esa-sceva/satcom-chunk-collection huggingface dataset:
# Alginate Silver Nanoparticles and Their Effect on Sperm Parameters of the Domestic Rabbit
## 3 Results and Discussion
### Analysis of Morphological Changes in Rabbit Sperm
[85], who found a significant decrease in all semen parameters analyzed by CASA in semen samples supplemented with xylose at a dose of 1 to 15 mM, after 24 h of treatment, compared to the control sample. In our study, silver nanoparticles produced using xylose as a reducing agent showed negligible damage to rabbit sperm in the comet assay, both in % Tail DNA and TM. In the morphological analysis, AgNP AlgX 24 h induced more primary and secondary damage compared to the Alg 24 h positive control and the negative control, the semen without the additional agent. Silver nanoparticles, or other nanomaterials, have been obtained for many years through chemical reduction methods of AgNO\({}_{3}\). The most commonly used reducing agent for obtaining nano metal forms is sodium borohydride (NaBH\({}_{4}\)), as indicated by numerous literature reports [86]. In addition to its strong reducing properties, including Ag\({}^{+}\) to Ag\({}^{0}\) during the synthesis of AgNP, it is an agent that additionally possesses properties to stabilize the nanostructure, and its use allows for the control of the size and shape of the obtained nanoparticles. This inorganic compound, NaBH\({}_{4}\), has also found application in other processes for obtaining ceramic materials, and it is a good hydrogen store, so it is used for the production of hydrogen gas [87]. Due to the general harmful effects of sodium borohydride, other reducing agents or methods for synthesizing AgNP are being investigated. The AgNP AlgB24h used in the comet assay showed no harmful effects on rabbit spermatozoa after 24 h exposure, as the observed damage in this group in % DNA content in the comet tail and tail moment values was similar to the negative control after 24 h, and significantly lower than the control alginate solution (Alg24h). Cytotoxic effects were demonstrated in the analysis of morphological changes within germ cells. AgNP AlgB24h induced the most secondary changes within the structure of the spermatozoa, >34% of secondary changes, and the proportion of normal cells after treatment with this agent fell to 65.91%, compared to the control groups and sodium alginate alone.
## 2 Results
### Differential Expression of Genes to Different Concentrations of Ammonium  
As shown in Table S1, from 15 leaves of the five groups (LNC, L1, L2, L3, and L4), a total of 76.07 million clean reads (78.34 million raw reads) were generated, pooled together, and then de novo assembled by Trinity. The transcriptome was assembled to 32,492 unigenes, with an N50 = 1617 bp. Principal component analysis (PCA) was used to capture the overall variance among the transcript data. The results illustrated that the gene expression in LNC, L1, and L2 groups were separated from each other while the L3 and L4 groups more closely resemble different PC directions with the first two PCs explaining 66% of the variation (Figure S1). The transcriptomic responses of _M. aquaticum_ to different concentrations of ammonium were analyzed. The numbers of genes, whose expression was significantly (\(p<0.05\)) altered based on different growth conditions, were shown in Figure 2 and Figure S2. Different concentrations of ammonium led to substantial transcriptomic responses in which 7721 genes were differentially expressed in _M. aquaticum_ (Table S2). Initially, the numbers of upregulated genes were more than that of downregulated genes in the L1 group while the numbers of downregulated genes increased to 2580 in the L4 group, as the ammonium greatly increased (Figure 2A). The four groups (L1, L2, L3, and L4) shared a total number of 699 differentially expressed genes (DEGs) in common (Figure 2B), of which 617 and 73 DEGs were down-regulated (Figure 2C and Table S3) and upregulated (Figure 2D and Table S3), respectively, among the four groups when compared with the LNC group.  
Figure 1: Effect of different concentrations of ammonium on the amino acid content in _Myiophylla aquaticum_ leaves. LNC: group treated with 0 mM ammonium. L1: group treated with 0.1 mM ammonium. L2: group treated with 1 mM ammonium. L3: group treated with 12 mM ammonium. L4: group treated with 36 mM ammonium values (means \(\pm\) SDs) were determined from three biological replicates. Different letters above the bars indicate a significant difference at 0.05.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 2 Related Work  
Unsupervised learning addresses the high annotation costs and difficulty in collecting negative samples, making it the mainstream method for image anomaly detection. Unsupervised learning methods can be divided into two main categories: reconstruction-based and feature embedding-based methods.
## 3 Experiments and Analysis  
In this section, two experiments were constructed to prove the effectiveness of the proposed method. In Experiment 1, the Case Western Reserve University (CWRU) bearing dataset was employed. In Experiment 2, a single point and compound bearing faults experiment was designed in the rotor-bearing experimental system. For purpose of a fair comparison, three conditions are the same as follows: (1) the length of each sample is 2048 points; (2) the SSDA is trained by two-thirds of the samples, and the rest samples are utilized to test the performance; (3) each experiment runs four times to reduce the randomness.
## 3 Preconditioned Precise Integration Time-Sweeping Method
### Time-Sweeping Wavefield Reverse Transformation Method  
To suppress interference and highlight anomalous information, this paper introduces a multi-window time-sweeping technique based on the preconditioned precise integration algorithm. First, different sweeping time windows are set, and the virtual wavefield is computed for each window. Then, a correlation analysis is performed between the virtual wavefield of each window and that of the full time period. If the correlation coefficient exceeds the predefined threshold, the corresponding virtual wavefields are combined through correlation summation to obtain the virtual wavefields for different windows. Finally, by stacking the virtual wavefields from different windows, the wavefield information for the entire virtual time profile is obtained [40]. The schematic diagram of the process is shown below [23] (Figure 5).
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 3 Problem Statement & System Model
### Graph-Based Model  
Graph theory refers to the mathematical constructs used to describe the whole network and the connectivity of the objects with each other. It also offers many approaches to problem solving. The subnet-based mechanism is shown through a graph in which all the objects are uniquely identified, which will also help in identifying the failure of nodes. The subnet is also known as a subnetwork, where a wider network is segmented into sections. The reason for choosing subnet deployment is to enhance the network's energy efficiency.  
It reduces the unnecessary traffic load on network routes, resulting in faster network speed. The graph-based system model is presented in Figure 3. In the graph, a set of vertices comprises nodes and a set of edges as connections for the representation of the network.  
Figure 3: Graph-based model.  
Figure 2: Sequence diagram of forest fire detection and counteraction.  
In this graph, different nodes are shown in the subnets, such as sensor nodes, alarm nodes, gateway nodes, drones as actor nodes, and a control room node. The powerful node of the system is selected as gateway, and drones are directly connected to the control room. There is a link among the pair of nodes, which means all the nodes in the subnet are connected to each other and the gateways. The subnets are linked with the control room through gateways with an edge.
###### Abstract  
The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt filtering parameters, predict signal reliability, and change the processing mode depending on the level of interference. In conditions of signal loss, noise, and unstable measurement reliability, traditional methods do not provide stable and accurate tracking, especially at the initial stages of radar operation. To address this issue, an intelligent method is proposed that integrates a probabilistic graphical evaluation and review technique (GETR) model, a recursive Kalman filter, and a measurement reliability prediction module based on a long short-term memory (LSTM) neural network. The proposed approach allows for the real-time adaptation of filtering parameters, fusion of local and global trajectory estimates, and dynamic switching between tracking modes depending on the environmental conditions. The dynamic weighting algorithm between model estimates ensures a balance between accuracy and robustness. Simulation experiments confirmed the effectiveness of the method: the root mean square error (RMSE) of coordinate estimation was reduced by 25%; the probability of tracking loss decreased by half (from 0.2 to 0.1); and the accuracy of loss prediction exceeded 85%. The novelty of the approach lies in integrating stochastic modeling, machine learning, and classical filtering into a unified adaptive loop. The proposed system can be adapted to various types of radar and easily scaled to multi-sensor architectures. This makes it suitable for practical implementation in both defense and civilian air object detection systems operating under complex conditions.  
Article  
30 May 2025  
Revised: 20 June 2025  
Submitted: 23 June 2025  
**Clation** [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Development of an Intelligent Method for Target Tracking in Radar Systems at the Initial Stage of Operation Under Intentional Jamming Conditions. _Appl. Sci._**2025**, _15_, 7072. [[https://doi.org/10.3390/app15137072](https://doi.org/10.3390/app15137072)]([https://doi.org/10.3390/app15137072](https://doi.org/10.3390/app15137072))  
**Copyright:** 62025 by the authors.  
Liconsec MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  
**Keywords:** radar station; adaptive filtering; neural network; noise-resistant support; stochastic modelling
## 3 Results
### Thirty-Day Storage Test  
#### 3.3.1 Microbial Stability  
To assess the microbial stability of the AP, a 30-day storage evaluation was performed, and the effectiveness of the treatments (at the previously selected processing conditions) for the inoculated _E. coli_ and indigenous microorganisms (TMAB and YM) was measured for AP samples processed and stored for 30 days at 5 \({}^{\circ}\)C. Analyses were performed every 10 days and the TMAB and YM results are summarized in Figure 4.  
Figure 3: Antioxidant activities of the AP before and immediately after treatment at the selected conditions for each technology, using the DPPH (**A**) and ABTS (**B**) assays. Bars with different letters on the same graph indicate a significant difference (_p_ < 0.05) between each technology.  
As confirmed, _E. coli_ was not detected in the treated AP samples immediately after each processing condition, as they resulted in a reduction of at least 5 logs. Furthermore, during the 30 days storage period, all treatments successfully prevented the growth of _E. coli_. Thus, the treated AP samples did not show any _E. coli_ CFU during the cold storage study in all the treatments studied (all treatments studied reduced _E. coli_ load below the minimum detection limit; 1.00 log CFU/g ). These results revealed a clear positive effect of these treatments on microbial load reduction after processing and throughout 30 days of cold storage.  
As expected, the control AP samples showed microbial growth over the 30-day storage period. The growth was around 3.9 log units for TMAB and 0.8 log units for YM. The BL technique successfully deactivated YM, and no instances of this microbiological group were found over the 30-day period. However, a small amount of TMAB remained after BL and was able to grow, reaching a level of 3.5 \(\pm\) 0.1 log CFU/g. The BL values for TMAB and YM indicated that pre-treatment alone had a minor impact on the microbiological safety of the AP. However, this method alone is insufficient to completely inactivate certain vegetative bacteria [26].  
As expected, the selected processing conditions for all technologies were effective in ensuring AP's microbial safety, as counts for the TMAB and YM were undetectable right after processing and throughout the 30 days of storage evaluation.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
###### Abstract  
Congestion in large cities is widely recognized as a problem that impacts various aspects of society, including the economy and public health. To support the urban traffic system and to mitigate traffic congestion and the damage it causes, in this article we propose an assistant Intelligent Transport Systems (ITS) service for traffic management in Vehicular Networks (VANET), which we name FOXS-GSC, for Fast Offset Xpath Service with hexaGonS Communication. FOXS-GSC uses a VANET communication and fog computing paradigm to detect and recommend an alternative vehicle route to avoid traffic jams. Unlike the previous solutions in the literature, the proposed service offers a versatile approach in which traffic road classification and route suggestions can be made by infrastructure or by the vehicle itself without compromising the quality of the route service. To achieve this, the service operates in a decentralized way, and the components of the service (vehicles/infrastructure) exchange messages containing vehicle information and regional traffic information. For communication, the proposed approach uses a new dedicated multi-hop protocol that has been specifically designed based on the characteristics and requirements of a vehicle routing service. Therefore, by adapting to the inherent characteristics of a vehicle routing service, such as the density of regions, the proposed communication protocol both enhances reliability and improves the overall efficiency of the vehicle routing service. Simulation results comparing FOXS-GSC with baseline solutions and other proposals from the literature demonstrate its significant impact, reducing network congestion by up to 95% while maintaining a coverage of 97% across various scenery characteristics. Concerning road traffic efficiency, the traffic quality is increasing by 29%, for a reduction in carbon emissions of 10%.  
VANET; ITS; vehicular route management 2023
## 3 Preliminaries
### Nash Equilibrium  
NE is a fundamental concept in game theory (GT), particularly suited for scenarios in which multiple decision makers, here the clients, interact in a shared environment and their outcomes are interdependent [21; 22]. In traditional non-cooperative games, each player aims to maximize its own payoff, assuming the strategies of others are fixed. However, in RBPS, the client selection process is modeled as a cooperative game, where clients are indirectly aligned through a shared reward function that integrates three system-wide objectives: model accuracy, battery efficiency, and privacy preservation. Each client evaluates its participation based on local conditions such as remaining battery level, data sensitivity, and expected accuracy contribution and responds accordingly. While the NE condition still applies, it is interpreted in a cooperative sense: no client has an incentive to deviate unilaterally from the collectively optimized strategy, as doing so would reduce its reward relative to the joint outcome.  
Mathematically, the NE is expressed as follows:  
\[u_{i}(s_{i}^{*},s_{-i}^{*})\geq u_{i}(s_{i},s_{-i}^{*})\quad\forall s_{i}\in S_ {i} \tag{2}\]  
where \(u_{i}\) denotes the reward (payoff) function for client \(i\), \(s_{i}^{*}\) is the equilibrium strategy of client \(i\), and \(s_{-i}^{*}\) represents the strategy profile of all other clients. At equilibrium, no client benefits by changing its selection strategy alone.  
In our framework, the payoff function \(u_{i}\) is computed using a weighted combination of the client's impact on accuracy, its battery consumption profile, and privacy sensitivity. These weights are adjusted dynamically based on the client's device type and real-time context. RBPS then centrally selects clients with cooperative intent, ensuring that participation decisions lead to an overall system-optimal outcome rather than allowing any single dimension (e.g., accuracy or fairness) to dominate the selection logic.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### WSN Challenges and Utility in Hydrology
#### 3.5.5 Network Costs  
The 52-node MICAz and IRIS network at the end of 2014 had a cost of $31,500 for the wireless motes, gateway, sensors, and other peripherals [32]. The CM5000-SMA (TelosB) mote includes built-in humidity and temperature sensors and does not require the use of an acquisition board for relay nodes as opposed to the MICAz or IRIS motes that use the MDA300 acquisition board ($179). Therefore, the adoption of the TelosB motes has significantly reduced the cost of relay nodes, from $330 (in the MICAz network) to $164, despite the TelosB ($110) being slightly more expensive than the MICAz or IRIS motes (both models about $99 each). These savings are also found for the soil sensor nodes (from $664 to $480), mainly due to the deployment of our inexpensive ($13) designed sensor boards (with 5 V voltage booster) instead of the MDA300, despite the increased cost for the MPS-2 (compared to the MPS-1) sensor. A new sap flow box design, which replaced the MDA300 by our sensor board ($9) without the 5 V voltage booster and does not require the use of AA or D batteries, has further contributed to the cost savings (from $464 to $257). The cost of the expanded 104-node (27 MICAz, 32 IRIS and 45 TelosB motes) network is approximately $50,000. Table 5 shows the distribution of sensors for each type of node.  
Figure 14: MobileDeluge, a hand-held mobile mote reprogramming tool.  
For the sake of comparison, a Decagon Devices EM50 data logger costs about $476 and is roughly equivalent to our WSN soil sensor node ($177 without the sensors) in terms of its capability to host external sensors.
# Multilayer Backbones for Internet of Battlefield Things +
## 1 Introduction
### Motivation and Work's Contributions  
In [5] we dealt with the heterogeneity of an IoBT network and modelled it as a _multilayer network_. We proved analytically that if we treat it as a plain union of independent subnetworks, then we do not reap efficient solutions for facilitating fast information routing. So, despite the past, very rich literature on the domination concept in ad hoc networks, the problem in IoBT networks is different, since these are multilayer networks and considering them as a single (flat) network or considering each layer in isolation and calculating dominating set produces either suboptimal or bad solutions. Therefore, the whole past literature is in principle inappropriate. Later, we incorporated in this first work energy issues [6], and we devised algorithms with fact-finding methods to address aspects of social sensing, e.g., characterize human assets/sources. We note here, that in [5] we developed distributed, communication-efficient algorithms based on the concept of node domination for choosing a set of nodes to be a backbone for a multilayer network, i.e., the IoBT network. In the biological sciences area, the work described in [20] developed centralized algorithms for computing dominating sets in multilayer networks.  
Nevertheless, neither the algorithms developed in [5] (and of course in [6]) nor those developed in [20] can adequately serve the purposes of an IoBT backbone network. The main issue with the former algorithms is that they do not produce very compact network spanners, so there is plenty of room for improvement with regard to the produced connected dominating set. This is very significant if we wish to design solutions scalable to enormous IoBT networks. The two main disadvantages of the latter algorithm is its centralized nature, and thus it can not work in IoBT, and additionally because it produces unconnected spanners, and therefore it can not guarantee network-wide flow of information. Thus, our article contributes the following:  
* It establishes the computation complexity (NP-completeness) of the problem of calculating a minimum connected dominating set for multilayer networks.
* It presents a new distributed algorithm, namely the _Cross layer Connected Dominating Set_ (\(CCDS\)) for calculating connected dominating sets for IoBT networks, by applying an efficient mechanism to reduce the size of the dominating set.
* It enhances _FAST-MDSM_[20] so as to produce connected dominating sets thus getting a new algorithm called _FAST-CMDSM_, and compares our proposed algorithm against the only existing competitors, namely the algorithm in [5] and _FAST-CMDSM_.  
The rest of this article is organized as follows: Section 2 formulates the problem and proves its complexity, and Section 3 develops distributed solutions for it. Section 4 evaluates the competing algorithms, and finally, Section 5 summarizes the article.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 7 Results and Discussion
### Gaussian Mixture Model (GMM)  
To further distinguish between honest, malicious, and system error \(Rep_{\text{vehs}}\), the following section discusses the results generated from using the Gaussian Mixture Model (GMM) that focuses on classifying and identifying the \(Rep_{\text{vehs}}\).  
Figure 16 shows the classification results generated from the Gaussian Mixture Model (GMM) to correctly identify the \(Rep_{\text{vehs}}\) types in all three scenarios: \(ACD\)-\(OCC\), \(ACD\)-\(ABS\), and \(ACD\)-\(RSL\). The results categorize \(Rep_{\text{vehs}}\) into three types: Honest, Malicious, and Erroneous \(Rep_{\text{vehs}}\).  
* Honest \(Rep_{\text{vehs}}\): In all three scenarios, honest \(Rep_{\text{vehs}}\) Represented by the (Blue Bars) show The majority of the cases, with approximately 80 \(Rep_{\text{vehs}}\) for each scenario which indicates a consistent pattern of correct reporting across scenarios.
* Malicious \(Rep_{\text{vehs}}\): As shown by the orange bars, a small proportion of \(Rep_{\text{vehs}}\) are identified as malicious. These \(Rep_{\text{vehs}}\) intentionally submit \(MR_{f}\) with a consistent number across scenarios, ranging from approximately 6 to 10.
* Erroneous \(Rep_{\text{vehs}}\): This is the smallest group, represented by the green bars. These \(Rep_{\text{vehs}}\) submitted incorrect reports \(MR_{f}\), resulted from an unintentional error and were not indicative of deliberate misbehavior. The false result in these MRs is due to sensor faults or environmental errors. The number of \(Rep_{\text{vehs}}\) with erroneous data differs slightly, with a minimal count observed in each scenario.  
In this classification, we highlight our proposed system's efficiency in making a difference between honest, malicious, and erroneous behavior, helping the RS maintain accuracy and filter out potentially disruptive or inaccurate reports.
## 6 Results and Discussion
### Descriptive Statistics of the Experimental Dataset Variables
#### 6.1.6 Porosity vs. UCS  
* A negative correlation (r \(\approx\) -0.78) indicates that samples with higher porosity exhibit lower UCS, underscoring porosity's critical role in strength reduction.