esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 7 Evaluation and Analysis
### Proposed Solution Analysis with Healthcare Application  
To examine the proposed solution's integrity, efficiency, and performance with real-world IoT usages, such as smart cities, smart homes, and other applications, the healthcare IoT network has been integrated with the proposed solution. The scenario of the application and network has been inspired from [46] and modified to fit with the proposed solution settings. We picture a sizable, chaotic hospital that sees hundreds of patients every day and is tremendously busy. The hospital is constantly checked for patients and other community hospitals, hundreds of physicians and other staff members commute daily, and therapeutic supplies that need to be used right away are frequently recalled. Healthcare professionals are assisted by information technologies, yet real-time monitoring of patient and asset mobility is not always reliable. Therefore, it becomes challenging to recognize and respond to critical situations in this complicated and chaotic circumstance. The proposed solution has been tested with packet overhead and energy consumption.  
In terms of packet overhead, as noticed in Table 4, it has been preserved with 128 bytes only compared with 1024 bytes of standard blockchain, as mentioned before.  
In terms of energy consumption, the consumption of the proposed solution is much better and very close to the basic work. The table at the bottom of Figure 10 shows the power consumption of the three primary tasks a miner performs: CPU, Transmit (Tx), and Listen (Lx). The energy consumption is very low, with 0.005 mJ for the CPU and 0.08 for Tx, due to the blockchain's lightweight mechanism and simple hashing.  
In terms of Lx, Figure 11 shows the power consumption, which is very low with 56.5 mJ at most in store-periods, 56.2 mJ in Store-Quer  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Packet flow** & **Proposed solution** \\ \hline From device to cloud & 128 \\ From cloud to device & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Packet overhead (bytes) of healthcare.  
Figure 10: Energy consumption overhead (CPU and Tx) of healthcare application.
## 3 Results
### Effects of Learning Methods on Route Learning  
To investigate the effects of the different learning methods on route learning, two Kruskal-Wallis tests were performed comparing the groups on the RLT at T1 and at T2. The tests showed no statistically significant differences between the groups (T1: H(2) = 3, 16, \(p\) = 0.21; Eta\({}^{2}\) = 0.05; T2: H(2) = 2.9, \(p\) = 0.23; Eta\({}^{2}\) = 0.04).  
In the absence of these group/ learning methods effects, for all the consecutive analyses, the three groups were taken together. A Friedman test examined whether route performance improved in the patients after training. This effect was significant \(\chi^{2}(1)\) = 13.8, \(p\) < 0.001; [PERSON]'s W = 0.53). As can be seen in Figure 2, the error rates clearly dropped from T1 to T2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Group** \\ **(Learning** \\ **Method**) \\ \end{tabular} } & \multirow{2}{*}{**Estimated**} & \multicolumn{2}{c}{**General Cognition**} & \multirow{2}{*}{**Verbal Working**} & \multicolumn{2}{c}{**Verbal Long**} & \multirow{2}{*}{**Attention**} & \multicolumn{2}{c}{**Central**} \\  & & & \multicolumn{2}{c}{**Screening**} & & \multicolumn{2}{c}{**Memory**} & & \multicolumn{2}{c}{**Memory**} & & \multicolumn{2}{c}{**Accuracy**} \\ \hline  & **M** & **SD** & **M** & **SD** & **M** & **SD** & **M** & **SD** & **M** & **SD** & **M** & **SD** \\ \hline \multicolumn{10}{c}{Passive (\(n=8\))} \\  & T0 & 98 & 14.3 & 20.9 & 3.2 & 50.7 & 10.7 & 27.0 & 4.4 & 33.3 & 9.2 & 47.0 & 8.9 \\  & T1 & - & - & 20.8 & 4.1 & - & - & - & - & - & - & - \\  & T2 & - & - & 20.5 & 2.7 & 51.5 & 6.7 & 24.1 & 3.1 & 36.8 & 11.3 & 50.3 & 8.6 \\ Trial-and-Error & & & & & & & & & & & & \\  & (\(n=9\)) & & & & & & & & & & & \\  & T0 & 104 & 15.0 & 20.1 & 5.4 & 48.6 & 7.6 & 26.6 & 4.3 & 29.0 & 11.5 & 44.6 & 7.5 \\  & T1 & - & - & 19.0 & 6.1 & - & - & - & - & - & - & - & - \\  & T2 & - & - & 19.7 & 5.7 & 49.6 & 10.4 & 23.8 & 2.9 & 37.1 & 10.5 & 46.9 & 9.1 \\ Errorless (\(n=9\)) & & & & & & & & & & & & \\  & T0 & 103 & 21.0 & 20.3 & 4.8 & 46.4 & 9.3 & 26.4 & 6.3 & 35.6 & 8.1 & 48.5 & 6.1 \\  & T1 & - & - & 20.7 & 4.0 & - & - & - & - & - & - & - & - \\  & T2 & - & - & 19.7 & 3.4 & 44.6 & 11.9 & 23.4 & 3.5 & 40.6 & 8.8 & 50.2 & 5.7 \\ \hline \hline \end{tabular} Note: IQ was estimated with the Dutch Adult Reading Test [43]. The general cognition screening outcome is presented by the raw total score on the MoCA [37].  
\end{table}
Table 2: Neuropsychological test scores per group (learning condition) in the pre (T0), midterm (T1), and post (T2) training measurements.  
Figure 2: Mean learning test scores (number of incorrect route decisions) and standard errors for the three training methods (groups) over the three training sessions.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 2 UAV Serving Ground Users
[24] presents the challenges of using millimeter-wave communications to connect UAVs either for access or for backhaul and focus on the challenges of detection, positioning, interference mitigation and spectrum sharing in UAVs. [25] proposes a stochastically geometry model to perform backhaul scenario for UABS in 2.6 and 73 GHz. In [26] a study of the backhaul at 3.5 and 30 GHz is done.
## 2 Literature Review
### Super-Resolution
#### 2.1.2 Information Theory  
The fact that we can do better than the resolution limit suggests that resolution is not an inherent property of an optical system. Instead, the optical system has the capacity to transmit a finite amount of information. The concept of information capacity was introduced by [PERSON] [7] and the concept of channel capacity in the presence of noise by [PERSON] [8]. Other important papers were by [PERSON] and [PERSON] [9; 10]. [PERSON] stated that it is not the spatial frequency bandwidth but the number of degrees of freedom that can be transmitted by the system that is invariant and gave an expression for the information capacity of a two-dimensional (2D) imaging system, including time [11]. He explained that resolution can be increased by making some trade-off between the parameters, which requires that some a priori information concerning the object is known. [PERSON] included depth information, time, and also the effect of noise in a 3D system [12]. This expression was later updated to include full 3D imaging, including polarisation [13], to give the number of degrees of freedom \(N\) as  
\[N=3(8 VB_{v}+1)\log_{2}\Bigl{(}1+\frac{s}{n}\Bigr{)}, \tag{1}\]  
where \(s\) and \(n\) are the signal and noise, respectively, \(V\) is the volume of the object, and \(B_{v}\) is the volume of the 3D transfer function. The theorem of invariance of the information capacity is the fundamental principle underlying all super-resolution schemes. We require sufficient information to allow the super-resolved image to be generated.  
[PERSON] investigated some examples of imaging systems [12]. Analytic continuation relies on the fact that the spatial frequency distribution of a spatially-limited object is analytic. Effectively, analytic continuation gives an increased spatial frequency bandwidth by trading off the signal-to-noise ratio (SNR). The gain in bandwidth, however, is very small for a substantial reduction in SNR. Further, if the object's field-of-view is limited to \(\sim\)20 resolution elements, a small additional improvement can be obtained. The incorporation of a priori information can also give further improvements.
## 3 Experimental Verification  
The induced voltages are measured by a reduced-scale experimental set-up of PV bracket system, as shown in Figure 7. Its sizes are \(a=0.45\) m, \(h_{1}=b=0.15\) m and \(h_{2}=0.05\) m.  
Figure 6: Subdivision for conductor loop plane.  
Rectangular loop 1 of 0.145 m \(\times\) 0.12 m and square loop 2 of 0.12 m \(\times\) 0.12 m are installed in the set-up. A fast impulse generator, whose circuit is shown in Figure 8[25; 26], is used to generate the impulse current \(i\) (15/295 ns). With \(i\) injected into the set-up, the induced voltages in the two loops are recorded by a digital oscilloscope with 200 MHz band width and their measured waveforms are shown in Figure 9. In fact, a conductor always has self inductance. The conductor also has the capacitance to ground, depending on its spatial location. As the inductance and capacitance are gratuitous (not deliberately included), they are called \"parasitic inductance and capacitance\", respectively [27]. In Figure 7, the average length of the loop wire is 0.505 m. The parasitic inductance and capacitance of the loop are non-ignorable for the fast transient. Meanwhile, the oscilloscope has the input and probe capacitances. The actual presence of such parasitic circuit parameters causes the oscillating phenomenon to occur on the measured waveforms. The waveforms calculated by the proposed method are also plotted in Figure 9 together with the measured waveforms. In the calculation, the estimated parasitic circuit parameters (\(R_{\text{P}}\), \(L_{\text{P}}\) and \(C_{\text{P}}\)) are given in Table 1. The measurement wire is quite short and its parasitic circuit parameters are ignored. A contrast between the measured and calculated waveforms indicates that a rough agreement appears between them. This confirms the validity of the proposed method.  
Figure 8: Circuit of impulse generator.  
Figure 7: Measurement on reduced-scale experimental set-up.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & \(\mathbf{R_{p}}\) **(f)** & \(\mathbf{L_{p}}\) **(uH)** & \(\mathbf{C_{p}}\) **(pF)** \\ \hline Loop 1 & 0.0082 & 0.22 & 148.0 \\ Loop 2 & 0.0068 & 0.19 & 114.0 \\ Oscilloscope & 50 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parasitic circuit parameters.  
Figure 9: Measured and calculated induced voltage waveforms. (**a**) Loop 1, (**b**) loop 2.
## 5 Evaluation  
### Datasets  
In our experiments, we utilize three benchmark datasets for image classification, namely, MNIST [16], Fashion-MNIST [17], and EMNIST-Letters [18]. We selected these grayscale image datasets because our work employs the Learnergy implementation of RBM [67], which is designed to handle grayscale images.  
The MNIST dataset [16] comprises 60,000 training instances and 10,000 testing instances. Each instance represents a handwritten digit formatted as a 28 \(\times\) 28 grayscale image. The learning objective is to classify these images into one of ten classes, corresponding to integer values ranging from 0 to 9.  
Similarly, Fashion-MNIST [17] consists of 60,000 grayscale images, with the training and testing split, image size, and format identical to those of MNIST. The goal is to classify each fashion product image (e.g., dresses, coats, and shirts) into one of ten classes from 0 to 9, each representing a different fashion product.  
The EMNIST-Letters dataset [18] is a collection of 28 \(\times\) 28 grayscale images of handwritten letters and contains 145,600 training examples and 20,800 testing examples. The learning objective here is to classify an image into one of 26 classes, corresponding to the letters from A to Z.  
Both MNIST [16] and Fashion-MNIST [17] datasets have separate test sets of 10,000 instances each, whereas the EMNIST-Letters [18] dataset has a separate test set of 20,800 instances. We use these test datasets to evaluate the performance of the global models trained by the central server.
## 4 Case Study: High-Frequency Tasks
In addition, there are also blocks here that are intended to read the values of the peaks detected by the previous process on the three channels of the maximum voluntary contraction signal. These will be used to normalize the signal that is translated with the ratio between the processed and filtered signal and the maximum value of the MCV calculated with the previous process. Following the normalization, the characteristic parameters of the signal are extracted, i.e., the average value, RMS, and maximum value. The program implemented on the Raspberry saves the data on the device in.txt format and sends it to a cloud platform to be subsequently post-processed (Section 4). The choice of a Raspberry Pi card is due to the fact that it has adequate memory for saving and sending the acquired data, because it has a large volume, and finally because it offers high performance. Furthermore, Raspy-GPIO allows the status of a GPIO to be viewed and its behavior to be modified. The GPIO zero Python library has ensured a quality leap in programming thanks to higher-level abstractions such as a motion sensor, light sensor, LED, and motor, and has allowed the adoption of an approach capable of managing the underlying hardware by reading or setting its state with more intuitive commands. The integrated signal acquisition, storage, and transmission system presents a different approach than other integrated systems [47, 48]. The proposed integrated system aims to allow the operator to analyze the signals and data remotely, while the patient can remain comfortably at home. While the authors of the paper [49] chose the Arduino Mega to avoid the memory and power limitations of smaller boards, as data acquisition and filtering required a large amount of memory, the proposed paper divides the process into two phases. The first focuses on the acquisition of the EMG signal, trying to eliminate interference and keep the signal unaltered; the second focuses on storage and transmission. The subdivision of the process ensures a better quality of the acquired and processed signal, at the expense of increasing the complexity of the system.
## 4 Data Processing Workflow
\begin{table}
\begin{tabular}{c c c} \hline \hline Rule 1: & If (Temp == high) AND (Chlorine == low) THEN (water == non-Useable); \\ \hline Rule 2: & If (Temp == high) AND (Chlorine == moderate) THEN (water == Useable); \\ \hline Rule 3: & If ((Temp == low) AND (Chlorine == high) THEN (water == Partial Useable); \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample rule for predicting the water quality. The hybrid strategy, which merges the wrapper and filter approaches, utilizes the ranking information provided by the filter method. Model validation is essential in chlorine level assessment and prediction in water monitoring systems. The purpose of model validation is to assess the accuracy of the model's predictions by comparing its predictions with the actual chlorine levels in the water. As mentioned above, the most common model validation approach divides the data into two parts: a training dataset and a testing dataset. The training dataset is used to train the proposed algorithm, while the testing dataset is used to validate the model's performance, as mentioned in Figure 4. The model is trained on the training dataset, and its predictions are compared with the actual chlorine levels in the testing dataset. This comparison calculates metrics such as recall, precision, and F-score. These metrics give a quantitative measure of the accuracy of the predictions and can be used to compare the performance of the proposed model. Figure 4: Water quality monitoring data processing workflow.
## 2 Stereo Reconstruction Algorithm for Unstructured Scenes
### SAD-FAST Feature Detection and Recombination Stereo-Matching System
#### 2.1.2 SURF Descriptor  
The SURF algorithm stands out for its good rotational and fuzzy robustness. After the feature corner points have been pinpointed by the SAD-FAST algorithm, our idea was to use these feature corner points to replace the scale- and selection-invariant feature points already identified by Hessian detector in the SURF algorithm. Before performing feature matching with image pairs, the position and orientation information for feature corner points needs to be computed to obtain and then generate the required 64-dimensional feature descriptors.
## 3 Results
### Effects of Stomatal Closure on the Variations in Carbon Assimilation and Fluorescence Emission  
An apparent decrease in both \(A_{\text{G}}\) and \(\text{g}_{\text{sw}}\) was observed within 30 min after ABA addition (Figure 5a-c), confirming that the ABA treatment effectively induced stomatal closure and inhibited leaf CO\({}_{2}\) assimilation. Moreover, in the context of the energy distribution in these treatment leaves, a similarly large reduction was also observed in \(\Phi_{\text{PSII}}\) (Figure 5d-f), thereby revealing the decline in photochemical quenching and lower light use efficiency of PSII. As a compensating pathway for dissipating the extra energy that could not be used in photochemistry, NPQ showed a significant increase following treatment (Figure 5d-f). More importantly, the apparent decreases in both TchlF\({}_{760}\) and \(\text{F}_{\text{s}}\) were consistently observed for all three ABA-treated leaves (Figure 5g-i): TchlF\({}_{760}\) and \(\text{F}_{\text{s}}\) decreased by 15.4 \(\pm\) 2.0% and 13.5 \(\pm\) 3.0%, respectively, after the treatment. We found that ChlF emission was well correlated with CO\({}_{2}\) fixation rates during stomatal closure invoked by ABA: TchlF\({}_{760}\) explained 99% and 73% of the variability in \(A_{\text{G}}\) after the treatment. These results confirm that far-red fluorescence radiance could respond to the ABA treatments, resulting in a reduction in the efficiency of dark reactions.  
Figure 5: Variation in soybean gross carbon assimilation (\(A_{\text{G}}\), \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), **(a–c)**, left \(y\)-axis), stomatal conductance to water vapor (\(g_{\text{sw}}\), mol m\({}^{-2}\) s\({}^{-1}\), **(a–c)**, right \(y\)-axis), quantum efficiency of photosystem II (\(\Phi_{\text{PSII}}\), **(d–f)**, left \(y\)-axis), nonphotochemical quenching (NPQ, **(d–f)**, right \(y\)-axis), total fluorescence radiance at 760 nm measured from both sides of leaves (TchlF\({}_{760}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\), **(g–i)**, left \(y\)-axis), and light-adapted steady-state PAM fluorescence (\(\text{F}_{\text{s}}\), **(g–i)**, right \(y\)-axis) for pre-treatment and post-treatment of ABA under irradiance of 800 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). The vertical green dashed line indicates the start of the ABA treatment. The three columns represent three individual soybean leaves. \(\text{n}_{\text{ABA}}=30\) for each leaf.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Data Preprocessing
#### 3.1.1. Invalid Data Deletion  
Maritime Mobile Service Identity (MMSI) is a unique 9-digit code recorded in the AIS static data to identify a vessel, based on which invalid data with non-9-digit MMSI codes are deleted from AIS data. Then, the trajectory point of the same vessel may be recorded several times during traveling. Therefore, when Equation (1) is satisfied, one of the duplicate track points is deleted to reduce data redundancy to ensure the accuracy and streamlining of the tracking data.  
\[T_{i}=T_{j}\ \&\ \text{Lat}_{i}=\text{Lat}_{j}\ \&\ \text{Lon}_{i}=\text{Lon}_{j} \tag{1}\]  
where \(i\) and \(j\) are the \(i\)th and \(j\)th trajectory points of the same vessel.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) Data Mining
#### 2.2.2 SAR Data Pre-Processing  
The whole pre-processing activity, for both ERS-1/2 and Sentinel-1 images, was performed with the SentiNels Application Platform (SNAP) v6.0 software. The pre-processing allowed, for each image, the correction of orbit errors and/or geometric distortions, all of which were introduced during the acquisition of the SAR scenes. In particular, the pre-processing methodology included:  
* \"Apply orbit file\": The orbit file (automatically downloaded by SNAP) provides an accurate position of the SAR image, and the updating of the original metadata of the SAR product.
* \"Radiometric Calibrate\": Calibration allows the creation of a SAR image where the value of each pixel is directly related to the radar backscatter of the surface, expressed in decibel (dB).
* \"Multilooking\": A characteristic of SAR images is the speckle, i.e., random noise that looks like a \"salt and pepper\" effect across the image, and affects the quality of the image, thus making SAR data interpretation more difficult. To remove such an effect, it is possible to use speckle filters or, alternatively, to multilook the image. A speckle filter is a compromise between speckle removal (radiometric resolution) and fine details preservation (spatial resolution). For Sentinel-1 images we applied \"Multilooking\" with 2 looks in azimuth by 2 looks in range [64]. For the ERS-1/2 images we applied \"Speckle Filtering\" using a Lee filter, which was selected among the possible filters to use according to the recommendations in [65], given that Lee filters are less degrading for the SAR image.
* \"Terrain Correction\": We converted the data from slant range geometry to the map geometry (UTM/WGS84 map projection) and corrected the main geometric distortions like layover, shadow and foreshortening, due to terrain relief, using the 3 arcsec Shuttle Radar Topography Mission (SRTM) [66] digital elevation model (DEM), as per the standard workflow of the SNAP software.
## 2 Methods and Materials
### Detection Approach
#### 2.3.2 Quality Assessment  
Having acquiring a clear image of ROI, we use another YOLOv5m model as the basic network for quality assessment, and propose an improvement described below. Note that the value of nc in prediction head is set to 2, as the solder joint needs to be judged as fine or bad.  
The difference between the qualified and unqualified solder joints is subtle and mainly concentrated in the contour and gray characteristics. To make the model focus more on these indistinctive characteristics and suppress other useless information, we introduced an attention structure, SE block [45], before the SPPF block in backbone. The SE block illustrated in Figure 8 mainly contains two operations of squeeze and excitation. First, the squeeze operation performs a global average pooling on the input feature \(x\in\mathbb{R}^{w\times h\times c}\) to encode the spatial feature on each channel into a constant statistic \(z\in\mathbb{R}^{1\times 1\times c}\). Then, in the excitation operation, two 1 \(\times\) 1 convolutional layers and non-linearity activations are used to fully capture channel-wise dependencies and yield the weight for each input channel. In addition, the channel-wise multiplication between the input and the learned weight \(z\in\mathbb{R}^{1\times 1\times c}\) results in the final output of the SE block. Formally, given an input \(x\in\mathbb{R}^{w\times h\times c}\), the output in SE block \(\tilde{x}\in\mathbb{R}^{w\times h\times c}\) can be represented as  
\[\begin{split} z&=F_{ex}(x)=\frac{1}{w\times h}\sum_{i =1}^{w}\sum_{j=1}^{h}x(i,j)\\ \tilde{z}&=F_{sq}(z)=\sigma(W_{2}\delta(W_{1}z))\\ \tilde{x}&=x\otimes\tilde{z}\end{split} \tag{1}\]  
where \(\delta\) and \(\sigma\) denotes the ReLU and Sigmoid activation functions respectively. \(W_{1}\) and \(W_{2}\) refer to the parameters of two 1 \(\times\) 1 convolutional layers used to reduce and increase the dimensionality respectively. By this means, our model can adaptively learn the importance of different channels for the current task and reassign their weights accordingly.