esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result Comparison
### Classification Approach  
For initial comparison of the classification models, the results of all CNN models were analyzed for all three classes. The derivatives of Vgg19, ResNet, Inception-V3, and MobileNet-V2 serve as the CNN models.  
The results are shown in the diagram in Figure 12. The training duration of the CNN models varied between 4 h and 12 h. The plot shows the test results for the error classes in the binary (Bin) and multilabel (Mul) approaches. The boxplot is composed of all four CNN architectures, with all detailed results listed in the Appendix A in Tables 12 and 13.  
Figure 11: Label distribution for binary and multilabel models.  
\begin{table}
\begin{tabular}{c c} \hline
**Options** & **Value** \\ \hline _Solver Type_ & adam \\ \hline _Mini Batchsize_ & 128 \\ \hline _Max. Epochs_ & 40 \\ \hline _Validation Frequency_ & 125 \\ \hline _Validation Patience_ & 8 \\ \hline _Shuffle_ & Once \\ \hline \multirow{4}{*}{_Learning Rate_  Binary_} & MobileNet: 0.0001 \\  & ResNet: 0.000001 \\  & Vgg19: 0.00001 \\  & Inception: 0.001 \\ \hline \multirow{4}{*}{_Learning Rate_  Multilabel_} & MobileNet: 0.0001 \\  & ResNet: 0.000001 \\ \cline{1-1}  & Vgg19: 0.00001 \\ \cline{1-1}  & Inception: 0.0001 \\ \hline \end{tabular}
\end{table}
Table 3: Training parameters for the CNN models.  
The scores for all methods are generally very high, with all results above 0.9. Comparison of the G-Mean and balanced accuracy values indicates that scatter is lower for the binary methods over all plausibility causes.  
Analysis of the highest classification results also reveals that the binary and multilabel variants achieve nearly identical results for the loads and mesh classes. The absolute differences are very small, in some cases in the third decimal digit. Only in the geometry class is the higher accuracy of the binary classification visible. In evaluation of the geometry cause, it is evident that this class was the most challenging in the classification, recognizable by the high dispersion of both classification variants.  
Because of low dispersion and higher accuracy for the geometry class of the binary classifiers, they will be examined in more detail. The aim is to compare the different CNN architectures against each other in order to obtain the best results.
## 4 Experiment and Analysis
### Experimental Results
#### 4.3.6 Detection Results of Different Algorithms under Low Contrast  
In this paper, the performance of the improved algorithm was compared with that of SSD, YOLOv3-tiny, and YOLOv4-tiny, respectively, and we calculated the _precision_, _recall_, \(F\)1 score, and _mAP_ for each algorithm with the _IOU_ threshold set to 0.5 and 0.75, as shown in Table 8. _IOU_ indicates the overlap between the prediction frame and the real frame. The higher the _IOU_ is, the higher the judgment standard is. From the table, it can be seen that when the _IOU_ was 0.5, the recall of the YOLOv4-tiny algorithm was higher than its precision, and the proposed method significantly improved the precision, better balanced the _precision_ and _recall_ indexes, and the \(F\)1 score and _mAP_ were also significantly improved. When the _IOU_ was 0.75, the accuracy index of each algorithm was reduced to a certain extent due to the high judgment standard, the _mAP_ of the improved algorithm in this paper was improved by 1.6%, and all other indexes were improved significantly. In a comprehensive view, the improved algorithm in this paper had a better performance compared with that of the other algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & _Precision_ & _Recall_ & \(F\)1 & _mAP_ \\ \hline Baseline & 92.00\% & 97.00\% & 95.00\% & 98.23\% \\ Baseline + SPP & 94.00\% & 96.00\% & 95.00\% & 98.27\% \\ Baseline + SPP + SE & 94.00\% & 96.00\% & 96.00\% & 98.30\% \\ Baseline + SPP + SE + FPN & 96.00\% & 97.00\% & 97.00\% & 98.79\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate** & _Precision_ & _Recall_ & \(F\)1 & _mAP_ \\ \hline
0.003 & 96.00\% & 97.00\% & 96.00\% & 98.81\% \\
0.0027 & 96.00\% & 97.00\% & 96.00\% & 98.71\% \\
0.00261 & 96.00\% & 97.00\% & 97.00\% & 98.79\% \\
0.0025 & 96.00\% & 97.00\% & 96.00\% & 98.85\% \\
0.0023 & 96.00\% & 97.00\% & 96.00\% & 98.72\% \\
0.002 & 96.00\% & 97.00\% & 96.00\% & 98.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Learning rate comparison experiment.  
To better reflect the performance of the algorithm, we compared the detection results of different algorithms in rainy and foggy weather and in a low-contrast environment with four types of loaders, excavators, fine sand, and stones, as shown in Figure 10. From the figure, we can see that the probability of object categories detected by other algorithms was lower than that of the algorithm in this paper. In general, the algorithm in this paper had the best overall performance and had a high recognition rate in each scene.
###### Abstract  
Most research conducted so far has primarily focused on pile-supported gravel embankments. The ability of solidified soil used as an embankment filling material has been verified, and a clear view on the performance of solidified soil embankments on piled foundations is rather limited. The three-dimensional unit cell models of pile-supported embankments are conducted to investigate the performance of solidified soil embankments in comparison to gravel embankments under static and dynamic loads. Then, a systematic parametric analysis is performed to investigate the effects of various factors, including the cohesion and friction of solidified soil, the velocity and wheel load of vehicles, the pile spacing, the height of embankments. The results show that, compared with the results of gravel embankments, the heights of the outer soil arch plane in solidified soil embankments reduces under static and dynamic loads, and the piles bear more load. In addition, the total settlements of solidified soil embankments decrease with increasing cohesions, and there is an economical cohesion of 25 kPa. The vehicle wheel load, pile spacing, and the height of embankment significantly influence the load transfer mechanism and total settlement of solidified soil embankment, while the friction angles and velocities have little effect on the total settlements and vertical stress. The relationship between the soil arch height and various parameters in solidified soil embankments is established by multiple regression analysis. This investigation highlights the advantage of solidified soil in reducing total settlement and provides an insightful understanding of the load transfer mechanism of solidified soil embankment on piled foundation.  
solidified soil; embankment fill materials; settlement; load transfer mechanism; soil arch
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 3 Stream-DBSCAN Water Quality Detection Clustering Model  
The Stream-DBSCAN model, proposed in this paper, integrates the benefits of easy merging in DBSCAN clustering algorithms with the high efficiency of Flink's distributed stream processing framework. This not only significantly reduces the time required to process massive data, but also reduces memory requirements. Furthermore, the DBSCAN algorithm is capable of noise reduction, filtering out anomalous data points, which results in improved clustering accuracy when dealing with large datasets.
## 4 Experiment and Result Analysis
### Reconnaissance Task Priority Assessment Results  
Before optimizing the reconnaissance task assignment, each evaluator, i.e., reconnaissance-capable UAV, evaluates the prior order of each task to each UAV using the evaluation method defined in Section 3.1.1. We set the number of negotiation hops to \(n_{c}=2\), and the UAVs will take other reconnaissance UAVs within 2 hops into consideration. For the scenario in Figure 3, the prior order of tasks to each UAV evaluated by different UAVs are shown in Table 5, and the tasks marked in bold are selected by each evaluator using Algorithm 2 to participate in this round of assignment.  
Taking the evaluation results of U7 as an example, the reconnaissance capable UAVs within 2 hops are U4, U5, U9, U14, U15, U19, U24, and U7 itself. The priority of task T15 ranks first for U7 since T15 is relatively close to U7, and the position of T15 can obtain more sufficient strike capability. Although T4 is the closest to U7, it gets a prior order of 4 th for U7 because the unknown potential synergistic relationship between T11 and T4 increases the risk of T4, and the low coverage of UAVs at T4 further reduces its priority.  
It can be found that there is a large gap between U4's task prior order as assessed by U7 and U4. T24 ranked after U4 because T24 has a great advantage in distance. However, the attention mechanism of U7 makes it pay more attention to the surrounding tasks, so the priority of T24 to U4 is suppressed in the evaluation of U7. This cognitive difference  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Evaluator** & **Task Prior Order to Each UAV** & **Evaluator** & **Task Prior Order to Each UAV** \\ \hline \multirow{4}{*}{U0} & U0: (**5**, **23**, **6**, **20**, **12**, **10**, **25**,...) & & U14: (**15**, **28**, **3**, **1**, **14**, **4**, **7**,...) \\  & U0: (**5**, **23**, **6**,  
caused by inconsistent environmental cognition or subjective preferences can be corrected during the negotiation process with the other party.  
Because each evaluator only considers several tasks with the highest priority in each round of allocation, the search space for task-allocation optimization can be effectively reduced.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### General IoT Applications
#### 3.4.3 Wireless Sensor Node for IoT  
The wireless sensor node developed by [34] is targeted for IoT-based applications. Regarding the processor and memory, the node is equipped with an ATMega88 micro-controller containing 8 kB flash memory and 1 kB SRAM. The BLE and nRF24 L01 RF (2.4 GHz) transceivers were utilized for communication. The communication ranges for the two transceivers are 10-30 m and 400-1000 m. The nRF24 L01 wireless module and the BLE transceiver (HM10 BLE) have data rates of 2 Mbps and 3 Mbps, respectively. The node houses temperature, humidity and current sensors.  
The node relies on both the Li-ion battery (3.6 V to 4.2 V) and supercapacitor for power supply. The current consumption of the RF transceiver (nRF24 L01) is 7-11.3 mA during the transmission mode, 9-13.5 mA during receiving mode, 26 \(\mu\)A during the sleep mode and 18 mA is the peak current. The transceiver's power consumption is 60 mW. The BLE communication module consumes 8.5-9 mA during the transmission and receiving modes, 0.78 \(\mu\)A in the sleep state, 15 mA peak current. Its power consumption is 10 mW.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Convergence of GATTRP  
GATTRP is based on a heuristic algorithm and the average time complexity of its convergence is complex and can be influenced by various factors, including population size, number of iterations, and randomness. Thus, it was difficult to produce an accurate expression. For its convergence, we could only estimate its time complexity as \(O(U_{max}\times O_{max}\times I_{max})\).
## 4 Discussion
### Feature Selection  
Feature selection is the key step before machine learning. Primarily, it was found that 163 ions were frequently detected (a 95% detection rate in malignant FNAB samples), and they were directly used as features for classifier training. The highest accuracy is 80.0%, obtained by an RF classifier with a sensitivity and a specificity of 74.1% and 87.0%, respectively. However, the accuracy, sensitivity, and specificity are not as high as the reported values, due to the poor homogeneity of the FNAB samples.  
Using the ion ratio is one way to eliminate the inhomogeneity of the clinical samples. For instance, [PERSON] reported the ability of the amyloid-\(\beta\) precursor protein (APP) 669-711/amyloid-\(\beta\) (A\(\beta\)) 1-42 and A\(\beta\) 1-40/A\(\beta\) 1-42 ratios, and their composites, to predict individual brain amyloid-\(\beta\)-positive or -negative status [20]. [PERSON] found that immunoglobulin \(\kappa\):\(\lambda\) ratios in cerebrospinal fluid determined at the time of diagnostic lumbar puncture could predict MS disease progression and may be useful prognostic markers for early therapeutic stratification [21]. [PERSON] showed that the LDL-C/ apolipoprotein B (Apo B) ratio may be considered as an indicator of coronary heart disease independent of the ASCVD risk score in patients with type 2 diabetes [22].  
However, no ion ratios were reported to homogenize the FNAB samples. Pairwise coupling will not ignore any potential ion ratios, and, consequently, 13,203 new features were generated from the 163 ions. However, the number of the new features was too large; therefore, a \(t\)-test was carried out to select features for classifier training (\(p<0.05\) between malignant and benign group, total 3630 features). With these new features, although the accuracy and sensitivity of the RF classifier decreased, the accuracy of the SVM and MLP classifiers improved by 12.0% and 26.0%, the specificity improved by 21.8% and 30.5%, and the sensitivity improved by 3.7% and 22.2%, respectively. Especially, the MLP classifier had an accuracy of 92.0%, which is comparable with the hepatic tumor (94%) [11], breast cancer (100%) [10], and pancreatic cancer classification (91.2%) [12]. The high accuracy, sensitivity, and specificity obtained proved that pairwise coupling was an effective way to eliminate the inhomogeneity of the FNAB samples.
## 4 FSOPSC with Two VVV Sets  
In view of the results of the previous section, it is natural to consider the possibility of using the best VVV set for each point of operation. In this section, the idea is shown to be feasible, and the regions assigned to each controller in the operating space \(\Omega\) are also stated to be easy to describe and use online.  
The previously computed performance maps for C1 and C2 (Figures 4 and 6) can be used to find the operating points where C2 outperforms C1. For these operating points, the optimal WF yields a lower value of \(e_{\alpha-\beta}^{o}\) for C2, that is, \(e_{\alpha-\beta}^{o}(\text{C2})<e_{\alpha-\beta}^{o}(\text{C1})\). The region, from now on named \(\Omega_{2}\), corresponds approximately to the operating points where \(65<I^{*}\times f_{e}<180\), as shown in Figure 8. This allows an easy implementation of the proposed FSOPSCC with two VVV sets.  
Figure 9 shows a diagram of the proposed FSOPSCC with the two VVV sets. The additions to the diagram in Figure 1 are marked with a different color, to clarify the novelty and added complexity of our proposal. As stated previously, the outer controller is responsible for speed/torque regulation and usually operates on a larger time scale than the stator current control. This controller is responsible for issuing the reference values for the stator's current control, \(I^{*}\) and \(f_{e}\).  
At this point, it is possible to check whether the actual pair \((I^{*},f_{e})\) belongs to \(\Omega_{2}\) simply by computing \(I^{*}\times f_{e}\) and comparing it with the limits indicated previously.  
Figure 7: Performance map for the proposed controller (C3) using the best set of VVV for each operating point.  
The extra operations needed to find the VVV set to be used at each sampling time are multiplication and two comparisons. These are easily implemented in any DSP. In addition, these additional computations impose a burden that is negligible compared to the rest of the operations: measurements, PI control, and optimization of the cost function. As a result, the proposed control scheme has almost the same computational load as the standard VVV method. For the particular hardware used in the experimental part (a TMS320F28335 DSP), the additional computational cost of the proposal is less thaN 0.5 \(\upmu\)s. Notice that the standard FSWPSC algorithm takes on the order of 50 \(\upmu\)s. Furthermore, the operating point does not change every sampling period. This means that the outer controller can select which of the two constellations will be used.
In parallel, many target groups, as are lactose intolerance consumers and those who have allergic reactions to dairy products, have increased in numbers the past few years [5]. Fruit juices are considered to be suitable substrates for probiotic bacteria viable cell production, as they already contain many beneficial nutrients including minerals, vitamins, fiber, and antioxidants [3]. As a result, more and more investigation is sought regarding the application of fruit juices as probiotic food carriers, as their main limitation is targeted on the survival of probiotics microorganisms throughout the harsh environmental and storage condition [5; 6]. Concerning this manner, scientific research has focused on the investigation of various probiotic strains (_L. plantarum_, _L. acidophilus_, _L. casei_, and _L. paracasei_) which show high tolerance against acidic environments and as a result, can grow and maintain their viability in fruit matrices [5; 7; 8]. After the selection of the appropriate strain, fruit juices are considered to be the most suitable substrates for probiotic bacteria cultivation, as they already contain beneficial nutrients, such as fiber, vitamins, minerals, and antioxidants [5; 8]. Among fruit juices, pomegranate seems to be a very good substrate for functional beverage production due to its high nutritional value and anti-oxidative, anti-inflammatory, antimicrobial, and anti-carcinogenic effects [9; 10]. There are some reports in the literature concerning the fermentation of pomegranate juice with probiotic lactic acid bacteria, showing in general that selected probiotic starters (i) enhanced the health benefits of the juice, (ii) increased the antioxidant activity and total phenolics content, and (iii) led to the production of desirable volatile compounds [7; 11; 12; 13; 14]. Recently, a novel potential probiotic was isolated from feta type cheese (_Lactobacillus paracasei_ K5) and was applied in preliminary fermentations of pomegranate beverage in order the viability to be determined during storage for 4 weeks at 4 \({}^{\circ}\)C [7]. The results were very encouraging since no significant decrease of viability was observed during the storage of the pomegranate beverage at 4 \({}^{\circ}\)C for 4 weeks [7]. Likewise, the main target of this study was to further determine other, also very important technological parameters, such as the composition of volatile compounds, antioxidant activity, and total phenolics content of pomegranate beverages produced after 24 h of fermentation, as well as during 4 weeks of cold storage. In addition, sensorial tests were conducted.
## 2 Materials and Methods
### Description of the Sampling and Measurement Procedures
Powdered sulfur, which is readily available, inexpensive, or can be obtained as a by-product of the Clauss process of natural gas desulphurization, is used to stabilize metallic mercury in the soil. The analysis of the sulfur safety data sheet used in the tests showed that it does not contain impurities (heavy metals, mono- and bi-cyclic aromatic hydrocarbons (BTEX), or poly and polycyclic aromatic hydrocarbons (PAHs)), which are listed, along with the permissible concentrations, in the documents concerning the soil quality standards in Poland [47]. The amount and manner of sulfur application for stabilizing mercury contamination in the soil were determined based on the laboratory test results. These tests evaluated the effect of Hg stabilization in soil with the addition of the optimal amount of sulfur (5-10%) and estimated the time required to achieve the best reduction in mercury emissions from soil to air [30]. The remediation procedure was designed so that the sulfur content introduced would constitute 5-10% of the soil involved in the process. The contaminated soil was loosened and mechanically mixed with the added sulfur. Due to the existing underground infrastructure of the facility, the entire remediation process was carried out manually. It was also crucial that the soil masses from the entire work area were displaced and mixed thoroughly during the treatment. To evaluate the effectiveness of the applied remediation procedure, the experimental area was divided into five sections, with measurement points established within each section to assess treatment efficiency. The effectiveness of the remediation was assessed from multiple angles, including:  
* Analysis of the total mercury content and mercury content capable of emissions in soil samples;
* Measurements of mercury emissions to air using a passive sensor meter and the diffusion chamber method. The studies on mercury emissions to the air were designed to track changes over time. To achieve this, three series of measurements were conducted: before the remediation procedure, 21 days after the procedure, and 90 days after the procedure.
###### Abstract  
In order to satisfy the performance requirements of the pyrotechnic ignition composition of a space mission under an extreme thermal environment, it is necessary to analyze and verify the thermal stability of magnesium/tellurium dioxide (Mg/TeO\({}_{2}\)) ignition composition at a temperature of 180 \({}^{\circ}\)C. The thermal stability of the ignition composition of Mg/TeO\({}_{2}\) and its components after exposure to 180 \({}^{\circ}\)C for 2-10 days was studied by means of apparent morphology analysis, differential scanning calorimetry (DSC), X-ray diffraction (XRD), content change analysis, and the _P-t_ curve test. The results showed that after exposure to 180 \({}^{\circ}\)C for 2-10 days, no obvious changes, such as ruptures, expansion, or shrinkage, were found by optical microscope, and no changes in morphology and surface details were found by scanning electron microscope (SEM). XRD showed that no other new substance was found in the mixture except magnesium hydroxide (Mg (OH)\({}_{2}\)). DSC showed that the main reaction peak temperature of the ignition composition of Mg/TeO\({}_{2}\) was after 500 \({}^{\circ}\)C and that no endothermic/exothermic reaction occurred before 380 \({}^{\circ}\)C. The exothermic pre-reaction took place at 381 \({}^{\circ}\)C to 470 \({}^{\circ}\)C, the weight loss ratio was within 0.71%, the content of the magnesium component varied from 0.49% to 0.90%, the peak pressure attenuation of the ignition composition of 360-mesh Mg/TeO\({}_{2}\) was 8.07%, and the pressure rise time was basically unchanged. The results showed that the ignition composition of Mg/TeO\({}_{2}\) had good thermal stability after exposure to 180 \({}^{\circ}\)C temperatures.  
180\({}^{\circ}\)C; thermal stability; apparent morphology; XRD; DSC; content change; _P-t_ test +
Footnote †: journal: Applied Physics Letters