esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Examination of 3D SIBIE Procedure  
### Application of Accelerometer Array for Detecting Signals  
#### 3.1.1 Reinforced Concrete Bridge Deck  
The 3D SIBIE procedure has been applied for existing structures. A RC bridge deck in service was targeted. Two different areas were selected for the case study after the crack inspection work. The crack inspection procedure is schematically described in Figure 9 with 4 steps as follows.  
1. Drilling a 5 mm diameter hole into the concrete deck from the surface of asphalt pavement
2. Injecting epoxy resin (in orange-colored to clearly see the crack)
3. Re-Drilling a 10 mm diameter hole at the same location after the resin is hardened
4. Inserting a flexible fiber optic borescope into concrete to take photos  
Figure 10 shows an example of the view from the fiberscope when it caught the crack filled with resin. In this way, damaged and non-damaged namely intact parts are chosen for the 3D SIBIE procedure. Figure 11 shows the 3D SIBIE result for the RC deck in the intact part and Figure 12 shows  
Figure 8: FFT signal data stacking procedure with/without each signal normalization procedure.  
Figure 9: Visual rack inspection procedure.  
that for RC deck in the damaged part where a lateral crack in concrete and also delamination between an asphalt pavement layer and a concrete layer was found. The sensor array was placed on the bottom surface of the deck and measurement was carried out for the 3D SIBIE procedure. The top surface denotes that of asphalt pavement and the bottom shows the bottom of the concrete layer both in Figures 11 and 12. In these figures, the thickness of the concrete layer is 0-180 mm, 180-250 mm in the fiber reinforced concrete (FRC) overlay, and the asphalt pavement is 250-300 mm from the bottom. In the intact part as shown in Figure 11, no sharply defined reflection intensity is found in the volume of interest while, for the damaged part as shown in Figure 12, the color gradations show strong reflections of elastic waves due to anomaly such as cracks or voids around the bottom of the RC deck (see the bottom area in the right side sectional view). Also, three layers consisting of concrete, FRC and asphalt can be classified by the 3D visualized image. As the strong reflections are obtained from the large difference of acoustic impedance of which the individual materials have, the delamination between the asphalt and the concrete layers, which can be visually confirmed, and sub-critical cracks, which cannot be visually confirmed, are possibly attributed to this fact.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 4 Reference Channel Model
### Mathematical Model of the Channel Transfer Function
Variables \(\gamma_{q}\) and \(\rho_{q}\) for \(q\in\{T,R\}\) are deterministic quantities that characterize the initial direction of motion of the MSs and the direction of their acceleration components, respectively. The AODs and AOAs are circular symmetric random variables having distributions \(p_{\phi}^{T}(\phi)\) and \(p_{\phi}^{R}(\phi)\), respectively. We consider that the function \(p_{\phi}^{R}(\phi)\) follows a von [PERSON] distribution given by [51]:  
\[p_{\phi}^{R}(\phi\;;\;\mu_{R},\kappa)=\frac{e^{\kappa\cos(\phi-\mu_{R})}}{2 \pi I_{0}(\kappa)} \tag{7}\]  
where \(\mu_{R}\) is the mean of the distribution, \(\kappa\) is known as the concentration parameter, and \(I_{0}(\kappa)\) is the modified zeroth order Bessel function of the first kind [51]. When \(\kappa\) increases its value, the values of \(\phi_{\ell}^{R}\) accumulate around its mean value. A particular case of this function is when \(\kappa=0\). In this case, the values \(\phi_{\ell}^{R}\) follow a uniform distribution over \([-\pi,\pi)\). The von Mises distribution in (7) allows modeling arbitrary propagation scenarios with isotropic scattering (\(\kappa=0\)) and non-isotropic scattering (\(\kappa>0\)). Considering that only one side of the radio link is affected by local interactions with IOs and that such interactions occur in the receiver side, the AODs \(\phi_{\ell}^{T}\) can be defined as [31]:  
\[\phi_{\ell}^{T}=\arctan\left(\frac{d_{\ell}^{R}\sin(\phi_{\ell}^{R})}{D+d_{ \ell}^{R}\cos(\phi_{\ell}^{R})}\right) \tag{8}\]where \(d_{\ell}^{T}\) and \(d_{\ell}^{R}\) depend on \(\phi_{\ell}^{R}\) and \(\phi_{\ell}^{R}\) as follows:  
\[d_{\ell}^{T} = \mathcal{G}_{T}(\phi_{\ell}^{T})\ =\ \sqrt{D^{2}+d^{2}-2 dD\cos(\phi_{\ell}^{R})} \tag{9}\] \[d_{\ell}^{R} = \mathcal{G}_{R}(\phi_{\ell}^{R})\ =\ d \tag{10}\]  
when the IOs are assumed to be located on a ring of radius \(d\) centered on \(\boldsymbol{\mathcal{O}}^{\prime}\).
## 2 Proposed Bearing Fault Diagnosis Method
### Signal Preprocessing
#### 2.1.1 Short-Time Fourier Transform  
STFT applies a Fourier transform for localization both in the frequency and time domains for signals that are time-varying or nonstationary [11]. The process for the STFT can be represented as follows.  
\[x_{1}[m]=x[m+lH]w[m] \tag{1}\]  
where \(m\in M\) {1, 2,... } is an index related to the beginning of the sliding extraction window (local time index), \(M\in\mathbf{N}\) is the analysis window length, \(l\in\) {0, 1,..., \(L-1\)} is the frame index, \(L\in\mathbf{N}\) is the number of frames, and \(H\in\mathbf{N}\) is the hop size. Further, the following discrete Fourier transform (DFT) is performed on every frame \(x_{1}[m]\) given a localized two-sided spectrum [11]:  
\[\dot{X}[k,l]= \frac{1}{M}\sum_{m-1}^{k}x_{i}[m]e^{-\frac{2m+k}{K}} \tag{2}\]  
where \(k\in\) {1, 2,..., K} is the frequency bin index and \(K\in N\) is the DFT size.  
Figure 1: Proposed bearing fault diagnosis method using vibration signals.  
The term \(\dot{X}[k,l]\) is called the STFT of \(x\)\([n]\) and corresponds to the local time-frequency behavior of the signal around the time index \(lH\) and frequency bin \(k\).
# JSON Schemas with Semantic Annotations Supporting Data Translation
## 5 Translating JSON Schema to XML Schema
### Algorithm
It creates an XSD element with an element name equal to the JSON data-carrying leaf-node name, and for each keyword, it translates it into an XSD attribute, respecting the following:  
* If the keyword is \"modelReference\", it creates an attribute with a name equal to \"sawsl:modelReference\", of which the value will be the semantic annotation. * If the keyword \"type\" has a value of \"array\", it will be translated according to the mapping table. In this case, there can be an \"items\" keyword at the same level with the array property data type; if this is the case, the content of the \"items\" keyword is analyzed and the datatype is translated according to the mapping table. * Any other keyword is treated according to the mapping table in Table 1.  
This is shown from lines 24 to 45, whereas the use of the mapping table, which is used for translating the \"type\" keyword with \"string\", \"integer\", \"number\", \"boolean\", \"array\", and \"object\", can be seen in line 43. After all attributes are created for the XSD element, the required array is inspected to evaluate the need for a minOccurs attribute with a value of 0. If the current property name is not in it, the minimum number of occurrences is set to 0. A similar approach was taken for translating the complement data proposed in [5]; however, since the number of levels of this JSON data, as proposed in Section 4.2, is known, it is not needed to navigate through it recursively. If there are complement data in the JSON schema, the \"annotation\" and \"appinfo\" elements are added to the document so the data can start being added (lines 56 to 57). For each item of the JSON array with the complement data objects, an XSD element is created and completed with attributes or node values, according to the JSON object properties (lines 58 to 67). To insert these attributes, the JSON object on the item is looped, extracting and inserting the JSON properties one at a time as XSD attributes or node values. It is verified if the property name equals \"a3 st:node-value\", and if so, this property value is directly inserted as the element node value; otherwise, an attribute is created with the property name and an attribute value is set with its value (line 60 to 66). A result of this algorithm applied to the JSON schema presented in Listing 4 is shown on Listing 6.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.9 Scalability  
Scalability of the system includes adaptation capability of the number of users and nodes in a system. System load of the proposed PoF based collaboration is already minimized by storing EMR and EHR off-the-chain. In addition, use of consortium blockchain rises the system scalability.
## 3 Analysis of Results
### Physical Phase Analysis of SiC Surface after Polishing  
The XRD results of SiC after dry polishing were compared with those of SiC before dry polishing, as shown in Figure 4. After importing the XRD data before and after dry polishing into Jade software, it wasfound that the same peaks appeared between 30\({}^{\circ}\) and 40\({}^{\circ}\) and between 70\({}^{\circ}\) and 80\({}^{\circ}\). The peak at 32\({}^{\circ}\) may be SiO\({}_{2}\) after software comparison analysis, and the intensity of the detected peak on the surface of the initial SiC was small. No oxygen appears, indicating that the content on the surface of SiC is small and not easy to detect. The intensity of the peak increased after dry polishing, indicating that silicon oxides were generated on the surface.  
The appearance and change of some micropeaks in the detection results may be (SiO\({}_{2}\))\({}_{\times}\), a class of microporous silicate inclusion compounds with a (4,2)-3D structure [22]. In the dry polishing test, a surface tribochemical reaction generated a layered structure.This layered structure may be due to oxidation during the solid-phase oxidant and SiC test period under the thermal effect of tribochemical reaction transformation of oxidation substances.This layered structure includes a multi-functional layer useful for redox, friction reduction, and anti-wear functions [23; 24].
## 3 Results
### Subgroup Analysis  
Subgroup analyses were conducted to explore whether the type of fabrication method or measurement technique affected the mean difference in terms of the fit accuracy between digital and conventional methods. The subgroup analysis aimed to determine whether specific subgroups of studies reported more consistent or superior outcomes regarding fit accuracy. When stratified by fabrication method, the results showed some variability in the fit accuracy across different digital techniques. Milling techniques (n = 2) reported a mean difference of \(-95.23\)\(\upmu\)m (SD = 15.20 \(\upmu\)m, \(p\) = 0.101), indicating a better fit accuracy than the conventional method. However, the variability was relatively large and the result was not statistically significant.  
The SLM subgroup (n = 3) showed a mean difference of \(-15.10\)\(\upmu\)m (SD = 8.23 \(\upmu\)m, \(p\) = 0.318), suggesting a smaller gap size and an improved fit accuracy compared to conventional techniques. However, the difference was not statistically significant.  
The DMLS subgroup (n = 1) exhibited a mean difference of 8.68 \(\upmu\)m (SD = 10.68 \(\upmu\)m, \(p\) = 0.566), indicating a slight increase in the gap size compared to conventional methods. This result suggests a potentially inferior fit accuracy, but the result was not statistically significant. The MJF subgroup (n = 1) showed a mean difference of 44.08 \(\upmu\)m (SD = 10.68 \(\upmu\)m,\(p=0.151\)), indicating a larger gap size compared to conventional techniques, which points to poorer fit accuracy. This difference was not statistically significant.  
The PEEK subgroup (n = 1) displayed a mean difference of \(-6.33\)\(\upmu\)m (SD = 10.68 \(\upmu\)m, \(p=0.659\)), indicating a slight improvement in the fit accuracy compared to conventional methods, but this result was not statistically significant.  
Although there were observable differences between the techniques, none of the results reached statistical significance (\(p>0.05\)). The differences between the SLM, DMLS, MJF, and milling techniques were relatively small and the findings should be interpreted with caution due to the limited number of studies in each subgroup. Larger, more standardized studies are required to better understand whether digital techniques, like SLM, consistently offer superior fit accuracy compared to other methods.
# Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
## 4 Experiments and Results
### Training Parameters and Evaluation Standards  
The size of all the real samples in the experiment was 28 \(\times\) 28, the training process was 500 epochs, and the batch size of each iteration was 64. The learning rate of the outer loop was 0.0001. The learning rate of the inner loop was a variable hyperparameter. If the inner loop parameter is fixed, there will be non-convergence if the parameter is too large, and there will be slow convergence if it is too small. The initial inner loop parameter was 0.01, and iterations converged to 0. In the MNIST reconstruction, the observation dimensions were respectively set as 196, 157, 118, 78, 39, 16, and 4 (extreme observation). In Fashion_MNIST reconstruction, the observation dimensions were respectively set as 78, 39, 16, and 4 (extreme observation).  
Cosine similarity and structural similarity index measures (SSIM) were used as the evaluation index of reconstruction accuracy. The mathematical expression of cosine similarity is shown in Equation (14).  
\[\text{similarity }=\cos(\theta)=\frac{A\cdot B}{\parallel A\parallel \parallel B\parallel}=\frac{\sum_{i=1}^{n}A_{i}\times B_{i}}{\sqrt{\sum_{i=1} ^{n}(A_{i})^{2}}\times\sqrt{\sum_{i=1}^{n}(B_{i})^{2}}}. \tag{14}\]  
Cosine similarity evaluates the similarity of two vectors by calculating the cosine of the angle between them. The positive value of cosine similarity is between [0, 1], where 0 means the two vectors are not correlated--the greater the similarity, the smaller the distance.  
The structural similarity index measure (SSIM) is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. The SSIM is defined as:  
\[\text{SSIM}(f,g)=l(f,g)c(f,g)s(f,g) \tag{15}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Versions** \\ \hline operating system & Windows10 \\ CPU & Core i5-10400 2.9 GHz \\ GPU & NVIDIA RTX2070 SUPER 8G \\ Python & python3.8 \\ Pytorch & pytorch1.10 \\ CUDA & CUDA version10.2 \\ cuDNN & cuDNN7.6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiment operating environment.  
\[\begin{cases}l(f,g)=\frac{2\mu_{f}\mu_{S}+C_{1}}{\mu_{S}^{2}+\mu_{S}^{2}+C_{1}}\\ c(f,g)=\frac{2\mu_{S}\mu_{S}+C_{2}}{\mu_{S}^{2}+\mu_{S}^{2}+C_{2}}\\ s(f,g)=\frac{\mu_{S}+C_{3}}{\mu_{S}\mu_{S}+C_{3}}\end{cases} \tag{16}\]  
where \(l\) is the luminance comparison function, the second term \(c\) is the contrast comparison function, and the third term \(s\) is the structure comparison function. The positive value of SSIM is between [0, 1]. The lower the value, the lower the correlation.
## 4 Bionic Morse Coding Method
### Information Modulation
#### 4.2.1 Step 1: Synchronization  
The first step at the receiver is to identify the mimicked signal. A variety of similar noises are present is the sea. The synchronization header plays an important role in the identification of the mimicked waveform and differentiating it with real sea noise. This is done through correlation with the synchronization vocal and verifying it by the checksum of bits in the synchronization header. If the  
Figure 6: Frame structure of bionic mimicry communication.  
numbers of bits are not same as defined in the synchronization header, it could be the ocean noise. The synchronization waveform with the checksum of bits should be known to the receiver.  
In this research we have used a humpback whale song for bionic communication. The synchronization signal is also composed of one segment of a humpback whale song. The time and transform characteristics of the synchronization waveform are shown in Figure 3a,f respectively. It is the broadband signal modulated with a wide range of frequencies from 0.1 to 3.5 kHz. The first segment of Figure 1b is used as a synchronization signal in this research. We can clearly notice from Figure 1 that all the segments are broadband in nature modulated with multiple frequencies. Figure 7 shows that the characteristics of humpback whale song with respect to the Doppler factor and auto correlation. The Doppler factor is calculated by change in frequency with respect to carrier frequency. Therefore it can be equated as  
\[\alpha=\frac{\Delta f}{f_{c}} \tag{3}\]  
where \(\alpha\) is the Doppler factor and \(\Delta f\) and \(f_{c}\) represents the change in frequency and carrier frequency respectively.  
The received signal from the channel is composed of the synchronization signal and the message signal. First the synchronization signal is identified using a correlation method. Figure 8 demonstrates the detection process of the synchronization signal from the received waveform. The high peak of correlation illustrates that the correct mimicked signal is received at the receiver. Since there are plenty of similar vocals present in the ocean, the synchronization signal is verified by checking the checksum of bits.  
Figure 7: Ambiguity function of a humpback whale song.  
After the verification of the correct synchronization received, it is removed from the received signal. Now the received signal consists of a message frame modulated with bionic Morse elements which holds covert information. Detection of bionic Morse elements and mapping it to covert information is briefly elaborated in Sections 4.2.3 and 4.2.5 respectively.
## 2 Methodology  
This section details the methodology employed for modeling the degradation data obtained through the accelerated testing of PEMFC stacks and evaluating their reliability under normal operating conditions. The section is structured as follows: Section 2.1 describes the durability test protocol, Section 2.2 introduces the bi-exponential model for characterizing cell-voltage degradation, Section 2.3 discusses the integration of the Arrhenius model to account for temperature-induced acceleration of the PEMFC stack, and Section 2.4 explains the application of a NLME model to distinguish between fixed and random effects within the model parameters. The proposed method operates under the assumption that individual cells within a PEMFC stack degrade independently.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 5 Experiments  
### Evaluation Metrics  
To evaluate the performance of the proposed method, three commonly used evaluation indicators in semantic segmentation are adopted in this paper, namely precision, recall, and mIoU. Since the calculation of these four indicators requires the use of four other basic indicators, True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN), we present the definition of them in Table 2. Then, the precision refers to the ratio of pixels correctly classified as positive by the model to all pixels classified as positive by the model, and the formula for calculating precision is  
\[precision=\frac{TP}{TP+FP} \tag{5}\]  
Figure 5: Some examples of the new roof segmentation dataset.  
The recall denotes the ratio of pixels correctly classified as positive by the model to all true positive pixels, and the value of the recall is given by the equation,  
\[recall=\frac{TP}{TP+FN} \tag{6}\]  
Finally, the intersection over union (IoU) is defined as the ratio of the intersection of the model predictions and the true values of each category.  
\[IoU=\frac{TP}{FN+FP+TP} \tag{7}\]
## 4 Results and Discussions
### Comparisons between Two Solutions
#### 4.1.2 Deflection and Radial stress  
Due to the fact that the relation of loads vs central deflection based on \(W_{m}\) or on \(P_{m}\) is quite equivalent, we may use this equivalence to check the consistency of deflection and radial stress. For the convenience of equivalence proof, we list the derivation of correlation of these perturbation solutions, as shown in Table 1.  
We note that Equation (74) is the relation of load vs central deflection for simply hinged, or is called as a general relation, as indicated above. For rigidly clamped edge, we have the relation, only by letting \(\lambda_{1}=0\),  
\[P_{m}=4 KW_{m}+\frac{V(73+50\lambda_{2})}{1080}W_{m}^{3} \tag{79}\]  
for movably clamped, we have, by letting \(\lambda_{1}=\lambda_{2}=0\),  
\[P_{m}=4 KW_{m}+\frac{73V}{1080}W_{m}^{3}+\cdots \tag{80}\]  
and for simply supported, we have, by letting \(\lambda_{2}=0\),  
\[P_{m}=\frac{4K}{2\lambda_{1}+1}W_{m}+\frac{V}{1080(2\lambda_{1}+1)^{4}}(360 \lambda_{1}{}^{4}+840\lambda_{1}{}^{3}+825\lambda_{1}{}^{2}+388\lambda_{1}+73 )W_{m}^{3} \tag{81}\]
# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 2 Methodology  
Articles were searched and identified from nine bibliographic databases: IEEE, Science Direct, MDPI, Web of Science, Scopus, Google Scholar, ProQuest, Springer, and Wiley Online Library. The primary keyword'remote sensing' and its synonyms were paired with  
Figure 1: The herbicides usage in controlling weeds by continent from 1990 to 2019.  
the secondary keyword 'weed' and the third keyword 'detection' and its synonyms, with Boolean operators. These keyword sets were used in each database search. In addition, a hand search was also run to ensure no related articles were missed. The search was conducted in the quarter of 2021.  
All search results were filtered based on five criteria: (1) the study must use remote sensing imagery and platform as the primary data input with at least three spectral bands (red, green, and blue), (2) the study must discuss the application of remote sensing techniques in weed detection, (3) the document must have reported the research conducted, (4) the included documents have been published up to the quarter of 2021, and (5) the articles must be in English.  
Next, the articles were screened by title and abstract to eliminate articles that did not meet the stated criteria. Finally, the full text of the remaining articles was carefully reviewed to decide whether they met the criteria or not. Lastly, details from selected articles were extracted and compiled into one giant spreadsheet. The details include citation information, study objective, remote sensing sensor, crop and weed types, approaches and technique used, accuracy assessment, study's implications, year of publication, and reference data.