esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 5 Experimental Results
### Implementation Details  
All experiments were implemented by MXNet [43], which is a scalable deep learning framework. We used the pretraining model ResNet-50 to initialize the network. All models were trained for eight epoches on our dateset, with a learning rate of 0.001 for the first six epoches, and 0.0001 for the next two epoches. The optimizer is Stochastic Gradient Descent (SGD) and weight decay and momentum were 0.0005 and 0.9, respectively. The programming language of the algorithm is Python. Considering the capacity of the GPU in Jetson TX2 and training efficiency, we trained the CNN models on a PC with one Nvidia GTX 1080 Ti to accelerate. Then the well-trained models by PC were ported into Jetson TX2.  
Table 1 shows the statistics of different types of anchors. S-4 has more positive samples and bigger average IoU of positive samples than B-4 and B-12. The number of anchors in B-12 has three times as many as S-4, but the anchors of S-4 still has better quality both in quantity and IoU overlap with ground truth.  
Figure 7: Some image patches under different conditions in our dataset.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Power System State Estimation (PSSE)
#### 4.2.2 Measurement Data  
Lacking measurement data will affect the observability analysis; as a result, classifying the different types of measurement data is necessary. The measurement data most works use can be classified into three categories: real-time measurement, pseudo measurement, and virtual measurement [53].  
Real-time measurement refers to direct measurement data like bus voltage, current, and frequency via sensors or other monitoring equipment gathered by the SCADA system. Because of the high dependency on the bandwidth and reliability of the communication infrastructures, especially in large-scale or distributed power systems, it is hard to guarantee to obtain real-time measurements.  
Pseudo measurement refers to the data generated by mathematical models (billing data and probability density) or algorithms (ML/DL) from historical system data when direct measurement is not possible or feasible [54]. Virtual measurements are typically characterised by low or zero-variance data, which can be effectively processed using the Lagrange multipliers algorithm [53].
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
###### Abstract  
In mobile ad hoc networks, network nodes cooperate by packet forwarding from the source to the destination. As the networks become denser, more control packets are forwarded, thus consuming more bandwidth and may cause packet loss. Recently, game theory has been applied to address several problems in mobile ad hoc networks like energy efficiency. In this paper, we apply game theory to reduce the control packets in dense networks. We choose a proactive routing protocol, Optimized Link State Routing (OLSR) protocol. We consider two strategies in this method: _willingness_always_ and _willingness_never_ to reduce the multipoint relay (MPR) ratio in dense networks. Thus, nodes with less influence on other nodes are excluded from nomination as MPRs. Simulations were used to confirm the efficiency of using our improved method. The results show that the MPR ratio was significantly reduced, and packet delivery ratio was increased compared to the conventional protocol.  
multihop networks; OLSR; game theory; mobile ad-hoc networks; willingness; multipoint relay (MPR) +
Footnote †: journal:  
_Future Internet_2020, 12, 47; doi:10.3390/f12030047
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 3 The Proposed Method
### Embedding Range Selection  
In order to remove possible bad bins, a better histogram embedding range selection method presented by [PERSON] et al. [15] is employed. In their method, the embedding range selection is determined by the following thresholds \(T_{1}\) and \(T_{2}\), as shown in Equations (3) and (4).  
\[T_{1}=\frac{M_{0}\times N_{0}}{n\times n}\times\alpha_{1} \tag{3}\]  
\[T_{2}=\frac{M_{0}\times N_{0}}{n\times n}\times\alpha_{2} \tag{4}\]  
where \(M_{0}\) and \(N_{0}\) are the height and width of an image, respectively, \(n\) represents the size of the block and \(\alpha_{1}\) and \(\alpha_{2}\) are a constant controlled by a secret key to adjust the embedding watermark, the values of which are in the range of \([0,1]\).  
For clarity, Figure 2 illustrates the comparison of two different embedding range selection methods. Figure 2a shows the embedding range selection method presented by [PERSON] et al., and Figure 2b presents a better embedding range selection method initially proposed by [PERSON] et al. [15]. Notably, some possible bad bins (the occurrence frequencies of pixels in these bins are zero or few) are removed successfully in Figure 2b. Based on this point, the robustness of the embedded watermark can be enhanced dramatically.  
Figure 1: The issue existing in the original watermark embedding design.  
Figure 2: Comparison of two embedding range selection methods: (**a**) [PERSON] et al.’s method and (**b**) [PERSON] et al.’s method.
## 3 Results and Discussion
### Electromagnetic Field on Different European Roads
#### 3.1.6 Slovenia  
In Slovenia, electric field measurements were carried out in the car cabin during driving on both motorways and expressways (between points P1 Sl-P2 Sl and P2 Sl-P3 Sl), as shown in Figures 17 and 18.  
The road section between points P1 Sl-P2 Sl covers part of the motorways (A34 and A1) and expressway H4. P2 Sl-P3 Sl covers part of the A1 and A2 motorways. The selected driving roads are part of the International E-road network-E 61 (between points P1 Sl-P2 Sl) and E70 (between points P2 Sl-P3 Sl).  
The median of \(E_{total}\) is 0.28 V/m for road section P1 Sl-P2 Sl and 0.29 V/m for road section P2 Sl-P3 Sl. The lowest \(E_{total}\) value in the two road sections is 0.11 V/m. The highest \(E_{total}\) values are 5.61 V/m and 6.46 V/m in the road sections P1 Sl-P2 Sl and P2 Sl-P3 Sl, respectively. Moreover, the results show that the \(E_{total}\) upper quartile is 0.37 V/m (P1 Sl-P2 Sl) and 0.40 V/m (P2 Sl-P2 Sl). We assume that obtained results are due to the fact that part of the road section between points P2 Sl-P3 Sl covers the Ljubljana Ring Road; therefore, more cellular network antennas are available close to the roads, which is one of the factors in the higher \(E_{total}\) values in the second road section.
# Sliding Mode Integral Separation PID Control for Hydrogen Fuel Cell Systems
## 3 Sliding Mode Integral Separation PID Control (SMC\(-\)Is\(-\)Pid)  
In this paper, a control strategy combining improved sliding mode control and four\(-\)stage integral separation PID control is adopted to control the oxygen excess ratio, electric stack temperature, and cathode and anode differential pressure in the hydrogen fuel cell system to improve the stability of the system's power generation. It can make full use of the advantage of the strong robustness of sliding mode control to ensure that the system can still operate stably in the face of various uncertainties and disturbances. It can also utilize the integral separation PID control to improve the static error so that the system can be closer to the desired output value when it reaches the steady state, which not only improves the dynamic response speed of the system but also enhances the steady state accuracy of the system. The control block diagram is shown in Figure 2.  
According to the proposed general control method, the key variables of the system studied in this paper are controlled hierarchically, and the specific control strategy is designed as follows: The status of the controlled variable error \(e\) and the contents of the controlled variable \(x\), which is currently collected by the sensors, are used to choose the control mode. When the absolute value of the error \(e\) between the status variable \(x\) and the desired value \(x_{\text{ref}}\) is less than or equal to the standard value of the error \(\phi\), the PEMFC system employs sliding mode control with improved convergence law (the switching control in sliding mode control employs improved convergence law control). When the absolute value of the error \(e\) is larger than the standard value of the error \(\phi\), the PEMFC system adopts the sliding mode integral separation PID control strategy (the switching control in the sliding mode control adopts the four\(-\)stage integral separation PID control).  
Based on control, the standard value of error judgment for OER, temperature, and pressure should be set at 0.2, 3 \({}^{\circ}\)C, and 200 Pa. Then the total control law is:  
\[u_{x}=\left\{\begin{array}{l}u_{\text{eq}}+u_{\text{conv}},|e|\leq\varphi\\ u_{\text{eq}}+u_{\text{IS$-$\text{pid}}},|e|>\varphi\end{array}\right., \tag{10}\]
## 7 Compliance with Maritime Regulations
### Regulatory Compliance Monitoring  
Autonomous vessels must continuously demonstrate adherence to a wide range of maritime regulations, from SOLAS and MARPOL to CII and local port rules. Here, AI agents function as real-time compliance monitors, automating surveillance, alerting, and documentation processes [127].  
These agents gather operational telemetry--such as vessel speed, fuel type, emission levels, ballast water status, and navigation logs--and compare them against prescribed regulatory thresholds [128; 129]. When deviations are detected, agents can issue automated corrective actions or alert operators before a violation occurs.  
For example, upon entering an Emission Control Area (ECA), the AI agent verifies fuel sulfur content via sensor input. If high-sulfur fuel is detected, it immediately switches to compliant reserves and logs the action in the audit trail.  
Compliance agents also generate standardized, regulator-ready reports by compiling data across subsystems, ensuring the documentation aligns with the MARPOL and CII templates. These reports are automatically time-stamped, encrypted, and sent to the designated maritime authorities or cloud platforms for verification [64].  
Advanced models go a step further, identifying patterns of near-non-compliance and flagging operations where efficiency or procedural drift may soon lead to violations. This supports proactive interventions and the creation of compliance risk heatmaps across fleet operations [36].  
Crucially, these agents are adaptive; when new IMO or EU directives are published, NLP-based modules can ingest and semantically interpret the changes, updating internal rule sets without full system reprogramming [120; 130].  
By taking over tedious, error-prone compliance tasks, AI agents free human operators from paperwork, while improving regulatory accuracy, speed, and traceability.
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 5 Simulation and Optimization Results
It can be observed that even with significantly different starting points, the final solutions from our two optimization runs are similar with respect to the objective function values. Figure 17: The S parameters for the final solution. Figure 18: The radiation patterns for the final solution. After optimization, the value of gain G\({}_{1+2}\) for any azimuth and any of the two body postures (standing or sitting) takes on values that are always greater than around \(-5\) dB, which can be regarded as a satisfactory result, considering the radiation patterns of the individual antennas (dipoles near the human body). This should enable the implementation of off-body communication. The worst value we obtained for on-body communication path loss was 93 dB. Assuming a hypothetical IEEE 802.15.6 transceiver with a \(-98\) dBm sensitivity (typical for existing ZigBee transceivers operating in the 2.4 GHz band, for instance, Texas Instruments CC2520) and a maximum transmission power of 0 dBm (5 dBm for CC2520), the link budget is positive, albeit with a low margin of 5 dB. Given these results, we conclude that our methodology for the WBAN physical layer optimization is successful.
## 6 Conclusions  
In this study, the design was initially executed considering the electromagnetic and mechanical characteristics of the STS420-J2 shaft PMSG, which is less expensive, but has paramagnetic characteristics. However, through heat treatment of the rotor, the paramagnetic characteristics of STS420-J2 were transformed into ferromagnetic characteristics, and the magnetic flux leaked to the shaft, increasing the leakage inductance. This increase in inductance caused a voltage drop and a decrease in the current of the generator while increasing the synchronous reactance, resulting in unsatisfactory performance. Subsequently, the shaft material was changed to Inconel 718, and the design and analysis were performed by applying the same design method. It was confirmed that Inconel 718 has a non-magnetic characteristics, and magnetic flux leakage to the shaft does not occur even when heat treatment of the rotor is performed. The experiment was conducted using a back-to-back system. As shown in Table 4, it was confirmed that the rated output, line-to-line voltage, phase current, phase resistance, and inductance were in agreement with the analysis, and the validity and reliability of the design method were verified.  
Figure 10: Line to Line voltage and phase current characteristics of Inconel 718 shaft material at load.  
Figure 9: Test bed and rotor assembly of high-speed PMSG.  
In this paper, a high-speed PMSG was designed using two different shaft materials considering the overhang effect and mechanical properties. Table 5 shows the material properties and cost of the designed PMSG. For the STS420-J2 material, due to its paramagnetic properties, one more analysis to predict the synchronous reactance after heat treatment is required. STS420-J2 material has lower rigidity than Inconel 718 material. However, considering the cost of about 14 times lower, the overall manufacturing cost can be reduced. In the case of Inconel 718, it has superior rigidity compared to STS420-J2 and has non-magnetic properties, so there is almost no error in analysis and experimentation. However, Inconel 718 is expensive and increases the overall manufacturing cost. As a result, STS420-J2 material is advantageous in terms of cost, while Inconel 718 material has high reliability, which is considered advantageous for PMGS design.  
J.-Y.C.; Conceptualization, review and editing, J.-LL; original draft preparation, formal analysis and validation, T.-K.B.; writing and formal analysis, H.-K.L.; validation and software, J.-H.W.; review and editing, J.N.; review and editing, All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1007353) and this research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2020R1A4A2002021). The authors declare no conflict of interest.
## 4 Discussion  
It was confirmed that the IRP-derived bed topography and radargram give information on detailed topography and the existence of water at ice bottom. The profile along the valley of the glacier confirmed that there is a large hill under anomalies A and B, where A is the uphill side and B is on top of it. Temporal change of velocity can be explained by the stick-slip mechanism at the interface of ice and bed. The existence of water at A is supported with high radar reflection comparable to that of the water in the hinge zone and the flat hydraulic head. Basal water in anomaly A might have lubricated the ice-bed interface, reducing the amplitude of stick-slip motion of ~1 cm/day. There is no such positive evidence of water in anomaly B lying at the top of the hill, having a relatively large amplitude of stick-slip motion of ~5 cm/day throughout the year. Crevasses were generated in B due to tensile stress caused by the underlying hill.  
The source of basal water in anomaly A could be either external or internal. External water could be supplied from surface or interglacial melt water [7]. If the surface melt water infiltration is the source of basal water, then the amplitude of anomaly should be reduced in the Antarctic summer, with increased lubrication of basal water and thus a reduction of the amplitude of stick-slip phenomenon [39; 40]. However, no seasonal tendency in velocity anomaly of A was observed as shown in Figure 5. Apart from the external sources of water, pressure melt can be an internal source of water, where water melts in the uphill and refreeze in the downhill side of the hill [41]. This may well explain the year-long low amplitude of the stick-slip mechanism in A, and relatively high amplitude in B. Other sources of water could be geothermal heat from the Mt. Melbourne volcano, which is located about 10 km from the study area. A study by [PERSON] et al. [42] detected a long-term slow surface change of the Mt. Melbourne volcano and suggested that the surface change was caused by a shallow depth of hot water. However, the small scale of circular anomalies isolated from the volcano weakens the possibility of geothermal heat being the source of water.  
The observation period in this study was only a year and a half, due to the limited availability of the COSMO-SkyMED one-day tandem InSAR pair for the study area. Long term observation of SAR data with a short temporal baseline is necessary to analyze the seasonal trend of the anomalies, the feasibility of the stick-slip mechanism, and the effect of basal water on ice velocity in more detail. The radargram provides much more information on reflecting targets than the ice bed topography, which is a byproduct of radargrams. It is also suggested that the flight paths of IRP survey need to be adjusted to pass the area of interest, which remains as a topic for further study.
## 4 Results and Discussion
### Transformer Condition Monitoring  
In the discipline of electrical engineering, transformer condition monitoring is the procedure of gathering and analyzing information regarding numerous transformer aspects to identify their condition and anticipate their potential for failure. This is accomplished by monitoring the trends and alterations in the critical parameters of transformers from their normal conditions [17]. Transformers are important assets in both electrical transmission and distribution networks. Their breakdowns can result in blackouts, endangering  
Figure 9: Type of research studies.  
people's lives and posing a risk to the environment. Furthermore, transformers are expensive equipment in a power system network. Effective condition monitoring facilitates the identification of transformers that are approaching failure and can enable data-driven, proactive maintenance strategies [23]. These transformers can be replaced under controlled circumstances to avoid catastrophic failures. The traditional condition monitoring techniques are manually performed, which presents a risk of inaccuracies due to human errors. This section presents critical parameters of transformers that can be monitored using IoT technology.
## 3 Results
### Analysis of Drought Characteristics of Stable Land Uses in the Shendong Mining Area
#### 3.3.1 Stable Region Meteorological Drought Characteristics  
A violin map combines the advantages of the boxplot and kernel density map, and can more intuitively distribute data and compare the distribution characteristics between different land uses. This helps us to find the trends in data more intuitively. In Figure 9, the drought characteristics in areas with stable land uses are illustrated. The figure indicates that the median of drought frequency of cropland is approximately 0.83 times per year, which is lower than that of grassland types with varying vegetation coverages. Among the various grassland types characterized by differing vegetation covers, those exhibiting higher medians of meteorological drought frequency were in the ranges of 30-45% and 45-60%, with a frequency of occurrence for both categories being 1.05 times per year. The category with vegetation coverage exceeding 75% exhibits a drought frequency of 0.85 occurrences per year. This suggests that an increase in grassland vegetation coverage correlates with a decrease in the frequency of meteorological droughts. The graph in the middle shows the duration of the meteorological drought. It can be found from the figure that the median duration of meteorological drought in croplands is much shorter than that in other grassland types, except for the high-coverage grasslands, as 5.75 months. In contrast, the median duration of drought in high-coverage grasslands was lower as 4.7 months. The median duration of drought in various grassland types with differing coverages ranged from 8 to 10 months, with low-coverage grasslands experiencing the longest drought duration of 9.75 months during periods of high drought intensity. On the contrary, the drought duration of croplands and high-coverage grasslands was more dispersed. In summary, increased vegetation cover in grasslands correlates with a reduced duration of meteorological dryness. This indicates that both vegetation coverages and land use types have certain effects on the duration of meteorological drought. The graph on the right shows meteorological drought intensity. Specifically, the median, quartile, and quartile quantiles of drought intensity of different land use types and different coverages in the study area had no significant differences. Only the medium-coverage grassland had a low value, and the meteorological drought intensity of other types was concentrated above 0.9, which indicated that the meteorological drought intensity in the study area was relatively stable and had low correlation with land use type and vegetation coverage.  
Figure 8: Areas with stable land uses in the Shendong mining area from 2001 to 2020.