esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Submerged Cavitating Jet Experiment
### Experimental Results and Analysis  
Seven aluminum blocks sized 50 mm \(\times\) 50 mm \(\times\) 10 mm were eroded, and the morphology before and after experiments is shown in Figure 5. It can be seen that except for nozzle No.6, the Helmholtz nozzle, the cavitation jets generated by other nozzles caused serious damage to the surface of the aluminum blocks. The damage manifests as a central dent with pitting around it, but the pitting caused by nozzle No.7 was almost negligible. It is not appropriate to determine the erosion performance of the nozzles solely based on the erosion morphology of aluminum blocks, thus the mass loss is listed in Table 3. It can be seen that the nozzles No.3, No.4, and No.5 caused the greatest mass loss to the aluminum blocks, so these three nozzles were selected to form the second group to test the erosion performance under a standoff distance of 50 mm.  
Figure 4: Erosion experiment.  
Figure 6 shows the morphology of the aluminum blocks before and after erosion in the second group. It can be seen that the damage of the three aluminum blocks all includes central dent and spitting around, wherein the dent caused by nozzle No.3 seems the deepest and the spitting caused by nozzle No.5 has the widest range. The mass loss of aluminum block caused by nozzle No.5 was the largest, as seen from Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Nozzle Number** & **Initial Mass [g]** & **Final Mass [g]** & **Mass Loss [mg]** \\ \hline
3 & 62.5468 & 62.5261 & 20.7 \\
4 & 63.1108 & 63.0954 & 15.4 \\
5 & 64.4566 & 64.4234 & 33.2 \\ \hline \end{tabular}
\end{table}
Table 4: Mass of the aluminum blocks before and after erosion in the second group (standoff distance of 50 mm).  
Figure 5: Morphology of the aluminum blocks before and after erosion in the first group (standoff distance of 30 mm).  
The central dent was attributed to high-pressure water flow, while the pitting around was caused by bubbles collapse. Therefore, the shallower the central dent and the wider the pitting range, the better the cavitation effect. In the first group, the nozzles No.3, No.4, and No.5 which have the expanded cylinder segment had better a erosion effect than the other nozzles. The expanded cylinder segment can prolong the lifespan of the cavitation bubbles, making more bubbles reach and collapse at the target surface, ultimately improving the erosion effect. Under the standoff distance of 30 mm, the predominant erosion effect was due to high-pressure water flow impact. However, the denudation caused by bubbles collapse became more pronounced under the standoff distance of 50 mm. Overall, nozzle No.5 in second group had the best erosion effect. This can be attributed to the spherical outlet, which facilitates the cavitation bubbles gathering together and collapsing at the target surface.  
When cavitation jet is applied to cleaning, e.g., ship hull cleaning, the erosion effect of nozzle No.5 was more favored, because the shallow central dent and wide pitting range mean less damage to the surface of the hull paint layer and larger cleaning range.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT  
Our proposed lightweight slice-based QoS manager was implemented as a solution to be integrated in SOFTWAY4 IoT. Our solution is lightweight since it is focused on non-3 GPP IoT environments considering simpler SDN implementations besides the complete framework such as MANO, OpenDayLight or OpenStack. Hence, this section provides a detailed setup of the proposed solution to achieve QoS in slices for Industry 4.0 and digital farming deployments.  
The implementation is split among three machines: Host; VM-Application and VM-Manager. The VM-Application can be described as the machine that stands between the cloud, services and tenant applications, being responsible for high-level tasks. It represents the FCMan module and, consequently, the Application Layer. VM-Manager's primary purpose is to run the SDN module, including the lightweight Ryu controller, with the additional task of hosting WebGM and all its administration capabilities, including the services that run on other machines. Thus, from the Network Operating System (NOS) perspective, the machine can be seen as the controller API. Both VMs run on the Host in a virtualisation environment with the Xen hypervisor [46], and all their components run inside Docker containers, guaranteeing the previously mentioned isolation of services and applications. In contrast with the VM-Application, the Host has the responsibility of performing the lower-level services and therefore represents the Data Plane. The SDWI service runs on the Host, and it communicates with the VMs employing OvS, which takes advantage of OpenFlow to update flow table entries in switches as the considered standard solution for the Southbound Interface. A schematic of the implemented setup and how it fits in the NOS architecture is depicted in Figure 4.
## 3 Results  
Results from reliability analyses are detailed in Table 1. Overall, results show both isometric hamstring (ICC 95% CI: 0.72 to 0.96) and quadriceps muscle strength (ICC 95% CI: 0.55 to 0.96). MVIC values range from moderate to excellent reliability. In addition, hamstring (ICC 95% CI: 0.19 to 0.89) and quadriceps (ICC 95% CI: 0.30 to 0.97). RFD-max values present the largest variability (Table 1). Isometric hamstring and quadriceps RFD metrics at different time phase (RFD\({}_{0-50}\), RFD\({}_{0-150}\) and RFD\({}_{0-250}\)) values range from moderate to excellent (ICC 95% CI: 0.52 to 0.95) and from poor to excellent reliability (ICC 95% CI: 0.22 to 0.91), respectively. Bias and LoA are presented in Bland-Altman plots (Figures 2A-E and 3A-E), and residuals were equally distributed with no presence of heteroscedasticity according to the visual inspection of the plots. Results from the H:Q reliability were good for the MVIC in both dominant (M \(\pm\) SD: 1.5 \(\pm\) 0.4 vs. 1.5 \(\pm\) 0.5, ICC = 0.86, [95% CI: 0.66, 0.95]; TE = 0.17 N, [95% CI: 0.13, 0.26]; CV = 11.8%, [95% CI: 8.9, 17.7]) and non-dominant legs (M \(\pm\) SD: 1.5 \(\pm\) 0.4 vs. 1.4 \(\pm\) 0.4, ICC = 0.76, [95% CI: 0.48, 0.90]; TE = 0.22 N, [95% CI: 0.17, 0.32]; CV = 14.8%, [95% CI: 11.7, 22.4]). In contrast, RFD metrics showed a large variability with insufficient reliability to compute the index (ICC = 0.08 to 0.75; CV% = 27 to 85%).
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 4 Hardware-Based Countermeasures for Memory and Cache Contention DoS Attack
### Our Proposed Mitigation Method
#### 4.2.2 Throttling Periods  
It is also crucial to determine how long we should maintain the throttling in order to fully suppress the effect of the malicious memory and cache contention. Figure 8 describes an example timing diagram for our throttling engagement and disengagement. To trigger the throttling mechanism, we should spend at least \(\textbf{{Thres}}_{\textit{acc}}\times\textbf{{Thres}}_{\textit{int}}\) cycles (in this work, \(500\times 100,000\) cycles) for profiling. Once our throttling mechanism is engaged, it lasts 500 million processor clock cycles. After that, the throttling mechanism is disengaged and the CPU returns back to the normal execution mode while resetting the profiling counters (i.e., CCounter, RCounter, and ACounter) to \"0\".  
We set the throttling phase to be ten times longer than the profiling phase, as shown in Figure 8. It means that we can recover performance and energy by at least over 90%, on average, as long as our detection mechanism correctly detects the malicious codes. Because our detection mechanism is hardware-based one that profiles runtime program execution patterns, we cannot forcibly terminate the detected malicious code (although the profiling phase is much shorter than the throttling phase), which is why we cannot achieve 100% recovery rates. This is a clear limitation of a hardware-based approach (the similar approach was also employed in [9]). 500 million cycles correspond to 250 ms in our simulated system, with 2 GHz CPU. We believe that it would be sufficient time to minimize the impact of the attack. Obviously, the recovery rate will vary, depending on the time periods of profiling and throttling phases though the investigation on the relationship between the recovery rate and profiling/throttling time periods is out-of-scope of this paper.  
The instruction fetch throttling is independently performed across the processing cores, because the fetch width throttling should be applied to each core. For example, once our detection mechanism detects the malicious program in Core1, only Core1 is engaged to the instruction fetch width throttling while the Core0 is not engaged. Once 500 million cycles have been spent since the throttling was engaged in Core1, it resumes the execution by recovering the instruction fetch bandwidth from \"0\" to \"3\" in Core1, returning back to the profiling phase.
## 1 Introduction
Although the papers provided accurate monitoring and identification of the anomalies during the printing operation, the defects and degradation on fabricated products are yet to be prevented. On that note, closed-loop control and monitoring platforms are proposed. On [28] a cyber-physical system (CPS) can provide an online data user interface where the printing parameters are displayed and according to the authors, they used SVM to extract the data features and a PID control is responsible to stabilize the nozzle temperature. They presented a promising technology; however previous studies have succeeded controlling the nozzle temperature by using PID control itself [29] and from the mentioned papers the existing monitoring and quality control program was able to detect the anomaly during or moments before it occurs making it difficult to prevent the fault from occurring. To overcome this, an artificial neural network-based monitoring system embedded for the FDM printing technique is proposed in this paper. Distinct from the presented papers, our proposed monitoring program provides a nozzle thermal estimation in future time-steps. The use of artificial neural networks in forecasting is becoming a trend in predictive maintenance [30] and have also been applied on other fields [31; 32]. The forecasted values are processed on an anomaly detection intended to detect possible irregularity on the nozzle temperature values that can lead to thermal degradation on the fabricated products as well as probable nozzle clogging that is one of the prominent printing failures in this type of printing technique [31]. A temporal convolutional network with a two-stage sliding window strategy (TCN-TS-SW) is proposed to perform the thermal estimation. TCN-TS-SW's ability to perform parallel computations that requires low memory for training is seen as an advantage towards the working environment that is presumed to receive a long sequence of data. To further reduce inference time and memory consumption, an initial sliding window technique to execute a coherent pre-processing procedure to improve the time series forecasting. The proceeding section talks thoroughly about the architecture of the TCN-TS-SW, furthermore, Section 3 discusses the experiment set-up. In Section 4 the performance evaluation is presented and lastly, the conclusion is summarized in Section 5.
###### Abstract  
Monitoring heavy equipment in real time is crucial for ensuring safety and operational efficiency at construction sites, yet achieving both high detection accuracy and fast inference remains challenging under diverse environmental conditions. Although previous studies have attempted to improve accuracy and speed, their findings often lack generalizability, partly due to inconsistent datasets and the need for more advanced techniques. In response, this study proposes an enhanced object detection method that integrates transformer-based backbone networks into the You Only Look Once (YOLO-version 10) framework. Evaluations conducted on a large-scale dataset of construction-site images demonstrate notable improvements in detecting the heavy equipment of varying sizes. Comparisons with other detectors confirm that the proposed model not only achieves higher accuracy but also maintains competitive processing speed, making it suitable for real-time deployment. Additionally, the dataset is made available for broader experimentation and development. These findings underscore the method's potential to strengthen on-site safety by providing more reliable and efficient heavy equipment detection in complex work environments, while also acknowledging areas for further refinement.  
heavy equipment detection; construction management; deep learning; object detection; YOLO; transformers Article
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 2 Shielding Grounding Optimization Method
### Multisim Conversion of Logical Expressions  
After the conversion of the truth table, the logical expression was then output using Multisim's logic conversion module, as shown in Figure 5. As the logical expression can organize and classify all grounding states, it is easier to identify than the truth table, and it can reflect the mapping relationship between the shield grounding state and shielding effect, which is more convenient for summarizing the law and has better guidance for subsequent shield grounding optimization. Firstly, the voltage level \(l\) (\(1\,\leq\,l\,\leq\,n\)) is determined, all the input states with \(Y_{l}=1\) are found, and the logical expression between the input and output is written in the form of the simplest and-or expression for all voltage levels. Taking Table 4 as an example, the final logical expression could be obtained as Equation (1):  
\[\left\{\begin{array}{l}Y_{1}=R_{2N}+R_{2F}\\ Y_{2}=R_{2N}\cdot R_{2F}^{\prime}+R_{2N}^{\prime}\cdot R_{2F}\\ Y_{3}=R_{2N}^{\prime}+R_{2F}^{\prime}\end{array}\right. \tag{1}\]  
where \(R_{2N}\) is grounded and \(R_{2N}^{\prime}\) is not grounded. Upon analyzing the above logical expression, determining the different grounding effect of the shield of cable \(C_{2}\) becomes very straightforward. If one wants to choose the state with the best shielding effect, then one should find the logical expression of \(Y_{1}\), i.e., the shield must be double-ended and grounded. If one wants to choose the state with the worst shielding effect, then one should find the logical expression of \(Y_{3}\), i.e., the ungrounded shield. The logical expression of \(Y_{2}\) shows that the cable is grounded and single-ended, the effect is medium, and the effect of near-end grounding is similar to that of far-end grounding, which is important in practice. As shield grounding is not always convenient, circuit debuggers are often located far from the signal transmitter and closer to the signal receiver. In the case of long cables, the shield's far-end grounding makes it easier to monitor and maintain the grounding state.
## 4 Security Benefits of Directional Antennas in WSNs
### Jamming  
Jamming is the deliberate act of broadcasting an inference radio signal aimed to disrupt wireless communication. This type of electromagnetic interference can be accomplished either in a simple manner when the jammer continually transmits interference signals or using more sophisticated approaches based on communication protocol vulnerabilities.  
Figure 3: Eavesdropping and jamming effect with directional antennas.  
Due to their particular radiation patterns, directional antennas can efficiently mitigate the effects of jamming attacks being able to safely communicate if the jammer's location is outside antenna's coverage sector (Figure 3). The scientific literature reveals some significant works in this domain.  
In [45], [PERSON] studied the effects of jamming attacks in a multihop _ad hoc_ communication network. By comparing the network connectivity index when either omnidirectional or directional antennas are used in jamming circumstances, the author proved a significant improvement in the second case. This result stands not only for randomly placed jammers but also for jammers optimally located in the network area. Moreover, the result can be extrapolated to diverse types of smart antennas able to concentrate the beam's power in the receiver's direction (e.g., sectored antenna or beamforming antenna) [57; 58].  
For mitigating the jamming effect in wireless sensor networks, [PERSON] _et al._[46] proposed a combined strategy that uses pre-distributed cryptographic keys in conjunction with sensor nodes able to switch from omnidirectional to directional antennas anytime a jamming attack is detected.  
In order to reduce unwanted interferences in randomly deployed wireless sensor networks [PERSON] and [PERSON] [47] suggested two possible solutions: equipping the nodes with directional antennas and establishing a superior limit of the duty cycle for each network node. While the first solution decreases the spatial area from where a jamming attack can be launched, the other decreases the temporal interval when a malicious attack can affect the node.
# A Novel Neural Network-Based Method for Medical Text Classification
## 3 Our Work
### Document Representation
#### 3.2.2 Sentence Decoder  
To extract sentences that are important to the meaning of the document, we use the attention mechanism and introduce a sentence level context vector \(v_{s}\) and use the vector to form a document representation. This yields  
\[u_{s}=\tanh(w_{s}h_{s}+b_{s}) \tag{15}\]  
\[a_{s}=\frac{exp(u_{s}*v_{s})}{\sum_{i=1}^{M}(exp(u_{s}*v_{s})} \tag{16}\]  
\[F_{c}=\sum(a_{s}*h_{s}), \tag{17}\]  
where \(w_{s}\) and \(b_{s}\) are represented as the weight and bias in the neuron. \(F_{c}\) is the document vector that summarizes all the information of sentences in a document. Similarly, the sentence level context vector can be randomly initialized and jointly learned during the training process.  
Finally, the comprehensive context representations \(F_{c}\) are obtained. The comprehensive context representations are considered as the features for text classification. In our model, the dropout layer and the sigmoid layer are used to generate the conditional probabilities over the class space to achieve classification [22]. The purpose of the dropout layer is to avoid overfitting.  
Currently, the cross entropy is a commonly used loss function to evaluate the classification performance of the models. It is often better than the classification error rate or the mean square error. In our approach, RMSProp optimizer [23] is chosen to optimize the loss function of the network. The model parameters are finetuned by RMSProp optimizer which has been shown as an effective and efficient algorithm. The cross entropy as the loss function can reduce the risk of a gradient disappearance during the process of stochastic gradient descent. The loss function can be denoted as follows in Equation (17).  
\[L=\frac{-1}{num}\sum_{cd}[y\ln o+(1-y)\ln(1-o)], \tag{18}\]  
where \(num\) is the number of training samples, \(cd\) represents the training sample, \(y\) is the label of the sample, \(o\) is the output of our model.  
In our method, the evaluation metric which measures the overall classification performance is accuracy \(p\).  
\[p=\frac{m_{c}}{m}, \tag{19}\]  
where \(m_{c}\) is the number of true classified medical texts and \(m\) is the number of whole medical texts.  
Compared with HAN, our method has been improved in the following ways:  
1. In sentence representation, our method uses convolutional layer to extract features and then through BIGRU and attention mechanism.
2. In model optimization, our method uses RMSProp optimizer to replace Adam which used in HAN.
## 4 Performance Analysis  
This section compares the graphics processing time using OpenACC for the considered 3D-FDTD problem in this study (dimensions of the volume \(1500\times 1500\times 1500\) m), with the execution time of this algorithm using a serial C programming language in CPU. Table 4 presents the comparison of the performance of OpenACC compared to CPU serial C. As can be seen in Table 4, the OpenACC execution time in an efficient state was 21.22 times that of the serial processing in the CPU without compromising precision. As can be seen in Table 3, the calculation time in the GPU process was drastically reduced by storing the variables of the float type (single precision). Although float variables (single precision) led to a decrease in the accuracy of the computation, this reduction was negligible. Figure 10 depicts the difference (computing error) between CPU and GPU processing for point A, at which the mountain height was zero (H\({}_{\text{m}}\) = 0). The error is smaller than 0.008% and can be considered insignificant. In addition, Figure 11 indicates the gain in the computation speed as a function of the size of the workspace. It can be seen that the GPU performance increases with larger simulation domains.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Programming Language** & **Processing Time(s)** & **Speed-Up** \\ \hline CPU serial C & 2231.59 & 1 X \\ OpenAcc & 105.15 & 21.22 X \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of OpenACC implementation.  
Figure 9: Horizontal magnetic field (H\({}_{\text{y}}\)) on a smooth ideal ground at observation point A. The height of the mountain is set at H\({}_{\text{m}}\) = 0 m. Results were calculated using the 2D-FDTD (solid line) and the 3D-FDTD (dashed line) methods.  
Figure 11: Gain in GPU runtime with respect to CPU for various numbers of nodes.  
Figure 10: (**a**) The blue solid line and pink line represent the CPU and GPU processing, respectively, (**b**) Numerical error between the CPU and GPU processing.
## 3 Data and Methodology
### Laboratory Testing of Rock Masses
#### 3.4.2 Rock Thin Section Analysis  
Rock thin section analysis involves preparing rock specimens in the form of thin slices, translucent slices, or probe slices. Subsequently, under the guidance of crystal optics and mineralogy principles, a polarized microscope was used to examine the optical properties of minerals for mineral identification. In the context of the lithology of the study area, four samples were chosen for experimentation, and the observation positions and magnifications were carefully adjusted to ensure optimal results. An Olympus polarizing microscope was used for this purpose, and microscope images at 25\(\times\), 50\(\times\), and 100\(\times\) magnifications were eventually obtained.
###### Abstract
In this paper, an algorithm is presented to address this challenge, enabling the discovery of transmitters in OCC systems where transmitters and receivers remain stationary even under such demanding conditions. The approach leverages both luminance parameters and the defined frame structure, providing robust transmitter detection. The algorithm is evaluated across various scenarios and lighting conditions. The remainder of this paper is structured as follows. To provide context, the OCC general literature is reviewed, and the sub-pixel scenario is defined in Section 1.1. In Section 1.2, prior research related to discovery algorithms in OCC and the sub-pixel scenario works is delved into. In Section 1.3, the impact of OCC on 6G is discussed. The software developed is detailed in Section 2, while Section 3 outlines the experimental design and key metrics of interest. Finally, the obtained results are presented in Section 4 and the conclusions drawn from this work are discussed in Section 5