esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Results
Then, we deduce that for all \(x,y\in C\),  
\[\|T^{n}x-T^{n}y\|\leq(1+\theta_{n})\|x-y\|=\|x-y\|+\theta_{n}\|x-y\|\leq\|x-y \|+\theta_{n}\operatorname{diam}(C).\]  
Hence, we get \(c_{n}:=\max\{0,\sup_{x,y\in C}(\|T^{n}x-T^{n}y\|-\|x-y\|)\}\leq\theta_{n} \operatorname{diam}(C)\), which immediately attains \(\sum_{n=0}^{\infty}c_{n}<\infty\). Therefore, by Theorem 1 we derive the result. Theorem 3: _Fix a convex, nonempty closed set in a real Hilbert space \(H\). Let the monotone mappings \(B_{1},B_{2}:C\to H\) be \(\alpha\)-inverse-strongly and \(\beta\)-inverse-strongly. Set \(F:C\to H\) a \(\kappa\)-Lipschitzian and \(\eta\)-strongly monotone with constants \(\kappa,\eta>0\) such that \(\tau=1-\sqrt{1-\rho(2\eta-\rho\kappa^{2})}\in(0,1]\), where \(0<\rho<\frac{2\eta}{\pi}\). Let \(T\) be an asymptotically nonexpansive mapping equipped with an asymptotic sequence \(\{\theta_{n}\}\). Let \(\{\beta_{n}\}_{n=0}^{\infty}:C\to C\) be a countable family of \(\ell\)-uniformly Lipschitzian pseudocontractions such that \(\Omega:=\bigcap_{n=0}^{\infty}\operatorname{Fix}(S_{n})\cap\operatorname{ GSVI}(C,B_{1},B_{2})\cap\operatorname{Fix}(T)\
eq\emptyset\) where \(\operatorname{GSVI}(C,B_{1},B_{2})\) is the fixed-point set of the mapping \(G:=P_{C}(I-\mu_{1}B_{1})P_{C}(I-\mu_{2}B_{2})\) with \(\mu_{1}\in(0,2\alpha)\) and \(\mu_{2}\in(0,2\beta)\). For any given \(x_{0}\in C\), let \(\{x_{n}\}\) be the sequence given by_  
\[ \begin{cases}&u_{n}=\gamma_{n}(x_{n}-S_{n}u_{n})+S_{n}u_{n},\\ &z_{n}=P_{C}(u_{n}-\mu_{2}B_{2}u_{n}),\\ &y_{n}=P_{C}(z_{n}-\mu_{1}B_{1}z_{n}),\\ &x_{n+1}=(1-\beta_{n})P_{C}(T^{n}y_{n}-\alpha_{n}\rho FT^{n}y_{n})+\beta_{n}x_ {n},\quad\forall n\geq 0,\end{cases} \]  
_where \(\{\alpha_{n}\}\), \(\{\beta_{n}\}\) and \(\{\gamma_{n}\}\) are the sequences in \((0,1]\) satisfying the following conditions:_  
_(i)_ \(\lim_{n\to\infty}\alpha_{n}=0\) _and_ \(\sum_{n=0}^{\infty}\alpha_{n}=\infty\)_;_  
_(ii)_ \(\lim_{n\to\infty}\frac{\theta_{n}}{\alpha_{n}}=0\)_;_  
_(iii)_ \(1\geq\limsup_{n\to\infty}\beta_{n}\geq\liminf_{n\to\infty}\beta_{n}\geq 0\)_;_  
_(iv)_ \(1\geq\limsup_{n\to\infty}\gamma_{n}\geq\liminf_{n\to\infty}\gamma_{n}\geq 0\) _and_ \(\lim_{n\to\infty}|\gamma_{n+1}-\gamma_{n}|=0\)_._  
_Assume that_ \(\sum_{n=1}^{\infty}\sup_{x\in D}\|S_{n}x-S_{n-1}x\|<\infty\) _for any bounded subset_ \(D\) _of_ \(C\)_, and let_ \(S:C\to C\) _be a mapping defined by_ \(Sx=\lim_{n\to\infty}S_{n}x\) _for all_ \(x\in C\)_, and suppose that_ \(\operatorname{Fix}(S)=\bigcap_{n=0}^{\infty}\operatorname{Fix}(S_{n})\)_. Then_ \(\{x_{n}\}\) _converges strongly to_ \(x^{*}\in\Omega\) _provided_ \(\lim_{n\to\infty}\|T^{n+1}y_{n}-T^{n}y_{n}\|=0\)_. In this case,_  
_(a)_ \(x^{*}\in\Omega\) _solves the VI:_ \(\langle F(x^{*}),x^{*}-x\rangle\leq 0,\ \forall x\in\Omega\)_;_  
_(b)_ \((x^{*},y^{*})\) _solves the GSVI (3) with_ \(y^{*}=P_{C}(x^{*}-\mu_{2}B_{2}x^{*})\)_._
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 3 System Model and Problem Formulation  
We considered a wireless network with \(N\) BSs serving a group of users in a 2-dimensional geometry. We used \(R_{BS}\) to denote the invalid probability of each BS, and \(N_{BS}\) to denote the number of connection restrictions for each BS. Let \(\mathcal{S}\) denote the set of all BS selection strategies, from which users choose the appropriate strategy \(s\in\mathcal{S}\). We used \(x\) to denote the position coordinates of UEs, and then \(s(x)\) can be expressed in detail as \((s_{1}(x),s_{2}(x),\ldots,s_{N}(x))\). Such a selection strategy means the probability that the user chooses to connect to BS \(i\) at location \(x\) is \(s_{i}(x)\), and we have \(s_{i}(x)\in[0,1]\) together with \(\sum_{i=1}^{N}s_{i}(x)=1\). Our optimization goal was to maximize the quality of the communication signal, so we define the reliability of the communication signal from the perspective of reliability modeling.  
Throughout this paper, \(\mathbb{P}[A]\) denotes the probability of event \(A\), \(d_{i}(x)\) denotes the distance between BS \(i\) and user location \(x\), \(R_{BS}(i)\) denotes the probability that BS \(i\) is invalid, \(s\) denotes the selection strategy, \(\mathbb{E}[\cdot]\) denotes the expectation operator, \(\alpha\) denotes the given significance level, and \(\lambda\) denotes the confidence interval.
## 4 ASK Modulation and Demodulation  
The amplitude shift keying (ASK) or on/off keying (OOK) is the simplest digital modulation used in wireless telemetry bio-devices and biomedical implanted devices [25]. In these types of modulation, no carrier is used during the transmission which minimizes the power consumption of the modulator [26]. The principle of ASK transmission is explained in [27] as shown in Figure 4 and as follows:  
\[S_{ASK}(t)=b(t)\times c(t) \tag{1}\]  
\[b(t)=\begin{cases}\sqrt{E_{b}}\ \ldots\ \ldots\ \ldots\ \ldots\ \ldots\ binary\ 1\\ 0\ \ldots\ \ldots\ \ldots\ \ldotsbinary\ 0\end{cases} \tag{2}\]  
\[c(t)=\sqrt{\frac{2E_{b}}{T_{b}}}\ \cos(\omega_{c}\ t) \tag{3}\]  
where \(b(t)\) is the binary message, \(c(t)\) is the carrier signal, \(E_{b}\) is the bit energy and \(T_{b}\) is the bit duration.  
Figure 3: Modulation techniques used in biomedical devices.  
In general ASK modulation is used in the implanted part for its simplicity and low power consumption. However, it has a number of limitations for high-bandwidth data transmission, high order filters with sharp cut off frequencies and large capacitors cannot be easily integrated for low-frequency RF applications [28]. There are two methods of ASK demodulation: coherent and non-coherent detection, however, most designers have used the non-coherent method due its simplicity and low power consumption [29]. The coherent method uses carrier phase information for detection. This method uses a product detector and a phase-locked beat frequency oscillator for detection. In non-coherent methods no carrier phase is used for detection based on filtering signal energy within allocated spectra and envelope detectors. The performance degradation of the non-coherent method is about 1-3 dB when compared to coherent detection, depending on _E_/_N_\({}_{0}\).  
Summaries in terms of frequency, data rate, power, size and application of ASK modulator for various telemetric wireless and implanted devices are shown in Table 1.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 2 Related Work  
With the rise of smart agriculture, numerous systems have emerged. Yet, most grapple with safety hazards, high costs and resource demands, dependency on internet connectivity, or poor performance. Amid the plethora of systems in the literature, our focus lies on the latest intelligent agricultural and animal intrusion detection methods.  
[PERSON] et al. suggest using the traditional electric fence, which shocks animals that cross the boundary [3]. While effective and easy to install, it requires a consistent and substantial power supply, along with regular maintenance. In contrast, our system remains unaffected during power outages and, importantly, does not pose risks to animals or people. In [4], authors analyze why traditional methods such as electric fencing are futile in some scenarios and have high costs.  
Cameras and computer vision are effective at identifying intruding animals. Some researchers [5; 6; 7] use deep learning algorithms to recognize animals captured by the camera at regular intervals. However, fixed interval detection wastes resources and may miss some animals. [PERSON] et al. [4] instead send images to a thin film transistor (TFT) display and use a flash light for better night images, which are more expensive and consume more power. Compared to computer vision, it is also harder for humans to accurately identify animals in images where they make up a small percentage. Instead of capturing images, [PERSON] et al. [8; 9] analyze videos, which is challenging to meet latency requirements and necessitates significantly higher computational power.  
Cloud-based infrastructures [4; 10; 11] are popular in smart agriculture for their powerful computing capabilities. In these systems, data are transmitted over the Internet to the cloud, where the data are stored and processed for decision-making. However, these systems rely on Internet connectivity, which may be unavailable in rural areas, and can result in high latency due to data transmission to the cloud.  
The systems proposed from 2017 to 2022 [12; 13; 14; 15; 16; 17; 18] that use infrared sensors lack specifics on sensor placement and algorithms. In [5], it fails to achieve better performance. The works presented in [3; 19; 20; 21] cannot support a large service coverage at a low cost.  
In comparison, our proposed system excels at accurately detecting and predicting animal locations while minimizing power consumption and transmission latency, and eliminating dependence on internet connectivity by leveraging LoRa communication protocol.
## 5 Conclusions  
The conventional driver drowsiness detection systems are vulnerable to extreme head rotation and occlusion. This study presents a new method to deal with such problems. First, it is shown that DB-ASM is more robust against occlusion than AAM; secondly, given that extreme head poses occur frequently during the natural driving condition in the car, PE-ASM is an extended version of DB-ASM by incorporating six extreme head pose cases into it. Evaluation result using BU face DB and our custom face DB suggests that it is particularly reliable against extreme head poses; thirdly, since our drowsiness detection system is based on the status of two facial features--head nodding and eye-blinking--it requires to make a judgment based on these information. HMMs are used in inferring whether the subject is drowsy or not in time sequence. It runs in the real-time basis on a PC. Our result suggests that it has a potential application in a commercial vehicle, in which the visual conditions are diverse and difficult to deal with.  
The following are available online at www.mdpi.com/2076-3417/6/5/137/s1, Video S1. This video demonstrates how both eye-bling and nodding of the driver are estimated and then Markov model makes inference according to their states in real-time.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A1A2006969) and by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the Global IT Talent support program (IITP-2015-R0134-15-1032) supervised by the IITP (Institute for Information and Communication Technology Promotion).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] collected the driving database; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 12: Detection of driver’s drowsiness using PE-ASM (Pose Extended – Active Shape Model) and HMM: PE-ASM is applied to the images of a driver (**top panel**), eye-blinking (**2****nd panel**), head pose (**3****rd panel**), HMM states (**4****th panel**) and the decision of driver’s drowsiness (**bottom panel**). Note that whenever blinking and nodding HMM states are overlapped, the system detects drowsiness of the driver, indicated as red rectangles, as shown in the bottom panel.
## 5 Summary and Conclusions  
Taking the settling basins out of use and discontinuing the discharge of mine water led to their transformation into freshwater bodies. In the case of the \"Hubertus I\" reservoir, the process was completed by pumping out salty water. In its place, poorly mineralised groundwater and rainwater flowed in, turning the reservoir into freshwater. A specific and only known case of turning an endorhei settling basin into a freshwater reservoir without gravitational drainage or pumping brine out is the Gliniok reservoir. In this case, the brine spontaneously discharged into the rock mass through a system of cracks (fissures) after high-energy tremors.  
Turning mine water settling basins into freshwater reservoirs (reducing water mineralisation) reduces the concentration of the main cations and anions in the water. To this day, the water in the reservoirs presents a different hydrochemical type than the control reservoirs. Also, the concentration of the main ions is higher than those found in waters of reservoirs of the same origin and kind of exploited raw material and not subject to secondary anthropopressure. The most significant changes occurred in the physicochemical properties of water in the vertical profiles of reservoirs. Most of all, water stratification and meromixis disappeared. Mixing processes have become typical for reservoirs in temperate latitudes.  
An interesting process was the change in the hydrochemical type of water in the Grunfeld reservoir caused by the decrease in water level. The observed change may indicate the direction of water transformation in reservoirs where the water level decreases, for example, due to climate change.  
Green energy transformation will cause more and more coal mines to be closed in the near future. The vast majority of these mines have settling basins for salty mine waters. The data presented in the article indicate that if pumping operations continue, the settling basins quickly turn into freshwater. Such reservoirs may begin performing new natural, social and recreational functions. Ichthyofauna reappeared in the Gliniok and Hubertus reservoirs. The observed processes indicate that after the end of exploitation, it would be beneficial to pump out the brines. The fresh underground water and rainwater flowing in their place may quickly cause the reservoir to renaturalise.  
Conceptualization, T.M., G.W., A.D., B.B.-Z. and J.K.; Methodology, T.M. and J.K.; Formal analysis, G.W., A.D., B.B.-Z. and J.K.; Resources, T.M. and J.K.; Writing--original draft, T.M.; Writing--review & editing, T.M. and J.K.; Visualization, T.M.; Project administration, J.K.; Funding acquisition, G.W., A.D. and B.B.-Z. All authors have read and agreed to the published version of the manuscript.  
This research was co-funded by the Medical University of Silesia agreement BNW-1-101/K/4/F.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
# Risk Perceptions on Social Media Use in Norway
## 5 Analysis and Results
### Risk Perceptions
#### 5.2.2 Categorical Analysis of Risk Perceptions when Sharing on Social Media
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Post Images** & **Category** & **N** & **Median** & **Mean** & **Std. Dev.** & **Std. Error** &
\begin{tabular}{l} **95\% CI** \\ **Lower** \\ \end{tabular} & **Upper** & **Min** & **Max** & **Sig** \\ \hline Facebook & Male & 187 & 2 & 2.04 & 0.691 & 0.051 & 1.94 & 2.14 & 1 & 4 & \\  & Female & 97 & 2 & 2.11 & 0.776 & 0.079 & 1.96 & 2.27 & 1 & 4 & \\  & Total & 284 & 2 & 2.06 & 0.72 & 0.043 & 1.98 & 2.15 & 1 & 4 & 0.4 \\ \hline Twitter & Male & 101 & 2 & 2 & 0.693 & 0.069 & 1.86 & 2.14 & 1 & 4 & \\  & Female & 29 & 2 & 2.24 & 0.577 & 0.107 & 2.02 & 2.46 & 1 & 3 & \\  & Total & 130 & 2 & 2.05 & 0.674 & 0.059 & 1.94 & 2.17 & 1 & 4 & 0.09 \\ \hline Reddit & Male & 94 & 2 & 1.87 & 0.883 & 0.091 & 1.69 & 2.05 & 1 & 4 & \\  & Female & 11 & 2 & 2.45 & 0.82 & 0.247 & 1.9 & 3.01 & 1 & 4 & \\  & Total & 105 & 2 & 1.93 & 0.891 & 0.087 & 1.76 & 2.11 & 1 & 4 & 0.04 \\ \hline Snapchat & Male & 172 & 2 & 1.66 & 0.643 & 0.049 & 1.56 & 1.75 & 1 & 4 & \\  & Female & 91 & 2 & 1.95 & 0.835 & 0.088 & 1.77 & 2.12 & 1 & 4 & \\  & Total & 263 & 2 & 1.76 & 0.727 & 0.045 & 1.67 & 1.84 & 1 & 4 & 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Differences in risk perceptions between groups on “How do you perceive risk when you conduct the following action on SoMe?”. If we further examine the perceived risk of participating in a debate on social networks, we can see that one gender has a higher perceived risk than the other, Figure 8 illustrates the difference between groups for Facebook. Doing an ANOVA analysis on genders and risk perception on debates on both Facebook and Twitter gives us a \(p=0.02\). Other than fitting with the pattern of women rating the risks higher, the difference on Reddit is smaller and insignificant with \(p=0.1\).
## 3 Channel Model
Mathematically, its PDF is expressed as follows:  
\[f_{h_{2}}(h_{2})=\frac{K}{\lambda}\bigg{(}\frac{h_{2}}{\lambda}\bigg{)}^{K-1} \mathrm{exp}\Bigg{[}-\bigg{(}\frac{h_{2}}{\lambda}\bigg{)}^{K}\Bigg{]}, \tag{5}\]  
with \(K>0\) being the shape parameter related to the scintillation index of the irradiance fluctuations and \(\lambda>0\) is the scale parameter related to the mean value of the irradiance [60]. Assuming \(E[h_{2}]=1\), then, according to [61], \(\lambda=\frac{1}{\Gamma(1+1/K)}\), whereas the scintillation index is provided by \(\sigma_{h_{2}}^{2}=\frac{\Gamma(1+2/K)}{\Gamma(1+1/K)^{2}}-1\approx K^{-11/6}\). Once we have presented the statistical PDFs associated with either the atmospheric and underwater channels, now we can use that information to write the expression of the cumulative distributive function (CDF) for the CSI-assisted AF-based TU-FSO system. Thus, we have the following. \[F_{\gamma}(\gamma)\approx\mathrm{Pr}[\gamma_{1}<\gamma\bigcup\gamma_{2}< \gamma]=F_{\gamma_{1}}(\gamma)+F_{\gamma_{2}}(\gamma)-F_{\gamma_{1}}(\gamma) F_{\gamma_{2}}(\gamma). \tag{6}\]  
In this last expression, \(\gamma_{1}\) and \(\gamma_{2}\) are the signal-to-noise ratios (SNR) corresponding to the terrestrial and the underwater FSO hops, respectively. Their associated CDFs are directly obtained by integrating their PDFs, which are shown in Equations (4) and (5), respectively, as follows:  
\[F_{\gamma_{1}}(\gamma_{1})=\sum_{r=1}^{\beta}\frac{m_{r}}{\Gamma(a)\Gamma(r)} \bigg{(}\frac{\gamma B}{\overline{\gamma_{1}}}\bigg{)}^{\frac{b-a+1}{2}}G_{1 \beta}^{2,1}\bigg{(}\frac{B\gamma}{\overline{\gamma_{1}}}\bigg{|} \begin{array} []{c}\frac{a-b+1}{2}\\ a,0,\frac{a-b-1}{2}\end{array} \bigg{)} \tag{7}\]  
\[F_{\gamma_{2}}(\gamma_{2})=1-\mathrm{exp}\Bigg{[}-\bigg{(}\frac{\gamma}{ \overline{\gamma_{2}}\lambda}\bigg{)}^{K}\Bigg{]}, \tag{8}\]  
where we have employed [62] (Eq. (6.592.2)) to obtain the CDF of the \(\mathcal{M}\) model. In Equations (7) and (8), \(\overline{\gamma_{1}}\) and \(\overline{\gamma_{2}}\) correspond to average SNRs of atmospheric and underwater fading channels, respectively. In addition, \(a=\alpha-r\), \(b=\alpha+r-1\) and \(B=\frac{a\beta}{\Omega^{{}^{\prime}}+\delta s^{\beta}}\), with \(\Omega^{{}^{\prime}}=\Omega+\xi_{c}+2\sqrt{\Omega\xi_{c}}\) representing the average power from the coherent contributions. Hence, by inserting (7) and (8) into (6), we obtain the resulting overall CDF. \[F_{\gamma}(\gamma)=1-\exp\left[-\left(\frac{\gamma}{\mathcal{T}2\lambda} \right)^{K}\right]\times\left\{1-\sum_{r=1}^{\beta}\frac{m_{r}}{\Gamma(\alpha) \Gamma(r)}\bigg{(}\frac{\gamma B}{\mathcal{T}_{1}}\bigg{)}^{\frac{b-a+1}{6} \mathcal{G}_{1,3}^{2,1}}\bigg{(}\frac{B\gamma}{\mathcal{T}_{1}}\bigg{|} \begin{array} []{c}\frac{a-b+1}{2}\\ a,0,\frac{a-b-1}{2}\end{array} \bigg{)}\right\} \tag{9}\]
## 2 Materials and Methods
### Analysis Methods Applied
#### 2.4.2 Machine Learning (ML) Analysis Using Classification Algorithms  
To complement the statistical analysis results, which are insufficient in accuracy, the second analysis for the experiments was implemented by using a machine-learning-based analysis model developed by Python as shown in Figure 2. This analysis model adopts multiple classification analysis algorithms, which have been commonly used as of recent. It tested the accuracy of classification to distinguish how much weight each construction worker was lifting with the sum of the left and right FSR sensing values (LSUM, RSUM, ASUM) measured, when a construction worker wearing smart safety shoes was not holding a heavy object (body weight +0 kg) and holding a different heavy object (body weight +5 kg-+20 kg).  
The analysis model consists of two major processes. The first process provides the processes of pre-processing data so that raw data collected and transmitted from the FSR sensors are effective for ML analysis. The second process provides the processes of performing ML-based classification analysis. The data preprocessing process includes the processes of converting raw sensing values (magnitude) into the unit of voltage values, defining and labeling variables necessary for analysis, setting independent variables (LSUM, RSUM, ASUM) and dependent variables (load), and dividing the sensing data values into train set and test set by using scikit-learn [31].  
The preprocessed data is subjected to a machine-learning-based classification analysis process by applying effective and useful classification analysis algorithms such as decision tree, random forest, GBM, and lightGBM. In the learning stage of this process, learning is performed through model selection and cross-validation by applying valid machine-learning algorithms. In the evaluation stage, the final model is selected, and finally, in the classification stage, the label is classified with new data, and the accuracy of the result is produced.  
Through this process, the classifier accuracy of the ML analysis model adopting each classification algorithm is confirmed by comparing and analyzing the actual weight measurement data and the weight classification data of the analysis model. Basically, the weight classification accuracy reveals how accurately the analysis model fits each weight of the object carried by each experimenter.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Experimental Settings
#### 3.2.2 Training Environment Description  
To evaluate the effectiveness of the proposed HRU-Net model for road extraction in remote sensing images, training, validation, and testing were conducted using the Massachusetts Road Dataset. The experiment was designed within the Pytorch (version 1.11.0) framework and developed using JetBrains PyCharm 2021. Python served as the programming language, while the hardware configuration included an Intel(R) Core(TM) [EMAIL_ADDRESS] GHz processor and an Nvidia GeForce RTX 3060 graphics card for acceleration. The memory size was 12 GB, and the operating system employed was Windows 10.3.2.3.