esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Diverse Model Stacking Ensemble
### Machine Learning
#### 4.4.3 Logistic Regression  
Logistic regression is a statistical model utilized for binary classification, which involves predicting the probability that an example belongs to a particular class. The model uses the sigmoid function (also known as the logistic function) to map the output of a linear equation into a probability value between 0 and 1. The sigmoid function has an S-shaped curve and is defined as  
\[\sigma(z)=\frac{e^{z}}{e^{z}+1}=\frac{1}{1+e^{-z}} \tag{5}\]  
where \(z\) is the linear combination of the input features and their respective weights.  
The logistic regression hypothesis function models the probability that an example belongs to a particular class (e.g., 1) as follows:  
\[P(y=1|x)=\sigma(\theta^{T}x) \tag{6}\]  
where \(\theta\) represents the parameter vector (weights) and \(x\) represents the input features.  
The logistic regression model is trained by minimizing a cost function, which quantifies the error between the predicted probabilities and actual labels. The most commonly used cost function is the log-loss (also known as cross-entropy loss) for binary classification:  
\[J(\theta)=-\frac{1}{m}\sum_{i=1}^{m}(y^{(i)}log(h_{\theta}(x^{(i)}))+(1-y^{(i )})log(1-h_{\theta}(x^{(i)}))) \tag{7}\]  
where \(m\) is the number of training examples, \(h_{\theta}(x^{(i)})\) is the predicted probability, and \(y^{(i)}\) is the true class label for the \(i\)-th example. To minimize the cost function, gradient descent or other optimization methods are employed to update the parameter vector \(\theta\) iteratively [92; 93].  
Logistic regression is a binary classification algorithm, and in order to use it for fine-grained emotion recognition, we use the One-vs.-Rest (OvR) approach, also known as the One-vs.-All approach. In this approach, we create multiple binary classifiers, one for each emotion class. During prediction, the algorithm runs the input through all classifiers and selects the class for which the classifiers give the highest probability of being in that class [94; 95].
## 3 Theoretical Analysis
### Symbol Error Rate and Packet Error Rate  
To compare the block orthogonal codes with other schemes, we analyze the packet error probability from Equation (3). The packet error probability of the block orthogonal codes, \(p_{ep}^{orth}\), is given by:  
\[p_{ep}^{orth}=1-(1-p_{es}^{orth})^{L_{wmp}/k} \tag{8}\]  
where \(L_{wmp}\) denotes the wake-up packet length in bits. To explore the performance of the varying modulation and encoding schemes, we analyze their symbol error rate (SER) and PER. For PWM modulation, we use the BER in [6]. In terms of other encoding schemes, we apply the system model in [23] to analyze their SER and PER. The relation between the symbol error probability, \(p_{es}^{ook}\), and bit error probability, \(p_{eb}^{ook}\), of binary coherent OOK modulation through a matched filter is given by:  
\[p_{es}^{ook}=p_{eb}^{ook}=Q(\sqrt{\text{SNR}_{b}})=Q\left(\sqrt{\frac{\text{ SNR}B_{s}}{R}}\right) \tag{9}\]  
The packet error probability of OOK modulation with 8B10B coding, \(p_{ep}^{\text{8B10B}}\), is given by:  
\[p_{ep}^{\text{8B10B}}=1-(1-p_{es}^{\text{ook}})^{1.25L_{\text{wmp}}} \tag{10}\]  
The packet error probability of OOK modulation with Hamming \((7,4)\) code, \(p_{ep}^{h74}\), that can detect and correct one error is given by:  
\[p_{ep}^{h74}=1-((1-p_{es}^{ook})^{7}+7p_{es}^{ook}(1-p_{es}^{ook})^{6})^{\frac {L_{wmp}}{4}} \tag{11}\]
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 4 Design of Network Coding Routing Protocol
### Initial Routing Construction
Frequent broadcasting beacon message can solve the node mobility problem but it brings low channel utilization and high energy consumption. Here we combine routing update and maintenance with data transmission, which improves the channel utilization greatly. The related algorithms are present in Section 4.4.
## 3 Materials: The Data  
In the current study, volatility means a fluctuation in output, not in resources. In terms of ANSPs, output includes metrics, e.g., flight hours, flights, movements at airports and flight distances. So in this case, we are particularly interested in variations in the number of flights. Eurocontrol offers a number of public and semi-public data sources, e.g., the ATM Cost-Effectiveness (ACE) data [35] and Performance Review Unit (PRU) data [36]. However, each of these datasets covers different temporal and operational levels. They also differ in regard to the years available. The key criterion for this study is the granularity of the data. Data on a daily basis is not sufficient to address short-term volatility, so the PRU database cannot be used. However, there is no publicly available data source for hourly flights. As an alternative, however, ANSPs have access to trajectory data, which can be analysed, for example, via the Network Strategic Tool (NEST) [37]. NEST provides both the actual and planned numbers of flights. The dataset used for short-term volatility was obtained by using a NEST assessment [38]. Hourly data are available for 2015-2019. Overall, the dataset contains 89 units. However, airspaces often contain overlapping areas. For example, the airspace ED contains all areas connoted to Germany, including parts of Maastricht UAC. However, flights are also available for EDCC (German Area Control Centres (ACCs)) and EDYYCC (Maastricht ACC in German airspace). Finally, there are units with a \"CTA\" suffix, so the _CC and -CTA airspaces may be subsets, but this is not the case for all observations. As a result, the database was split up in advance to avoid double counting. The differences between LP and LPCC airspace are illustrated in Figure 2.  
The times indicated are in UTC/GMT and refer to the time stamp when a flight enters the unit. Note that the accuracy of these data is limited in 3 dimensions: time (1 min), vertical distance (400 ft) (or 1000 ft in the climb/descent-phase) and lateral distance (10 NM). A preliminary analysis of the actual data presented in Tables 1-3 shows a clear overdispersion in many hour ranges. That is, the variance is well above the mean in all cases. Data for Germany are used to illustrate the analysis, but the comments and findings can be generalised to all FABEC countries, as shown below. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{5}{c}{**Values**} \\ \cline{2-6}  & **Mean** & **Volatility** & **Variance** & **95 th Percentile** & **Variance/Mean** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 1: Historic mean, variance and risk for Germany (EDCC) depending on the hour. Figure 2: Portuguese airspace according to NEST—LP (**left**) and LPCC (**right**). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{6}{c}{**Weekday**} \\ \cline{2-8}  & **Mon** & **Tue** & **Wed** & **Thur** & **Fri** & **Sat** & **Sun** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 2: Mean number of flights in Germany depending on day of the week.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Application Challenges
#### 5.1.2 Power Usage  
In CPS water system applications, the sensing device is a key component. A sensor essentially comprises of sensing, data processing, data storage, and a communication unit powered by a battery source. The sensors use wireless communication technology to communicate and are deployed to provide a continuous update on the state of water supply network facilities for water quality and leakage analysis, among other factors, which are anticipated to operate uninterrupted for several years. This raises the issue of energy consumption of the sensors and sensor nodes. The main sources of energy consumption by the sensor and sensor nodes are the communication task, followed by the computation and sensing tasks. During the communication task, more energy is consumed. The energy consumed during wireless transmission of a single bit of data may be about 1000 times higher than that used for a single 32-bit data computation [114]. Table 4 shows the energy consumption trend of a typical TelosB sensing platform adapted from [115]. As may be observed in Table 4 and Figure 10 [116],  
Figure 9: CPS water supply network monitoring application challenges.  
the energy consumed during the communication task is relatively higher than those consumed during data processing and storage. Additionally, the actuator nodes possess greater computational and communication capacities and a higher energy budget compared to sensors [34]. While both have power constraints problems, these key characteristics of sensors and the sensor nodes make energy consumption a key challenge in the application of a CPS for monitoring large-scale water supply systems.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Node Activation  
With regards to node activation, many existing studies propose alternating nodes between Sleep and Active modes, or regulating the sensing range of the nodes. The authors in [22] however, proposed to use both approaches together by setting up the minimum sensing radius for each sensor in the Active state. This reduces the active node's density, thereby reducing useless Media Access Control (MAC) layer interference, which would otherwise consume lots of power.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 3 DBR with an LA Increasing on a Cell-by-Cell Basis
\(L_{8}\) is the distance between the center of current LA (C) and the center of the farthest cell from C. \(U_{8}\) is the distance between the center of the current LA (C) and the center of the nearest cell (from C) that is not included in the current LA. \[L_{8}<D_{8}<U_{8}\]  
when \(N=8\), assuming one side is 1 in a hexagonal cell, then \(L_{8}=\frac{5}{2}\) and \(U_{8}=\frac{\sqrt{31}}{2}\) can be obtained. \[\frac{5}{2}<D_{8}<\frac{\sqrt{31}}{2}=2.78\]  
For example, if we set to \(D_{8}=2.6\), a DBR for \(N=8\) can be defined. Figure 5 shows the LA and cell 0 (central cell) of the LA for a cell-by-cell DBR for \(N=4\) and \(N=8\). In Figure 5a, when a UE exits the current LA, it registers its new LA such that the cell in which it registers is one of the central cells of the new LA as shown in the figure. Similarly, in the case of \(N=8\), a UE registers its new LA such that the cell in which it registers is the central cell of the new LA as shown in Figure 5b. Similarly, we can define the configuration of an LA and the center of the LA for various \(N\) to calculate the range of each reference distance. Table 1 below summarizes the configuration of the LA with increasing \(N\), the center of the LA, and the range of the reference distance (\(L_{N}<D_{N}<U_{N}\)). The following notations are defined to analyze the total cost on radio channels:  
\(C_{U}\): Location registration cost in one hour  
\(C_{V}\): Paging cost in one hour  
\(U\): The location registration cost for one registration  
\(P\): The paging cost for one cell  
\(N\): Number of cells in an LA  
\(T_{c}\): Interval between two calls (r. v., \(T_{c}\) -Exp[\(1/\lambda_{c}\)], E(\(T_{c}\)) = \(1/\lambda_{c}\))  
\(T_{m}\): Staying time in a cell (r. v., E(\(T_{m}\)) = \(1/\lambda_{m}\))  
\(R_{m}\): Time between the arrival of the call, and the time when the UE moves out of the cell  
\(f_{m}^{s}(s)\) The Laplace-Stieltjes Transform for \(T_{m}\) (=\(\int_{t=0}^{\infty}e^{-st}f_{m}(t)dt\))  
We also assume the following to obtain the total cost on radio channels:  
* When the UE enters a neighboring cell, the probability of selecting one of the neighboring cells is 1 out of 6. * The incoming and outgoing calls are generated with rates \(\lambda_{i}\) and \(\lambda_{o}\), respectively, according to the Poisson processes and the staying time in a cell, while \(T_{m}\) follows a general distribution with the mean \(1/\lambda_{m}\). Note that by the additional property of the Poisson processes, the incoming calls with the rate \(\lambda_{i}\) and outgoing calls with the rate \(\lambda_{o}\) will form total calls with a rate \(\lambda_{c}\) (= \(\lambda_{i}\) + \(\lambda_{o}\)) [26].
## 2 Traditional Video Application Methods over CVIS
[16] proposed a multipath solution based on a disjoint algorithm to increase the transmission rate and reduce the delay. [PERSON] et al. [17] proposed an interference-aware multipath video streaming (I-MVS) framework which aims to reduce packet error rate. A method to reduce transmission cost, backhaul burden and transmission delay is named video cache transmission. It mainly caches the video to the roadside unit (RSU), and then the RSU transmits the video stream to the user. [PERSON] et al. [18] proposed a dual-time-scale dynamic cache scheme to reduce the backhaul burden and improve video quality. [PERSON] et al. [19] proposed a cache-based cooperative video transmission scheme in cellular networks which improves the QOE. [PERSON] et al. [20] proposed an active video content caching (RCC) scheme to minimize transmission delay. [PERSON] et al. [21] designed cache placement and short-term transfer strategies for long-term-aware transmission to improve the QOE. Video target detection is mentioned to solve such problems as predicting a traffic situation, tracking a trajectory and identifying vehicle types, so as to improve traffic convenience and driving safety. [PERSON] et al. [22] collected target parameters through video graphics detection, and predicted vehicle delay, which is defined as the extra time the vehicle spends at an intersection, by using support vector machine (SVM) and artificial neural network (ANN) algorithms. Comparing the two algorithms, it was concluded that ANN is the best-fitting model for estimating the delay of signalized intersections. [PERSON] et al. [23] obtained datasets from traffic data equipment, such as cameras, at seven road intersections of the most congested roads in South Africa, and developed an ANN model that divides these data sets into thirteen inputs and one output. The results showed that the accuracy of the ANN prediction method is effective. The traditional video transmission method can show good performance in a simple CVIS environment. However, the algorithm of the traditional method converges slowly, and the model optimization is more difficult, due to more interference and the large data scale in complex environments.
_Algorithms_**2014**, \(7\), 418-428; doi:10.3390/a7030418  
_algorithms_  
ISSN 1999-4893  
www.mdpi.com/journal/algorithms  
_Article_  
Target Channel Visiting Order Design Using Particle Swarm Optimization for Spectrum Handoff in Cognitive Radio Networks  
[PERSON] 1,2,*, [PERSON] 3, [PERSON] 4 and [PERSON] 1,2  
1 Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314033, China; E-Mail: [EMAIL_ADDRESS]  
2 School of Telecommunications Engineering, Xidian University, Xi'an 710071, China  
3 School of Telecommunications Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; E-Mail: [EMAIL_ADDRESS]  
4 Nanhu School, Jiaxing University, Jiaxing 314001, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-573-8367-8066.  
_Received: 29 March 2014; in revised form: 29 July 2014 / Accepted: 8 August 2014 / Published: 18 August 2014_  
**Abstract:** In a dynamic spectrum access network, when a primary user (licensed user) reappears on the current channel, cognitive radios (CRs) need to vacate the channel and reestablish a communications link on some other channel to avoid interference to primary users, resulting in spectrum handoff. This paper studies the problem of designing target channel visiting order for spectrum handoff to minimize expected spectrum handoff delay. A particle swarm optimization (PSO) based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithm performs far better than random target channel visiting scheme. The solutions obtained by PSO are very close to the optimal solution which further validates the effectiveness of the proposed method.  
**Keywords:** particle swarm optimization; cognitive radio; spectrum handoff; target channel
## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Attribute** & **Description** & **Domain Range** \\ \hline Class & Class label & Non-recurrence, Recurrence \\ age & Age in years & 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, \\  & whether the patient is pre- or & 70–79, 80–89, 90–99 \\ menopause & post-menopausal during treatment & get40 or lt40 or premeno \\ tumor-size & Tumour size (in mm) & 0–4, 5–9, 10–14, 15–19, 20–24, 25–29, 30–34, \\  & 35–39, 40–44, 45–49, 50–54, 55–59. \\ inv-nodes & Total axillary lymph nodes that contain & 0–2, 3–5, 6–8, 9–11, 12–14, 15–17, 18–20, \\  & metastatic breast cancer & 21–23, 24–26, 27–29, 30–32, 33–35, 36–39 \\ node-caps & If tumour penetrated in lymph node capsule & Yes or no \\ deg-malig & Histological level of the tumour & 1, 2 or 3 \\ breast & Which side of breast is affected & Right or Left \\ breast-quad & Breast is partitioned into four quadrants & right-up, left-up, right-low, left-low, \\  & with nipple as the centre. & central \\ irradiat & Patient’s radiation (X-rays) therapy history & Yes or no \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Breast Cancer dataset details [79]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Attribute** & **Description** & **Domain Range** \\ \hline age & Age & 1–100 years old \\ sex & The gender of a person & 1 = Male; 0 = Female \\ cp & Uncomfortable chest pain & General angina/non-anginal \\  & & pain/asymptomatic/atypical angina/ \\ Trestbps & blood pressure at rest & Measured in mm Hg after admitted to medical centre \\ chol & Serum Cholesterol level & Measured in mg/dL \\ fbs & Fasting period blood sugar level & 0: \textless{}120 mg/dL; 1: \textgreater{}120 mg/dL \\ Restecg & Electrocardiography outcome at rest time & Values of 0,1 or 2 \\ Oldpeak & Exercise induced ST depression prior to rest & 3.05–3.81 \\ exang & exercise induced angina & 1 = yes; 0 = no \\ smoke & Smoker or not & Value: 1 = yes; 0 = no \\ Slope & ST segment peak exercise slope & 1: Upsoling; 2: Flat; 3: Downsloping \\ Ca & Major vessels count & 0–3 \\  & thal & maximum heart rate achieved & 3 = normal; 6 = fixed defect; 7 = reversible defect \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Heart Disease dataset details [79]. Figure 5 demonstrates the general workflow model of the framework proposed in this study. Chronic disease datasets are the input file to which pre-processing is applied to detect and eliminate irrelevant features. Moreover, the missing values in datasets or occurrence of repetitive values are handled through data pre-processing. Missing and repetitive values are substituted with the mean value from that column. Finally, the data is ready to be processed using the attribute selection tool.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management  
#### 4.5.1 PowerMatching City  
Management of the energy and the grid in overall has been addressed by the \"PowerMatching City\" project carried out in The Netherlands. Its objective has been to depict the grid of the future. Therefore, smart appliances, smart meters, renewable sources and storage volumes have been integrated in the system. The scope has been to examine how the system would perform and how people would react to this initiative. It has been noticed that the various technologies provide flexibility and give the possibility for the system to expand, whereas the system itself could adapt to fluctuating consumer demands. A VPN network has been used for the communication requirements between the households and the central servers. Local connections are realized by a separate ADSL connection. Such connections could also be provided by a broadband internet connection [98].
### Reanalysis Data
Because the observation conditions are highly consistent, the measurements from both sensors can be directly compared to identify and correct systematic biases, improving cross-sensor consistency. In [74], [PERSON] et al. conducted a cross-calibration of the long near-infrared bands between MODIS and VIIRS. By identifying matched observations with similar viewing and illumination geometry, they calculated radiance ratios to quantify sensor differences and adjust VIIRS measurements, leading to improved consistency in downstream ocean color products such as chlorophyll-a concentration. Figure 2 displays several examples of the aforementioned data, and Table 1 provides a comprehensive summary of ocean data, including their sources, characteristics, and typical applications in ocean research. It is worth noting that while several remote sensing datasets, such as Sentinel-1 SAR, are accessible on platforms like Google Earth Engine (GEE) [75; 76], others, like in situ buoy data, are not included. As a result, understanding the original data sources becomes crucial for fully utilizing these datasets. Ocean data, encompassing observational, model, and reanalysis data, form the foundation for understanding and addressing ocean and climate-related challenges. Observational data provide real-time measurements while model and reanalysis data offer large-scale, consistent information. Together, these datasets support comprehensive analyses of the changes in ocean and climate.