esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 8 Potential Applications in Other Fields
### Healthcare  
In healthcare, data security, privacy, and integrity are paramount, particularly when dealing with sensitive patient information and medical records. AE-PBFT can provide a robust foundation for blockchain-based healthcare systems by offering secure and efficient consensus mechanisms for managing electronic health records (EHRs), medical research data, and insurance claims. The node trust management model of AE-PBFT ensures that only trusted participants, such as hospitals, clinics, and insurance companies can access or contribute to the blockchain. Malicious nodes attempting to tamper with or misuse patient data are quickly identified and excluded, ensuring data integrity and patient privacy. Furthermore, the dynamic consensus group mechanism can adapt to varying network conditions, such as during large-scale medical emergencies, ensuring consistent performance and fault tolerance. AE-PBFT's low communication overhead and high throughput make it ideal for real-time data sharing between healthcare providers, enabling seamless collaboration and improving patient care outcomes.
## 4 Proposed Model of SACFIR and SAMCFIR
### Network Forwarding Phase (NFP) of SACFIR and SAMCFIR
Every commodity has the following elements which must be be defined:  
\(\tau^{l}_{i,j}\)= Number of transmissions of \(l\) routed on \(E_{FECHs}\) from \(i\) to \(j\);  
\(\mathbf{T}^{l}\)= Total number of transmissions, transmitted on \(V_{FECHs}(S^{l},D^{l})\);  
\(\tau_{i,j}=\sum_{l=1}^{L}\tau^{l}_{i,j}\) Total number of transmissions on communication link from \(i,j\)  
So, every \(FECHs_{V}\) has following communication responsibilities:  
\[X(FECH_{V})=\{j\in V_{FECHs}\mid(CH_{V},j)\in E_{FECHs}\} \tag{34}\]  
\[Y(FECH_{V})=\{j\in V_{FECHs}\mid(FECHs_{V},j)\in E_{FECHs}\} \tag{35}\]  
while, \(X(FECH_{V})\) identify selected \(FECHs_{V}\)that can be accessed by \(FECH_{V}\), similarly \(Y(FECH_{V})\) are \(FECHs_{V}\) that can communicate directly with \(FECH_{V}\) in the communication area. The enhancement of total deliverable traffic introduces a parameter \(\phi\) that indicates total traffic fraction of \(\mathbf{T}^{l}\) communicated within network. The optimum \(\phi^{*}\) is the particular value of \(\phi\) that satisfies the following objective function:  
\[Max\left\{\sum_{l=1}^{L}\phi\times\mathbf{T}^{l}\right\} \tag{36}\]  
Further, conservation equations and non-negativity must be satisfied by all flows:  
\[\sum_{j\in X(FECH_{V})}\tau^{l}_{i,j}-\sum_{j\in Y(FECH_{V})}\tau^{l}_{i,j}= \begin{cases}\phi\times\mathbf{T}^{l}&if\ S^{l}=FECH_{V}\\ -\phi\times\mathbf{T}^{l}&if\ D^{l}=FECH_{V}\\ 0&otherwise\end{cases}  \tag{37}\]  
\[\tau^{l}_{i,j}\geq 0\ for\ (i,j)\in E_{FECHs} \tag{38}\]  
\[\phi\in[0,1] \tag{39}\]  
From the above equation it is clear that positive transmission indicates source \(FECH_{V}\), negative transmission represents destination \(FECH_{V}\) and the zero value represents intermediate \(FECH_{V}\). Modeling interference to judge the capacity of the first cluster C of FECHs is initialized by adjacent \(\mathbf{S}^{l}_{i}\) to \(FECH_{V}\)  
\[\mathbf{C}^{l}_{1}=\{(i,j),j\in V_{FECHs}\} \tag{40}\]  
Similarly, for every node j A(i),cluster \(\mathbf{C}(i,j)^{2},\mathrm{j}\) contains every path that has one of its destinations in A(i) and has no relation with \(\mathbf{C}(i,j)^{1}\):  
\[\mathbf{C}(i,j)^{2}=\{(j,k)\mid k\
eq i\} \tag{41}\]  
From above clusters, it is convenient for the SDN controller to develop a group of \(G_{FECHs}\) generated by all intermediate nodes within two hops from the source \(i\), which can be represented by:\[G_{CH_{i}}=\left\{C_{1}^{l}+\bigcup_{j}C_{(i,j)^{2}}\right\} \tag{42}\]  
\[\sum_{k=1}^{K}\sum_{(i,j)\in C_{i}^{b}}\tau_{i,j}^{l}+Max\left\{\sum_{k=1}^{K} \sum_{(i,j)\in C_{i}^{b}}\tau_{i,j}^{l}\right\}\leq F_{i}\forall G_{FECHs_{i}} \tag{43}\]  
From the above outcomes, the SDN central controller is able to transfer the nonlinear constraint into a group of linear constraints and develops a linear model for our problem. In this way, during the forwarding phase, SDN controller contributes at the BS level and invests a lot of resources on the path selection computational function to manage WSNs nodes efficiently at the infrastructure level.
# Low-Profile Harmonic Transponder for IoT Applications
## 1 Introduction
Thus, this diode is considered to be one of the most sensitive diodes at zero bias and is, thus, suitable for the employed application. The diode is matched to the antennas, using the T-matching network; thus, the transponder is of a single layer. No lumped components are used, so the weight is only limited to the diode and carrier substrate. To assess the diode CL, the equivalent transponder circuit is extracted. Moreover, biasing the diode is also elaborated upon an alternative solution to reduce the diode CL. The structure of this paper is as follows; Section 2 introduces the proposed transponder prototype and the corresponding circuit model. Section 3 investigates the diode conversion loss. The experimental results are demonstrated in Section 4. Finally, Section 5 concludes the paper.
## 3 Experiments and Results
### Experimental Results
#### 3.2.2 Results of Cooling Batteries Onoard Drones with/without a Fuel Stick
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flight** & **Battery Temperature** & \(\Delta T(^{\circ}C)=B_{2\text{Test}_{k}}-B_{2\text{Test}_{k+1}},K\geq 1\) & **Flight** & **Flight** \\ \hline
**Test** & **Takeoff** & **Landing** & **(Decrease in Battery Temperature** & **Time** & **Time** & **Gained** \\
**Name** & \(B_{1}\)\({}^{\circ}C\) & \(B_{2}\)\({}^{\circ}C\) & **Using the Fuel Stick)** & **(minutes)** & **(seconds)** \\ \hline
1 & 30.11 & 52.63 & N/A & 6.58 & N/A \\
2 & 30.33 & 48.27 & 4.36 & 7.06 & 28.8 \\ \hline
3 & 24.1 & 45.9 & N/A & 6.44 & N/A \\
4 & 26.9 & 37.8 & 8.1 & 6.61 & 10.2 \\ \hline
5 & 23.4 & 48.6 & N/A & 5.62 & N/A \\
6 & 28 & 44.0 & 4.6 & 6.31 & 41.4 \\ \hline
7 & 29.2 & 46.4 & N/A & N/A & N/A \\
8 & 34 & 42.7 & 3.7 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of various tests conducted with batteries of various capacities, from different manufacturers. Figure 12: Flight test 1—flight parameters, with and without fuel cell. Figure 13: (**a**) Flight test 5—temperature of battery without fuel stick-based cooling; (**b**) Flight test 6—temperature of battery with fuel stick-based cooling. Flight tests 5 and 6—decrease in battery temperature via fuel stick-based cooling. Images captured by thermal imaging device 2.
## 3 Case Study
### Casting Mold Liquid Surface Segmentation Experiment and Result Analysis
#### 3.2.2 Training Strategy  
The training strategy proposed in this paper for segmenting casting mold liquid surfaces using the improved DeepLabV3+ primarily involves two steps:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Serial Number** & **Casting Date** & **Recording Duration** & **Whether Liquid Aluminum Leakage Occurs** \\ \hline
1 & 11 March 2023 & 1 min 20 s & Yes \\
2 & 16 March 2023 & 50 s & No \\
3 & 21 March 2023 & 50 s & Yes \\
4 & 7 April 2023 & 56 s & Yes \\
5 & 13 April 2023 & 48 s & Yes \\
6 & 15 April 2023 & 2 min 10 s & Yes \\
7 & 18 April 2023 & 53 s & No \\
8 & 20 May 2023 & 1 min 43 s & Yes \\
9 & 28 May 2023 & 2 min 15 s & Yes \\
10 & 5 June 2023 & 58 s & No \\
11 & 20 July 2023 & 1 min 12 s & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Partial dataset information.  
Figure 9: Research objects and data collection examples. (**a**) Diagram of the casting workshop. (**b**) Camera installation location. (**c**) Monitoring area.  
* Model pre-training: This paper adopts a transfer learning strategy for feature extraction, as illustrated in Figure 10. The model undergoes pretraining on the VOC dataset to create pretraining files in.pth format, after which the pre-trained weights are loaded into the model.
* Model training: The imported pre-trained model weights are used as input for retraining with the casting mold liquid surface dataset. The training process continues until the model reaches convergence.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 5 Conclusions  
This work presented a compression algorithm combining principal component analysis (PCA) and discrete wavelet transform (DWT) for improved data compression. The main algorithms are supported by thresholding, quantization, and entropy encoding strategies that prune less representative data from the dataset. We discussed the fundamentals of data compression based on the involved algorithms and the proposed algorithm combining them. The proposed method was then evaluated on a dataset of seismic data collected during an oil and gas survey leading to compression ratios of up to 31:1 while preserving relevant characteristics of data in frequencies of interest in the ranges of 0-20 Hz, 15-65 Hz, and 60-105 Hz, even though the compression leads to high reconstruction errors in total.  
Unfortunately, not all configurations of the algorithm produce better compression ratios than their individual counterparts, meaning that the algorithm must be tuned for the specific dataset being compressed. However, this also means that the user can adjust how much reconstruction error the compression can tolerate. In essence, the compression strategy outlined in this study streamlines the management of the extensive data volumes generated by seismic reservoir monitoring surveys carried out by oil and gas companies. However, further work is required to demonstrate that the information loss from aggressive compression does not significantly interfere with downstream data processing to extract pertinent insights from the seismic data acquired during such surveys.  
M.M.d.L., conceptualization, writing--original draft preparation, review, and editing; J.L.R., writing--review and editing; M.W., writing--review and editing; A.A.F., supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by Petroleo Brasileiro S.A. under grant agreement no. 2022/0082 and the Fundacao de Amparo a Pesquisa e Inovacao do Estado de Santa Catarina (FAPESC).  
The data used in this paper are part of the Jubarte Permanent Reservoir Monitoring available at [26].  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 7: Reconstruction difference of PCA + DWT + LZW + quantization, 40 principal components energy threshold = 0% quantization = 2%.
## 3 Methodology
### The Multi-Head Attention Mechanism  
Multi-head attention [23] is a key technology in deep learning for processing sequence data. Utilizing multiple attention heads in parallel allows the model to learn and capture diverse relationships and patterns across the different subspaces of the input sequence. This mechanism enhances the model's ability to express sequence data, making it particularly useful for tasks with complex relationships, such as natural language processing (NLP). Compared to single-head attention, multi-head attention can flexibly focus on multiple aspects of the input sequence, thereby improving the model's representational power.  
In this study, we employed a 12-head attention mechanism due to its ability to handle various features of the input sequence simultaneously, capturing richer and more diverse semantic information from the text. The decision to use 12 attention heads was based on a balance between the computational efficiency and the model's performance. Experimental validation showed that this design achieves the best performance in capturing complex behavioral patterns in telecom fraud detection. Additionally, the widespread success of multi-head attention in NLP tasks further supports its effectiveness in learning intricate relationships, providing a solid theoretical foundation for our approach. Equations (1) and (2) are the calculation formulas for the multi-head attention mechanism:  
\[MultiHead(Q,K,V)=Concat(head_{1},head_{2},...head_{12})W^{O} \tag{1}\]  
\[head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}),i=1,...12 \tag{2}\]  
Here, \(Q\), \(K\), and \(V\) represent the query, key, and value, respectively; \(W_{i}^{Q}\), \(W_{i}^{K}\), and \(W_{i}^{V}\) represent the corresponding weight matrixes, respectively; \(W^{O}\) represents the output weight matrix; and \(head_{i}\) represents the output of the \(i\)-\(th\) head.
# Simulating Ecological Functions of Vegetation Using a Dynamic Vegetation Model
## 2 Materials and Methods
### Calculation of Ecosystem Service Functions  
Referring to related studies on the ecological effects of vegetation on the LP [37], the ecological effects of vegetation on the LP was calculated based on the annual average SC, CS, and WC.  
The average annual SC of the vegetation (\(\Delta A\), t\(\cdot\)ha\({}^{-1}\)yr\({}^{-1}\)) was calculated based on the soil loss equation [38], as follows:  
\[\Delta A=R\times K\times L\times S\times(1-C\times P) \tag{1}\]  
where \(R\) is the rainfall erosivity factor (M\(\cdot\)mm\(\cdot\)ha\({}^{-1}\)\(\cdot\)h\({}^{-1}\)\(\cdot\)yr\({}^{-1}\)), calculated using down-scaled monthly precipitation data.  
\[R=\sum_{1}^{12}\left(1.735\times 10^{(1.5\log_{10}{(\frac{p^{2}}{p})}-0.8188)}\right) \tag{2}\]  
where \(pi\) represented the monthly average precipitation, \(p\) represents the average annual precipitation, and \(K\) is the soil erodibility (t\(\cdot\)ha\({}^{-1}\)\(\cdot\)h\(\cdot\)ha\({}^{-1}\)\(\cdot\)M\({}^{-1}\)\(\cdot\)mm\({}^{-1}\)). The data were obtained from the National Geographic Resource Science SubCenter, National Earth System Science Data Center, National Science & Technology Infrastructure of China ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn))) (accessed on 15 May 2021). \(L\) and \(S\) are the slope length factor and slope factor, respectively, based on the digital elevation model data of the study region, which were obtained using the terrain factor calculation tool. \(C\) is the cover-management factor, which was quantified according to its mathematical relationship with the simulated LAI and the vegetation coverage [39]:  
\[\left\{\begin{array}{cc}C=1&f_{c}=0\\ C=0.6508-0.3436\lg f_{c}&0<f_{c}<78.3\%\\ C=0&f_{c}\geq\ 78.3\%\end{array}\right. \tag{3}\]  
where \(fc\) is the vegetation coverage:  
\[f_{c}(\theta)=1-e^{-G(\theta)LAI/\cos\theta} \tag{4}\]  
\(G(\theta)\) is the projection of the leaves in the incident direction of the sun, which characterized the light interception ability of the leaves; \(\theta\) is the incident zenith angle of the sun. When the vegetation leaves are randomly distributed in a spherical shape, \(G(\theta)=0.5\). By simulating the LAI, the vertical vegetation coverage of forest, shrub, and grass can be accurately estimated [40]. \(P\) is the support practice factor, which was assigned a value of 1 in this study [39].  
The CS of the vegetation was calculated using the GPP data (kg\(\cdot\)m\({}^{-2}\)) of the model simulation results.  
Soil moisture was negligible when calculating the multi-year mean water conservation (WC) (mm), which was quantified by the following formula [41]:  
\[WY=P-ET \tag{5}\]  
where \(P\) is the annual average precipitation, calculated using monthly precipitation data, and \(ET\) is the result simulated by the model.
## 2 Faraday Effect  
In 1845, [PERSON] discovered a difference in refractive index of glass for left-handed and right-handed circularly polarized light induced by an external magnetic field. [PERSON] showed in 1854, that the angle of rotation of linearly polarized light is proportional to the strength of the magnetic field and the cosine of the angle between the field and the propagation direction of the light wave. This rotation can be expressed mathematically by:  
\[\theta_{f}=\int_{L}\
u\overline{B}\cdot\overline{dl} \tag{1}\]  
where, \(V\) is the material Verdet constant, which is both dispersive and temperature-dependent [1, 2], \(B\) is the magnetic flux density vector and \(dl\) is the differential vector along the direction of propagation. This effect is called the Faraday effect or linear magneto-optic effect and can be used to build optical current sensors. Figure 1 illustrates the polarization rotation due to a parallel external magnetic field on a magneto-optical material, such as, glass.  
The Faraday effect is distinct from the inherent circular birefringence (called optical activity) that appears in some materials, in that its sign depends on the direction of the magnetic field with the respect to the direction of propagation of the light [3]. That is to say, it is non-reciprocal. Therefore, if the same light passes through the same medium, but with opposite propagation direction, the rotation of polarization will be cumulative  
The Faraday effect is present, to some extent, in all materials and its characteristics are related to the other magnetic properties of the material. It varies with temperature, much more in paramagnetic and ferromagnetic than in diamagnetic materials. It is linear with magnetic field in diamagnetic materials but saturates in ferromagnetic materials, and its magnitude generally diminishes with increasing wavelength.  
Figure 1: Faraday effect in linearly polarized light.
## 3 Metasurface Antennas in Small Cavities for Telemetry Applications  
Very small cavity sizes are considered here, namely of 20 and 16 mm. They are intended to be integrated into small flying vehicles, and therefore must be extremely mechanically robust to support shocks and high accelerations.
## 5 A Note on Orbit Fitting and Orbit Extrapolation Accuracies
### Accuracy of Orbit Fitting  
The set of 15 F/NAV broadcast parameters (Section 2), including six Keplerian and nine perturbation corrections, has limited ability to reproduce true dynamic orbits. The orbit error is expected to be small for a short fitting interval and increase with interval length. Figure 1a shows the average accuracy over the entire constellation for a fit interval of 10 min. The orbit error is decomposed in the radial, along-track and cross-track components. Over such short intervals, the broadcast orbit model is very accurate, with fitting errors below the mm-level.  
Figure 2a shows the maximum recorded Root-Mean-Square Error (RMSE) over the entire constellation for fitting intervals of 10-120 min, while Figure 2b presents results for fitting intervals of 2-6 h.  
The maximum 3D (total) fitting error grows from a cm-level for two-hour intervals, a dm-level for a typical broadcast fit interval of 4 h, to an m-level when using 5 to 6 h intervals. These plots show that a cm-level accuracy is only guaranteed within 1 to 2 h fitting intervals. Therefore, any orbit determination method based on this orbit parametrization is always affected by an error equal to or larger than the orbit fitting error.  
Figure 1: Average absolute post fit error and extrapolation error for the 24 MEO satellites when fitting 15 perturbed Keplerian parameters to dynamic orbits. The fitting interval is set to 10 min. (**a**) Post fit error. (**b**) Extrapolation error.
# Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data
## 3 Research Design  
### Case Study  
The selected study area is the Colombo city of the western province of Sri Lanka. Colombo is the commercial capital of Sri Lanka, as well as the country's main economic hub. The study was conducted in Colombo not only because it is the commercial capital of the country but also because it is one of the best examples in the Sri Lankan context of a rapidly changing urban form influenced by globalization and triggered by economic changes. Additionally, it is the most densely populated area--over 52,000 per square miles.  
This study used the Keywords-Hashtag (K-H) network mining method to identify popular keywords and hashtags tweeted or circulated together. K-H network mining acts as a content-based approach that was designed to enable related keywords and hashtags to discover from the links among keywords and hashtags. Such related K-H networks were explored algorithmically. Accordingly, this study identified 52 Lynchain elements. From them, 24 Lynchain elements were removed due to low occurrence (<10). This study considered the identified 28 Lynchain elements as the main keywords and their associated hashtags to mine tweets. After, a bounding box was demarcated, covering the location of the aforesaid 28 Lynchain elements. As in Figure 1, the case study is bounded by the bounding box coordinates of 6.88, 79.81 (Lower Left) and 6.98, 79.89 (Upper Right).  
Figure 1: Case study area.  
The identified 28 elements were: Altair Residential Condominium (ARC), Arcade Independence Square (AIS), Beddagana Wetland Park (BWP), Beira Lake (BL), Bellanvila Park (BP), Bandaranaike Memorial International Conference Hall (BMICH), Borella Cemetery (BC), Colombo City Center (CCC), Diyatha Uyana (DU), Colombo Fort (CF), Galle Face (GF), Galle Face Hotel (GFH), Gangaramaya Temple (GT), Hilton Hotel (HH), Kelaniya Temple (KT), Kingsbury Hotel (KH), Liberty Plaza Building (LPB), Lotus Tower (LT), Mount Lavinia Beach (MLB), One Galle Face Building (OGFB), Parliament (P), Pettah Market (PM), Port City (PC), Savoy Cinema (SC), Shangri La Hotel (SLH), Viharamahadevi Park (VP), World Trade Center (WTC), and Zoo (Zoo)
# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 4 FL Node Selection Algorithm Based on DQN
### MDP Model
#### 4.1.3 Reward Function  
The server selects end devices with sufficient resources, such as CPU cycle frequency, to participate in the model update until the desired accuracy is achieved. The server selects the optimal end devices via the reward function to make the best decision.  
When an agent takes a one-step action based on a node selection policy, the environmental information changes and a reward value is obtained to evaluate the action. In this paper, we propose a reward function based on federated learning's test accuracy, while considering a maximum time delay constraint during action selection.  
\[R(s,a)=-\alpha_{I}\frac{T_{i}}{T_{i}^{\max}}-\frac{1}{\sum\limits_{d\in N^{ \prime}}\beta_{i,d}}L^{i}\left(x_{test},y_{test};\omega_{g}^{i}\right) \tag{9}\]  
The maximum latency \(T_{i}\) of the clients participating in the iteration (2).  
The execution action source described above is a policy \(\pi\), which is a mapping from the state space to the action space, resulting in the selection of appropriate actions for different states, i.e.,  
\[a_{i}^{t}=\pi\left(s_{i}^{t}\right) \tag{10}\]  
The goal of the MDP model is to obtain an optimal policy that maximizes the expected cumulative reward of reinforcement learning when taking appropriate actions based on the corresponding states, i.e.,  
\[\pi^{*}=\arg\max E\left[\sum\limits_{t=0}^{\infty}\alpha^{t}r_{i}^{t}\right] \tag{11}\]  
where \(\alpha^{t}\) is the discount factor, whose value decreases with time.  
The discount factor plays a crucial role in deep reinforcement learning by weighting the importance of future rewards. It determines to what extent the agent discounts future rewards when making a decision. The value of the discount factor typically decreases over time, since future rewards are uncertain and risky, and the agent's prediction of future rewards becomes increasingly unreliable over time. Furthermore, more distant future rewards generally have higher uncertainty than more immediate rewards, since they are influenced by more factors. Therefore, decreasing the value of the discount factor can help reduce the effect of distant rewards and focus more on current rewards, making deep reinforcement learning more robust and reliable.