esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
### Node Forward Capacity (NFC) Model  
The NFC is the delivery probability of the message from the neighbor node to the DN, which consists of the NA and the NEF [31]. We divide the message TTL into \(l\) time intervals with \(\tau\) as the time unit, the \(m\)-th time interval is \(\tau_{m}\) (\(m=1\), \(2\), \(\ldots\), \(l\)). The NA \(D_{j}\left(\tau_{m}\right)\) is the frequency of a certain node \(N_{j}\) encountering other nodes in time \(\tau_{m}\), which is given by  
\[D_{j}\left(\tau_{m}\right)=\frac{\left|S_{j}\left(\tau_{m}\right)\right|-\left| S_{j}\left(\tau_{m-1}\right)\cap S_{j}\left(\tau_{m}\right)\right|}{\left|S_{j} \left(\tau_{m-1}\right)\cup S_{j}\left(\tau_{m}\right)\right|}, \tag{6}\]  
where \(S_{j}\left(\tau_{m}\right)\) and \(S_{j}\left(\tau_{m-1}\right)\) represent the sets of the nodes that \(N_{j}\) encounters with in time \(\tau_{m}\) and \(\tau_{m-1}\), respectively.  
If \(N_{j}\) frequently encounters the \(N_{b}\) in time \(\tau_{m}\), \(N_{j}\) is preferred as the RN to assist the message transmission from \(N_{a}\) to \(N_{b}\). Thus, the NEF \(F_{j,b}\left(\tau_{m}\right)\) can be defined as  
\[F_{j,b}\left(\tau_{m}\right)=\frac{E_{j,b}\left(\tau_{m}\right)}{E_{j,1}\left( \tau_{m}\right)+\ldots+E_{j,b}\left(\tau_{m}\right)+\ldots+E_{j,N}\left(\tau_ {m}\right)}=\frac{E_{j,b}\left(\tau_{m}\right)}{\sum\limits_{k=1}^{N}E_{j,k} \left(\tau_{m}\right)}, \tag{7}\]  
where \(E_{j,b}\left(\tau_{m}\right)\) represents the number of encounters between \(N_{j}\) and \(N_{b}\) in time \(\tau_{m}\), and \(E_{j,k}\left(\tau_{m}\right)\) represents the number of encounters between \(N_{j}\) and all nodes in time \(\tau_{m}\).  
To sum up, the NFC \(Q_{j,b}\left(\tau_{m}\right)\) from \(N_{j}\) to \(N_{b}\) in time \(\tau_{m}\) can be defined as  
\[Q_{j,b}\left(\tau_{m}\right)= D_{j}\left(\tau_{m}\right)F_{j,b}\left(\tau_{m}\right)=\frac{ \left|S_{j}\left(\tau_{m}\right)\right|-\left|S_{j}\left(\tau_{m-1}\right) \cap S_{j}\left(\tau_{m}\right)\right|}{\left|S_{j}\left(\tau_{m-1}\right) \cup S_{j}\left(\tau_{m}\right)\right|}\times\frac{E_{j,b}\left(\tau_{m} \right)}{\sum\limits_{k=1}^{N}E_{j,k}\left(\tau_{m}\right)}. \tag{8}\]
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 3 Interpolation Methods  
In this section, a set of methods for interpolation of complex SAR data is introduced. Note that all the described methods, with the exception of nearest-neighbor interpolation, involve the phase-control procedure that takes an important place in the processing of complex SAR data. The proposed interpolation methods can further be incorporated into different backprojection algorithms.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 4 Simulation Setup, Cost Modeling, and Optimization
### Optimization  
The challenge of designing a hybrid renewable energy generation model is stated as an optimization problem that has the goal of decreasing NPC while taking into account a variety of schemes and operation limitations. The major objective of this research is to reduce NPC by minimizing energy shortages/outages and grid pressure by maximizing the use of renewable energy. The objective function of the system is expressed as follows [25]:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Constraints** & **Value** \\ \hline \multirow{8}{*}{**Battery**} & Round trip efficiency & 85\% \\  & BSOCmin & 30\% \\  & Vnom & 6V \\  & Qnom & 360 Ah \\  & Lifetime throughput & 1075 kWh \\  & CC & USD 300/Unit \\  & RC & USD 300/Unit \\  & OMC/Year & USD 10 \\ \hline \multirow{8}{*}{**Converter**} & Efficiency & 95\% \\  & Operational lifetime & 15 Years \\ \cline{1-1}  & CC & USD 400/kW \\ \cline{1-1}  & RC & USD 400/kW \\ \cline{1-1}  & OMC/Year & USD 10/Year \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._\[\begin{split}\text{Minimize}&\text{NPC}\\ \text{Subject to}& E_{PV}+E_{BG}>E_{BS}\\ & E_{PV}+E_{BG}+E_{Batt}=E_{BS}+E_{Losses}\\ & E_{Surplus}=E_{Gen}-E_{BS}-E_{Losses}\\ & E_{Battmin}\leq E_{Batt}\leq E_{Battmax}\end{split}\] (17a) where ( \[E_{Losses}\] ) is the summation of losses from converter and battery. The complementary effect of solar PV and biomass generators can fulfill the base station energy demand and ensure 100% energy availability as presented in constraint (17a). The combined utilization of solar PV and BG in addition to battery storage can supply the load requirement and associated losses as stated in constraint (17b). According to constraints (17c), a certain percentage of surplus electricity is put in storage in the battery, and the remaining portion is conveyed to the electrical grid. The constraint (17d) specifies that the storage capacity must not exceed the upper edge nor fall below the lower limit.
# Effect of Geometrical Factors on Torsion in Cold Roll Forming of the Lower Side Beam of a Car
###### Abstract  
Cold roll forming is a continuous metal forming process used to produce a large variety of profiles. A significant product of cold roll forming is asymmetric profiles, whose application is expanding in the construction of cars, houses, bridges, etc. Torsion is one of the common defects in the cold roll forming process of asymmetric section steel and sometimes seriously affects cold roll forming products. With the development of the economy and technology, many asymmetric cold roll-formed parts are being gradually applied to automobiles. Three-dimensional finite element analysis models of 12 stands with 700L sheet material were carried out using the professional COPRA RF and MSC MARC software, taking the lower beam of an automobile as the research object. Five geometric factors of the lower side beam were researched, and the influence rules of different geometric elements on the forming torsion were analyzed. We found that with the increase of web width, flange width, and vertical edge height and the decrease of the corner radius and strip thickness, the torsion defects of cold roll forming were improved to different degrees. The results also showed that the width of the flange has the most significant effect on torsion.  
cold roll forming; asymmetric section steel; finite element simulation; torsion defect; geometric factor +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Roles of AI in DFU Management  
AI significantly contributes to managing DFUs through various tasks, including classification, prediction, segmentation, detection, generative AI, and smartphone applications. Classification models, such as CNNs, are highly effective in categorizing DFUs into conditions like infection or ischemia, providing essential diagnostic support to clinicians. Similarly, prediction models, using algorithms such as ANNs, forecast key outcomes like the likelihood of healing or the risk of amputation, equipping healthcare providers with actionable insights for early interventions.  
Segmentation, another significant task, focuses on delineating wound boundaries using models like UNet, enabling the precise measurement of wound size, depth, and progression. Wound measurements are essential for tracking healing and guiding treatment strategies. Detection involves identifying DFUs within complex image backgrounds and serves as an essential first step before classification or segmentation. Models like EfficientNet excel in this task, making them particularly valuable in remote monitoring scenarios.  
Despite its potential, researchers have explored generative AI less frequently in DFU research. However, it addresses a data scarcity challenge. Generative AI leverages models like GANs and diffusion models to synthesize realistic datasets, enhancing the performance, reliability, and generalizability of classification and prediction models. Finally, smartphone applications provide an efficient platform for integrating AI into healthcare management. These applications help perform real-time wound detection, classify wound types, and measure wound dimensions, reducing the need for frequent clinical visits.
## 3 Results and Analysis
### Discussion on Stress Wave Energy Transfer of jointed Specim  
It is noteworthy that the variation of the reflected energy coefficient and transmitted energy coefficient at the joint inclination angles of 30\({}^{\circ}\) to 60\({}^{\circ}\) are different from those at 0\({}^{\circ}\) and 90\({}^{\circ}\). This phenomenon can be attributed to the following main reasons:  
1. JRC directly affects the unevenness and contact area. With JRC increasing, the degree of unevenness and contact area increases. At joint inclination angles of 0\({}^{\circ}\) or 90\({}^{\circ}\), the uneven joint surface results in a decreased relative contact area, leading to reduced energy transfer. Conversely, at joint inclination angles of 30\({}^{\circ}\) to 60\({}^{\circ}\), the uneven joint surface increases the relative contact area, facilitating energy transfer.
2. The unevenness of joint surface results in the scattering and reflection of incident waves. At joint inclination angles of 0\({}^{\circ}\) or 90\({}^{\circ}\), scattering and reflection significantly impede wave propagation and reduce the transmission energy coefficient. Moreover, at joint inclination angles of 30\({}^{\circ}\) to 60\({}^{\circ}\), the hindrance to wave propagation is less pronounced, thereby increasing the transmission energy coefficient.
3. The propagation paths of stress waves in jointed rocks vary with the joint inclination. At joint inclination angles of 0\({}^{\circ}\) or 90\({}^{\circ}\), stress waves propagate perpendicular to or parallel to the joint surface. Hence, increasing JRC enhances the scattering and reflection of stress waves, reducing stress wave transmission. Conversely, at joint inclination angles of 30\({}^{\circ}\) to 60\({}^{\circ}\), the wave propagation path becomes relatively oblique. The increased JRC weakens the scattering and reflection of incident waves, thereby increasing stress wave transmission.  
The influence of joint roughness and inclination on stress wave propagation in jointed rocks is highly complex. Additionally, the effects of joints on stress wave propagation vary with joint morphologies and inclinations. The roughness of different parts of the joint surface is not uniform, and joint rock surface failure can involve one or multiple failure modes at various joint inclinations. For instance, for 0\({}^{\circ}\) or 90\({}^{\circ}\) joints, jointed rocks primarily exhibit splitting tensile failure as the dominant failure mode, with minimal sensitivity to joint roughness. However, for a 45\({}^{\circ}\) joint, shear failure is observed in jointed rocks with low roughness, while high roughness leads to tensile shear failure. The investigated joint surface roughness and inclination angle in this study represent only one specific scenario of jointed rocks. Further research is required to explore various joint morphologies and inclination angles.
## 2 SITHON
### Imaging payload
#### 2.3.4 Image processing  
For every camera exposure the corresponding GPS and IMU data are automatically registered together with the image data to create a new _data package_. The data package is forwarded to a server PC system (Pentium IV, 2 GHz), installed on the ground station or on board the aircraft (depending on the chosen communication architecture). The _data package_ is decoded on the server PC, and transformed to useful products before the next camera frame is captured. The following operations are carried out on the server station in real time:  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline \multirow{2}{*}{**Flying velocity**} & \multirow{2}{*}{**Flying height \(H\)**} & **Time interval in seconds** & **Time interval in** \\  & & **- stereoscopic view** & **seconds - monoscopic** \\  & & **(overlap 60\%)** & **view (overlap 0\%)** \\ \hline
160 & 1,000 & 3 & 7 \\
200 & _1,000_ & 2 & 6 \\
160 & 1,500 & 4 & 11 \\
200 & 1,500 & 3 & 8 \\
160 & 2,000 & 6 & 14 \\
200 & 2,000 & 4 & 11 \\ \hline \end{tabular}
\end{table}
Table 4: Time interval of successive image acquisitions for different combinations of flying velocity, flying height and image overlap percentage. FPA physical dimension _l = (6,24 mm) x \(w\) (8,32 mm).  
Figure 5: (a) Camera opening at the bottom of the aircraft CESSNA 310Q using an inertial gyroscopic platform GSM300, (b) Design for camera fit to the camera opening at the bottom of the aircraft, (c) Camera and IMU devices rigidly attached.  
image geo-rectification and projection,
* identification of temperature alarms (image processing),
* display of the geographic coordinates of detected hot spots and fire fronts.  
Direct image geo-referencing is based on a) the combination of the GPS data for the positioning of the camera exposure centre, and b) the inertial measurements returning the camera's orientation angles at the time of exposure. The restored and ortho-rectified thermal or video images are projected to the reference cartographic projection system, and undergo a number of additional processing operations including global image thresholding, in order to detect areas with temperatures exceeding a predetermined value (hot spots). The generated temperature and hot spots map products are then combined with existing background and asset maps of the area under monitoring.  
Figure 6(a) illustrates the set of operations undertaken by the software package, which has been developed for sensor control and input raw data processing to derive a set of usable information products. Moreover, Figure 6(b) summarizes the operations applied on the server PC station including data package decoding, image thresholding and geo-referencing.  
Figure 6: (a) S/W operations for sensor control and input raw data processing; (b) operations applied on the server PC station for data package decoding, image thresholding, and image geo-referencing.
# Analysis of Mining Waste Dump Site Stability Based on Multiple Remote Sensing Technologies
## 2 Study Area and Dataset
### Dataset  
Previous studies have shown that most sliding body instability is caused by rainfall, in addition to individual rock and soil performance [44]. At the test site, the rainy season generally lasts from May until September. Therefore, the stability of the dump site during these four months is crucial to local safety. After looking at the archives, a descending stack of 13 COSMO-SkyMed images covering the study area and acquired from 6 May to 11 September 2015 was collected and employed in our time series analysis. As one of a new generation of high-resolution SAR satellite missions, COSMO-SkyMed is a military-civilian dual-use satellite constellation of four satellites established by the Italian Ministry of Defense and the Italian Space Agency [45]. The constellation works in an X-band, with multiple imaging modes and full polarization. The images are acquired by stripmap mode, with similar look angles of approximately 27 degrees. The thresholds of 0.27, 200 days and 680 m were applied for coherence, temporal and perpendicular baselines, respectively. As a result, 35 interferograms were generated from the 13 SAR images. The spatial and temporal baseline distributions are depicted in Figure 3, where the asterisks indicate the SAR images and blue lines represent the interferograms. The topographic phase was simulated and removed from the interferograms using the TanDEM-X DEM of 3-arc-second resolution (with spatial sampling of 90 m \(\times\) 90 m) covering the study area [46; 47]. TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is a German earth observation  
Figure 2: North slope of the Xudonggou dump site, where the thirteen ground measurements are marked with red triangles.  
radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical spatial baselines between the satellites of 120-150m, a global Digital Elevation Model (DEM) has been generated. The absolute height error of TanDEM-X DEM is with about 1m an order of magnitude [47].
# Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm
###### Abstract  
In this work, we proposed a high-efficiency and low-complexity encoding algorithm and its corresponding implementation structure during the design and implementation process of an LDPC encoder and decoder. This proposal was derived from extensive research on and analysis of standard encoding algorithms and recursive iterative encoding algorithms, specifically targeting the problem of high computational complexity in encoding algorithms. Subsequently, we combined binary phase-shift keying modulation mode and additive white Gaussian noise channel transmission with the min-sum decoding algorithm to realize the (1536, 1024) LDPC codec. This codec was uniformly quantized with a (6, 2) configuration, executed eight iterations, and achieved a 2/3 code rate in the IEEE802.16e standard. At the bit error rate (BER) of \(10^{-5}\), the codec's BER obtained by the proposed coding algorithm was about 0.25 dB lower than the recursive-iterative coding algorithm and was about 1.25 dB lower than the standard coding algorithm, which confirms the correctness, effectiveness, and feasibility of the proposed algorithm.  
LDPC code; high-efficiency and low-complexity coding algorithm; MSA; IEEE802.16e
## 3 Discussions of Experimental Results
### Effect of the Relative Unit Length
At \(AoA=10^{\circ}\), the absolute value decrement of the moment coefficient of the DCFJ312-OF1/2 airfoil over the OA312 airfoil is 2.0% smaller than that of the DCFJ312-OF1/2 airfoil over the OA312 airfoil. At \(AoA=20^{\circ}\), the absolute value decrement of the moment coefficient of the DCFJ312-OF1/2 airfoil over the OA312 airfoil is 3.5% greater than that of the DCFJ312-OF1/2B airfoil over the OA312 airfoil. In summary, the maximum lift coefficient and stall \(AoA\) of the DCFJ312 rotor airfoil with the smaller _RUL_ are slightly greater than that of the DCFJ312 rotor airfoil with the larger _RUL_. Other aerodynamic characteristics of DCFJ312 airfoils with various _RULs_ have little change. The reason may be that DCFJ312 airfoils with the same \(OF\) and different _RULs_ have similar turbulent mixing effects although different spanwise vortex structures are generated.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 2 Preliminary
### Polar Code Construction  
Polar code is a kind of linear block code with codeword length \(N=2^{n}\). Its generator matrix can be written as \(G_{N}=B_{N}F^{\otimes n}\), where \(B_{N}\) is the bit-reversal matrix used for permutation and \(F^{\otimes n}\) is the \(n\)th kronecker power of \(F=\left(\begin{smallmatrix}1&0\\ 1&1\end{smallmatrix}\right)\). In his original paper, [PERSON] constructed \(N\) new binary input multiple output channels called bit-channels with indices in \(1,2,\cdots,N\). As \(N\) goes to infinity, the capacities of these bit-channels polarize to either \(0\) or \(1\). [PERSON] also proved that the ratio of bit-channels with capacity larger than \((1-\delta)\) to all bit-channels goes to \(C\), where \(C\) is the capacity of original channel and \(\delta\) is an arbitrary small positive number. The operating principle of polar coding is the placement of information bits in bit indices relating to bit channels with large capacities with known bits placed in the remaining positions. [PERSON] used Bhattacharyya parameter \(Z\) as the boundary for the capacity of the bit-channels. However, calculating \(Z\) is hard for most channels. In practice, the Gaussian approximation proposed in [12] is simple and performs well enough to evaluate the bit-channels. To perform polar encoding, the first step in code construction is to choose \(K\) best positions as the information set and assign the remaining positions as the frozen set. By placing information bits at the information positions and \(0\) at the frozen positions, we obtain the message vector \(U\) and the resulting codeword \(X=UG_{N}\).
# Design of an Open Remote Electrocardiogram (ECG) Service
## 4 A Prototype for a Remote ECG Service
### MCU to HTTP User Agent Interface  
The unit communicates with the MCU dedicated to networking using the embedded UART running at 115,200 baud. They exchange 250 frames per second, each containing 33 8-bit characters as seen above, so that the resulting data rate is of \(33\times 250=8250\) octets per second. Serial protocol overhead is limited to start/stop bits: since communication is reliable and real-time there is no need for parity check and flow control. Thus, the required baudrate is of 82,500 baud, still comparable with that anticipated in Section 3, and the protocol is ready to transfer six samples--more than required--and a timestamp. In theory, such a baudrate is consistent with UART configuration: in Section 5, we verify this statement in practice.
## 3 Materials and Methods
### Classification Methods
#### 3.2.7 Radial Basis Function Network  
The radial basis function network (RBF) is an artificial neural network that uses radial basis functions as activation functions. The output of the RBF network is composed of neuron parameters and radial basis functions of the inputs [34].
## 3 Advanced Techniques in Remote Sensing of EBC  
Similar criteria were applied to choose the remote sensing techniques discussed below, including promising algorithms or methods in image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of RS and GIS. Although these techniques are reviewed separately, they are frequently integrated in practice. For example, data fusion can be implemented to remotely sensed data before they are classified by advanced classifiers in order to improve classification accuracy.
# Research on the Coupled Bending-Torsional Flutter Mechanism for Ideal Plate
## 5 Conclusions  
In this paper, the modal decoupling of bending-torsional-coupled flutter is carried out through the theory of the \"incentive-feedback\" mechanism, and the flutter mechanism of the ideal plate is analyzed. From the above analysis, the following conclusions are drawn:  
1. The main sources of vertical modal damping in an ideal plate are twofold: firstly, the aerodynamic positive damping induced by vertical velocity self-excitation in the vertical vibration system; secondly, the torsional vibration system, where vertical velocity self-excites a twisting moment, and the twisting displacement self-excites aerodynamic positive damping that feeds back to the vertical vibration system.
2. The main sources of damping for the ideal flat plate's torsional mode come from two aspects: firstly, the self-excited aerodynamic positive damping due to torsional velocity in the torsional vibration system; secondly, the vertical displacement self-excites the vertical vibration velocity through lift-induced vertical excitation in the vertical vibration system, and the vertical velocity self-excites the aerodynamic negative damping feedback to the torsional vibration system.
3. The torsional displacement of the ideal plate produces the aerodynamic negative damping through \"incentive-feedback\", which is greater than its torsional velocity and self-excited positive damping, resulting in the divergence of its torsional vibration. Because the self-excited aerodynamic forces are coupled with each other, and the vertical vibration system diverges at the same time under the excitation of torsional self-excited lift, bending-torsional-coupled flutter occurs.
4. The \"incentive feedback mechanism\" indicates that in the process of exciting vertical vibrations, the torsional displacement of an ideal flat plate is caused by self-excited lift. The vertical vibration system produces a negative stiffness \(-m_{h}\omega_{sa}^{2}\), significantly greater than the stiffness \(K_{h0}-H_{4}\) of the vertical vibration system, resulting in a cosine value of the phase difference between the torsional displacement due to self-excited lift and the vertical vibration displacement response being less than zero. In other words, the phase difference angle between the torsional displacement due to self-excited lift and the vertical vibration displacement response lies in the interval of \(90^{\circ}\) to \(180^{\circ}