esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis  
### The Equivalent Flexural Stiffness (EFS) of Contact Interface  
The contact model of the shaft segment with the contact interface consists of two shaft segments and a contact interface (see Figure 1). The contact interface is in a series relationship with two elastic shaft segments. In the actual contact, the contact interface consists of rough contact surfaces of two shaft segments interacting with each other and has no thickness on the macroscopic level. The calculation method for the equivalent flexural stiffness (EFS) of the contact interface is given below.  
Based on the statistical model in rough surface contact established by [PERSON] and [PERSON] [PERSON] and [PERSON] (1965), the contact course between two rough surfaces is actually the contact behavior of the micro-convex bodies distributed on them. The relation between the pressure and the distance of the rough contact surface is given by:  
\[P=\frac{4}{3\sigma\sqrt{2\pi}}\eta A_{nom}\ E^{\prime}\beta_{0} \frac{1}{2}\int_{d_{0}}^{\infty}(z-d_{0})^{\frac{3}{2}}e^{-\frac{z^{2}}{2 \sigma^{2}}}dz \tag{1}\]  
Here, \(P\) is the pressure of the contact rough surfaces. \(\sigma\) is RMS (root mean square) of micro-convex body height distribution. \(\eta\) is the distribution density of the micro-convex body. \(\beta_{0}\) is the average micro-convex body radius of curvature. \(E^{\prime}\) is the equivalent Young's modulus. \(A_{nom}\) is the nominal contact area of the contact surface. \(d_{0}\) is the distance of two reference contact planes when the preload is applied. \(z\) is the parameter of the micro-convex body height.  
The EFS of the contact interface can be given by:  
\[G_{r}=\frac{\partial M}{\partial\theta}=-\frac{2}{\sigma\sqrt{2 \pi}}\eta\ E^{\prime}\beta^{\frac{1}{2}}\int_{A_{nom}}\int_{d_{0}}^{\infty}(z- d_{0}-y\theta)^{\frac{3}{2}}e^{-\frac{z^{2}}{2\sigma^{2}}}\cdot y^{2}dzdA_{nom} \tag{2}\]  
Figure 1: The ith elastic shaft segment with the contact interface.  
Here, \(M\) is the bending moment which is applied to the contact interface. \(y\) is the radial parameter of the contact surface. \(\theta\) is the angle of rotation between two contact planes.  
By numerical methods, it can firstly calculate \(d_{0}\) according to Equation (1), then substitute \(d_{0}\) into Equation (2) to obtain EFS \(G_{r}\) at different rotation angles.
## 2 Preliminaries
### Homomorphic Encryption  
Homomorphic encryption is a cryptographic primitive that allows third parties to perform arithmetic operations on ciphertexts without decrypting them. It provides the same result as encrypting after operating in cleartext messages.  
More formally, an encryption scheme is called homomorphic over an operation \(*\) if it supports the following property:  
\[E(m_{1})*E(m_{2})=E(m_{1}*m_{2})\]  
where \(E\) is the encryption algorithm and \(m_{1},m_{2}\) belong to \(M\) the set of all possible messages.  
An HE scheme consists of four algorithms [29]: _KeyGen_, _Enc_, _Dec_, and _Eval_. _KeyGen_ generates a pair (public key, private key) for the asymmetric configuration and a secret key for the symmetric version. _Enc_ is the encryption algorithm and _Dec_ is the decryption algorithm.  
While the three algorithms (_KeyGen_, _Enc_, _Dec_) are common to conventional cryptosystems, an additional algorithm is needed for homomorphic encryption schemes, called the _Eval_ algorithm. This algorithm is defined as follows:  
\[Eval(f,C_{1},C_{2})=f(m_{1},m_{2})\]  
where \(Dec(C_{1})=m_{1},Dec(C_{2})=m_{2}\) and \(f\) is a function that can be addition or multiplication.  
Based on the number (limited or unlimited) and the type of operation (addition or multiplication), HE is classified into three types of schemes: Partially Homomorphic encryption (PHE), Somewhat Homomorphic Encryption (SWHE), and Fully Homomorphic Encryption (FHE). PHE allows only to perform one type of operation on unlimited way. When the operation is addition, like in the Paillier Scheme [30], we say it is an Additive Homomorphic Encryption (AHE). When it is the multiplication, we say that it is a multiplicative scheme, like in the RSA scheme.  
SWHE allows for both operations, but the number of operations is limited. On the other hand, FHE allows making unlimited operations of both types. This type of scheme was possible after the Gentry breakthrough in 2009 [31].
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
## 1 Introduction
Therefore, the P2G used in this paper can convert surplus wind power into natural gas or hydrogen and store it in natural gas pipelines during periods of low load or peak output of renewable energy, and can then provide the users with energy by converting the stored gas to electricity in the event of a shortage of electricity or thermal energy, thereby achieving the bi-directional energy conversion. The present paper aims to investigate a scheduling strategy for integrated energy system, based on gas hydrate technology and power-to-gas technology, and at combining them with heat and power cogeneration to improve wind power consumption and utilization capacity in the integrated energy network. The electro-thermal integrated energy system with P2G Function proposed in this paper incorporates hydrogen as clean energy into existing gas supply networks, which realizes the complementary transformation of power to gas. The combined system can produce high quality power and clean secondary energy, which provides a new way to solve the problem of wind power consumption in power system. The remainder of the paper is organized as follows. In Section 2, the technology and application of P2G are introduced. In Section 3, the day-ahead dispatching model of the electro-thermal integrated energy system with P2G is proposed. In Section 4, case studies are conducted to demonstrate the benefit and effectiveness of the proposed model and method.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Spatial Distribution of Estimates  
To qualitatively assess the spatial prediction performance of each model, we visualized the estimated aboveground carbon stock (Mg/ha) over representative subregions in the Liuchong River Basin. Figure 5 presents a visual comparison across six models: (a) true-color satellite imagery, (b) ground-truth carbon stock maps derived from field measurements, and predictions from (c) Enhanced ResNet (ours), (d) ViT, (e) GBDT, and (f) RF. Each subregion was sampled to reflect different landscape types, including mountainous forests, mixed vegetation zones, and anthropogenic areas.  
The visual comparison highlights clear differences in spatial fidelity and predictive coherence. Traditional models like RF and GBDT tended to produce overly coarse and spatially fragmented predictions. These models frequently failed to detect fine-scale variability and exhibited systematic underestimation in high-carbon areas, especially along ridge-lines and densely forested valleys. The ViT model offered better spatial continuity than tree-based methods but introduced unnatural block artifacts due to its fixed patch-based encoding, which lacked explicit spatial overlap.  
In contrast, the Enhanced ResNet generated predictions that more closely matched the ground truth, both in value distribution and spatial structure. It effectively delineated boundaries between high- and low-carbon zones, preserved continuous forest patterns, and responded well to variations in canopy density and elevation. Notably, our model showed robustness in both high-resolution and complex terrain subregions, likely due to the multiscale residual blocks and channel-wise attention mechanisms that enhanced spatial representation and spectral discrimination.  
Overall, the Enhanced ResNet provides visually coherent and topographically consistent carbon stock maps, supporting its suitability for fine-scale forest carbon monitoring in ecologically diverse and mountainous landscapes.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **RMSE (Mg/ha)** & **MAE (Mg/ha)** & \(\mathbf{R^{2}}\) \\ \hline Random Forest (RF) & 30.27 & 28.31 & 0.622 \\ Gradient Boosting (GBDT) & 29.95 & 27.88 & 0.643 \\ Vision Transformer (ViT) & 29.55 & 21.52 & 0.719 \\
**Enhanced ResNet (Ours)** & **23.02** & **17.15** & **0.773** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different models on the test set. Bold is the best result.
###### Abstract
In the second part of the section, dedicated to RC analysis, some of the papers mentioned above [5; 19; 20; 21] are presented, in which the RC assessment methods are analyzed. Finally, they conclude that although all three RC assessment methods have some advantages and limitations, the ISO 2631 standard is widely used. A review of the most recent methods for evaluating and improving RC of the railway vehicles is also found in reference [28]. In this context, it is shown that the Sperling index method does not reflect the effect of the duration of exposure to vibrations, but that this index offers advantages to designers, giving the possibility to improve the damping of the lateral and vertical suspension of the vehicle by assuming that the vertical and lateral motions are decoupled. It is also summarized that the comfort indices corresponding to various assessment approaches vary due to different reasons, including the acceleration measurement, index calculation, and evaluation thresholds. All comfort indices have advantages and disadvantages, so using several comparison and reference methods in the evaluation of vibration comfort is recommended. The present paper is part of the area of research described above, focusing on the investigation of RC evaluation methods, but with a completely different approach. In the paper, the effect of frequency weighting, corresponding to the Sperling index method, is analyzed both for the evaluation of RQ and for the evaluation of RC, on the dynamic response of the railway vehicle carbody to vertical vibrations. These aspects help outline how the frequency weighting functions influence the RC and RQ assessments according to the Sperling index method. The numerical simulation applications were developed based on an original 15 degrees of freedom model of the vehicle-track system, which includes the rigid and flexible modes of the vehicle, as well as an equivalent model for the track. This model considers important factors for vehicle vibration, namely the carbody's structural flexibility, the system through which the longitudinal forces are transferred between the bogies and the carbody, and the elasticity of the wheel-rail contact. The carbody vibration is calculated in an original manner, represented as the form of the PSD of the acceleration for three different analysis cases: unweighted PSD of the acceleration, and frequency-weighted PSD of the acceleration with the corresponding weighting functions for RQ and RC evaluation. These analyses lead to important conclusions regarding the effect of the two weighting functions on the dominant vibration modes of the carbody, which are then correlated with the results for the Wz indices used in the evaluation of RQ and RC.
## 4 Experiments and Results
### Implementation Details  
We implemented our model using the Baidu PaddlePaddle [46] library with CUDA 11.6 and cuDNN 8.5. We ran our model on an NVIDIA GeForce RTX 3060 GPU (with 12 GB memory) and Ubuntu 20.04.  
During the training phase, we chose the SGD [47] optimizer with momentum of 0.9 and a weight decay of \(4.0\times 10^{-5}\). We adopted the warm-up strategy and the poly-learning rate scheduler to optimize the training process, the warm-up initial learning rate was set to \(1.0\times 10^{-4}\) and increased to \(1.0\times 10^{-2}\) at 1200 iteration steps, and then, the learning rate was decreased by 0.9 at every epoch until it reached \(1.0\times 10^{-4}\). The training process converged after around 110 epochs with a batch size of 8. When training and testing, we resized each image to \(3\times 256\times 256\) as the input size.
## 3 Neighbor Verification through Low-Power Beacons
### Sketch of Solution Approach  
See Figure 1 that depicts the movements of two honest vehicles \(U\) and \(V\) on a road strip, and two attackers \(K\) and \(K^{\prime}\) on the roadsides. An attacker \(K^{\prime}\) is less than \(d_{B}\) apart from a potential victim \(V\), where \(d_{B}\) is the maximum distance that a BSM beacon can reach. Note that the attacker can use the standard Tx power (e.g., 23 dBm) or increase \(d_{B}\) by using an amplifier or directional transmission. Without the proposed low-power beacon check scheme, \(K^{\prime}\) can inject false positions or other safety-critical information in its BSM, \(BSM_{K^{\prime}}\), and coax \(V\) into believing it and elicit a dangerous and unnecessary reaction (e.g., hard braking). But with the proposed low-power beaconing, each vehicle is designed to trust only those vehicles that are within the low-power beacon range \(d_{W}\ll d_{B}\). It checks the condition not by the location information in neighbors' BSMs, but per the physical constraints of wireless communication. Notice that it does not require the addition of new hardware or modification of the standard WAVE OBU, because the message Tx power can be explicitly controlled by application in WAVE short message protocol (WSMP) [30].  
In our solution approach, each vehicle broadcasts special beacons carrying the sender's randomly chosen identifier at much lower Tx power, in addition to BSM beacons. For convenience, we will call this special beacon \"whisper\" in the rest of this paper. Only if a neighbor echoes the received whisper identifier in its own whispers, the neighbor is trusted. In Figure 1, the remote attacker \(K^{\prime}\) does not hear these whispers of \(V\)'s, as it is not within \(d_{W}\) from \(V\). Thus \(K^{\prime}\) cannot include \(V\)'s identifier in its whispers, so \(V\) rejects the BSMs from \(K^{\prime}\) as suspicious. For an attacker to mount the false position attack, therefore, it must come within the distance \(d_{W}\) to the roadside. Moreover, it should exchange whispers with a passing vehicle (e.g., \(U\)) to enable an attack. For example, the attacker \(K\) in Figure 1 has a chance to mount the attack. Even in this case, the window of attack is limited to \(\pm 2\ d_{W}\) in principle. So, with a sufficiently small \(d_{W}\), we can drastically reduce the attack window for \(K\). Furthermore, we can introduce an extension to this baseline scheme to further limit the attackers that come within \(d_{W}\) from the roadside, which we will discuss in Section 3.6. Recollect that the purpose of limiting the Tx power of the whispers is to let only the neighbor vehicles in close proximity, but not a remote attacker, hear them. Finally, a desirable fallout from using a small Tx power for whispers is that it helps suppress the increase of the channel utilization due to this additional security measure to a small value. Even with this added security mechanism, therefore, we can still keep a larger chunk of the channel bandwidth for ordinary BSMs. Below, we discuss the details of the sketched idea.
# Research on Fracture Mechanism and Stability of Slope with Tensile Cracks
## 3 Analytical Method for Safety Factor of Slope with Crack
### Analytical Calculate Process  
According to the limit equilibrium method, the safety factors under different conditions can be obtained by explicit methods. In the three failure modes of slope, the vertical crack is considered to be part of the slip surface. Cracks of any depth and location are considered in the optimization process. Thus, the dimensionless coefficient \(\lambda\) is not a constant value. To obtain the optimal solution of crack, a minimum safety factor method is proposed for the present mechanism. Taking the calculation of the safety factor as an example, a self-programmed computer procedure by FORTRAN95 languages was used for calculations, and the procedure is expressed in the following steps.  
Step1: Input the slope morphology and parameters, including slope height \(H\), inclination angle \(\beta\), cohesion \(c\), and friction angle \(\varphi\).  
Step2: Input the dimensionless crack depth coefficient \(\lambda\) (\(0<\lambda<2\)), which is increased by an unchanging incremental step, and the equal-step is \(0.1\).  
Step3: Determine the slip surface parameters and select the slope failure modes. Then calculate the safety factor, if \(\left\lvert F(i)-F(i+1)\right\rvert<0\), the crack depth optimization process will end. Otherwise, the procedure enters Step2.  
Step 4: Determine the minimum safety factor and the crack parameters and evaluate the stability of slope.  
The calculation process for the optimal crack depth and the minimum safety factor using the present method are shown in Figure 7.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 1 Introduction
STARFM used spatial information from fine resolution Landsat imagery and temporal information from coarse resolution MODIS imagery to produce surface reflectance in both spatial and temporal resolution, which was particularly useful in detecting gradual changes over large land areas [2]. Some improvements of STARFM, such as the spatial temporal adaptive algorithm for mapping reflectance change (STAARCH) [20], enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) [21], and unmixing-based STARFM (USTARFM) [22], have been developed to reduce the limits of STARFM in predicting disturbances when the changes are transient and not recorded in the base Landsat images, in reducing the dependence on temporal information from homogeneous patches of land cover at the MODIS pixel scale, and in handling the directional dependence of reflectance as a function of the sun-target-sensor geometry described by the Bidirectional Reflectance Distribution Function (BRDF). Studies have shown that STARFM is extendable to other biophysical properties [13], such as evapotranspiration (ET) [23], land surface temperature (LST) [24] and LAI [25], so long as the multi-scale input data streams are comparable and spatially and temporally consistent. Therefore, the application of STARFM may be available in generating time series high resolution GPP and NPP. Some studies on the application of STARFM in generating high resolution GPP and NPP have been performed. For example, high resolution chlorophyll index (CI), which involved NIR and green spectral bands, was generated by STARFM and then was used to retrieve GPP [26]. Time series Normalized Difference Vegetation Index (NDVI) was generated by STARFM, and then was assimilated into LUE models to estimate GPP [27; 28] and NPP [29; 30]. But the relationship between MODIS and Landsat vegetation index varied due to different cloud contamination, aerosols and viewing angles, which could introduce some uncertainties into GPP/NPP estimation. In this condition, developing a robust GPP and NPP downscaling method with high accuracy is of great significance. The aims of this paper are (i) to generate high resolution fraction photosynthetically active radiation (FPAR) and LAI from a downscaling method based on STRAFM, (ii) to obtain high resolution GPP and NPP estimates by using a light use efficiency model and to validate the estimates. Results of this study indicated that downscaling methods are applicable in generating time series high resolution GPP and NPP.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
###### Abstract  
This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN.  
Internet of Medical Things; SmartBAN; human monitoring system; industry use case; cross-layer design +
Footnote †: journal: Electronics  
14 February 2021  
11 March 2021  
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**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Discussion
This is probably because it can dynamically strike a compromise between energy conservation and system efficiency. The beam testing outcomes establish the estimated number of RF chains needed to maximize EE. When the UE is equipped with \(M_{rx}\) antennas, it receives \(M_{y}\) data streams from the BS, which has \(M_{tx}\) antennas. To attain optimal EE, the beam training procedure maximizes the quantity of RF chains \(M^{tx}{}_{rf}\) required at the transmitter and receiver. Based on system parameters, like the state of the downlink queue or the battery level of the UE, the DRL block allows dynamic switching of energy-efficient DRL-EE. Using spatial multiplexing, the RBT-DRL-EE mode prioritizes energy savings when the UE's packet queue is backlogged and its battery level falls below 50%. RBT-DRL's dynamic behavior guarantees a trade-off in energy consumption. Figure 5.5 EE versus time step for memory and processing optimization. Figure 13 shows SE vs. time steps for the three strategies RBT-DRL, DRL, and beam training. Though it varies significantly depending on the dynamic channel and system conditions, beam training consistently yields larger SE than the other two methods. DRL exhibits relative stability and modest SE values, showing environmental adaptability without achieving the peak SE attained by beam training. In contrast, RBT-DRL prioritizes EE over spectral performance, maintaining the lowest SE. Based on the results of the beam training procedure, the expected number of RF chains needed to achieve the ideal EE is calculated. Using \(M_{tx}\) antennas, the BS transmits \(M_{s}\) data streams to a UE that has \(M_{rx}\) antennas. It is assumed that both the UE and the BS have RF chains that are \(M^{tx}{}_{rf}\) and \(M^{rx}{}_{rf}\) and \(M_{s}\leq M^{tx}{}_{rf}\leq M_{tx}\) and \(M_{s}\leq M^{rx}{}_{rf}\leq M_{rx}\). DRL-EE and DRL-SE configurations are dynamically switched by the DRL block based on system conditions. The reward function for EE is defined by the equation \(R^{\varpi}_{n}\), which takes into account achievable efficiency, beam training overhead, and RF chain usage. To ensure convergence and scalability across different system sizes and channel conditions, the DRL agent undergoes a rigorous training procedure that includes hyperparameter tweaking (learning rate, trade-off factor) to optimize long-term rewards. While balancing computing complexity and energy cost, this dynamic behavior facilitates real-time decision-making.