cultural, digital, academic, and \\ \hline Berber Latin & Azul, mlih ca? & formal contexts \\ \hline Arabic & \(\xi\)L\(\  
Figure 1 illustrates the complex morphological structure characteristic of Tarifit through representative examples that demonstrate the agglutinative nature of the language and the computational challenges it presents for automated processing.
# Measurement of the Impact Loads to Reduce Injuries in
## 2 Materials and Methods
### Study Design
The jumps and respective landings were filmed using two video cameras, one Sony (camera C1) and one Canon (camera C2), each with an acquisition frequency of 100 Hz (Figure 6). Before the jumps were performed, the cameras were calibrated and positioned relative to the platform position allowing the initial jump calibration data to be obtained. The objective of camera C1 was to obtain the maximum height of the jump (Figure 7), and the main goal of camera C2 was to obtain images of the maximum flexion of the knees of the gymnasts (Figure 8) and to characterize the landing. The foot position at landing (first image of ground contact) of the gymnasts (on the forefoot, midfoot, or rearfoot) was evaluated (Figure 9). The way landing is performed, mainly by the lower limb joints, can have a direct influence on a greater or lesser predisposition to injury occurrence. It was considered that the maximum flexion of the knees occurred when the position of the greater trochanter was the closest to the ground (Figure 8). Tracker software (Open Source Physics--Video Analysis and Modeling Tool--5.1.5) was used to extract the values of the maximum height of the jumps, using the gymnast's heel as a reference, and also the distance of the greater trochanter to the ground. The calibration was performed according to the software instructions and using a reference rectangle (3 \(\times\) 2 m) aligned with the sagittal plane of motion. The method of Direct Linear Transformation (DLT) allowed the transformation of 2D virtual coordinates to real coordinates. Figure 5: Example of calibration test for the in-house-built platform. Figure 6: Test setup diagram. To understand the relationship between the maximum vertical load and the form of landing, the ratio between the maximum vertical load on landing and the weight of the gymnast was computed (Equation (1)). \[Ratio=\frac{MaximumLoad(N)}{GymnastWeight(N)} \tag{1}\]
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology
### Techniques for Detecting Coverage Holes  
Three categories will be used to categorize the existing coverage hole identification techniques. They are connectivity-based, range-based, and location-based. Any coverage holes in the target zone may be properly located using location-based algorithms [9]. The necessity for precise geographic data has a significant impact on the applicability of some data since it might be challenging to obtain in specific circumstances. Range-based techniques only employ restricted distance data between nearby sensors to discover coverage holes in the classification that follows. Furthermore, it could still be too costly to collect accurate  
Figure 2: Coverage holes: (**a**) multiple in a WSN; (**b**) coverage holes and network boundary.  
distance information between nodes. Connectivity-based estimations are particularly significant because the availability of data is anything but trustworthy. Homology-theory-based algorithms stand out among the rest of these, which use logarithmic devices to evaluate systems' topological characteristics.
## 3 Results  
### IAA Production  
In Figure 1A, IAA concentrations produced by single strains with and without Trp are reported. For _G. diazotrophic_ and _H. seropedica_, IAA concentrations were higher than respective controls (\(p<0.05\)) while for _A. brasilense_ and _B. ambigaria_ no significant difference were shown (\(p>0.05\)). _H. seropedica_ was the best IAA producer; however, if we consider the number of cells present in the medium (Figure 1B) _G. diazotrophic_ showed the highest production, followed by _A. brasilense._ Lowest IAA production was shown by _B. ambigaria_. Average values of IAA concentrations were recorded when the four strains were grown together (Figure 2A). As shown in Figure 2B, the CFU mL\({}^{-1}\) obtained for each strain from mixed broth cultures were similar to those obtained by single cultures.  
Figure 1: (**a**) Indole-3-acetic acid (IAA) production of the strains grown in single cultures (\(n=3\)); (**b**) CFU mL\({}^{-1}\) of the strains grown in single cultures. In the Figure: Cnt, broth culture without Trypanian; Trp, broth culture added with Tryptophan; A, _A. brasilense;_ B, _B. ambigaria_; G, _G. diazotrophica_; H, _H. seropedicine._ Error bars are standard error of the means. Means followed by different letters are significantly different according to [PERSON]’s LSD test (\(p<0.05\)).  
Figure 2: (**a**) IAA production in binary and mixed cultures of strains (\(n=3\)); (**b**) CFU mL\({}^{-1}\) of the single strains grown in a mixed broth culture of all strains (\(n=5\)). In the Figure: Cnt, broth culture without Tryptophan; Trp, broth culture added with Tryptophan; A, _A. brasilense;_ B, _B. ambigaria_; G, _G. diazotrophica_; H, _H. seropedicine._ Error bars are standard error of the means. Means followed by different letters are significantly different according to [PERSON]’s LSD test (\(p<0.05\)).  
In Figure 2A, IAA concentrations found in binary broth cultures in the presence and in the absence of Trp are shown. Except for binary culture of _B. ambigira_ + _H. seropedicae_, the IAA concentrations in Trp-added medium were higher than control (_p_ < 0.05). Generally, IAA range concentrations were similar to those obtained by single cultures. However, in the absence of Trp, the presence of _A. brasilense_ negatively influenced the synthesis of auxins. In Figure 3, the CFU mL\({}^{-1}\) obtained for each strain from each binary culture are shown. Slight differences in their loads were recorded for _A. brasilense_, _B. ambigira_, and _G. diazotrophicus_ in the different binary cultures. Instead, _H. seropedicae_ loads recorded strong decreases when cultured with _B. ambigira_ and _G. diazotrophicus_ in the presence and in the absence of Trp, respectively.
## 3 Field Trialed System
### Remote Controlled Tractor
#### 3.2.3 Remote View from the Tractor  
For the remote control of a tractor, it is essential to see where the tractor is going. Therefore, various cameras (listed in Table 5) were tested during the trial to provide views from the tractor. For safety reasons, a human driver was also in the tractor cockpit, ready to take manual control if needed.  
The shortest latency was obtained using the H.264 codec. Other codecs available from the cameras were H.265 and MJPEG. The Hikvision camera with the Chrome browser extension provided the best results on the screen-to-screen, with a delay of about 100 ms. For the other cameras, the delay was about 250 ms. Multiple cameras were tested simultaneously by opening their views on separate screens, but the best results were obtained using a single-camera setup. When several video transmissions were used, the video would lag and freeze from time to time.  
The cameras were connected using two different router setups. With the Goodmill router, the camera traffic was forwarded through a cloud server, and with the Teltonika  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CAN Messages** & **CAN Data** & **SCTP** & **TCP** & **Total** & **Extra bytes** & **Overhead** \\ \hline
1 & 13 & 48 & 50 & 184 & 171 & 93\% \\
2 & 26 & 60 & 63 & 200 & 174 & 87\% \\
4 & 52 & 88 & 89 & 224 & 172 & 77\% \\
8 & 104 & 140 & 141 & 280 & 176 & 63\% \\
16 & 208 & 244 & 245 & 384 & 176 & 46\% \\
32 & 416 & 452 & 453 & 592 & 176 & 30\% \\
64 & 832 & 868 & 869 & 1008 & 176 & 17\% \\
96 & 1248 & 1284 & 1285 & 1424 & 176 & 12\% \\
100 & 1300 & 1336 & 1337 & 1472 & 172 & 12\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network traffic efficiency, depending on how many CAN frames are transmitted at once.  
router, the traffic was directly forwarded using port forwarding. Dynamic DNS (DDNS) was also used to allow a connection to the cameras with dynamically changing IP addresses.
## 2 Theoretical Framework and Methods
### Analysis Process
#### 2.3.5 Obstacle Degree Analysis  
Through the obstacle degree model, it is possible to accurately quantify the degree of obstacles posed by various evaluation indicators in the resilience of subway stations to flood disasters, thereby identifying key factors that significantly constrain resilience performance. At the same time, it clarifies the core factors affecting evaluation results and specifies the extent to which key constraints impact the overall evaluation. Based on this, it can precisely diagnose the issues faced by subway stations' resilience to flood disasters and propose targeted measures for enhancing resilience.  
The core of the obstacle degree model lies in its three main measurement indicators: obstacle degree, factor contribution degree, and indicator deviation degree. The calculation process of this model is as follows:  
\[F_{j}=V_{j}\times w_{j} \tag{25}\]  
\[D_{j}=1-A_{j} \tag{26}\]  
\[O_{j}=\frac{F_{j}\times D_{j}}{\sum F_{j}\times D_{j}} \tag{27}\]In the formula, \(A_{j}\) represents the standardized value of the indicator, \(V_{j}\) represents the weight of the \(j\)-th criterion layer indicator, and \(W_{j}\) represents the weight of the \(i\)-th individual indicator to which the \(j\)-th criterion layer indicator belongs.