esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 3 System Model  
### Network Model  
An in-hospital real-time healthcare patient monitoring network is assumed to evaluate the performance of the proposed methods. A patient monitoring block with 15 patients is considered. Each patient is assumed to be a WBAN with five sensor nodes and a body controller. The sensor nodes collect the body vital signs such as blood glucose, blood pressure, body temperature, ECG and EEG. The measured data are given to the body controller which is deployed on the human body. The patient monitoring system consists of 15 body controllers which form the tree structure for the proposed model. The body controller transfers the data to the fog node (central coordinator) using the proposed algorithms. The fog node assigns priority to the data and sends the prioritized data to the physician through the cloud server to meet the emergency situations. The data processing and computation are done within the fog node and only the consolidated report is sent to the physician through the cloud server. The local server in the proposed network is called here as the cloud server. The cloud server assigned here is mainly to connect to the external network. The fog node avoids congestion in the network, reduces the computation time by performing all operations in the fog node itself. It also minimizes the storage size and redundant data package (only important data is sent to the server), and decreases the time delay between the source and the destination.  
The designed MCPS algorithm is used to transfer data towards the central coordinator. The developed fuzzy based dynamic time slot allocation is utilized to improve the reliability and network lifetime.
###### Abstract  
Within the scope of the work, the possible use of fused deposition modeling (FDM) technology in executing rapid prototypes of cutting tools for aluminum sheets was systematically studied. Relevant investigations have thus far mainly concentrated on tools for the 3D printing of bent and deep-drawn pieces, yet the implementation of FDM tools in cutting has been insufficiently covered. This study aims to determine the characteristics of FDM cutting tools, such as wear and tear, dimensional stability, and cutting efficiency. Various tool designs were tested under different wall thicknesses and orientations with respect to the feed of Al99.5 sheets with thicknesses of 0.22 mm and 0.3 mm. According to the results, in the best case, three-dimensional printed PLA tools performed six cuts with no burrs and an acceptable wear level due to the IT tolerances (IT9 and IT10). Tools with thicker walls and more appropriate orientations were found to be more robust. However, some designs failed when subjected to greater loads, revealing a deficiency in some of the strength properties of the material. These observations suggest that it is possible to create 3D printed tools for modeling and small-scale production at considerably cheaper and faster rates than conventional methods. Future work will integrate advanced materials and designs to enhance tool performance, further solidifying FDM as a transformative approach in industrial tool manufacturing. With this research, the authors wanted to demonstrate that FDM technology can also be used to produce a classic sheet cut, which, of course, is still of great importance for prototyping or setting up production processes. This research demonstrated that FDM printing can play a role in this area.  
3D printing; PLA; FDM; rapid prototype; 3D scanning; optimization; part-off die; cut-off die; GOM ATOS; sheet metal +
Footnote †: journal: Applied sciences
# Model Test on Grouting Properties of Alluvial Filler Soil
## 5 Theoretical Analysis of Grouting Volume and Its Quantitative Method
### Theoretical Grouting Volume
#### 5.2.1 Theoretical Grouting Volume  
(1) According to the soil condition at the test site, the scanning route for geo-radar was drawn up, and the parameters of GPR scanning were determined. Then the geo-radar was scanned according to the proposed scanning route to the soil before grouting, as shown in Figure 7.  
Figure 7: Geological radar scanning before grouting.  
(2) When the grouting was completed after seven days, the geological radar test was carried out to scan the grouted soil.  
(3) By analyzing the data from the geological radar test results, the volume of the soil grouting filling area was obtained. The radar images (line 4) are before and after grouting.  
The grouting zone was preliminarily determined by the comparison and analysis of geo-radar images before and after grouting by the Geo-radar test. The geological radar scanning results showed that the grouting zone was roughly a rectangular region with its depth, length, and width equaling 2 m, 3.25 m, and 3.25 m. The total volume of the grouting area was \(V=21.125\) m\({}^{3}\).
## 7 Results and Discussion
### Impact of Device Heterogeneity on FL Performance
#### 7.1.1 Impact on Accuracy Across Datasets  
Across all datasets, as shown in Figures 2-5, fully heterogeneous (3-type) pools consistently delivered superior and more stable accuracy compared to homogeneous (1-type) and semi-heterogeneous (2-type) pools.  
* Figure 2: while all pools performed well, homogeneous smartphone-only pools reached 91% accuracy more quickly. However, the fully heterogeneous 3-type pool achieved a similar result while maintaining a smoother convergence path due to the diverse contributions from different devices.
* Figure 3: the 2-type and 3-type heterogeneous pools outperformed the homogeneous 1-type pools. IoT-only and edge-only pools struggled to reach competitive accuracy, with the 3-type pool maintaining steady gains through the mid and late rounds.
* Figure 4: the performance gap widened, with homogeneous 1-type pools overfitting quickly or slowing down, particularly the smartphone-only pools. In contrast, the 3-type heterogeneous pools preserved accuracy and reached 88% with less fluctuation.
* Figure 5: this more complex task, requiring deeper models and better generalization, showed that homogeneous 1-type pools plateaued below 75%. The 3-type heterogeneous pools, however, steadily climbed to 78%, highlighting the importance of device diversity as model complexity increases.  
As the datasets become more abstract and challenging, the advantage of including multiple device types becomes increasingly evident. Heterogeneous participation leads to better generalization, helping to avoid bias toward any one class of local data.
# LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
## 2 Related Works
### Traditional and Deep Learning-Based Log Anomaly Detection  
Early approaches to log anomaly detection primarily relied on rule-based systems and supervised machine learning models, such as Support Vector Machines (SVM), Random Forest (RF), and Decision Trees (DT), which used structured fields like timestamps, IP addresses, and port numbers as features [28; 29; 30]. These methods showed promising results when sufficient labeled data were available, and log structures remained consistent. However, they often struggled with generalization across heterogeneous environments and were brittle under unseen log formats [7; 8].  
To address the limitations of feature engineering and static modeling, researchers explored unsupervised and semi-supervised techniques--including k-means clustering, DBSCAN, Isolation Forest (IF), and One-Class SVM (OCSVM)--which required no labels and aimed to model normal behavior for outlier detection [31; 32; 33; 34]. Among these, IF gained traction for its ability to detect rare events by recursively partitioning feature space.  
As log data grew in complexity and volume, deep learning methods gained popularity for their ability to learn patterns directly from raw sequences.  
Autoencoders were applied to learn compact representations and detect anomalies based on reconstruction errors [35].  
Models like DeepLog [15] and LogAnomaly [11] leveraged LSTM-based sequence modeling to capture temporal patterns across log events, enabling an improved detection of anomalous behavior in sequential logs. These models performed well on benchmark datasets but were often sensitive to minor log structure changes and lacked interpretability.  
While these techniques improved detection performance, they lacked robustness to evolving log schemas and struggled with semantic understanding, especially when logs were unstructured or generated from varied sources such as Endpoint Detection and Response (EDR) agents or application-specific tools [19; 22]. This ultimately motivated the shift toward transformer-based and LLM-enhanced approaches, which are discussed in the following section.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods
### Overview of the Study Area
#### 3.1.1 Terrain Topography  
The southeastern part of the 10 disaster-hit counties (cities) is adjacent to the Chengdu Plain, which is known as the \"heavenly country\", and has a flat topography with little undulation, making it an important crop-growing area in China since ancient times. The western part of the study area has peaks such as Mount Siguniang and Xuelongbao, so it can be found in conjunction with Figure 1b that the overall topography of the 10 hardest-hit counties (cities) is high in the west and low in the east. The central part of the study area is the Longshan Mountain Range, which is relatively high in elevation, while the western part of the area gradually rises in relief and transitions to the Tibetan Plateau.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Military Applications  
The military domain is not only the first field of human activity that used WSNs but it is also considered to have motivated the initiation of sensor network research. Smart Dust [4] is a typical example of these initial research efforts, which were performed in the late 90 s in order to develop sensor nodes which despite their very small size would be capable of accomplishing spying activities.  
The technological advances achieved since then made WSNs capable of supporting various operations [5]. In Figure 4, the main subcategories of the military applications of WSNs which namely are battlefield surveillance, combat monitoring, and intruder detection, are illustrated along with the types of sensors that are most commonly used in them.  
Specifically, Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE), and Toxic Industrial Material (TIM) sensors may be used to detect the presence of such substances. To detect intrusion, WSNs may use infrared, photoelectric, laser, acoustic, and vibration sensors. Similarly, RAdio Detection And Ranging (RADAR), LIght Detection And Ranging (LIDAR), LASer Detection And Ranging (LADAR) and ultrasonic sensors are used by nodes in WSNs in order to detect the distance from objects of interest. Likewise, LADAR and infrared sensors are used for imaging purposes.  
Figure 4: The subcategories of the military applications of WSNs and the types of sensors they use.  
Figure 3: Overview of the most popular categories of applications of WSNs.  
Additionally, the flexibility that WSNs have in their structure enables them to adapt to various requirements. For instance, in battlefield operations large-scale WSNs consisting of many thousands of nodes, which are non-manually deployed, are used. In urban warfare and force protection operations, WSNs used consist of hundreds of manually deployed nodes. In other-than-war operations all scales of WSNs and deployment methods are used [5].  
WSNs that have been developed for battlefield surveillance, combat monitoring, and intruder detection are examined in the following subsection.
## 1 Introduction
A binary offset carrier modulation with 1.023 MHz spreading code chipping rate and 1.023 MHz square wave subcarrier frequency, sine phased, which is denoted as BOC(1,1). TMBOC(6,1,4/33) is produced by replacing four of each 33 spreading symbols in the pilot component with BOC(6,1) spreading symbols, while retaining BOC(1,1) for all other spreading symbols in the pilot and also for all of the data spreading symbols [15]. With respect to the satellite orbit, GPS III and QZSS are also different. QZS-1, QZS-2, QZS-4 belong to inclined geo-synchronous satellite orbit (IGSO) satellites, QZS-3 belongs to geostationary Earth orbit (GEO) satellites, and GPS III belongs to medium Earth orbit (MEO) satellites. The anti-noise, anti-interference and multipath performance of Binary Phase Shifted Keyed (BPSK) and BOC modulation have been evaluated theoretically [16; 17]. [PERSON] et al. [18] have verified the ranging performance of BPSK and BOC modulated signal in the open sky and other situations and the results of the evaluation were given. Combined with GPS, [PERSON] et al. [19] verify that the BOC modulated signal broadcast by QZS-1 has better performance than the BPSK modulated signal in the positioning error. The positioning performance of the Software-defined Receiver (SDR) by means of QZSS has been reported in the literature [10; 20]. Many studies [21; 22; 23; 24; 25; 26] use Precise Point Positioning (PPP) for GNSS such as GPS and GLONASS, and analyze the QZSS observations and the positioning effect. However, the effect of the latest L1C signals on positioning results is still lacking. The first satellite launched by GPS III in December 2018 has already broadcast the L1C signal with TMBOC modulation. While the anti-interference, anti-noise and other benefits of the TMBOC over the BOC have been theoretically assessed [15], no in-orbit, operational assessments have been published. We have developed methods to make a direct comparison of the ranging difference of TMBOC(6,1,4/33) and BOC(1,1) from GPS III and QZSS. In view of the fact that there is barely one satellite in the current GPS III constellation, we propose a joint positioning method to use GPS III for positioning service, and verify its feasibility by means of the experiment in Xi'an. The ideal way to make a direct comparison of anti-interference and anti-noise performance is to use TMBOC(6,1,4/33) and BOC(1,1)-modulated signals emanating from the same constellation geometry. The use of satellites of the same time period rather than the two periods of close proximity is the key to accurate comparison as the ranging and positioning results are sensitive to the slightest difference in the constellation geometry. Therefore, the experiments in Sanya made a detailed comparison of positioning error between the TMBOC(6,1,4/33) and BOC(1,1) modulated signals of GPS III + QZSS and QZSS-only.
## 4 Simulation Study and Discussion
Furthermore, we illustrate the effect of the TD feature on the controller performance when stochastic noise is not present in System (3), and the test results are given in Figures 13-15. From the above comparison results, it follows that the TD feature will have a certain effect on controller performance for the ship heading system (3) whether there is stochastic noise or not. By comparison, we can conclude that, by the appropriate parameter selection of the TD, the desired heading tracking performance and control performance can be ensured. From the above experiment results, we can obtain the following conclusions:  
(1) Stochastic noise in complex sea conditions does have an impact on the system performance, especially when there is a huge stochastic effect resulting from excessive wind and waves, and it will have a terrible impact on the control system. Therefore, it is of great practical significance to consider the stochastic noise in system modeling and study the related control issues for safe navigation;  
(2) The novel TD-based adaptive heading tracking control scheme has a simple structure, and it only needs the knowledge of \(y_{d}(t)\) and its one-order time derivative \(\dot{y}_{d}(t)\), while the drawback of the \"explosion of terms\" is cleverly prevented by taking the boundary \(s_{2d}=\alpha_{2d}-\alpha_{1}\) in the designed compensating signals into account; thus, the computational work is reduced greatly, which is very conducive to practical ship control;  
(3) The developed TD-based control scheme can achieve a good control effect by means of proper parameter selections. However, the parameter tuning is a nontrivial and tedious task. In order to obtain the desired heading control performance, large \(R_{2},a_{21},a_{22}\) and small \(l_{21},l_{22}\) are needed, but it should be noted that, too large \(R_{2},a_{21},a_{22}\) and too small \(l_{21},l_{22}\) will result in bad heading tracking and controller convergence.
## 4 Analysis of Experimental Results
### Evaluation Results
Specifically, in the leakage scenario, this algorithm consistently maintains a low leakage rate across different false alarm rates, only slightly increasing to 1.67% at the highest false alarm rate. Conversely, other algorithms demonstrate a significant increase in leakage rates as the false alarm rate rises, with the sliding-window method peaking at a leakage rate of 58.34% at an 11% false alarm rate. Figure 10: Average batch rate of correct trajectories for different algorithms with different false alarm rates in the cloud context. Figure 9: Average starting batch rate of correct trajectories for different algorithms with different false alarm rates in trajectory intersection scenarios. In the experimental comparison of performance evaluation of trajectory initialization algorithms, the study analyzed and compared the performance of the MDF-clustering algorithm with several other commonly used algorithms in the context of target tracking. The analysis included handling false trajectories, maintaining accurate initialization rates, initializing frames, and controlling the missed batch rate, showcasing significant advantages in accuracy and robustness. Target tracking in complex dynamic environments requires maintaining a high initialization rate and a low false alarm rate while also ensuring accuracy and stability in monitoring.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 2 Numerical Calculation Method  
### Energy Dissipation Wear Model  
Temperature variations in the contact zone alter the coefficient of friction between the surfaces in contact. Energy dissipation models in existing theoretical wear models are not affected by the coefficient of friction. Therefore, the energy wear model is used as the theoretical model for the study. According to the energy model [26], the amount of wear is proportional to the energy consumed between surfaces, which is expressed as  
\[\mathrm{V} =\mathrm{K_{E}E_{d}} \tag{1}\] \[\mathrm{E_{d}} =\int\mathrm{QdS} \tag{2}\]  
where V is the wear volume, \(\mathrm{K_{E}}\) is the energy wear model wear coefficient, \(\mathrm{E_{d}}\) is the energy consumed by wear, Q is the frictional shear force, and S is the specimen displacement amplitude.  
Differential transformation of Equations (1) and (2) is carried out to obtain the local wear depth at position x, which is expressed as  
\[\mathrm{dh(x)}=\mathrm{K_{E}q(x)ds(x)} \tag{3}\]  
where q(x) is the local shear stress at position x and ds(x) is the increment of the local relative slip distance.  
During fretting, shear stress varies. Therefore, the integral form of the model is utilized. For a two-dimensional contact surface, the wear depth at moment t and at position x can be expressed as  
\[\mathrm{h(x,t)}=\mathrm{K_{E}}\int\limits_{0}^{t}q(x,t)ds(x,t) \tag{4}\]  
where q(x, t) is the corresponding shear stress at moment t and ds(x, t) is the increment of the relative slip distance at moment t.
# Substrate-Integrated Waveguide Microwave Sensor for Water-in-Diesel Fuel Applications
## 3 Applicator Design  
The aim of the design is to obtain a low cost, planar/low-profile and compact (of few square centimeters) microwave applicator, providing good performance in terms of sensitivity and resolution, even if operating at a single frequency, to be employed in a simple online set-up, for example including a microwave source and an MW power meter.  
The employed dielectric substrate is Rogers RT/duroid 5880 (\(\epsilon_{r}=2.2\), \(\tan\delta=0.0009\)) of commercial thickness \(h_{sub}=1.575\) mm, whereas metallic parts are made of copper (\(\sigma=5.8\times 10^{7}\) S/m) with thickness \(h=0.035\) mm.  
The design and characterization of the SIW applicator is performed with reference to both the air and water-in-diesel surrounding medium (background). In the case of the water-in-diesel background, the measured complex dielectric constant of Figures 2 and 3 have been considered.
## 2 System Description  
In this paper, we have considered a two-degree-of-freedom mechanical system consisting of two mechanical oscillators having mass, spring, and damper, coupled with each other [4]. The displacements and the velocities of the two masses are considered as the system's state variables. The four state variables make the system four dimensional. The schematic representation of the system is shown in the Figure 1.  
The mechanical system consists of two masses \(m_{1}\) and \(m_{2}\), connected with a rectangular shaft. When an external excitation is applied, the shaft rotates with an angular frequency \(\omega\) in the counterclockwise direction. As the shaft is rectangular in size and is rotating, the spring constants of the coupled system also vary periodically. While applying the external excitation in the system, the two masses \(m_{1}\) and \(m_{2}\) move back and forth on  
Figure 1: The Schematic representation of a system composed of two oscillators connected with periodically variable stiffness. (Color online).  
the hard surfaces. This movement is done by the real rolling bearings attached with the masses [27]. The resistance force \(F_{\text{RI}}(\dot{x}_{i})\) of the system is composed of two functions, one is viscous damping \(C_{i}\dot{x}_{i}\) and another one is the force due to the dry friction. \(x_{1}\) and \(x_{2}\) are the displacements of the two masses \(m_{1}\) and \(m_{2}\) from the equilibrium positions. The inclusion of the four pairs of magnets with the identical polarities in the system creates the repulsive magnetic force \(F_{\text{SI}}(x_{i})\), where \(i=1,2\), acting on the two masses. Each pair of magnets have been placed at a fixed distance \(\delta\). In our work, we keep varying the angular frequency \(\omega\) of the system, keeping the remaining parameters fixed and observing the state variables' dynamics under varying \(\omega\) parameter conditions.