esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Road Design for Wind Farms in China
## 1 Introduction  
With the increasing demand for energy conservation and emission reduction and the reduction in the cost of generating electricity from renewable energy sources, global wind energy and other new energy generation industries have developed rapidly. In terms of wind power generation, according to the data of the Global Wind Energy Association, by the end of 2021, the total installed wind power in the world was 837 GW, of which China ranked first with 338.31 GW. According to the data from the State Energy Administration, the cumulative installed capacity of wind power in China reached 328.5 GW in 2021, accounting for 13.8% of the total installed capacity of domestic power generation, next only to hydropower and thermal power. Wind power is green energy. Compared with other new energy generation technologies, wind power has the advantages of a short construction period, mature technology, high reliability, and low cost, and has a very broad development prospect.  
Domestic and foreign scholars have carried out relevant research on wind farm construction and concluded that the difficulty of road construction in mountainous wind farms directly affects the cost of wind farm construction. Road construction in mountainous wind farms occupies only a small part of the investment cost of wind farms, generally about 10%. However, it accounts for more than 50% of the construction cost of civil engineering and is a very important part of project cost control. On the other hand, with the large-scale development of wind power projects in recent years, the terrain resources, which are very advantageous to the construction of wind power, are becoming increasingly tense. Thetype of onshore wind farm is pushing forward in areas where the wind resources, such as hills and mountainous areas, are better but the relative investment in the construction of wind power projects increases greatly. During equipment transportation during the construction period of the wind power plant, the tower, blade, engine room, wheel hub, and transformer of the wind power unit are all heavy pieces, which have special requirements for wind power roads and make road design difficult. Therefore, it is very important to systematically study the road of wind farms.  
Through a lot of research on relevant literature on the road design of wind farms, this paper summarizes and compares the design methods of road route selection optimization, circular curve design, and longitudinal section design. This paper summarizes the causes and characteristics of water and soil losses in wind farms, lists the methods for predicting the intensity of water and soil losses in wind farms at present, sums up the measures for preventing water and soil losses in wind farms based on the previous studies, and puts forward the measures system for preventing water and soil losses in wind farms. Finally, according to the existing research results and the development status of the wind power industry, the problems faced by wind power road construction are summarized and its development prospects are forecasted.
## 4 Correlating \(T_{\text{max}}\) with the Consistency Limits  
Using tabulated data presented for inorganic fine-grained soils reported on pages 187-191 of [PERSON] [8], this section investigates new correlations between the consistency limits and \(T_{\text{max}}\) (Figure 6). As expected, there are clear trends of \(T_{\text{max}}\) increasing overall for increasing \(w_{\text{L-FC}}\), \(w_{\text{P}}\), and \(l_{\text{P}}\) (Figure 6a-c), with the _clay_ soils mobilizing significantly greater toughness than the _silt_ soils. Compared to the data plot against \(l_{\text{P}}\) (Figure 6c), there appears to be a strong demarcation between _clay_ and _silt_ evident for plotting the data in the \(T_{\text{max}}\) versus \(w_{\text{P}}\) chart of Figure 6b. The authors have included a tentative demarcation line (i.e., \(T_{\text{max}}=1.1\times w_{\text{P}}-20\)) in Figure 6b for distinguishing between _clay_ and _silt_ soils, whose original classification, as reported in [PERSON] [8], was based on the Casagrande plasticity chart. As indicated in Figure 6c, the decision on the positioning of the _clay-silt_ demarcation line in the \(T_{\text{max}}\) versus \(w_{\text{P}}\) chart was aided by the fact that, when the data were plotted in the Casagrande plasticity chart, three of the investigated soils were plotted on the A-line boundary. Figure 6d presents a plot of \(T_{\text{max}}\) against the \(w_{\text{P}}/w_{\text{L-FC}}\) ratio, termed the \"plasticity ratio\" by [PERSON] and [PERSON] [57]. They showed that the \(w_{\text{P}}/w_{\text{L}}\) ratio inversely correlates with SSA, which is consistent with greater remolding toughness for increasing SSA [10]. From an inspection of Figure 6d, with an increasing plasticity class level from CL to CE, the value of \(T_{\text{max}}\) reduces due to the increasing \(w_{\text{P}}/w_{\text{L-FC}}\) ratio, according to Equation (4):  
\[T_{\text{max}}=70-98\times w_{\text{P}}/w_{\text{L-FC}}\ (n=55,\,R^{2}=0.76) \tag{4}\]  
Figure 5: Relationship between maximum remolding toughness \(T_{\text{max}}\) and the plasticity index to liquid limit (\(l_{\text{P}}/w_{\text{L-FC}}\)) ratio (after Moreno-Maroto and Alonso-Azcarate [11], prepared using data extracted from [PERSON] [8]).
## 2 Miniaturized SAR Images  
HISEA-1, the first miniaturized SAR in the HISEA series, was launched on 22 December 2020. Despite its compact weight of only 185 kg, HISEA-1 can deliver high-quality SAR images with a spatial resolution of 1 m and a swath width of up to 100 km [18]. The subsequent satellites in the series, Chaohu-1 and Fucheng-1, each weigh approximately 300 kg and were launched on 27 February 2022 and 7 June 2023, respectively. Currently, a number of studies have shown that HISEA-1 performs well in several important applications, including sea surface wind retrieval [25], ocean wave retrieval [26], floodwater segmentation [27], and ship detection [28]. These capabilities are crucial for accurate environmental monitoring and disaster assessment, enhancing our ability to respond to natural disasters and monitor maritime activities.  
Operating at the C-band frequency with single VV-polarization (vertical transmit and vertical receive), these miniaturized SAR systems support four distinct imaging modes: StripMap, Spotlight, TOPSAR, and ScanSAR (including Narrow ScanSAR and Extra ScanSAR). The StripMap and Spotlight modes offer swath widths ranging from 20 km to 25 km, suitable for detailed, high-resolution imaging. In contrast, the TOPSAR and ScanSAR modes provide broader coverage--with swath widths between 80 km and 100 km--by utilizing multiple sub-swaths. This broader coverage, however, comes at the expense of slightly reduced imaging quality.  
The data products generated by these satellites are classified into two primary levels: Level-1 single look complex (SLC) and Level-2 orthorectification geolocation (ORG) images. The Level-2 ORG images are derived from the Level-1 SLC images through processes that include radiometric calibration and terrain correction. This ensures that the ORG images have a spatial resolution of 10 m by 10 m for both ScanSAR and TOPSAR modes, balancing high resolution with extensive coverage. All three SAR images used in this study are Level-2 products. The specific TCs captured in these images include TC Noru, TC Doksuri, and TC Koinu. TC Noru was imaged on 23 September 2022 at 13:23 UTC, and at that time, it was classified as a Tropical Storm according to the standards set by the China Meteorological Administration (CMA). TC Doksuri was captured on 23 July 2023 at 01:36 UTC, also classified as a Tropical Storm. TC Koinu, however, was observed on 04 October 2023 at 02:41 UTC and intensified to a Severe Typhoon, indicating a much stronger and more intense storm system.  
The specific parameters of these SAR images, along with the corresponding details of the TCs, are summarized in Table 1. This table includes information such as the classification of each TC, the imaging time, the spatial resolution of each SAR image, the imaging modes, the satellite orbit direction, and the location of the TC.
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 2 Experimental Procedure
### Accelerated Corrosion Method  
It is common knowledge that natural corrosion is a relatively slow process, with a required period for the onset of corrosion, even for marine environments. The accelerated technique adopted in the present study was the impressed current density method, where a power supply is used to induce corrosion by applying a direct electric current. In this provision, the embedded reinforcing steel bar acts as the anode, a stainless-steel bar acts as the cathode and the 5% sodium chloride solution (NaCl) by the weight of water in which all specimens were immersed acts as the electrolyte (Figure 3).  
The selected content of the NaCl solution better simulated the case in the regions of Mediterranean basin, where the hot climate results in a higher salinity of the seawater, in contrast to northern countries. Moreover, in the Mediterranean basin, the salinity of the sea increases during the summer periods due to the high temperatures recorded [18; 19].  
In light of the real severity of coastal conditions, during the corrosion periods of the specimens, ponding cycles of wetting and drying conditions were applied because splash and tidal environments are the most aggressive among the three cases, given the high chloride concentrations [19; 20]. Specifically, in light of the current climatic conditions in the Mediterranean basin, a cyclic exposure was adopted, with 9 h wet in the electrolyte mode and 15 h in the dry mode.  
With a view to achieving different levels of corrosion damage on reinforcing steel bars, all specimens remained in the tanks for different exposure times, inducing a current density equal to 1.0 mA/cm\({}^{2}\) corresponding to a current equal to 0.105 A, based on [PERSON]'s law. Taking into account the disparity between the theoretical predicted value of mass loss provided by [PERSON]'s law and the actual recorded mass loss, as already highlighted by several studies [21; 22], all specimens remained in the corrosion cells up to a desired theoretical value of mass loss of 15%.  
Finally, it is worth mentioning that a certain length equal to 210 mm in the middle of the steel bars was allowed to corrode during the corrosion experiments and the remaining length was protected and carefully covered with wax, so as not to allow failure near the grips during the mechanical tests. For the evaluation of corrosion damage, only the exposed surface was examined.
## 4 Classification of MAC Protocols Based on Channel
### Centralized Multi-Channel MAC Protocols
#### 4.5.1 ReMAC  
The main objective of the reliable MAC protocol is to connect vehicles with a multi-channel hybrid medium of access. It reduces collision and hidden terminal problem and deals with high mobility by randomly minimizing transmission delay. Here, the modeled system consists of RSUs placed at regular intervals in a two-way highway with vehicles moving at high speed. For determining the next RSU, the vehicle's direction is considered, and the channel is assigned before it enters the range of next RSU. FDMA-based access is used, dividing the CCHs and SCHs into a sub-frequency channel of same bandwidth. This mechanism avoids collision and provides reliable and efficient communication. Each sub-frequency channel are divided into time slots by TDMA. Figure 10 demonstrates the network model or structure of ReMAC.  
The separation of a guard band is chosen to be wide to minimize the channel interference. A joining network channel (JNCH) for new vehicles is used when they initially enter the network; then, the request is sent to RSU from JNCH by CSMA. The vehicle at the beginning adjusts to communicate with control channel and data channel and then sends a channel request packet to RSU by CSMA from JNCH. The RSU upon receiving VSIP [87] allocates a data channel to the vehicle and time slots from convenient channel, taking into account the moving direction of vehicles. The updates are provided in the VCAT table. When RSU receives a package present in the private network within its range, it searches for the vehicle's information in the VCAT table. The table is updated on a regular basis and channel is allocated per the requirement. The calculation of network drop time (DT) is carried out to check whether the vehicle is dropped out of the network or still present, and the vehicles not sending heartbeats within 0.5 s are disconnected. The channel and slot allocation algorithm is based on FDMA by selecting a backward frequency band and a forward frequency band.  
The protocol provides better performance in channel access rate, collision avoidance, high throughput, and minimized delay in dense city scenarios. It also improved the scalability of the network and adapted to frequent diverse topological changes. There is  
Figure 9: Time frame structure of VAT MAC [85].  
communication overhead (at RSUs and OBUs) along with high energy consumption that need to be optimized.
## 5 Results
### Performance Tests
#### 5.1.2 Network Congestion Image Transfer Control  
Network congestion image transfer control was tested in a controlled manner with modems completely aligned and stable bandwidth conditions. Two scenarios are presented in this subsection. The first one shows an adaptive mode where the operator is only able to adjust the desired TBF. In the second scenario, the operator can change the mode to a manual mode, decreasing and fixing the values of scale and quality. This mode can be used when the use of the image is not a priority (navigating, folding, or unfolding the arm; planning a trajectory; etc.).  
Figure 10 shows how the adaptive mode allows the controller to adjust the quality and the scale depending on the RFL. The desired TBF in Figure 10 starts at 0.6 s; at this point, there are some oscillations from 0.5 s to 0.7 s, which result in adjustments in the quality and the scale. The second step shows an increase in the desired TBF; under these specific bandwidth conditions, even when the quality and the scale reach their maxima, the real RFL does not approach the desired TBF. Finally, the desired setpoint decreases several times, as evidenced in the quality and scale graph as a decrease in both variables.  
As in Figure 10, Figure 11 represents some changes in the desired TBF setpoint, except for a period from 45 s to the 60 s where the manual mode is executed. In this period, the scale and quality were fixed at 20% and 50%, respectively. This means that the real RFL does not follow the desired TBF signal. After second 60, the adaptive mode is activated, and the desired TBF is increased to 1 s, increasing quality and scale.  
Figure 9: Experiment 4–iPerf results after an aligned half-round trajectory using a 6 MHz optical speed.
## 2 Theoretical Analysis
### Normal-Tangential Contact
#### 2.2.2 Low Normal Force Level  
If the maximum static frictional force on the surface of the contact point is less than the tangential force required for either sphere crushing, relative sliding between the two particles will occur before breakage (see Figure 5a), which results in a reduction in the contact area (see Figure 5b). The decrease in contact area leads to the increase in force per unit area on the surface of the contact point. It is assumed that when the relative slip between the two spheres is small, the increase in force per unit area on the surface of the contact point does not cause a secondary occurrence of local compaction. With the increasing uniform load \(q\) at the contact surface, the values of \(K_{mob}\) at each point inside the sphere along the \(z\)-axis increase. Once the \(K_{mob}\) value at a point of the inner sphere along the \(z\)-axis passing through the center of the bottom surface of the conical core reaches \(K\), the whole sphere crushes (see Figure 5c). Meanwhile, the relationship between the normal and tangential forces at the moment of particle crushing is \(F_{lcr}=\mu F_{ncr}\). When the normal force is small enough, the relative slip is large, resulting in a small contact area between the two particles and thus local compaction under the contact surface caused by stress concentration occurs again rather than integral crushing.  
To sum up, the relationship between the normal and tangential forces in the normal-tangential contact test when the overall crushing occurs is obtained using the following:  
\[F_{lcr}\ =\ \left\{\begin{array}{cc}\mu F_{ncr},&(F_{mmin}<F_{ncr}<\frac{ \sqrt{F_{lcr}^{2}-F_{ncr}^{2}}}{\mu})\\ \sqrt{F_{\sigma}^{2}-F_{ncr}^{2}},&(\frac{\sqrt{F_{\sigma}^{2}-F_{ncr}^{2}}} {\mu}\ \leq\ F_{ncr}\ \leq\ F_{\sigma})\end{array}\right. \tag{21}\]  
where \(F_{mmin}\) is the minimum normal force required for spherical particle crushing under normal-tangential contact conditions. If \(F_{n}<F_{mmin}\), there will be a secondary occurrence of local compaction at the contact point rather than particle breakage. Different from the  
Figure 4: (**a**) A plot of \(K_{mob}\) along the line passing through the center of the compacted area and in the direction of the resultant force for the following material parameters: \(\sigma_{c}=50\) MPa, \(\sigma_{t}=3\) MPa, \(\
u=0.3\), \(a=2.7\) mm and \(R=25\) mm; (**b**) the size and shape of the conical core; (**c**) a sketch showing the breakage form of a sphere.  
normal contact test, both the rupture surface and the conical core have the same inclination that takes the value of the following:  
\[\theta=\left\{\begin{array}{ll}\tan^{-1}\frac{F_{nr}}{F_{nr}},&(F_{min}<\ F_{nr}<\frac{\sqrt{F_{G}^{2}-F_{nr}^{2}}}{\mu})\\ \tan^{-1}\mu.&(\frac{\sqrt{F_{G}^{2}-F_{nr}^{2}}}{\mu}\ \leq\ F_{nr}\ \leq\ F_{cr})\.\end{array}\right. \tag{22}\]  
The bottom area and the height of the conical core are solved using Equations (4) and (19), respectively.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Comparison with Other Approaches  
Table 9 summarizes the AD solutions designed for edge deployment, focusing on model types, device capabilities, and support for on-device training and FL. While many prior studies utilize AEs for efficient unsupervised AD, they mostly rely on more resource-rich and computationally superior hardware, such as Raspberry Pi, Jetson Nano, or desktop-class CPUs. Some works do target constrained hardware like ESP32 and STM32 MCUs [28; 29], or Arduino Nano 33 BLE Sense [33], but these approaches typically focus on inference or incremental updates rather than full on-device training or FL. When on-device training or FL is performed, it usually involves more powerful platforms or simulated multi-device environments rather than real deployment on constrained devices.  
In contrast, our work demonstrates full on-device training and inference on highly constrained real-world ESP32 MCUs. Compared to other studies, our work features one of the lowest memory requirements for model storage, ranging from 25 to 120 KB, which is substantially smaller than many related works that use models sized in megabytes (e.g., 12 MB in [26] or 29-42 MB in [42]) or omit memory details altogether. Despite the ESP32's limited resources, our models achieve competitive inference latencies between 9 and 42 ms, which is comparable to or better than the results reported on more capable edge devices, such as the Raspberry Pi 4 or Jetson Nano (13.1 and 19.6 ms, respectively [42]). Furthermore, unlike most approaches that use quantization or pruning to further reduce latency and memory footprint, we perform end-to-end training from scratch using standard 32-bit floating-point precision without simplifying the implementation and preserving performance at the same time.  
Furthermore, we extend this setup into a practical FL framework involving multiple physical ESP32 devices communicating over Wi-Fi. Unlike related FL studies that use more powerful hardware or simulate multi-device setups, our implementation supports real-world testbed decentralized learning under both IID and realistic non-IID data conditions. Through extensive evaluation across diverse data partitions and corruption levels, we demonstrate the feasibility and resilience of privacy-preserving decentralized learning on ultra-low-power embedded systems. Our solution stands out by enabling the full FL process on constrained devices, closing the gap between traditional edge-based AD, where training occurs centrally and only inference is performed on-device, and advanced collaborative learning, typically run on high-performance hardware.
## 6 Conclusions  
A methodology to obtain daily mean top-of-atmosphere RSF (Wm\({}^{-2}\)) from AVHRR is presented. The different steps consist of: (1) a narrowband-to-broadband conversion of the AVHRR reflectances using empirical regressions with CERES observations; (2) an anisotropy correction using ADMs resulting in hemispherical albedo; and (3) a temporal interpolation between the available instantaneous albedos using a flexible diurnal cycle model.  
This methodology is subsequently demonstrated by validating a full year (2008) of RSF daily means with the CERES SYN1 deg data record, both on daily and subdaily scales. Several orbital configurations are tested, each excluding particular satellites from the constellation. Early afternoon and mid-morning orbits are proven essential for the daily mean RSF retrieval.  
[PERSON] carried out the main research, visualizations, and wrote the article. [PERSON] supervised the work and reviewed the text, including interpretation of the results. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Climate Monitoring Satellite Application Facility (CM SAF) of EUMETSAT.  
**Acknowledgments:** The authors are grateful to the Atmospheric Science Data Center at NASA Langley Research Center for providing the CERES data used in this work. The daily TSI time series and the ERA5 data have been obtained from the Copernicus Climate Data Store (CDS).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Control for Ship Course-Keeping Using Optimized Support Vector Machines
###### Abstract  
Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.  
ship course-keeping; support vector machines; L2-gain design; uncertainties +
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