esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The assessment of habitat quality and geological disaster susceptibility provides crucial decision support information for local authorities and policymakers, as well as a scientific foundation for land use planning, environmental protection, and strategies to prevent geological disasters. In mountainous areas, disaster prevention policies should account for differences in ecological environments. In zones with a lower habitat quality, it is advisable to implement a combination of engineering and biological measures, enhance ecological protection, expand green areas, and bolster ecosystems' self-restoration capabilities. Conversely, in regions with a higher habitat quality, there should be an intensified focus on geological disaster research and monitoring. It is also essential to encourage rational land development and utilization while strictly prohibiting indiscriminate excavations at slope bases to preserve mountain stability.  
On the macro level, the impact of habitat quality on susceptibility to geological disasters is minimal when observing the entire county. However, the ecological environment, the geological environment, and the disaster environment are closely interrelated, influencing and constraining one another. The geological environment significantly affects the relationship between habitat quality and susceptibility to geological disasters. In Pingshan County, regions with similar geological environments exhibit a negative correlation between habitat quality and susceptibility to geological disasters. Therefore, while habitat quality providessome guidance in assessing susceptibility to geological disasters, it should not be the sole criterion for determining susceptibility levels.  
When selecting evaluation indicators for geological disaster susceptibility, it is crucial to thoroughly consider the disaster-bearing environment. For instance, when conducting geological disaster susceptibility assessments in mountainous areas with better ecological environments, it may be appropriate to reduce the weight of the ecological indicators and incorporate measures of human activities, such as land use types, to enhance the accuracy and reliability of the assessment. Conversely, in mountainous regions where high-quality habitats are significantly disturbed by human activities, greater attention and the development of targeted prevention measures are necessary to mitigate potential disaster risks.  
Writing--original draft preparation, M.Z.; writing--review and editing, A.Z.; writing--review and editing, S.C.; writing--review and editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the General Program of the National Natural Science Foundation of China, grant number 42277166, and Hebei GEO University Science and Technology Innovation Team Project, grant number KJCYTD-2021-08.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 3 Dynamic Hard Shoulder Running Strategy Based on Hidden Markov Model
### Traffic Breakdown Probability Calculation  
In order to detect traffic breakdown incidents, the specific manifestation of traffic breakdown must first be defined. The breakdown state can be defined as an abrupt drop in vehicle speed that fails to recover above a critical speed over a period of time. To address the differences in the characteristics of traffic flow on different lanes, the paper adopted the cross-sectional speed detection method to define the breakdown state of a roadway in a hierarchical manner. A road's breakdown situation must be clear and definite before defining its breakdown state. Define the breakdown as a state in which the vehicle speed is less than the critical speed \(V_{c}\) (km/h) and does not recover above \(V_{c}\) for \(T_{c}\)(min), where \(V_{c}\) is the critical speed. The limit of \(T_{c}\) is to prevent misjudging transient deceleration as a traffic breakdown.  
The physical meaning of \(V_{c}\) is that a breakdown is considered to have occurred when the speed is less than that value. Intuitively, if the speed is close to \(V_{c}\), a rapid and widespread deceleration is likely to occur, leading to a breakdown in the traffic flow. Therefore, the confirmation of \(V_{c}\) is to find a speed point at which a significant fluctuation occurs when the vehicle speed reaches this value.  
Therefore, \(V_{c}\) can be determined through the following three steps:  
1. Calculate the average speed \(V_{mean}\) for a continuous period of time \(t_{c}\) (min);
2. Calculate the standard deviation \(\sigma\) of the speed in \(t_{c}\);
3. Repeat the previous two steps to calculate the standard deviation \(\sigma\) of all average speeds \(V_{mean}\), respectively. The critical speed \(V_{c}\) is the \(V_{mean}\) corresponding to the greatest value of \(\sigma\). The formulas for each parameter are as follows: \[V_{mean}(t)=\frac{\sum_{i=t}^{t+t_{c}-1}\ q_{i}v_{i}}{\sum_{i=t}^{t+t_{c}-1} \ q_{i}}\] (6) \[\sigma=\sqrt{\sum_{i=t}^{t+t_{c}-1}\ p_{i}(v_{i}-\overline{v}_{t})^{2}}\] (7) \[p_{t}=\frac{q_{t}}{\sum_{i=t}^{t+t_{c}-1}\ q_{i}}\] (8)  
where \(q_{t}\) is the flow rate at moment \(t\); \(\overline{v}_{t}\) is the average speed on the road at moment \(t\); \(V_{mean}(t)\) is the weighted average speed during period \(t_{c}\) (min) starting at moment \(t\), and the weights are the traffic flow at the corresponding moments; \(\sigma\) is the weighted standard deviation of the speeds during period \(t_{c}\) starting at moment \(t\), and the weights are \(p_{t}\).  
Figure 3: Traffic breakdown diagram.  
According to the setting method of \(V_{c}\) and the definition of single-lane breakdown, single-lane breakdown incidents can be detected from the raw data, which can be carried out through the changes in the speed of each lane in the studied road section.
## 3 Context Modeling
### Context Model  
At the core of the proposed context model lies the thing--the abstract building block of the IoT. It is associated with the _who_ feature. Things can be users, devices or other objects, and they are defined by a list of properties that are usually profiled data. There are two main observations that need to be captured by the context model: the state of each thing and the interactions among them. In essence, both types of observations can be combined into one, introducing the axiom that \"Each observation includes one or more entities in a temporal and spatial extent\". The _where_ and _when_ features are thus integrated into the scheme. Furthermore, many observations include a target entity, the _what_ feature, which may be one or more entities that are related to the observation in a passive way (e.g., an activity undertaken by an individual). The combined set of thing(s), location, time, and target(s) defines an observation, which is captured per activity. Thus, the proposed context model is responsible for representing all those sets of observations. Finally, the _why_ feature is derived data from the inference procedure, or explicit user input. [PERSON] et al. [47] present their CA meta-ontology with a hierarchical structure that includes a general layer and a domain-specific layer. They describe six contextual classes, exploiting the 5 Ws schema to represent any possible contextual information. Although conceptually their work succeeds in modeling context efficiently, it is missing the implementation aspect that allows the identification of data sources and flow and the representation of the inference process and output. Influenced by this meta-model, the following core contextual classes are used as the basis of the proposed model:  
* Thing: User or device.
* Location: Any entity that describes a location of varying range, such as point, area, room, building, district, etc.
* Time: Any period of time that has a meaningful representation, e.g., lunchtime or commute time. Specific timestamps are not objects of the class.
* Activity: Any potential activity or status of a thing.
* Asserted context: The set of contextual parameters that infer the identification or triggering of an activity.  
Those five core classes are connected with relationships as illustrated in Figure 1. Each observation tuple is represented as a single instance of this relationship graph for a single observation. This instance describes an activity (durative or instant) or status change that occurs at a specific time and includes things that are located somewhere, while the event is identified or triggered based on asserted context data. Each class is analyzed in the following sections.
# 5G-IPAKA: An Improved Primary Authentication and Key Agreement Protocol for 5G Networks
## 7 Conclusions  
In this paper, according to the analysis of the latest version of the 5G AKA protocol, we point out seven shortcomings of this protocol, including that \(SUC1\) can be replayed without being found, mutual authentication between the _UE_ and the \(SN\) cannot be established, \(K_{SEAF}\) cannot reach an agreement, the location privacy of the _UE_ can be compromised, DoS attacks against the \(SN\) can be formed, attacks based on _MAC_ failure can be performed, and PFS cannot be provided.  
To overcome these shortcomings, we propose a 5G-IPAKA protocol. Compared with the latest version of the 5G AKA protocol, the main improvements of the 5G-IPAKA protocol include that the pre-shared key between the _UE_ and the \(HN\) is replaced with a derivation key of the pre-shared key, the challenge-response mechanism for the \(SN\) is added, the mutual authentication and key confirmation between the _UE_ and the \(SN\) is added, and the _MAC_ failure procedure is replaced with a timeout mechanism on the \(HN\).  
Accordingly, we summarize the 5G-IPAKA protocol into two cases, and then use the mixed strand space model for mixed protocols to formally analyze the security of the 5G-IPAKA protocol. As a result, mutual authentication and injection among the _UE_, the \(SN\), and the \(HN\) are established. Therefore, the 5G-IPAKA protocol is secure in the mixed strand space model.  
Based on the further discussion and comparative analysis, the 5G-IPAKA protocol can overcome the above shortcomings of the latest version of the 5G AKA protocol, and is better than the recently improved 5G AKA protocols in overcoming these shortcomings. In addition, the 5G-IPAKA protocol is efficient and backward-compatible.  
Recently, some authors also point out that the protection mechanism of SQN can be defeated due to its use of XOR in the 5G AKA protocol. This paper does not consider this security problem, and we will further study this security problem in the future.  
Methodology, Y.X.; formal analysis, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.61741216, 61402367), Shaanxi Science and Technology Co-ordination and Innovation Project (No.2016 KTTSGY01-03), National Key Research and Development Program (No. 2018 YFC08242-04), and New Star Team Project of Xi'an University of Posts and Telecommunications.  
Not applicable.  
The authors declare no conflict of interest.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Discussion and Analysis Based on Performance Metrics
#### 5.2.1 Analysis Based on RMSE  
In this part, the values of the RMSE in the papers are discussed. Table 15 shows the details of each paper's RMSE based on the numerical range. In this table, papers [11; 12; 24] used the CNN-based model; papers [35; 36; 37; 38; 39; 45; 46; 47; 57; 65] used the LSTM-based model; papers [71; 72; 73] used the DNN-based model; papers [27; 29; 31] used the RNN-based model; and paper [86] used another deep learning method.  
It was clear that papers [24; 57] achieved the best performance using the DNN model, having RMSEs smaller than 0.001. Papers [11; 29; 31; 32; 36; 38; 71; 73] had great performance, while papers [27; 35; 37] did not have low RMSEs.  
Among all the papers, 33% of the CNN papers had an RMSE below 0.001, 33% of the CNN papers were in the range of 0.01-0.1, and 33% of the CNN papers were in the range of 0.1-1. 36% of the LSTM papers were in the ranges 0.01-0.1 and 1-10 each, and the rest  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RFMSE Range** & **Reference No.** \\ \hline RMSE & [11; 12; 24; 27; 29; 31; 32; 35; 36; 37; 45; 46; 71; 72; 73; 86] \\ \hline \(<\)0.001 & [24; 57] \\
0.001–0.01 & [31; 73] \\
0.01–0.1 & [11; 29; 32; 36; 38; 65; 71] \\
0.1–1 & [12; 72; 86] \\
1–10 & [39; 45; 46; 47] \\
10–100 & [37] \\ \(>\)100 & [27; 35] \\ \hline \hline \end{tabular}
\end{table}
Table 15: Analysis based on Root Mean Square Error (RMSE).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Metrics** & **Reference No.** \\ \hline RMSE & [8; 12; 24; 27; 29; 31; 32; 35; 36; 37; 38; 45; 46; 71; 72; 73; 86] \\ MAPE & [10; 12; 29; 31; 35; 38; 40; 41; 60; 65; 73] \\ MAE & [12; 24; 29; 32; 35; 37; 38; 40; 42; 66; 71; 72] \\ Accuracy & [3; 13; 16; 19; 22; 23; 25; 26; 28; 29; 34; 36; 45; 50; 52 –54; 56; 58, 61 –64; 67; 68; 74; 87; 89] \\ F-measure & [3; 14; 18; 19; 26; 28; 53; 68] \\ Sharpe ratio & [14; 18; 71; 77; 82; 84; 88; 90; 91] \\ CEQ & [14] \\ Return rate & [14; 16; 18; 21; 23; 33; 70; 71; 78; 79; 84; 87; 88; 89; 91] \\ Mean Test Score & [15] \\ MSE & [22; 24; 30; 31; 35; 38; 39; 70; 72; 81] \\ AE & [11] \\ Precision & [28; 53] \\ Recall & [28; 53] \\ R2 & [35] \\ Error Percentage & [20] \\ MCC & [34] \\ HMAE & [42] \\ HMSE & [42] \\ CORR & [70] \\ SMAPE & [73] \\ Volatility & [71] \\ IC & [48] \\ AR & [48] \\ IR & [48] \\ Score & [55] \\ \hline \hline \end{tabular}
\end{table}
Table 14: Analysis based on performance metrics.  
were distributed equally in ranges <0.001, 10-100, and above 100, with 9% in each range; and 33.3% of the DNN papers were in the ranges 0.001-0.01, 0.01-0.1, and 0.1-1 each. The RNN papers were distributed equally in the ranges of 0.001-0.01, 0.01-0.1, and above 100, i.e., 33% in each range. The only paper that used another deep learning method had an RMSE in the range of 0.01-0.1.
## 2 Methodology
### Key Strata Deflection Solving Model
After solving Equation (1), the equation can be obtained as follows:  
\[\left\{ \begin{array}{l}w_{1}(x)=e^{m_{1}x}\big{[}C_{11}\cos(m_{1}x)+C_{12} \sin(m_{1}x)\big{]}+e^{-m_{1}x}\big{[}C_{13}\cos(m_{1}x)+C_{14}\sin(m_{1}x) \big{]}+\frac{q}{k_{1}}\ (\text{Area I})\\ w_{2}(x)=e^{m_{2}x}\big{[}C_{21}\cos(m_{2}x)+C_{22}\sin(m_{2}x)\big{]}+e^{-m_{ 2}x}\big{[}C_{23}\cos(m_{2}x)+C_{24}\sin(m_{2}x)\big{]}+\frac{q}{k_{2}}\ (\text{Area II})\\ w_{3}(x)=e^{m_{3}x}\big{[}C_{31}\cos(m_{3}x)+C_{32}\sin(m_{3}x)\big{]}+e^{-m_{ 3}x}\big{[}C_{33}\cos(m_{3}x)+C_{34}\sin(m_{3}x)\big{]}+\frac{q}{k_{3}}\ (\text{Area III})\\ \end{array} \right. \tag{2}\]  
where \(C_{ij}(i=1\)-\(3\); \(j=1\)-\(4\)) represents undetermined coefficients of the general solution, which is related to the overlying load, elastic foundation coefficient, bending stiffness, and length of the key strata; \(m_{i}\) is a differential parameter, which can be calculated by \(m_{i}=\sqrt[4]{k_{i}/4 Et}\). The rotation angle \(\theta\), bending moment \(M\), and shear force \(Q\) of the key strata can be computed by  
\[\left\{ \begin{array}{l}\theta(x)=\frac{\mathrm{d}w_{i}(x)}{\mathrm{d}x}\\ M(x)=-Et\frac{\mathrm{d}^{2}w_{i}(x)}{\mathrm{d}x^{2}}\\ Q(x)=-Et\frac{\mathrm{d}^{3}w_{i}(x)}{\mathrm{d}x^{3}}\\ \end{array} \right. \tag{3}\]  
Based on the continuity condition and boundary conditions, the following equations can be concluded:  
\[Z_{i}(x_{\mathrm{R}})=Z_{i-1}(x_{L}),\ w_{i}(-\infty)=w_{i}(+\infty)=0 \tag{4}\]  
Figure 2: Deflection mechanical model of key strata with grouting into overburden separation. where \(Z\) represents the parameters of \(w\), \(\theta\), \(M\), and \(Q\); \(i\) represents the serial number of the elastic foundation; \(x_{R}\) and \(x_{L}\) are the coordinates at the right and left interfaces of a certain segment in the elastic foundation beam. Once the length of the elastic foundation beam, the overlying load, and the elastic foundation coefficient are determined, the undetermined coefficients of the general solution in Equation (2) can be ascertained by combining Equations (2)-(4). This process can be efficiently performed using MATLAB R2023b software, and further details are omitted here due to space constraints.
# Adaptive Clustering of Users in Power Domain NOMA
## 7 Conclusions  
We presented a new method of clustering users based on evolutionary algorithms. The method was evaluated in a NOMA downlink scenario with dynamic power allocation. The evolutionary algorithm DenStream, which served as the basis for the proposed model, was chosen due to its ability to form clusters that adapt to new samples--that is, to new users who join a cell.  
A relatively uniform overall data rate was observed in the results, even considering a temporal variation in the channel gain of all users and the ingress of new users into the cell, thereby, reinforcing the conclusion that the model is robust to these variations. Compared to OMA, the proposed system maintained the expected performance gain for NOMA, making better use of the dynamic power allocation method as it follows the channel state variations. The solution presents a competitive complexity concerning other solutions found in the literature, while it stands out for its online operation.  
As the proposed method considered the state of the user's channel to be clustered, a new user may not be grouped as soon as they arrive at the network cell if the channel state does not meet the system restrictions at that time. However, as the system tracks variations in each user's communication channel, that user can still be grouped at a later time. The signal can still be transmitted by OMA. The system restrictions are adjusted by an initial parameterization that depends on the modeled communication channel.  
The computational complexity of the proposed system increased linearly with the increase in the number of users; even so, it would be possible to use parallel processing to reduce the processing time of the method in networks with many users.  
In future work, we intend to analyze the effects of correlation between channels over the proposed clustering method, which was not addressed in this work. Furthermore, we intend to optimize the proposed system considering a new dynamic power allocation method, and a study of algorithms based on game theory will be performed.  
Methodology, Y.P.S. and L.F.Q.S.; Writing--original draft, Y.P.S.; Writing--review and editing, L.F.Q.S. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES) Finance Code 001.  
Not applicable.  
The authors thank the Laboratory of Parallel Architecture for Signal Processing (LAPPS) of the Federal University of Rio Grande do Norte (UFRN) for providing computational resources.  
The authors declare no conflict of interest.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 2 Matrix Theory in Modern Wireless Communication Systems
### Matrix Representation of OFDM
\right]=\left[ \begin{array}{cccccc}h_{0}&h_{1}&\cdots&h_{M }&0&\cdots&0\\ 0&h_{0}&\cdots&h_{M-1}&h_{M}&\cdots&0\\ \vdots&\ddots&\ddots&\ddots&\ddots&\ddots&\vdots\\ 0&\cdots&0&h_{0}&\cdots&h_{M-1}&h_{M}\\ \vdots&\ddots&\ddots&\ddots&\ddots&\ddots&\vdots\\ h_{2}&h_{3}&\cdots&h_{M-2}&\cdots&h_{0}&h_{1}\\ h_{1}&h_{2}&\cdots&h_{M-1}&\cdots&0&h_{0}\end{array} \right]\left[ \begin{array}[] {c}x_{N-1}\\ x_{N-2}\\ \vdots\\ x_{0}\end{array} \right]+\left[ \begin{array}{c}v_{N-1}\\ v_{N-2}\\ \vdots\\ v_{0}\end{array} \right], \tag{8}\]  
which can be written compactly as  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\boldsymbol{\
u}.\]  
It turns out that \(\mathbf{H}\) is a circulant matrix with the generator vector \([h_{0},0,\cdots,0,h_{M},\cdots,h_{1}]^{T}\). According to the properties of circulant matrices [9], \(\mathbf{H}\) admits the eigenvalue decomposition  
\[\mathbf{H}=\mathbf{U}\boldsymbol{\Lambda}\mathbf{U}^{H},\]  
where \(\boldsymbol{\Lambda}\) is a diagonal matrix whose diagonal elements stand for the eigenvalues of \(\mathbf{H}\) and \(\mathbf{U}\) is a unitary matrix whose rows correspond to the columns of DFT matrix \(\mathbf{W}\) in (5). In other words, \(\mathbf{U}=\mathbf{W}^{H}\). Thus, one can infer the following relations:\[ \begin{split}\mathbf{Y}&=\mathbf{Wy}\\ &=\mathbf{W}(\mathbf{Hx}+\mathbf{\
u})\\ &=\mathbf{W}(\mathbf{HW}^{H}\mathbf{X}+\mathbf{\
u})\\ &=\mathbf{W}\mathbf{U}\mathbf{A}\mathbf{U}^{H}\mathbf{W}^{H} \mathbf{X}+\mathbf{W}\mathbf{\
u}\\ &=\mathbf{\Lambda}\mathbf{X}+\tilde{\mathbf{\
u}}.\end{split}  \tag{9}\]  
It turns out that by exploiting DFT and IDFT operations and inserting the cyclic prefix, OFDM transforms a frequency-selective channel into \(N\) orthogonal flat-fading subchannels without ISI, which helps to reduce the complexity of the receiver decoder. Due to its advantages in combining the energy of all the propagation paths of the channel and enabling a reduced complexity equalization, OFDM has been adopted in numerous communications standards (ADSL, WLAN, LTE, etc.) and has been also used in conjunction with the MIMO technique in what it is currently referred to as the MIMO-OFDM technology [10].
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 7 Security Analysis  
[PERSON] must compare the quantum states, which he obtained from [PERSON], to verify whether the eavesdropper has intercepted the quantum channel. By analysing the parallel quantum channels through a public channel, [PERSON] and [PERSON] need to monitor [PERSON]'s interception. Let us assume that [PERSON] shares four sequences of bits through quantum channels simultaneously with [PERSON]. By comparing the quantum bits and the corresponding transformation they used for it, [PERSON] and [PERSON] detect the presence of [PERSON] in the time slot. They find that the first channel error occurs, while the other channels are safe in the meantime. It should be noticed that according to the no-cloning quantum theory, any change of the quantum state can be detected. However in the case of the authentication protocol successfully applied in the initial stage of the device, the disturbance of an eavesdropper rarely happens. This is because the eavesdropper does not recognise the exact keys since the quantum states have been encoded in each quantum connection with a secret rotation angle, only recognized by the legitimate parties.