esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Foundations and Model Description
### Model Description  
The model considers three distinct states for computers within the network: susceptible (S(t)), infected (I(t)), and recovered (R(t)), based on [27; 28; 29; 30]. Four parameters govern the transitions between these states: \(b\) (the rate of new computers joining), \(\epsilon\) (recovery rate), \(d\) (removal rate), and \(\lambda\) (the infection rate).  
\[\begin{cases}\frac{dS}{dt}=b-\lambda S(t)I(t)-dS(t),\\ \frac{dI}{dt}=\lambda S(t)I(t)-\epsilon I(t)-dI(t),\\ \frac{dR}{dt}=\epsilon I(t)-dR(t).\end{cases} \tag{8}\]  
It is noteworthy that the recovered compartment R in System (8) exhibits no influence on the dynamics of the susceptible or infected compartments. Consequently, this study will be restricted to the following reduced system:  
\[\begin{cases}\frac{dS}{dt}=b-\lambda S(t)I(t)-dS(t),\\ \frac{dI}{dt}=\lambda S(t)I(t)-\epsilon I(t)-dI(t).\end{cases} \tag{9}\]  
The discretization and dynamical analysis in this work are based on model (9). This choice is motivated by the discrete nature of real-world data, which makes discrete models more suitable for simulating virus transmission. To obtain a discrete approximation of model (9), the forward Euler scheme with a unit time step is employed, yielding  
\[\begin{cases}S(\sigma+1)=b-\lambda S(\sigma)I(\sigma)-dS(\sigma)+S(\sigma), \\ I(\sigma+1)=\lambda S(\sigma)I(\sigma)-\epsilon I(\sigma)-dI(\sigma)+I(\sigma).\end{cases} \tag{10}\]  
Equation (10) provides a discrete-time approximation using the forward Euler method. This scheme is widely adopted due to its simplicity and effectiveness in digital simulations. The validity of this discretization is supported by its ability to preserve essential qualitative features of the continuous model, such as stability and positivity of solutions. Additionally, the accuracy of this method has been verified through extensive simulations and alignment with theoretical predictions. Convergence results for similar discrete schemes can be found in [24; 25], further supporting the reliability of the adopted approach.  
To highlight the dynamics in the first-order difference setting, we also present the classical discrete SI model as  
\[\begin{cases}\Delta S(\sigma)=b-\lambda S(\sigma)I(\sigma)-dS(\sigma),\\ \Delta I(\sigma)=\lambda S(\sigma)I(\sigma)-\epsilon I(\sigma)-dI(\sigma). \end{cases} \tag{11}\]  
In light of the preceding considerations, attention is now turned to the analysis of the following discrete-time fractional model for the propagation of computer viruses using the Caputo-like difference operator:  
\[\begin{cases}{}^{c}\Delta_{\rho}^{\eta}S(\sigma)=b-\lambda S(\sigma+\eta-1)I( \sigma+\eta-1)-dS(\sigma+\eta-1),\\ {}^{c}\Delta_{\rho}^{\eta}I(\sigma)=\lambda S(\sigma+\eta-1)I(\sigma+\eta-1)- \epsilon I(\sigma+\eta-1)-dI(\sigma+\eta-1).\end{cases} \tag{12}\]  
where \({}^{c}\Delta_{\rho}^{\eta}\) denotes the Caputo-like discrete fractional difference operator of order \(\eta\) with base point \(\rho\).
# Time Management in Wireless Sensor Networks for Industrial Process Control
###### Abstract  
This paper addresses the critical challenge of time management in wireless sensor networks (WSNs) applied to industrial process control. Although wireless technologies have gained ground in industrial monitoring, their adoption in control applications remains limited due to concerns around reliability and timing accuracy. This study proposes a practical, low-cost solution based on commercial off-the-shelf (COTS) components, leveraging the IEEE 802.15.4-2020 standard in Time-Slotted Channel-Hopping (TSCH) mode. A custom time management algorithm is developed and implemented on STM32 microcontrollers paired with AT86 RF212B transceivers. The proposed system ensures a sub-millisecond synchronization drift across nodes by dividing communication into a structured slot frame and implementing precise scheduling and enhanced beacon-based synchronization. Validation is performed through experimental setups monitored with logic analyzers, demonstrating a time drift consistently below 600 microseconds. The results confirm the feasibility of using synchronized wireless nodes for real-time industrial control tasks, suggesting that further improvements in hardware precision could enable even tighter synchronization and broader applicability in fast and critical processes.  
artificial sensor networks (WSNs); industrial process control; time synchronization; real-time systems; embedded systems; network time management Article
# A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
## 3 Experimental Results
### Configuration and Parameter Settings  
The data are divided with a 70-30 ratio, where 70% of the data is used for training purpose and the remaining 30% data to test the proposed system. The experiments are performed on a core i5 system with 6 GB RAM. To classify the recorded EEG data into expert-novice level based on extracted features from the fourteen-channel headset, three different classification algorithms were trained, including naive Bayes, SVM and MLP. The MLP used in this study consists of a hidden layer with 15 neurons, and the network is fully connected such that all neurons in each layer have a connection with all neurons in the following layer. The network is trained using the back-propagation algorithm, and cross entropy is used as the cost function. The weights are initialized randomly from a zero mean Gaussian distribution. The learning follows the stochastic gradient descent (SGD) algorithm and selects the optimized weights for the neuronal connections giving an accurate classification of game player expertise level. The network hyper-parameters including the learning rate are selected using grid search. For the SVM classifier, a linear kernel is used.
## 4 Proposed Architecture
### High-Level Architecture Overview  
The system architecture, illustrated in Figure 2, is composed of four core layers: the Perception Layer, the Local MQTT Broker, the Cloud Integration Layer, and the Application Layer. Each layer has a distinct responsibility in the secure acquisition, transmission, processing, and presentation of healthcare data, from sensor-level interactions to cloud-based storage and clinical dashboards.
## 8 5G Potentials and Promises for Robot Applications
### Vehicle-to-Everything Standard  
The Third-Generation Partnership Project (3 GPP) deploys the Vehicle-to-Everything (V2X) based on Dedicated Short-Range Communications, which includes Vehicle to Vehicle (V2V), Vehicle to Network (V2N), or Vehicle to Infrastructure (V2I). The cellular V2X standard based on the 5G air interface is a fascinating feature for cooperative robotic missions, specifically through introducing the sidelink (SL), which permits vehicles to directly exchange information without other parts of the network being involved. This will play a vital role in cooperative tasks and localisation, whether the operation is within the coverage area or without BS coverage. Furthermore, the V2X feature not only allows communication of the vehicles witheach other, but also with the infrastructure and even the internet. This increased connectivity improves the efficiency of the cooperative systems, enhances localisation accuracy, and makes some previously infeasible missions possible. For example, in an environment with adverse NLOS effects from the BS, which results in erroneous measurements, one or some parts of the vehicles can play the role of BSs or act as a pseudo BS.
## 5 Conclusions  
The CVA-CVapp has shown to be useful for CVA measurement. The test-retest and inter-rater reliability scores showed excellent results for the CVA-CVapp of the measurement of the CVA in healthy controls, in volunteers with neck pain, and in TTH. The CVA-CVapp showed a high correlation with the Kinovea web tool for the assessment of the CVA.  
The use of a CV-based application in this study has shown a remarkable level of reliability and validity, indicating its usefulness as an objective tool for evaluating CVA in a standing position. Nevertheless, it is important to exercise caution when interpreting the results. The application was solely utilized for measuring CVA, and as a result, the conclusions cannot be extended to other cervical angles. Furthermore, the results' applicability to other subgroups of cervical patients is limited.  
Conceptualization, A.C.-U., X.M. and A.M.-A.; methodology, A.C.-U., X.M., F.D., S.C.-B., L.C.-L. and A.M.-A.; software, X.M. and A.M.-A.; formal analysis, F.D. and S.C.-B.; investigation, A.C.-U., X.M., F.D., S.C.-B., A.P., L.C.-L. and A.M.-A.; resources, A.C.-U. and X.M.; data curation, F.D., L.C.-L. and A.M.-A.; writing--original draft preparation, A.C.-U., X.M., F.D.,S.C.-B., A.P., L.C.-L. and A.M.-A.; writing--review and editing, A.C.-U., X.M., F.D., S.C.-B., A.P., L.C.-L. and A.M.-A.; supervision, A.C.-U., X.M., F.D., S.C.-B., A.P., L.C.-L. and A.M.-A.; project administration, A.C.-U., X.M. and A.M.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Universitat International de Catalunya with the registration number FIS-2022-010.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data analyzed in this study are included in this published article. The dataset is available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Space-Constrained Scheduling Optimization Method for Minimizing the Effects of Stacking of Trades
## 3 Space-Constrained Scheduling Optimization Method
### Executing CPM and GA Chromosome Encoding  
SSO obtains the activity duration (\(D_{i}\)) and predecessor (\(PS_{i}\)) information from the \(N_{D}\) and executes the CPM algorithm using the given \(D_{i}\) and \(PS_{i}\) (Step 6), as shown in Equation (6). The computed schedule data (i.e., early start times (\(esi_{i}\)), early finish times (\(ef_{i}\)), late start times (\(ls_{i}\)), and late finish times (\(lf_{i}\)) of each activity (\(i\)) calculated by the CPM are stored in a matrix (\(M_{C}\)) (Equation (7)). The total float (\(lf_{i}\)) of each activity (\(i\)) is calculated as the difference between the late start time (\(ls_{i}\)) and early start time (\(es_{i}\)) (Equation (8)). Afterward, SSO compiles activities with total floats (\(tf\)) greater than zero (i.e., \(lf_{i}>0\)) as noncritical activities (\(N_{CP}\)) and sets \(j\) as an index of \(N_{CP}\) (Step 9).  
\[[es_{i},ef_{i},ls_{i},lf_{i}]=CPM(D_{i},PS_{i}),\;i=1:I \tag{6}\]  
\[M_{C}=[es_{i},ef_{i},ls_{i},lf_{i}],\;i=1:I \tag{7}\]  
\[tf_{i}=ls_{i}-es_{i},\;\forall i \tag{8}\]  
SSO uses two strategies for minimizing space interference. The first is selecting an alternative for the execution patterns of each activity, and the second is deferring the start times (\(st_{i}\)) of noncritical activities (\(N_{CP}\)). GA chromosomes are composed of a set of genes. In SSO, the GA chromosome is composed of a pair of the index (\(k_{i}\)) of the alternatives of execution patterns for activity (\(i\)) and the number of deferred days (\(sd_{i}\)) of the start time for the activity (\(i\)) (Figure 4). The index (\(k_{i}\)) of alternatives of execution pattern is smaller than or equal to the number of alternatives (\(K_{i}\)) of occupation density function for activity \(i\) found in Step 6 (\(k_{i}\)\(\leq\)\(K_{i}\)). The number of deferred days (\(sd_{i}\)) is always zero if activity \(i\) is a critical activity (\(sd_{i}\) = 0, \(i\)\(\leq\)\(N_{CP}\)), and it is smaller than or equal to the total float (\(tf_{j}\)) found in Step 9 if activity \(i\) is a noncritical activity (\(sd_{i}\)\(\leq\)\(tf_{j}\), \(I\subset N_{CP}\)). The serial number of a gene pair is the same as the index (\(i\)) of activity. The search range of the optimal solution (\(S_{S}\)) is the number of combinations of the execution pattern alternatives and the range of deferrable days of activities (Equation (9)).  
\[S_{S}=\prod_{i}(k_{i}\times sd_{i}) \tag{9}\]  
Figure 4: Encoding a chromosome.
# A Survey of CNN-Based Network Intrusion Detection
## 7 Other Factors of CNN-Based IDS Experiments
### Vector-to-Matrix Format Conversion  
The matrix format is established by the dimensional transformation of every feature vector. For example, the reshaping of 49 feature vectors into a 7 \(\times\) 7 matrix exemplifies the remolding of network data into the matrix to enable their input into the convolutional neural network. The elimination of a feature with a high correlation in 121 features is enabled by the ANOVA F-test, as demonstrated in a particular research paper [47]. This is implemented by converting the dimensional vector from 1 \(\times\) 122 into 11 \(\times\) 11.  
Most CNN-based IDS research converted a feature vector into 2D matrices and filled the redundant parts with zeros. Imposing a correlation in the vicinity of the matrix elements is unavoidable in the transformed 2D matrix, which resembles a grayscale image. The implementation of data clustering serves to mitigate the influence of the imposed correlation. Discrete processing occurs for distinct portions of the data, where the data are separated into n parts, in line with previous knowledge of the data. For example, in one study [25],features were divided into primary, content, host-based, and time-based network traffic statistics. This data clustering approach facilitates learning high-level relationships between global features that other classification algorithms would ignore.
# Indole Degradation in a Model System and in Poultry Manure by _Acinetobacter_ spp.
## 3 Results
### Factors Affecting Indole Degradation  
#### 3.3.1 Effect of Temperature and pH  
MSM medium amended with 117.15 mg/L (1.0 mM) of indole was inoculated with 100 uL inoculants and incubated for 96 h at temperatures of 25, 28, 31, 34, and 37 \({}^{\circ}\)C, and 5, 6, 7, 8, and 9 pH values.  
Effect of temperature: The percent degradation efficiency of indole increased as the incubation temperature rose from 25 \({}^{\circ}\)C to 31 \({}^{\circ}\)C. Peak degradation efficiency was observed at 31\({}^{\circ}\)C and declined as temperature increased to 37 \({}^{\circ}\)C (Figure 4).  
Effect of pH: Indole degradation using the two strains was also affected by the pH of the culture media solutions. Both strains and the mix exhibited maximum indole degradation efficiency (%) at pH 6. Strain TAT1-6A and the mix showed increased indole degradation efficiency between pH 6 and 7 (Figure 5).  
Figure 4: Effect of temperature on indole removal efficiency (%) at pH 6 and 117.15 mg/L (1.0 mM) of indole concentration incubated for 96 h. Error bars: Mean \(\pm\) SE of three replicates.  
Figure 3: Indole removal capability of a mix of two strains (NTA1-2A and TAT1-6A) at different initial concentrations (mg/L). Symbols: Solid circle, 58.58; open circle, 117.15; solid triangle, 175.73; open triangle, 234.30; solid square, 292.87; open square, 351.45; error bars, mean \(\pm\) SE of three replicates at 31 \({}^{\circ}\)C and pH 6.
###### Abstract  
The H2020-project OPTIMA concept of smart sprayer relies on several functionalities, including variable nozzle spacing for bed-grown carrots, based on an air-assisted boom sprayer. A prototype boom was designed and evaluated though canopy deposition, ground losses, and spray drift potential. Four bed spray configurations, including various nozzle types, angles, and sizes (XR8004, combination of AIUB8504/AI11004, Al8004, and XR8002) at the most appropriate nozzle spacing and height, were tested and compared to a broadcast application (XR11004). Deposition measurements were performed on carrots in bins at early and full-grown stages with respective target zone width of 1.4 m and 2.2 m. Spray drift potential measurements were performed following ISO 22401, 2015. The spray boom was equipped with an air sleeve providing different air speeds (0, 4, 8 m s\({}^{-1}\)). The relative depositions at both growth stages showed a significant effect of spray configuration and lowest values were found for the broadcast application. The configurations consisting of air inclusion nozzles generated the lowest drift potential compared to the broadcast application, although not significantly different. Bed spray configurations can thus improve canopy depositions and spray drift potential compared to a conventional broadcast application when the boom height and the nozzle spacing are adjusted to the growth stage.  
bed-grown crop; air support; spray deposition; spray drift potential; smart sprayer; nozzle type +
Footnote †: journal: _Sciences_
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 1 Introduction  
In sports activities, the use of wearable devices has a significant impact on providing reliable feedback to users. Undoubtedly, the most dependable information concerns time measurement, as it is independent of the user's movement or the placement of the wearable device. However, with current technological advancements, a product that solely measures time is no longer attractive in the market. The demand for multifunctionality is significantly higher now than it was a decade ago. Users expect their wearable devices to perform various tasks beyond timekeeping, such as counting laps, steps, strokes, and stairs, monitoring heart rate, interacting with user interfaces, enabling GPS localization, displaying smartphone notifications, or even processing payments.  
Focusing on motion monitoring, most wearable devices available on the market provide feedback data but not real-time feedback during movement. One such commercially available system is the Cometa Wave, which includes inertial sensors capable of rapid and accurate data acquisition, real-time communication with external devices, and onboard flash memory for offline data storage. The system supports integration of up to 36 IMU and EMG sensors, enabling full-body motion analysis [1].  
Research using the Cometa Wave system (Cometa Systems, Inc., Newburg, MO, USA) in swimming applications typically involves recording data in the device memory, followedby offline analysis to better understand aquatic motion patterns [2,3]. As this system currently lacks a direct competitor offering similar features, it is priced at a premium, with configurations costing several thousand euros depending on the number of devices used [4].  
Although the Cometa system provides reliable IMU data, it lacks real-time feedback for swimmers during performance. This limitation is critical, as swimmers are unable to assess the quality of their strokes while in the water. Movement verification is only possible post-session using a desktop application that downloads and analyzes the stored sensor data.  
This paper proposes an improved approach for monitoring human movement in water using ESP32 microcontrollers (Espressif Systems, Shanghai, China) and MPU6050 accelerometer and gyroscope sensors (TDK InvenSense, San Jose, CA, USA). The system employs a pattern recognition algorithm based on Dynamic Time Warping (DTW) to evaluate each swimming stroke [5]. After each attempt, the system informs the swimmer whether the stroke was performed correctly, based on pre-recorded reference patterns stored on the device. A vibration motor provides immediate vibrational alert by activating briefly when a stroke does not match the expected movement pattern.  
For this study, three monitoring devices were placed on the left arm--at the wrist, elbow, and shoulder. Each device, comprising an ESP32, an MPU6050 and a 500 mA LiPo battery, costs approximately 30 euros, resulting in a total system cost of about 90 euros.
The application of appropriate (\(m\), \(n\))-threshold schemes generates a clear-cut method of secret division, shadow distribution among protocol participants, as well as the required number of shadows necessary to reproduce the original message. In linguistic threshold schemes, there is a need to determine the number of shadows necessary to recreate the concealed information. In order to reproduce the original message by linguistic threshold schemes, it is necessary to put together:  
* The required \(m\) number of shadows, without the linguistic shadow; this is an example of a classic (\(m\), \(n\))-threshold scheme,
* The required \(m\) number of shadows with the linguistic shadow; this is an example of a linguistic threshold scheme containing semantic information regarding the meaning of the shared data. * The required \(m\) number of shadows, with the required \(k\) number of parts of the shared linguistic shadow; this is an example of a linguistic threshold scheme, in which, to reproduce the original information with its linguistic description, it is necessary to reproduce the linguistic shadow by means of \(k\) parts of the divided linguistic shadow. The \(k<m\) number is the necessary number of parts of the shared linguistic shadows required to reproduce the original message. At the same time, in order to reproduce the entire original message, it is necessary to put together \(k\) parts of the linguistic shadow and \(m-k\) parts of the remaining shadows. In linguistic threshold schemes, it is therefore necessary to define the role and significance of the concealed information. The larger their impact, the greater the number of the linguistic shadow parts required in the process of recreating the original message. In this way, it is possible to determine the required \(k\) parts of the linguistic shadow as well as the dependence on the \(m\) number, i.e., the number of shadows required to reproduce the shared message in linguistic (\(m\), \(n\))-threshold schemes. By means of application in the linguistic threshold schemes, the rules of grammar description and formalisms of the meaning of data interpretation, these protocols can be used both to guarantee the security of the concealed data and the meaning information contained in the protocols executed. Furthermore, they can also be used to describe the meaning of the protected secret. Linguistic threshold schemes are a universal data protection tool enhanced by elements of meaning description and interpretation of the shared information; therefore, they can be used by various entities to conceal the meaning of secret (confidential, restricted, strategic) data.
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)  
In 2004, Zelinka introduced SOMA (Self-Organizing Migrating Algorithm) [59], a new class of stochastic optimization algorithms. Evolutionary algorithms work on populations of candidate solutions that are evolved in generations (i.e., two parents create one new individual, called the offspring) in which only the best-suited (or fittest) individuals are likely to survive. Instead, SOMA which can works on a population of individuals, is based on the self-organizing behavior of groups of individuals in a \"social environment\", e.g., a herd of animals looking for food.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 2 Faults in DC Microgrids  
Protection of converter-based DC microgrids is an evolving topic that has been relatively poorly researched. The variety of power sources, possible operating points, converter types and control modes result in challenging protection system set ups. This section presents the two most common types of faults that the protection system deals with--low-impedance faults (LIF) and high-impedance faults (HIF), each with a different response.