esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 1 Introduction  
The need for personal safety is increasingly being recognized by victims of various crimes, including dating violence, sexual assault, domestic violence, and retaliation crimes, as well as minor stalking [1]. Personal safety smartwatches are equipped with real-time location tracking and emergency reporting capabilities [2, 3]. Although these devices ensure the safety of victims through location-based services, they have limitations in recognizing and preventing at-risk situations in advance. Therefore, this paper focuses on acquiring new data for accident prevention and aims to develop an easily usable smartwatch application based on this research. Since the smartwatch application interacts with the user based on autonomy, it can be personalized to the situation without the need for direct control [4] and can send appropriate emergency SOS signals, providing an effective personal safety measure.  
This study proposes a threatened behavior detection algorithm and application that operates in real-time on a smartwatch. It utilizes the acceleration sensor of an inertial measurement unit (IMU). The accelerometer in an IMU can measure linear accelerations, including gravitational accelerations [5]. Typically, fall detection research involves attaching sensors to the waist or chest, where it is easier for the system to detect falls. However, in real-life environments, this may reduce usability or comfort [6]. Considering the ease ofwear and sustainability, this study conducts data collection and testing using a wearable watch that can be worn on the wrist.  
This study categorizes four main movements: walking, running, desk work, and at-risk movements, and analyzes time series data in the time domain and frequency domain to distinguish these behaviors. This research primarily investigates the effectiveness of 1D convolutional neural networks (CNNs) in processing frequency domain data. It is hypothesized that this approach facilitates efficient computation even with limited training datasets. Furthermore, we consider different input time settings, ranging from 1 to 6 s, to determine their impact on the accuracy of behavior recognition. The ultimate goal is to develop a real-time motion detection application suitable for wearable watch environments, aimed at enhancing personal safety by recognizing potentially at-risk movements.  
This paper is structured as follows. Section 2 presents the related work from the literature. Section 3 describes the data collection method, the machine learning model used, the 1D CNN structure, and the algorithm of the real-time validation app. The experimental results and real-time verification results of the proposed model are analyzed in Section 4. The research directions and conclusions are proposed in Sections 5 and 6.
## 3 Results and Discussion  
The target of designing the 1D scanning system using two galvanometers is to meet the ISO standard and have a more effective working range compared to the 2D galvanometer scanner. Through the workflow diagram, there are three main parts for building this scanning system, including part 1: simulating an f-theta lens model with the same specifications as a real f-theta lens; part 2: simulating a 2D scanner and 1D scanner with two mirrors and evaluating the telecentricity and the roundness of the beam spot; and part 3: implementing the practical experimental setup, including measuring the parameters and evaluating the results, as well as comparing the results of the simulation and experiment. All parts of this study are presented in detail in Figure 4.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Algorithms for Transmission Scheduling of Physiological Signals
#### 3.4.3 Method 3: WFQ with Round Robin and Empty Buffer (WFQ_RR_Empty)  
Based on Method 2, each queue of WFQ follows the FIFO principle and the bandwidth is assigned according to the weight of the flow. Although the priority consideration allows higher priority packets to be served first, and the weight assignment enables a fair scheduling opportunity among queues. However, in order to reduce the interaction and the number of rounds between flows, as well as to obtain more important signals at one time, after the bandwidth is allocated according to the weight, the highest priority queue will be clear first before it is rounded to the next queue. As shown in Figure 13, each physiological signal is placed in its corresponding queue, where the bandwidth is allocated after configuring Equation (2) of each queue. Thus, the highest-priority ECG signal will be transmitted first until the queue is empty, and then the second-priority queue is consulted.
## 5 Conclusions  
The FTIR data demonstrates that DBD plasma treatment effectively induces polymerization of sodium 4-styrene sulfonate (NaSS) within porous filter paper and creates a stable PSS matrix crosslinked with N, N-methylene bisacrylamide (MBAA) and divinyl benzene (DVB). The optimized use of DBD plasma under mild conditions enabled thorough polymerization without initiators or excessive thermal energy. Among tested configurations, the PSS crosslinked with 0.5% DVB exhibited the highest methylene blue (MB) removal, up to 99.4%, over four consecutive filtrations. This crosslinked network demonstrates stability and reusability, aligning with sustainable water purification goals. Future directions include investigating this method's scalability, testing additional monomer systems, and developing DBD plasma-modified filter materials with tailored properties for diverse environmental and biomedical applications. These findings highlight the broader potential of non-thermal plasma in creating robust, biocompatible, and efficient polymer-based adsorbents for environmental applications.  
Conceptualization, S.A.K.V. and H.-F.J.; Methodology, A.P. and H.-F.J.; Validation, S.A.K.V.; Formal analysis, S.A.K.V. and H.-F.J.; Investigation, S.A.K.V., L.F. and A.P.; Data curation, L.F. and A.P.; Writing--review & editing, L.F. and H.-F.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement**: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Short-Run Contexts and Imperfect Testing for Continuous Sampling Plans
## 1 Introduction  
[PERSON] developed the initial continuous sampling plan, referred to as CSP-1, as an effort to ensure a high level of quality for items without the burden of 100% inspection [1]. Under CSP-1, some defective items escape to customers because of a reduced inspection rate. [PERSON]'s work included analytical formulae for performance metrics that are easy to use. However, those formulas were designed for long-run production contexts, and therefore do not apply to finite-size batches of items. In addition, those formulas were designed under the assumption of perfect testing, and therefore do not apply to imperfect testing.  
[PERSON]'s original long-run framework has been adapted to handle imperfect testing, and the appreciable effect of imperfect testing on the performance parameters of the sampling plan is well-understood [2]. The original long-run production framework was also adapted to account for short-run contexts [3, 4]; however, the assumption of perfect testing was retained. In these references, analytical formulae were derived using a Markov chain modeling approach that was later generalized by a renewal-process approach so that more general continuous sampling plans could be analyzed [5]. Formulas for performance metrics of CSP plans resulting from the renewal-process approach were implemented in FORTRAN code [6]. It should be emphasized, however, that the formulas implemented in the FORTRAN code reflect an assumption of perfect testing.  
This research develops a simulation algorithm for CSP-1 plans to provide a mechanism to understand the combined impact of short-run contexts and imperfect testing. To the best of our knowledge, this is the first attempt to combine these two important practicalities of CSP-1 sampling contexts. The simulation algorithm implements a test effectiveness parameter that enables recognitionthat defective items can escape the test procedure. A key output of the simulation is the probability distribution for the number of defective items that escape to customers. An application that answers sampling design questions for the United States Navy is presented and comparisons with analytical formula are discussed. User-friendly R code that implements the simulation algorithm is provided.
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 4 Discussion  
### Spatial and Temporal Trends of CUE and WUE  
The CUE of vegetation in Inner Mongolia was determined in this study by using MODIS data from 2001 to 2020. The results indicated that Inner Mongolia had a multi-year mean value of CUE of 0.656, which agrees with the assumption of [PERSON] et al. (2021) [36] that the mean value of CUE in dry and semi-arid regions is more than 0.5. Multi-year mean CUE decreased spatially from west to east in Inner Mongolia because of the region's higher altitude, higher humidity, and colder temperatures, which all lead to lower ecosystem energy consumption via autotrophic respiration and more efficient carbon sequestration and transfer in the vegetation [37]. Since grassland ecosystems are less complicated than forest ecosystems, and since photosynthesis fixes more solar energy during the development of vegetation while burning more energy for respiration, the CUE of grassland is higher than that in forests [38; 39; 40; 41]. Consistent with the findings of [PERSON] et al. [42] on the distribution of NPP/GPP, the general distribution of CUE demonstrated that regions with sparse vegetation were greater than dense vegetation areas. The regional distribution of WUE exhibited an upward tendency from the southwest to the northeast, and, in contrast to CUE, WUE was noticeably greater in forest environments than in grassland habitats. The reason for this is because grassland ecosystems have less plant cover, poorer productivity, and higher surface evapotranspiration [43]. There was a correlation between the geographic distribution of the different ecological zones and the distribution of WUE. The northern part of Hulumbieer, Inner Mongolia, has coniferous and mixed forests, with sufficient water and heat resources and high vegetation cover; under the same conditions, this ecological zone has stronger photosynthesis, and thus will produce more organic matter than other regions. Moreover, the changes in temperature were small, reducing evapotranspiration [44; 45; 46; 47]. In contrast, desert grasslands have little vegetation cover, low productivity, and higher soil evaporation, resulting in relatively low WUE values in these areas [48].
## 2 Materials and Methods
### ROS Analysis  
Cells were seeded in 96-well plates (PerkinElmer, Waltham, MA, USA) coated with poly-D-lysine (Sigma-Aldrich, St. Louis, MO, USA) at a density of 1.5 \(\times\) 10\({}^{4}\) cells/well and incubated for 8 h. Subsequently, the cells were treated with 0, 10, 50, and 100 ng/mL LPS (L3129; Sigma-Aldrich) dissolved in PBS and 50 U/L IFN-7 (R&D systems, Minneapolis, MN, USA) for 24 h. The cells were then treated with 0, 1, 5 and 10 \(\upmu\)M \(\beta\)-lactolin for 6 h prior to staining, followed by incubation with 10 \(\upmu\)M 5-(and-6)-chloromethyl-2,7-dichlorhydrofluorescein diacetate acetyl ester (CM-H2 DCFDA; Invitrogen, Waltham, MA, USA) and 0.5 \(\upmu\)g/mL Hoechst 33,342 (Dojindo, Kumamoto, Japan) in Hanks' balanced salt solution (Gibco) for 30 min at 37 \({}^{\circ}\)C. The culture medium was replaced with fresh Hanks' balanced salt solution. Fluorescence was measured using an Operetta CLS\({}^{\upmu\text{M}}\) instrument (Perkin Elmer) at excitation and emission wavelengths of 460-490 nm and 500-550 nm (CM-H2 DCFDA), and 355-385 nm and 430-500 nm (Hoechst), respectively. Fluorescence intensity per cell was analyzed using the Harmony 4.9 software (Perkin Elmer) and expressed in relative fluorescence units (RFU).
## 3 Test Data Analysis
### Z Vibration Level Analysis
#### 3.5.2 Z Vibration Level and Insertion Loss Analysis  
Take the data measured when 20 trains pass through the test section, analyze and statistically obtain the V\({}_{\text{LZ}}\) value and the vibration difference V\({}_{\text{LZ}}\) of the tunnel wall measurement point of each test section under the two track bed forms. In order to compare the Z vibration levels of the track components under different line conditions, the above corrected Z vibration levels are counted, as shown in Figure 24.  
It can be seen from Figure 24 that after the vehicle speed is corrected to the same speed, the Z vibration level of the tunnel wall in the steel-spring-floating slab track segment is significantly smaller than that in the ordinary monolithic track segment. For the ordinary monolithic track tunnel wall, the Z vibration level is located at 70.83-76.54 dB. The Z vibration level of the tunnel wall at the steel-spring-floating slab track is located at 46.41-49.33 dB. The insertion loss V\({}_{\text{LZ}}\) of the straight line and the curved segment are 24.42 dB and 27.21 dB, respectively, and the average insertion loss V\({}_{\text{LZ}}\) is 25.82 dB, indicating that the steel-spring-floating slab track has a significant vibration isolation effect.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Numerical Value** & **Unit** \\ \hline Basic vehicle length & 19.0 & m \\ Body distance & 12.6 & m \\ Bogie length & 2.995 & m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensional parameters of subway type B car.  
Figure 21: Comparison of frequency division vibration level transfer rate of each track component. (a) Rail–track bed; (b) Track bed–tunnel wall.  
Figure 23: Time domain curve of rail dynamic displacement. (**a**) Straight line segment—ordinary monolithic track; (**b**) straight line segment—steel-spring-floating slab track; (**c**) curved segment—ordinary monolithic track; (**d**) curved segment—steel-spring-floating slab track.  
Figure 22: Comparison of transfer coefficients between track components. (**a**) Rail–track bed; (**b**) track bed–tunnel wall.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 6 Risk Analysis
### Safety Impact Taxonomy  
This chapter provides an in-depth cybersecurity risk analysis of the autonomous emergency braking AEB system using the STRIDE methodology. The analysis evaluates and categorizes each AEB component within severe and major safety impact levels, facilitating a structured approach to identifying and mitigating threats that, while not always classified as high-risk, pose critical implications for vehicle safety. This evaluation underscores the necessity of robust security measures to ensure the AEB system's reliability and the overall safety of vehicle operations.  
As outlined in Table 4, the most significant safety threats arise from spoofing, tampering, and denial-of-service attacks on critical components. The ESC system is the most vulnerable component, as attacks targeting it can cause the entire system to malfunction, posing a substantial risk to the safety of vehicle occupants. The ESC plays a central role in system operations, making it a critical single point of failure. Similarly, the SAS, which detects the steering wheel's position, is another highly sensitive component. In the event of spoofing, the SAS may deliver incorrect data to the ESC, while a denial-of-service attack could prevent it from transmitting data altogether. Both scenarios critically compromise vehicle safety, further underscoring the components' importance.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attack** & **Component (s)** & **Attack Vector** & **Countermeasure** \\ \hline Spoofing & ESC, EMS/TCU, ACU, Radar & Physical access in the case of hardware components and remote access by leveraging the external communication of the EMS/TCU. & Implementing digital signatures and authentication protocols. \\ \hline Tampering & ESC, EMS/TCU, SAS, WSS, Radar & Physical access to the component. & Implementing security measures for wiring cables connected via the OBD port. \\ \hline Repudiation & ESC, ACU & The unauthorized external manipulation of a component’s functionality. & The implementation of multi-factor authentication and privileged access management. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analyzed STRIDE severe threats for safety.
# Train Service Design for Rail Transit Cross-Line Operation Applying Virtual Coupling
## 3 Mathematical Model
### Objective Function
#### 3.1.3 Operation Costs  
This study examines two primary components of operation costs, the train operating cost and train usage cost, as represented in Equation (36), where \(C_{1}\) represents the train operating cost, and \(C_{2}\) represents the train usage cost.  
\[C=C_{1}+C_{2} \tag{36}\]  
The train operating cost \(C_{1}\) represents the expenses incurred for the distance traveled during train service. In CO systems, the train composition of line \(l\)\(n_{l}\), operation frequency of line \(l\)\(f_{l}\), operating distance of line \(l\)\(D_{l}\), and train operation cost per kilometer \(\lambda\) can be calculated using Equation (37).  
\[C_{1}=\lambda\cdot\sum_{l=1}^{3}D_{l}\cdot f_{l}\cdot n_{l} \tag{37}\]  
The train usage cost \(C_{2}\) consist of wear and tear, depreciation, etc. The train usage cost is related to the number of carriages in service and can be calculated by the train composition of line \(l\)\(n_{l}\), operation frequency of line \(l\)\(f_{l}\), the full cycle time of line \(l\)\(T_{l}\), and the train depreciation cost per min \(\omega\). Unlike traditional trains, VC trains are equipped with multiple sensors, communication equipment, and other technologies to facilitate coupling operations. The train usage cost is calculated using Equation (38).  
\[C_{2}=\omega\cdot\sum_{l=1}^{3}\left[\frac{f_{l}\cdot n_{l}\cdot T_{l}}{60}\right] \tag{38}\]
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Path Optimization  
The reversed path planning in D* Lite is beneficial to replanning when there are dynamic obstacles. However, there may also be some redundant turns in the path, which increases the path length. Figure 4 shows a simple path planned by the D* Lite. We find that when D* Lite starts to plan a path from the goal node to the start node, it detects an obstacle at position \(b\) and makes a left turn towards position \(a\) to avoid the obstacle. Finally, it finds a path from the goal node to the start node. However, we find that the path between position \(a\) and the goal node is not optimal. It contains an unnecessary turn towards position \(b\). We can reduce the path if we can let the USV drive straight from position \(a\) to the goal node.  
To optimize the path, we set the line from the current node \(s\) to the goal node as \(l\). We compare the \(rhs(s)\) value with the length of \(l\). If there is no obstacle along the line \(l\) and the \(rhs(s)\) value is smaller than the length of \(l\), then we replace the original path segment with the straight line. Figure 5 shows the main procedure of path planning and optimization.  
The main steps of the planning and optimization precedure are as follows:  
1. Plan the path using D* Lite.
2. Start to move from the start node \(S_{start}\) to the goal node \(S_{goal}\).  
Figure 4: Path planned by D* Lite.  
Figure 3: Next position calculation with steering angle limitation.  
Set the current node as \(s\) and calculate the _rhs_ value of its all the successor nodes. Let node \(s^{\prime}\) represent the node with the smallest _rhs_ value of the successor nodes.
4. Set a line \(L\) from node \(s\) to the goal node \(S_{goal}\).
5. If there is no obstacle along line \(L\), 1. Let \(D\) be the length of \(L\). 2. If \(D\geq\textit{rhs}(s^{\prime})+c(s,s^{\prime})\), move to node \(s^{\prime}\); go to step (3). 3. Otherwise, if \(\alpha>maxTurn\), move to node \(s^{\prime}\); go to step (3). Otherwise, move from node \(s\) to the goal node \(S_{goal}\) directly.
6. Otherwise, if there is an obstacle along line \(L\), 1. Move to node \(s^{\prime}\). 2. Go to step (3).
## 4 Results and Discussion
### Sensitivity Analysis  
This study performed the sensitivity analysis to investigate the factors affecting the results. It is important to analyze the correlation between the factors and the results because the results can be changed depending on the variation of the factors. In this study, four factors are investigated for the sensitivity analysis: failure rate, repair time, redundant equipment, and modified preventive maintenance schedule. The reliability data used in this study are mainly from OREDA, and its mean value is utilized. The values can be different depending on the target conditions. OREDA predicts the failure rate with 90% confidence interval. The confidence interval describes the amount of uncertainty associated with a sample of a population. The sensitivity analysis was performed for the lower and upper limits of the failure rates. The repair times utilized in this study were also mostly from OREDA. The employed active repair time considers only the time when actual repair work is being done. It does not contain time to shut down the unit, issue the work order, wait for spare parts, start-up after repair. Some variation exists between the active repair time and the actual downtime. (The reason why OREDA only considers the active repair time is that the required time for the preparation and return to the normal operation are different depending on the location of the installation.) The additional repair time is taken into account. The availabilities with and without redundant equipment are estimated to examine its effect on the availability. Finally, the availability is calculated with different preventive maintenance schedules.  
Figure 14: Component criticality depending on the stages.  
Figure 13: Availability depending on the stages.
## 5 Discussion
### Limitation and Future Works
#### 5.3.2. Consideration of Tactics Interaction  
In this study, we incorporated three tactical components: Stiffness Tactic \(T_{k}\): Controls stiffness based on velocity feedback to minimize the difference between actual and target speed. Landing Posture Tactic \(T_{\theta}\): Adjusts landing posture by feeding back the velocity difference between steps to minimize acceleration between strides. Force Tactic \(T_{f}\): Regulates force control based on the energy state to achieve a target energy condition. Among these, \(T_{k}\) and \(T_{\theta}\) both utilize velocity feedback \(e_{\
u}\), indicating a potential interdependency. Additionally, since \(T_{f}\) operates based on the abstract physical quantity of energy, it may interact indirectly with the other tactics.  
While each tactic's gain could be optimized independently, in this study, we optimized them collectively to adapt stride and cadence to each individual while ensuring stable running motion. If too many tactical parameters are introduced, redundancy may compromise stability, whereas an insufficient number of tactics may fail to capture individual characteristics effectively. Based on our findings, we estimate that the proposed tactics provide a necessary and sufficient basis for adaptive individual motion generation.  
Although we did not conduct an explicit analysis of the interdependence between tactics, their interactions were inherently considered in the optimization objective function. A more detailed investigation into these interactions will be addressed in future research.