esa-sceva/satcom-chunk-collection huggingface dataset:
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 6 Discussion  
The presented DRLLA approach does not directly optimize for low PER but for data rate, while the PER adjusts according to the channel state. IEEE 802.11p, on the other hand, is optimized for reliability with PER as a fixed parameter, while the throughput then adjusts according to the channel quality. Therefore, due to these different objectives, neither the throughput nor reliability of the produced results can be directly compared to the state of the art.  
The results of the experiments carried out in Section 5 exhibit that DRLLA can be applied to the various examined channels. When a clear decision is possible, the agent recommends almost solely a single MCS during the entire episode of a static SNR level experiment, as expected and performed by traditional LA algorithms. Whenever the system reaches an SNR level where the transmission could benefit from a combination of more than one MCS, the agent suggests up to three primary actions to be used within the same episode, which is a noticeable difference from typical LA algorithms. As the sweeping SNR experiments hint, DRLLA is able to quickly adapt to changing channel states.  
DRLLA approaches the theoretical data rate on the AWGN and NLOS channels well. Even on the real-world channel, DRLLA manages to establish a transmission rate of almost 16 Mbps at a static SNR level of 15 dB. Increasing the SNR to 50 dB improves the data rate heavily, hinting that some channel effects included in the prerecorded channel are not covered by the observation and action space but can be tackled with a higher SNR. Stated channel effects are the cause of a slightly higher PER compared to the AWGN and NLOS channel.  
The sometimes relatively large gap between the theoretical data rate and the effective data rate of an MCS hint that the agent prefers a high throughput over a low PER. This can be attributed to the chosen reward function. The difference in theoretical data rate between  
Figure 9: The behavior of the PPO reinforcement learning approach on an AWGN channel with a static SNR of 15 dB.  
the individual MCS is rather large in the IEEE 802.11p definition. Therefore, a single packet transmitted with a higher MCS index is often valued higher than several successfully transmitted packets with lower MCS indexes. Since our reward function focuses on high throughput, the agent's propensity towards higher MCS indexes despite potentially not matching the channel quality makes sense.  
In contrast to the state of the art for LA, the proposed method benefits from multiple factors, including the experience or knowledge stored in its neural networks, its capability to learn the characteristics of the environment, and the ability to adapt to unseen conditions over time.  
Even though DRLLA adds some computational complexity, the advantages over traditional techniques outweigh the drawbacks of additional overhead, especially since modern hardware and model compression techniques can greatly reduce the runtime.
## Materials and Methods
### Constraint Condition  
It is essential to reach the requirements of actual engineering for reconstruction parameters to introduce a constraint condition in the process of calculation [27]. First, the setting of the circular curve and transition curve should conform to the design specification within a certain range, and is generally an integer multiple of 10 m or 5 m. Second, the track lining distance should not be too long, otherwise it will increase the amount of engineering required, especially in key areas such as bridges, tunnels, and stations. An overly long track lining distance will cause engineering risks. Third, some constraints can simplify the objective function to a certain extent. For example, when the first and second transition curves are equal, the center of the circular curve must be on the angle bisector of the first and second intermediate straight lines [28].  
In this article, the constraint condition in the railway reconstruction optimization model is divided into three categories. Geometric constraints simplify the reconstruction model. The control point constraint can make the construction position correct. Specification constraints can ensure that construction quality is kept. Considering the above objective function (Equation (6)), the final optimization model can be expressed as  
\[\begin{array}{ll}Q=&MIN\sum\limits_{i=1}^{n}\left(D_{i}(\mathbf{X})\right)^{ 2}\\ S.T.&l_{\min}\leq l\leq l_{\max}\\ &\text{R}_{\min}\leq R\leq R_{\max}\\ &D_{i}(\mathbf{X})\leq D_{\max}\quad i=1,2,\cdots,n\\ &f_{1}(k_{1},k_{2},b_{1},b_{2})=f_{2}(O(x_{o},y_{o}))\end{array} \tag{7}\]  
In Equation (7), the circular curve radius after reconstruction should be limited to values between \(\text{R}_{\min}\) and \(R_{\max}\). Meanwhile, \(l_{\min}\) and \(l_{\max}\) express the upper and lower bounds of the first and second transition curves. The track lining distance of the control point cannot exceed \(D_{\max}\), and \(f_{1}(k_{1},k_{2},b_{1},b_{2})=f_{2}(O(x_{o},y_{o}))\) expresses that the circle center is on an angle bisector of the first and second intermediate straight lines.
# Telerehabilitation--A Viable Option for the Recovery of Post-Stroke Patients
## 3 Who Can Benefit from TR?  
Many studies (Table 1) have assessed the efficacy of TR on different deficits in chronic stroke and the most agreed-upon conclusion was that there is no inferiority in comparison with usual care on patient autonomy, activities of daily living and motor function [5; 11; 19; 20]. However, there are no clear recommendations made in this field because of the heterogeneity of existing data. Therefore, more studies are needed in order to have a complete image of the most suitable ways to use TR in stroke rehabilitation.  
Using TR could make it possible to discharge patients from rehabilitation clinics after they have become accustomed to the therapeutic exercises and provide continuity of treatment by using telecommunication technology [5]. In this way, we appreciate that the chances to lose the improvements gained during hospitalization are reduced and the patients can benefit from a guided rehabilitation process in the comfort of their homes. Research suggests that the less motivated patients, those with a low degree of autonomy, and those who have difficulties using technology benefit more from in-person therapy, while highly motivated patients, and patients accustomed to the internet and different devices benefit most from TR because they can exercise as long as they want to, without the time restrictions of face-to-face training or in-clinic programs [6].
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 2 Background
### Related Works
It also does not account for or cover the bit-flipping attack. The prevention of replay attacks is also addressed by [PERSON] et al. [22]. The authors describe how an attack can be detected from the received signal strength indication (RSSI) value. If the variation of RSSI value for an ED known to be static is detected, the security mechanisms are activated. The article deals mainly with sniffing and spoofing, with the communication being eavesdropped on by the device that is already admitted to the network, which is rather a strong implication. In this study, we consider eavesdropping on the communication by an external device that is not in the network and demonstrate how data captured during OTAA activation could be used for reconstruction of the keys. [PERSON] et al. [27] describe a replay attack focused on the DevNonce value. They detail the generation of a random DevNonce value and show that this value can be generated using a predictable value jammer. This results in reducing the entropy of this information, thus allowing a replay attack to be performed. Albeit discussing the join procedure in detail, the study does not deal with eavesdropping and individual attacks. The empirical studies, which are dealing with LoRaWAN security, are relatively rare. Among these, the work of [PERSON], [PERSON] and [PERSON] presented in [28] should be noted. In this study, the authors present the ChirpOTLE framework allowing practical evaluation of LoRaWAN security and report its use for investigating the potential of ADR and beacon spoofing in the context of denial-of-service attacks. This is worth mentioning that the ChirpOTLE is based on common off-the-shelf hardware, including LoRa transceivers; however, the focus of this study is on denial-of-service attacks rather than replay and bit-flipping attacks, which we deal with. In contrast to the previously discussed articles, in the current work, we report the design of a platform allowing empirical validation of the LoRaWAN security, implying the use of SDR as a tool for eavesdropping of communication and subsequent reconstruction of keys. This allows the implementation of versatile attacks, including, as we demonstrate in what follows, the ones belonging to the MitM class. Note that MiTM attacks are among the most widespread for other IoT-grade wireless communication technologies and, thus, are likely to be also used one day against LoRaWAN devices and networks. Further, the authors of the previous studies mainly dealt with replay attacks and sniffing for data communication, implying that the attacker is already admitted to the network. In this study, we relax this assumption and demonstrate the possibility of launching an MitM attack by intercepting the OTAA procedure by an external device, which is not a part of an existing LoRaWAN network.
## 3 RS Applications in Ocean
### Sea Level (SL)
#### 3.4.3 Gravimeter  
Ocean mass and steric changes cause variation in SL [251]. The ocean mass is associated with exchanges of ice and water mass with oceans, affecting the regional and global gravity. Therefore, it is possible to measure SL changes through gravimeters [252]. For example, the GRACE mission observes the Earth's gravity, enabling the measurement of GMSL change associated with ocean mass alterations [253]. Although GRACE provides global coverage of the Earth's gravity, it is difficult to employ it for regional studies due to the existing uncertainties in low spatial resolution gravity data [230]. For instance, [PERSON] et al. [230] applied RL05 GRACE monthly data provided by the Center of Space Research (CSR) and the GeoForschungsZentrum (GFZ) in Potsdam to quantify the global SL change. The atmospheric and ocean de-aliasing model was also used to remove the contributions of atmospheric surface and ocean bottom pressures. Finally, the gravity observations revealed a GMSL rise of about 2.14 \(\pm\) 0.12 mm/yr. Additionally, [PERSON] et al. [254] employed satellite gravimetry and altimetry data to evaluate the Nile Delta-Mediterranean Sea interactions. Gravity observations, acquired from GRACE, revealed an erosion rate over the eastern and western parts.
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 3 Error Rate Analysis  
In this section, we present the PDF of the composite received signal phase in Fourier series form and derive the novel expression for Fourier coefficients. After that, by applying FSM, we present the formula for SEP in convergent series form, and give the proof of convergence of this series, as well as the estimation of the upper bound for truncation error. In addition, we present asymptotic analysis when average SNR tends to infinity.
## 3 Methodology
### HAR Using a Decision Tree
#### 3.5.3 Building a DT from Training Data  
To build a DT from training data, DT is built based on gain and gain ratio. The training data is divided into two classes \(P_{i}\) (acceptable level) and \(N_{i}\) (unacceptable level). The information needed to identify the classes of an element is made based on the following information.  
\[Info(P_{i},N_{i})=-\frac{P_{i}}{P_{i}+N_{i}}\log_{2}\frac{P_{i}}{P_{i}+N_{i}} -\frac{N_{i}}{P_{i}+N_{i}}\log_{2}\frac{N_{i}}{P_{i}+N_{i}}, \tag{15}\]  
Figure 10: Convergence of MOTIONSENSE optimized data along the 3D axes.  
The training set is partitioned based on features \(X_{i}\). into different classes based on the weighted average. The _Info_(\(P_{i}\),\(N_{i}\)) is represented with _Info_(\(TS\)) as follows:  
\[Info(X_{i},TS)=\sum_{i=1}^{n}\frac{|TS_{i}|}{|TS|}Info(TS_{i}) \tag{16}\]  
Next, the information gain is calculated with the difference in the basis of features to identify the desired elements. The information gain on each element of the feature \(X_{i}\) is defined as:  
\[Gain(X_{i},TS)=Info(TS)-Info(X_{i},TS), \tag{17}\]  
The classification decisions are made based on the greatest value of the gain. Such a decision process is repeated until all the features are properly classified. Each node in \(DT\) is located with the greatest gain. The gain has a shortcoming effect, where the number of features is too big. To cope with this issue, the gain ratio is calculated based on gain information and split information. The split information is the split values of two classes \(P_{i}\) (acceptable level) and \(N_{i}\) (unacceptable level), which are represented with \(TS\). The gain ratio is defined as:  
\[Gainratio(X_{i},TS)=\frac{Gain(X_{i},TS)}{split(X_{i},TS)} \tag{18}\]  
\[split(X_{i},TS)=\sum_{i=1}^{n}\frac{|TS_{i}|}{|TS|}\log_{2}\!\left(\frac{|TS _{i}|}{|TS|}\right) \tag{19}\]  
A DT is a learning model that uses the information gain to evaluate a target node, and the complete function uses divide-and-rule, a no-return strategy, and a top-down approach. Every branch of a node subset acts recursively and builds Decision Tree nodes and leaves until the classification model is achieved [37]. The final result is shown in Figure 11.  
Figure 11: Classification result of the Decision Tree algorithm on the IM-AccGyro dataset.
# Influence of the Motion of a Spring Pendulum on Energy-Harvesting Devices
## Nomenclature  
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}}
**Symbols and Abbreviations** & **Description** \\ \(F(t)\) and \(M(t)\) & External harmonic force and torque. \\ \(C_{1}\) and \(C_{2}\) & Damping coefficients. \\ \(k_{1}\) and \(k_{2}\) & Linear and nonlinear stiffness coefficients. \\ \(l_{0}\) & Spring's normal length. \\ \(g\) & Gravitational acceleration. \\ \(x(t)\) and \(\theta(t)\) & The elongation and swaying angle of the spring. \\ \(v(t)\) and \(q(t)\) & Output voltage and current. \\ \(R\) & Radius of the circular path. \\ \(\Omega\) & Angular velocity. \\ \(O\) & Suspension point of the dynamical system. \\ \(X\), \(Y\) & Axes in the plane. \\ \(V\), \(T\) & Potential and kinetic energies. \\ \(\gamma_{j}\left(j=1,2\right)\) & Linear coupling coefficients for the piezoelectric and electromagnetic circuits. \\ \(R_{p}\) and \(R_{m}\) & Resistive loads of the piezoelectric circuit and electromagnetic one. \\ \(c_{p}\) and \(l_{m}\) & Capacitance of the piezoelectric and the inductance of the coil. \\ \(\epsilon\) & Small parameter. \\ \(a\), \(b\) & Amplitudes. \\ \(\theta_{1}\), \(\theta_{2}\) & Modified phases. \\ \(\omega_{1}\), \(\omega_{2}\) and \(\Omega_{1}\), \(\Omega_{2}\) & Frequencies. \\ \(f_{1}\), \(f_{2}\) & Amplitudes of harmonic force and torque. \\ \(\sigma_{1}\), \(\sigma_{2}\) & Detuning parameters. \\ EH & Energy harvesting. \\ PEH & Piezoelectric energy harvester. \\ FEM & Finite element method. \\ DOF & Degrees of freedom. \\ AMS & Approach of multiple scales. \\ EOM & Equations of motion. \\ ODEs & Ordinary differential equations. \\ ME & Modulation equations. \\ \end{tabular}
## 5 Frequency Hopping Techniques
### Channel Aware, Probabilistic-Channel-Usage Techniques
#### 5.3.1 Weighted Random Frequency Hopping  
In the Weighted Random Frequency Hopping (WRFH) technique, the hopping set contains all of the \(K\) channels, just like with the standard RFH, but when deciding the hopping sequence, a non-uniform probability distribution is used so that channels with higher gains are more likely to be selected [24]. The probability, \(P_{k}\), of selecting channel \(k\) is given by  
\[P_{k}=\frac{|H_{k}|^{2}}{\sum_{i=1}^{K}|H_{i}|^{2}}\quad k\in\{1,2,\ldots,K\} \tag{12}\]  
where \(H_{k}\) is the same channel gain introduced in Equation (5). This equation is analogous to the normalized power metric in the MFH technique (Equation (6)). For each hop, the selection of the channel is carried out by a cumulative metric, \(C_{k}\), as it is done with the MFH technique:  
\[C_{k}=\sum_{i=1}^{k}P_{i} \tag{13}\]  
Figure 10: Channel selection evolution in the Advanced Frequency Hopping (AFH) technique when varying the \(\alpha\) parameter: (**a**) \(\alpha=1\); (**b**) \(\alpha=0.1\); (**c**) \(\alpha=0.01\).  
The index of the \(n\)th channel is given by the value of \(k\) such that  
\[C_{k-1}\leq y_{n}<C_{k}\quad n\in\{1,2,\ldots,K\} \tag{14}\]  
where \(y_{n}\) is a pseudo-random number over [0,1). This is the main difference with the MFH technique, as the corresponding \(y_{m}\) numbers in the MFH were \(M\) equally spaced values over [0,1). So, in the WRFH technique, all the channels can be selected with more or less probability, while in the MFH technique, there were only \(M\) selected channels that would be used with equal probability.  
Figure 11 illustrates the differences between the RFH, MFH and WRFH techniques. As an example, 20 frequency hops have been considered for the three hopping techniques. In the case of the random and weighted random techniques, this results in 20 different channels, whereas in the MFH technique, some or all of the selected 10 best channels have to be repeated. Because of its random behaviour, the RFH technique selects some channels with a very low gain. This is partially avoided by the WRFH as the cumulative metric around the lowest gain channels is hardly increased and is less likely to be intercepted by the horizontal lines representing the \(y_{n}\) random values. However, as the number of channel hops approaches infinite, it would be inevitable to hop over the channels with the least gain, which does not happen with the MFH technique.  
Figure 11: Comparison of channel selection between different frequency hopping techniques: **(a)** Random Frequency Hopping (RFH); **(b)** Matched Frequency Hopping (MFH); **(c)** Weighted Random Frequency Hopping (WRFH). In the RFH and MFH cases, horizontal lines are equally spaced while in the WRFH case, horizontal lines are randomly spaced.
## 2 Materials and Methods  
### Study Area  
The research was conducted in Northeast China Tiger and Leopard National Park (Figure 2), spanning geographical coordinates 129\({}^{\circ}\)5\({}^{\prime}\) E-130\({}^{\circ}\)23\({}^{\prime}\) E and 43\({}^{\circ}\)19\({}^{\prime}\) N-44\({}^{\circ}\)10\({}^{\prime}\) N,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **ICESat-1/GLAS** & **ICESat-2/ATLAS** & **GEDI** & **TECIS/CASAL** \\ \hline Launch date & 2003 & 2018.09 & 2018.12 & 2022.08 \\ Platform & ICESat & ICESat-2 & ISS & TECIS \\ Nominal altitude & 600 km & 500 km & 405 km & 506 km \\ Wavelength & 1064/532 nm & 532 nm & 1064 nm & 1064 nm \\ Pulse repetition & 40 Hz & 10 kHz & 343 Hz & 40 Hz \\ Ground tracks & 1 & 6 & 8 & 5 \\ Measurement concept & Full waveform & Photon counting & Full waveform & Full waveform \\ Footprint size & \(\sim\)70 m & 17 m & \(\sim\)25 m & 25 m \\ Along-track distance & 170 m & 0.7 m & 60 m & 180 m \\ Across-track distance & 2.5–15 km & 3.3 km & 0.6 km & 4 km \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of operational parameters for ICESat-1/GLAS, ICESat-2/ATLAS, GEDI, and TECIS LiDAR systems.  
Figure 1: Cross-track relationships of CASAL lasers and distributions of ground tracks.  
with a total area of approximately 5600 km\({}^{2}\). The region experiences a continental humid monsoon climate, characterized by windy and dry springs, short and hot summers, cool autumns with rapid temperature declines, and prolonged cold winters. The mean annual temperature is 5 \({}^{\circ}\)C, with annual precipitation ranging from 450 mm to 750 mm, predominantly concentrated between May and September. The terrain exhibits complex geomorphological diversity, primarily comprising low-to-medium mountains, valleys, and hills. The area harbors exceptionally rich temperate forest flora, with forest coverage reaching 93.32%. Dominant forest types include broadleaf forests, deciduous coniferous forests, and a limited proportion of evergreen coniferous forests. The extended winter season provides an extended temporal window for light detection and ranging (LiDAR) monitoring, while the structurally simplified and well-defined tree canopies during this period offer optimal data acquisition conditions.
## 2 Simulation Model and Method
### Governing Equation  
The following assumptions are made in the simulation calculation: (1) the flow is steady-state; (2) the fluid is continuous; (3) the fluid is incompressible; (4) there is no source term in the energy equation. Therefore, the governing equations of incompressible flow in the numerical simulation can be simplified:  
Mass conservation equation:  
\[\
abla\times V=0 \tag{1}\]  
Figure 1: Schematic of impingement cavity model. (unit: mm). (**a**) Impingement cavity model. (**b**) Size of impingement cavity.  
where \(\
abla\) is gradient operators and \(V\) is the velocity vector.  
Momentum conservation equation:  
\[\rho\frac{dV}{dt}+\
abla\times(\rho VV)=\ -\
abla p\ +\ \
abla\cdot\tau+S_{\text{M}} \tag{2}\]  
where \(\rho\) is air density, \(t\) is time, \(p\) is static pressure, \(\tau\) is viscous stress and \(S_{\text{M}}\) is dynamic source term.  
Energy conservation equation:  
\[\rho\frac{du}{dt}+p\cdot(\
abla\cdot V)=\
abla\cdot(\lambda\
abla T)+\Phi+\rho q ^{\prime} \tag{3}\]  
where \(u\) is internal energy, \(\lambda\) is fluid thermal conductivity, \(T\) is fluid temperature, \(\Phi\) is viscous dissipation function and \(q^{\prime}\) is heat flux.
## 4 Results
### Impact of HOEs on Active Systems  
The fire fatalities in residential buildings vary across different countries and statistical data extrapolated from the literature indicate a range between \(7.3\times 10^{-6}\) to \(1.3\times 10^{-5}\) death/year in Australia, Canada, New Zealand, and the UK [45]. In this study, the calculated ERL with active systems indicate a range between \(2.23\times 10^{-6}\) to \(5.01\times 10^{-5}\) which compares reasonably well with the literature. With no active safety systems, the ERL range from \(1.68\times 10^{-3}\) to \(8.38\times 10^{-3}\) (Trial Design 16). As expected ERL values are higher in the absence of active safety systems.  
Trial Design 1 provides the most comprehensive fire safety systems as it includes sprinklers, BOWS, smoke detectors, and egress protection but is also most influenced by HOEs. Indeed, Trial Designs 1-9 consist of more complicated systems for fire safety, while Trial Designs 10-15 fire safety systems are less complicated. In general, these more complicated systems result in the lowest ERL.  
HOEs have significant risk impact ranging from an average of 19.7-33% (average values of cases) with combinations of sprinklers, BOWS, smoke control systems, and smoke detectors for Trial Designs 1-9. For design options 10-15, HOEs had a lower impact on risk, ranging from 6.5% to 16.3% (average value of cases). HOEs have limited or no impact when active systems are absent.  
\begin{table}
\begin{tabular}{c c} \hline
**Cases** & **Pearson Correlation Coefficient** \\ \hline All & 0.867 \\ \#1 & 0.945 \\ \#2 & 0.878 \\ \#3 & 0.863 \\ \#4 & 0.889 \\ \#5 & 0.760 \\ \#6 & 0.905 \\ \#7 & 0.944 \\ \hline \end{tabular}
\end{table}
Table 11: Correlation between HOE/ERL% and the number of active systems.  
Figure 4: HOE/ERL% ratio vs. active systems.