esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Dataset Generation  
To produce the required datasets, the equations of the proposed model outlined in Sections 3.1 and 3.1.1 were implemented using MATLAB simulations. The values of the simulation parameters are shown in Table 2. The datasets will be used to train models that will be put on all transmitting devices to prevent interference from occurring during any medical reception. There are 20,311 records, each record representing a unique combination of the distances between AX and everything (\(d_{AX}\)), the required signal-to-interference-plus-noise-ratio threshold (\(SINR_{th}\)), the AV transmission power (\(P_{A}\)), CUE transmission power (\(P_{C}\)), D2D transmission power (\(P_{D}\)), and V2V transmission power (\(P_{V}\)).
## 2 Establishment of SDM for Large Karst Caves
### Establishment of SDM  
Owing to the diversity and complexity of cave development, the edges of caves are irregular. We assume an irregular karst cave as shown in Figure 2, and take as the analysis object one of the typical sections, labeled typical section I, as shown in Figure 3.  
Figure 1: Research design.  
Figure 3: Schematic diagram of the SDM (spatial decomposition method).  
Figure 2: Schematic diagram of the spatial relationship between the karst cave and tunnel.  
According to Figures 2 and 3, the surrounding wall of the karst cave space is the cave outline. In fact, it is a three-dimensional (3D) closed surface of the cave contour. Here, typical section I (Figure 2) is selected, such that the cave contour line is a closed curve, as shown in the middle line in Figure 3. Owing to the erosion of the surrounding rocks in the process of karst development, a certain thickness of an unstable structure layer could be formed in the surrounding rock of the karst cave. The tunnel outline (line g) is the shape of the outermost protective layer in the design. At the top of the tunnel profile, we draw an artificial tangent called the tunnel top line (line c), while at the bottom of the tunnel contour we draw a tangent called the tunnel bottom line (line d). There is typically a certain thickness of fillings in karst caves, which may be gravel, clay, sand, etc., and the treatment of fillings is also an important part of karst treatment; therefore, the filling line (line e) should be drawn. Water is the most common filling material in karst and has a significant impact on tunnel construction, which is represented by the water level (line f). Lines a-g are collectively referred to as spatial decomposition lines (SDLs). SDLs are continuous surfaces rather than closed curves or line segments. For example, the water level (line f) represents the water surface in the project.  
Using SDLs, the cave space can be divided into eight space parts: the upper space of the cave (\(\mathbb{D}\)), the tunnel cavity space (\(\mathbb{Z}\)), the karst cave space on the two wings of the tunnel (\(\mathbb{3}\)), the arch bottom cave space (\(\mathbb{Q}\)), the water space (\(\mathbb{Q}\)), the filling space (\(\mathbb{R}\)), and the unstable surrounding rock space (\(\mathbb{Z}\)). The karst cave space on the two wings of the tunnel can be divided into the left and right space parts, respectively, \(\mathbb{3}\)-L and \(\mathbb{3}\)-R. The specific space numbers are shown in Figure 3 \(\mathbb{1}\)-\(\mathbb{Z}\).
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 4 Architecture Improvements for Multi-Target Detection
### Small-Target Detection Layer  
In the original YOLOv8 model, the network will calculate three feature maps of different scales. These three feature maps will be used to predict targets of different sizes in the image.  
The features extracted by the YOLOv8 backbone network are shown in Figure 6.  
The YOLOv8 backbone takes 640 x 640 images as input and generates three feature maps through 8\(\times\), 16\(\times\), and 32\(\times\) downsampling, corresponding to resolutions of 80 x 80, 40 x 40, and 20 x 20. According to the Feature Pyramid Network (FPN) principle, deeper layers capture rich semantic information and are suitable for large-object detection, but repeated downsampling leads to loss of spatial details, making small-object detection more difficult. In contrast, shallow layers retain finer location information and are better for detecting small targets.  
To address this, an additional feature map from a 4\(\times\) downsampling layer is incorporated into the fusion process to enhance the network's ability to detect small objects. The improved feature extraction structure is shown in Figure 7.  
To enhance small-target detection, a 4\(\times\) downsampled feature map is introduced into the feature fusion network, generating a new 160 x 160 scale feature. Although this feature map has a smaller receptive field and less semantic information, it retains more spatial detail, making it beneficial for locating small objects.  
Figure 6: Original YOLOv8 feature extraction model.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **EN** & **SSIM** \\ \hline Laplace & 6.461 & 0.931 \\ Ours & 7.193 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of fusion methods based on EN and SSIM.  
Deep feature maps obtained through multiple downsamplings contain rich semantic information but may lose fine details and precise location cues. In contrast, shallow feature maps preserve detailed spatial information but are more susceptible to noise and lack semantic understanding. To leverage the strengths of both, the improved model adopts a feature fusion structure based on FPN and PAN, as shown in Figure 8. FPN enhances semantic propagation from deep to shallow layers, while PAN improves the transfer of location information from shallow to deep layers. This fusion strategy enables the network to extract more robust and discriminative features across scales, effectively improving small-object detection performance.
###### Abstract  
Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.  
solar output power forecasting; combined prediction model; variational model decomposition; deep belief networks; auto-regressive and moving average model +
Footnote †: journal: Applied Sciences  
Article
# A Numerical Approach for the Filtered Generalized Cech Complex
## 5 The Cech.scale Algorithm
1 Calculate \(\
u_{M}\);
2\(\mu^{*}\gets\
u_{M}\);
3 if\(\rho_{M}(\mu^{*})\geq 0\)then
4 return\((\mu_{M}=\mu^{*};c_{M})\)
5 end if
6 for\(1\leq i<j<k\leq|M|\)do
7\(N\leftarrow\{D_{i},D_{j},D_{k}\}\);
8 Calculate \(\
u_{N}\);
9 if\(\sqrt{4/3}\,\
u_{N}\geq\mu^{*}\)then
10 if\(\rho_{N}(\
u_{N})\geq 0\)then
11\(\mu_{N}\leftarrow\
u_{N}\);
12 else
13\(\mu_{N}\leftarrow\texttt{bisection}(\rho_{N},\
u_{N},\sqrt{4/3}\,\
u_{N})\)
14 end if
15 if\(\mu_{N}>\mu^{*}\)then
16 Update \(\mu^{*}\leftarrow\mu_{N}\);
17
18 end if
19
20 end if
21 return\((\mu_{M}=\mu^{*};c_{M})\)
```  
**Algorithm 3**Cech.scale. Theorem 1: _For any 2-disk system \(M\), the Algorithm 3 has as output the Cech scale \(\mu_{M}\) of \(M\), and the unique intersection point \(\{c_{M}\}=\bigcap_{D_{i}\in M}D_{i}(c_{i};\mu_{M}\,r_{i})\)._  
Proof: If \(\rho_{M}(\
u_{M})\geq 0\), the algorithm returns the right data: steps (1)-(5). On the other hand, by Helly's Theorem (cf. [20]) the 2-disk system \(M=\{D_{1},\ldots,D_{m}\}\), as a finite family of convex sets in the plane, has a nonempty intersection \(\bigcap_{D_{i}\in M}D_{i}\) if, and only if, \(D_{i}\cap D_{j}\cap D_{k}\
eq\emptyset\) for every triplet \(1\leq i<j<k\leq m\). Let \(\mu\) be the maximal Cech scale over every triplet in the disk system \(M\), i.e.,  
\[\mu=\max\{\mu_{N}\mid N=\{D_{i},D_{j},D_{k}\}\subset M\}.\]  
It follows that every \(\mu\)-rescaled triplet has a nonempty intersection. Therefore, the \(\mu\)-rescaled 2-disk system \(M_{\mu}\) also has the nonempty intersection property. Moreover, \(\bigcap_{D_{i}\in M}D_{i}(c_{i};\mu r_{i})\subset\bigcap_{D_{i}\in N\subset M }D_{i}(c_{i};\mu r_{i})\) for every triplet \(N\subset M\). Therefore, \(\mu\) is actually the Cech scale of the 2-disk system \(M\), this is, \(\mu_{M}=\mu\). In steps (6)-(19), the algorithm search the scale \(\mu\) systematically, over every triplet \(\{D_{i},D_{j},D_{k}\}\subset M\), updating the maximal scale found if necessary in steps (15)-(17). By Lemma 6, every Cech scale calculation over any triplet, requires just one application of the bisection method. This implies the correctness of the algorithm. Additionally, the condition in step (9) avoids calculating unnecessary Cech scales of triplets \(N=\{D_{i},D_{j},D_{k}\}\). In effect, if \(\lambda^{*}\) is the maximal Cech scale found until the verification of the triplet \(N\), and the condition in step (9) does not satisfy, i.e.,  
\[\mu_{N}\leq\sqrt{4/3}v_{M}<\lambda^{*},\]  
then, whatever is the Cech scale of \(N\), it would be not greater than \(\lambda^{*}\). The computational evidence to support the Algorithm 3 is more efficient than Algorithm 2, is given in Figure 7. The graphic shows the average time (in seconds) to computation of both algorithms, with respect to the number of disks in a randomly generated 2-disk system (see Remark 1).
## 3 Application of Generic Patterns for an SOA IDS
### Detectable Anomalies in Use Cases  
#### 3.4.1 Detectable Anomalies in Medical Use Case  
An effect chain of different RPCs can be built upon the angiography system, pausing the ventilator when taking an image and previously locking the OR table and afterwards restarting the ventilator. Furthermore, the timing is also interesting, as an imaging procedure and thus the pausing of the ventilator should approximately be identically in each iteration. When the angiography system pauses the respiration, the NIDS can check for the restarting of the respiration in a plausible time span. Furthermore, it could check  
Figure 6: Exemplary timeline for the occurrence of Service IDs within an SOA network.  
that the HIDS sensor of the angiography system reports an imaging process during the respiration pause. The publish/subscribe causality check can be applied to the movement of the OR table together with the angiography system. During an imaging procedure, the movements of both systems must match in a certain workspace around the patient. In another scenario, the movements outside the restricted area of the OR table are detected by the supervisor NIDS.  
Some systems create virtual pivot points for synchronized movements [62]. If both systems can determine this pivot point, it can be checked if both points are approximately identical during an imaging procedure. This can be monitored with an according to context plausibility check. Furthermore, the NIDS can check that the OR table is moving despite the lock having been set by the HIDS sensor of the OR table or the angiography system. In addition, the OR-Light camera can check in an advanced scenario that the OR table is moving. Therefore, the IDS sensors of the individual systems can be used to supervise other systems in the OR network. Another example is that an angiography system is in a state representing imaging in progress, while the payload data of a GetPosition service of an OR table indicates that a patient is unreachable for imaging.  
In the OR context, it is hard to tell how many subscribers or publishers of services are present because each OR has an individual setup. Nevertheless, there is a chance to determine the according to amount for example at the start of the surgery. Another possibility is to determine static features during the commissioning process in which new medical devices are introduced into the OR [27]. These approaches are very static and might lack in practicability. Another approach is to couple the subscribers to the current devices with the so-called _location context_ of SDC because they need to be especially reliable. Otherwise, a device from a different OR is able to control a device within another OR. In this way, a stationary state can be monitored.
## 3 Results and Discussion
### Comparison of Different Extraction Methods
#### Estimation of Cell Disruption by Scanning Electron Microscopy (SEM)  
For evaluating the relationship between yield of flavonoids and cell disruption, SEM was applied to observe the structure of raw and treated samples by different extraction methods (UAE, HRE and UHRE). Different extraction methods have different influences on the tissue of Mairei Yew leaves (Figure 7A-D). Figure 7A obviously shows that the external surface of the tissues of untreated sample was intact. Using HRE, some of the cells were partially destroyed (Figure 7B), and more cells were destroyed by UAE (Figure 7C); however, most cells were totally destroyed and collapsed using UHRE (Figure 7D). This showed that UHRE destructs cell walls more efficiently, which resulted in the higher yield of flavonoids.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Kinetic Model**} & \multicolumn{4}{c}{**Extraction Method \({}^{*}\)**} \\ \cline{2-4}  & **UAE** & **HRE** & **UHRE** \\ \hline _Phenomenological_ & & & \\ Equation & & & \\ A\({}_{1}\) & 0.720 & 0.721 & 0.913 \\ A\({}_{2}\) & 0.130 & 0.046 & 0.141 \\ Correlation coefficient (R\({}^{2}\)) & 0.9948 & 0.9976 & 0.9972 \\ Standard error (SE) & 0.019 & 0.012 & 0.016 \\ _Pseudo-second order_ & & & \\ Equation & & & \\ B\({}_{1}\) & 6.854 & 21.571 & 4.932 \\ B\({}_{2}\) & 1.215 & 1.192 & 0.964 \\ Correlation coefficient (R\({}^{2}\)) & 0.9887 & 0.9879 & 0.9802 \\ Standard error (SE) & 0.027 & 0.027 & 0.042 \\ _Power law_ & & & \\ Equation & & & \\ C\({}_{1}\) & 0.314 & 0.175 & 0.426 \\ C\({}_{2}\) & 0.224 & 0.299 & 0.207 \\ Correlation coefficient (R\({}^{2}\)) & 0.9540 & 0.9153 & 0.9340 \\ Standard error (SE) & 0.055 & 0.072 & 0.077 \\ \hline \hline \multicolumn{4}{l}{\({}^{*}\) UAE, HRE and UHRE are ultrasonic-assisted extraction, heating reflux extraction, and ultrasound and heat–reflux} \\ \end{tabular}
\end{table}
Table 4: The kinetics models and parameters for extraction of flavonoid by UAE, HRE and UHRE.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 6 Experimentation
### Baselines  
We compare the performance of the RL agent with different heuristic methods. Each of these heuristic methods follows a fixed policy to distribute jobs in the Dew environment. They are the most common approaches for distributing jobs in Dew environments but, unfortunately, none of these algorithms dominates in every possible scenario [7]. In practice, depending on many factors, their performance can be strong or mediocre. In that regard, an RL agent has the advantage of being able to adapt its behavior to any particular situation. Specifically, we compare the RL agent with the following heuristic methods:  
* _Enhanced Simple Energy-Aware Scheduler (E-SEAS)_[32]: Before every allocation, the scheduler computes a score for each device which considers the node's current battery level, computation capabilities, and the number of jobs currently in its queue. Then, the device with the lowest score is selected.
* _Round Robin (RR)_[8]: The traditional RR algorithm gives one task to each device in a circular order until all tasks have been assigned.
* _Weighted Round Robin (W-RR)_[7]: Each device is assigned a weight proportional to its computing power. Then, when giving tasks to the devices in circular order, those with higher weights will be assigned more jobs at once.
* _Weighted Random (W-Rand)_[7]: Similarly to W-RR, the weight of each device is proportional to its computing power. The scheduler will then sample a random device when assigning a job, but those with higher weights will have a higher chance of being selected.
* _Batch Processing Algorithm (BPA)_[7]: In the same way as E-SEAS, the scheduler computes a score considering the node's current battery level, computation capabilities, and job load measured in how many operations are needed to finish each task. Then, the device with the lowest score is selected.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 2 Related Work
### Audio Adversarial Example Detection
#### 2.2.3 Logits Difference Detection  
Originally from computer vision, [PERSON] et al. [24] and [PERSON] et al. [25] discovered the divergence of logit distribution between the adversarial examples and normal images. They [24] trained a Long Short-Term Memory (LSTM) network to analyze logit sequence features in the semantic space. This method does not require understanding the model details or modifying the network and can detect adversarial examples with a success rate of more than 90%. However, each speech is divided into tens of frames for the ASR task [26], while the logit structure of each frame is identical to that of the image classification task. [PERSON] et al. [9] used the logit statistical feature difference between the original speech and the adversarial example, added noise to the logits of the speech to be detected and observed the changes in the transcription results. This method has a high detection rate for white-box [12], black-box [17] and physical attack [14] adversarial examples.  
The above detection method usually judges all speech to be detected. However, this paper is more concerned with the voice that smart homes or autonomous driving systems will soon accept. They are either derived from normal speech or adversarial examples carefully constructed by the attacker to perform the targeted attack. The command execution system will automatically reject transcribed speeches as meaningless text. Our approach assumes that adversarial examples that perform target and nontarget attacks have different logit feature distributions. If this assumption is true, concentrating on the identification of adversarial examples intended to perform targeted attacks can increase the detection success rate.
## 3 3D Sliding Force Balance Analysis
### Overall Instability
#### 3.1.1 Model Building  
In the absence of a retaining wall to protect the natural foundation, soil equilibrium can be disrupted by factors such as the presence of slurry, leading to the collapse of the trench wall. To analyze this instability, a wedge-shaped failure section with inclined sliding was assumed to exist, consisting of an upper triangular prism and a lower triangular pyramid (Figure 6). The limit equilibrium theory was applied, with assumptions made for various parameters, including the angle between the bottom sliding face and the horizontal plane (\(\alpha=45\ +\ \varphi/2\)), the height of the failure body (\(H\)), the depth of the slurry liquid surface (\(H_{\text{s}}\)), the depth of the groundwater level from the surface (\(H_{\text{w}}\)), the height of the lower part of the wedge (\(H_{\text{c}}\)), the length of a single excavation in the trench formation process (\(L\)), and the included angle (positive angle) of the adjacent trench segments (\(\beta\)), as illustrated in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Thickness** & **Gravity** & **Poisson** & **Cohesion** & **Internal** & **Compression** & **Permeability** \\  & **(m)** & **(kN/m\({}^{3}\))** & **Ratio (\(-\))** & **(kPa)** & **Friction** & **Modulus** & **Coefficient** \\  & & & & & & & & (MPa) & **(m/s)** \\ \hline Plain fill & 4.7 & 18.7 & 0.35 & 12 & 8 & 6.75 & 1.20 & 10 [6] \\ Clay 1 & 5.8 & 18.9 & 0.4 & 31 & 7.8 & 6.5 & 5.00 & 10 [6] \\ Silty clay 1 & 7 & 19.3 & 0.4 & 30.8 & 9.6 & 6.62 & 1.60 & 10 [7] \\ Clay 2 & 10.5 & 18.8 & 0.38 & 30.5 & 9 & 8.84 & 5.00 & 10 [6] \\ Silty clay 2 & 31.4 & 20.1 & 0.35 & 31.3 & 12.2 & 8.52 & 1.10 & 10 [7] \\ Peaty soil & 9.8 & 13.9 & 0.35 & 15 & 8.6 & 7.24 & 1.70 & 10 [8] \\ Dolomite & 50.8 & 21.6 & 0.3 & 110 & 45 & 26 & 5.00 & 10 [5] \\ limestone & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geotechnical physical and mechanical parameters.  
Figure 5: Enclosure structure of the diaphragm wall.  
The destruction body is divided into two parts, including the upper triangular prism (abcdef) and the lower triangular prism (defg). The directions of each force are shown in Figure 7.
## 2 Theoretical Research on Indirect Measurement Method Based on Vehicle-Bridge Coupling
### Influence of Road Roughness  
In practical measurement, vehicle response is not only related to vehicle performance and bridge performance, but also related to road roughness. Additionally, uneven bridge deck is the main source of motivation that affects vehicle vibration, especially vertical vibration of vehicles [21], and naturally affects indirect detection methods based on vehicle response.  
According to the mathematical definition, road surface irregularity is a random function, and the harmonic superposition method can simulate a stationary random process. The main idea is to express road roughness as the sum of a large number of sine and cosine functions with random phases. It has the characteristics of fast calculation speed and high accuracy. The road surface irregularity value at each position is calculated by the harmonic superposition method, and the calculation formula is as follows  
\[r(x)=\sum_{k=1}^{N}\alpha_{k}cos(\omega_{k}x+\varphi_{k}) \tag{24}\]  
\[\alpha_{k}=\sqrt{4G_{d}(n_{k})\Delta n} \tag{25}\]  
\[n_{k}=n_{1}+\left(k-\frac{1}{2}\right)\Delta n\ k=1,\ 2,\ \ldots N \tag{26}\]  
Figure 3: The response spectrum of this paper.  
\[\Delta n=\frac{n_{2}-n_{1}}{N} \tag{27}\]  
In the formula: \(r(x)\) is the uneven sample waveform; \(\omega_{k}\) is the circular frequency, with a value of \(2\pi n_{k}\); \(\varphi_{k}\) is a random phase angle, with a value between \([0,\,2\pi]\); \(N\) is a sufficiently large integer, which is an equal fraction of the spatial frequency; and \(G_{d}(n)\) is the pavement power spectrum function.  
The harmonic superposition method is used to simulate road irregularity, and MATLAB software is used to simulate the irregularity sample functions of three pavement grades A-C, as shown in Figure 4.  
It can be seen from Figure 4 that the pavement irregularity value increases with the decrease in pavement grade. The peak irregularity values of three pavement grades A-C are 5.78 mm, 11.49 mm, and 27.86 mm, respectively.