esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights in Potato Leaf Freezing by Infrared Thermography
###### Abstract  
Infrared thermography has been widely used to study freezing processes in freezing resistant plants but hardly in freezing susceptible species. _Solanum tuberosum_ leaves get frost killed at \(-3\)\({}^{\circ}\)C and are unable to frost harden. The basic nature of frost injury to potato leaves is not clear. By employment of infrared differential thermal analysis (IDTA) in combination with viability assessment, we aimed to clarify the mechanistic relationship between ice formation and frost injury. During controlled freezing of potato leaves two distinct freezing events were detected by IDTA. During the first freezing event, the ice wave propagated via the xylem and spread out within 60 s throughout the whole leaf. When leaves were rewarmed after this freezing event, they did not show any frost injury symptoms. We suggest that this non-lethal first ice wave is restricted to the extracellular space. When leaves remained exposed after this exotherm, a second freezing event with a diffuse freezing pattern without a distinct starting point was recorded. When thawed after this second freezing event, leaves always showed frost damage suggesting intracellular freezing. The freezing behavior of potato leaves and its relation to frost damage corroborates that control of ice nucleation is a key for frost protection.  
Keywords:differential thermal analysis; freezing susceptible; ice nucleation; ice propagation; ice tolerance +
Footnote †: journal: Applied Sciences
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion
### Gross Primary Production (GPP) Validation  
The GPP from the EC (observed), the modeled GPP, and the GPP extracted from MOD17A2H are shown in Figure 3. It is observed that there is a greater difference in GPP values between the two areas during the rainy season as a response to greater rainfall availability (Figure 3). In other periods, which are drier, DC captures CO\({}_{2}\) closer to SC. It was expected that this difference would be less noticeable in the dry season due to the loss of leaves (senescence) [5]. It shows that rain is the limiting factor in CO\({}_{2}\) capture since the smallest difference between GPP in the two areas occurred in the period with less water availability (August to February). This result corroborates previous results [13]. The authors observed that the difference between the GPP estimated and the measured data is much greater in the wet season than in the dry season. Even though MOD17A2H GPP greatly underestimates the measured data, it manages to differentiate the seasonal behavior of the two study areas. It is worth mentioning that this estimate was made on an 8-day basis, and the value presented here corresponds to the average for the period.  
In a study carried out in northern Australia, the authors of [63] found an R2 between GPP measured and GPP_MOD17 for savanna vegetation equal to 0.32. As for the Caatinga area, the authors of [13] obtained a reasonable value of 0.43 for R\({}^{2}\). In this study, the R\({}^{2}\) values between the GPP observed and GPP MOD17A2H (Table 2) were 0.76 and 0.65 for DC and SC, respectively, which are satisfactory. Between the GPP observed and GPP modeled, the values of R\({}^{2}\) were smaller, i.e., 0.28 and 0.12 for DC and SC, respectively. However, the average values were closer to the in-situ measurements. The modeled GPP presented the smallest difference (mean absolute err--MAE) in relation to the ground measurements, showing for the DC, an MAE value of 3.2 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), while for the GPP MOD17A2H, the MAE was 4.0 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\). The modeled GPP presented better results (with smaller absolute errors), since it is calculated by associating in situ measurements with orbital data. However, spatially, the GPP MOD17A2H presented better results, particularly in the differentiation of land cover types between the two study areas.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 3 Remote Sensing Target Detection  
The existing Yolov5 target detection system is developing rapidly, but there are still some bottlenecks in the detection of small targets, such as in satellite images that have a large field of view and a large size.  
This section shows the Yolov5 network and the process of detecting small targets on satellite images.  
Figure 1: The procedure of an image compression and target detection system on a satellite.
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results
### Results
#### 3.3.3 Localization of DME Regions  
For performance evaluation of DME detection from retinal images, we have used the MESSIDOR dataset. FRCNN precisely localized the macula region and Softmax layer classify normal and DME-affected regions. Visualization results of DME localization are shown in Figure 4, FRCNN localized the macular edema region at regressor layer with a mean average precision of 0.943.
## 3 Background
### Web Application Firewalls (WAFs)  
A Web Application Firewall (WAF) is a security mechanism designed to protect web applications by monitoring, filtering, and blocking malicious HTTP/S traffic. Unlike traditional firewalls that safeguard networks, WAFs focus on protecting the application layer (Layer 7 of the OSI model) [22]. They are commonly used to defend against web-based attacks such as SQL injection (SQLi), Cross-Site Scripting (XSS), and Distributed Denial-of-Service (DDoS) attacks. WAFs match incoming traffic against a database of known attack patterns. Some notable WAFs include AWS WAF, Microsoft Azure WAF, CloudFlare WAF, Imperva, Fortinet, and ModSecurity. Table 1 compares the open source ModSecurity WAF and the commercial AWS and Cloudflare WAF along multiple features such as types of attack detection mechanisms supported, ease of customization, and the manner in which threat intelligence is incorporated.  
In this research, we use ModSecurity [11], since it is an open-source WAF and used in conjunction with Apache and Nginix servers. It uses rules to identify and mitigate malicious HTTP/S requests. These rules form the backbone of ModSecurity's ability to protect web applications by defining patterns, behaviors, and conditions that determine what traffic should be allowed, denied, or flagged for review. Each ModSecurity rule is written in its SecRule directive and typically includes the following:  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Feature** & **ModSecurity** & **AWS WAF** & **Cloudflare WAF** \\ \hline \multirow{2}{*}{**Rule-based**} & \multirow{2}{*}{OWASP CRS + Custom Rules} & Managed Rules + Custom Rules & Managed Rules \\ \hline \multirow{2}{*}{**Machine Learning**} & \multirow{2}{*}{Optional (via integrations)} & Integrated with & \\  & & AWS ecosystem & Integrated, advanced capabilities \\ \hline \multirow{2}{*}{**Behavioral Analysis**} & Limited (custom & Available (via & \\  & scripts required) & AWS services) & Advanced \\ \hline \multirow{2}{*}{**Case of Customization**} & \multirow{2}{*}{High (fully open-source)} & Medium & Medium \\ \hline \multirow{2}{*}{**Threat Intelligence**} & None (manual & \\  & updates needed) & AWS threat feeds & Cloudflare threat feeds \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of SQLi detection mechanisms in WAFs.  
1. Input to Monitor--specifies which part of the request or response to inspect (e.g., headers, parameters, or body).
2. Operator--defines the condition to match (e.g., checking for specific patterns or keywords).
3. Action--Specifies what to do if the rule is triggered (e.g., deny, log, or redirect).  
The OWASP CRS [12] is a set of generic attack detection rules for use with ModSecurity. Commercial WAFs employ similar proprietary rule sets. The security rules are manually updated by cybersecurity personnel in response to observed threats. In this work, we demonstrate how generative AI can be used to **proactively and automatically** enhance the robustness of ModSecurity. It should be noted that the proposed GenSQLi framework is generic and can be applied to strengthen any WAF against SQLi attacks.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 5 Performance Evaluation  
Analytical results of the proposed COSB formulated from R-DTMC are validated with that obtained from the simulation for the proposed COSB in an event-driven simulator, network simulator-3 (NS-3) version 3.24 [11]. To evaluate the performance of COSB, we compare simulation results with BEB, and two of the related contention window-scaling algorithms; enhanced collision avoidance (ECA) mechanism [12], and exponential increase-exponential decrease (EIED) backoff algorithm [13]. These comparison protocols (ECA and EIED) are selected for their characteristic of not resetting the value of W to its minimum value \(W_{min}\). ECA uses a deterministic backoff value \(B=W_{min}/2\) instead of resetting W to \(W_{min}\) after successful transmission. The \(W\) value is exponentially increased after each unsuccessful transmission and is halved after each successful transmission in the EIED mechanism. The limitations of these protocols is that the performance of ECA is limited to the number of contenders below the deterministic cycle length \(W_{min}/2\), while EIED increases/decreases the contention window value without knowing the channel collision probability. The analytical model is validated with a network of \(n\) WEs ranges from 5 to 50 (a typical office floor deployment of IEEE 802.11 ax standard [3]), where each WE is within the coverage area of the others (no hidden terminal). The WEs are set to be in saturation state (always willing to transmit). The specific MAC and PHY layer parameters are listed in Table 1.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 2 Background
### Low Power Wide Area Network (LPWAN)
#### 2.1.1 LoRa  
This is one of the most popular and successful techniques, consisting of a protocol stack that is specified by LoRa Alliance [11]. LoRa operates over a long-range physical layer on unlicensed frequency bands while using the chirp spread spectrum (CSS) modulation technique, with the key features, such as low data rate, low complexity, and flexible operating classes for various applications, which enables a massive number of nodes per single gateway.
## 5 Results  
Measurements of the reflected ultrasonic signal were made at different oil heights and references. These measurements were compiled depending on the variable oil temperature and the selected oil type. The measured data shaped into an envelope of the received ultrasonic signal with specific amplitude were subjected to regression analysis and ANOVA analysis of inter-dependencies. The obtained dependency algorithms can be used to significantly refine and understand the envelope shaping of the received ultrasonic signal. Physically, it was assumed that the amplitude of the envelope of the received ultrasonic signal and its steepness are directly dependent on the height and temperature of the oil, but the selected analyses helped to pinpoint the inter-dependencies and thus enable more precise detection of the edge location of the received ultrasonic envelope. Below is a selected dataset on which the inter-dependencies and the resulting analyses by regression analysis and ANOVA analysis are presented.  
If the motor oil level of 30 mm is mentioned, it is clear that there is almost no dependence between the amplitude of the reference and the temperature. On the other hand, the amplitudes of the level can be well described by the second-degree polynomials; see Figure 5a. In the case of the motor oil level of 60 mm, it can be said that temperature does not have a significant influence on the amplitude of reference. On the contrary, there is a clearly visible trend for the amplitude of the motor oil level that can be described by the second-degree polynomials; see Figure 5b. Measurement data analysis revealed very low amplitudes for both reference and level signals for temperatures that are close to zero. In the case of the reference signal, the amplitude is relatively stable. The amplitude of the level signal is constantly increasing up to a temperature of 100 \({}^{\circ}\)C. Amplitudes related to the motor oil level might be described quite well by the second-degree polynomials; see Figure 5c. In the given representation, the amplitude (mV) is denoted by the variable \(Y\), while the temperature (\({}^{\circ}\)C) is represented by the variable \(X\).  
Experimental data related to \(\Delta t\), amplitude starting from the level, slope of the level amplitude, and amplitude from the reference were selected according to the motor oil level in the level measurement device. Data were tested by one-way ANOVA (equal variances were not assumed). As can be seen, there is no statistically significant difference among the \(\Delta t\) groups. Amplitude start from the oil level revealed a statistically significant difference for the two pairs of 30-90 mm and 30-60 mm. Moreover, the slope exhibited a significantly different pair of variables (30-90 mm). Finally, the amplitude of reference did not show any statistically significant difference among all three groups.  
In this way, selected data of variables representing the physical values of the received ultrasonic signal were analyzed to confirm the significant oil height-dependent differences in the variable temperature settings.
# ICT in Rural Areas from the Perspective of Dairy Farming: A Systematic Review
## 3 Results
### Production (18 Documents)  
The growing urban-rural inequalities and the accelerated depopulation have become a general concern in the fight against the rural decline and urbanization all over the world [119], more concretely, the new course of the politics in the EU is increasingly oriented towards the promotion of small agro-zootechnical productions from a sustainable point of view, fostering the return to agricultural activities by young people [120].  
In terms of the agricultural development, as demonstrated in surveys carried out by [121] to Irish farmers, it has always affected--and has been affected by--the context in which it operates, both in the past and in modern days; in such a way that three clear business dimensions by the farmers can be distinguished: Diversification (for example, care, education, and farmers' market), finalization (that is to say, abandonment of the dairy sector), and maximization (the use of consumables which are external to the farm in order to maximize the efficiency per hectare) of the production. Concretely, in terms of milk production, it has been seen that it has drastically increased during the second half of the XX century, as a result of the agricultural modernization, which stimulated the specialization, intensification, and scale enlargement [73; 122; 123], which makes the extension of sustainable agriculture necessary [124]. Currently, in this field, there is a growing demand at a global level of dairy products, which brings deep changes in this industry [125; 126; 122], giving room to the opening of new market niches, where increasing the efficiency of milk production is key to survive in the market [14]. All this has brought a growing need of knowledge about the usefulness of the information and communication technologies in order to achieve the goals proposed by the market of sustainable agriculture, that is to say, to improve the agricultural productiveness and increase the incomes [79; 127; 128; 129]. To this effect, strategies focused on the creation of the value chain, transparency, and traceability will also be necessary [116].  
Dealing with the number and type of farm businesses, according to studies conducted by [130] from the analysis of data of dairy farms provided by Farm Business Survey of England and Wales (FBS), it is observed that in spite of the high efficiency of milk per cow in many industrialized countries in the European Union, as is the case in Wales and England, the dairy production continues with a long-term tendency to the reduction in the number of farm businesses and intensification, which is fostered by socio-economic and political aspects. In this context of the reduction and intensification of dairy farms, other surveys such as those elaborated by [131] in Australia show the importance of guaranteeing the best practices with the use of the ICT to all those farmers who consider expanding their business in terms of cattle management, operations, finances, human resources, and strategic administration.
## 3 Methodology
### SBas-InSAR  
SBAS-InSAR was first introduced by [PERSON] et al. in 2002 and is now commonly used in the monitoring and prevention of geological hazards such as landslides and ground subsidence [42]. SBAS-InSAR utilizes pre-existing SAR image datasets and partitions them into different subsets based on various temporal and spatial baseline thresholds. It employs the least squares method to compute the deformation time series for each subset. Additionally, the singular value decomposition (SVD) technique is used to combine the results from individual subsets and obtain the deformation sequence spanning the entire time period [43].  
In this study, the processing of 128 scenes of C-band Sentinel-1A ascending orbit data from January 2021 to March 2023 was conducted (Figure 1). The maximum normal baseline set for this calculation is 2%, and the maximum temporal baseline is 50 days. Figure 4 depicts the temporal and spatial baseline maps obtained based on the configured parameters.  
Figure 4: Temporal and spatial baselines of the SAR datasets employed in this study. (**a**) Time-position plot illustrating InSAR image acquisitions; (**b**) Time-baseline plot depicting interference pairs of SAR images.
# Method for Handling Massive IoT Traffic in 5G Networks
## 10 Results and Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**IoT Devices** & **Traffic Load** \\ \hline
1000 & 0.07 \\
2000 & 0.14 \\
3000 & 0.2 \\
4000 & 0.27 \\
5000 & 0.34 \\
6000 & 0.41 \\
7000 & 0.47 \\
8000 & 0.54 \\
9000 & 0.61 \\
10,000 & 0.68 \\
11,000 & 0.74 \\
12,000 & 0.81 \\
13,000 & 0.88 \\
14,000 & 0.95 \\
15,000 & 1.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Devices** & **100** & **200** & **300** & **400** & **500** \\ Without multiplexing & 0.0012 & 0.0007 & 0.0008 & 0.0012 & 0.0010 \\
**Number of Devices** & **600** & **700** & **800** & **900** & **1000** \\ Without multiplexing & 0.0017 & 0.00127 & 0.00097 & 0.00077 & 0.0011 \\ \hline
**Number of Devices** & **100** & **200** & **300** & **400** & **500** \\ With multiplexing & 0.0031 & 0.0026 & 0.0020 & 0.0015 & 0.0022 \\
**Number of Devices** & **600** & **700** & **800** & **900** & **1000** \\ With multiplexing & 0.0011 & 0.0008 & 0.0007 & 0.0005 & 0.0003 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 95% confidence intervals for simulation results in Figure 7. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Setting** \\ \hline MCS & 26 \\ Maximum RN resource blocks & 5 \\ Number of IoT devices & 1000, 2000, 3000,..., 15,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation parameters. The packet delay performance of the system in both categories is compared in Figure 9. Error bars in the figure depict standard deviation. In low load scenarios, the delay performance without relaying is better than with relaying. The logic behind this behavior is that RN has to wait until arrival of \(n_{max}-overhead\) bytes at RN, while no such waiting time is required if there is no relaying. However, once the traffic load increases, the environment without relaying fails to handle the load and large average delays (that do not fit to the scale used in Figure 9) are observed. In the scenario of 7000 IoT devices, this notion is clearly illustrated. However, in the environment with relaying, the traffic load of even up to 14,000 IoT devices is efficiently handled.
## 4 Methodology
### PRV Acquisition and Processing  
To ensure reliable PRV estimation, signal quality checks were applied to the PPG signals prior to analysis, and segments with excessive artefacts were excluded from further processing. Pulse rate variability (PRV) was then computed from the PPG signals using a peak detector specifically designed for pulse waveforms [27]. The analysis was performed using a 5-min sliding window, following established conventions in the literature [28]. Two standard time-domain metrics were calculated, as defined in Equations (1) and (2). These metrics were chosen due to their established use in reflecting autonomic nervous system dynamics, with RMSSD being sensitive to parasympathetic activity and SDNN capturing overall heart rate variability [28].  
\[RMSSD=\sqrt{\text{mean}((RR_{i+1}-RR_{i})^{2})} \tag{1}\]  
\[SDNN=\sqrt{\frac{\sum_{i=1}^{N}(RR_{i}-mRR)^{2}}{N-1}} \tag{2}\]  
Here, \(RR\) denotes the time (in seconds) between two consecutive R peaks, \(RR_{i}\) is the \(i\)-th RR interval, \(mRR\) is the mean of all RR intervals, and \(N\) is the total number of RR intervals.  
To obtain frequency-domain features, we followed the methodology described by [PERSON] et al. [29].  
The extracted PRV features were subsequently concatenated with vision-based features, resulting in a combined feature matrix of shape \(31\times 250\), where 31 denotes the number of feature channels and 250 the number of time steps. Principal Component Analysis (PCA) was then applied row-wise for normalization, using parameters computed on the training set.