esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 4 Discussion
### Effectiveness of eDNA Monitoring
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Variables** & **Temp** & **Salinity** & **EC** & **TP** & **TN** & **TOC** & **TIC** & **TC** & **Chla** \\ \hline Temp & **1** & \(-\)0.192 & \(-\)0.216 & **0.505** & **0.464** & \(-\)**0.599** & \(-\)0.275 & \(-\)0.220 & 0.422 \\ Salinity & \(-\)0.192 & **1** & **0.996** & \(-\)0.586 & \(-\)0.532 & 0.301 & 0.435 & **0.486** & 0.006 \\ EC & \(-\)0.216 & **0.996** & **1** & \(-\)**0.615** & **0.564** & 0.325 & 0.440 & **0.478** & \(-\)0.034 \\ TP & **0.505** & \(-\)**0.586** & \(-\)**0.615** & **1** & **0.904** & \(-\)**0.505** & \(-\)**0.634** & \(-\)**0.715** & 0.192 \\ TN & **0.464** & \(-\)**0.532** & \(-\)**0.564** & **0.904** & **1** & \(-\)0.424 & \(-\)**0.680** & \(-\)**0.645** & 0.200 \\ TOC & \(-\)**0.599** & 0.301 & 0.325 & \(-\)**0.505** & \(-\)0.424 & **1** & 0.127 & 0.355 & \(-\)0.248 \\ TIC & \(-\)0.275 & 0.435 & 0.440 & \(-\)**0.634** & \(-\)**0.680** & 0.127 & **1** & **0.877** & 0.195 \\ TC & \(-\)0.220 & **0.486** & **0.478** & \(-\)**0.715** & \(-\)**0.645** & 0.355 & **0.877** & **1** & 0.263 \\ Chl\(-\)_a_ & 0.422 & 0.006 & \(-\)0.034 & 0.192 & 0.200 & \(-\)0.248 & 0.195 & 0.263 & **1** \\ Marine & \(-\)0.197 & **0.879** & **0.878** & \(-\)0.343 & \(-\)0.195 & 0.314 & 0.051 & 0.151 & \(-\)0.082 \\ Fresh & 0.123 & \(-\)**0.888** & \(-\)**0.884** & 0.332 & 0.199 & \(-\)0.286 & \(-\)0.061 & \(-\)0.175 & 0.024 \\ Estuarine & 0.269 & \(-\)**0.856** & **-0.856** & 0.349 & 0.188 & \(-\)0.337 & \(-\)0.041 & \(-\)0.123 & 0.139 \\ First consumer & 0.195 & \(-\)0.283 & \(-\)0.271 & 0.164 & 0.120 & 0.152 & \(-\)0.329 & \(-\)0.269 & 0.159 \\ Second consumer & 0.011 & 0.387 & 0.373 & \(-\)0.329 & \(-\)0.070 & 0.159 & 0.239 & 0.346 & 0.026 \\ Producer & \(-\)0.054 & \(-\)0.319 & \(-\)0.307 & 0.288 & 0.040 & \(-\)0.194 & \(-\)0.162 & \(-\)0.281 & \(-\)0.064 \\ Symbiotic & 0.032 & \(-\)0.335 & \(-\)0.326 & 0.266 & 0.182 & 0.158 & \(-\)0.298 & \(-\)0.301 & 0.220 \\ Non-indigenous & \(-\)0.002 & \(-\)0.348 & \(-\)0.331 & 0.284 & 0.021 & \(-\)0.137 & \(-\)0.195 & \(-\)0.295 & \(-\)0.008 \\ Indigenous & 0.002 & 0.348 & 0.331 & \(-\)0.284 & \(-\)0.021 & 0.137 & 0.195 & 0.295 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Linear relationship (Pearson correlation) between environmental factors (temperature (Temp. ), Salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)) and classified OTU sequences based on habitat types, feeding habits and natives (Bold: significant relationship).
## 3 Results and Discussion
### Microstructure and Mineralogical Analyses
#### 3.3.3. MIP  
A mercury intrusion porosimetry (MIP) test was employed to assess the pore distribution and structure of the remolded loess and carbonated solidified loess subjected to different numbers of freeze-thaw cycles (Figure 17). The cumulative pore volume analysis indicated that the remolded loess had a significantly larger pore volume. In contrast, in carbonated solidified loess, substances such as CL and magnesium carbonate (MgCO\({}_{3}\)) effectively fill the pore spaces, leading to smaller pore diameters and lower overall pore volume. The pore size distribution analysis reveals that remolded loess, as a low-plasticity clay, typically exhibits a bimodal pore size distribution. This characteristic arises from the presence of fine particles within the loess, which partially fill and block the pores, influencing pore connectivity. Consequently, two primary mercury intrusion pathways are formed: one dominated by pores between soil particles, and another by fewer inter-aggregate pores. As the number of freeze-thaw cycles increases, several key changes occur in the pore structure. Some soil particles and hydration products break into smaller fragments, reducing the number of large pores, while increasing the abundance of small pores. Additionally, structural damage to soil particles and carbonation reaction products leads to particle rearrangement and reorganization, further increasing the proportion of small pores. The experimental results reveal distinct differences in the pore characteristics of remolded and carbonated solidified loess. The remolded loess contained more pores, directly impacting its thermal conductivity. Under freeze-thaw conditions, the denser structure of carbonated solidified loess reduces structural degradation, leading to a smaller decline in thermal conductivity compared to remolded loess. Consequently, carbonated solidified loess exhibits superior thermal performance stability under freeze-thaw cycling conditions.
# Adsorption of Coxsackievirus in Sediments: Influencing Factors, Kinetics, and Isotherm Modeling
## 3 Results and Discussion
### Effect of Different Factors on Virus Adsorption
#### 3.1.2 Temperature  
The graph in Figure 3b illustrates the adsorption data of CA16 at different temperatures. It is evident that there is an increased adsorption of CA16 onto clay as the temperature rises from 10 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. At equilibrium, the reduction in CA16 concentration rises from 2.17 log to 2.5 log. Furthermore, the adsorption rate of CA16 increases with the temperature elevation. This temperature-dependent enhancement can be attributed to the accelerated reaction rate between viruses and adsorbents. The increased temperature induces higher molecular dynamics, facilitating adsorption reactions and ultimately boosting the virus's adsorption capacity. This observation aligns with previous studies [53]; for instance, [PERSON] and [PERSON] [54] reported a significant increase in the adsorption rate of bacteriophages MS2 and DX174 by quartz sand when the temperature increased from 4 \({}^{\circ}\)C to 20 \({}^{\circ}\)C. Additionally, they found that the adsorption of bacteriophage MS2 was higher compared to QX174, and montmorillonite exhibited higher adsorption than kaolinite. Hydrophobic forces played a crucial role, as the stability of hydrophobic interactions increases at higher temperatures. [PERSON] et al. further suggested that if adsorption is partially governed by hydrophobic forces, the adsorption process will accelerate with an increase in temperature [16].
## 4 Conclusions  
This work focused on the passivation of stainless steel (AISI 304 and AISI 316) and Inconel 600 in an alkaline solution that simulates reinforced concrete at room temperature. The films are formed by cyclic voltammetry and under natural conditions (OCP). The results obtained show:  
1. The films formed by CV are thicker for AISI 304 than for AISI 316 and Inconel 600. The determination of the residual current density in the passivation zone showed that the films formed on the three alloys are protective. However, the presence of molybdenum in the chemical composition of AISI 316 enables the formation of a film that is more stable and more resistant to ion transport. Impedance measurements confirm these results.
2. EIS measurements have shown that the resistance of spontaneously formed films increases with immersion time for all three alloys. As with CV-formed films, AISI 316 also seems to be the alloy that forms the most resistant film. The film formed on Inconel 600 becomes stable faster than those formed on stainless steel. However, the electrochemical measurements did not show better protection for the film formed on Inconel 600 under these experimental conditions.
3. The AFM measurements show that the growth of the oxide film is under the control of the bare substrate topography and roughness until an immersion time is reached where coalescence occurs. The coalescence time depends on the substrates. After coalescence, the small oxide grains develop on the entire surface until they cover it and, therefore, form a stable homogeneous film that becomes more and more protective with increasing immersion time. This layer is also very chemically homogeneous, as shown by SKPFM images. The passive layer is, therefore an efficient barrier against corrosion. We have clearly highlighted the correlation between EIS measurements and topographic modifications.  
Conceptualization, N.M., N.E.H. and J.L.B.; Methodology, N.E.B., N.M., N.E.H. and J.L.B.; Validation, N.E.B. and J.L.B.; Formal analysis, N.E.B., K.D.K. and J.L.B.; Investigation, N.E.B., H.R., E.D., A.F., K.D.K., M.M.H. and J.L.B.; Resources, E.D., A.F. and K.D.K.; Data curation, H.R. and M.M.H.; Writing--original draft, N.E.B. and J.L.B.; Writing--review and editing, N.E.B. and J.L.B.; Supervision, N.M. and J.L.B.; Project administration, N.M. and J.L.B.; Funding acquisition, N.E.H. and M.M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the French-Algerian exchange and research program Tassili (Project code CMEP no. 10 MDU801).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors are grateful to both universities they belong for the financial support (researcher grants), of this work.
## 3 Single Factor Variable Sensitivity Analysis
### The Minimal Width of the Convergence Plenum (\(w_{\text{cp}}\))  
Figure 12a shows that, in terms of the maximal temperature, changing \(w_{\text{cp}}\) can satisfy the criterion \(T_{\text{max}}\) < 313 K in three kinds of Q0, and the maximum temperature drops by an average of 2.5 K. As can be seen from Figure 11b, considering the maximal temperature difference, \(\Delta T_{\text{max}}\) dropped by an average of 3.3 K. In order to ensure that \(\Delta T_{\text{max}}\) is within 5K, \(w_{\text{cp}}\) must be greater than 5.5 mm (Q0 = 0.050), 6.5 mm (Q0 = 0.045), and 7.8 mm (Q0 = 0.040). Compared with other structural parameters, changing \(w_{\text{cp}}\) can easily make the maximal temperature difference be within 5K.  
In summary, the maximum temperature in the battery pack is not sensitive to changes of \(\theta_{\text{dp}}\) and \(w_{\text{dp}}\). The changes of \(\theta_{\text{cp}}\) and \(w_{\text{cp}}\) can significantly reduce the maximum temperature, but a very small \(\theta_{\text{cp}}\) will cause the maximum temperature to exceed 313 K. From  
Figure 11: The effect of the minimal width of the divergence plenum on the temperature distribution for various inlet airflow rates. (**a**) effect on maximal temperature, (**b**) effect on maximal temperature difference. (\(\theta_{\text{dp}}\)= 15\({}^{\circ}\), \(\theta_{\text{cp}}\)= 15\({}^{\circ}\), \(d_{0}\) = 1 mm, \(w_{\text{cp}}\) = 5 mm).  
Figure 10: The effect of plate angle of the divergence plenums on the temperature distribution for various inlet airflow rates: (**a**) effect on maximal temperature, (**b**) effect on maximal temperature difference. (\(d_{0}\) = 1 mm, \(\theta_{\text{cp}}\)= 15\({}^{\circ}\), \(w_{\text{dp}}\) = 5 mm, \(w_{\text{cp}}\) = 5 mm).  
the maximal temperature difference analysis, the structural variable that has the greatest influence on \(\Delta T_{\text{max}}\) is \(\theta_{\text{cp}}\), followed by \(w_{\text{cp}}\), and finally \(\theta_{\text{dp}}\). When \(w_{\text{dp}}\) changes, it is found that the smallest \(w_{\text{dp}}\) can not only reduce \(\Delta T_{\text{max}}\), but also reduce the battery pack area. In order to ensure that \(\Delta T_{\text{max}}\) is within 5K, only adjusting \(\theta_{\text{cp}}\) requires a larger angle to meet the temperature condition and thus wastes space. In contrast, the temperature condition can be satisfied by adjusting \(w_{\text{cp}}\) in a small range. It can be seen that \(d_{0}\) is inversely proportional to \(T_{\text{max}}\) and \(\Delta T_{\text{max}}\), so the minimum \(d_{0}\) can increase the cooling performance and save space.
# Stable Analysis of Compressive Principal Component Pursuit
## 2 Notations and Main Results
### Main Results  
Pertaining to Problem (1), we have the result as follows.  
**Lemma 1** ([11]).: _Assume there exists a feasible solution \(x=(X_{1},\ldots,X_{\tau})\) to the optimization Problem (1). Suppose that each of the norms \(\|\cdot\|_{(i)}\) is decomposable at \(X_{i}\), and that each of the \(\|\cdot\|_{(i)}\) majorizes the Frobenius norm. Then, \(x\) is the unique optimal solution if \(T_{1},\ldots,T_{\tau}\) are independent subspaces with_  
\[\|\mathcal{P}_{T_{i}}\mathcal{P}_{T_{j}}\|<\frac{1}{\tau-1}\ \ \forall i\
eq j\,,\]  
and there exists an \((\alpha,\beta)\)-inexact certificate \(\hat{\Lambda}\), with  
\[\beta+\frac{\alpha\sqrt{\tau}}{\sqrt{1-(\tau-1)max_{ij}\|\mathcal{P}_{T_{i}} \mathcal{P}_{T_{j}}\|}}\times\frac{1}{min_{l}\lambda_{l}}\leq 1\,.\]  
The main contribution of this paper is the stability analysis of the solution of CPCP; the main Theorem of [13] can be regarded as a special case of our result (although the main idea of proof is similar to the paper [13], there are some important differences here). Next, we will provide the proposed related convex programming (2) is stable from small entry-wise noise under board condition. The main result of this paper is provided below.  
**Theorem 1**.: _Assume \(x_{\diamond}=(X_{1,\diamond},\ldots,X_{\tau,\diamond})\), \(\hat{x}=(X_{1},\ldots,X_{\tau})\) are the solutions of the optimization Problems (1) and (2), respectively. Suppose that each of the norms \(\|\cdot\|_{(i)}\) is decomposable at \(X_{i,\diamond}\), and each of the \(\|\cdot\|_{(i)}\) majorizes the Frobenius norm. Then, if \(T_{1},\ldots,T_{\tau}\) are independent subspaces with_  
\[\|\mathcal{P}_{T_{i}}\mathcal{P}_{T_{j}}\|<\frac{1}{\tau-1}\ \ \forall i\
eq j\]  
_and there exists an \((\alpha,\beta)\)-inexact certificate \(\hat{\Lambda}\), with_  
\[\beta+\frac{\alpha\sqrt{\tau}}{\sqrt{1-(\tau-1)max_{ij}\|\mathcal{P}_{T_{i}} \mathcal{P}_{T_{j}}\|}}\times\frac{1}{min_{l}\lambda_{l}}\leq 1\,, \tag{4}\]  
_then for any \(Z_{0}\) which is limited by \(\|Z_{0}\|_{F}\leq\delta\), the solution \(\hat{x}\) to the convex programming (2) obeys_  
\[\sum_{i}\|\hat{x}_{i}-x_{i,\diamond}\|_{2}^{2}\leq C(n,\tau,\alpha,\beta)\delta ^{2}\,, \tag{5}\]  
where \(C(n,\tau,\alpha,\beta)\) is a numerical constant only depending upon \(n,\tau,\alpha,\beta\).
# A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
## 2 Materials and Methods
### Experimental Methodology  
To conduct empirical experiments and measurements, two devices were constructed in accordance with the specifications outlined in Section 2.1. A testing and validation methodology was subsequently devised, founded upon the principles of reliability, stability, performance evaluation, and image quality. This methodological framework involves the systematic and continuous execution of tests spanning a duration of 2 months, employing both devices within a controlled environment. These tests cover the evaluation of the device across its operational states, encompassing the acquisition of data pertaining to energy consumption and the quality assessment of captured images.  
The testing and validation process is described in detail below.  
1. The device initializes and establishes a connection to the remote service (Connection State).
2. It awaits configuration via the Web API (Configuration Waiting).
3. Images are transmitted at fixed intervals specified in the configuration (Monitoring State).
4. Remote configuration is carried out via the Web API (Camera Testing).
5. Configuration mode transitions to WebServer (Web Server State).
6. Streaming is initiated for remote configuration via WebServer.
7. Image quality is assessed using metrics, including RMS contrast, Image Entropy, Image sharpness metric, Natural Image Quality Evaluator (NIQE), and Modulation Transfer Function (MFT).
8. The device returns to the state described in point 3 (Monitoring State).  
In order to expose the device to rigorous and sustained stress testing, image captures were acquired at one-minute intervals, as evidenced by the consumption study depicted in Figure 13, with the most frequent sampling interval typically observed in applications of this nature set at 6 h. To evaluate the remote configuration, four test scenarios were designed in a controlled environment with varying natural and artificial lighting conditions. This enabled the reconfiguration of the device by adjusting the parameters listed in Table 4 via the web API (Table 3) and/or the web server. The images captured during this testing  
Figure 12: WebServer configuration interface.  
phase were automatically evaluated using the proposed quality metrics, which facilitated the selection of the optimal configuration.  
These tests are instrumental in the identification of potential reliability and durability issues that may emerge during prolonged, continuous usage, allowing for necessary corrections prior to the device's deployment in a real-world environment.
## 1 Introduction
### Literature Survey  
In the literature, various metaheuristic optimization techniques have been proposed and investigated for the training of the artificial neural network. The research in [30] used a hybrid AI model to predict the speed of wind at different coastal locations where PSO was applied to train an ANN. A comparison was made between the support vector machine, ANN, and hybrid ANN based wind speed prediction models, and it was observed that the root mean square error (RMSE) was the minimum for the hybrid PSOANN model. In [31], a hybrid ANN-PSO model was used to extract maximum power from PV systems. Different test scenarios were considered, and it was observed that the maximum power was tracked by the ANN-PSO technique. The research in [32] explored a hybridized ANN-BPSO (binary PSO) model to control renewable energy resources in a virtual power plant. Simulation results clearly showed that the best energy management schedule was obtained by the hybrid ANN-BPSO algorithm. The work in [33] developed a hybrid model of an ANN and ant lion optimization (ALO) algorithm for the prediction of suspended sediment load (SSL). In [34], a new hybrid intelligent artificial neural network (NHIANN) with a cuckoo search algorithm (CS) was proposed to develop a forecasting model of criminal-related factors. Based on different performance parameters like mean absolute percentage error (MAPE), it was observed that not only did CS-NIHANN train the model faster but also obtained the optimal global solution.  
In recent years, the trend of combining various metaheuristic techniques for improved performance has risen, and many cooperative metaheuristics techniques have been proposed in the literature [35]. To predict energy demand, the genetic algorithm and particle swarm optimization were combined in [36]. Different parameters, like electricity consumption per capita, income growth rate, etc., were used as input for the hybrid prediction model. It was concluded that the performance of the ANN-GA-PSO based model was better than the ANN-GA or ANN-PSO models. In [37], a modified butterfly optimization (MBO) position updating mechanism was improved by utilizing the exploitation capabilities of the multi-verse optimizer (MVO). The work in [38] combined the exploration capabilities of a sine cosine algorithm (SCA) with a dynamic group-based cooperative optimization algorithm (DGCO) for efficient training of a radial basis function neural network. In [39], the performance of a salp swarm algorithm was greatly improved by integrating Levy flight and sine cosine operators. In [40], an improved Java algorithm was proposed, which used Levy flight to provide a perfect balance between exploration and exploitation.
## 3 Results and Discussion
### Excitation Source Contribution Analysis and Critical Transfer Path Identification  
#### 3.3.1 Analysis of Vibration Contributions from Excitation Sources to the Chassis Frame  
To determine the contributions of the multiple excitation sources to the chassis frame, the vertical accelerations from the conveyor trough-threshing frame connection point, the hydraulic cylinder-chassis frame articulation point, the threshing cylinder, the vibrating screen, and the engine were imported into MATLAB R2023b for analysis. Under conditions where all excitation components of the combine harvester were operating stably, the improved OTPA method was used to calculate the contributions of the cutting header, threshing cylinder, vibrating screen, and engine to each measurement point on the chassis frame via the five defined transfer paths. The results are presented in Figure 12. The total contribution of each transfer path to the vertical vibration of the chassis frame is shown in Figure 13.  
The results show that transfer path 2 (cutting header \(\rightarrow\) conveyor trough \(\rightarrow\) hydraulic cylinder \(\rightarrow\) chassis frame) has the largest total contribution to the vertical vibration of the chassis frame, reaching a vibration acceleration level of 117.6 dB. This finding, identifying the cutting header assembly as a primary contributor transmitted through a rigid hydraulic connection, is highly significant. It aligns with the general understanding that major working components are primary vibration sources. For instance, [PERSON] et al. [45], in their study on a similar combine harvester, also identified the engine and threshing system as major sources of vibration. Our work provides a more granular insight by quantifying the specific path through the hydraulic cylinder as the most critical one, which contributes more than other paths. This suggests that while the engine is an inherent source, the structural design of the cutting header's mounting system plays a dominant role in amplifying and transmitting vibrations to the chassis. The second largest contributor is transfer path 5 (engine \(\rightarrow\) chassis frame), with a total contribution of 98.2 dB. As the core power source of the machine, the engine is an inherent and major source of vibration due to combustion  
Figure 11: Amplitude–frequency distribution curves of vertical vibration at each measurement point on the chassis frame.  
impacts and the imbalance of rotating components. Although installed with vibration isolation mounts, its vibration energy remains a significant factor influencing the frame's vibration. In contrast, path 1 (cutting header \(\rightarrow\) conveyor trough \(\rightarrow\) threshing frame \(\rightarrow\) chassis frame) has the smallest total contribution, at only 4.8 dB. This is likely because the path is relatively long and involves multiple component connections, leading to significant attenuation of vibration energy through structural damping and dissipation at connection interfaces during transmission.