esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Furthermore, specific measures have been implemented to mitigate the adsorption/desorption effects characteristic of NH\({}_{3}\) at trace concentrations. These include inert coatings on the cell walls and pipes, and carefully regulated flow rates to ensure homogeneous renewal of the analysis volume. These actions are designed to mitigate non-linear phenomena, memory effects, and long response times that could compromise measurement at the ppb level. Although direct calibration at ultra-low concentrations was not performed at this stage, the detection limit (LOD) was determined experimentally from statistical analysis of background noise and system repeatability under controlled conditions. This approach reliably estimates the sensitivity of the system in the ppb range. However, it is recognized that cross-validation using standard mixtures in the sub-ppm and ppb range will be necessary to empirically confirm the extrapolation of the response. This step is planned as future work and will be presented in subsequent publications. The results obtained with the MPC-PAS system are very promising, but they also show certain limitations. Despite achievements in improving the optical path and the signal, current technologies still face significant challenges in terms of sensitivity and long-term stability. However, MPC-PAS systems are more accessible and less complex compared to other approaches, such as systems using quantum cascade lasers (QCL) or QEPAS, making them an excellent option for industrial applications and field measurements. On the other hand, the results show that, in terms of monitoring industrial gases or ammonia emissions, the multipass cell photoacoustic system could be an efficient and cost-effective option. The enhancement of signal by increasing the optical path is a strategy that has proven effective and is worthy of further optimization. As previously mentioned, the mid-infrared offers deeper absorption lines for a wider range of gases, which would improve sensitivity and lower detection limits. Recent research, such as that of [PERSON] et al. [23], suggests that the transition to MIR could be the key to overcoming the limitations of near-infrared photoacoustic systems. This transition would not only enhance the detection of gases such as NH\({}_{3}\), but also address gases of industrial interest, including H\({}_{2}\)S and NO\({}_{2}\), which exhibit more pronounced absorption characteristics in the MIR. The miniaturization of photoacoustic spectroscopy systems has the potential to unlock new applications, particularly in real-time environmental monitoring. According to [PERSON] et al. [28], compact MPCs and portable systems are beginning to have a significant impact on gas monitoring in the field. The development of portable systems that can perform real-time measurements without sacrificing accuracy would represent a key advance.
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 3 Case Studies
### Case Study in an Online Mode  
Previous offline case studies verified the merits of the proposed strategy based on integrated load curve and filtering method 2. An online case study is thus conducted here using filtering method 2 and integrated load curve.  
Time series of three types of loads, as in offline case studies, are also used here. For comparison, case 5 optimizes at each time point using only current load measurement without consideration of load curve prediction or segmentation. In case 6, the proposed strategy is applied, as shown in Table 3.  
In case 6, for each hour in a daily cycle, hourly load forecasts are made for the next day and for each type of load. The forecasts and previous observations form a 48-h load curve, which is then filtered using method 2 in (2) and segmented. If the current time is considered as a separation point, then optimization is applied based on averaged load level in an associated segment. In Figure 8a, black line indicates load curve observed, the red line predicted ones. Filtering is conducted for 48-h time series, and then the current time, i.e., 9 am, is detected as a separation point. Thus, optimization is conducted based on average load during current segment, i.e., time period from 9 am to 12 am. In Figure 8b, optimization should be based on load level at current time, i.e., 7 am, because it is detected as a separation point.  
Figure 9a shows comparisons of two cases w.r.t. network losses. It is found that case 6 presents relatively lower losses. Figure 9b,c illustrate that tap changes produced by case 6 are less than that in case 5. Again, the tap changing times have positive correlation with the segment number involved.  
Figure 8: Illustration of observed and predicted load curves in case 6, at time 9 am (**a**) and 7 am (**b**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case No.** & \begin{tabular}{c} **Using Predicted** \\ **Load Series** \\ \end{tabular} & \begin{tabular}{c} **Segmentation** \\ **Applied** \\ \end{tabular} & \begin{tabular}{c} **Filtering** \\ **Method Used** \\ \end{tabular} & \begin{tabular}{c} **Use Integrated** \\ **Curve** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Segments** \\ \end{tabular} \\ \hline Case 5 & No & No & – & – & 24 \\ Case 6 & Yes & Yes &
\begin{tabular}{c} method 2 as in \\ (2) \\ \end{tabular} & Yes & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters for cases 5 and 6.  
Table 4 presents comparisons in terms of device adjusting times and network losses. Case 5 produces eight tap changing times while case 6 only output six. Network losses in case 5 are slightly less than in case 6. The computation time consumed by case 5 is two times more than that in case 6. This evidence that the proposed strategy gave a satisfactory performance in both optimization results as well as computational efficiency.
## 2 Background on Internet of Things (IoT) and Sensors
### Applications of IoT in Emergency Management  
IoT technology has a wide range of potential applications in emergency management, from early warning systems to real-time monitoring and response. One of the key applications of IoT in emergency management is in the area of disaster detection and warming [27; 28; 29; 30]. IoT sensors can be used to detect natural disasters such as earthquakes, hurricanes, and floods, and to issue real-time alerts to authorities and the public [31; 32; 33]. These alerts can help to reduce the impact of disasters by giving people time to evacate or take other appropriate action. Another application of IoT in emergency management is in the area of real-time monitoring and response. IoT sensors can be used to monitor a range of environmental and infrastructure factors, including air quality, water levels, and structural integrity. These data can be used to identify emerging emergencies and to coordinate a rapid response by emergency responders [34; 35; 36; 37; 38]. IoT technology can also be used to track the movement of people and resources during emergencies [10; 39]. For example, wearable IoT devices can be used to track the location and condition of emergency responders, while GPS-enabled devices can be used to track the location of emergency vehicles and supplies. This information can be used to coordinate a more effective response and to ensure that resources are deployed where they are most needed. Another application of IoT in emergency management is in the area of post-disaster recovery and reconstruction [40; 41]. IoT sensors can be used to monitor the condition of buildings and infrastructure in the aftermath of a disaster, and to identify areas that require repair or reconstruction. This information can be used to guide the allocation of resources and to ensure that recovery efforts are targeted and effective.  
The potential applications of IoT in emergency management are vast and varied. By harnessing the power of IoT technology, it is possible to improve the detection, response, and recovery from emergencies and to reduce the impact of disasters on individuals and communities.  
Figure 1: Generic architecture of the Internet of Things.
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 4 Experimental Tests  
Two scenarios (i.e., in-lab and practice scenarios) have been designed: (i) to check the correct functioning of real-time biosignals acquisition, and (ii) to correlate biosignals monitoring with the NASA-TLX subjective questionnaires. For both scenarios, the biosignals tools described in Section 3 have been used.  
The first scenario, named in-lab scenario, is devoted to divided attention tests, where users perform some exercises in front of a computer and their biosignals are recorded and monitored. Moreover, NASA-TLX questionnaires are provided at different stages to extract the biosignals of users.  
The second scenario, named practice scenario, focuses on outdoor tasks to be performed in a controlled but real environment, where users must navigate through collapsing structures with visual and auditive divided attention exercises.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 2 Architecture of Digital Twin Monitoring System
### Overall Architecture of Digital Twin Monitoring System
#### 2.1.3 Data Processing System  
The data processing system is the core component of the digital twin monitoring system for the roadheader and the data center of the entire framework. The data generated by the multi-valued attribute physical entity, the digital twin virtual entity, and the user interaction system all need to be aggregated in the data processing system for analysis and processing. Essentially, the data processing system is a data engine that encapsulates various types of data, digital models, functional algorithms, tool programs, and data interfaces required during the operation of the digital twin monitoring system for the roadheader, such as the standardized encapsulation and storage management functions of twin data, data transmission protocols, data correction and analysis algorithms, cloud computing data interfaces, user function extension interfaces, etc.
###### Abstract  
A mobile ad-hoc network (MANET) is a network of mobile nodes that dynamically form a transitory network lacking any existence of infrastructure and any form of centralized management. Nodes in ad hoc networks are powered by batteries with a limited lifespan and communicate in a restricted bandwidth. The unpredictable environment of a MANET may run into a major concern in the routing mechanism, therefore the need for a routing protocol with robust performance is still one of the key challenges in MANET deployment. In this work, a comparative comparison and extensive simulation analysis have been carried out for three major routing protocols: destination sequenced distance vector (DSDV), dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV). Protocol evaluation has been extended by considering several simulation arrangements, different classes of traffic load patterns and diverse performance metrics. Based on packet rate change, node quantity and node speed, simulation scenarios were generated. Protocols were investigated against energy consumption, throughput, lost packets, routing load and packet delivery fraction for three types of traffic load patterns regular, irregular and joint traffic. DSR and AODV protocols proved to be more reliable when joint traffic was implemented when node speed and packets variations are considered. DSDV protocol verifies outstanding response over other protocols in terms of energy consumption when either regular or irregular traffic is applied. The simulation results for DSR protocol have verified the superiority over other protocols in 9 simulation scenarios when diverse metrics are considered. DSDV showed optimal performance in 7 cases, especially at low packet rates and in networks with minimum number of nodes. Similarly, AODV protocol showed outstanding performance in 6 scenarios, when higher packet rates and node mobility are considered.  
MANET; DSDV; DSR; AODV Article
## 1 Introduction
For instance, the positioning accuracy of the GPS is typically within the meter range. Further, the autonomous positioning error model can determine the precision required for measurements of polarized angles, thus enabling the autonomous positioning method to achieve meter-level accuracy. In summary, by analyzing the global error distribution, one can effectively design and optimize the parameters of the autonomous global positioning system, enhancing its reliability and stability. This paper is organized as follows: Section 2 introduces the fundamental principles of real-time global positioning methods. Section 3 develops the error model by employing partial derivatives and outlines the conditions under which the proposed error model is valid. Section 4 verifies the accuracy of the proposed error model through numerical simulation. Finally, Section 5 summarizes the spatial and temporal distribution properties of the proposed error model.
# Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
## 3 Related Work
### Gradient of the Unlabeled Data  
For the case of unlabeled data \(\mathbf{x}_{i}\in\mathbf{X}_{u}\), the ground truth distributions \(t_{k}(y_{i})\) are not given, so it is impossible to obtain the gradient of the cross entropy loss, \(\
abla\mathcal{J}_{i}(\theta)\). To deal with this problem, self-training and teacher/student learning-based methods generate a pseudo ground truth distribution using a pre-trained model. Figure 2 shows the pseudo label generation process of self-training and teacher/student learning using a pre-trained model, \(P_{\theta_{s}}(y|\mathbf{x}_{i})\).  
Figure 2: The pseudo label generation process of self-training and teacher/student learning.  
As shown in Figure 2, self-training-based approach generates a label, \(\tilde{y}_{i}\), which produces the maximum posterior probability from a pre-trained model \(\theta\) for an unlabeled input feature \(\mathbf{x}_{i}\), and it is taken as a pseudo ground truth label, as follows [13; 14; 15]:  
\[\tilde{y}_{i}=argmaxP_{\theta}(y_{i}=k|\mathbf{x}_{i}) \tag{17}\]  
A confidence measure is then carried out to decide whether the sample is to be selected or not. Finally, the gradient of the unlabeled data can be obtained as follows:  
\[\
abla\mathcal{J}_{i}(\theta)=\begin{cases}-\
abla\log P_{\theta}(\tilde{y}_{ i}|\mathbf{x}_{i}),&\text{CM}_{\theta}(\tilde{y}_{i})\geq\gamma\\ 0.0,&\text{CM}_{\theta}(\tilde{y}_{i})<\gamma\end{cases} \tag{18}\]  
where \(\text{CM}_{\theta}(\tilde{y}_{i})\) is the confidence measure and \(\gamma\) is a threshold [22]. In some cases, normalized \(\text{CM}_{\theta}(\tilde{y}_{i})\) can be used for the pseudo ground truth distribution as follows:  
\[\
abla\mathcal{J}_{i}(\theta)=-\text{CM}_{\theta}(\tilde{y}_{i})\
abla\log P_ {\theta}(\tilde{y}_{i}|\mathbf{x}_{i}) \tag{19}\]  
However, the confidence measure is usually carried out as post-processing using external knowledge, such as language model, so it can increase the complexity of the semi-supervised learning.  
To alleviate the complexity of the confidence measure in the self-training methods, the teacher/student learning methods use the output distribution of a pre-trained model as a pseudo ground truth distribution \(t_{k}(y_{i})\), as follows [12]:  
\[t_{k}(\tilde{y}_{i})=P_{\theta}(\tilde{y}_{i}=k|\mathbf{x}_{i}) \tag{20}\]  
So, the gradient of the unlabeled data can be obtained from  
\[\
abla\mathcal{J}_{i}(\theta)=-\sum_{k=1}^{c}t_{k}(\tilde{y}_{i})\
abla\log P_ {\theta}(\tilde{y}_{i}=k|\mathbf{x}_{i}) \tag{21}\]  
In some sense, teacher/student learning can be understood as top-\(n\) pseudo label selection, from the viewpoint of self-training.
## 3 Materials and Methods
### Data Forwarding Method  
In our scenario, the sensor nodes are dynamically moving due to water flow. In addition, the environment and neighborhood topology of each sensor node keep changing. We adopt a reinforcement learning-based method by which sensor nodes can distributively learn from the changing environments to forward data. This section describes the data forwarding method in detail. Specifically, we present the learning model, the learning method to choose a relay and the algorithm for packet forwarding.
## 4 Formulation of the Proposed Modeling Framework with MESSs
### Distribution Generation (DGs) Constraints  
The following is a limitation of DGs (Wind and PVs) and their capacity, which is expressed in the following constraints.  
\[0\leq P_{e,\delta,s}^{Wind}\leq P_{e}^{(W)\ unit}\times\tau_{t,s}^{Wunit}\qquad \forall e\in\mathbb{N}_{w},t\in T,s\in\omega \tag{49}\]  
\[0\leq Q_{e,\delta,s}^{Wind}\leq P_{e}^{Wind}\times\tan(\cos^{-1}(PF_{W})) \qquad\forall e\in\mathbb{N}_{w},t\in T,s\in\omega \tag{50}\]  
\[0\leq P_{e,\delta,s}^{PV}\leq P_{e}^{(PV)\ unit}\times h_{e}^{PV}\times\tau_{t,s}^{PV}\qquad\forall e\in\mathbb{N}_{pv},\,t\in T,s\in\omega \tag{51}\]  
\[0\leq Q_{e,\delta,s}^{PV}\leq P_{e,\delta,s}^{PV}\times\tan(\cos^{-1}(PF_{PV})) \qquad\forall e\in\mathbb{N}_{pv},\,t\in T,s\in\omega \tag{52}\]  
\[\left\{\begin{array}{c}\sum\limits_{e\in E_{pv}}\hbar_{e}^{PV}=1,\text{ if }\delta_{e}^{PLs}=1\\ \hbar_{e}^{PV}=0,\text{ otherwise}\end{array}\right\} \tag{53}\]  
where (49) and (50) define, respectively, the active and reactive power restrictions of wind-DGs installed at selected MGs with MESSs, while PV-DG active and reactive power restrictions are denoted in (51) and (52). Constraint (53) was implemented to ensure that the installed PV-DGs are at any MGs that include PLs.
# Improved Short Memory Principle Method for Solving Fractional Damped Vibration Equations
## 4 Examples Analysis  
### Free Vibration  
The single-degree-of-freedom fractional-order-damped free vibration differential equation is selected taking \(\alpha=0.5\) conveniently finds the analytical solution. The calculation is carried out in five cases for 20 s: (1) step size \(h=0.001\), no truncation; (2) step size \(h=0.0001\), no truncation; (3) step size \(h=0.001\), the number of coefficient truncation items \(N_{t}=1000\), step size magnification \(n=5\); (4) step size \(h=0.0001\), the number of coefficient truncation terms \(N_{t}=2000\), the step size magnification \(n=15\); (5) step size \(h=0.0001\), the number of coefficient truncation terms \(N_{t}=1000\), and the step size magnification \(n=30\). The resulting curve nearly coincides with the exact solution (Laplace transform method) because the error obtained by this method is much smaller than the classical short memory principle. Therefore, the error between the numerical results and the exact solution (i.e., numerical solution minus the exact solution) is compared. The calculation error results are shown in Figure 11. All cases tend towards the equilibrium position, and the two lines with step size of \(h=0.001\) and \(h=0.0001\) have no coefficient truncation, indicating that the smaller the step size is, the smaller the error will be. Two error curves have \(h=0.001\), i.e., the red dashed line without coefficient truncation and the green dotted line with coefficient truncation. Although the curves are nearly coincident, the amount of computation with coefficient truncation is much less than that without coefficient truncation. Although the error of the fourth case is not consistent with the fluctuation position of the error of the second case without coefficient truncation, the error amplitude is roughly  
Figure 10: Schematic diagram of extracting function value by the first enlargement of step size (\(nN_{t}<k\leq n^{2}N_{t}\) and \(k\geq n^{2}N_{t}\)).  
the same, also showing the effectiveness of coefficient truncation. In the fifth case, although the initial step size is very small, the effective time of the basic step size is only \(L_{1}=N_{t}h_{1}=0.1\), and the step magnification is extremely large, so the error is relatively large.  
When using this method, the number of truncated items and the step size magnification must be coordinated with each other. When the number of truncated items is small, the step size magnification must be reduced, and when the number of truncated items is large, the step size magnification can be appropriately increased. Taking the last numerical result of the second and fourth cases as an example, the comparison of the calculation amounts show that 200,000 items participate in the addition calculation without coefficient truncation, but only approximately 4622 items participate in the addition calculation after the truncation.
###### Abstract
The ESP32 microprocessor is known for its low cost compared to traditional NICs used for CSI data collection [16]. Despite its potential, the ESP32 remains largely untapped in Wi-Fi research for identifying sleep apnea measuring RR. Previous studies [17; 18] have utilized the ESP32 with physical instruments for monitoring, but these methods can hinder sleep quality due to their intrusive nature. Recent research employing CSI on five patients has been demonstrated, however, the system was not explicitly tested across varying breathing rates (normal, fast, and slow) [19]. Our experiment aims to measure RR at different rates to identify discrepancies with ground truth across various breathing patterns. Additionally, we introduce a new strategy to correlate Wi-Fi signal fluctuations with RR by addressing noise in raw CSI data, employing a series of signal processing methods--Hampel Filtering [20], Gaussian Filtering [21], Linear Interpolation [22], and Butterworth Low Pass Filtering [23]. These four filters implemented in sequence removes noise and outliers from raw CSI data. However, noise caused by external movements and elements that generate distortions in the rooms other than subject maybe still available in the treated CSI data. Therefore, we detect such anomalies and remove it. Furthermore, extraction of RR is applied using local peak function [24] approach. The major contributions of this study are listed below:  
1. The ESP32 microcontroller is utilized to identify sleep apnea by measuring RR, offering a cost-effective, compatible, and low-complexity approach. 2. A series of signal processing methods--Hampel Filtering, Gaussian Filtering, Linear Interpolation, and Butterworth Low Pass Filtering--are introduced to reduce noise contained in raw CSI data. Furthermore, noise generated from external movement other than the subject are also considered for CSI data refinement. 3. Measurement of RR is implemented at different rates--Normal (12-16 ppm), Fast (>16 ppm), and Slow (<12 ppm)--to assess the effectiveness of the proposed approach in both normal and sleep apnea conditions.