esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Robust Image Classifier Using Non-Overlapping Image Compression Filters
## 4 Experimental Results
### Time Consumption of Model Training  
The time consumption of model training, which refers to the total time spent completing the entire model training process, is one of the core parameters for evaluating model efficiency in the fields of machine learning and deep learning. Multiple factors collectively affect the time cost of model training, among which the architectural design and complexity of the model are particularly crucial. For structurally complex models, such as deep neural networks, parameter optimization and model adjustments often require more computational resources and time consumption.  
In our experiment, all five defense strategies used three CNN architectures as classification models after processing the data, with the main differences reflected in the data processing strategies. In order to compare the time cost differences among the five different defense strategies, we adopted a unified network architecture as a benchmark. Specifically, the number of iterations for the ResNet34 architecture was set to 100, while the number of iterations for VGG16 and Inception-ResNet-v2 was set to 20. In addition, we used two datasets and divided them into training and validation sets in an 8:2 ratio. In the experiment, DIP adopted optimized parameter settings, with a THRESHOLD value of 87, and a PIXELS value of 659, while LPIF used an optimal low-pass filter size of 7. To reduce experimental errors, we repeated the model training process for each defense method three times.  
Table 7 provides a detailed list of the time costs required for the five different defense methods during model training. Through comparative analysis, our defense method showed significant advantages in terms of time consumption. Due to the use of a larger image filter size (659 \(\times\) 659), the DIP method significantly increased the number and complexity of model parameters, thereby prolonging the training time. Data Filtering increased the size of the training data by incorporating adversarial samples into the training process. UDFilter required additional computing resources and utilized adaptive learning algorithms to generate a universal defense filter. In contrast, our method effectively reduced the size of the processed image by optimizing the image-filtering process, thereby reducing the number and complexity of model parameters and achieving more efficient model training. This demonstrates that our defense method reduces the computational overhead during the training process of image classification models without sacrificing robustness, making it a lightweight and efficient defense strategy.
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Internal/Subsurface Defects
#### 3.2.2 Acoustic Emission Testing (AE)  
Another method that can be used for the NDT of steel bridges is Acoustic Emission Testing (AE). AE is implemented to monitor materials experiencing dynamic processes, including structural loading, corrosion, and weld heating and cooling.  
The methodology of this technique works according to acoustic emissions generated as elastic waves (Figure 15) by the onset or progression of cracks and other discontinuities in steel elements. These waves are radiated outward from the vicinity of crack tips in a circular pattern and are sensed by the sensors (transducers) attached to the surface of the test piece seen by the AE monitor. Before the installation of the transducers, the coating should be removed at the attachment sites. In some cases, magnetic hold-downs are employed to couple the transducers to a test piece. For monitoring bridge components with the AE method, multiple transducers are typically used. To locate cracks and remove interference signals from noise sources, the transducers are to be placed on a test piece in geometric arrays. Figure 16 indicates the application of AE testing for the inspection of steel bridges [10].  
Figure 14: Cracks in a drum weld detected using ToFD technology [38].  
Figure 13: Principle of Time-of-Flight Diffraction Ultrasonic Method with a transmitter (transducer) on the left side of the figure and receiver on the opposite side [38].  
Figure 15: Schematic of AE technique (NDT Resource Center at Iowa State University).  
The AE has various applications for steel bridge elements including for rolled shapes, plates, welded connections, pins, and cable fittings. This method can only be used to detect the onset of a defect or the progression of existing anomalies; it cannot be used to detect existing defects without major activity. The AE method has been used successfully for detecting wire breaks in cable-stayed bridges and fatigue cracking in orthotropic bridge decks [42]. It should be stated that this method requires special equipment and training for the inspectors.
# Offloading of Atomic Tasks in Satellite Networks:
## 4 Method
### Algorithm Design
#### 4.2.1 Maximum Task Flow Transmission
We first obtain the number of tasks that need to be unloaded on each satellite (line 5) and then use the classic Ford-Fulkerson [40] algorithm to calculate the maximum traffic path and traffic distribution on the graph \(G^{{}^{\prime}}\) at the current moment (line 6). If the number of unloadable tasks on a satellite \(s\) is greater than the maximum number of tasks allowed in the maximum stream result, only the number of tasks allowed in the maximum stream result will be allowed to be transmitted. Conversely, the number of tasks to be offloaded on the transmission satellite is allowed (lines 7-11). Finally, the results of all task offloading assignments during the period are returned. ```
1:Given a directed graph \(G=(V,E,T)\), Given time range \(\tau\), Given the atomic task request queue \(v_{q}\) on each satellite. 2:The number of atomic tasks that can be unloaded by each satellite in a certain period \(O_{timeRange}(V)\). 3:set \(timeRange=\tau\)
4:set \(G^{{}^{\prime}}=G(V,E)\)
5:repeat
6:repeat
7:Calculate the number of tasks that need to be unloaded by the satellites \(O_{t}(V)\);
8:\([route,f]\)=FordFulkerson(\(G^{{}^{\prime}}\));
9:if\(task(s)>f(s,v)\)then
10:\(O_{t}(s)=f(s,v)\)
11:else
12:\(O_{t}(s)=task(s)\)
13:endif
14:until\(V\)
15:until\(timeRange\)
16:return\(O_{timeRange}(V)\)
```  
**Algorithm 1** Optimal allocation of task offloading quantity
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 5 Conclusions  
To eliminate the power supply problem of a traditional MR damper, a new self-powered MR damper control system was developed based on electromagnetic energy harvesting technology. Unlike other self-powered MR dampers, the MR damper and therotary DC generator are not at the same location on the stay cable in this paper. The feasibility and effectiveness of the new system for suppressing cable vibration was experimentally evaluated via a 21.6 m inclined cable model. The test results indicate that the developed self-powered MR damper system can provide more cable damping than a passively operated MR damper with constant voltage. The increased supplemental modal damping ratios of the cable in the first four modes are mainly attributed to two factors. One is that the self-powered MR damper can exhibit negative stiffness behavior for the case of the higher-order mode of the cable, which is capable of enhancing the control performance; the other is that the rotary DC generator in the self-powered MR damper system also provides supplemental damping to the cable.  
Since the main purpose of this paper is merely to demonstrate the feasibility of the self-powered MR damper system in reducing cable vibration, the system may not show the best possible performance in the test. Hence, the optimum design of the system should be considered in later research, including the relative locations of the generator and the MR damper, the sizes and parameters of both the rotary DC generator and the MR damper, and the effective transmission radius of the sprocket.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 51308214 and 51578151) and the National Basic Research Program of China (973 Program) (Grant No. 2015 CB057702 and 2015 CB060000). [PERSON] and [PERSON] conceived the idea and designed the experiments; [PERSON] performed the experiment and analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] reviewed and revised the paper.  
The authors declare no conflict of interest.
## 3 Experiments Evaluation
### Algorithm Behavior on Simulated Data
#### 3.1.3 Recovery on the Rank of Fixed Subspace  
As the special sparse recovery problem depends on the fixed subspace, in the third simulated experiment we demonstrate how the rank of the fixed subspace affects recovery. To observe the algorithm behavior, we fix the signal sparsity \(\rho_{s}\)as 5% and generate a series of simulated signals with different rank setting from rank = 1 to rank = 100. Figure 3 shows the recovery results with CS measurement ratio \(\rho_{cs}\) as 30%, 50%, and 70% respectively. It can be clearly seen that given moderate enough CS measurements the proposed algorithm can guarantee the recovery with up  
Figure 1: Sparse recovery performance with respect to signal sparsity.  
to a relative high rank of the fixed subspace. For example, with the CS measurement \(\rho_{cs}=50\%\) the sparse signal can be stably separated from the fixed subspace with up to rank \(=40\).
## 2 Materials and Methods
### Chromatographic Fingerprinting by Peak Features Alignment across Chromatograms  
To comprehensively map the detectable metabolites signatures of hazelnut, data processing was based on a workflow developed to track untargeted and targeted components across multiple chromatograms. The process is termed UT fingerprinting [38] and was validated for complex fractions of volatiles [29; 39; 40; 41] in biological fluids metabolomics [42; 43; 44] and food metabolomics [30; 37]. It performs untargeted and targeted pattern recognition of individual 2D peaks by template matching algorithms [45].  
The UT fingerprinting work-flow included four major steps:  
* Step-1: Individual chromatograms were imported by the data processing software (GC Image, GC Image LLC, Lincoln, NE, USA), rasterized according to the \(P_{M}\), and pre-processed for baseline subtraction and peak detection. The detection threshold was set at 150 S/N, as previously validated [23].
* Step-2: The untargeted feature template was created by a dedicated program of the GC Image suite (i.e., Image Investigator(tm)) by cross-matching peak templates from all analyzed chromatograms (24 QCs + 36 samples). After re-alignment of 2D-peak patterns, peaks that consistently matched across all-but-one chromatograms were annotated as _reliable_ peaks and included in the _feature template_. For peak matching a spectral similarity direct and reverse match factors (DMF and RMF) constraint was applied with the NIST (National Institute of Standards and Technology) similarity algorithm [46] using threshold values \(\geq\) 750 [23].
* Step 3: After feature template generation, the template was pruned by removing solvent peaks, column(s) bleed, and interferements, before proceeding with peaks targeting. Compounds targeting was a supervised process that made putative identifications from an MS library based on spectral similarity [46] using threshold values DMF \(\geq\) 900, RMF \(\geq\) 930, and \({}^{1}\)D retention-index (\(l^{T}\)) coherence (\(l^{T}\)\(\pm\) 15 units).  
At this point, the feature template combined untargeted and targeted features--i.e., a UT template. Untargeted peaks correspond to reliable peaks defined at Step-2 but that remained unidentified applying the criteria of Step-3. Targeted peaks are those reliable peaks putatively identified at Step-3.  
* Step 4: The UT feature template was then matched to each sample chromatogram thereby recognizing re-aligned peak features, which were exported for further data elaboration. The output was a data matrix of UT peaks together with \({}^{1}\)D and \({}^{2}\)D retention times (\(l_{R}\), \({}^{2}t_{R}\)), compound names for target analytes, fragmentation spectra, selected ions responses, total ions response, etc.
To evaluate the accuracy of the trained models in a specific domain, it is essential to compare the predictions of various algorithms with the true outcomes of the data that we already know about. For instance, DT models are exclusively exhaustive for classification because their structures utilize the \"if-then\" rule. Additionally, DTs have the propensity to over-fit by generating too many branches that might reflect anomalies such as noise or outliers until meeting a termination condition. The artificial neural networks (ANNs) are inspired by the way the neurons of the brain function, as understood by neurobiologists a long time ago, and they mathematically simulate the way information is processed according to the principles of network topology. In this paper, different types of classification algorithms have been compared with our proposed algorithm in terms of accuracy. As formerly mentioned, each algorithm has its own mechanism or strategy for training a suitable model. Some utilize probabilistic approaches such as NNs, some partial differentiation (linearequations) to update their weights, such as the case of KNN and the nearest neighbor, and others, such as SVM, use linear algebra. A quick study of the available literature shows that, in terms of accuracy and effectiveness, the family of ANN algorithms is highly adaptable for the classification of all types of data starting from text to images and videos, in addition to audio and other standard numerical datasets [10]. Nevertheless, the availability of multiple algorithms is a great advantage, as each of them has its own peculiarity in dealing with different challenges in various datasets. Thus, some algorithms work perfectly in some cases while in other cases they are weak in terms of model accuracy. In this study, a novel classification algorithm called Core Classifier Algorithm (CCA) is presented. It is based on the cores which represent the characteristics and features of each class and can be used to classify new points based on their similarity to the cores. K-means clustering algorithm is part of the proposed algorithm to minimize the effects of data distribution irregularities such as outliers and overlapping and to simulate the learning process in NNs. Of note, the results of the K-means algorithm are not constant and change based on various implementations, but the main reason for using K-means was to exploit its ability to generate cores and increase the accuracy of the model. Whereas NNs go through multiple iterations of adjusting weights and error rate for each feature until they achieve the most optimal results, CCA uses multiple iterations of K-value to better represent different distributions until it converges to the most optimal one, in this way achieving high accuracy even on data with very disparate distribution. The rest of the study is organized as follows: Section 2 for literature review, Section 3 for proposal algorithm, Section 4 for the experimental analysis and results, and Section 5 for conclusion and future work.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 3 Results
### Simulation Results
#### 3.1.2 No. 2 Simulation Results  
Figures 8-10 and Table 5 compare the performance of the Chi-square and the proposed method in the GNSS ramp fault scenario. In Figure 8, the test statistic caused by a ramp fault in SV-1 shows that both methods can detect the ramp fault and that the proposed method can detect it faster. In Figure 9, when the ramp fault first appears, neither method can detect the fault. When the ramp fault increases, the proposed method can detect the fault faster, then detect and isolate the fault satellite pseudo-range measurements with sub-filters to ensure positioning accuracy. Table 5 proves that the proposed method has lower RMS results. The box diagram of position errors also shows the superiority of the proposed method in Figure 10. Positioning accuracy can be improved by more than 60%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Sources**} & \multicolumn{2}{c}{**Fault Information**} & \multicolumn{1}{c}{**Fault Duration**} \\ \hline \multirow{3}{*}{Position (m)} & longitude & 2.0 & 0.9 \\  & latitude & 2.8 & 0.7 \\  & height & 6.7 & 1.1 \\ \hline \multirow{3}{*}{Velocity (m/s)} & east velocity & 0.08 & 0.06 \\  & north velocity & 0.13 & 0.07 \\ \cline{1-1}  & up velocity & 0.41 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMS of navigation errors obtained by simulation under the single-satellite step fault.  
Figure 8: The test statistic of fault detection obtained by simulation under the single-satellite ramp fault.  
Figure 7: The box diagram of fused position errors under the single-satellite step fault.
## 4 Results and Experiments
### Multi-Class Classification  
The performance metrics for multi-class classification across various models on the CICIDS2017 and NF-BoT-IoT-v2 datasets are summarized in Table 31, highlighting the results of our proposed models, Autoencoder-CNN and Transformer-DNN. On the CICIDS2017 dataset, the CNN and DNN models achieved accuracy, precision, recall, and F-score values of 99.94%, while the Autoencoder model recorded slightly lower values at 99.93%. Autoencoder-CNN model outperformed these, achieving 99.95% for all metrics, with the Transformer-DNN model demonstrating the best performance at 99.96% across accuracy, precision, recall, and F-score. For the NF-BoT-IoT-v2 dataset, the CNN model delivered an accuracy of 97.87%, precision of 97.91%, and recall and F-score values of 97.87%. The Autoencoder model showed slightly lower performance, with 97.81% for all metrics except precision, which was 97.87%. The DNN model achieved an accuracy of 97.83%, precision of 97.88%, recall of 97.83%, and an F-score of 97.82%. Autoencoder-CNN model demonstrated the best overall performance, achieving 97.95% for accuracy, recall, and F-score, and 97.97% for precision. The Transformer-DNN model closely followed, achieving accuracy, recall, and F-score values of 97.90% and precision of 97.98%.These results underscore the superior performance of our proposed models, Autoencoder-CNN and Transformer-DNN, in multi-class classification, demonstrating their reliability and effectiveness across both datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Metric** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{6}{*}{CICIDS2017} & CNN & 99.83\% & 99.83\% & 99.83\% & 99.83\% \\ \cline{2-6}  & Autoencoder & 99.73\% & 99.73\% & 99.73\% & 99.73\% \\ \cline{2-6}  & DNN & 99.88\% & 99.88\% & 99.88\% & 99.88\% \\ \cline{2-6}  & Autoencoder-CNN & 99.90\% & 99.90\% & 99.90\% & 99.90\% \\ \cline{2-6}  & Transformer-DNN & 99.92\% & 99.92\% & 99.92\% & 99.92\% \\ \hline \multirow{6}{*}{NF-BoT-IoT-v2} & CNN & 99.97\% & 99.97\% & 99.97\% & 99.97\% \\ \cline{2-6}  & Autoencoder & 99.94\% & 99.94\% & 99.94\% & 99.94\% \\ \cline{1-1} \cline{2-6}  & DNN & 99.96\% & 99.96\% & 99.96\% & 99.96\% \\ \cline{1-1} \cline{2-6}  & Autoencoder-CNN & 99.98\% & 99.98\% & 99.98\% & 99.98\% \\ \cline{1-1} \cline{2-6}  & Transformer-DNN & 99.98\% & 99.98\% & 99.98\% & 99.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 30: Performance metrics in binary classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Metric** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{6}{*}{CICIDS2017} & CNN & 99.94\% & 99.94\% & 99.94\% & 99.94\% \\ \cline{2-6}  & Autoencoder & 99.93\% & 99.93\% & 99.93\% & 99.93\% \\ \cline{1-1} \cline{2-6}  & DNN & 99.94\% & 99.94\% & 99.94\% & 99.94\% \\ \cline{1-1} \cline{2-6}  & Autoencoder–CNN & 99.95\% & 99.95\% & 99.95\% & 99.95\% \\ \cline{1-1} \cline{2-6}  & Transformer–DNN & 99.96\% & 99.96\% & 99.96\% & 99.96\% \\ \hline \hline \end{tabular} \end{table
## 3 Literature Reviews
### Leach  
There are major routing protocols for WSN such as clustering-based routing protocols, TinyOS beaconing, directed diffusion, geographic routing, minimum cost forwarding, rumor routing and energy conserving topology maintenance [4]. Clustering-based routing is famous for its energy-efficient features. One of protocols in this family is LEACH. LEACH rotates randomly the role of CHs in order to evenly distribute the energy load among the sensors in the network. It incorporates a data aggregation scheme into its routing protocol to reduce the amount of information that CHs should transmit to the BS. LEACH considers a network model where a fixed BS is located far from sensors and all sensors in the network are homogeneous and energy-constrained. LEACH assumes that every node can reach the BS by transmitting with sufficiently high-power. Figure 1 summarizes LEACH protocol in steps.  
The various symbols denote:  
\(H,A_{i},BS\): A CH, ordinary node, and BS, respectively;  
\(\mathcal{G}\): The set of all nodes in the network;  
\(\Rightarrow\), \(\rightarrow\) : Broadcast and unicast transmissions, respectively;  
\(id_{X}\): Id of node \(X\);  
**adv**, **join_req**, **sched**: String identifiers for message types;  
\((id_{X},t_{X})\) : A node id \(X\) and its time slot \(t_{X}\) in its cluster's TDMA schedule;  
\(d_{X}\): Sensed data from node \(X\);  
\(\mathcal{F}(\) \()\): Data aggregation function.  
LEACH divides network operation processes into rounds. Every round consists of two phases, _setup phase_ and _steady-state phase_. Cluster formation is done in the setup phase. This phase is comprised of three steps. At first, the self-elected CH candidates advertise the **adv** message to become CHs for that round. In the next step, based on the received signal strength of the **adv** message, nodes send the **join_req** message to a CH they want to join. In the last step CHs send the **sched** messages  
Figure 1: LEACH protocol.  
containing TDMA time slots to their cluster members. After cluster formation, LEACH enters the steady-state phase. The actual communication takes place in this phase. At first cluster members send their sensor readings to the CH. Then the CH aggregates the readings and sends the aggregated data to the BS.  
LEACH only concerns energy efficiency. Although its randomized rotation of CHs' roles inherently provides protection for the aggregator nodes, LEACH does not take the security aspect into consideration. Thus several schemes, such as SLEACH [13], SecLEACH [14], SC-LEACH [15], Armor LEACH [16] and MS-LEACH [17], have been proposed to secure LEACH.