esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 VRDeepSafety Simulation Environment
### V2X Communication Integration
#### 3.7.4 Communication Workflow  
The VRDeepSafety framework uses a range of communication message formats to increase situational awareness, decision-making, and the safety of autonomous vehicles. These communications allow for the real-time sharing of important information among vehicles, roadside units, and infrastructure, allowing for coordinated activities and proactive responses to changing road conditions. The V2X communication system's primary message types are summarized below:  
One of the primary message types is the BSMs. They provide vehicle status data, enabling immediate decision-making and reducing accident likelihood in emergency situations, allowing timely assessment of potential collision risks.  
Complementing this, the CAM maintains situational awareness by broadcasting vehicle location, speed, and direction, enhancing traffic flow and reducing road conflicts in dynamic driving environments.  
Furthermore, DENMs are crucial for alerting vehicles to hazards like accidents, road blockages, and adverse weather conditions, especially in areas with limited visibility or rapid conditions.  
In summary, the communication message types in the V2X system--BSMs, CAMs, SPaT, and DENMs--work together to improve safety and optimize the transportation environment. By enabling vehicles to share information about their status, road conditions, and emerging risks, these messages support coordinated decision-making and proactive safety measures. By embedding these message formats within the VRDeepSafety framework, autonomous vehicles can react to real-time traffic conditions, improving safety and traffic efficiency.  
Figure 3 illustrates the real-time data exchange within the VRDeepSafety environment via V2X communication. AVs transmit BSMs and DENMs to RSUs and receive information such as BSMs and SPaT from RSUs. RSUs exchange BSMs, DENMs, CAMs, and SPaT information with the centralized traffic management system. This multi-directional communication enables coordinated, proactive responses to dynamic road conditions, improving traffic flow, efficiency, and safety.  
The communication flow is important in improving decision-making. Vehicles collect data on speed, location, and braking, which are sent to traffic management systems via V2X. The systems process the data and create a traffic overview, which is sent to all vehicles so everyone can respond in sync and improving situational awareness.  
Including V2X in VRDeepSafety improves accident prediction and mitigation. By sharing real-time data, the system can anticipate and respond to impending threats. This enhances the safety of self-driving vehicles and traffic flow, decreases delays, and increases overall transportation efficiency.
# Geospatial Assessment of the Territorial Road Network by Fractal Method
## 2 Statement of the Problem  
### Classical Indicators of the Transport Provision of Territories  
The concept of transport development of territories is associated with such concepts as transport provision, which reflects the quality level of transport services for facilities and the population. Obviously, the more developed the network of communication lines in a particular region, the higher these indicators are. To assess transport provision, quantitative indicators are usually used that express the ratio of the length of tracks to a unit area of a territory, or to a certain number of residents, production volumes, or other factors. For example, there are the coefficients of [PERSON] (1), [PERSON] (2), and [PERSON] (3) [8]:  
\[k_{E}=\frac{L}{\sqrt{SH}} \tag{1}\]  
\[k_{G}=\frac{L}{\sqrt{SN}} \tag{2}\]  
\[k_{U}=\frac{L}{\sqrt[3]{SHt}} \tag{3}\]  
where \(L\) is the length of the transport network in km, \(S\) is the area of the developed territory in thousands of km\({}^{2}\), \(H\) is the total population in thousands of people, \(N\) is the number of settlements, and \(t\) is the total weight of the cargo sent to the territory.  
When exploring the transport provision of a territory, it is not valid to compare the length of the transport networks with the area of the territory. It is assumed that moving away from the areal component and analyzing the level of complexity of the transport network itself may become more rational. A good example is a study of the calculation of the transport provision of a territory based on the theory of fractals, which is displayed in [25]. In contrast to the indicators in Equations (1)-(3), an indicator based on the fractal dimension excludes the area of the territory and takes into account the structural features and complexity of the road network itself, where the fractal dimension of each cell of the territory reflects a certain level of its density of the road network.
## 2 Sensor Selection and Layout
To ensure the reliability and accuracy of the measurements, the experiments were conducted with repeated trials. A total of 60 trials were performed, with each trial measuring the heart rate and breathing frequency for a duration of 60 min. The number of trials was determined based on a power analysis, which estimated that 30 repetitions would provide a 95% confidence level for detecting differences within an error margin of \(\pm\)8%. This approach was chosen to minimize statistical errors and ensure the reliability of the results. The layout of the sensors in the smart cushion plays a critical role in the accuracy and stability of physiological data collection. The MEMS sensors are strategically placed in the backrest and seat cushion areas to monitor the heart rate and respiratory frequency. This placement ensures that signals are reliably captured regardless of the driver's posture during normal driving operations. Optical fiber sensors are embedded in the seat's pressure contact areas to measure real-time changes in body pressure and posture. These sensors not only detect pressure variations but also, when combined with vehicle data from the Electronic Vehicle Identification (EVI) system, contribute to a more comprehensive understanding of the driver's fatigue and stress levels. Electrodermal activity (EDA) sensors, worn by volunteer drivers on their fingers, wrists, or forearms, further enhance the monitoring of fatigue and stress responses. To maintain consistent and accurate physiological measurements, an adaptive calibration system has been designed to mitigate errors caused by sensor movement or environmental interference. This system adjusts the sensor placement dynamically to accommodate individual differences in body shape and driving posture, ensuring high-quality data across diverse driving conditions. A reference signal, established by monitoring the driver's baseline heart rate and respiratory rate over time, is used to calibrate the collected data in real time. Additionally, a Kalman filter is employed to fine-tune the signal, compensating for changes in the sensor position or individual physiological variations. By integrating MEMS sensors, optical fiber sensors, and EVI technology, the system ensures the continuous, real-time monitoring of key physiological parameters, such as the heart rate and respiratory rate. When combined with vehicle status data, this multimodal approach provides a holistic evaluation of the driver's physiological state, enhancing both the accuracy and robustness of monitoring. This integrated system offers real-time, reliable data collection, ensuring safety and performance across various driving scenarios.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 3 Proposed Dereverberation Method  
The block diagram of the proposed dereverberation method is shown in Figure 1. In the upper path, the CGMM-based MVDR beamformer is used to enhance the input signal \(\mathbf{y}\) (\(l\)) collected by the microphones so that we can obtain the enhanced signal \(x_{\mathrm{b}}\) (\(l\)). The PSD of the desired signal used for the Kalman filter is approximately equal to the power spectral density \(\phi_{x_{\mathrm{b}}}(l)\) of the \(x_{\mathrm{b}}\) (\(l\)). In the lower path, according to the delayed signals \(\mathbf{t}\) (\(l\)) of the input signal \(\mathbf{y}\) (\(l\)), the MCLP is performed based on the Kalman filter to estimate the reverberation signal \(z\) (\(l\)) by linear prediction. The input to the Kalman filter-based linear predictor is \(\mathbf{t}\) (\(l\)), and the corresponding output is the estimated reverberation signal \(z\) (\(l\)). By subtracting \(z\) (\(l\)) from \(x_{\mathrm{b}}\) (\(l\)), we can finally obtain the enhanced signal \(s\) (\(l\)). Different from ISCLP [12], this paper uses the MVDR beamforming to replace the GSC, because the GSC has the shortage of speech distortion, that is, if the steering vector of the desired speech used in the GSC is not accurate, the desired speech will be inevitably destroyed by its block matrix. In particular, in the ISCLP, the PSD of the target signal is estimated by a complex algorithm, which contains some prior information [12], whereas the proposed method uses a simpler approach without any prior information to estimate the PSD of the target signal.
## 3 Results and Discussion
### The Impact of Different Parameters on the Efficency of IME Process
#### 3.2.4 Effect of Time of the Process  
Changes in COD removal in m-IME (pH 3) over time for two doses of Fe/GAC microelectrodes were observed: 40/4 and 40/10 g/L are presented in Figure 4.  
An increase in the efficiency of COD value reduction in the IME process was observed with the time of the process up to a certain limit. The highest increase in the efficiency of the m-IME process was observed up to 15 min; for both microelectrode doses of Fe/GAC, 40/4 and 40/10 g/g/L, the COD removal was 25.9% and 29.5%, respectively. The process carried out for 120 min allowed us to reduce the COD value by an additional 12.5% in the case of sample 1.1 and by 15.1% in the case of sample 1.3 (Figure 4). Due to the fact that, in most cases, the highest increase in COD removal was observed during the first 120 min, that time was considered to be the optimal process time. Extending the process time by another 120 min resulted in an increase in COD removal by about 7%.  
Figure 4: Changes in D COD for m-IME (pH 3) over time for doses of Fe/GAC microelectrodes: 40/4 g/L—sample 1.1 and 40/10 g/L—sample 1.3.  
Although there are several publications in which the authors give the optimal time of the IME process, there is a lack of detailed research on the effect of the time of the process and the effectiveness of COD removal. Additionally, the results obtained by different authors are practically impossible to compare due to the differences in the conditions of the process. Different authors suggest different optimal times for the IME process: 20 min [31], 60 min [28; 31; 34] and 90 min [2; 30]. In column experiments, the concept of bed-leachate contact time, regulated flow time [5] or HRT/retention time [2; 28] is used; the authors report 60 min as the optimal process time, but the comparison of their results with the results obtained in stationary reactors can only be approximate. [PERSON], on the other hand, conducting a column experiment, proposed a total time of 90 min for the sequential IME process (\(\Delta\) COD 73.7%) (without aeration--60 min (COD 48.9%)) (with aeration--90 min (58.5%)) [2; 3; 4]. In the case of the experiment conducted in stationary reactors, 60 min was also considered to be the optimal process time [29; 34] (a COD removal of 90.27% and 76.7%, respectively). On the other hand, [PERSON] achieved the maximum COD removal (45%) in the first 20 min of the IME process [31]. The longest process time (90 min) is proposed by [PERSON] (\(\Delta\) COD 75-80%) [33].  
During the studies on the effectiveness of m-IME, an increase in pH was observed over time, which is probably a result of the electrode reactions (reaction 7). Figure 5 presents the results of the studies on the changes in pH during the z-IME process from 15 to 240 min.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 3 Materials and Methods  
This section proposes different types of polling mechanisms for LoRaWAN in more detail. After introducing the basics of the technology, two polling alternatives for Class B and two for Class C are described. They can be used to perform polling in different environments in terms of time requirements, number of nodes, and consumption. Nevertheless, with the described polling systems, the majority of polling applications involving a LoRaWAN are covered; furthermore, these alternatives can be combined to adapt dynamically to environment and application changes.
# Effects of the Damping Parameters on the Opening and Closing Characteristics of Vent Valves
## 1 Introduction
[20] studied the influence of the position and size of the damping hole on the performance of the solenoid valve and concluded that the electromagnetic force can be enhanced by reducing the size of the damping hole and the width of the working air gap while keeping the damping hole away from the center of the armature. [PERSON] et al. [21] made the shift-regulating valve better meet the requirements of construction machinery by controlling the proper matching of the spring stiffness and damping hole. [PERSON] et al. [22] studied the influence of the fluid path on the flow pulsation and instantaneous pressure in a piston cavity from the perspective of the damping hole, buffer chamber and throttle hole. [PERSON] et al. [23] analyzed the influence of the damping holes on the dynamic response of the spool of the relief valve by establishing mathematical and physical models. [PERSON] et al. [24] studied the influence of the arrangement of damping holes on the overall flow characteristics of the pilot globe valve and the local flow characteristics around the spool. [PERSON] et al. [25] proposed a perforated damping sleeve that changed the internal geometry of the valve and conducted experimental and numerical studies on the flow dynamics on the damper sleeve disc to determine the differences in the flow field. [PERSON] et al. [26] simulated the pressure drop characteristics of the pilot-controlled globe valve (PCGV) with different structural parameters and concluded that in order to ensure the normal operation of the PCGV, a larger pilot pipe diameter and medium orifice diameter should be selected in the design progress. The pressure drop increases with the increase in the inlet velocity or pilot pipe diameter and decreases with an increase in the valve diameter or orifice diameter. [PERSON] al. [27] proposed a simplified structure of the pilot pressure reduction valve (PRV). It was concluded that the orifice diameter and pilot lift, which affect the pressure drop, are important design variables for controlling the opening of the main valve. The above scholars have studied the pressure characteristics of the valve, the dynamic response of the damping hole to the valve body, the flow characteristics and the pressure loss. These studies have promoted the improvement of valve performance. However, there is no method to quickly and effectively obtain the influence of the damping parameters on the system characteristics. In this paper, the system modeling of a two-stage partial pressure control pneumatic valve is carried out. The reliability of the model is verified by experiments, and the influence of the valve damper hole diameter and nozzle diameter on the valve's opening and closing characteristics is analyzed by parametric simulation.
# Reality Check: Experimental Studies on Microplastics Lack Realism
## 1 Introduction  
Ever since the discovery of microplastics (MPs) in the ocean, there have been large numbers of laboratory studies on their effects. Many studies have been conducted on aquatic organisms, many of which consume these pollutants, and some of which are eaten by humans. Other experimental studies have been on mammalian surrogates (rats or mice) or tissue cultures to provide evidence for potential impacts of these ubiquitous pollutants on humans. For both types of studies, questions have arisen about the lack of realism. For example, [PERSON] and [PERSON] (2018) noted that the levels detected in the environment are orders of magnitude lower than those used in studies of effects on biochemistry, reproduction, growth, feeding, and inflammation in aquatic organisms [1]. Similarly, [PERSON] et al. (2020) who performed a meta-analysis of the literature, concluded that there was a mismatch between field studies and the laboratory studies, which tend to use different plastic shapes (fibers being the most common in the field; spheres in the lab), virgin MPs in the lab as compared to aged MPs with biofilm in the field, and different sizes of MPs [2]. Fibers have been found to be the most common MP type in aquatic ecosystems [3]. These fibers come predominantly from washing of synthetic textiles, but also from cigarette butts [4]. The degree of effect detected is a response to concentration, shape, particle size, and polymer type. Comparing concentrations used in most lab exposures to the concentrations and sizes of MPs in the environment, [PERSON] et al. (2020) noted that 17% of the concentrations used in lab studies have been seen in the environment, and that 80% of the particle sizes used in experimental studies are below the sizes found in most environmental sampling [2].  
[PERSON] et al. (2020) in a general review found that toxicity of plastic particles depends on particle size, concentration, particle condition, exposure time, polymer type, and shape [5]. Other factors, relating to the experimental subject included species, sex, developmental stage, and food availability. Effects that have been found in experimental studies were on energy metabolism, feeding, growth, activity, metabolism, physiological stress, pathology, immune system, intestinal damage, development, hormonal regulation, and cell death. Photosynthetic disruption was reported in phytoplankton.  
In this review, we examine effects of MPs on aquatic animals, and on mammalian surrogates and cell cultures, representing risks to human health. We view these studies in terms of what type of MP was chosen for experimentation: shape (e.g., spheres in experiments, fibers in the environment) and size, concentrations used, length of time of exposure, and coating (e.g., chemical additives, biofilms, \"virgin\"). Critically, in some cases we show that the dearth of environmentally relevant data emphasizes what needs to be done, rather than what we already know.
###### Abstract  
When an insulated-gate bipolar transistor (IGBT) open-circuit fault occurs, a three-phase pulse-width modulated (PWM) converter can usually keep working, which will lead to system instability and more serious secondary faults. The fault detection and diagnosis of the converter is extremely necessary to improve the reliability of the power supply system. In order to solve the problem of fault misdiagnosis caused by parameters disturbance, this paper proposes a robust accuracy weighted random forests online fault diagnosis model to accurately locate various IGBTs open-circuit faults. Firstly, the fault signal features are preprocessed by using the three-phase current signal and normalization method. Based on the test accuracy of the perturbed out-of-bag data and the multiple converters test data, a robust accuracy weighted random forests algorithm is proposed for extracting a mapping relationship between fault modes and current signal. In order to further improve the fault diagnosis performance, a parameter optimization model is built to optimize hyper-parameters of the proposed method. Finally, comparative simulation and online fault diagnosis experiments are carried out, and the results demonstrate the effectiveness and superiority of the method.  
three-phase PWM converter; IGBT open-circuit fault; fault diagnose; robust accuracy weighted; random forests
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 4 Results and Discussions
### Findings  
From the data analyzed and real-life experience while using the MNO network in real-time, the trends and patterns can be summarized in the following findings:  
1. The RSRP or any other relevant KPI is not sufficient enough on its own to determine the QoS of an MNO, as observed from the UE.
2. To get better MBB performance in Lagos, especially the sample space researched, a thorough mapping and possible replanning of the routes and the hardware used may be necessary.
3. From the probability distribution functions, the chances of getting the best value obtained from the PSO optimization are 0.02, 0.018, and 0.035 in UNILAG, Ikorodu, and Oniru VI, respectively.
4. For instance, in areas such as UNILAG, the QoS of one user at a location with good receptivity is greatly dwarfed by the experience of a user at a location with poor reception.
5. Though there seems to be a consistency in value with the data obtained in Oniru VI, the measured values generally indicated poorer user experience in that area.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 2 Theoretical Background
### Related Work
Similarly, in [27], the focus was on improving video quality in social clouds. These clouds formed a basis for which voice and video calling services in budding social networks and gaming were developed [29]. Physical KPIs explored certain relationships between the QoE and the QoS [30; 31; 32].
# Networked Roadside Perception Units for Autonomous Driving
## 5 Evaluation
### Experimental Evaluation
#### 5.1.1 Indoor Test  
As shown in Figure 6, we conducted the indoor test by placing the routers and hosts on a desk. Between the routers, the distance was nearly 3 m. For the RSPU, the Rosbag was recorded at an intersection at the Hongo campus. For the vehicle, it was recorded at the school. The duration of Rosbags was 4 min 35 s, which were replayed in the vehicle hosts and RSPU. Meanwhile, an average of 57 messages was created the RSPU as per second. All measurements were made as many as five times.  
Figure 7 illustrates the findings of the indoor experiment. Figure 7a shows that the overall average was T was 70.7 ms, and Tpc occupied 80% of it. As illustrated in Figure 7b, the peak delay relating to \(T_{pc}\), \(T_{hr}\), \(T_{rr}\), and \(T_{rh}\) were nearly 80 ms, 3 ms, 20 ms, and 3 ms, respectively. Therefore, the autonomous vehicle was able to receive the cooperative perception message in nearly 100 ms even in the worst-case scenarios; 10 Hz, which is also the maximum frequency for CAM, is the frequency of point cloud measurement. For this reason, the result demonstrates that the proposed system is capable of delivering the message both within the point cloud measurement interval and within the CAM interval.