esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Blockchain Technology
### Challenges in Blockchain-Based IoV  
Blockchain-based IoV architectures are predisposed to challenges such as security, trust and privacy concerns, performance issues, consensus algorithm optimization problems, and a selection of appropriate incentive mechanisms [8; 10; 25]. Another challenge faced by BC-based IoV systems is storage and scalability concerns [10; 27; 61]. Due to the voluminous amount of data generated in IoV systems, there is usually not enough storage capacity to accommodate these data [10]. Choosing the appropriate consensus mechanism can be one effective way of handling the scalability issues [27]. Proof-based consensus mechanisms do not suffer scalability concerns as individual components in the network do not require validation. Other issues with regards to appropriate choice of consensus mechanisms include fault tolerance level, mode of communication, and energy consumption requirements [27].  
The use of BC introduces concerns relating to encryptions that limit access to data [10]. Private BC only permits few authorized participants into the network, and this limits access to data. The use of public BC is limited, and often discouraged by researchers [8]. This is due to its open nature, which ultimately impacts the privacy and security concerns of the IoV system. Recent efforts ae now considering lightweight encryption schemes to lower the communication overhead [8; 99].  
Choice of Blockchain Architecture  
The appropriate choice of the blockchain platform for a particular application could critically impact the performance of the system [105]. For multi-layered architectures, testing utilizes several platforms that provide dedicated features to enhance adaptability. Some of the well-used blockchain platforms include Hyperledger, IOTA Tangle, Ethereum blockchain, etc.  
Researchers are projecting that ML or AI would be an efficient solution to the many BC-based IoV challenges [25], assisting, particularly, with the huge amount of generated data in the IoV system.
## 3 Results  
To describe the health index of the wind turbine operation state proposed in this paper, two 2 MW direct-drive wind turbine units (WT1 and WT2) of the same model on a wind farm were selected for study. SCADA data were selected from four days (7 September to 10 September); however, WT1 broke down suddenly due to yaw bearing tooth fault on 10 September. Some parameters of the wind turbines are shown in Table 2.  
Based on the SCADA data recorded during the normal operation of the wind turbine on the first day, a data relation standard model was established. Using wind speed and power data as an example, the relationship between wind speed and power data is obtained as follows:  
\[P_{\text{std}}(v)=5316.85-2618.04v+403.17v^{2}-17.34v^{3} \tag{17}\]  
If the window width is set at 24 h and the window increment is set at 1 h, the change rule of wind turbine generator operating health index is obtained via Equation (16), as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Rated power (kW) & 2000 & Cut-in wind speed (m/s) & 3.5 \\ Rotor diameter (m) & 82.6 & Rated wind speed (m/s) & 12 \\ Tower height (m) & 80 & Cut-out wind speed (m/s) & 25 \\ Rated rotor speed (rpm) & 17 & Maximum wind speed (m/s) & 70 \\ Blade weight (kg) & 6750 & Blade length (m) & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the 2 MW wind turbines investigated in this study.  
The window data of the 30 th and 50 th hours with their running state model curves are shown in Figure 8. The wind turbine unit was shown to operate normally at the 30 th hour, and the state model curve at this time was not very different from the standard model curve, showing fluctuations within the normal range. At the 50 th hour, the operation of the wind turbine generator set was abnormal, and the state model curve at that time differed from the standard model curve. However, it was difficult to observe the obvious difference between the two data scatter plots. This result also showed that the operation health index of wind turbine generator set in Equation (16) could describe a change in the operation state of the wind turbine generator set. In the authors' previous research [26], we used a small probability method to judge the operation state of the wind turbine. The statistical analysis of the wind turbine health index was carried out in normal operation phase to obtain the distribution function. Then, according to the hypothesis of small probability event, the threshold value of the health index of wind turbine abnormal state was determined. Compared with the small probability method, the proposed health index was more sensitive to the abnormal state of the wind turbine, and the calculation consumption was lower.  
To further analyze the factors that affect the operation health index of a wind turbine, the SCADA wind speed and output power data of the four days were processed with different window widths, window increments (i.e., time steps), and other window and modeling parameters.
## 4 Interactive Compression
### Interactive data compression  
[PERSON] and [PERSON] in [7] consider the following problem: \"Given two random variables \(X\) and \(Y\) with entropies \(H(X)\) and \(H(Y)\) and joint entropy \(H(X,Y)\), and two persons \(P_{X}\) and \(P_{Y}\), such that \(P_{X}\) knows \(X\) and \(P_{Y}\) knows \(Y\), suppose that the two persons wish to communicate over a noiseless two-way channel so that at the end of the communication process they both know \(X\) and \(Y\). How many bits on the average must they exchange and what are the optimal codes?\"  
They prove that at least \(H(X|Y)+H(Y|X)\) bits must be exchanged on the average and that \(H(X,Y)+2\) bits are sufficient and that if the joint probability \(p(x,y)\) is uniform then the average number of bits needed is close to \(H(X|Y)+H(Y|X)\). They also discuss randomized protocols that can reduce the amount of communication if some probability of communication error is acceptable.
## 2 Methodology
\[V_{6}=Ve+V_{45}sen\theta_{2}+V_{56}sen\varphi_{2}=Ve+V_{45}sen\theta_{2}+V_{45} \frac{cos\theta_{2}}{cos\varphi_{2}}sen\varphi_{2}=Ve+V_{45}(sen\theta_{2}+cos \theta_{2}lg\varphi_{2}) \tag{9}\]  
\[V_{6}=V_{1}\left[\left(\frac{tg\theta_{1}+tg\varphi_{1}}{1-tg\alpha g\theta_{ 1}}\right)+\left(1+\frac{\left(tg\theta_{2}+\frac{tg\theta_{1}+tg\varphi_{1}}{ 1-tg\alpha g_{1}}\right)\cdot lg\alpha_{2}}{1-tg\alpha_{2}lg\theta_{2}}\right) \left(tg\theta_{2}+tg\varphi_{2}\right)\right] \tag{10}\]  
where \(V_{1}=Ve\) is the input velocity to each module. Applying the UBT will calculate the dimensionless ratio \(P/2k\) to obtain the value of the final load. \[\frac{dW}{dt}=\dot{W}=k\cdot\omega\cdot\left[\overline{AD}\cdot v_{45}+ \overline{DB}\cdot v_{56}+m\cdot\overline{AB}\cdot v_{5}\right]=P\cdot\omega \cdot b_{2}\cdot V_{1} \tag{11}\]  
and considering that:  
\[ \begin{array}{ll}tg\theta_{2}=\frac{v_{2}}{h_{2}};tg\theta_{1}&=\frac{v_{1} }{h_{1}};tg\varphi 1=\frac{b_{1}-v_{1}}{h_{2}};sent\theta_{2}=\frac{v_{2}}{ \sqrt{x_{1}^{2}+h_{2}^{2}}};cos\theta_{2}\\ &=\frac{b_{2}}{\sqrt{x_{2}^{2}+h_{2}^{2}}};\ sen\varphi_{2}=\frac{b_{2}-v_{2}}{ \sqrt{h_{2}^{2}+(b_{2}-x_{2})^{2}}};cos\varphi_{2}\\ &=\frac{h_{3}}{\sqrt{h_{3}^{2}+(b_{2}-x_{2})^{2}}};\end{array}  \tag{12}\]  
we obtain:  
\[\dot{W}=k\cdot\omega\cdot\left[\frac{x_{2}}{sen\theta_{2}}\frac{V_{1}+V_{5}sen \alpha}{cos\theta_{2}}+\frac{(b_{2}-x_{2})}{sen\varphi_{2}}\frac{V_{1}+V_{5}sen \alpha_{2}}{cos\varphi_{2}}+m\cdot\frac{b_{2}}{cos\alpha_{2}}\cdot\frac{V_{1} \cdot\left(tg\theta_{2}+\frac{tg\theta_{1}+tg\varphi_{1}}{1-tg\alpha g\theta _{1}}\right)}{cos\alpha_{2}-sen\alpha_{2}lg\theta_{2}}\right] \tag{13}\]  
The modular approach has been implemented with the possibility of incorporating friction both by adhesion (Tresca, \(m\)) (Equation (14)) or sliding (Coulomb, \(\mu\)) (Equation (15)) [16], responding in each case to a different expression. \[\begin{array}{ll}\frac{P}{2k}=\frac{1}{2b_{2}}\cdot\left[\left(\frac{x_{2}^{ 2}+h_{2}^{2}}{h_{2}}+\frac{(b_{2}-x_{2})^{2}+h_{2}^{2}}{h_{3}}\right)\left(1+ \frac{\left(tg\theta_{2}+\frac{tg\theta_{1}+tg\varphi_{1}}{1-tg\alpha g\theta _{  
As stated above, the connection between modules is defined by the exit velocity from one module that is the same than the entrance velocity to the next one, denoted by \(V_{e}\), and whose value can be determined by Equation (16).  
\[V_{6}=V_{1}\left[\left(\frac{tg\theta_{1}+tg\varphi_{1}}{1-tg\alpha_{2}g\theta_ {1}}\right)+\left(1+\frac{\left(tg\theta_{2}+\frac{tg\theta_{1}+tg\varphi_{1} }{1-tg\alpha_{2}g\theta_{1}}\right)\cdot tg\alpha_{2}}{1-tg\alpha_{2}tg\theta _{2}}\right)\left(tg\theta_{2}+tg\varphi_{2}\right)\right] \tag{16}\]  
The calculation of the ratio \(P/2k\) combining the influence of both modules (\(P_{A+B}/2k\)) is determined by Equation (17), where some weight factors are included, considering the geometry of each module by means of the width. \[\frac{P_{A+B}}{2k}=\frac{\frac{P_{A}}{2k}\cdot b_{1}+\frac{P_{B}}{2k}\cdot b_{2 }}{\left(b_{1}+b_{2}\right)} \tag{17}\]
## 2 Basic Theory
### Skeleton Simulation in Single-Light-Source Scenario
#### 2.2.2 Temporal-Spatial Aggregation Method
Based on the translation components \(\Delta u_{(t_{i},j_{c})}\) and \(\Delta v_{(t_{i},j_{c})}\), a translation transformation matrix \(A_{t_{i}\to j_{c}}\) can be established for the translation calculation as shown in Equation (29d). For the convenience of further illustration, the extraction procedure of matrix \(A_{t_{i}\to j_{c}}\) is simplified in Equation (30). The function \(Par\) is introduced to present translation matrix extraction procedure illustrated from Equation (29a) to Equation (29d). \[A_{t_{i}\to j_{c}}=Par(\{{{\left\{{{{Jp_{t_{i}}^{k}}}\right\}},{{ \left\{{{Jp_{j_{c}}^{k}}}\right\}}}}}\}) \tag{30}\]  
In order to maintain a consistent expression system, the translation transformation is presented in the same format with Equations (14) and (15). When the translation transformation in Equations (31) and (32) is deployed synchronously on the light source \(S_{t_{i}}\) and human shadow contour \(Sh_{t_{i}}\) for each frame, all the transformed human shadows \(Sh_{t_{i}}^{\prime}\) fit the spatial coordinates of the central human pose \(P_{t_{c}}\) in each simulated scenario. \[Sh_{t_{i}}^{\prime}=F(Sh_{t_{i}},A_{t_{i}\to j_{c}}) \tag{31}\]  
The position of light source \(S_{t_{i}}\) applies the same transformation \(T_{t_{i}\to j_{c}}\) along with the related shadow \(Sh_{t_{i}}\), simulating multiple light sources \({S^{\prime}}_{t_{i}}\) in the single frame. \[S_{t_{i}}^{\prime}=f(S,A_{t_{i}\to j_{c}}) \tag{32}\]  
The temporal-spatial aggregation procedure illustrated above is presented in Algorithm 3. With more than two positional distinctive light sources simulated in the same frame, the skeleton synthesis procedure presented in Equation (24) can be applied on the simulated human shadow set \(\{Sh_{t_{i}}^{\prime}\}\) and the corresponding light source set \(\{S_{t_{i}}^{\prime}\}\). Based on Equation (33), the skeleton \(Sk_{j_{c}}\) of pose \(P_{j_{c}}\) can be synthesized under a single light source scenario as shown in Figure 4d. The detailed human skeleton synthesis procedure under a single light source scenario is illustrated in Section 3. \[Sk_{j_{c}}=Syn(\{Sh_{t_{i}}^{\prime}\},\{S_{t_{i}}^{\prime}\}) \tag{33}\]
## 4 Results and Discussion
### Stability Map of the Static System
Our previous study [27] demonstrated that the stable effect due to system mass and the unstable effect by channel-to-channel interaction could prevail in a multi-channel system. In this case, it seems that the stable effect would dominate over the unstable effect. However, when a more asymmetric heat flux distribution of 1.4:1.0:0.6 is set, the parallel three channel system would become more unstable than the single channel one. This resulted from the more dramatic unstable channel-to-channel interaction being triggered by the more difference among channels. In addition, the normal operating state of N\({}_{P\text{-}SUB}\) = 1.55 and N\({}_{TPC}\) = 3.232, as denoted as S1 in Figure 3b, is substantially distant from the stability boundaries of all systems. This infers that it is a very stable operating point.
## 4 Performance Evaluation
### Simulation Results
#### 4.2.1 Performance Comparison Under Different Proportions of Selfish Nodes  
Figures 4 and 5, respectively, present the delivery ratio and average delivery delay of each scheme under varying proportions of selfish nodes. The results indicate that as the proportion of selfish nodes increases, the performance of all opportunistic transmission schemes shows a declining trend. However, the proposed opportunistic transmission scheme for integrated UAV-ICV networks exhibits the smallest performance degradation. When the proportion of selfish nodes increases from 20% to 50%, the delivery ratios of the proposed scheme, Scheme 1, Scheme  
it prioritizes nodes with higher cooperation levels for relay transmission. Furthermore, the proposed scheme fully considers the energy and cache size of nodes, effectively improving the delivery ratio and reducing the average delivery delay.  
Based on the aforementioned findings, it is evident that in VDTNs, the average delivery delay frequently exceeds 1000 s, attributed to the dynamic characteristics of their operational environment. The reason is that integrated UAV-ICV networks are designed to handle intermittent connectivity and high mobility, where continuous communication cannot be guaranteed. Particularly, UAVs and ICVs are in constant motion, leading to frequent changes in the network topology and connectivity, which results in disconnections. VDTNs typically rely on \"store-carry-forward\" transmission mechanisms, where data are temporarily stored at relay nodes until a viable communication opportunity arises, at which point the data can be forwarded. Despite such high delays, VDTNs remain valuable in specific scenarios, where the timely delivery of information is not always critical, but ensuring high reliability and extensive coverage is critical. For example, VDTNs can be used in applications such as real-time traffic management and entertainment information services, where data can be delivered in a delayed manner but still provide valuable services to users. The main difference between VDTNs and traditional direct data transmission is that VDTNs do not require continuous end-to-end connectivity.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 3 Materials and Methods  
### Dataset  
For natural image dehazing, there are some datasets for training the dehazing network models, for example, FRIDA [33], Hazy Cityscapes datasets [34], D-Hazy [35] and RESIDE [36]. The RESIDE dataset includes a large amount of indoor and outdoor clear images and synthetic hazy images. It has been widely accepted as a benchmark dataset for natural image dehazing research in recent years. Due to the huge difference between remote sensing images and natural images, these datasets cannot be directly applied to dehazing remote sensing images. At present, datasets for remote sensing image dehazing mainly include the haze detection and removal dataset [37], Haze1K [27] and RICE [38]. These datasets include some non-uniform haze but are limited to the visible light band and have no multispectral data. In this research, we build our dataset by collecting Sentinel-2 images from January 2021 to January 2022, from 112\({}^{\circ}\) E, 36\({}^{\circ}\) N in central China to 120\({}^{\circ}\) E and 29\({}^{\circ}\) N in eastern China. The band information of Sentinel-2 is shown in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sentinel-2 Bands** & **Central Wavelength (\(\upmu\)m)** & **Resolution (m)** \\ \hline Band 1—Coastal Aerosol & 0.443 & 60 \\ Band 2—Blue & 0.490 & 10 \\ Band 3—Green & 0.560 & 10 \\ Band 4—Red & 0.665 & 10 \\ Band 5—Vegetable Red Edge & 0.705 & 20 \\ Band 6—Vegetable Red Edge & 0.740 & 20 \\ Band 7—Vegetable Red Edge & 0.783 & 20 \\ Band 8—Near Infrared & 0.842 & 10 \\ Band 8—Vegetable Red Edge & 0.865 & 20 \\ Band 9—Water Vapor & 0.945 & 60 \\ Band 10—Shortwave Infrared—Cirrus & 1.375 & 60 \\ Band 11—Shortwave Infrared & 1.610 & 20 \\ Band 12—Shortwave Infrared & 2.190 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 bands.  
We chose 9 bands of 10 m and 20 m, which are commonly used in earth observation, including Band 2, Band 3, Band 4, Band 5, Band 6, Band 7, Band 8, Band 11 and Band 12 for research. We also selected 30 sets of hazy and haze-free image pairs as experimental data. Figure 2 shows an example of those image pairs.  
The image size of 10 m resolution images is 10,980 \(\times\) 10,980, while the image size of 20 m resolution is 5490 \(\times\) 5490. We first chose the hazy areas of the collected images and the corresponding areas of the haze-free images. We then resized the image size of the 10 m resolution training data to be \(1024\times 1024\) and resized the 20 m resolution training data to be \(512\times 512\). The selected area was randomly cropped. We finally obtained 1500 sets of 9-band hazy and haze-free data pairs as the training data set for this research.
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments  
### Baseline Models  
In our experiments, we compare the proposed ThinDyUNet model with four state-of-the-art semantic segmentation models: UNet [41], pyramid attention network (PAN) [42], pyramid scene parsing network (PSPNet) [43], and TransUNet model [44]. Each model is re-implemented and retrained using the UAV semantic segmentation dataset. Details of each model are shown in the next subsections.
## 2 Potential magnetic sensors for space applications
The next step in the development of magnetic sensors was the integration of sensing elements with MEMS-Micro-Electro-Mechanical Systems technology. This was the approach used by the Japanese companies Aichi Steel Corp and Aichi Micro Intelligent Corp who developed a MI-based sensor with a 1 nT resolution using MEMS technology [43]. The MI effect consists in the variation of the penetration depth of a ferromagnetic material when the magnetic field changes. Finally there are groups that have developed real MEMS-based magnetic sensors [44] like resonant silicon magnetometers based on vibrating cantilevers [45-49] and torque magnetometers [50]. These sensors, which are not COTS, detect mechanical forces or torques on thin magnetic films deposited on a cantilever by capacitive, inductive or optical methods [51]. This kind of device is highly sensitive being able to measure magnetic moments in the order of 10\({}^{-17}\) Am\({}^{2}\) (and 10\({}^{-23}\) Am\({}^{2}\) at cryogenic temperatures). However these techniques require very controlled set-ups that cannot be easily achieved in the normally noisy and changing environments of spacecrafts. Aside from the magnetic head of the sensors, their front-end has also experienced a huge evolution lately. Until this century, electronic devices could have either analog or digital interfaces but in general the integrated front-end was composed of discrete ICs-Integrated Circuits. With the invention of the FPGA- Field Programmable Gate Array in 1984 by [PERSON], this technology of programmablelogic was implemented in some magnetic sensors in laboratory developments and also in a commercial level. Nowadays FPGA and ASIC-Applied Specific Integrated Circuit compete for miniaturization of the conditioning electronic of the sensors offering a low power solution. The ASIC option seems to be, for instance, the approach of Honeywell for the AMR sensors for mobile phone technology accurate electronic compassing, magnetometry, telematics, vehicle detection, position sensing and security systems. Both have advantages and disadvantages. FPGAs are more versatile but there is only a few work done with analogic FPGAs. Besides, so far the density of ASICs is higher to that of FPGAs. What is clear is that this line of miniaturization is the line of the future for the conditioning electronic of both COTS and highly qualified devices.
## 4 Discussion
### Limitations and Recommendations  
There are limitations to our study. Data collection was from one cohort in the integrated curriculum, while it was collected from two cohorts in the traditional curriculum. The synergistic effect of the two-year data may have reduced the difference observed between the two curriculum exposures. Since a convenient sampling method was used in the three assessments, the results may not be a true reflection of the competencies of the entire class. In addition, the 2019 cohort had two prior exposures to the questionnaire in their second-year pre and post-exposure assessments. The repeated exposures may have given them an edge over the 2017 and 2018 cohorts who completed the questionnaire once in their final year of study. As the assessments did not count for course grades, the participants' stakes were low, which may have affected their performance. Infant growth assessment skills were examined as a written assessment rather than the traditional objective structuredclinical examination or practical evaluation due to inadequate manpower. The assessment process may undermine the results of that section.  
Clinical nurse practitioners traditionally offer MCH at the primary care level. Increased multidisciplinary training would develop the competencies of entry-level graduates towards team-based care for South Africa's decentralised district-based primary care, which is crucial for health system strengthening [40].  
Further research to assess practising pharmacists' MCH knowledge, attitude, and practices in South Africa should be carried out to inform the introduction of a continuing professional development programme.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods
### Description of Testing Tools  
In the context of C-ITSs, precise validation of message content and adherence to standards are critical for ensuring system interoperability and safety. To address these needs, specialized testing tools have been developed. These tools are primarily designed to verify the compliance of messages with relevant standards, as well as to facilitate the transmission and reception of messages under controlled conditions. In this section, we provide an overview of the key tools used in our testing framework, focusing on their specific applications and functionalities.
## 4 Simulation and Analysis  
For the coupled structures of the VLL and PLL, most of the dynamics are eliminated by the VLL, while the remaining small portion of the frequency and phase differences are eliminated by the PLL. According to the analysis in [31], the serial structure of the VLL and PLL has better tracking performance than a parallel structure under high dynamic  
Figure 8: Implementation for HD-VLL-NCO.  
conditions. Therefore, we first simulate different bandwidths of the HD-VLL and select an appropriate bandwidth. Next, the VLL and HD-VLL are both simulated for the purpose of comparing their dynamic adaptations and steady-state tracking performance. Then, the VLL-SA-PLL and HD-VLL with a serially assisted PLL (HD-VLL-SA-PLL) are simulated to analyze the effect of two VLLs on the PLL's tracking performance. Finally, the HD-VLL-NCO is simulated and compared with the HD-VLL.  
The simulation parameters are set as follows: the DS code rate is 10 Mchips/s, the loop update period is 5 ms, the carrier center frequency is 1.5 GHz, the minimum hopping interval is 50 kHz, the number of hopping points is 4096, and the initial velocity error for tracking is 10 m/s.