esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Predicting customer churn is essential for telecommunications companies to maintain profitability. However, training models on historical models leads to performance degradation when they are applied to future conditions--a phenomenon known as concept drift. We employ a sliding-window approach that separates the training and testing time windows, creating a future-based \"true test\". Using unique real data, we show that a CatBoost classifier model trained on older data can remain relevant when new, unseen intervals are used. A key innovation of our work is the use of 40-day \"partial churn\" labels; a model trained on these labels accurately predicts 90-day churn by simply adjusting the decision threshold. Out of the six modeled scenarios, in the main realistic scenario, CatBoost retained an accuracy above 0.798 and an F1 of near 0.704, reflecting its robustness even under real-world delays and potential drift. Overall, our findings emphasize that models do not necessarily \"expire\" with time; rather, their performance varies according to when they are tested. This research underscores the importance of a truly future-based evaluation (instead of artificial splits) and offers practical guidance for earlier churn detection when facing real-world data delays.  
concept drift; customer churn prediction; gradient boosting classifier; machine learning; sliding-window approach; telecom churn; CatBoost Article
## 3 Results and Discussion  
### Relationship between the Proposed Fluorescence Index and LNC  
The chlorophyll fluorescence spectrum shows a positive correlation with LNC. Thus, the fluorescence characteristic peaks at 685 nm and 740 nm increased with the increasing of LNC. However, the fluorescence peak at 685 nm is less intensive than that at 740 nm because the reabsorption by the chlorophyll pigment at 685 nm is stronger than that at 740 nm [34]. Thus, the effect of LNC on the fluorescence signal at 740 nm is more apparent than that at 685 nm. The relationship between the fluorescence intensity at 740 nm and at 685 nm with the changing of LNC is shown in Figure 1.  
Figure 1 shows the changes of fluorescence peaks at 740 nm and 685 nm with increasing LNC. The results show that the fluorescence peak intensity at 740 nm has a linear positive correlation with fluorescence peak intensity at 685 nm with increasing LNC. \(R^{2}\) can reach up to 0.945. In addition, the slope of linear regression analysis is 1.242, which means that the relative change of fluorescence peak intensity at 740 nm is larger than that at 685 nm when the LNC increased. This shows that the intensity of the fluorescence peaks at 740 nm and 685 nm are related to LNC. Therefore, the result demonstrated that the proposed fluorescence index can be applied in the LNC monitoring. Then, the relationship between the proposed fluorescence indices calculated using Equations (2)-(4) and LNC was established and shown in Figure 2.  
Figure 1: Relationship between the fluorescence characteristics peak intensity at 740 and 685 nm with increasing leaf nitrogen concentration (LNC).  
Figure 2 demonstrates that the proposed fluorescence index exhibited a closely positive linear correlation with LNC. The \(R^{2}\) values are 0.508, 0.771, and 0.732 for fluorescence indices \(K1\), \(K2\), and \(K3\), respectively. \(K1\) demonstrates that fluorescence intensity at 740 nm increases faster than that at 685 nm with increasing LNC. \(K2\) (\(R^{2}\) = 0.771) and \(K3\) (\(R^{2}\) = 0.732) are superior to \(K1\) (\(R^{2}\) = 0.508) for the detection of a smaller change in LNC because \(K2\) and \(K3\) displayed larger slope and lower scatter than \(K1\). These results are consistent with those obtained by previous studies [35,36]. Therefore, the proposed fluorescence index can be applied in LNC estimation.
## 3 Results and Discussion
### pH
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Source** & **Sum of Squares** & **DF** & **Mean Square** & **_F-Value_** & **_p-Value_** & \\ \hline Model & 3.20 & 1 & 3.20 & 1837.44 & \textless{}0.0001 & significant \\ X\({}_{3}\) & 3.20 & 1 & 3.20 & 1837.44 & \textless{}0.0001 & \\ Residual & 0.0226 & 13 & 0.0017 & & & \\ \hline Lack of Fit & 0.0218 & 11 & 0.0020 & 4.96 & 0.1795 & not significant \\ \hline Pure Error & 0.0008 & 2 & 0.0004 & & & \\ \hline Total & 3.22 & 14 & & & & \\ \hline \(R^{2}\) = 0.9930, adj \(R^{2}\) = 0.9924, CV = 0.95, Adeq Precision 83.00 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Effect of the independent variables on the dependent variables and statistical significance for \(pH_{1}\) of extracts obtained in vessels with a diameter of 35 mm. Figure 5: Dependence of pH on time, ultrasound amplitude, and solvent concentration for extracts obtained in vessels with diameters of (**a**) 35 mm, (**b**) 25 mm. CV values (0.95% and 0.94%) suggest minimal deviations between the experimental and predicted values, indicating high experimental reliability and precision. A regression analysis was employed to develop two models (Equations (9) and (10)) that describe the effect of input variables on the pH value. \[pH_{1}=3.118+0.021X_{3} \tag{9}\]  
\[pH_{2}=3.191+0.019X_{3} \tag{10}\]  
The pH value of the obtained extracts ranges from 3.72 to 5.09 for the vessel with a diameter of 35 mm and up to 4.97 for the vessel with a diameter of 25 mm depending on the other conditions of the experiment. The available literature reports do not contain data on the pH value of rowan fruit extracts.
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
## 3 Results
### Future of AV Deployment in Rural Areas
#### 3.3.4 Necessary Technological Modifications  
Technological advancements play a crucial role in overcoming AV localization, detection, and navigation challenges in rural areas. To address issues like limited GNSS signals under tree canopies, outdated point cloud maps, and high environmental variability, [PERSON] et al. (2023) [65] introduced a sensor fusion-based localization framework integrating data from GNSS, LiDAR, INS, and vehicle odometry. A loosely coupled extended Kalman filter and weighted gate mechanism improved state estimation, reducing lateral deviations by 71% and ensuring safe localization in complex rural settings. Field tests validated its reliability in maintaining accurate positioning despite GNSS disruptions and outdated maps. While cloud networking and edge computing have enhanced AV functionality in urban areas, rural deployments face challenges due to poor road conditions and limited telecommunications infrastructure for V2I connectivity. Connectivity improvements, including V2I and V2V systems paired with machine learning models, can address challenges such as tight curves and poor lane visibility [43]. [PERSON] et al. (2024) [66] tackled rural road detection using a lightweight MobileSAM model optimized with Apache TVM on a Jetson Nano. Field tests suggest future optimizations could improve speed and accuracy, enhancing AV navigation in underdeveloped regions. Field test-based studies emphasized that enhanced sensors, such as LiDAR, radar, and cameras, are critical for detecting obstacles like farm equipment, wildlife, and unmarked roads, especially under low-visibility conditions such as fog, snow, and shadows [48; 76; 93]. HD maps play a key role in navigating areas without traditional lane markings, while virtual speed limits and adaptive throttle adjustments enhance safety on curves and blind hills. For gravel roads, AVs shift closer to the center to avoid loose gravel, and a \"nudge\" feature adjusts positioning for oncoming traffic [74]. Advanced traction control and adaptive suspension improve stability on uneven surfaces, while decision-making algorithms handle dynamic responses to intersections, stops, and yield-controlled areas [74]. Enhanced parking automation uses Open Space Planner software for angle parking and redefines parallel parking spaces as short additional lanes to accommodate AVs with larger turning radii [77]. Battery efficiency optimization through advanced capacity and thermal management systems ensures extended operational hours during high-demand periods, such as air conditioning usage [45; 93]. Weatherproofing and obstacle detection upgrades further enhance performance in diverse conditions.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### Multi-Objective Function Optimization Problem  
The method is characterized as a multi-objective function optimization problem and aims to reach the six objective functions described in this subsection. The first objective function aims to maximize the _LRP_ value obtained for a connection between a smart meter \(v_{i}\) and a key device position \(k_{j}\) (1):  
\[\text{maximize }LRP_{v_{i}\to k_{j}},\ \ \forall\ \ v_{i}\in\{\mathcal{V}\},\ k_{j}\in\{ \mathcal{K}\} \tag{1}\]  
where \(\mathcal{V}=\{v_{1},\ldots,v_{u}\}\) is the set of smart meters and \(\mathcal{K}\) is the set of key devices positioned. To reduce the installation costs, the second objective function aims to minimize the number of key devices positioned (2).  
\[\text{minimize }|\mathcal{K}| \tag{2}\]  
where \(\mathcal{K}\) will be positioned in a subset of the set of candidate positions \(\mathcal{C}\ =\ \{c_{1},\ldots,c_{z}\}\). Considering the key devices positioned, the third objective function aims to maximize the number of key devices installed in poles with distribution automation, _DA_, equipment (3):  
\[\text{maximize }|\mathcal{K}_{DA}| \tag{3}\]  
where \(\mathcal{K}_{DA}\subseteq\mathcal{K}\). Positioning a key device on poles with DA minimizes the setup cost, as it ensures that the device will be installed at a point where the backhaul network has already been configured. The fourth objective function maximizes the number of smart meters connected to a key device to construct the set of connected SMs and to optimize the use of each key device (4):  
\[\text{maximize }|\mathcal{V}_{con_{k_{j}}}| \tag{4}\]  
where \(\mathcal{V}_{con_{k_{j}}}\) is equal to the number of smart meters \(v_{i}\in\{\mathcal{V}\}\) connected to a key device \(k_{j}\in\{\mathcal{K}\}\). The fifth objective function maximizes the average _LRP_ value for the solution (5):  
\[\text{maximize }\left(\frac{1}{n}\sum_{i=1}^{n}LRP_{v_{i}\to k_{j}} \right),\ \ \forall\ \ v_{i}\in\{\mathcal{V}\},\ k_{j}\in\{\mathcal{K}\} \tag{5}\]  
where \(n\) is the number of connected smart meters. The last objective function minimizes the percentage of unconnected smart meters, \(P_{u}\), evaluated at each iteration of the method by the stopping criteria (6). The maximum percentage must be adjusted according to project requirements, and it is set as a parameter for the method execution:  
\[\text{minimize }P_{u},\ \ \forall\ \ v_{i}\in\{\mathcal{V}\},\ k_{j}\in\{ \mathcal{K}\} \tag{6}\]  
where \(P_{u}\leq P_{u}^{max}\), and \(P_{u}^{max}\) represents the stopping criteria.  
The placement of routers and gateways is considered an NP-hard problem, characterized by multiple objective functions, demanding complex resolution. The use of an AI-driven heuristic approach (as proposed by this study) aims to reduce its complexity and reach a solution for the problem in a reasonable processing time. Therefore, it is useful in planning large-scale scenarios.
## 3 Results and Discussions
In this work, one DOF of the exoskeleton transmitted movement to the distal and middle phalanx as this move is naturally due to the tendons' anatomy and attachment points. Currently, there is a simple interface that allows the finger to move from four positions: claw, proximal pointing down, extended, and flexed finger. Nevertheless, the rehabilitation exercises are not limited to these positions, and more complex rehabilitation routines could be implemented in the system thanks to its flexibility in the future. In Figure 7a, the variation in the patients' work areas according to the parametric model can be appreciated; this means that for each patient, the ROM is better suited individually, which is a great advantage because it allows for a wider variety of rehabilitation compared to fixed rehabilitation gloves. While there are systems with three DOFs for each finger, such as the one presented in [35], or a more comprehensive work area, they have the disadvantage of often being bulky and adding difficulty in design, manufacture, and control, and also increasing the cost of the system. Moreover, the difference in the work area is not too significant. An example of the work area that may be lost by using 2 DOF compared to a 3 DOF mechanism is shown in Figure 7b. For this comparison, an index finger of 73 mm in length is used as a reference and the ROM for each articulation was determined using the average ROM stated in [28]. It is apparent from Figure 7b that, in some parts, the 3 DOF model has an advantage over the mechanism of this work. Nevertheless, because this mechanism has more ROM in the MCP and DIP joints, it also has some work areas that the 3 DOF model cannot reach. The green dotted line in Figure 7b represents the only trajectory that a 1 DOF finger rehabilitation mechanism could follow; this is a very limiting factor on these kinds of devices that trade the simplicity of the mechanism over the versatility and variety of rehabilitation exercises. Figure 7: **(a)** Work area comparison between subjects. **(b)** Average work area with 3 DOF vs. work area of this mechanism using an index finger of 73 mm.
###### Abstract  
The importance of accurately monitoring rangeland degradation dynamics over decades is increasing in Linxia rangeland, the birthplace of the Yellow River in China. Since 2000, the Chinese government has implemented the \"Grain for Green\" program and enforced a grazing ban in Gansu province, one of the most degraded provinces, to mitigate the problem of rangeland degradation. The effects of these policies are controversial and have become a topic of public concern. In this study, a grading system was established for the estimation of Linxia rangeland degradation. Degrees of rangeland degradation were interpreted and the spatio-temporal dynamics of the degraded rangeland through several study periods were mapped and monitored using the Linear Spectral Mixture Analysis method on Landsat Thematic Mapper (TM)/ETM+ (Enhanced Thematic Mapper Plus) images for the years of 1996, 2001, 2006, and 2011. The results demonstrated that the time around the year 2001 appeared to be a turning point of the rangeland degradation reversion course, as the rangeland degradation reversed significantly since 2001. From 1996 to 2001, the total degraded area in Linxia rangeland increased from 2922.01 km\({}^{2}\) to 3048.48 km\({}^{2}\) (+4.33%), and decreased by 4.54% to 2909.97 km\({}^{2}\) in 2011; the non-degraded rangeland gradually increased from 602.74 km\({}^{2}\) to 710.01 km\({}^{2}\), an increase of 17.80%. Degraded rangeland vegetation was restored significantly during 2001-2011: the area of slightly degraded rangeland increased by 3.71% and 3.83% annually during 2001-2006 and 2006-2011 intervals, respectively, and the area of moderately and severely degraded rangeland decreased annually by 4.77% and 2.41% from 2001 to 2006, and 4.58% and 0.81% during 2006-2011, respectively. These results indicated that the \"Grain for Green\" program and grazing ban policy, together with other ecological impacting factors, helped reverse the rangeland degradation and promote the rehabilitation of rangeland vegetation.  
Keywords: rangeland degradation; spectral mixture analysis; \"Grain for Green\" program; grazing ban policy; Linxia rangeland +
Footnote †: journal: Applied Soft. 2017
# Evaluation of Local Security Event Management System vs. Standard Antivirus Software
## 2 Review and Background Knowledge
### Obfuscation Techniques  
Years ago, a small modification in a piece of malware was able to circumvent antivirus software, so for each virus, we had several versions as if they were different developments (only manual analysis was able to find a common origin to assign a suitable virus name). However, the evolution of antivirus software with heuristics and AI, has been very effective in detecting variants of existing malware. The remaining challenge is to detect malware that uses obfuscation techniques, which is the focus of this research.  
To avoid detection, several techniques have been developed. There are diverse techniques currently available to any attacker that can be applied individually or in combination to evade the defenses of any organization.  
**Self-encryption**: The use of encryption algorithms or functions that contain a key included in the body of the malware that allows it to perform the encryption or decryption function in an automated manner [32].  
**Polymorphism**: It uses a polymorphic engine to mutate itself and keep its original code intact [33; 34; 35].  
**Metamorphism**: It can transform itself according to its capabilities to translate, edit, and rewrite its own code each time it infects a computer system [36; 37].  
**Armouring**: It is programmed to prevent any attempt at analysis or disassembly, making it impossible to know the original code [38].  
**Stealth**: It completely or partially hides its presence in the system by intercepting requests of the operating system to interact with infected objects (boot sectors, file system elements, memory, etc.) [39; 40].  
**Covert channels**: It uses unauthorized communication channels and manipulates them in an unconventional way to transmit information undetected by anyone other than the entities operating the covert channel [41; 42; 43].  
These obfuscation techniques make detection process very hard because the antivirus software is not able to read and analyze the code as in the case of any other static file. Moreover, due to the internal randomness of the obfuscation process, it is not possible to create signatures of the encrypted code because each sample of the same malware is different. In this paper, we propose to detect such kind of hidden malware by analyzing its behavior based on the chain of actions executed. Very recently evasion techniques have been applied to ransomware as well to avoid being detected while running. As an example, LockFile [44] performs file encryption by mapping the file to memory to avoid interaction with the hard drive during the process.
## 1 Introduction
Considering various scenarios of major power outages, a parallel restoration strategy utilizing the A* algorithm was proposed in [24], which can provide reliable power supply to consumers. While GFM-ESSs have the ability to provide inertia to a power system, the configuration cost of GFM-ESSs is notably higher compared with grid-following energy storage systems (GFL-ESSs). Furthermore, GFM-ESSs share similar functions with GFL-ESSs in terms of power balancing and peak load regulation. Most of the existing studies focus on the capacity planning or the optimization of control strategies for a single type of ESS, making it a critical issue to study the capacity ratio between GFM-ESSs and GFL-ESSs. Inspired by the above-mentioned research, a hybrid optimization method is proposed for the coupled placement and capacity allocation of GFM-ESS and GFL-ESS construction. This method, which is applicable to the field of power system planning, addresses the problem of utilizing the minimum amount of GFM-ESSs to solve the issue of insufficient inertia support capability in power systems, thereby enabling the stable operation of a renewable energy power system. The main advancements are delineated as follows:  
Based on the network equations of a power system, this study presents a method for evaluating the inertia support capability of generators in the power system. A virtual inertia emulation framework is subsequently formulated for GFM-ESSs, explicitly integratingfrequency dynamics characteristics. Taking the integration of RESs into account, the frequency relationship between the generators and the network nodes is deduced, and according to the frequency relationship, the inertia distribution characteristics of the power system are characterized by using the node frequency regulation coefficient. Furthermore, a method for the optimization configuration of ESSs considering the inertia distribution characteristics of the power system is proposed. This method, which can quantify the configuration requirements of GFM-ESSs, aims to enhance the inertia support capacity of generator nodes and reduce the operation cost of the power system. In summary, the major contributions of this study are mainly reflected in two aspects: (1) the method for evaluating the inertia distribution characteristics considering the grid structure of a power system and (2) an optimization configuration method for site selection and capacity determination of GFL-ESSs and GFM-ESSs considering inertia distribution characteristics. The remainder of this study is structured as follows: Section 2 assesses the inertia support capability of different generator nodes in the power system using network equations and introduces a method to characterize the distribution of inertia resources in the system. Section 3 evaluates the demand for GFM-ESSs in the power system and designs an optimization configuration method for ESSs based on multiple operation scenarios. Section 4 verifies the effectiveness of the evaluation method and the proposed configuration method on the IEEE 9-bus system and IEEE 39-bus system, respectively. Section 5 provides a summary of this study.