esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geological Setting  
The investigated river sediments come from the Ionian and Tyrrhenian seaboard of the Calabrian coastline (Figure 1), and they derive from the dismantling of the Serre Massifs and Aspromonte due to erosion processes. These Massifs belong to the Calabria-Peloritani Orogen (CPO), which is the peri-Mediterranean orogenic Alpine nappe system that comprises the whole Calabria and the north-eastern sector of Sicily [12]. It includes the Sila and Catena Costiera Massifs in northern Calabria, the Serre and Aspromonte Massifs in central and southern Calabria and the Peloritani Mountains in Sicily [12].  
The Serre Massif (Central Calabria) is characterized by outcrops of granitoids and Variscan metamorphic rocks. A thin early Mesozoic sedimentary cover is present only along the Ionian margin. According to recent studies [12; 13; 14], the Serre Massif represents a nearly complete continental crust section, which is divided into lower, middle and upper crustal portions. The lower one is composed of granulitic and migmatitic complexes, whereas the middle one is made up by late-Variscan acidic granitoid rocks, such as tonalites, granodiorites and granites. Conversely, the upper crustal portion consists of high- to low-grade metapelites (Mammola and Silo-Pazzano complexes) [13; 14].  
The Aspromonte Massif (Calabria), together with the Peloritani Mountains (Sicily), constitutes the southern sector of the CPO (Calabria Peloritani Orogen). This Massif was generated by the juxtaposition of three different tectonic units. The Madonna di Polsi Unit is the lowermost unit and it is composed of greenschist to amphibite facies metamorphic rocks showing only Alpine metamorphism. The higher-most unit is represented by the Stilo Unit, made up of phyllites, schists and paragneisses derived from the variscan metamorphism of Paleozoic sediments. The Aspromonte Unit, which is sandwiched between the Stilo and Madonna di Polsi Unit, exhibits high-grade metamorphites and late-variscan granitoids [12; 13; 14].  
Paleozoic and early Mesozoic rocks of both Serre and Aspromonte Massif are covered by sedimentary deposits ranging in age from the Late Cretaceous to the Pleistocene.
### Contribution and Novelty  
Although several studies have reported on the CAES system considering economic issues, few studies have analyzed the CAES system in coordination with P2G facilities integrated with electricity and natural gas infrastructures from economic and environmental perspectives [25; 28; 41; 46]. To the best of the authors' knowledge, the economic, technical,and environmental advantages of the coupled CAES-P2G-wind turbine-based system leveraging DRP have not been investigated in prior works. Thus, the economic-environmental dispatch strategy for a hybrid MCMG system based on a multi-objective optimization framework is analyzed in this work. The coordinated scheduling of the MCMG system, including wind plant, CAES, and P2G facilities, can play a vital role in satisfying the power system's energy requirements in an eco-friendly manner. Table 1 summarizes the taxonomy of reviewed papers in operation studies of hybrid multi-energy systems. The differences between the operation problem of this work and previous works are specified.  
According to the above description, this work focuses on the optimal eco-emission assessment of coordinated CAES, P2G, DRP, and wind farm hybrid MCMG systems participating in the day-ahead market. The proposed coordinated operation approach aims to find the Pareto-optimal set by minimizing the total operation cost and pollution as well as facilitating more integration of RESs into the hybrid MCMG system enabled with P2G and CAES systems. The proposed system serves both electrical and gas loads. Wind energy, load demand, and power price are considered random parameters and modeled via stochastic programming. Besides, the weighted sum fuzzy-based decision making is employed to solve the proposed multi-objective model. Briefly, the main novelty of this research is highlighted as:  
* Proposing a hybrid MCMG system consists of CAES, P2G, and wind farms, contributing to environmental and economic benefits.
* Investigating the coordinated operation of coupled CAES-P2G to facilitate more integration of wind energy resources into the power system.
* Proposing multi-objective scenario-based stochastic programming to the eco-emission analysis of the proposed MCMG and extending the weighted sum fuzzy-based decision-making to solve the model.
* Employing the shiftable strategy of DRP in the hybrid system as a flexible source to mitigate the load curve under probable behavior of wind power, load demand, and power market price.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 5 Learning with Errors (LWE)
### The LWE Cryptosystem
Its security, as with any cryptographic system, depends on the specific parameters chosen and the method used. LWE is a legitimate and well-studied cryptographic concept within the field of lattice-based cryptography. As an ongoing and dynamic field of study, the security of LWE cryptographic algorithms is continuously and consistently investigated and evaluated. Many cryptographic primitives and schemes, including encryption and digital signatures, have been constructed using the assumptions provided by LWE, so it remains a prominent area of study and is considered a promising candidate for the future of computing science. There are several variants and extensions of the LWE problem, each offering different performance properties and security implications that find applications in the field of lattice-based cryptography. Among these variants are Ring-LWE, Binary-LWE, Dual-LWE and Multilineal-LWE [50; 51; 52]. Module-LWE [53] has recently received particular attention as a candidate of NIST [37] chosen in the third round of the competition; it is a key-encapsulation mechanism with a security that relies on the hardness assumptions over module lattices [54].
## 2 Preliminaries and Problem Formulation
### Preliminaries on Technical Lemmas  
We hereby present several pivotal lemmas, These lemmas play an important role in the main conclusion of the paper, which uses Lyapunov stability theory to demonstrate that the designed control protocol can achieve finite-time consensus in heterogeneous multi-agent systems.  
**Lemma 1** ([29; 34]).: _If the signed digraph \(\mathcal{G}\) is structurally balanced, then there exists a diagonal matrix \(\mathcal{W}=\mathrm{diag}\{w_{1},w_{2},\ldots,w_{N}\}\) with \(w_{i}=\pm 1(i=1,2,\ldots,N)\) that transforms the signed digraph into an unsigned graph, which is called the gauge transformation. For graph \(\mathcal{G}\), if the adjacency weights \(a_{ij}\geq 0\), then \(w_{i}=w_{j}\). If \(a_{ij}\leq 0\), then \(w_{i}=-w_{j}\). The nodes in the same subset have the same value of \(w_{i}\) such that \(\mathcal{W}\mathcal{A}\mathcal{W}=\bar{\mathcal{A}}\), where \(\mathcal{A}=(a_{ij})_{N\times N}\), and \(\bar{\mathcal{A}}=(|a_{ij}|)_{N\times N}\) is the matrix of the corresponding unsigned digraph._  
**Lemma 2** ([35]).: _For real matrices with appropriate dimensions \(X\) and \(Y\), there exists a positive constant \(\varepsilon\) such that_  
\[X^{\mathrm{T}}Y+Y^{\mathrm{T}}X\leq\varepsilon X^{\mathrm{T}}X+\frac{1}{ \varepsilon}Y^{\mathrm{T}}Y\]  
**Lemma 3** ([35]).: _For any \(x_{i}>0(i=1,2,\ldots,N)\) and \(0<p\leq 1\), the following inequalities hold:_  
\[(\sum_{i=1}^{N}x_{i})^{p}\leq\sum_{i=1}^{N}x_{i}^{p}\leq N^{1-p}(\sum_{i=1}^{N }x_{i})^{p}\]  
**Lemma 4** ([36]).: _If \(\mathcal{K}=\min_{i=1,2,\ldots,N}\{\frac{\alpha}{1+\alpha}(\sum_{j=1}^{N}a_{ij }-\sum_{j=1}^{N}a_{ij})+b_{i}\}>0\), then the inequality \(\mathcal{X}^{\mathrm{T}}M\mathrm{sig}(\mathcal{X})^{\alpha}\geq\mathcal{K} \sum_{i=1}^{N}|\mathcal{X}_{i}|^{\alpha+1}\geq 0\) holds, where \(\alpha>0\), \(\mathcal{X}=[\mathcal{X}_{1},\mathcal{X}_{2},\ldots,\mathcal{X}_{N}]^{\mathrm{ T}}\in R^{N}\), \(M=L+B\)._  
**Lemma 5** ([30; 37]).: _Suppose that a function \(V(t)\) is continuous and non-negative when \(t\in[0,+\infty)\). If there exist two constants \(c>0\) and \(0<\alpha<1\) so that the derivative of \(V(t)\) satisfies_  
\[\dot{V}(t)\leq-cV^{\alpha}(t),\forall t\geq t_{0},V(t_{0})\geq 0\]  
_then \(V(t)\) satisfies_  
\[V^{1-\alpha}(t)\leq V^{1-\alpha}(t_{0})-c(1-\alpha)(t-t_{0}),t_{0}\leq t\leq T,\]  
_and \(V(t)\equiv 0,\forall t\geq T\), with the settling time \(T\) given by_  
\[T=t_{0}+\frac{V^{1-\alpha}(t_{0})}{c(1-\alpha)}\]
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.4 System Latency (SL)  
During the experiment, the sensor data collection frequency was adjusted and the average system latency for the BBCN model was recorded, as shown in Table 5.  
At a 30 Hz data collection frequency, the latency of the BBCN model is controlled within 80 ms, meeting real-time requirements. Latency slightly decreases with an increase in data frequency, as high-frequency collection reduces the waiting time for data buffering. Compared to other models, the GNSS model has the lowest latency, but lacks local perception and data fusion capabilities. The Vision-SLAM model has the highest latency, mainly due to the higher computational demands of visual SLAM. The BBCN model achieves  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Dynamic Obstacle Avoidance Success Rate (\%, mean \(\pm\) std)** \\ \hline BBCN & \(95\pm 2\) \\ GNSS & \(40\pm 5\) \\ Vision-SLAM & \(80\pm 4\) \\ GNSS-Vision & \(90\pm 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Obstacle avoidance success rate comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distance Range (m)** & **Obstacle Count** & **Actual Path (m, mean \(\pm\) std)** & **Ideal Path (m)** & **PEF (mean \(\pm\) std)** \\ \hline
6–8 & 5 & \(7.4\pm 0.3\) & 7.2 & \(1.03\pm 0.04\) \\
8–10 & 10 & \(9.6\pm 0.4\) & 9.3 & \(1.03\pm 0.04\) \\
10–12 & 15 & \(11.5\pm 0.5\) & 11.0 & \(1.05\pm 0.05\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Path planning efficiency performance of BBCN Model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Frequency (Hz)** & **Average Latency (ms, mean \(\pm\) std)** \\ \hline
10 & \(92\pm 7\) \\
20 & \(85\pm 6\) \\
30 & \(80\pm 5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: System latency vs. data frequency.  
real-time performance close to that of the GNSS-Vision model, demonstrating good stability and real-time capability.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Physical Layer Challenge-Response Authentication (PHY-CRAM)
#### 3.1.1 Physical Layer Challenge  
Suppose that a legitimate IoT device wants to be authenticated by a BS with a shared secret key, while an intrusion device that has no knowledge of the secret key tries implementing impersonation attacks. They use \(L\) subcarriers to communicate with each other. If the IoT device sends a request signal to the BS requesting authentication, the BS transmits a pilot signal to the IoT device to perform the channel estimation for the physical layer challenge. The challenge signal from the BS in a time domain can be represented as follows:  
\[x_{C}(t)=\sum_{l=1}^{L}\sqrt{\frac{2E_{s}}{T}}\text{cos}(2\pi f_{l}t), \tag{1}\]  
where \(E_{s}\) and \(T\) denote the energy per symbol and the symbol duration, respectively. In addition, it is assumed that sinusoids denoted by \(\text{cos}(2\pi f_{l}t)\) are sufficiently separated so that the \(L\) sinusoids are all orthogonal to each other. Then, the channel phase of the received signal at the IoT device through the \(l\)th subcarrier is given by the following:  
\[Y_{C}(f_{l})=e^{i\theta_{l}}|H(f_{l})|X_{C}(f_{l})+n(f_{l}), \tag{2}\]  
where \(H(f_{l})\) and \(n(f_{l})\) are the \(l\)th channel coefficient, and noise term, respectively, which are assumed to be independent and identically distributed CSCG random variables, i.e., \(H(f_{l})\sim\mathcal{CN}(0,\sigma_{h}^{2})\) (for Rayleigh fading) and \(n(f_{l})\sim\mathcal{CN}(0,\sigma^{2})\). Then, the channel phase can be obtained as follows: \(e^{i\theta_{l}}=\frac{Y_{C}(f_{l})}{|Y_{C}(f_{l})|}\), where \(\theta_{l}\) is the \(l\)th estimated channel phase. The estimated channel phases are used for encapsulation of a secret key in the following physical layer response.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 2 Problem Description  
### Train Operation and Train Delay  
Metro train operations involve the movement of trains between stations along railway tracks, which consist of multiple blocks. Here, a metro station refers to a physical station where passengers board and alight from trains, while virtual stations are defined as the boundary points between two adjacent blocks along the railway tracks. To ensure safe and efficient operations, specific running times are calculated for different blocks of a metro line, taking into account factors such as curves, slopes, switches, and distances between stations. These running times are categorized into different block running time levels, each associated with a recommended speed curve [46]. This comprehensive management and scheduling approach ensures the smooth operation of the metro transit system, ultimately providing passengers with an enhanced travel experience. The relationship between block running time levels and the corresponding recommended speed curve is depicted in Figure 1.  
Figure 1: Schematic diagram of different block running time levels.  
Train delays are the phenomenon whereby trains deviate from their scheduled arrival or departure times, caused by some unexpected events. When addressing train delay issues, it is crucial to understand how delays propagate throughout the system [47]. The impact of delay events can be categorized into three stages: initial delay, secondary delay, and delay propagation, as illustrated in Figure 2. In the figure, the horizontal axis represents time, while the vertical axis represents the physical stations and the virtual stations. Physical stations are represented by a solid green line, while virtual stations are denoted by dashed lines. The black line represents the initial train schedule, while the red dashed line illustrates the adjusted actual train schedule, reflecting deviations resulting from delays. Furthermore, the blue dashed line represents a modified train schedule with different block running time levels, indicating potential adjustments aimed at mitigating delays.  
In Figure 2, the initial delay occurs when the first train fails to depart according to its original timetable at the fourth physical station. It signifies the first occurrence of a train deviating from its scheduled departure time. The secondary delay manifests as a consequence of the initial delay. It may lead to a reduction in the train headway or necessitate the waiting of other trains for the delayed train, thus affecting their planned operations. This propagation of delays refers to the gradual expansion of the initial delay. When a train experiences significant delays, it can trigger a chain reaction, subsequently causing delays in subsequent trains and affecting an increasing number of trains over time. This propagation phenomenon spreads throughout the transportation network, disrupting the normal operation of the system.
# Evaluation and Prediction of Ecological Benefits in Song-Liao River Basin
## 4 Discussion
### Future Prediction and Development Planning  
Markov chain is a commonly used tool for ecological benefit prediction. The prediction results of Loess Plateau from 1995 to 2030 show that the ESI simulation based on cellular automata is effective [76]. However, compared with the ecological benefit prediction research with different economic development models [77], the neural network model used in this study can ensure the objectivity of the prediction results. Figure 7 shows the spatial distribution characteristics of ESI and EQI predicted in 2030 based on the neural network model, and analyzing the future trend will help to formulate the development plan and resource development plan. Figure 9 shows the possible changing trend of ESI and EQI in SRB during 2020-2030. ESI and EQI showed a trend of improvement in most areas, and some areas showed a trend of deterioration. Combining Figure 9a and 9b, it could be found that the distribution of the Worse region is consistent, mainly distributed in the northern region and the Songhua River basin in the central and eastern regions, which is consistent with the prediction results based on the CLUE-S model [78]. Combined with Figure 8d, it could be found that after 2020, the Barren in the northern part of SRB will increase significantly, and the Woodland will decrease gradually. Hence, the prediction model determines that ESI and EQI in this area will show a downward trend in the future. Some studies have pointed out that the prevention and control of Grassland desertification is still the focus of future project implementation [79]. To solve this problem, the Chinese government has made a plan for the new phase of the Three-North Shelterbelt Project. It will invest sufficient funds for vegetation restoration and forest quality improvement.  
The analysis of macro-ecosystem structure changes shows that although the overall area of forest land shows a downward trend during 1990-2020, Figure 4c shows extensive new afforestation in SRB. Many of these newly planted forests will be able to develop afforestation carbon sink projects suitable for the CCER (Chinese Certified Emission Reduction) market, which will also help China realize the goal of carbon peaking and carbon neutrality. On the other hand, through the assessment of ecosystem service and ecosystem quality in SRB from 1990 to 2020, it is found that although the ESI and EQI indexes in SRB are fluctuating and rising, the growth rate in some areas is declining. This phenomenon might be related to the fact that the existing large-scale natural forests have entered maturity, and the carbon fixation rate has decreased. These areas are suitable for developing natural forest management methodology suitable for the CCER (Chinese Certified Emission Reduction) market and continuously improving the rise of ESI and EQI through reasonable forest management models.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation
### Comparison with State-of-the-Art Methods
#### 5.3.4 The Number of CHs in the Network  
The nodes in the sensing field are installed through the distributed algorithm. Therefore, the distributions of nodes in the sensing field are not even. The previously designed clustering protocols use the distributed clustering algorithm for CH selection, which increases the computational overhead on all the nodes. Another problem is that the optimum numbers of CHs are also not guaranteed through this distributed algorithm. If the selected number of CHs is not optimal, this causes the resources to deplete very quickly. In this proposed model, we introduce a new mechanism which restricts the algorithm to select the optimal number of CHs in each round. Figure 14a depicts the comparison of the number of CHs selected per round in SEED, ABC, CEEC, and ZBR. The proposed model always chooses the optimal number of CHs in comparison with selected state-of-the-art clustering protocols. While the other clustering protocol selects the CHs through distributed algorithm, so, their selection criterion is not very good. Consequently, the number of CHs in ZBR and ABC vary from 10%-50% CHs per round during the lifetime of the network. The performance of our algorithm remains very consistent when we increase the node densities from \(N=100\) to \(N=300\), it always meets the optimality criterion for CHs selection as depicted in Figure 14b.
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 6 Conclusions  
We evaluated the efficacy of an application for personal safety watches using time-series analysis based on IMU sensors to classify everyday movements and at-risk situations. We analyzed data that included routine actions such as walking, running, and desk work, as well as at-risk motions like lifting and pushing. Various machine learning models (SVM, SGD, \(k\)-NN, random forest, and 1D CNN) were tested for classification accuracy. Notably, the 1D CNN model demonstrated superior performance, especially in analyzing the characteristics of time-series data in the frequency domain. By adjusting the input timing and amount of training data, we maintained high accuracy even with short input times, proving effective for real-time detection of at-risk situations. The developed smartwatch application was tested in real environments, detecting at-risk situations in daily life and providing appropriate alerts. These results not only advance personal safety technology but also highlight the potential for application in various real-time monitoring systems.  
Conceptualization, J.Y.; methodology, H.O. and J.Y.; software, H.O.; validation, H.O. and J.Y.; formal analysis, J.Y.; resources, H.O.; data curation, H.O.; writing--original draft  
Figure 9: Web-based dashboard connected to a real-time app for desk work.  
preparation, H.O.; writing--review and editing, H.O. and J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Protection Technology for Socially Vulnerable Individuals program (www.kipot.or.kr) funded by the Korean National Police Agency (KNPA, Korea) [Project Name: Development of an Integrated Control Platform for Location Tracking of Crime Victim based on Low-Power Hybrid Positioning and Proximity Search Technology /Project Number: RS-2023-00236101].  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Sungshin Women's University (SSWUIRB-2024-061, approved on 20 November 2024).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available upon reasonable request made to the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflicts of interest.
# Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
###### Abstract  
In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5', 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.  
automated vehicles; conditionally automated driving; take-over; driving parameters; data mining Article