esa-sceva/satcom-chunk-collection huggingface dataset:
# Brainware Computing: Concepts, Scopes and Challenges
## 5 Challenges and Opportunities
### Privacy and Security  
In the proposed Brainware computing architecture, robots collect and share data with the nearest edges. Inter-edge communication needs to share data together. Sometimes, robots may collect and send sensitive information, which is the fundamental concern of privacy.  
If a robot is set for service in a home, office or any other confidential place, a lot of private information can be collected by the cameras and microphones. In this case, Brainware Computing performs federated learning to keep sensitive data confidential.  
To ensure the privacy, we suggest two approaches for two different purposes: one is federated learning [53] for training using local sensitive data, and the second one is a deep learning encoder-decoder [54] approach (see Figure 6) to ensure end-to-end encryption and decryption facilities in all the sender and receiver end during data sharing among the edges. According to the approach, during data sharing, each robot encodes data using the encoder part of the model and shares only the encoded features with the nearest edges. Coordinating communication blocks includes the decoder of the encoder-decoder network and decodes the encoded features, which has been sent by the robots. Each edge holds data privacy using the encoder-decoder approach. However, in Brainware Computing systems, only the encoded features of all kinds of data such as images, voices, metadata etc., are shared to ensure privacy. More specifically, images and voice data are encrypted using convolutional and recurrent neural network-based encoder-decoders because of their better performance. Moreover, in terms of global learning using local sensitive data, Brainware Computing uses federated learning [53] strategy where the local model in the robot learns from the local data. Global learning blocks in cloud computing perform global federated averaging to obtain a globally generalized trained model.  
For the purpose of security, there is a section called registry and discovery in the edge clouds. The following concern should be maintained to ensure security for the Brainware Computing. [PERSON] et al. [55] and [PERSON] et al. [56] introduced some techniques to ensure the security for fog and edge computing, respectively. Being motivated by these concepts, in Brainware Computing, we suggest the following steps:  
* Federated learning to hold the data privacy in edge computing;
* Encryption of metadata and access verification and authorization: Registry and discovery block is responsible for this task;
* To detect unauthorized access the block includes intrusion detection systems (IDS) and multiple step verification systems;
* Analysis and identification of User Behaviour Profiling (UBP).  
Figure 6: Encoder–decoder example for transferring the encoded features of images instead of actual data.
## 3 Proposed CS-DDSG Scheme
### Procedures of CS-DDSG
```
1: The probability of selecting source nodes: \(P_{1}\); The probability of forwarding: \(P_{2}\); The number of measurements: \(M\);
2:
3: Measurement vector: \(y\); Measurement matrix: \(\mathbf{\Phi}\); Stage 1:
4:for\(i=1\):\(N\)
5:\(S(i).id=[i]\);
6:\(S(i).data=x_{i}\);
7:endfor Stage 2:
8: Nodes select themselves with the probability \(p_{1}\) and broadcast their packets;
9:\(N_{2}=0\);
10:for\(i=1\):\(N\)-\(p_{1}\)
11:for\(j=1\):\(N\)
12:if node \(i\) receives the broadcasting data from node \(j\)
13:\(S(j).id=[j,i]\);
14:\(S(j).data=x_{j}+x_{i}\);
15:\(N_{2}=N_{2}+1\);
16:endif
17:endfor
18:endfor
19:endfor
20:
21:endfor
22:endfor
23:
24: The receiving nodes in Stage 2 forward their update packets with probability \(p_{2}\). 25:\(N_{3}=N_{2}p_{2}\);
26:for\(loop=1\):max
27:if\(N_{3}\leq 1\)
28:break
29:endif
30:if node \(j\) forwards its packets
31:for\(i=1\):\(N\)
32:if node \(i\) has not received a packet and hears node \(j\)
33:\(S(i).id=[i,j]\);
34:\(S(i).data=x_{i}+x_{j}\);
35:\(N_{3}=N_{3}+1\);
36:endif
37:endfor
38:endif
39:endif
40:endif
41:The reception nodes in the stage 3 forwarding their packets with probability \(p_{2}\). 42:endfor Stage 4:
43:The mobile sink queries \(M\) nodes to generate \(\mathbf{\Phi}\) and \(Y\). 44:\(\mathbf{\Phi}=zeros\) (\(M,N\))
45:if node \(i_{k}\) are queried
46:\(\mathbf{\Omega}_{k}=S(i_{k}).id\);
47:\(\mathbf{\Phi}(k,\mathbf{\Omega}_{k})=1\);
48:endif
49:Return \(\mathbf{\Phi}\) and \(Y\). ```  
**Algorithm 1** The CS-DDSG algorithm
## 5 Experiments and Results
### Qualitative Analysis
#### 5.2.2 Text Recognition  
Since we have character-level annotations, the goal is to group them into words before they are fed into the OCR model for recognition. Figure 23 illustrates the post-processing outputs of easy samples after the text heatmap regression discussed in Section 5.2.1. Figure 23a-d shows the easy predictions with clean horizontal texts without tilted signboards. Figure 24 shows the challenging scenarios when the horizontal texts are rotated or tilted. Figure 24a-d shows the dilation masks for all the text classes combined for hard samples. Figure 24e-h shows the corresponding word grouping performed in the post-processing step. These images, when cropped for the OCR model, lead to poor character recognition. OCR for rotated images is still a challenging task in the field of AI. We fixed this issue to some extent, as shown in Figure 24i-l, using improved contour masking, but with the penalty of increased post-processing time. Figure 25 illustrates the prediction differences between the YOLOv3 and our model. It can be seen that the YOLOv3 model performs well on large and medium objects, whereas our anchorless model performs well on small objects. It is safe to say that our model performs well on small objects, in our case, text characters.  
Table 1 illustrates some of the OCR model predictions for easy and hard samples. \"CTC Output\" indicates the labels predicted by the OCR model, which are then decoded into words from the dictionary indicated by \"Decoded Label\". The model fails to detect texts that are very small because of poor image quality after cropping. The OCR dataset was generated randomly up to a maximum length of eight. Better predictions may be obtained if the dataset is generated with similar words used when text augmenting the 3D model dataset. We found that the model is more accurate for English alphabets compared to Japanese kanji characters.  
Figure 23: Character level predictions to word grouping for easy samples (**a–d**).  
Figure 22: Activation maps of the intermediate layers of hourglass model (**a**) Without attention modules; (**b**) With attention modules.  
Figure 24: Character level predictions to word grouping for hard samples. (**a–d**) Dilation masks; (**e–h**) Character to word grouping; (**i–l**) Improved character to word grouping.  
Figure 25: Cont.
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 3 Results and Discussion
### Study on the Influencing Factors of Single-Tooth Broken Rock
#### 3.2.4 Single Spherical Tooth Crushing Specific Work  
As shown in Figure 10, the specific energy of rock breaking can be regarded as the slope of the rock volume fracturing curve, which reflects the efficiency of utilizing impact energy. When the drilling pressure is less than 1.4 kN, the specific energy increases with the rise in impact energy. At a drilling pressure of 1 kN, the specific energy of the 16 mm spherical tooth is significantly higher than that of the 22 mm spherical tooth. However, when the drilling pressure exceeds 1 kN, the values for both sizes become similar. When the drilling pressure surpasses 1.4 kN, the specific energy of the 22 mm spherical tooth exceeds that of the 16 mm spherical tooth. At a drilling pressure of 1.4 kN, the 22 mm spherical tooth reaches a peak at an impact energy of 35 J. When the drilling pressure is 1.8 kN, the 22 mm spherical tooth demonstrates consistently higher specific energy than the 16 mm counterpart.  
This indicates that when the drilling pressure is below 1.2 kN, the 16 mm spherical tooth has a higher impact energy utilization rate. However, when the drilling pressure exceeds 1.2 kN, the 22 mm spherical tooth exhibits a more efficient utilization of impact  
Figure 10: The comparison diagram of crushing specific work of 16 mm and 22 mm spherical teeth under different impact energy.  
energy. Additionally, in the high drilling pressure range, the specific energy tends to stabilize. As a result, at higher drilling pressures, a smaller impact energy is sufficient to achieve greater specific energy for rock breaking.
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 3 Compact Bat Algorithm
### Bat-Inspired Algorithm  
A recent new population-based algorithm, BA [1] has drawn inspiration from the bat's echolocation of the species called the microbats for searching prey. The updated solutions of BA are constructed based on three primary characteristics including echolocation, frequency, and loudness. Bats use echolocation to locate the prey, background frequency to send out the variable wavelength, and loudness to search the victim. Solutions for the BA are generated by adjusting parameters, e.g., frequencies, loudness, and pulse emission rates of the bats, according to the evaluation of the objective function. Formulas for updating the positions and velocities of BA in d-dimensional search space are as follows:  
\[f_{i}=f_{min}+(f_{max}-f_{min})\times\beta \tag{8}\]  
where \(f_{i}\) is the frequency for adjusting velocity change, \(f_{min}\) and \(f_{max}\) the minimum and maximum frequency of the bats emitting the pulse, respectively, and \(\beta\) is a generated vector randomly based on the distributed Gaussian \(\in[0,1]\). A frequency assigned initially for each bat in a uniform range \(\in[f_{min}\), \(f_{max}]\). BA updates the vectors of the bat locations and velocity \(x\), and \(v\) in search space of d-dimensional.  
\[v_{i}^{t}=v_{i}^{t-1}+\left(x_{i}^{t-1}-\ x_{best}\right)\times f_{i}, \tag{9}\]\[x_{i}^{t}=x_{i}^{t-1}+v_{i}^{t}, \tag{10}\]  
where the superscript of \(t\) is the current iteration, and \(x_{best}\) is the global best solution. Generating new location of the bats in exploiting phase strategy is formulated as:  
\[x_{i}=x_{i}+\varepsilon\times A^{t}, \tag{11}\]  
where \(\varepsilon\) is a random variable in the range \(\in[-1,\ 1]\), and indicates the weight for the loudness of the bats at the current generation. The loudness of bats \(A\) is defined as:  
\[A_{i}^{t+1}=\ \alpha\times A_{i}^{t}\, \tag{12}\]  
where \(\alpha\) is a variable constant. The symbol denotes the rate of the pulse emission \(r\) and \(\in[0,\ 1]\). The pulse emission rate is calculated as:  
\[r_{i}^{t+1}=r_{i}^{0}\times\left[1-e^{-\gamma\times t}\right], \tag{13}\]  
where \(\gamma\) is the constant variable. In the process, this rate \(r\) is considered as the control to switch the global and local search. If a random number is greater than \(r\), a local search with a random walk is triggered.
# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 2 Proposed Methodology
### Convolutional Neural Networks (CNN)
\[f_{r}=ReLU(x_{i})=max(0,x_{i}) \tag{2}\]  
Equation (2) produces zero for negative inputs and linearly conveys the input for positive inputs. The activation layer is followed by pooling layers which simplify the output by performing nonlinear down-sampling, thus reducing the number of parameters that the network needs to learn, controlling the overfitting by progressively reducing the spatial size of the network and reducing the computational burden in the network. Those layers are referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution. The final convolution, in turn, often involves backpropagation in order to more accurately weight the end product. Though the layers are colloquially referred to as convolutions, this is only by convention. Mathematically or technically, it is a sliding dot product or cross-correlation. This is significant for the indices in the matrix, in the sense that it affects how the weight is determined at a specific index point. This special design of CNNs renders them capable to successfully capture the spatial and temporal dependencies in an image through the application of the relevant afore-described filters. The architecture shows a better fitting to the image dataset due to the reduction in the number of parameters involved and reusability of weights. In other words, the network can be trained to understand the sophistication of the image better. In this context, a CNN-based methodology is analyzed to evaluate its performance for forecasting applications with short period datasets with the motivation behind the CNN-based approach being its wide usage for image recognition applications. More specifically, the CNN models exploit the spatial locality of the pixel data in order to recognize an image. Similarly, in load time series, time locality, expressed by data stationarity and autocorrelation, forms the main motivation for utilizing CNN-based models in load forecasting applications. In our approach we transform the time series data into image-like data, taking advantage of the autocorrelation exhibited by time series. Therefore, the first step is to perform statistical analysis and test the data for stationarity. If the data are not stationary then a second step of stationarity analysis is conducted by deploying a Unit Root Test, in order to capture more complex forms of stationarity, which is the case in most datasets.
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 3 Numerical Simulation of Mountainous Terrain and Wire  
### Model of Mountainous Terrain and Wire  
Transmission lines in mountainous terrain are inevitably set up in valleys. An acceleration effect occurs when the wind field passes through the front mountain. The obstruction of the back mountain further complicates the change in the wind field, which has a greater impact on the transmission lines. In this study, an approximately round hammer mountain was selected, and the wire was located between the two mountains. The three-dimensional model of the mountainous terrain and the wire's route direction are shown in Figure 2, where the direction of the incoming wind is perpendicular to the wire, L is the wire length (L = 60 m), D is the bottom diameter of the mountain (D = 40 m), H is the mountain height (H = 27.5 m), and d is the distance between the bottoms of the two mountains (d = 20 m). The wire is 20 m above the ground.  
When creating the initial state of the wire, the initial strain and elastic modulus were assigned to the wire, and the load and constraint were applied to obtain the arc sag shape of the wire that met the equilibrium state. The fixed points on both sides of the wire were 20 m high from the ground. The detailed wire parameters are shown in Table 1.
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
## 2 Data and Methods
### Clustering Method  
#### 2.3.1 K-Means Clustering Algorithm  
The K-Means clustering algorithm is a commonly used distance-based clustering algorithm. It achieves the clustering goal by minimizing the Sum of Squared Error (SSE), that is, obtaining the minimum sum of the squares of the distance from each sample point to the center of its cluster. Its calculation formula is shown in Equation (9).  
\[SSE=\sum_{i=1}^{K}\sum_{x\in C_{i}}\|x-\mu_{i}\|^{2} \tag{9}\]  
where \(K\) represents the number of clusters, \(C_{i}\) represents the set of sample points in the \(i\)-th cluster, \(x\) represents the sample point in \(C_{i}\), \(\mu_{i}\) represents the centroid of the \(i\)-th cluster, and \(\|x-\mu_{i}\|^{2}\) represents the square of the Euclidean distance between the sample point \(x\) and the cluster center \(\mu_{i}\). Euclidean distance is the default method used by the K-Means clustering algorithm to measure the distance from the sample point to the cluster center. Its calculation method is shown in Equation (10).  
\[d(x,\mu)=\sqrt{\sum_{j=1}^{n}\bigl{(}x_{j}-\mu_{j}\bigr{)}^{2}} \tag{10}\]  
The working principle of the K-Means clustering algorithm is to randomly select \(K\) samples as the initial cluster center, then calculate the distance between each sample and the initial cluster center, and classify each sample into the cluster with the smallest distance based on the obtained distance. After completing the division of each sample, each cluster starts a new round of iteration, that is, re-find the cluster center and calculate the clustering of each sample with the new cluster center so as to re-cluster each sample. This iterative process is repeated until the cluster center no longer changes, that is, the distance between samples in each cluster reaches the maximum value.
# Progress in THz Rectifier Technology: Research and Perspectives
### THz Diode Technology: State of the Art on Metal Multi-Insulator Metal (MI\({}^{n}\)M) Diode
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oxide** & **Dielectric Constant** & **Thickness (nm)** & **Cut-Off Frequency** \\  & & & **(THz)** \\ \hline Al\({}_{2}\)O\({}_{3}\)–TiO\({}_{2}\) & 0.304–1.34 & 2 & 29.3 \\ Al\({}_{2}\)O\({}_{3}\)–ZnO & 0.304–3.57 & 2–3 & 27.0 \\ Al\({}_{2}\)O\({}_{3}\)–HfO\({}_{2}\) & 0.304–3.92 & 2 & 25.9 \\ TiO\({}_{2}\)–ZnO & 1.34–3.57 & 2–3 & 10.1 \\ TiO\({}_{2}\)–HfO\({}_{2}\) & 1.34–3.92 & 2–3 & 9.88 \\ ZnO–HfO\({}_{2}\) & 3.57–3.92 & 2–3 & 4.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different combinations of oxide layers and optimized thickness to match the peak intensity frequency range. Reprinted with permission from ref. [72]. Copyright 2021 IOP Publishing. Figure 16: The I-V response of diode for different oxide combinations. Reprinted with permission from ref. [72]. Copyright 2021 IOP Publishing. The results indicate that insulators with a significant difference in electron affinity enhance the responsivity of the M1I1I2M2 diode. The recent studies on MI\({}^{\text{TM}}\)M discussed in this subsection are summarized in Table 6.
## 3 Results and Discussion  
### Specific Volume, pH, and TTA of Fermented Dough  
The leavening capacity of starter is the most important factor that affects the quality of steamed bread. The specific volumes, pH, and TTA values of fermented doughs were determined and are shown in Figure 1. The specific volumes of the fermented doughs were all >2.0 mL/g, and fermented doughs were considered to meet the requirements for making steamed bread [4]. The specific volumes of AFDs were in the range of 2.19-2.37 mL/g, and those of FDs fermented by the same CTSs were 2.06-2.25 mL/g. This was consistent with the previous study of [PERSON] et al. [9], who demonstrated that the addition of 0.1% and 0.2% Na\({}_{2}\)CO\({}_{3}\) could significantly increase the specific volume of buckwheat CSB to 2.07 and 2.16 mL/g, respectively, by neutralizing the organic acids.  
The pH values of AFDs ranged from 4.47 to 6.22, and the TTA values were from 1.6 to 3.3. For FDs, the pH values ranged from 4.16 to 5.44, and the TTA values were from 2.0 to 5.6. The pH and TTA values differed among the doughs fermented by CTSs from different regions. The acidity of fermented dough was mainly related to the microbiota in the starters [16]. The FD from BZ showed the highest pH value (5.44) and the lowest TTA value (2.0) among all of the FD samples, indicating its lower content of organic acids, which could be related to low acid-producing capacity of the microorganisms in the dough ecosystem. Comparing AFDs with the corresponding FDs, adding alkali raised the pH values by 16.9%; the result was similar to that reported by [PERSON] et al. [10].  
Overall, the specific volume and acidity of fermented doughs varied in different regions. The addition of alkali could increase specific volume and decrease acidity in fermented dough. These findings can be explained in two ways. First, the complex origin of raw materials and the various production processes of CTSs bring different microorganisms into CTSs, leading to different fermentation properties of doughs, including gas production  
Figure 1: (**A**) The specific volume of alkali-free fermented wheat dough (FD) and alkali-added fermented wheat dough (AFD). (**B**) The pH values of FD and AFD. (**C**) The total titratable acid (TTA) values of FD and AFD. WW, WN, YC, LY, LK, HZ, BZ, and WH represent the FDs and AFDs fermented by Chinese traditional starters (CTSs) from eight regions of China. * \(p\) \(<\) 0.05; **_n_ \(<\) 0.01.  
and acid accumulation [11]. Second, organic acids accumulated in doughs are neutralized by hydroxide from Na\({}_{2}\)CO\({}_{3}\) dissolution, which promotes the generation of CO\({}_{2}\), and provides a suitable environment for microorganisms to maintain high gas-producing capacities [9; 17].
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
## 5 Simulation and Comparison
### Parameter Selection  
We show the differentiator order selection and the batch size selection by simulating two cases. A 2-channel TIADC with the mismatch coefficient \(\beta=0.2\) is used for these two cases.  
In the first case, the effect which the differentiator order has on the algorithm performance is surveyed. Simulation parameters except the differentiator order are set as Table 3. The results are depicted in Figure 11. One can achieve better SINAD and LSSR along with higher differentiator's order. A 40-order differentiator is enough when the mismatch coefficient is below 0.2 and input spectrum occupies the lower 90% fraction of the Nyquist band. Moreover, the differentiator's order also has a connection with its pass-band (PB) width. For example, a 20-order differentiator is enough if the input signal only occupies the lower 80% fraction of the Nyquist band.  
In the second case, the effect which the batch size for coefficient identification has on the calibration precision is surveyed. The input signal is set as Table 3, and the differentiator order is set as 40. The results are depicted in Figure 12. One can achieve better SINAD and LSSR along with larger batch  
Figure 11: The (**a**) SINAD and (**b**) LSSR after two stages of calibration for different orders of differentiators.  
Figure 10: The (**a**) signal-to-noise-and-distortion (SINAD) and (**b**) Largest-Signal-to-largest-Spurious-component-Ratio (LSSR) for different mismatch coefficients before and after calibration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency Range** & **Signal Type** & **Batch Size** & **Differentiator Order** \\ \hline
0 \(\sim\) 0.45\(f_{s}\) & 10-tone sinusoids & \(N=\) 16,384 & \(N_{d}=\) 60 \\ \hline
90 & 80 & 80 & 80 \\  
size for identification, because the cost function is established under the circumstance where \(N\rightarrow\infty\) as (17). However, the resource consumption also increases with the batch size. Batch size of 512 is moderate for both dynamic performance and resource consumption.  
In this subsection, we show the connection between the calibration precision and the differentiator order or the batch size. Eventually, differentiator order of 20 and batch size of 512 are selected for moderate dynamic performance and resource consumption.