esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Applications in Agribbusiness: A Systematic Review
## 3 Results and Discussion
### A Qualitative Systematic Review
#### 3.2.2 Blockchain Application for Energy in Agribusiness  
The current stage of agricultural development seeks to face the constant increase in production with the use of new equipment and facilities. This makes the agriculture sector a major consumer of energy. In this context, more efficient management of energy resources in agriculture is a fundamental requirement.  
[PERSON] et al. [44] proposed a study to minimize the consumption of photovoltaic energy in greenhouses by using blockchain. In this system, the index that measures the ratio of photovoltaic consumption is defined to assess the condition of consumption in each greenhouse. As a result, the optimal load operation scheme is always recorded on the blockchain. The authors conclude that the blockchain control strategy can effectively improve local energy consumption. This reduces costs with the purchase and losses of energy, improving the quality of electric grid voltage in the farms.  
Another application that is also being tested is the development of an irrigation system that minimizes energy consumption, especially in times of drought. In this field of blockchain application, [PERSON] et al. [45] proposed a study on the use of photovoltaic energy to power a soil improvement system. According to the authors, this technology can help small farmers to better manage water resources during periods of drought.  
Blockchain applications for energy purposes have encouraged users to join the renewable energy system. Smart systems are being tested by several industries related to energy production in agriculture. Many of these blockchain applications are still in the pilot project phase, but they already show considerable results in training energy producers and distributors. The technology also acts as a facilitator in the negotiation, bringing greater flexibility, playing an important role in the storage of data, and guaranteeing security in the transactions and commercialization of energy.  
[PERSON] [46] presents a digital coupon or cryptocurrency that could be introduced into the trade of waste, energy and by-products, such as fertilizers or raw materials, among farmers and entrepreneurs. This system could maximize the use of agriculturalwaste by encouraging farmers and companies to work together in the production and commercialization of energy.
# Groundwater Prediction Using Machine-Learning Tools
## 3 Techniques Used
### Multivariate Linear Regression  
The level of correlation between the predictors and the output variables are usually estimated by regression models to determine their relationship form [42]. In linear regression, the mean square error is used to fit the models and to assess the performance of the trained models on the testing set [42; 43]. In general, MLR is used to discover the hyperplane that best fits all individual data points [42]. For simplicity, in the following sections, MLR will be abbreviated as LR.
## 4 Experiments
### Cross Track Error  
The cross track error (XTE) is defined as the distance between the planned sounding profile (planned USV track) and the actual unit position. The sounding profiles are represented by a line connecting two defined WPs. Figure 3 presents the XTE, which equals \(l_{\perp}\). Assuming that the sounding profile is defined by two waypoints \(WP_{i}\Big{(}X_{W_{i}},Y_{W_{i}}\Big{)}\) and the next waypoint \(WP_{i+1}\Big{(}X_{W_{i+1}},Y_{W_{i+1}}\Big{)}\), and the actual unit position is an interpolated GPS position with coordinates \(\hat{\mathrm{p}}_{\mathrm{GPS}_{n_{R}}}\Big{(}\hat{\mathrm{X}}_{\mathrm{G}_{ i}},\ \hat{\mathrm{Y}}_{\mathrm{G}_{i}}\Big{)}\) at the measurement number \(n_{R}\), then the actual XTE can be calculated in accordance with the following formula:  
\[XTE_{n_{R}}=\frac{\Big{|}\Big{(}Y_{W_{i+1}}-Y_{W_{i}}\Big{)}\hat{\mathrm{X}}_{ \mathrm{G}_{i}}-\Big{(}X_{W_{i+1}}-X_{W_{i}}\Big{)}\hat{\mathrm{Y}}_{\mathrm{ G}_{i}}+X_{W_{i+1}}Y_{W_{i}}-Y_{W_{i+1}}X_{W_{i}}\Big{|}}{\sqrt{\Big{(}Y_{W_{i+1}}-Y_{W _{i}}\Big{)}^{2}+\Big{(}X_{W_{i+1}}-X_{W_{i}}\Big{)}^{2}}} \tag{15}\]
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 3 An Idea of the Asynchronous Radio Localization
### Protocol Layer Description  
The protocol layer of the asynchronous radionavigation system results from the way it works as described above. The key issue is the organization work and measurement activity of reference stations. These stations send out signals in a spreading way, containing so-called location messages, which contain two types of location data used to perform location calculations described by dependencies (12)-(15). The location data are divided into two groups. These are:  
* Basic location data placed in the basic location telegram containing:
* identification number, longitude and latitude of the reference station,
* EIRP (dBm) of the signal radiated from the reference station antenna,
* position height (m a.s.l.) of the reference station antenna;
* Additional location data to be included in the additional location message, including:
* identification numbers of the reference stations from which radio signals containing measured time differences were received;
* current time differences between themselves and other reference stations, which is measured between the broadcasted signal by itself and signals received from neighboring stations.  
As a reminder, the localized UT terminal receives all the signals with the messages from the reference stations within reach of where the terminal is located. This way of organizing the work of the reference stations and the UT location terminal makes the system work in self-organizing mode, provided that UT receives a signal from at least three reference stations.  
In addition, all reference station transmitters are assumed to operate at the same power and carrier frequency, which facilitates the construction and operation of these stations' receivers. However, it forces the cyclic (intermittent) character of these transmitters' work. The random suspension (switching off) mode of the transmitters is based on [PERSON]'s layout, in which the duration of transmitting a navigation message consisting of the two abovementioned location messages was assumed as a random variable X, which can be expressed as follows:  
\[p(X=k)=\frac{\lambda^{k}}{k!}\cdot e^{-\lambda},\textit{for}\ k\in<0,1,\ldots), \tag{16}\]  
where \(\lambda\) indicates the average time of transmission of the navigational telegram. The generator of pseudo-random numbers with such a distribution was performed using the algorithm developed by [PERSON] [23]. As a result, the total time of switching off each of the transmitters was assumed to be 8 s. The time necessary to stabilize the operating conditions of the transmission track during the transmitter reclosure was taken into account. Moreover, on the basis of the performed tests, it was assumed that the average time of transmitting the navigation wire should be 30 s. With this in mind, using [PERSON]'s distributed timetable, the time of data acquisition in receivers of reference stations and the UT terminal was assumed to be 40 s.
## 2 Main Concepts
### Multi-Frame Reconstruction
#### 2.2.3 Multi-Exposure  
The technique based on multi-exposure consists of integrating images with different exposures to form a final full-exposure image [56]. Typically, LDR images are captured with different exposure times so that each image retains detail in specific regions of the scene. The reference image, usually the one with medium exposure, serves as the basis for fusion, while the other images are used to recover missing information in overexposed or underexposed regions [26].
###### Abstract  
To develop and evaluate deep learning models for cardiac arrest rhythm classification during cardiopulmonary transuscitation (CPR), we analyzed 508 electrocardiogram (ECG) segments (each 4 s in duration, recorded at 250 Hz) from 131 cardiac arrest patients. Compression-affected segments were recorded during chest compression, while non-compression segments were extracted during compression pauses or immediately after return of spontaneous circulation (ROSC) declaration. One-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) models were employed for four binary classification tasks: (1) shockable rhythms (VF and pVT) versus non-shockable rhythms (asystole and PEA) in all ECG segments; (2) the same classification in compression-affected ECG segments; (3) pulse-generating rhythms (ROSC rhythm) versus non-pulse-generating rhythms (asystole, PEA, VF and pVT) in all ECG segments; and (4) the same classification in compression-affected ECG segments. The 1D-CNN model consistently outperformed the RNN model across all classification tasks. For shockable versus non-shockable rhythm classification, the 1D-CNN achieved accuracies of 91.3% and 89.8% for all ECG segments and compression-affected ECG segments, respectively, compared to 50.6% and 54.5% for the RNN. In detecting pulse-generating rhythms, the 1D-CNN demonstrated accuracies of 90.9% and 85.7% for all ECG segments and compression-affected ECG segments, respectively, while the RNN achieved 92.2% and 84.4%. The 1D-CNN model demonstrated superior performance in cardiac arrest rhythm classification, maintaining high accuracy even with compression-affected ECG data.  
c 2025, 15, 4148 +
Footnote †: journal: Applied sciences  
Article
## 3 Experiments and Results
### Discussions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Data Source** & **Target Spot** & \begin{tabular}{c} **Object** \\ **Detection** \\ \end{tabular} & \
\begin{tabular}{c} **Object** \\ **Identification** \\ **and Tracking** \\ \end{tabular} &
\begin{tabular}{c} **Feature** \\ **Extraction** \\ \end{tabular} & **Used Features** \\ \hline
**Ref**[13] & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Unsignalized \\ mid-block \\ crosswalk \\ \end{tabular} & x & n/a & x &
\begin{tabular}{c} Pedestrian safety \\ margin \\ \end{tabular} \\ \hline
**Ref**[14] & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Signalized \\ intersections \\ \end{tabular} & x & n/a & x &
\begin{tabular}{c} Crossing speed, \\ pedestrian delay, \\ etc. \\ \end{tabular} \\ \hline
**Ref**[15] & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Signalized \\ intersections \\ \end{tabular} & x & n/a & x &
\begin{tabular}{c} Gender, vehicle \\ type, crossing speed, \\ etc. \\ \end{tabular} \\ \hline
**Ref**[17] & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Signalized \\ intersections \\ \end{tabular} & x & n/a & x &
\begin{tabular}{c} TTC, pedestrian \\ gait, etc. \\ \end{tabular} \\ \hline
**Ref**[40] & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Signalized \\ intersection \\ \end{tabular} & + & + & + &
\begin{tabular}{c} TTC, etc. \\ \end{tabular} \\ \hline
**Ref**[41] & \begin{tabular}{c} Driving \\ simulator \\ data \\ \end{tabular} & \
\begin{tabular}{c} Mid-block \\ crosswalk \\ \end{tabular} & x & n/a & n/a &
\begin{tabular}{c} Maximum \\ deceleration, TTC, \\ PET, etc. \\ \end{tabular} \\ \hline
**Ref**[42] & \begin{tabular}{c} Thermal \\ video \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Unsignalized \\ crosswalk \\ \end{tabular} & x & n/a & x &
\begin{tabular}{c} Object speed, PET, \\ yielding \\ compliances, \\ conflict rates, etc. \\ \end{tabular} \\ \hline
**Ref**[43] & \
\begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & Intersections & n/a & + & + &
\begin{tabular}{c} Object trajectory, \\ TTC, etc. \\ \end{tabular} \\ \hline
**Ref**[44] & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} School zone \\ roads \\ \end{tabular} & n/a & + & + &
\begin{tabular}{c} Object trajectory, VV \\ \end{tabular} \\ \hline
**Proposed** & \begin{tabular}{c} CCTV \\ camera \\ \end{tabular} & \
\begin{tabular}{c} Unsignalized \\ crosswalk \\ \end{tabular} & + & + &
\begin{tabular}{c} VV, VA, PV, DVP \\ and DVC \\ \end{tabular} \\ \hline \hline \end{tabular}
**Note: x: Manual extraction, +: Automated extraction, n/a: Not applicable, TTC: Time to collision, PET: Post-encroachment time.**  
\end{table} 
Table 5: Comparison table for characteristics of previous systems with our approach.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 2 V2A Charging Mode
### V2B Charging Scenario  
V2B is defined as the energy transfer of EV battery to a building, e.g., a building located in a school or a company, for supporting its internal loads [9], as shown in Figure 3. Through V2B, the operation cost of the managed building can be lowered. Meanwhile, as a countermeasure, V2B can be arranged to mitigate the potential power mismatch between day-ahead scheduling energy demand and real-time power consumption [14].  
EV users can perform V2B when they park their cars in a smart garage of a single building; this charging mode is attractive in several aspects: (1) V2B can help facilitate the participation of buildings in demand side management; to be more specific, it can reduce the on-peak load of buildings through load shifting operation; (2) V2B can be operated for emergency backup purposes, to generate power for power restoration of buildings when outage management is needed; (3) besides the energy regulation of buildings, V2B also provides extra profit for EV owners; (4) V2B is simpler than V2G, and is regarded as a more near-term alternative to V2G [9].
## 3 Methods
### Effect of Spatial Intensity Nonuniformities in Co-Occurrences  
#### 3.2.1 Distortion in Co-Occurrences from Spatial Intensity Nonuniformity  
The statistics of the products \(b_{i}.I_{u_{i}}\) are modeled as the convolutions of \(C_{u_{i}u_{i}}\) and \(C_{u_{0}u_{1}}\) with the respective PSF of the intensity distortions that are non-stationary to account for the spatial multiplication. The effects of \(b_{i}\) in \(\mathcal{N}_{\rho}\) around \(\mathbf{x}_{0}\) are approximated by the zero-order term \(b(\mathbf{x}_{0})\) that scales the auto-co-occurrences of \(C_{u_{i}u_{i}}\) radially and the first-order term \(\
abla b(\mathbf{x})|_{\mathbf{x}_{0}}\) that rotates them around the origin in the co-occurrence space [49]. The PSF affecting \(C_{u_{i}u_{i}}\) is represented in polar coordinates \((r_{i},\phi_{i})\). The \(\sigma_{r_{i}}\) of the PSF is scaled linearly with \(r_{i},\sigma_{r_{i}}\propto r_{i}\). The \(\sigma_{\phi}\) of the PSF increases with \(\rho\), is largest along the diagonal and is zero along the axes. The application of the PSFs of the distortions to \(p_{u_{i}}(u_{i})=C_{u_{i}u_{i}}\) produce \(p_{v_{i}}(v_{i})=C_{v_{i}v_{i}}\) with convolution  
\[C_{v_{i}v_{i}}(r_{i},\phi_{i})=C_{u_{i}u_{i}}(r_{i},\phi_{i})\ast p_{b}(r_{i}; 0,\sigma_{r_{i}}^{2})\ast p_{b}(\phi_{i};0,\sigma_{\phi_{i}}^{2}),i=0,1, \tag{3}\]  
which represents the auto-co-occurrences of the distorted images.  
The effects of the zero-order terms of \(b_{i}\) on the joint-co-occurrences \(C_{u_{0}u_{1}}\) of \(u_{i}\) are in Cartesian coordinates. The \(\sigma_{\eta_{i}}\) of the PSFs are linearly related with \(\eta_{i}\), \(\sigma_{\eta_{i}}\propto\eta_{i}\)[50]. The diagonal relation between \(\eta_{i}\) and \(r_{i}\) is used, \(\eta_{i}=r_{i}/\sqrt{2}\). The application of the PSFs of the distortions to \(p_{u_{0}u_{1}}(u_{0},u_{1})=C_{u_{0}u_{1}}\) produces \(p_{v_{0}v_{1}}(v_{0},v_{1})=C_{v_{0}v_{1}}\) with convolution  
\[C_{v_{0}v_{1}}(\eta_{0},\eta_{1})=C_{u_{0}u_{1}}(\eta_{0},\eta_{1})\ast p_{b}( \eta_{0};(\sigma_{\eta_{0}}\eta_{0})^{2})\ast p_{b}(\eta_{1};(\sigma_{\eta_{1} }\eta_{1})^{2}), \tag{4}\]  
which represents the joint-co-occurrences of the distorted images. The PSFs of the distortions are assumed separable.
# A Single Motor-Driven Focusing Mechanism with Flexure Hinges for Small Satellite Optical Systems
## 2 Materials and Methods
### Design of Flexure Hinge (FlexHe)  
The primary objective of FlexHe is to maximize the displacement in the \(z\)-axis (De-space), while at the same minimizing Tilt and De-center in the \(x\) and \(y\) axes. Accordingly, the structure should be reinforced to resist both bending and torsion, yet be compliant in tension and compression. Moreover, the dimensions should satisfy the requirements that were introduced in the aforementioned section. The presented FlexHe can reduce structural complexity, as well as achieve promising mechanical characteristics. The slits of the FlexHe are geometrically arranged in a zig-zag pattern. The best structure with a relatively high bending stiffness and a more compliant axial stiffness was identified by means of the Finite Element Method.
# Reverse Time Migration Imaging Using SH Shear Wave Data
## 2 Application Background and Method Principles
### Depth Domain Velocity Model Building for SH Shear Wave Data  
The depth domain velocity model building is very important for the effect of depth domain imaging, and P-wave and SH shear wave data are simultaneously acquired in this study area [33]. In depth-domain imaging, the reflector of the subsurface structure, the P-wave and SH shear wave, should be matched [34]. Generally, in the case of the combination of shear wave or converted wave and P-wave imaging, a priority will be given to determine the P-wave velocity (V\({}_{\rm P}\)) during the model establishment, and then a P-to-S wave velocity ratio will be estimated and used together with the V\({}_{\rm P}\) (P-wave velocity) to establish the model of SH shear wave velocity (V\({}_{\rm S}\)). In this study area, due to the gas  
Figure 1: Pre-stack time migrated P-wave seismic section in a gas-cloud region of Sanhu.  
clouds and the difficulty of obtaining effective signals of P-waves, it is difficult to establish a velocity model of V\({}_{\rm P}\). While the pure SH shear wave can propagate through the rock frame without being affected by gas and fluid, the pure SH shear wave data can be directly used to establish a model of the SH shear wave velocity vs. (SH shear wave velocity).  
First of all, we separately used the SH shear wave and P-wave velocity models. The establishment of P-wave velocity model refers to the establishment of SH shear wave velocity model since SH shear wave data in the region has a better signal-to-noise ratio in the survey and is more credible in the absorption area. Then, the velocity ratios of the P- and SH shear waves in this region are estimated, and the obtained ratios are taken as constraints. In order to better match the imaging of the P-wave and SH shear wave in the depth domain, we also need to estimate the change in the velocity ratio of the P-wave and SH shear wave by using the pre-stack time migration image. There are many methods of depth domain velocity model building, and we mainly use layer- and grid-based tomography. After the establishment of the two models, the spatial P-wave-to-SH shear wave ratio mentioned above is used to obtain the interlayer travel time in the depth domain model, calculate the proportion of model correction, and apply it to the correction of the P-wave model. In the actual constraint process, the velocity correction is about 1-3%, which can be attributed to weak anisotropic media.
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 5 Experimentation
### Data Description  
We analyzed the open source car hacking data which included four datasets: _DoS_ attack, _spoofing the drive gear_, _spoofing the RPM gauge_, and _fuzzy_ attack. The datasets were created by logging the CAN network 2.0B traffic via the OBD-II port from a real vehicle, while message injection attacks were performing [27; 30]. Each dataset contained 300 intrusions of message injection for 3 to 5 s time lapse with a total of 30 to 40 min of CAN traffic.  
In particular, in the _Do5_ attack database attack messages of 29 zero bits CAN ID, the most dominant in the CAN bus were injected every 0.3 milliseconds; the aim was the tampering of the CAN network accessibility. Since the most dominant CAN ID wins the bus, all other ECUs are precluded to access the network and send messages.  
In the _spoofing gear/RPM_ databases, the attack messages of CAN IDs related to gear/RPM information were injected every 1 millisecond. The injected messages transmitted information about the driver gear and the RPM gauge, respectively, changing the original status on the instrument panel. Data were labeled for normal and attack traffic data.  
In the _fuzzy_ attack database, the attack messages were similar to the _Do5_ attacks, but CAN IDs and DATA values were injected totally at random every 0.5 milliseconds impairing the vehicle functionality.  
An overview of the dataset is shown in Tables 1 and 2.  
All the datasets have the same structure and include: timestamp, recorded time in seconds, CAN ID, identifier of CAN message in HEX, DLC, number of data bytes from 0 to 8, DATA [0-7], data values (byte), and labels T or R where T represents the injected attack messages while R represents the normal messages.
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 3 Optimization Problem
### Data-Based Likelihood Probability  
This subsection presents the derivation of the data-based likelihood probability based on the detected data symbols. The probability \(p_{r,k}\) in (6) is expressed using the following conditional probability formula,  
\[p_{r,k} =\mathbb{P}(q_{r}[n]=1|\mathbf{s}[n]=\mathbf{s}_{k})\] \[=\frac{\mathbb{P}(q_{r}[n]=1,\mathbf{s}[n]=\mathbf{s}_{k})}{ \mathbb{P}(\mathbf{s}[n]=\mathbf{s}_{k})} \tag{10}\]  
where the probabilities in (10) are, respectively, defined as  
\[\mathbb{P}(q_{r}[n]=1,\mathbf{s}[n]=\mathbf{s}_{k}) =\frac{\sum\limits_{m\leq n}\bar{q}_{r}[m]\,\mathbb{1}\left\{k[m] =k\right\}}{n},\] \[\mathbb{P}(\mathbf{s}[n]=\mathbf{s}_{k}) =\frac{\sum\limits_{m\leq n}\mathbb{1}\left\{k[m]=k\right\}}{n}. \tag{11}\]  
Here, \(\bar{q}_{r}[n]\) is a function whose value is 1 if \(q_{r}[n]=1\) and 0 otherwise. \(\mathbb{1}\left\{\mathcal{E}\right\}\) is one if event \(\mathcal{E}\) is true; otherwise, it is 0. The probabilities in (11) can not be computed at the receiver because the transmitted symbol index \(k[m]\) is not available. Therefore, they are approximated using the detected symbol index \(\hat{k}[m]\), which is expressed as  
\[\mathbb{P}(q_{r}[n]=1,\mathbf{s}[n]=\mathbf{s}_{k}) \approx\frac{\sum\limits_{m\leq n}\bar{q}_{r}[m]\,\mathbb{1}\left\{ \hat{k}[m]=k\right\}}{n},\] \[\mathbb{P}(\mathbf{s}[n]=\mathbf{s}_{k}) \approx\frac{\sum\limits_{m\leq n}\mathbb{1}\left\{\hat{k}[m]=k \right\}}{n}. \tag{12}\]  
These approximations are similar to the true probabilities for a sufficiently large \(n\)[32]. Subsequently, the data-based likelihood probability is obtained as follows:  
\[p_{r,k}^{da}\approx\frac{\sum\limits_{m\leq n}\bar{q}_{r}[m]\,\mathbb{1}\left\{ \hat{k}[m]=k\right\}}{\sum\limits_{m\leq n}\mathbb{1}\left\{\hat{k}[m]=k \right\}}. \tag{13}\]