esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This methodology effectively mitigates data paucity challenges while maintaining annotation fidelity through intelligent style-transfer mechanisms. Crucially, latent space manipulations within foundation models facilitate cross-domain adaptation across diverse geographical regions and seasonal variations [46], thereby addressing the generalisation constraints prevalent in conventional CV systems. For practical deployment, GenAI can augment knowledge distillation methodologies to transform complex vision architectures into computationally lean models deployable on edge computing platforms [47; 48; 49], thereby systematically addressing the operational limitations inherent to rural agricultural infrastructure. Furthermore, sophisticated simulation environments, powered by generative techniques, are leveraged to create targeted training datasets that systematically include challenging scenarios like occlusions and variable crop geometries, essential for robust autonomous harvesting systems. This approach yields two critical advancements: (1) it substantially enhances the capability to detect objects even when they are occluded, and (2) it significantly reduces the need for field datasets that are laborious to collect. These outcomes collectively underscore the transformative potential of GenAI in enabling robust CV solutions for precision agriculture. Building upon this transformative potential, this survey provides a comprehensive examination of GenAI's burgeoning role in revolutionising CV for smart agriculture. We chart the evolution from established CV techniques to cutting-edge GenAI-driven solutions, elucidating how these novel approaches address persistent challenges like data scarcity, environmental variability, and the need for adaptable models. The primary achievements of this review are threefold: firstly, it offers a structured analysis of how GenAI, particularly through diffusion models and GANs, facilitates the generation of diverse, high-fidelity synthetic agricultural data, thereby enhancing model training, resilience, and cross-domain adaptability. Secondly, it presents an extensive overview of state-of-the-art applications where GenAI-augmented CV is driving innovation, spanning crop health monitoring, autonomous harvesting, precision field management, livestock and aquaculture surveillance, and agricultural product quality control. Finally, this survey critically assesses the remaining operational and ethical challenges while outlining promising future research directions, providing a holistic perspective on the ongoing integration of GenAI into the fabric of Agriculture 4.0. Figure 1 provides a visual summary of the methodological spectrum from traditional CV to GenAI, highlighting key applications across smart agriculture domains.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model
### PPO Algorithm
 \tag{32}\]  
where \(\delta_{t}=r_{t}+\gamma V(s_{t+1})-V(s_{t})\) is the TD error, \(\lambda\) is the GAE parameter, and \(\gamma\) is the discount factor. In this research, we use the PPO algorithm to solve the discrete-time Markov decision process proposed above, thereby implementing a DRL-based dynamic SFC orchestration algorithm for LEO satellite networks. According to the modeling method described above, at the beginning of each time slot, the algorithm receives several user service function chain deployment requests while updating the underlying physical network state, including the current network topology state and the network topology states for several future time slots. At this point, the system will first calculate the status of all deployed SFCs based on the latest information, determining which SFCs require readjustment due to constraint violations caused by these changes. After that, the algorithm calculates readjustment actions according to the PPO algorithm's training process and updates network parameters based on readjustment results. Once all readjustment requests are completed, the system begins processing arrived user service function chain deployment requests, with the algorithm calculating deployment actions according to the PPO algorithm's training process and updating the network parameters based on deployment results. For any time t, the action space A can be represented as \(A=\{a_{t}|a_{t}=(X_{t},Y_{t},Z_{t})\}\). For a large LEO satellite constellation network, the number of possible physical links formed by inter-satellite links between any two satellites is exponential to the number of nodes. Therefore, for each sub-action decision \(z_{t}\in Z_{t}\), its possible action space is also enormous, and multiple virtual link mapping actions need decisions for each SFC. If the above action space \(Z_{t}\) is fully incorporated into the agent's decision action space, the complexity of this space would be unacceptable. In a service function chain in-orbit service orchestration scenario, we notice that most deployment scheme routing solutions tend to be distributed among the K-shortest paths subset between SFC input and output nodes, while deployment schemes outside this range are mostly discarded due to unacceptable delays caused by excessive link distances [35]. Therefore, to solve the problem of oversized action space, we reduce the action space to \(A=\{a_{t}|a_{t}=(X_{t},Y_{t})\}\), where \(Y_{t}\) is the set of K-shortest paths between SFC input and output nodes ranked by delay, and \(X_{t}\) is the set of physical nodes along these routing paths. For action \(a_{t}\), the agent will simultaneously decide sub-actions \(x_{t}\in X_{t}\) and \(y_{t}\in Y_{t}\). The pseudocode of the algorithm is presented in Algorithm 1, and the flowchart in Figure 1 illustrates the algorithmic workflow. Figure 1: Flowchart of DRL-based dynamic SFC orchestration algorithm for LEO satellite networks.
## Materials and Methods  
### Study Site  
Lake Sevan (40\({}^{\circ}\)23\({}^{\prime}\)N, 45\({}^{\circ}\)21\({}^{\prime}\)E) is a high-altitude lake located approximately 1900 m above sea level in the Gegharkunik province of Armenia. It is one of the largest lakes in the entire South Caucasus region and one of the largest freshwater high-mountain lakes in Eurasia with a maximum length and width of approximately 70 km and 50 km, respectively. Morphologically, Lake Sevan is divided into two basins, the Major and Minor Sevan, which differ in their time of formation, origin, and average depth. According to data from 1 January 2017 [32], the total lake surface area is 1278.6 km\({}^{2}\) with a mean depth above 26 m. Surface areas of deeper Minor Sevan and a shallower Major Sevan are 338.6 and 950.0 km\({}^{2}\), respectively [32]. Lake Sevan drainage basin now covers 4721 km\({}^{2}\)[42]. The lake is fed by 28 rivers and drained by the Hrazdan River, which originates at the northwest extremity of the lake. The main inflow sources of the lake are river waters, water inflow from the Vorotan-Arpa and Arpa-Sevan tunnels, and groundwater inflow. Main outflow components are the Hrazdan River, evaporation from the lake surface, water discharges, and groundwater outflow. According to the Ministry of Emergency Situations of the Republic of Armenia, the water level altitude was 1900.43 m by 1 January 2020, and had increased to 1900.73 m by 11 August 2020 [33].  
We determined the coastal zone boundaries according to the natural shoreline of Lake Sevan, as reconstructed from historical small-scales bathymetric maps from 1929, i.e., before the long and dramatic human-induced drop of the lake water level. Several key sites in this area that display the most pronounced changes in shoreline since the 1930s reflect diversity of coastal types (in terms of their geomorphometry and prevailing processes) and current land-use patterns were chosen for repeated UAV-based surveys (Figure 2). It should be noted that the number of sites was limited due to legislative restrictions concerning UAV surveys in some areas (the mouth of Martuni River).
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 3 Security Measures for VSS
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Attacks** & \begin{tabular}{c} **Mechanisms that Help to** \\ **Prevent Attacks** \\ \end{tabular} & **Description** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Spyware and malware \\ \end{tabular} } & \multirow{4}{*}{Anti-virus solution} & \begin{tabular}{c} Commercially available anti-virus solutions can scan \\ system and cache files, messages and emails as well as \\ website URLs. They can also prevent spyware and \\ malware by preventing access to a suspected \\ phishing website. \\ \end{tabular} \\ \hline IP spoofing & Firewall & \begin{tabular}{c} Firewall stops unauthorized inward or outward \\ connections. It can prevent various types of network \\ attacks by blocking unauthenticated wireless networks. \\ \end{tabular} \\ \hline Man-in-the-middle, visual layer attacks. & Secure API & \begin{tabular}{c} Cryptography-based API can provide various secure \\ functionalities to the application developer. \\ \end{tabular} \\ \hline Malware, DoS, covert channel attacks. & Access control & \begin{tabular}{c} Access control not only limits the access of processes but \\ also limits the user to resources and/or services, thus \\ limiting risk from malicious applications. \\ \end{tabular} \\ \hline Covert channel attacks, DoS, spyware, & Authentication & \begin{tabular}{c} The user can logn to a device only after being \\ authenticated to prevent its unauthorized use. \\ \end{tabular} \\ \hline Covert channel attack, spyware. & Spam filter & \begin{tabular}{c} Spam filtering applications block unwanted calls, \\ messages and emails. They can also prevent spam from \\ reaching to the user’s inbox. \\ \end{tabular} \\ \hline Covert channel attacks, malware attacks, & Pre-testing & \
\begin{tabular}{c} Pre-testing can ensure application security by \\ preventing malware. It can also validate applications \\ and give proper access authority to \\ application developers. \\ \end{tabular} \\ \hline All types of attacks. & Regular update &
\begin{tabular}{c} Smartphone applications can prevent various types of \\ attacks by regularly updating from various \\ manufacturer-provided platforms. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of security mechanisms. All latest software/firmware updates must be promptly applied as and when they are released by the manufacturers. * If a cloud-based system is deployed, only authentic verified vendors should be chosen. * One must be aware of all the latest cyber security standards being adopted as well as study all recent attacks to gain a better understanding of new techniques employed by attackers.
# Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
## 1 Introduction
### Background
Although cultured cells and organoids can be used as a substitute, these also require specialized care and maintenance [2]. Conversely, non-biological phantoms and simulated tissue have been used to accurately model organic tissues [12; 13; 14; 16]. Brain phantoms have been produced from a range of materials, including solids and liquids [14; 15]. To accurately model the effects of applied electrical stimulation, the brain phantom must contain electrolytic compounds comparable to the original organ [10]. Although computer simulations have been used for estimates, phantom tissue enables experimental results without requiring human or animal data [10; 14; 34; 35]. Many materials have been used as brain phantoms, with variable costs and preparation times. Different studies use different metrics, making direct comparisons more difficult [12; 13; 14; 16]. Thus, directly comparing different electrical stimulation modalities on the same phantom becomes challenging. A wide range of electrical stimulation devices are marketed and sold as non-medical therapeutic devices [33; 36]. The efficacy of these \"off-label\" devices is controversial in the research literature, even compared to medically approved devices [9]. The range of manufacturers and relatively low cost of electrical stimulation devices have resulted in a range of products, from hobbyist kits to high-quality devices [6]. The potential reliability and safety of these devices could be more easily evaluated with a phantom [13; 16]. Different circumstances and experimental research often require the rapid, iterative optimization of stimulation for specific circumstances. Having a consistent performance metric, and readily prepared phantom and measurement method could streamline the optimization of the stimulation parameters. A low-cost, low-complexity phantom could enable the affordable and economical testing of consumer electrical neuromodulation devices, without the risk of hurting animal or human subjects, while economizing on time and money.
In this way, GC\(\times\)GC-MS allows detection and identification of a much larger number of volatile compounds compared to conventional GC-MS [23]. Regardless of the existing great potential, only a few studies have utilized GC\(\times\)GC to investigate wine volatile aroma profiles, while studies which used GC\(\times\)GC for varietal characterization and differentiation were extremely rare. Several authors reported more or less detailed GC\(\times\)GC volatile aroma profiles of particular monovarietal wines, such as Cabernet Sauvignon [24], Sauvignon Blanc [25], Shiraz [9; 26] or Syrah [12], Pinotage [21], Chardonnay [27], and Verdicchio [28], but none of them directly compared them to or differentiated them from other monovarietal wines of similar typology. In this way, despite detailed profiles determined in some cases, it still remained unknown which compounds and in which amounts are typical for a given variety and whether they could differentiate it from other monovarietal wines. The only two studies which utilized GC\(\times\)GC and succeeded in differentiating several monovarietal wines did not report actual concentrations of all the identified volatile compounds [20; 29]. The aim of this study was to utilize the potential of two-dimensional gas chromatography with time-of-flight mass spectrometry (GC\(\times\)GC-TOF-MS) technique, in combination with headspace solid-phase microextraction (HS-SPME) and multivariate statistical tools, as a more efficient approach to characterize and differentiate monovarietal white wines based on their volatile aroma compound composition. Profiling by GC\(\times\)GC was combined with conventional GC-MS analysis of major wine volatile compounds to obtain more comprehensive aroma profiles. Special attention was devoted to terpenes, often highlighted as key parietal markers in wine. The approach was applied to characterize and differentiate Croatian wines made from indigenous grape varieties, with each variety represented by a rather heterogeneous group of wines with respect to geographical microlocation and agroecological conditions, viticultural practices, harvest date, and grape processing and wine production parameters. It was expected that GC\(\times\)GC-TOF-MS would be extremely effective in providing novel in-depth information for efficient white wine varied differentiation.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 3 Aquatic Platform
### Functionalities  
The main purpose of this platform is to sense and process data from the sensors in the USVs and Buoys and send it through the DTS to the server, which is accessible to aquaculture workers and useful for researchers to improve or maintain the network and aquaculture performance. With the data collected, this platform is used for data analysis, and in an enterprise context, it can be used to prevent sub-optimal welfare and mortality or unwanted events, or even to control the automatic feeding portions of each tank.  
In terms of navigation, the platform is capable of completing a mission or task as a team while maintaining connectivity between each USV. It allows the implementation of machine learning algorithms to improve the team's organization. Due to the connectivity to a DTS, this platform can assign new missions at any time and from everywhere to each USV via the MC.  
Moreover, the platform is connected to a mobile App, that can be used to collect data from the device and alerts the user for a specific trigger inserted. The mobile app also allows for the visualization of the data collected.  
The Web dashboard allows for a map visualization of the data, showing every location where data was collected and allowing the user to watch the most recent values. It also contains graphs that show the evolution over time of any particular sensor. Using the dashboard, it is possible to define threshold values that, when toppled, a notification is sent to the dashboard. Mission routes are defined also using the dashboard, with the locations defined using a map. After being created, they can be started and also stopped, if they are still in queue.  
This platform is also scalable because it can admit a change in the number of devices without changing the code or configuration files and it is robust as it can recover and still complete the mission when a vehicle malfunctions or shuts down.
## 3 Methodology
### Data Preprocessing
#### 3.4.1 Feature Engineering  
In the context of this study, feature engineering is an essential technique for gaining greater insight. The dataset includes a wide range of variables, but it is crucial to enhance these features through clever combinations and adjustments. These engineered variables are meticulously constructed to reveal complex correlations by utilizing domain knowledge relevant to football. This method greatly enhances the depth of analysis by enabling the discovery of hidden patterns and relationships.  
1. **Player Workload:** The 'player workload' variable classifies football players as 'Rare Starter', 'Average Starter', and 'Frequent Starter', depending on how frequently they start matches. This variable provides insights into workload differences and their consequences on injuries and performance. These cut-off points were derived from a review of historical data and existing literature on player health and performance, providing a structured approach to monitor and manage player workload effectively. Number of matches started: 1. Less than 10 \(\Rightarrow\) Rare starter 2. More than 10 but less than or equal to 25 \(\Rightarrow\) Average starter 3. More than 25 \(\Rightarrow\) Frequent starter 2. **Player Usage:** Based on the number of minutes played, the 'Player Usage' variable classifies football players as 'Squad Rotation Players', 'Sporadic Players', or 'Crucial Players'. This emphasizes the importance of specific players to their teams and provides information about their usage's impact on performance, fitness, and injury risk. Number of minutes played: * Less than 1140 min \(\Rightarrow\) Sporadic Player * Between 1140 and 2280 (inclusively) minutes \(\Rightarrow\) Squad Rotation Player * More than 2280 min \(\Rightarrow\) Crucial Player 3. **Age Category:** Based on age, the 'Age Category' variable classifies football players as 'Youngster', 'Prime', or 'Vetearan'. This sheds light on the impact of age on career duration, injury risk, and performance. It aids players in understanding how age and experience affect their roles and output. Age falling between: * 16 and 23\(\Rightarrow\) Youngster * 24 and 31 \(\Rightarrow\) Prime * 32 and above \(\Rightarrow\) Vetera 4. **Average Minutes per Match** By determining a player's average minutes played per match, the 'Average Minutes per Match' variable offers a fair assessment of player playing time. This helps in determining the consistency of player participation in games, which may affect injury risk and performance. It serves as a starting point when examining how player workload, injury, and performance are related. Average Min/Match = (Minutes Played)/(Matches Played)
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Parametric Analysis on G-Shaped Patch Antenna
#### 3.6.2 Effect of Variation of V\({}_{\mathrm{W}}\)  
Adjusting the feed width of the G-shaped patch antenna to 5, 6, and 7 mm indicated that 6 mm provides optimal impedance matching and maximum bandwidth. When Vw deviates from 6 mm, the antenna exhibits higher S\({}_{11}\) values, demonstrating both dual-band and narrow frequency range (Figure 13b).  
Figure 13: S\({}_{11}\) at different dimensions of the G-shaped patch antenna: (**a**) Lt, (**b**) Vw, and (**c**) K.
# Evaluation of Ultrasound Accuracy in Acute Appendicitisitis Diagnosis
## 3 Results  
The study included 132 patients. The majority of the patients (59.8%) were within the age range of 21-40 years. (Table 1) shows socio-demographic characteristics of the patients and accuracy of the US diagnosis. There was no significant association between the accuracy of the US diagnosis and the sex (\(p\) = 0.42) and age (\(p\) = 0.4) of the subjects.  
Only 61 of the 132 patients in the study were accurately diagnosed with the use of ultrasound. Just 44 of them were diagnosed with appendicitisis (true positives) and 17 without appendicitisis (true negatives). However, 69 patients who had positive histopathologic results received a negative US diagnosis (false negatives), and 2 patients with negative histopathologic findings had a positive US diagnosis (false positives). The analysis revealed that the sensitivity of the US was 38.9% (95% CI 29.9 to 48.5), the specificity was 89.5% (95% CI 66.8 to 98.7), the positive predictive values (PPV) was 95.7% (95% CI 85.3 to 98.8), and the negative predictive values (NPV) was 19.8% (95% CI 16.6 to 23.4). The overall diagnostic accuracy was 46.2% (95% CI 37.5 to 55). The features of the 2 subjects that were incorrectly diagnosed as positive (false positive) included diameter \(\geq\)7 mm (1 patient) and free fluid (1 patient), and both patients were female.  
(Table 2) shows differences in expertise (years) of sonographers and their diagnostic performances in diagnosing appendicitisis. Sensitivity and specificity were found to be not significantly different between all subgroups. A comparison performed between the true  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Patients (\%)** & **Number of Accurate Diagnosis (\%)** \\ \hline
**Sex** & & \\ Male & 48 (36.4\%) & 20 (32.8\%) \\ Female & 84 (63.6\%) & 41 (67.2\%) \\ \hline
**Age range** & & \\
1–20 & 37 (28\%) & 18 (48.6\%) \\
21–40 & 79 (59.8\%) & 36 (47.4\%) \\
41–60 & 7 (5.3\%) & 4 (50\%) \\
61–80 & 7 (5.3\%) & 1 (14.3\%) \\
81–100 & 1 (0.76\%) & 0 (0.0\%) \\ \hline \hline \end{tabular}  
No significant association between the accuracy of the US and the sex and age of the subjects.  
\end{table}
Table 1: Socio-demographic characteristics of the patients and accuracy of the ultrasound diagnosis.