esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influence of Cutting Environment on Output Parameters  
Grey relation analysis grades, presented in Table 6, show that out of the three tested cutting environments, MQL produced the lowest outputs. As noted by [PERSON] et al. (2018), during the drilling of 6063 non-porous aluminum, MQL provides the most effective lubrication of the tool, which greatly reduces the coefficient of friction between the workpiece material and tool edge. The cold compressed air cutting environment had the lowest grey relation grade, indicating the lowest effect on the output parameters. Although the implementation of a vortex tube provides low cooling temperatures, the fact that the cutting zone is located deep within the workpiece material means that it is inaccessible to the jet of cold air, which leads to higher cutting temperatures and higher workpiece adhesion. As mentioned by multiple authors, high-porosity materials generally have low thermal conductivity. In addition to the discontinued chips produced by the drilling of metal foams, this leads to excess heating of the tool, which further increases workpiece adhesion ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2020).  
The highest deviations were observed while drilling with a cold compressed air cutting environment because this cutting environment does not provide any lubricating properties. Adequate lubrication, which was achieved using cutting fluid or MQL, helped in reducing the porosity's effect on hole deviation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multirow{2}{*}{**Initial Parameter Set**} & \multicolumn{2}{c}{**Optimal Parameter Set**} \\ \cline{3-4}  & & **Predicted** & **Experimental** \\ \hline Parameter & & & \\ combination & A1B1C1 & A1B1C2 & A1B1C2 \\ \hline \(F_{t}\) (N) & 3.3 & & 2.7 \\ \hline \(a\) (\({}^{\circ}\)) & 2.46 & & 1.88 \\ \hline \(L_{BUE}\) (mm) & 0.38 & & 0.15 \\ \hline \(H_{BUE}\) (mm) & 0.22 & & 0.29 \\ \hline GRG & 0.7064 & 0.8099 & 0.8204 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Confirmation test results.
## 4 Result and Discussion
### Edge Contour Extraction of Structures
The presence of image noise can lead to the generation of pseudo edges. On the other hand, the real signal on the edge of the structure may also be smoothed out by the Gauss spatial filter, which will cause the edge of the structure to be discontinuous and the edge information missing as shown in Figure 11. Figure 11a shows that the lower edge contour of the bridge deck is relatively continuous. Therefore, the lower edge of the bridge deck is used as the characteristic contour of the test beam to extract the overall deformation of the structure; the position of edge contour extraction in this paper is shown in Figure 11b. Because the space position of the camera is fixed and the same perspective transformation method is used in each load grade, the edge contour before and after deformation can be directly extracted and compared without looking for fixed points in each load grade. The pixel coordinates of the lower edge of the bridge deck are extracted, and the original edge contour of the structure is obtained under each load grade, as shown in Figure 12.  
The result shows that the deflection of the specimen increases with the load grade. Since the edge line of the specimen is obscured near the right bearing by the reaction frame, the edge information is partially missed. By contrast, the information of the left edge is well established. Considering the parallel change of the contour line near the bearing on the left, it can be inferred that the rigid displacement of the specimen exists under each load condition. When loa  
Figure 11: Feature contour extraction; (**a**) edge of the test beam affected by environment; (**b**) sketch map of edge extraction position. Figure 12: Edge line of lower edge of bridge deck. the rigid displacement reaches its largest. This large displacement is due to the fact that the bearings are not in close contact with the reaction frame before loading. After the load of 100 kN, the bearings will be tightly contacted with the reaction frame. However, due to the deformation of the reaction frame since it is not ideally stiff, a small amount of rigid body displacement still exists in all the load conditions. Besides, it can be found that the edge contour of the specimen is piecewise continuous under every single load condition, with the step change occurred at the four connections between the segments. During the fabrication, the specimen is first divided into five segments and each of the segments is manufactured independently. Then, the separated segments are assembled at the four points together, resulting in the inevitable assembly error. As a result, the shape of the specimen will change abruptly at those assembly points, which in turn lead to the step change as reflected in the edge contour.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System  
[PERSON]  
1 Laboratory of Renewable Energy, Embedded System and Information Processing, National School of Applied Sciences, Mohammed First University, Oujda 60000, Morocco; [EMAIL_ADDRESS] (C.E.F.); [EMAIL_ADDRESS] (B.H.)  
[PERSON]  
2 Department of Building Services, \"[PERSON]\" Technical University of Isai, 700050 Isai, Romania 22 Department of Building Services, \"[PERSON]\" Technical University of Isai, 700050 Isai, Romania 22 Department of  
transport heat through a boiling-condensing process to the ambient air [13]. Some recent related review articles also provide updates on the important aspects of PV/T cooling systems [14; 15; 16; 17; 18].  
Water cooling methods provide improved performance over air cooling methods due to the increase in heat-carrying capacity of water over air [19]. [PERSON] et al. [20] studied the performance of a PV/T module with parallel plate flow channel without absorber plate both numerically and experimentally at outdoor conditions. They observed that the overall efficiency increases numerically by 22.1% and experimentally by 20% with an increase in water inlet velocity from 0.0003 to 0.0007 m/s under an irradiation level of 1000 W/m\({}^{2}\) and inlet water temperature of 34 \({}^{\circ}\)C.  
A novel compound system through connecting a PV/T system with a solar collector in series was proposed by [PERSON] et al. [21]. The results showed that using the optimum condition given by Taguchi-based gray relational optimization, electrical, thermal and total power generation of the system increased by 23.86%, 35.78%, and 34.75%, respectively.  
A hybrid photovoltaic/thermal (PV/T) collector with serpentine exchanger was studied by [PERSON] et al. [22] showing that combining both types of heat and electrical offers a better thermal performance with an overall thermal efficiency around 79.43%.  
Different parameters for enhancing heat transfer performance of photovoltaic thermal systems was investigated by [PERSON] et al. [23]. Results revealed that PV/T electrical and thermal efficiency increase with both Reynolds and Prandtl number and a maximum reduction in cell temperature of 10.2 \({}^{\circ}\)C was achieved for increasing channel depth from 10 to 20 mm.  
Even with the large number of papers related to the PV/T system, there is little research work that treats enhancement of flow parameters, e.g., the inlet velocity and water film thickness simultaneously for an effective cooling of PV cells. This paper presents a transient numerical PV/T model based on an energy balance equation developed in MATLAB software and resolved by the Runge-Kutta numerical method. This model is built based on the three layers PV model validated with the NOCT model. Moreover, the energy balance equations for the cooling unit (absorber 1, fluid, absorber 2) are driven from a PV/T model validated experimentally in previous work [24]. Thus, based on the developed model, a parametric study was conducted to determine the optimal flow parameters.
## 2 Materials and Methods
### Description of the Study Design and Sample  
In order to ensure a more personalized experience and accurate data collection, a subsample of patients was extracted from the total population to participate in the HRS pilot. This pilot took place over a four-week period, where patients received daily notifications on the mobile app. The total number of participants in the pilot was 37, including 17 patients, 10 caregivers, and 10 healthcare professionals. This allowed for a more hands-on approach to survey administration and ensured that all patients were able to provide their feedback in a consistent and reliable manner. By selecting a subsample, it was also possible to gain a more detailed understanding of the impact of the TeNDER system on a smaller group of patients. Table 1 summarizes all data related to the pilot, including the number of patients, caregivers, and healthcare professionals involved in the study. The study population was composed of patients with chronic diseases, considered as main diseases if PD, AD, or CVD.  
The scope of the study was the health consortium partner centers: SERMAS (primary care centers of the Madrid Health Service, Madrid, Spain), APM (Parkinson's Association of Madrid, Madrid, Spain), UNITOV (Hospital of the University of Rome Tor Vergata, Rome, Italy), SKBA (Neurological Hospital, Schoen Clinic, Prien, Germany), Spomincica (Department of Neurology, University Medical Center, Ljubljana, Slovenia).  
This study made a specific selection criteria for patients, caregivers, and health professionals to be included in the pilot. Patients had to be over 60 years and able to understand the local language. They could not be unwilling to use the technologies or have a life expectancy less than 6 months. Caregivers were required to have knowledge of technology and provide support to the patient. Health professionals had to be socio-health professionals and have at least one year of professional experience. In Table 2, further details are referred to for the selection criteria. All of the participants in the study had to accept and understand their participation and sign the informed consent form.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Number of Patients** & **Group** & **Number** \\ \hline Alzheimer & 8 & Caregivers & 10 \\ Parkinson & 3 & Professionals & 10 \\ Cardiovascular & 6 & Devices & 28 \\ \hline Total patients & 17 & Total & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of participants and devices used in the pilot study.  
Figure 1: Health recommender system integration with TeNDER ecosystem.  
However, it is important to note that the data collected in this study may be subject to certain biases. For example, self-report measures may be subject to recall bias, and the data collected by the sensorial ecosystem devices may be influenced by factors such as device accuracy and user adherence. These limitations should be taken into account when interpreting the results of the research.
# Evaluation of Glottal Inverse Filtering Techniques on
## 1 Introduction
In this paper, first, a two-stage analysis methodology for the comparison of glottal inverse filtering techniques based on a common reference dataset is introduced. Second, current state-of-the-art GIF techniques based on IAIF and QCP approaches, which have been partially compared in the literature, are exhaustively evaluated following this analysis methodology on OPENGLOT Repository I [32] by computing various well-established GIF error measures. As the original repository only contains male formant frequencies for producing vowels with different phonation types and fundamental frequencies, the experiments are extended by including the female counterparts from the original reference study [19]. The paper is organised as follows. Section 2 presents the analysis methodology and the evaluated GIF methods in a nutshell. Next, Sections 3 and 4 detail the conducted experiments and the obtained results, which are discussed in Section 5. Finally, the conclusions and future work are presented in Section 6.
## 2 Materials and Methods
### Cell Culture and In Vitro Assay
#### 2.2.3 Evaluation of Cell Viability with the MTT Test  
Assessment of the cytotoxicity of the tested compounds (deltarasin, Kobe0065, FTS) on lung cancer cells was performed using the MTT test. The A549 cell lines were treated with drugs doses selected on the basis of the publicly available data cited within the manuscript [15; 19; 20; 21]. In the first step, a cell suspension at a density of 1.5 \(\times\) 10\({}^{4}\) cells/mL was seeded into appropriately designed 96-well plates in a volume of 100 \(\upmu\)L of cell suspension/well. After incubating the cells for 24 h, the plates were loaded with test compounds at various concentrations in the medium. The following concentrations of drugs were used for the study: deltarasin (1.25; 2.5; 5 uM); Kobe0065 (2; 10; 20 uM); FTS (50; 100; 200 uM) (See Supplementary Figure S1 for chemical structures of compounds [22; 23; 24]). The cells were exposed to the compounds for 24 and 48 h. After the incubation was completed, an enzymatic reaction was carried out with mitochondrial dehydrogenase. In the first stage of the MTT test, the plates were loaded with 100 \(\upmu\)L of 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide per well and left for two hours for incubation. In the next stage, the formed crystals of formazan were dissolved with solubilization solution and the plates were read colorimetrically at 570 nm. Cell viability was then calculated for each concentration of test compounds. At least three independent experiments were performed. IC50 values (the concentrations required to inhibit growth by 50%) were calculated byusing a linear regression equation i.e., Y = Mx + C. Here, Y = 50, M and C values were derived from the viability graph for the A549 cell line.  
Assessment of HRAS and KRAS Gene Expression after Exposure to Deltarasin, Kobe0065 or FTS in A549 Cells
# A Snapshot on the Occurrence and Risk Assessment of Organic Pollutants in an Urban River
## 3 Results and Discussion
### PAHs and PCBs Presence in Surface Sediments  
PAHs were categorized in three groups: (1) 2-3 ring; (2) 4 ring; and (3) 5-6 ring (Table 3). A four-color system, indicating risk, has been adopted, as described above. In F1, the three groups demonstrated very similar total concentrations: 2-3 rings (15.75 ng\(\cdot\)g\({}^{-1}\) dry weight (dw)); 4 rings (14.98 ng\(\cdot\)g\({}^{-1}\) dw); and 5-6 rings (12.82 ng\(\cdot\)g\({}^{-1}\) dw). In F2, the highest concentrations were observed for 2-3 ring PAHs (18.88 ng\(\cdot\)g\({}^{-1}\) dw) and 4 ring PAHs (28.11 ng\(\cdot\)g\({}^{-1}\) dw), and for 5-6 ring PAHs, the total concentrations were 5.93 ng\(\cdot\)g\({}^{-1}\) dw). In F2, the highest concentrations were observed for 2-3 ring PAHs (18.88 ng\(\cdot\)g\({}^{-1}\) dw) and 4 ring PAHs (28.11 ng\(\cdot\)g\({}^{-1}\) dw), and for 5-6 ring PAHs, the total concentrations were 5.93 ng\(\cdot\)g\({}^{-1}\) dw. F3 presented the lowest concentrations for all groups. The most abundant PAHs in this sampling site were 2-3 rings (5.25 ng\(\cdot\)g\({}^{-1}\) dw). Indeed, the highest \(\Sigma\) PAHs were observed in F2 (52.92 ng\(\cdot\)g\({}^{-1}\) dw), followed by F1 (43.55 ng\(\cdot\)g\({}^{-1}\) dw). The lowest \(\Sigma\) PAHs (10.39 ng\(\cdot\)g\({}^{-1}\) dw) were detected in F3. Nevertheless, in F1 and F2, the higher weight molecular PAHs (HPAHs) were predominant. A similar pattern was observed for Tiber River (Italy) and Danube (Serbia). Nevertheless, the contamination level due to PAHs can be considered low compared to other European river sediments (Table S2). Indeed, the observed concentrations in the Fervenca were similar to the lowest concentrations observed in the European rivers. These concentrations can be explained by the fact that the Fervenca River is located in a region with low population density and little industrialization. The diagnostic ratio indicated the dominance of PAHs with pyrolitic origin with a small petrogenic contribution. The mixed combustion of biomass and fossil fuels seems to be the primary source of pyrogenic PAHs (Table 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ratios** & **F1** & **F2** & **F3** & \
\begin{tabular}{c} **Limit Values for** \\ **Petrogenic Source** \\ \end{tabular} &
\begin{tabular}{c} **Limit Values for** \\ **Pyrogenic Source** \\ \end{tabular} \\ \hline Ant/(Ant + P) & 0.38 & 0.43 & 0.59 & \textless{}0.1 & \textgreater{}0.1 \\ F/(F + Pyr) & 0.26 & 0.40 & 0.78 & \textless{}0.4 & \textgreater{}0.5 a  \\ BaA/(BaA + Chr) & 0.39 & 0.75 & 0.73 & \textless{}0.2 & \textgreater{}0.35 b  \\ IcdP/(IcdP + BphiPer) & 0.48 & 0.96 & 0.82 & \textless{}0.2 & \textgreater{}0.5 c  \\ LMW/HMW & 0.57 & 0.55 & 1.02 & \textgreater{}1 & \textless{}1  \\ Total Index & 8.01 & 14.72 & 20.69 & \textless{}4 & \textgreater{}4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PAH diagnostic ratios calculated for the analyzed samples and their limit values, indicating pyrogenic/petrogenic sources.
## 4 Conclusions  
In this study, a shock detector was developed and tested, which registers shock data, the location of the shipment on a map, and provides real-time information. Using various sensors, such as accelerometers and advanced microcontroller technology, the status and movement of shipments can be accurately monitored. The results of the study show that the shock detector is an effective tool for monitoring and analyzing the shocks experienced by the shipments. By measuring the force, direction, and duration of the impact, it is possible to determine the strength of the impact and its impact on the shipments. In addition, the integrated parcel tracking function allows us to determine the location of the shipment, and real-time data transmission ensures that all information is available promptly. During a shock, the detector always records 32 shock values (this can be changed as needed). As the results show, the impact values in the X, Y, and Z directions vary between \(-0.5\) and \(2\) g with a measurement range from \(5\) to \(32\). As we can see from the results, the highest values of the overall impact vector are at the beginning of the impact, which reach about \(4.5\) g, and about in the middle of the measurements when values vary from \(2\) to \(4.5\) g. In the future, continuing this research, it is planned to improve the capabilities of the smart impact detector (depending on market needs), e.g., integrating a hybrid system of satellite and ground tracking, etc. The detector developed during this study could cost around 70 EUR. The shock detector helps to reduce risk and ensure the safety of shipments by quickly identifying and reacting to possible damage or shocks experienced during the transportation process. A shock detector can be useful not only in logistics and  
Figure 11: Estimation of the total vector of impact strength.  
transportation, but also in other areas where it is important to monitor and evaluate the movement of objects and shocks. These research results open up new opportunities in the field of tracking shipments, introducing innovative tools that help better control and protect shipments in transit. This is important from both a business and consumer point of view, as it ensures the reliable and safe delivery of shipments.  
Methodology, G.B. and S.K.; Software, I.S. and S.K.; Formal analysis, G.G.; Writing--original draft, Z.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 2 Materials and Methods
### Grad-CAM Method  
The Grad-CAM method [44] can be used to determine saliency areas. This method is useful for assessing and understanding the decision-making of neural networks. The Grad-CAM method utilizes the gradient information flowing into the final convolutional layer to assign weight values to each neuron for a particular decision, and it is a generalization of the Class Activation Mapping (CAM) method. It highlights important class-specific regions of the input images during one forward propagation and partial back propagation for each image.  
As depicted in [44], to obtain a \(L^{C}_{Grad-CAM}\) saliency map for class \(c\), we first calculate the gradient of the result of the last layer of the network before softmax \(y^{c}\) with respect to the feature map activation \(A^{k}\) of the convolutional layer. After obtaining the multidimensional gradients, the global average pooling technique is used to show the importance of each feature map for specific classes (over two dimensions: width \(i\) and height \(j\)). Therefore, to calculate the neurone weight \(a^{c}_{k}\), which represents a partial linearization of the deep network downstream from \(A\), the following equation is used:  
\[a^{c}_{k}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{c}}{\partial A^{k}_{ij}}. \tag{1}\]  
The weights \(a^{c}_{k}\) are multiplied by the corresponding feature maps \(A^{k}\), we sum up the components of the final saliency map, and the weighted combination of forward activation maps followed by the ReLU is performed as follows:  
\[L^{C}_{Grad-CAM}=ReLU(\sum_{k}a^{c}_{k}A^{k}). \tag{2}\]
## 5 Results and Discussion  
### Comparisons with Prototype Measurements  
To appraise the modeling accuracy, both numerical and physical, the results of shuice flow discharge and water stage are compared. With the ACDP technique, field measurements are also made on several occasions to determine river discharges under varying operation conditions. Analysis of the field data leads to [PERSON]'s \(n_{\text{pt}}\) = 0.022-0.026, which is used in the numerical model. To satisfy \(\lambda_{\text{n}}\) = \(n_{\text{pt}}\)/\(n_{\text{m}}\) = \(\lambda^{1/6}\), \(n_{\text{m}}\) = 0.012-0.015, which is achieved through repeated surface finishing in the physical model.  
For three \(e\) and \(N\) combinations, Figure 10 compares the shuice discharge capacity, i.e., the \(Q\) variations as a function of \(\Delta Z\), where \(\Delta Z\) (m) = \(Z_{\text{c}\text{s}\text{d}}\) - \(Z_{\text{c}\text{s}\text{s}}\) = \(Z_{\text{u}}\) - \(Z_{\text{d}}\). It shows that the model tests and the numerical simulations give comparable results and their agreement with the field data is generally good. At \(e\) = 0.5 and \(N\) = 3, the results are slightly underestimated. At \(e\) = 2 and \(N\) = 6, the modeling leads to some overestimation. At \(e\) = 4 and \(N\) = 6, the results are much closer to each other. As shown in Figure 11, the modeled  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Data Range** & **Source** \\ \hline \(\omega_{\text{s}}\) (m/s) & 0.0002 & Field measurements \\ \(\tau_{\text{d}}\) (N/m\({}^{2}\)) & 0.06–0.10 & [49] \\ \(\tau_{\text{e}}\) (N/m\({}^{2}\)) & 0.10–0.20 & [50] \\ \(M\) (kg/m\({}^{2}\)s) & 0.0002–0.02 & [50] \\ \(\gamma_{0}\) (kg/m\({}^{3}\)) & 1460 & Field measurements \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values adopted in the model.  
Figure 9: Meshed computational domain. The domain is covered by 15,350 cells, comprising 307 streamwise and 50 transverse.  
reservoir water levels at CS1-CS4 in cases 1 and 2 are compared with the field data. The modeled results fit the field data well, with errors below \(\pm 3\)-5 cm (in prototype dimension), which indicates good performance of both models. The study also demonstrates that it is essential to satisfy the roughness criterion (\(\lambda_{\mathrm{n}}\)), which would otherwise give rise to significant errors in water stage.  
Figure 11: Prototype \(Z\) comparisons among physical model, numerical model, and field data. (**a**) case 1; (**b**) case 2.  
Figure 10: Comparisons of the \(Q\)-\(\Delta Z\) relationship among physical, numerical, and prototype measurement results.