esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Effect of Nozzle Configuration Under Different Fluid Temperatures  
Figure 9 shows the effects of nozzle configuration on the erosion action of the jets under various fluid temperatures. In this group of experiments, the inlet and ambient pressures were set constant at 40 and 10 MPa, respectively. The standoff distance increased from 1 to 10. The fluid temperature was sequentially adjusted from 290 to 350 K.  
Figure 8: Effect of nozzle configuration on rock erosion volume of the SC-CO\({}_{2}\) jet under different ambient pressures and constant \(\Delta P\): (**a**) 2 MPa; (**b**) 10 MPa; (**c**) 18 MPa; (**d**) 26 MPa.  
As can be seen from the curves in Figure 9, the fluid temperature not only affects the erosion capability of the SC-CO\({}_{2}\) jet, but also affects the enhancement of erosion performance achieved by using the Laval nozzles. Specifically, the maximum erosion volumes for the three nozzles show an increasing trend with the increasing fluid temperature. This indicates that the SC-CO\({}_{2}\) jet has a stronger erosion capability at relatively higher temperatures. This is due to the fact that with the growth of fluid temperature the diffusivity of the SC-CO\({}_{2}\) fluid increases. What is more, the increase of diffusivity is very significant when the temperature increases from a liquid temperature to a supercritical temperature, which results in the obvious enhancement of erosion ability of the SC-CO\({}_{2}\) jet when the temperature increases from 290 to 310 K [9,11]. Moreover, compared with the commonly used convergent nozzle, the use of Laval-1 nozzle increases the rock erosion volume by 12.5%, 21.8%, and 26.7%, respectively, corresponding to fluid temperature of 310, 330, and 350 K, respectively; while the employ of Laval-2 nozzle increases the erosion volume by 37.2%, 49.2%, and 53.7%, respectively. These data indicate that, with the increase of fluid temperature, the enhancement of rock erosion ability of the SC-CO\({}_{2}\) jet by the Laval nozzles is more and more obvious though the inlet and ambient pressures remain constant. However, at temperature of 290 K, the convergent nozzle has better erosion performance than both the two Laval nozzles, which means that the Laval nozzle cannot effectively play its role in accelerating the liquid CO\({}_{2}\) fluid at temperature of 290 K.
## 3 Approach
### Rail Track Intrusion Detection  
Further analysis of rail track intrusion by aligning the tracked area of the orthogonal map with the LiDAR point clouds in the track coordinate system. Analysis of the point cloud within the rail track area along the distance axis, i.e., the \(z\)-axis, on a distance-by-distance basis in meters. In other words, after determining the track area in space according to Equation (10), as shown, a sliding window is set along the \(z\)-axis direction, measured in meters. Then, the region is inspected meter by meter to detect any intrusion.  
\[R_{area}=\left\{(X,Y,Z)\in R^{3}\Big{|}L_{right}(Z)<X<L_{left}(Z)\right\} \tag{10}\]  
During the rail-obstacle search process, we set two primary criteria for intrusion detection. One criterion is height: if there is a cluster of points higher than the track plane, it is suspected to be an intrusion. Additionally, to enhance robustness and mitigate the impact of adverse weather (raindrops, snowflakes, etc.) and noise from the equipment itself, we incorporate point cloud density as a judgment criterion. Theoretically, the point cloud density of intrusions decreases with distance but it differs significantly from noise.  
Whereas the height constraint is a constant value \(H=0.4\) m, the constraint on point cloud density, through experimentation, is found to be ideally modeled as a quadratic decay. This is illustrated as \(\Theta=N-a^{*}z^{2}\) where \(N\) is set to 420 and \(a\) is set to 0.01. The rail-obstacle detection process is shown in Algorithm 2. For the intrusion detection process, Figure 10 illustrates an example using a turnout as a reference.  
Figure 10: The clustered railway point cloud undergoes correction and reconstruction. Then, obstacle detection is performed within the track area.
## 3 The Model of IKDC
### Using Improved KNN and the Average Distance to Determine the Core Domains  
After determining the cluster centers and noise points, IKDC first removes the noise points from \(X^{D}\), uses the improved KNN algorithm to determine the initial core domains in the set \(X^{D}-\Omega\), denoted as \(q_{j}(j\in[1,K])\), and then continuously expands the range of the core domain through the average distance to obtain the final core domain.  
**Definition 2** (the \(K\)-nearest neighbors).: _Given a data space \(X^{D}\), for any point \(p\) in the dataset, its \(K\)-nearest neighbor representation is shown in Equation (7)._  
\[K_{p}=\{q_{j}|j\in[1,K]\} \tag{7}\]  
_where \(K\) is the number of nearest neighbors._  
**Definition 3** (the average similarity).: _Given a data space \(X^{D}\) and the point \(p\), the average similarity is shown in Equation (8)._  
\[d_{p}^{avg}=\frac{1}{K}\sum_{1}^{K}\tilde{d}_{pq_{j}} \tag{8}\]  
_where \(\tilde{d}_{pq_{j}(j\in[1,K])}\) is the distance based on the isolation kernel between point \(p\) and its \(j\)-th nearest neighbor._  
Based on the initial core domain, the final core domain is obtained by continuously expanding the core domain range through the average similarity, as shown in Equation (8). More details are shown in Algorithm 1.  
```
1:Input: cluster center \(c=\{c_{1},\cdots,c_{r},\cdots,c_{k}\}\), data space \(X^{D}-\Omega\)
2:Output: core domain \(\{c_{1}^{p},c_{2}^{p},\cdots,c_{k}^{p}\}\)
3:for\(r=1\), \(r<=k\)do
4: find \(K\)-nearest neighbors of \(c_{r}\to KNN_{c}\);
5:\(C_{r}^{p}=[c_{r};KNN_{c_{r}}]\);
6: initialize \(Q=KNN_{c_{r}}\);
7:while\(Q\) is not emptydo
8: select the head point \(p\);
9: find its \(K\)-nearest neighbors \(KNN_{p}=\{q_{1},\cdots,q_{j},\cdots,q_{K}\}\);
10:\(d_{p}^{avg}=\frac{1}{k}\sum_{1}^{K}d_{pq_{j}}\);
11:for each point \(q_{j}\) in \(KNN_{p}\)do
12:if\(d_{pq_{j}}<=d_{p}^{avg}\)then
13:\(C_{r}^{p}=C_{r}^{p}\cup q_{j}\);
14: add \(q_{j}\) to the end of \(Q\);
15:endif
16:endfor
17: remove \(p\) from the head of \(Q\);
18:endwhile
19:endfor
20:return\(\{c_{1}^{p},c_{2}^{p},\cdots,c_{k}^{p}\}\)
```  
**Algorithm 1** Determining the core domain based on the average similarity
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Forensic Features Based
#### 4.1.4 JPEG Compression  
JPEG compression is considered a non-malicious manipulation. It is performed to compress the image to meet social platform storage compliance. The three basic processes of JPEG compression are discrete cosine transform, quantization, and entropy coding. On the decoding end, the procedure is reversed. A method for detecting JPEG compression was created by [PERSON] and [PERSON] [62]. It would initially determine whether or not a picture has been JPEG compressed. Once the compression signature has been evaluated, compression parameters are estimated. A function to calculate the maximum likelihood for the quantizer step was developed. MLE estimation was devised, which could be used to assess the usage of the quantization table. [PERSON] proposed Error Level Analysis (ELA) [63], which uses the fact that the JPEG resaving error is not linear. The method was to resave the JPEG images with a known rate and then compute the difference. In an uncompressed image, all pixels in the picture are not at their local minima, but when compressed, they achieve their local minima. [PERSON] et al. [64] exemplified a method based on double JPEG2000 compression to spot and locate the manipulated areas in tampered images. The method utilizes the fact that there is a statistical difference in single and double JPEG2000 compression. The difference sums to double quantization of the sub-band DWT coefficients, which brings in specific artifacts visible in the histograms of the Fourier transforms of the DWT coefficient. However, this technique could detect single and double compression only. The method would have a different accuracy if multiple compressions were made. [PERSON] et al. [65] created a fully automatic model for spotting manipulated images by inspecting the Double
# An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
## 5 Experimental Results
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case Study**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Fitness**} & \multirow{2}{*}{**Shapiro-Wilk**} & \multirow{2}{*}{**Wilcoxon**} \\  & & & **Median** & & **Maximum** & \(p\)**-Value** & \(p\)**-Value** \\ \hline \multirow{3}{*}{3 UAVs} & RS & 1.373 & 1.546 & 1.619 & 0.022 & \textless{}0.001 \\  & EA.ucx & 1.351 & 1.392 & **1.440** & 0.063 & 0.848 \\  & EA.dcx & **1.341** & **1.383** & 1.482 & 0.014 & — \\ \hline \multirow{3}{*}{5 UAVs} & RS & 3.273 & 3.513 & 3.722 & 0.923 & \textless{}0.001 \\  & EA.ucx & 3.071 & **3.229** & 4.171 & \textless{}0.001 & — \\  & EA.dcx & **2.998** & 3.236 & **3.467** & 0.885 & 0.929 \\ \hline \multirow{3}{*}{10 UAVs} & RS & 5.574 & 6.533 & 7.929 & 0.434 & \textless{}0.001 \\  & EA.ucx & 5.389 & 5.634 & 6.865 & \textless{}0.001 & 0.030 \\ \cline{1-1}  & EA.dcx & **5.168** & **5.538** & **6.497** & 0.015 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the optimisation process comparing RS and EA (10 scenarios). Best results are in bold. it is hard to visualise a 3D representation of the swarm members, all the graphs show a sphere-like shape in contrast with the initial positions depicted in Figure 6.  
Figure 11: UAVs’ final position for the 90 testing scenarios per case study and algorithm.
## 5 Discussion
### Comparison Results with Baseline Deep Learning Models on Position-Independent HAR  
The preceding section concentrated on HAR, which relies on position information. The proposed Att-ResBiGRU was evaluated against five fundamental deep neural networks (CNN, LSTM, BiLSTM, GRU, and BiGRU), demonstrating superior performance. In this part, we assess the position-independent proficiency of Att-ResBiGRU by comparing it with established recurrent and convolutional baseline architectures. The evaluation used the Opportunity, PAMAP2, and REALWORLD16 datasets, employing consistent experimental protocols.  
Examining the outcomes of the position-independent human activity recognition comparison presented in Figure 6 across the Opportunity, PAMAP2, and REALWORLD16 benchmark datasets revealed several noteworthy observations. Initially, our proposed Att-ResBiGRU framework consistently attained the highest average F1-scores, reaching 86.69%, 96.44%, and 96.23% on the dataset compilations. This underscores its advanced generalization capabilities.  
Specifically, when confronted with the intricate activities in the Opportunity dataset, the performance of the proposed model surpassed that of RNN variants such as LSTM and GRU by over 15% while also outperforming the CNN baseline by nearly 10% in terms of the F1-score. The incorporation of residual bidirectionality and attention mechanisms demonstrably enhances disambiguation fidelity.  
These advantages become even more evident when assessing the daily activity datasets of PAMAP2 and REALWORLD16. On PAMAP2, we surpassed the performance of BiGRU by 2.56%, and on REALWORLD16, we surpassed alternative fusion approaches by more than 1.95% in terms of the F1-score, substantiating our model's efficacy in recognizing sequential daily living motions.
## 1 Introduction
We applied a multi-modal fish monitoring method using a case study in the Austrian Alps based on a three-staged approach:  
* Seasonal patterns of fish presence in the headrace channel using long-term assessments from single-beam echosounders,
* Observations of fish behavior (spatial distribution) in front of the trash rack with acoustic cameras,
* Assessment of local flow field hydrodynamics via combined time-averaged flow velocity and pressure turbulence measurements. When combined, the different data sources can provide key parameters to assess the relevance of downstream migration and to develop suitable measures regarding fish passage.
# Security Audit of a Blockchain-Based Industrial Application Platform
## 4 Attacks and Countermeasures
### Attacks
#### 4.1.4 Data  
The content of the data can be changed in various ways.  
* _Condition monitoring and OPC UA server_: an attacker uses compromised/stolen credentials to gain access and to impersonate the account. Typically, account hijacking is done through phishing, sending fake emails to the user, password guessing, or a variety of other tactics. By accessing these systems, it is possible to easily send valid fake data. An attacker could also compromise the active session and gain access to the transmitted data. _These two targets are explained together because of their similarities._Brute Force Attack
* _Condition monitoring and OPC UA server:_ the attacker tries to gain access to simple password-protected data by randomly trying passwords. Since these systems have no rate limiting, the attacker will eventually succeed. _These two targets are explained together because of their similarities._  
* _Auxiliary DB:_ read and write access enables various actions such as reading, modifying, or deleting the information. Data inconsistency can also occur if the VPN tunnel crashes and the data in the blockchain and the Auxiliary DB are no longer synchronized.  
Malicious Smart Contracts
* _Peers in blockchain:_ Since a smart contract is simply a piece of code that is executed on the peers, it is possible to exploit flaws in the code by transmitting specially crafted data. Furthermore, if a malicious smart contract is installed on the network, it can manipulate the data it receives.  
Sensor Data/User Data
* _Network gateway:_ attackers can steal important information, including passwords and intellectual property, by gaining access to the network. It is possible to steal user identities, to send the wrong information to the system, or to manipulate the data by impersonating an authorized user.
* _OPC UA server:_ by modifying OPC UA data, it is possible to trigger data checks that can either shut down the machine or at least falsify information about the product, leading to quality problems.
* _Sensor:_ radio signals can interfere with communication between the sensors and the device by affecting the signal-to-inference ratio, resulting in intentional (or unintentional) crosstalk.  
* _Auxiliary DB:_ unauthorized access by a malicious actor can result in altered data. The attacker can also retrieve confidential information (e.g., bulk sensor/process data from machines).
## 4 Discussions  
### ET, Yield and Economic Benefits  
In order to understand the difference in ET between cabbage during the growth period and other similar studies in other regions, we collected the estimated ET for cabbage from previous studies (Table 3). As shown in the table, the ET of cabbage during the growth period varied from 141.4 mm to 296.3 mm in different studies. In this study, the total ET of cabbage during the growth period was 260.1 \(\pm\) 24.2 mm. Based on the literature research, it can be found that there is a lack of research on the driving factors of ET in cabbage agricultural ecosystems in Northwest China. By comparing the ET during the growth period of other agricultural ecosystems in the arid region of Northwest China, it can be found that the ET of cabbage during the growth period is lower than that of other crops such as maize [47], wheat [48], and cotton [49].  
During the experimental period, the average yield of cabbage was 8374.7 kg ha\({}^{-1}\), with an economic benefit of CNY 70,046.7 ha\({}^{-1}\). From an economic perspective, the comprehensive economic benefits of cabbage are better than other crops, such as maize [50], wheat [51], and soybean [52], which are mainly grown in this region. Considering the current situation of drought and low rainfall in this area, planting only one rotation of cabbage per year can reduce irrigation and increase economic benefits compared to planting other crops, thereby achieving the goal of water conservation and increasing income. It is worth noting that, unlike the grain crops, the price of cabbage fluctuates greatly and the initial investment is high, which may limit the large-scale cultivation of cabbage in Northwest China.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lat** & **Lon** & **Site** & **Type of the** & **Irrigation** & **Length of** & **ET (mm)** & **Ref.** \\ \hline
41\({}^{\circ}\)21\({}^{\prime}\) N & 114\({}^{\circ}\)68\({}^{\prime}\) E & \
\begin{tabular}{c} Zhangjiakou, \\ China \\ \end{tabular} & Chinese cabbage & Drip irrigation & 59 & 214.8 &
\begin{tabular}{c} [PERSON] et al., \\ 2020) [53] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of \(ET\) for different cabbage studies. Values of total \(ET\) during the growth period (total ET \(\pm\) standard deviation).  
Figure 7: Scatter plot of \(ET_{m}\) and \(ET_{s}\).
## 3 Results
### Monitoring of Retrofitted BCJs
#### 3.2.1 Statistical Damage Index RMSD  
Figure 8 presents the RMSD values of the epoxy-bonded PZT patches of Specimen 1. The PZT Left exhibits steady growth from 4.5% at the third loading step to 13.9% at the end of the seventh loading step, which is owed to the extensive crack formation in the patch's vicinity, resulting in the activation of the C-FRP rope to mitigate the cracking growing rate. During the eighth and final loading step, the joint panel displays significant cracking, as reflected by the sharp increase in the RMSD values of PZT Left to 18.3%.  
In contrast, the RMSD values of PZT Right increase up to the fourth loading step, reaching 6%, and remain unchanged up to the sixth loading step. In the final loading steps, the RMSD values of PZT Right show slight decreases. Although PZTs were placed in C-FRP ropes symmetrically, they exhibited different RMSD results due to the unsymmetrical cracking pattern. Furthermore, the BCJ's hysterical performance was unsymmetrical, as discussed in Section 3.1.  
Similarly, Figure 9 presents the RMSD values of the epoxy-bonded PZT patches of Specimen 2. The RMSD values of both patches exhibit an upward trend throughout the loading sequence. The RMSD values of PZT Left increase slowly up to the fifth loading step, reaching 3.7%. Subsequently, the RMSD values of PZT Left increase more steeply, reaching up to 6.7% at the end of the loading sequence. The RMSD values of the PZT Right indicate a notable increase in its RMSD volume ratio at the fourth loading step, reaching 4.7%, which remains relatively stable up to the sixth loading step. This can be attributed  
Figure 7: Comparison of the BCJ panels’ cracking pattern at the end of the loading sequence. (a) Specimen 1 and (b) Specimen 2.  
to the extensive cracking propagation in the vicinity of the patch and the activation of the C-FRP rope, contributing to mitigating shear deformations and thereby enhancing the resistance of the retrofitted joint. Eventually, the RMSD values of PZT Right rise to approximately 9% at the seventh loading step and 12% at the eighth loading step, reflecting the development of stresses on the rope as it strives to withstand the extended cracking of the joint. This observation aligns with the specimen's mechanical response and the dense mesh of cracks that formed in the joint panel.  
The RMSD values of the PZT patches attached to the C-FRP ropes align with the mechanical response and the cracking pattern formed in both specimens' joint panels. Therefore, PZT patches can provide insightful information regarding the performance of the retrofitting technique. The proposed monitoring approach detects micro-fracture development within the C-FRP rope or the adjacent concrete through the exceptional sensi  
Figure 8: RMSD values of epoxy-bonded PZT patches of Specim 1.  
Figure 9: RMSD values of epoxy-bonded PZT patches of Specim 2.  
tivity of PZT transducers. Consequently, the PZT transducers are suitable for continuous monitoring of retrofitted BCJs.
###### Abstract  
Agricultural water use represents more than 70% of the world's freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 23 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = \(-\)18.8 mm. Finally, among four Harfield seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.  
irrigation timing and amounts; surface soil moisture; data assimilation; particle filters; FAO-56; Sentinel-1; semi-arid Mediterranean region; winter wheat 2021132667[[https://doi.org/10.3390/rs13142667](https://doi.org/10.3390/rs13142667)]([https://doi.org/10.3390/rs13142667](https://doi.org/10.3390/rs13142667))