esa-sceva/satcom-chunk-collection huggingface dataset:
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 2 Method
### Loss Imbalance
#### 2.2.3 Boundary Imbalance  
We observe boundary imbalance caused by label confusion in the training of surface defect segmentation models. We find that the points at the boundary of the defect, i.e., whose neighboring points have different labels from itself, constantly produce very large loss values compared with its small ratio in the image. Our experimental observation plotted in Figure 2 shows that points at the boundary of the defect can hold as much as 30% loss value, even if they are no more than 0.01% of all pixels. The loss of boundary points are hundreds (even more than one thousand) times the non-boundary points, and they do not converge in training.  
Our explanation to this phenomenon is label confusion. Figure 3 illustrates label confusion at the boundary. Each point-wise prediction of \(Y\) has a fixed receptive field associated with the neurons in CNN. For two neighboring points, their receptive fields almost completely overlap. Research [20] on CNN architecture shows that the neurons mainly focus on the features that lie at the center of their receptive field. Given almost the same feature input, the model is trained to make different predictions at the two sides of the label boundary. This makes boundary points naturally hard and noisy examples. Fitting these noisy examples can lead to over-fitting and impair the segmentation accuracy on other points.  
Figure 3: A graphical illustration of label confusion at boundary points. For two nearby points (blue and green solid dots) at the boundary of the defect, the receptive field and the perceived image features for the two points are very similar, but they are trained to make different predictions.  
Figure 2: The ratio of loss contributed by boundary pixels and the mean loss value of boundary pixels over non-boundary pixels observed in the training of U-Net on KolektorSDD2 with cross-entropy loss. On average, boundary pixels are 0.008% of all pixels.  
The core of label confusion suppression is how to measure the degree of confusion. In Section 2.3.3, we propose an entropy-based method to suppress label confusion and avoid boundary imbalance of loss.
# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 3 The Proposed Methodology of ECFAR for Burst Detection and Pipeline Fault Diagnosis
### Burst Detection Use Enhance Constant Fault Alarm Rate
The mean over the reference windows consisting of \(N\) values presents the background noise estimation of this algorithm. Because the peaks are not located at one cell, but rather extend across a range of cells, the reference window is not placed nearby the test cell \(C_{T}\). Those cells that are directly next to \(C_{T}\) are called neighbor cells. In addition, \(\alpha\) is a constant for each cell and is evaluated depending on window size \(N\) as well as the given probability of a false alarm \(P_{fa}\). With the above detector, we assume the data input into the detector is from a single pulse; thus, with no pulse integration, the threshold factor can be shown as  
\[\alpha=N\Big{(}P_{fa}^{-1/N}-1\Big{)} \tag{3}\]  
where \(P_{fa}\) is the required false alarm rate. If the value in the cell \(C_{T}\) exceeds the threshold value \(T\), the comparator declares that an impulse is located in the cell \(C_{T}\). The \(P_{fa}\) is chosen to satisfy the Neyman-Pearson theorem for detection [27]  
\[P_{fa}=\int\limits_{\{x:\lambda(x)>\gamma\}}f(x\big{|}H_{0})dx \tag{4}\]  
where the \(\lambda(x)\) is the likelihood ratio, which is defined as \(\lambda(z)=\frac{f(\lambda\big{|}H_{1})}{f(\lambda\big{|}H_{0})}\). \(H_{0}\) and \(H_{1}\) stand for the absence of impulse and for its presence hypothesis, respectively, and \(f(\cdot)\) is the probability density function. Figure 2: The proposed method of the Enhanced Constant Fault Alarm Rate (ECFAR) algorithm for burst detection. Instead of using the raw signal from the sensor, which has a high level of noise, we proposed a pre-processing method in which the integrated signal was used as the input to the impulse detector algorithm. First, the rectification and Hilbert-Huang transform were used to convert the signal segments into the analytic form. The envelope signal was received by simply taking the modulus of the analytic signal. Then, the integrated process was employed. The integrated signal had a response similar to the power variation in the signal. Therefore, the method was sensitive to the sudden variation in the signal created by the impulse.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Aspects with Influence in PLC Systems Design
#### 5.2.6 PLC Performance Assessment  
PLC communications networks are very special due to their constraints. The main constraint can be found in network configuration once the architecture has been chosen. In many telecommunication systems, network planning can be controlled deciding where network equipment is going to be placed (with limitations in each case: e.g., base stations cannot be placed in any location). In the case of the electricity grid, service points and the places where PLC signals must propagate are fixed, and more important, the characteristics of the grid itself (cable types, specificities, _etc._) are generally not known, or known to an extent that does not allow predicting PLC behavior. Thus, if ex-ante planning is severely limited, ex-post performance assessment is needed to understand if the results obtained are in accordance with the needs, and to understand where any grid modification or network adjustment is needed. Performance assessment of point-to-point connectivity is easy to make and there are many procedures and best practices [132]. This is completely different for shared media technologies, and especially for those which are NB (control traffic consumes an important share of the useful bandwidth).
## 2 Preliminaries
For any interval \(I=[\ell_{I},r_{I}]\) of size \(W\), Theorem 4 in Section 4 gives a data structure \(\mathcal{D}_{I,\epsilon}\) that uses \(O(\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W})\log\log W)\) space, supports \(O(\log(\frac{\epsilon B}{\log W})\cdot(\log\frac{1}{\epsilon}+\log\log W))\) update time, and enables us to obtain, for any item \(a\) and any time \(t\in I\), an estimate \(\hat{f}_{a}([t,r_{I}])\) of \(f_{a}([t,r_{I}])\) such that  
\[|\hat{f}_{a}([t,r_{I}])-f_{a}([t,r_{I}])|\leq\epsilon f_{*}([t,r_{I}]) \tag{1}\]  
Given \(\mathcal{D}_{I_{1},\epsilon},\mathcal{D}_{I_{2},\epsilon},\dots\) where \(I_{i}=[(i-1)W+1,iW]\), we can obtain, for any \(W^{\prime}\leq W\), an estimate \(\hat{f}_{a}([s,\tau_{\text{cur}}])\) of \(f_{a}([s,\tau_{\text{cur}}])\) where \(s=\tau_{\text{cur}}-W^{\prime}+1\) as follows. * Let \(I_{i}\) and \(I_{i+1}\) be the intervals such that \([s,\tau_{\text{cur}}]\subset I_{i}\cup I_{i+1}\). * Use \(\mathcal{D}_{I_{i},\epsilon}\) to get an estimate \(\hat{f}_{a}([s,iW])\) of \(f_{a}([s,iW])\), and \(\mathcal{D}_{I_{i+1},\epsilon}\) an estimate \(\hat{f}_{a}([iW+1,(i+1)W])\) of \(f_{a}([iW+1,(i+1)W])\). * Our estimate \(\hat{f}_{a}([s,\tau_{\text{cur}}])=\hat{f}_{a}([s,iW])+\hat{f}_{a}([iW+1,(i+1) W])\). By Equation (1), we have  
\[|\hat{f}_{a}([s,iW])-f_{a}([s,iW])|\leq\epsilon f_{*}([s,iW]) \tag{2}\]  
and  
\[|\hat{f}_{a}([iW+1,(i+1)W])-f_{a}([iW+1,(i+1)W])|\leq\epsilon f_{*}([iW+1,(i+ 1)W]) \tag{3}\]  
Observe that any item that arrives at or before the current time \(\tau_{\text{cur}}\) must have timestamp no greater than \(\tau_{\text{cur}}\); hence \(f_{a}([iW+1,(i+1)W])=f_{a}([iW+1,\tau_{\text{cur}}])\) and \(f_{*}([iW+1,(i+1)W])=f_{*}([iW+1,\tau_{\text{cur}}])\), and Equation (3) is equivalent to  
\[|\hat{f}_{a}([iW+1,(i+1)W])-f_{a}([iW+1,\tau_{\text{cur}}])|\leq\epsilon f_{*} ([iW+1,\tau_{\text{cur}}]) \tag{4}\]  
Adding Equations (2) and (4), we conclude \(|\hat{f}_{a}([s,\tau_{\text{cur}}])-f_{a}([s,\tau_{\text{cur}}])|\leq\epsilon f _{*}([s,\tau_{\text{cur}}])\), as required. Our data structure \(\mathcal{D}_{\epsilon}\) is just the collection of \(\mathcal{D}_{I_{1},\epsilon},\mathcal{D}_{I_{2},\epsilon},\dots\). Note that we only need to physically store in \(\mathcal{D}_{\epsilon}\) the data structures \(\mathcal{D}_{I_{i},\epsilon}\) and \(\mathcal{D}_{I_{i+1},\epsilon}\) where \([\tau_{\text{cur}}-W+1,\tau_{\text{cur}}]\subseteq I_{i}\cup I_{i+1}\). The intervals of the earlier ones will no longer be covered by the sliding window and the corresponding \(\mathcal{D}_{I,\epsilon}\)'s can be thrown away. Together with Theorem 4, we have the following theorem. **Theorem 2**: _The data structure \(\mathcal{D}_{\epsilon}\) solves the \(\epsilon\)-approximate counting problem. The space usage is \(O(\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W})\log\log W)\) and it supports \(O(\log(\frac{\epsilon B}{\log W})\cdot(\log\frac{1}{\epsilon}+\log\log W))\) update time._
# Co-Simulation of Multiple Vehicle Routing Problem Models
## 3 The Designed Model
### Description of Atomic VRP Models
#### 3.1.2 Second VRP Model  
VRP of the second model uses a similar algorithm routing and sequencing of the first model. The difference is that each ant in this model tries to reduce the number of vehicles to visit all clients and to add all possible ones whose demand does not exceed the capacity of the current vehicle. The algorithm of the second model is formulated as follows, see Algorithm 2.  
```
0:\(R\), \(ant\_colony\), \(Q(k)\);
0:\(route\_map\);
1:for(\(a\in ant\_colony\)) do
2:\(route\_map\gets r_{0}\);
3:\(R\gets R-r_{0}\);
4:\(C\gets R-r_{0}\);
5:\(k\gets 0\);
6:\(Q(k)\gets 0\);
7:\(i\gets r_{0}\);
8:while\(R\
eq\varnothing\)do
9:while\(C\
eq\varnothing\)do
10:for(\(r_{j}\in C\)) do
11:\(p_{ij}\gets probability\_of\_point_{i}\_to\_choose\_point_{j}\);
12:endfor
13:\(r_{s}\gets r_{j}\_with\_highest\_p_{ij}\);
14:\(C\gets C-r_{s}\);
15:\(Q(k)\gets Q(k)+demand(r_{s})\);
16:if\(Q(k)<capacity\_of\_vehicle\)then
17:\(route\_map\gets route\_map+r_{s}\)
18:\(i\gets r_{s}\)
19:\(R\gets R-r_{s}\);
20:endif
21:endwhile
22:\(k\gets k+1\);
23:\(Q(k)\gets 0\);
24:\(route\_map\gets route\_map+r_{0}\);
25:\(i\gets r_{0}\);
26:endwhile
27:endfor
28:return\(route\_map\_of\_the\_best\_ant\_solution\);
```  
**Algorithm 2** Routing and sequencing of the second model  
In Algorithm 2, the ant checks if its current load allows it to add the demand of the chosen client and verifies the vehicle's capacity violation. If yes, the ant visits the specified client (adding client's demand and indicating it as selected). Otherwise, the ant selects another unvisited node according to the calculated probability. When all clients have been visited, the ant ends the current trip with empty load (returning to its colony) and the algorithm begins a new trip. We improve the solution by local search and we use a two-opt local search algorithm in the end of both Algorithms 1 and 2.
###### Abstract  
This paper addresses the issue of agile attitude maneuver control for low-Earth-orbit satellites during short arc segments for multi-target observations. Specifically, a configuration design for Control Moment Gyroscopes (CMGs) and a hybrid control law are provided. The control law is adept at avoiding singularities and escaping singular planes. Subsequently, an optimal time-based attitude maneuver path-planning method is presented, rooted in the relationship between Euler angles/axis and quaternions. Furthermore, a novel satellite attitude maneuver controller is developed based on a piecewise power-reaching law for variable structure sliding mode control. The paper theoretically demonstrates that the proposed piecewise power reaching law possesses two favorable properties regarding convergence time. On the other hand, the designed reaching law maintains continuity at all stages, theoretically eliminating buffeting. The simulation results demonstrate that the proposed controller achieves an Euler angle control precision of \(\pm 0.03^{\circ}\) and angular velocity accuracy of \(\pm 0.15^{\circ}\)/s, fulfilling the demands of multi-objective observational tasks. Compared to conventional power reaching law controllers, the convergence time is reduced by 3 s, and Euler angle accuracy is improved by 70%. This underscores the effectiveness of the proposed algorithm.  
agile attitude control; piecewise power-reaching law; variable-structure sliding mode control; control moment gyroscopes; quaternions +
Footnote †: journal: Applied Sciences
## 2 Materials and Methodology
### Numerical Simulation
#### 2.3.2 Thermal Boundary Conditions  
A total of six thermal boundary conditions are applied considering HS exterior surfaces to be system boundaries depicted in Figure 3. The HS bottom surface is identified as the input heat flux boundary while the remaining five surfaces are marked as mixed-mode heat loss boundaries employing radiation and convection heat loss coefficients represented by Figure 3.  
The convection heat loss is computed from Equation (5) as:  
\[Q_{con}=h_{con}\times A(T_{Sur}-T_{amb}) \tag{5}\]  
The convective heat loss coefficients for the HS surfaces are calculated by applying the Nusselt number (\(Nu\)) relationship:  
\[h_{con}=\frac{K}{L}Nu \tag{6}\]  
with \(K\) and \(L\) being the conductivity of air and characteristic length, respectively.  
The horizontal and vertical surfaces of the HS are treated as plates subjected to uniform heat flux and the \(Nu\) correlation for plates are applied as given in Equations (7) and (8) [47]:  
\[Nu_{horiz}=0.453\;Re_{L}^{0.5}\;Pr^{\frac{1}{3}} \tag{7}\]  
\[Nu_{vert}=0.664\;Re_{L}^{0.5}\;Pr^{\frac{1}{3}} \tag{8}\]  
where \(Pr\) is the Prandtl number calculated from fundamental air properties as  
Figure 3: HS boundary conditions.  
\[Pr=\
u\;c_{p}/K \tag{9}\]  
The Reynolds number (\(Re_{L}\)) is calculated using the relation  
\[Re_{L}=\frac{VL}{\
u} \tag{10}\]  
where \(\
u\) and \(V\) denote the viscosity and air velocity, respectively.  
The film temperature is calculated as the average of surface and ambient temperatures as  
\[T_{f}=\frac{(T_{sur}+T_{amb})}{2} \tag{11}\]  
The radiative heat loss is calculated from Stefan-Boltzmann law:  
\[Q_{rad}=\sigma\varepsilon(T_{Sur}^{4}-T_{\infty}^{4}) \tag{12}\]  
where \(\sigma\) is the Stephan-Boltzmann constant, \(\varepsilon\) = 0.97 is the external emissivity, \(T_{\text{Sur}}\) is the surface temperature of HS sides and \(T_{\infty}\) is the infinity temperature.
## 3 Results
### Inter-Segmental Coordination  
The three exemplar CRP plots presented (Figure 1) denote differences in pelvis/thorax segmental coordination for both the dominant and non-dominant take-off legs within the 180\({}^{\circ}\) jump. As shown in Figure 1, all participants tended to remain in-phase with minimal variability during the loading/take-off phase of the jump irrespective of the take-off leg. All three participants remain in-phase with low variability throughout the unilateral 180\({}^{\circ}\) jump when taking off from the non-dominant leg. Participant C employs a similar pattern of inter-segmental coordination (and variability) irrespective of the take-off leg. Participants A and B show a tendency to move out-of-phase and demonstrate high levels of CRP variability throughout the landing phase of the unilateral 180\({}^{\circ}\) jump.  
The solid red line represents the mean relative phase angle of all five jumps with a phase angle of 0 representing completely in phase or both the thorax and pelvis rotating together at the same rate. A phase angle \(>\) 0 (positive angle) indicates that the thorax is rotating ahead or leading the pelvis. A phase angle \(<\) 0 (negative angle) indicates the pelvis is rotating ahead or leading the thorax. The loading phase is represented by the first \(\sim\)20%. The landing phase is represented by the last \(\sim\)50%.  
Figure 1: Exemplar continuous relative phase plots.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Phase** & **Variable (degs)** & **Left** & **Right** & \(p\)**Value** & **Mean Diff [95\% CI]** & **Effect Size (d)** \\ \hline Loading & Flex/Ext & \(-12.0\pm 7.5\) & \(-9.2\pm 5.5\) & 0.27 & 2.8 [12.1, \(-9.3\)] & 0.43 \\  & Add/Abd & \(-3.6\pm 3.1\) & \(-4.3\pm 2.4\) & 0.46 & 0.7 [4.3, \(-3.6\)] & 0.26 \\  & Axial Rot. & \(4.4\pm 6.0\) & \(0.8\pm 4.6\) & 0.09 & 3.6 [11.1, \(-7.5\)] & 0.68 \\ \hline Landing & Flex/Ext & \(-1.2\pm 3.8\) & \(-1.1\pm 3.1\) & 0.95 & 0.1 [6.5, \(-6.4\)] & 0.03 \\  & Add/Abd & \(-7.8\pm 4.1\) & \(-7.0\pm 3.3\) & 0.60 & 0.8 [6.7, \(-5.9\)] & 0.22 \\  & Axial Rot. & \(-18.0\pm 9.0\) & \(-18.7\pm 8.0\) & 0.82 & 0.7 [12.9, \(-12.2\)] & 0.08 \\ \hline \hline \end{tabular} Mean \(\pm\) Standard Deviation. Cohen’s d calculated for effect size (0.2\(-\)0.5 are considered small, 0.5\(-\)0.8 considered moderate and \(>\)0.8 considered a large effect).  
\end{table}
Table 3: Pelvis alignment for both loading and landing phases. Flex—Flexion; Ext—Extension; Add—Adduction; Abd—Adduction.  
Vertical error bars indicate the standard deviation at points of the CRP plot. Therefore, larger vertical bars indicate greater movement variability jump to jump, while smaller bars represent less variability.  
In phase is represented by a relative phase angle of 0. Out of phase (thorax-led) is represented by a positive relative phase angle.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
###### Abstract
Our exploration covers both LOS and NLOS communication conditions, looking at two crucial industrial communication modes: many to one, in which several transmitters send data to a single receiver, and one to many, in which a single transmitter sends data to several receivers. Several parameters were considered in the simulations. These included the use of an 868 MHz frequency band, a 125 kHz bandwidth, two spreading factors (7 and 12), and two coding rates (4/5 and 4/8). In addition, two channel types were used to obtain results closer to the real environment. The results demonstrate a clear advantage of forward error correction (FEC), based on the Hamming code built into LoRa's physical layer: for a coding rate of 4/8, LoRa sees its PER performance improve considerably, with impressive SNR gains ranging from 7 dB to 11 dB. But the contribution doesn't stop there. By integrating an IIR or FIR filter into the LoRa architecture, we observe additional SNR gains ranging from 2 to 6 dB, although the optimal choice between these filters is intrinsically linked to the specific needs of the industrial environment in question. Taken together, these contributions illustrate LoRa's robustness in demanding industrial scenarios while providing a framework for maximizing its performance. This paper is organized as follows: Section 2 presents a review of the most significant work on the evaluation of LoRa technology in industrial environments. Section 3 takes a deeper look at LoRa technology as well as channel and noise interactions. In Section 4, forward error correction based on Hamming error correction, as well as IIR and FIR filters, are discussed. Section 5 scrutinizes the modulation and demodulation processes of LoRa, particularly in the context of noisy channel conditions. In Section 6, the spotlight is on evaluating LoRa's performance, with a keen focus on forward error correction using hamming error correction, complemented by an exploration of filtering methods. The paper draws to a close with Section 7, encapsulating the primary conclusions and hinting at prospective avenues for subsequent research in this field.
# A Review of Video Object Detection: Datasets, Metrics and Methods
## 3 Video Object Detection Methods
### LSTM-Based
By using only the short-term temporal information, it was difficult to deal with these situations. The long-term temporal context information was also exploited via the convolutional LSTM, in which the feature maps of the distant preceding frame obtained from the memory function was propagated to acquire more information. The important sub-network (temporal Conv LSTM) is shown in Figure 6. Given the feature map at the time step t, the state and output from the time step t-1, the output \(H_{t}\) and the updated state \(S_{t}\) at the current time step t are computed as Equation (2). The long-term temporal information is stored, propagated and employed. Then, the feature maps extracted on the current frame as well as the warped feature maps and the output of the LSTM were concatenated to obtain the aggregated feature maps. Finally, the aggregated feature maps were inputted into a detection sub-network to obtain the detection outcome on the current frame. By utilizing both the short- and long-term information, this method achieved an accuracy of 75.5% mAP at a high speed on the ImageNet VID dataset, indicating a competitive performance for online detection. \[ \begin{array}{l}FG_{t}=\sigma(W_{FG}*concat(F_{t},H_{t-1})),\\ I_{t}=\sigma(W_{I}*concat(F_{t},H_{t-1})),\\ O_{t}=\sigma(w_{O}*concat(F_{t},H_{t-1})),\\ C_{t}=tanh(w_{C}*concat(F_{t},H_{t-1})),\\ S_{t}=(f_{t}\times S_{t-1})+(I_{t}\times C_{t}),\\ H_{t}=tanh(S_{t})\times O_{t}.\end{array}  \tag{2}\]  
where the \(FG_{t}\), \(I_{t}\), \(O_{t}\) and \(C_{t}\) denote the output of Forget Gate, Input Gate, Output Gate and the information branch at the time step t, respectively. Their weights are represented by \(W_{FG}\), \(W_{I}\), \(W_{O}\) and \(W_{C}\). \(\sigma()\), \(\times\), \(+\), \(*\) represent the activation function, element-wise multiplication, element-wise addition and 3*3 convolutions operations, respectively.
###### Abstract  
With the continuous development of information technology, network security risks are also rising, and the ability to quickly perceive network threats has become an important prerequisite and an important means to cope with network risks. Currently, there are various types of network attacks and complex attacking techniques, and the large differences between them have led to the difficulty of collecting and recognizing the common characteristics of network attacks. Considering the regular temporal fluctuations in network attacks, a method for network security situational awareness, based on an enhanced Particle Swarm Optimization Bidirectional Long Short-Term Memory (BiLSTM) network model, is proposed. By gathering and organizing critical information within the network, an encapsulated Wrapper feature selection algorithm is utilized for the extraction of element features. The refined Particle Swarm Optimization algorithm is applied to optimize the parameters of the BiLSTM network, enabling a rapid convergence and enhancing the training efficiency, thus effectively identifying the categories of network attacks. The experimental results show that the MAPE for the proposed model has been diminished to 0.36%, while the sMAPE is 2.654%. Additionally, the fitting coefficient attains a value of 0.92. This indicates a high level of recognition and precision exhibited by the proposed model in detecting network security risk behaviors. Furthermore, in contrast to the traditional CNN neural network, the proposed model is more compact, which significantly reduces the computational overhead and allows for efficient network security situational awareness.  
computer applications; network security posture; situational awareness; BiLSTM neural network +
Footnote †: journal: Applied sciences