esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractional Order Processing of Satellite Images
## 3 Results
### Grey-Scale Edge Detectors vs. Color Based Edge Detectors  
As explained before, color edge detection algorithms were implemented and tested with the whole data set due to their good results in other applications. All the six methods were adapted and applied in the main algorithm in order to perform color-based edge detection. The best results of the performance assessment for these versions of the algorithms are presented above and are compared with the corresponding best grey-scale result, using index \(\Delta J\):  
\[\Delta J=J_{\text{Color}}-J_{\text{Grey-scale}} \tag{20}\]  
The results of this comparison are presented under the form of histograms (in semilogarithmic plots) for each detector in Figure 24.  
Figure 23: Best results for image 38 processed using LoG algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \(k\) & \(\sigma\) & **Threshold** & \(\kappa\) & \(J\) & \(D\) & **Sensitivity** & **Specificity** \\ \hline
**Integer Canny** & - & 0.2 & - & - & 0.5180 & 0.6825 & 0.5365 & 0.7167 \\
**Fractional Canny** & - & 0.6 & - & 2.4 & 0.7356 & 0.8477 & 0.7600 & 0.7359 \\
**Integer Sobel** & - & - & 0.1 & - & 0.0768 & 0.1427 & 0.0775 & 0.9271 \\
**Fractional Sobel** & - & - & 0.9 & 0.5 & 0.9600 & 0.9796 & 0.9997 & 0.6710 \\
**Integer Roberts** & - & - & 0.1 & - & 0.0618 & 0.1163 & 0.0622 & 0.9451 \\
**Fractional Roberts** & - & - & 0.1 & -1.7 & 0.9613 & 0.9803 & 0.9990 & 0.6877 \\
**Integer LoG** & - & - & 0.01 & - & 0.0887 & 0.1630 & 0.0898 & 0.9076 \\
**Fractional LoG** & - & - & 0.1 & 1.8 & 0.9637 & 0.9815 & 0.9994 & 0.7046 \\
**CRONE** & 2 & - & - & 3 & 0.9612 & 0.9802 & 0.9995 & 0.6818 \\
**Fract. Deriv. Mask** & - & - & 0.9 & 2.3 & 0.9625 & 0.9809 & 0.9995 & 0.6936 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best performance results using Grey-Scale detectors on image 38.  
Figure 22: Best results for image 38 processed using Roberts algorithms.
## 4 Results and Validation
### Small-Scale Testing
#### 4.1.2 Flexible Structure Monitoring
Figure 19: Dynamic response of the 2-DOF structure in the frequency domain: (**a**) SVD spectrum of the reference SHM system. (**b**) SVD spectrum of the low-cost SHM system. The modal damping ratio related to the first dominant vibration mode was calculated by the logarithmic decrement method from the free-vibration dataset. For this purpose, a band-pass Butterworth filter, with cut-off frequencies at 0.10 Hz and 4.00 Hz, was implemented on free-vibration signals to hold the 1.95 Hz natural frequency content. Then, from a set of representative acceleration peaks, the logarithmic decrement was computed over every peak couple separated by six cycles to estimate the damping ratio. The same procedure was performed for the free-vibration signals on the DOF 1. Nevertheless, the damping ratio of the second dominant vibration mode was not considered due to its low modal contribution and the considerable disturbance produced by the filtering process [35]. Figure 22 indicates the exponential free-decay window selected from both SHM systems on the DOF 2 and a group of representative acceleration peaks from the ADXL355 RBP3 accelerometer. By gathering a complete dataset of damping ratio results from measurement channels 1 and 2, the mean damping ratio and its coefficient of variation were calculated through both SHM systems and are indicated in Table 8. The mean damping ratio determined by the WSN monitoring system presented a relative error of 15.68% regarding the result from the wired monitoring system. The coefficients of variation of the mean damping ratio  
Figure 21: Modal assurance criterion for the mode shapes estimated. Figure 20: Mode shapes estimated by the monitoring systems. from both monitoring systems did not exceed the state-of-the-art limit reported in [35], suggesting relative standard deviation or coefficient of variation values up to 53.0%.
# A Domain-Based, Adaptive, Multi-Scale, Inter-Subject Sleep Stage Classification Network
## 5 Conclusions  
In this paper, a domain-based adaptive multi-scale network was proposed to improve subject-specific sleep staging based on a single-lead ECG. We presented a multi-scale residual feature encoder to extract various details, which supports the model to deal with the feature extraction of single-lead ECGs in different situations to improve the test results. Taking the domain shift caused by individual differences and acquisition conditions into consideration, we utilized MMD to optimize the feature encoder so that the features from the confused domain were obtained to make the task of sleep stage classification more generalized. In addition, to enhance the performance of the model, the multi-class focal loss was used to reduce the negative impact of class imbalance on the learning of the model, and the loss of sequence prediction was added to the classification task to assist the model in the judgment of sleep staging. Finally, the methods achieved excellent performance in terms of accuracy and Cohen's Kappa in the three public datasets and four types of cross-dataset modes. Hence, for sleep monitoring, this approach, which outperforms the state-of-the-art solutions based on single-lead ECGs, makes contributions to the analysis of the subjects' sleep structures under comfortable conditions.  
Although the overall performance of the proposed model on the test sets is excellent, there are limitations in its robustness to different subjects. For subjects whose feature distribution is too far from the distribution of the training set, the aligning effect of domain adaptation is limited, resulting in significantly below-average accuracy of sleep staging for these subjects. The question of how to improve the prediction accuracy of the model for the sleep staging of these people so that the model can adapt to extreme distribution gaps will become a future task, seeking to make the methods of sleep staging more robust. It is a major challenge to apply the proposed method to sleep monitoring based on ECGs or other single physiological signals on wearable devices that are comfortable and portable for users.  
Conceptualization, Z.Z. and M.T.; methodology, Z.Z.; software, M.T.; validation, Z.Z. and M.T.; formal analysis, Z.Z. and M.T.; investigation, Z.Z. and M.T.; writing--original draft preparation, M.T.; writing--review and editing, Z.Z.; visualization, Z.Z. and M.T.; supervision, Z.Z; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript. This work was funded by the CGYMS Innovation Fund (2019-ZTKJ-0202).  
Not applicable.  
The authors thank the editors and anonymous reviewers for their constructive suggestions. The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
## 9 5G and its Relationship with LPWAN  
As stated earlier, 2G, 3G, and 4G cellular technologies are primarily targeted towards human-oriented voice, data, and video applications. NB-IoT and LTE-M were key initiatives by the 4G LTE technology to address LPWAN-oriented applications.  
The upcoming 5G technology is a fundamental paradigm shift from previous generations [42; 43]. In addition to handling human-oriented voice, data, and video-based applications at significantly higher performance and functionality, 5G is being defined to address M2M applications in the IoT space [44]. The key improvements in 5G in reference to 4G technology relevant to M2M oriented applications are peak data rate (20x), area traffic capacity (100x), network energy efficiency (100x), and connection density (10x). Latency is also decreased by a factor of 10 with respect to the 4G standard. The relative cost per GByte is also estimated to decrease by a factor of three with respect to a similarly configured 4G network. In addition to operating in the sub-6 GHz frequency spectrum, similar to currently existing cellular access and LPWAN technologies, the 5G operating spectrum is being extended to 100 GHz. This is generally termed as mmWave region operation. Clearly, operation at these frequencies has several challenges including high path loss, deterioration of propagation characteristics owing to environmental issues, and susceptibility to blockage owing to obstructions. These issues are mitigated using multiple input multiple output (MIMO) antennas and the use of steerable narrow beams directed towards individual users or devices.  
5G is being defined in terms of three suggested solutions. In addition to enhanced mobile broadband (eMBB) primarily targeted at multimedia content and data applications, 5G provides massive machine type communication (mMTC) to cover a wide range of IoT applications and ultra-reliable low latency communications (UR-LLC) for the delivery of critical communications. As the M2M applications continue to expand and more challenging network requirements come into play, 5G wireless will have a major competitive advantage in becoming the network of choice for IoT-based applications including applications currently being addressed by LPWAN solutions. It is expected that, in the upcoming future, 5G and existing LPWAN technologies will form complementary hybrid network solutions in enabling and accelerating IoT deployments. 5G and selected LPWAN will interrupt with each other, especially as the 5G new radio (NR) is being designed to interface with multiple technologies. 5G will provide an effective backhaul connection for LPWAN access networks for connecting to the centralized entities, for example, the cloud. Whereas typically, LPWAN is a very powerful solution for applications requiring primarily periodical or event-based transmission of small messages, 5G will address IoT applications requiring high throughput data speeds and very low latency.
## 4 Conclusions  
This study was able to derive 12 defect categories and sub-defects using the content analysis technique, draw the cause and effect relationship between embedded software defects, and derive core defects using the DEMATEL method. After studying the data yielded throughout the different stages of the study's analyses, the results show that the core embedded software defects were the external interface defect, the hardware interrupt defect, device driver defect, timing error, and task management defect.  
What this study does is integrate and organize pure software defects and embedded software defects from around the world, opening avenues for other researchers to improve software quality. This study also helps mitigate the risks that come from critical defects that might not have been tested. Moreover, the impact relationships between defects can be better mapped through the diagrams presented here. Lastly, using the cause and effect relationships, this study constructed a basis for estimating defect weights. Future studies may validate and use them as criteria for targeting embedded software defects.  
There are also many industrial applications for this study. First, by eliminating the time required to collect and classify the defects, one may immediately inspect and target any defects that may be present. Second, when developing an embedded system, systems can remove defects more efficiently and effectively using a guide that orders defects by importance. Third, analyzing the priorities of the defects may facilitate a more accessible selection of the appropriate embedded software test technique. Lastly, when performing a fault injection test, this study suggests that more defects can be injected and tested in the source code where core defects are likely to occur.  
In this study, embedded software defects were classified into 12 defect categories and sub-defects. Moreover, the influence relationship of defects was analyzed for each of the 12 defect categories andclassifying the defects into cause group defects and effect group defects. However, the weight of the defect was not completely calculated, and the influence relationship of the sub-defects was not analyzed. Therefore, future studies that derive the weights of sub-defects and studies that analyze the influence relationship of sub-defects to identify cause defects and effect defects at the level of sub-defects are essential. In addition, future researchers can look into how to improve the defect removal rate while conducting embedded tests (such as defect injection tests) using the defects derived in this study, compared to the existing tests.  
Conceptualization, S.M.H. and W.-J.K.; methodology, S.M.H. and W.-J.K.; validation, W.-J.K.; investigation, S.M.H.; resources, S.M.H. and W.-J.K.; writing--original draft preparation, S.M.H., writing--review and editing, S.M.H. and W.-J.K.; project administration, S.M.H. and W.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Actual Case Analysis
#### 4.2.1 Case 1  
A fault occurs at L\({}_{\text{SW}}\). Lp\({}_{71}\)m and Lp\({}_{33}\)m operate, and then CB\({}_{71}\) and CB\({}_{33}\) trip. However, the alarm message for CB\({}_{33}\) is lost. The alarm messages are briefly described as Lp\({}_{71}\)m(0), Lp\({}_{33}\)m(5) and CB\({}_{71}\)(25). The fault diagnosis process is as follows:  
1. \(CBS=\{\)CB\({}_{71}\)\(\}\) can be obtained from the alarm message. Traverse \(CBS\) and search for suspected faulty components. There are a subset \(COM_{71}=\{\)L\({}_{\text{SW}}\), L\({}_{\text{XQ}}\), B\({}_{3}\), B\({}_{7}\}\), and a total set \(COMS=\{\)L\({}_{\text{SW}}\), L\({}_{\text{XQ}}\), B\({}_{3}\), B\({}_{7}\}\).  
Figure 6: The wiring diagram of the power system.  
\(\rm L_{SW}\) is selected for fault diagnosis. The \(\mathbf{\alpha}_{p(11)}^{0}\), \(\mathbf{T}_{p(11)}^{0}\), \(\mathbf{\alpha}_{p(12)}^{0}\), and \(\mathbf{T}_{p(12)}^{0}\) of the fault diagnosis model for the \(\rm CB_{71}\)and \(\rm CB_{33}\) subnets are as follows. \[\left\{\begin{array}{l}\mathbf{\alpha}_{p(11)}^{0}=[0.9833,0.2,0.2,0.9913,0.2,0.2, 0,0,0,0]^{\rm T}\\ \mathbf{T}_{p(11)}^{0}=[[25,25],[25,25],\varnothing,[0,0],\varnothing,\varnothing, \varnothing,\varnothing,\varnothing]^{\rm T}\\ \end{array}\right.\] (36) \[\left\{\begin{array}{l}\mathbf{\alpha}_{p(12)}^{0}=[0.2,0.2,0.2,0.9913,0.2,0.2, 0,0,0,0]^{\rm T}\\ \mathbf{T}_{p(12)}^{0}=[\varnothing,\varnothing,\varnothing,[5,5],\varnothing, \varnothing,\varnothing,\varnothing,\varnothing]^{\rm T}\\ \end{array}\right.\] (37) 3. The diagnosis results for the \(\rm CB_{71}\) subnet are \(\alpha_{p10(11)}=0.9873\) and \(T_{p10(11)}=[-40,-10]\). The diagnosis results of the \(\rm CB_{33}\) subnet are \(\alpha_{p10(12)}=0.5567\) and \(T_{p10(12)}=[-35,-5]\). The diagnosis results of \(\rm L_{SW}\) are \(\alpha_{(1)}=0.7915\) and \(T_{(1)}=[-35,-10]\), and its measured value \(\eta_{(1)}=0.8540\), which is greater than 0.6. Thus, \(\rm T_{1213}\) is a faulty component. 4. Similarly, the measured values of \(\rm L_{XQ}\), \(\rm B_{3}\) and \(\rm B_{7}\) are 0.2363, 0.14, and 0.2085, respectively, and they are non-faulty components.
## 3 Dcmm
### Segmenting the Basic Score  
Next, the K-Means clustering algorithm is applied to segment the basic score. The K-Means algorithm is a commonly used clustering method that divides data points into different clusters or groups, so that similar data points are close to each other, while dissimilar data points are farther apart. This algorithm is an unsupervised learning method, as it does not require prior knowledge of the categories of data points but automatically identifies and groups them. By using the K-Means algorithm, nodes are divided into levels according to performance, and tasks are divided into corresponding performance nodes according to computing task resource requirements, which can reduce the amount of calculation of Euclidean distance. Typically, the K-Means algorithm can be broken down into the following six steps:  
1. **Inputs:** Input the dataset \(S_{n}=\{S_{1},S_{2},\ldots,S_{i},\ldots,S_{m}\}\) (\(S_{n}\) represents the set of all basic scores) and the maximum number of iterations \(max\_interactions\) are provided.
2. **Initialization:** Define the number of clusters \(K\) (In this article, K = 3), and initialize the cluster centroids \(C^{(0)}=\{C_{1}^{(0)},C_{2}^{(0)},\ldots,C_{K}^{(0)}\}\). The initial cluster of centroids can be randomly chosen.
3. **Assignment:** For each data point \(S_{i}\), calculate its distance to each cluster centroid \(C_{j}\), typically using the Euclidean distance measured by the formula: \[d(S_{i},C_{j})=\sqrt{\sum_{k=1}^{n}{(S_{jk}-C_{jk})^{2}}}\] (10) Subsequently, assign each data point \(S_{i}\) to the cluster with the nearest centroid \(C_{j}\) where \(j\) is determined by the following formula: \[j=arg\ min_{j}\ d(S_{i},C_{j})\] (11)
4. **Update Centroids:** For each cluster \(j\), calculate the new cluster center \(C_{j}^{(t+1)}\) as the mean of all data points in that cluster: \[C_{j}^{(t+1)}=\frac{1}{|C_{j}|}\sum_{S_{i}\in C_{j}}x_{i}\] (12) where \(|C_{j}|\) represents the number of data points in the cluster \(j\), and represents the iteration number.
5. **Convergence Check:** Usually, K-means iterates until a stopping condition is met, such as when the cluster centers no longer undergo significant changes (or changes are below a certain convergence threshold \(\epsilon\)), or when the maximum number of iterations \(max\_interactions\) is reached. This can be expressed as: \[\left\|C_{j}^{(t+1)}-C_{j}^{(t)}\right\|<\epsilon\] (13)
6. **Outputs:** The final result of the K-Means algorithm includes the ultimate cluster centers \(C^{(t)}=\{C_{1}^{(t)},C_{2}^{(t)},\ldots,C_{K}^{(t)}\}\) and the set of data points assigned to each cluster \(Z_{i}\subset\{Z_{1},Z_{2},\ldots,Z_{n}\}\).  
Finally, all basic scores are divided into K categories \(C^{(t)}=\{C_{1}^{(t)},C_{2}^{(t)},\ldots,C_{
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 3 Results
### Large Ideal Reflector Array
#### 3.1.4 Temporal Distribution of Retroreflected Waveforms  
The use of distinct patches with focusing and/or space-reversal spreads the energy of the retroreflected waveform over time, because each patch can contribute a distinct reflection to the impulse response. Figure 10 shows two examples of a retroreflected sound envelope and its cumulative sum for the four patch types. The surface distance is 2 m, and patch size is 1.2 m. The additional latency from space-reversal for 1.2 m patches is \(\tau_{\text{min}}=4.9\) ms. To match the other analyses of the large reflector, the impulse responses in Figure 10 were octave-band filtered (6 th order 125 Hz-4 kHz center frequencies) and speech-A-weighted before being recombined, and this process spreads the impulse energy in time.  
The source-receiver position had little effect on the reflected sound from the simple surface, but had a large effect for the focusing patch. In those cases, the reflected sound event was relatively compact in time. Space-reversal and focusing space-reversal patches yielded extended periods of reflected energy, with contributions from distant patches evident. The source-receiver position did not change the overall temporal spread of reflected energy for those patches. For non-focusing space-reversal surfaces, the cumulative energy sum grew relatively slowly, indicating that numerous patches contribute to the total energy. However, for focusing space-reversal patches, significant energy contributions came from the closest patch and its neighbors, with distant patches making negligible contributions to the total energy.  
Figure 9: Octave-band distribution of retroreflected energy levels for 25 collocated source-receivers (large reflector), showing individual values (thin lines) and energetically averaged values (thick lines): (**a**) space-reversal patches (**b**) focusing space-reversal patches.
## 4 Optimization of Channel MIMO Capacity by Combining OSTBC and Polarization-Diversity with XPI
### Diversity Transmission Using OSTBC-MRRC MIMO Technique

Table 4: Transmission OSTBC coding operation. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(t\) & \(t+T_{s}\) \\ \hline \(YX_{1}\) & \(Y_{1}(1)\) & \(Y_{1}(2)\) \\ \(Z_{1}(1)\) & \(Z_{1}(2)\) \\ \hline \(YX_{2}\) & \(Y_{2}(1)\) & \(Y_{2}(2)\) \\ \(Z_{2}(1)\) & \(Z_{2}(2)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Received signal \(Y\) and additive white Gaussian noise (AWGN) signal \(Z\). In short,  
\[Y=H_{TR}S+Z \tag{13}\]  
where \(S\) is the OSTBC matrix symbol, \(Z\) is the independent complex Gaussian random variable noise, and \(H_{TR}\) is the MIMO channel response matrix under Quasistatic Flat-Rayleigh-Fading assumptions. In addition, \(H_{TR}\) is:  
\[H_{TR}= \begin{bmatrix}h_{11}&h_{12}\\ h_{12}^{*}&-h_{11}^{*}\\ h_{21}&h_{22}\\ h_{22}^{*}&-h_{21}^{*}\end{bmatrix}  \tag{14}\]  
An important feature of this OSTBC coding matrix is the orthogonality between its columns [24]. If the condition  
\[column_{1}^{H}column_{2}=0 \tag{15}\]  
is met, the column vectors are orthogonal. The operator \((\ \cdot\ )^{H}\) is the complex transpose. For the \(H_{TR}\) case,  
\[ \begin{bmatrix}h_{11}^{*}&h_{12}&h_{21}^{*}&h_{22}\end{bmatrix} \cdot \begin{bmatrix} h_{12}\\ -h_{11}^{*}\\ h_{22}\\ -h_{21}^{*}\end{bmatrix} =0\]  
This critical feature allows the symbols to be decoded and Equation (10) to be obtained. It can also be seen by presenting Equation (9) as the following matrix:  
\[\tilde{S}=(H_{TR})^{H}Y\]  
Next, (13) is substituted into the above equation to obtain  
\[\tilde{S}=(H_{TR})^{H}(H_{TR}S+Z)=\sum_{i=1}^{2}\sum_{j=1}^{2}\lvert h_{ij} \rvert^{2}\mathbf{I}_{2}S+(H_{TR})^{H}Z\]  
which is the same as Equation (10). \(\mathbf{I}_{2}\) denotes an identity matrix of size 2. Performance of OSTBC-OFDM Combined with Dual-Polarization Model, Under Rain Distortion in mmWave Channels  
In this section, we formulate and demonstrate the formulation and optimization of the communication model described in Section 2, in combination with OSTBC-OFDM, XPI, and polarization techniques. The OSTBC operation on \(H_{TR}\) leads to:  
If we neglect the power leakage \(XPI_{T},XPI_{R}\), meaning \(A=C=D=0\), and \(B=1\), the channel response matrix, \(H_{TR}\) becomes (4). To optimize the system performances, for example, the BER, energy efficiency, spatial-separation, and also to maximize the capacity of the throughput with DP, and to add precision to the process of the CSIR estimation, the receiver must approximate three different stages one by one. The first stage is to estimate the MIMO-multi-path fading response channel,\(\tilde{H}_{TR}\). The next step is to generate a receiver-transmitter convention on one of the four optimal cases (according to the optimization process we will present below) and to adjust the XPI mechanism in the receiver-transmitter to meet the selected condition. Finally, it is necessary to measure parameters that we are interested in improving in the receiver.