esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Outage and Error Probability
## Appendix C Derivation of \(P_{out}\)  
The general expression for outage probability derived in Section 3.3 Equation (11) is given as:  
\[\begin{array}{lll}P_{out}&=&Pr(\gamma_{SD}\leq\gamma_{0})Pr(\gamma_{SR_{1}D}+ \gamma_{SD}\leq\gamma_{0}|\gamma_{SD}\leq\gamma_{0})\\ &&Pr(\gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SD}\leq\gamma_{0}|\gamma_{SR_{1}D }+\gamma_{SD}\leq\gamma_{0})\cdots\\ &&Pr(\sum_{i=1}^{m}\gamma_{SR_{i}D}+\gamma_{SD}\leq\gamma_{0}|\sum_{i=1}^{m-1} \gamma_{SR_{i}D}+\gamma_{SD}\leq\gamma_{0})\end{array} \tag{17}\]  
By limiting \(m\) to 3, we get:  
\[\begin{array}{lll}P_{out}&=&Pr(\gamma_{SD}\leq\gamma_{0})Pr(\gamma_{SR_{1}D}+ \gamma_{SD}\leq\gamma_{0}|\gamma_{SD}\leq\gamma_{0})\\ &&Pr(\gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SD}\leq\gamma_{0}|\gamma_{SR_{1}D}+ \gamma_{SD}\leq\gamma_{0})\\ &&Pr(\gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SR_{3}D}+\gamma_{SD}\leq\gamma_{0}| \gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SD}\leq\gamma_{0})\end{array} \tag{18}\]Using the law of conditional probability, i.e., \(P(A|B)=\frac{P(A\cap B)}{P(B)}\), we get:  
\[P_{out} = Pr(\gamma_{SD}\leq\gamma_{0})\times\frac{Pr(\gamma_{SR_{1}D}+ \gamma_{SD})}{Pr(\gamma_{SD}\leq\gamma_{0})}\times\frac{Pr(\gamma_{SR_{1}D}+ \gamma_{SR_{2}D}+\gamma_{SD})}{Pr(\gamma_{SR_{1}D}+\gamma_{SD})}\] (C3) \[\times\frac{Pr(\gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SR_{3}D}+ \gamma_{SD})}{Pr(\gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SD})}\] \[= Pr(\gamma_{SR_{1}D}+\gamma_{SR_{2}D}+\gamma_{SR_{3}D}+\gamma_{SD} \leq\gamma_{0})\]
## 2 Proposed Vehicle Detection and Tracking System
### Bright Blob Segmentation  
Given an RGB image retrieved from a vehicle-mounted camera, the vision-based system first extracts bright blobs that are potential vehicle lights. Because the main focus of this system is to detect  
Figure 3: Mean value of a* channel images: (**a**) headlight; (**b**) taillight.  
Figure 2: Learning flow for training multiclass Adaboost classifier.  
and track vehicles based on vehicle lights, the nonvehicle lights such as street lamps and traffic lights should be removed from the road scene image. To filter out nuisance lights and save the computational cost, a region of interest (ROI) in which the bright blob segmentation is performed is determined. Based on the observation and the study [7], all the objects located above the virtual horizon are screened out. Thus, the ROI is the region below the virtual horizon (see Figure 4). After locating the ROI, an automatic multilevel thresholding [17] and HSV color thresholding [12] are applied to preliminarily extract potential bright blobs that might be headlights or taillights. As a result, a binary image containing the extracted bright blobs is created (see Figure 5). Then, the contours of extracted blobs are
## 4 Platform Architecture and User Interface
### Preprocessing  
The preprocessing module illustrated in Figure 5 provides a visual representation of the successive steps of the preprocessing pipeline analyzed in Section 3.2. In the final step of the module, the results of the synchronization between the video frames and the GPS data take the JSON format and are inserted into the communication queue of the message broker. This module can support multiple workers to process many requests at the same time.
## 4 Simulation Results
### Performance Metrics and Simulation Environment  
In our simulation, we used the following metrics to validate the performance of the proposed EDACP-CS protocol:  
* _Energy consumption:_ Is measured by the total number of the network energy dissipation.
* _Network lifetime:_ Is the time interval from the start of operation (of the sensor network) until the death of the last alive node.
* _Throughput:_ Is the rate of data sent from the CHs to the BS over the network lifetime, measured per round per epoch.
* _Stability period:_ Is the time interval from the start of network operation until the death of the first alive node. We call this period as stable region or period.
* _Number of clusters:_ Indicates the number of clusters generated per round.  
The simulation parameters are summarized in the following (Table 1).  
Figure 5a shows the network topology comprised of 100 nodes corresponds to DWT basis. The networked data shown in Figure 5b can be presented with \(K=10\) nonzero coefficients after DWT transform. Note: The locations of nodes are generated as random values drawn from the standard uniform distribution on the open interval (0, 100).  
Figure 4: Data communication in case of error.
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 2 Related Work and Problem Statement
### Problem Statement  
Due to its preconfigured and hardware-constrained design, an existing LTE-LAA network only operates in an allocated licensed band along with an unlicensed band [44; 45]. Consequently, only the users who have access to this allocated licensed band of the LTE-LAA network can enjoy the benefit of a higher throughput. The full potential of the LTE-LAA system can be further realized if users from multiple operators (i.e., multiple licensed bands) can access this LTE-LAA network. SDR can be an enabling technology in this context. The research question becomes how to couple SDR with the LTE-LAA system and manage resources in this SDR-enabled LTE-LAA network so that users, irrespective of their operators, can benefit from the LTE-LAA network in the form of a higher throughput and lower energy consumption.  
In this work, we propose an approach which utilizes SDR technology to introduce open-access or shared, SDR-based BSs which support LTE-LAA and can serve users from different operators. The proposed approach adds flexibility to HetNets, increases the overall data rate for the users, and enhances energy efficiency. Hence, its benefits can be realized by both operators and users.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.1 Comparative Experiment of Mainstream Lightweight Network Performance  
To objectively reflect the performance of the YOLOX-Nano network, this study also trained other lightweight YOLO models and the standard YOLOv3 model using the same parameters and settings for comparison with the YOLOX-Nano model. The comparative results of these five object detection networks are presented in Table 1. It is evident that under the same testing set, the \(mAP_{0.5}\) value of the YOLOX-Nano network reached 82.51%, which is higher than that of the other models. Furthermore, the \(mAP_{0.5}\) value of this network shows an increase of 3.44%, 10.71%, 0.72%, 1.27%, and 1.93% compared to YOLOv3, YOLOv4-Tiny, YOLOv5-S, YOLOv7-tiny, and YOLOv8-Nano, respectively. In the context of debris flow geological disaster detection in remote sensing images, the _Recall_ rate is an essential metric for assessing the model's coverage of targets, as missing debris flow targets could lead to severe consequences. Observing the _Recall_ values, it is discernible that the YOLOX-Nano network model demonstrates approximately a 15% increase in the _Recall_ rate compared to other lightweight YOLO network models. Therefore, with similar _Precision_ rates, the significant improvement in the _Recall_ rate of the YOLOX-Nano model results in its composite performance indicators, the \(F1\) score and \(mAP_{0.5}\) value, being higher than those of other benchmark models, making it most suitable as a base model for debris flow disaster target detection in remote sensing images.
## 2 Materials and Methods
### Statistical Analysis  
For the descriptive analysis, the mean and standard deviation were calculated. The ICC and CV were determined to analyze the relative and absolute reliability of the variables that were measured more than twice. The Shapiro-Wilk test was performed to analyze whether the variables followed a normal distribution. Furthermore, the homogeneity of the variances was verified through the Levene test. To analyze the differences by playing position, a one-way ANOVA was applied. For the post hoc analyses between playing positions, the Tukey or Games-Howell test was used, depending on the homogeneity of the variances. The effect size (ES) was calculated using partial Eta2 (0.01: small, 0.06: medium, 0.14: large). Correlations were calculated through Pearson's or Spearman's \(r\) depending on the normality of the variables, in order to study the degree of association between anthropometric and physical fitness variables. The \(r\) values were interpreted as trivial (0.00-0.09), small (0.10-0.29), moderate (0.30-0.49), large (0.50-0.69), very large (0.70-0.89), almost perfect (0.90-0.99), and perfect (1.0) [44]. The significance level was established at \(p\leq 0.05\), and the confidence interval (CI) at 95% was calculated for all the measurements. Simple linear regressions were also calculated. The statistical analyses were conducted using SPSS IBM(r) software v.22 (New York, NY, USA).
###### Abstract
With the development of technology and the growth of application demand, the proportion of constellations used for satellite communication and earth observation is increasing, currently even reaching 68% [45]. In recent years, microsatellites are mainly used for earth observation and remote sensing, and large satellite constellations make many simultaneous and distributed measurements or observations possible, such as for disaster monitoring, and increase temporal resolution of collected data [45; 46]. Moreover, the use of higher frequencies than the S-band (mainly for telemetry) and X-band (for data transmission), such as the Ku-, K-, and Ka-bands, has become more widely available, which allows higher data rates [45; 47]. These are the latest technologies for large spacecraft and enable real-time transmission and acquisition of high-definition photos and videos required for disaster emergency response, but they are still emerging technologies in the field of small aerial remote sensing platform. In this paper, we present a novel technical solution for an aerial remote sensing platform based on real-time satellite communication to achieve rapid arrival at disaster sites, high-quality acquisition of the latest disaster site information, and real-time transmission of on-site information. The innovation of this technical solution lies in the application of civilian heavy-duty unmanned aerial vehicles, self-developed orthographic image stabilization platform, and particularly airborne mobile communication devices that can achieve a transmission rate of no less than 2 Mbps in the Ku band. Additionally, as the actual performance of the aerial remote sensing platform potentially is influenced by multiple factors [14; 42; 47], we innovatively conducted double verification for the feasibility of this technical solution through both ground and flight tests. That is, we evaluate the performance of the proposed aerial remote sensing platform and its applicability for real-time emergency monitoring in disaster events by verifying the image stabilization capability of the image stabilization platform and the communication capability of the airborne satellite communication system in ground and flight tests.
## 3 Two-Stage MG Operation Model Considering Interactive Power Deviation
### Intraday Model Considering Interactive Power Deviation
We organize the above intra-day model inequalities into the following compact form:  
\[\mathbf{\Omega}_{\text{RG}}^{\text{FR}}=\{\mathbf{y},\mathbf{\Delta}\mathbf{w}|\mathbf{\text{A}} \mathbf{x}+\mathbf{\text{B}}\mathbf{y}+\mathbf{C}(\mathbf{w}+\mathbf{\Delta}\mathbf{w})\leq\mathbf{\text{b}}\} \tag{17}\]Here, \(\mathbf{\Omega}_{\text{RG}}^{\text{FR}}\) represents the set of linear inequality constraints formed by the MG's intra-day variables, including renewable energy, load, controllable units, and the interaction power with the ULG. \(\mathbf{x}\) denotes the day-ahead scheduling, \(\mathbf{x}=\left[\mathbf{P}_{\text{DA}}^{\text{MG}}\quad\mathbf{P}_{\text{DA}}^{\text{CG}} \right]^{\text{T}}\). \(\mathbf{P}_{\text{DA}}^{\text{CG}}\) is the row vector representing the output of controllable units in the day-ahead scheduling during the time period \(T^{\prime}\). \(\mathbf{P}_{\text{DA}}^{\text{MG}}\) is the row vector representing the interaction power output between the MG and the ULG in the day-ahead scheduling during period \(T^{\prime}\). \(\mathbf{y}\) represents the intra-day adjustable resources of the MG, \(\mathbf{y}=\left[\mathbf{P}_{\text{ID}}^{\text{load,trans}}\quad\mathbf{P}_{\text{ID}}^{ \text{CG}}\quad\mathbf{\Delta}\mathbf{P}_{\text{ID}}^{\text{MG}}\right]^{\text{T}}\). \(\mathbf{\Delta}\mathbf{P}_{\text{ID}}^{\text{MG}}\) is the row vector representing the allowable deviation in the interaction power between the MG and the ULG at each moment during the intra-day UFR analysis period \(T^{\prime}\). \(\mathbf{P}_{\text{ID}}^{\text{CG}}\) is the row vector representing the output of controllable units at each moment during the intra-day UFR analysis period \(T^{\prime}\). \(\mathbf{P}_{\text{ID}}^{\text{load,trans}}\) is the row vector representing the output of flexible loads at each moment during the intra-day UFR analysis period \(T^{\prime}\). \(\mathbf{w}\) denotes the forecasted renewable generation. \(\mathbf{\Delta}\mathbf{w}\) represents the fluctuation of renewable energy. In this paper, depending on the MG being evaluated, this fluctuation can refer to PV units, wind turbine units, or even the sum of both. \(\mathbf{b}\) represents the right-hand side of the constraints. **A,B,C** are the corresponding constant matrices.
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 2 EUV-Induced Plasma
### Key Parameters of EUV-Induced Plasma
#### 2.2.1 Electron Density  
When considering Equation (3), one would expect an initial plasma density--primary due to photo-ionization by EUV photons--that varies over several orders of magnitude, depending on the local gas pressure, EUV power, EUV beam waist, and gas type. The highest plasma density can most likely be found in the foci, where the EUV light has the highest flux. Most of the laboratory experiments have utilized a xenon-based discharge produced plasma (DPP) source, operating at a typical frequency of 500 Hz, as a source of EUV radiation. For reference, typical laser produced plasma (LPP) sources in the field use repetition rates that are two orders higher, i.e., up to 50 kHz [7; 15]. Of course, as time after the EUV pulse passes, the plasma density may further increase due to consecutive electron impact ionization (dependent on the background gas pressure), followed by a decrease due to plasma expansion in the free space. Finally, plasma termination occurs at a surface in the vicinity of the plasma (typically on ms time scales for cylindrical measurement volumes of 10 cm in height and 10 cm in radius [16]). Which of these processes is dominant depends on local conditions and the considered time scales. Overall, measurements of the plasma density--while using low power EUV sources--have demonstrated values up to 10\({}^{17}\) m\({}^{-3}\)[17].
## 3 Hierarchical Classification
### Log Analysis Using Thematic Hierarchical Codes
By removing messages with duplicate themes and keeping only one per theme, templates and their original semantics are preserved, significantly reducing the log size and increasing its efficiency. Let \(L\) be the set of all lines in the 'datacod' file, where each line \(l_{i}\) is an element of \(L\), i.e., \(l_{i}\in L\). Each line \(l_{i}\) is a vector \(v_{i}=(w_{i}c_{i})\), where \(w_{i}=(w_{i1},w_{i2},\ldots,w_{in})\) and \(c_{i}=(c_{i1},c_{i2},\ldots,c_{in})\), with \(w_{ix}\) being a token or word and \(c_{ix}\) a hierarchical code. Let \(n_{i}\) be the number of lines with identical hierarchical codes up to line \(i\), i.e. :  
\[n_{i}=\sum_{j=1}^{i}1\mid c_{i}=c_{j}\]  
The transformation of the 'datacod' file to one without lines with identical codes is performed using the function \(f\), which is applied to each vector \(v_{i}\) such that:  
\[f(v_{i})= \begin{cases}w_{i}\,c_{i}&\text{if }n_{i}=1\\ \emptyset&\text{if }n_{i}>1\end{cases} \]  
The resulting log file is reduced to a set of vectors \(\{v_{i}\}\), where each \(v_{i}\) is the result of applying the function \(f\) to each line \(l_{i}\) of the original file. We can describe the complete process on the 'datacod' file as: \(\{v_{i}|v_{i}=f(v_{i})\,\forall\,v_{i}\in L\}\). In the second step, rows with similar meaning codes are removed from the resulting log. To determine which rows are similar, the themes of all messages are compared using a specificity threshold, which is a percentage of the shortest code's length among the two codes to be compared. Another threshold is set to determine the percentage of codes of the same theme needed to consider that two messages belong to similar themes. Finally, the last step starts from the log resulting from the previous module and removes all hierarchical codes, leaving only the messages. The final log presents one message per existing template in the original log, simplifying the representation and improving the system's efficiency.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 3 The Need for Trust-Based Information Fusion  
Information fusion is a process that allows us to estimate and assess situations by collecting diverse and sometimes conflicting information from various information sources. This process involves integrating information from these multiple information sources to produce specific and comprehensive yet unified estimate about the situation. Multiple information sources such as sensors generate information which is collected by sink node(s), where the information fusion processes take place. These processes may include transformation, reduction, integration, or replacement [8; 14; 15; 16]. Finally, an output is generated that is hopefully more useful than that provided by any individual information source.  
Key challenges during this information fusion process arise from dealing with information sources' behaviour issues such as trustworthiness, honesty, and accuracy. These challenges are many folds. First, an information source or a sink device can be untrustworthy. That is, they can intentionally modify the collected datasets or fused information. Second, a dishonest information source can intentionally lie for their own benefit and thus submit wrong information. Third, honest information sources have different levels of accuracy relating to their imprecise individual trust metrics. These different levels of accuracy arise because nodes can evaluate the same situation differently. As such, the concept of accuracy enables review-based mechanisms to function with imprecise metrics.  
Therefore, before relying on the fused information, we must ensure trustworthiness, honesty, and accuracy for our information sources and also for our sink devices. In a nutshell, we want to avoid the problem of fusing untrustworthy or dishonest information which we believe is a key factor to achieve the full benefits of smart applications.
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 3 Results
### Simulations for Static Obstacles
#### 3.1.2 No Passage through Closely Spaced Obstacles and Oscillations in Narrow Passages  
This simulation is designed for the algorithm to handle more obstacles with narrow channels, especially for the conditions of no passage through closely spaced obstacles and oscillations in narrow passages. The static environment consists of five kinds of static obstacles with different shapes (combined curve, combined polygon, concave polygon, triangle and point). Their actual boundaries are marked by black solid lines, and their characteristics and potential parameters are shown in Figure 12. The initial states and results of the simulations are shown in Table 6.  
These two experiments keep safe distances from static obstacles, and there are no oscillations in passages. The total simulation time is very short, approximately 0.6 s in 10 repeated trials.
# A Novel Robust Control System Design and Its Application to Servo Motor Drive
## 4 The Determination of Sliding Mode Control Law  
The second part, \(u_{smc}=-q\text{sgn}(\sigma)\), of (25) is used to guarantee the existence of sliding conditions. Typically, a large quantity of \(q\) is chosen to reach the desired performance, with it invariant to perturbation and disturbance. However, this is the primary source leading to chattering.  
The action of the integrator can overcome the total perturbation (20). To consider the output, \(z_{1}(t)\), of the integrator by the inputs of reference \(r(\mathbf{t})\) and disturbance \(w(t)\):\[Z_{1}(s)=\frac{s^{2}+(a_{2}+bk_{3})s+a_{1}}{s^{3}+(a_{2}+bk_{3})s^{2}+(a_{1}-bk_{2} )s-bk_{1}}\times R(s)+\frac{-1}{s^{3}+(a_{2}+bk_{3})s^{2}+(a_{1}-bk_{2})s-bk_{1}} \times W(s) \tag{26}\]  
If the two inputs are assumed as step signals,  
\[R(s)=\frac{r}{s}\text{ and }W(s)=\frac{w}{s} \tag{27}\]  
then  
\[\lim_{t\rightarrow\infty}z_{1}(t)=\frac{a_{1}r-w}{-bk_{1}} \tag{28}\]  
With the same procedures, we have the following results:  
\[\lim_{t\rightarrow\infty}z_{2}(t)=\lim_{t\rightarrow\infty}e(t)=0 \tag{29}\]  
and  
\[\lim_{t\rightarrow\infty}z_{3}(t)=0 \tag{30}\]  
i.e., the total perturbations of reference input and external disturbances exist at the output of the integrator. Through the linear control part, \(u_{L}=-\mathbf{k_{1}^{T}z}\), the steady-state output of the controller is from the state \(z_{1}\) only, which can eliminate the total perturbations.  
However, the transient response for the integral state feedback control cannot be guaranteed due to the uncertainties of parameters and external load. The second part of (25) is used to keep the state, \(\mathbf{z}\), on the sliding surface.  
The main weak point of SMC control is the chattering phenomenon due to the sign function. Choosing a big value of \(q\) easily leads to the occurrence of sliding mode. On the contrary, the performance of sliding mode control is destroyed due to chattering, especially under steady-state conditions. Thus, the design of \(q\) should be considered for the steady-state performance. The control law of (23) is modified as  
\[-q\text{sgn}(\sigma)=-q\frac{\sigma}{|\sigma|+\varepsilon} \tag{31}\]  
to solve the problem of chattering. In (31), \(\varepsilon\) is a small positive value and \(q\) is chosen to fit the inequality of (24). With the defined control law (31), once the system states are kept on the sliding surface, the extra control law becomes linear control, which avoids the occurrence of chattering.