esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chemical Analysis
#### 2.4.5 Lipid Oxidation  
The present study assessed the peroxide value as an indicator of initial oxidation and the p-Anisidine value as an indicator of secondary oxidation [41]. To determine the peroxide value of pesto sauce after various processing methods, the oil part was first separated by centrifuging (5415D, Eppendorf, Germany) control samples at 4000 rpm for 5 min and keeping the oil at 4-6 \({}^{\circ}\)C. Chemical solutions for the titration method were prepared, including an acetic acid-chloroform solution, saturated potassium iodide (7681-11-0, Merck, Germany) solution, 0.1 M sodium thiosulfate (7772-98-7, Sigma-Aldrich, Germany) solution, 10% sodium lauryl sulfate (Riedel-de Haen, Hanover, Germany), and 1% starch (9005-84-9, Thermo Fisher, Rochester, New York, NY, USA) indicator solution. The titration technique required weighing 5 g of extracted oil, dissolving it in a mixture of acetic acid (64-19-7, VWR, Amsterdam, The Netherlands) and chloroform (64-19-7, VWR, Amsterdam, The Netherland), and adding potassium iodide solution. The mixture was then titrated using sodium thiosulfate until a color change appeared. To liberate all iodine from the chloroform layer, sodium lauryl sulfate and starch indicator were added, and then titrated until the blue color disappeared [42]. The volume of titrant used in both blank and control samples was recorded, and the peroxide value was determined using the following equation:  
\[\text{Pero oxide value (milliequivalents / peroxide /1000\ g\text{  test  portion})}=\frac{(S-B)\times M\times 1000}{Mass\ of\ test\ portion\ (g)}\]  
\[\text{S}=\text{Volume of titrant used for control samples (mL)}\]  
\[\text{B}=\text{Volume of titrant used for blank (mL)}\]  
\[\text{M}=\text{Molarity of sodium thiosulfate (Na}_{2}\text{S}_{2}\text{O}_{3})\text{ standard solution}\]  
To determine the p-anisidine content of the pesto sauce control sample, p-anisidine reagent (S8426458346, Sigma-Aldrich, Germany) was made by dissolving 0.25 g of p-anisidine in 100 mL of glacial acetic acid (64-19-7, VWR, The Netherlands), protected from light with aluminum foil. For the measurement, 4 g of the oil was diluted with isooctane in a 25 mL volumetric flask, and then 1 mL of this solution was diluted with 9 mL isooctane (540-84-1, Merck, Germany). The absorbance (A\({}_{\text{b}}\)) of this solution was measured at 350 nm using UV-Visible spectroscopy, with isooctane serving as the blank. The oil solution and isooctane were then combined with 1 mL of p-anisidine reagent in separate test tubes,agitated, and left for 10 min. The absorbance (A\({}_{\text{s}}\)) of the oil solution was measured again at 350 nm, using the isooctane mixture as the blank. The p-anisidine value was eventually obtained using the following equation [43]:  
\[\text{p-anisidine value}=25\times(1.2A_{s}-A_{b})\times\frac{dilution\ factor}{mass\ of\ oil\ portion\ (g)}\]
## 2 Materials and Methods
### 2D-ERT Measurements in Farms  
Three prospecting lines were performed per farm. The Wenner model configuration was selected for this study [8]. Thirty-six electrodes per line were placed at an equally spaced distance of \(a\) (0.35 m) covering a surface distance \(b\) (12.25 m) between the first and last electrodes on the line. Soil depth probing was achieved by changing the inter-electrode spacing \(a\) in successive increasing passes. The prospecting lines were placed in parallel and covered \(2\pm 0.1\) m of soil depth. The lines were separated from each other by 66 cm on the surface. One transversal line was carried out to fit the resistivity values. A direct electrical current was supplied into the subsoil to generate a difference of voltage measured in surface. The tomography was performed using the software Res2 Dinv (GEOTOMO, Penang, Malaysia). The 2D-ERT values were expressed in Ohm-m. Each plot showed in the results section corresponds to the \"inverse model resistivity section\".  
Figure 2: Locations of the assessed farms in the municipalities of San Vicente de Chucuri, Arauquita, and Muzo (in red), part of the major cacao producing districts in Colombia (Santander, Arauca, and Boyaca, respectively). As shown on the map, the presence of geological faults/granitoids formations, emerald mines, and petroleum pipelines near the farms are highlighted.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 5 Experimental Results
### Comparison of Schemes in Terms of Precision and Recall  
The performance of the schemes can be evaluated using different metrics such as recall and precision, defined by Equations (6) and (7), respectively.  
\[Recall=\frac{\textit{True Positive}}{\textit{True Positive}+\textit{False Negative}} \tag{6}\]  
\[Precision=\frac{\textit{True Positive}}{\textit{True Positive}+\textit{False Positive}} \tag{7}\]  
The recall is the true positive rate or sensitivity; it measures the probability of ground truth objects being detected correctly. Figure 8 shows the recall of the four schemes. We observe that our proposed Mask R-CNN + Tiles scheme exhibited better performance than the other schemes, especially for the reading room and YouTube-8M datasets. Its recall and true positive rate are always high, indicating that it can detect objects much more accurately than the remaining schemes. By contrast, the MobileNet-SSD scheme has the lowest recall value; even in the reading room dataset, it achieved a recall value of only 0.112.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Resolution of Image** & \begin{tabular}{c} **Number of** \\ **Images Used** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **People** \\ \end{tabular} & \
\begin{tabular}{c} **Average Number of** \\ **People Per Image** \\ \end{tabular} \\ \hline Classroom &
\begin{tabular}{c} 1024 \(\times\) 1024, \\ 1024 \(\times\) 576, \\ 576 \(\times\) 1024 \\ \end{tabular} & 100 & 738 & 7.38 \\ MICC people counting & 640 \(\times\) 480 & 100 & 885 & 8.85 \\ PIROPO & 704 \(\times\) 576 & 100 & 431 & 4.31 \\ YouTube-8M & 1440 \(\times\) 1080 & 100 & 1836 & 18.36 \\ Reading room & 1920 \(\times\) 1072 & 100 & 2401 & 24.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing dataset description.  
Figure 8: Recall values of four schemes.  
Figure 9 shows the precision of the four schemes. The precision values of three schemes (YOLO v3, Mask R-CNN, and proposed Mask R-CNN + Tiles schemes) remained consistently high. For the MobileNet-SSD scheme, the five testing datasets had significantly different precision values.  
A sample frame illustrates the different detection results of the four schemes, as shown in Figure 10. The MobileNet-SSD and YOLO v3 schemes had 38 and 30 false-negative detections, respectively. The Mask R-CNN scheme significantly improved the false-negative detection to 17. Our proposed Mask R-CNN + Tiles scheme had only two false-negative detections, demonstrating its excellent performance.  
Figure 10: Comparison of MobileNet-SSD, YOLO v3, Mask R-CNN, and Mask R-CNN + Tiles detection schemes for reading room dataset.  
Figure 9: Precision values of four schemes.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author ID** & **Primary Aim** & **SMF Modality Used** & **Groups Present** & **Modality for OTM** & **Assessment** & **Assessment Period** & **Impat of SMF Observed** \\ \hline \hline
**[PERSON] et al.**[22] & Evaluation of SMF combined with springs on OTM in guinea pigs & Orthodontic coil spring with magnets attached spring) & 3 SMF coil springs and spring combined with coil spring & Assessment of control & 10 days & Significant OTM was observed in the SMF + coil spring group, followed by SMF group and coil spring group respectively \\ \hline
**[PERSON] et al.**[23] & Evaluation of magnet-induced SMF on OTM in rats & Orthodontic appliance with magnets attached & 2 (SMF and non-SMF) and molar movements & Measurement of inciser and molar movements & 2 weeks & Significant OTM was observed in the SMF group non-SMF group \\ \hline
**[PERSON] et al.**[24] & Evaluation of magnet-induced SMF on OTM in rats & Orthodontic appliance with magnets attached & 2 (SMF and non-SMF) & Measurement of inciser and molar movements & 2 weeks & Significant OTM was observed in the SMF group non-SMF group \\ \hline
**[PERSON] et al.**[25] & Evaluation of magnet-induced SMF on OTM in rats & Orthodontic appliance with magnets attached & 3 (SMF, SMF combined with appliance force and neither SMF nor force) & Measurement of central incisor and maxillary left 1 st molar movements & 2 weeks & Significant OTM was observed in the SMF + force group, followed by force-only group \\ \hline
**[PERSON] et al.**[26] & Evaluation of magnet-induced SMF on OTM in rats & Orthodontic appliance with magnets attached & 2 (SMF and non-SMF) & Haematoxylin and eosin stain staining of sagittal section of first molars & 2 weeks & No statistical difference was observed between SMF and non-SMF groups with respect to OTM \\ \hline \end{tabular}
\end{table} 
Table 1: Demographic variables pertaining to the papers considered for inclusion in the study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author ID** & **Year** & **Sample Size** & **Region** & **Study Design** & **Age of Sample Size** \\ \hline
**[PERSON] et al.**[22] & 1995 & 18 guinea pigs & Unspecified & Experimental (animal-based) & 2 weeks old \\ \hline
**[PERSON] et al.**[22] & 2007 & 44 Wistar rats & Unspecified & Experimental (animal-based) & 7 weeks old \\ \hline
**[PERSON] et al.**[24] & 2008 & 34 Wistar rats & Japan & Experimental (animal-based) & 6 weeks old \\ \hline
**[PERSON] et al.**[25] & 2021 & 105 BALB/c mice & China & Experimental (animal-based) & Unspecified \\ \hline
**[PERSON] et al.**[26] & 2000 & 32 Wistar rats & Australia & Experimental (animal-based) & 9 weeks old \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Representation of study objectives, modalities, and outcomes assessing SMF usage in articles included in this review. Figure 4: Impact of SMF in OTM in terms of RR as observed in the studies included in the review [22, 23, 24, 25]. blue square: OR of each study; Black rhombus: pooled OR.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 4 Results and Analysis
### Network Load (bits/s)  
In Figure 6a-f, the graph represents the performance of a network in terms of network load by varying the number of UAVs, speed, and area dimensions. The \(X\)-axis represents simulation time and \(Y\)-axis indicates the load in bits per second. DSR had the highest network load in scenarios where the speed of the UAVs was high. This was due to the repetitive path finding by the reactive manner before each packet delivery in a highly dynamic network. ZRP had the lowest network load for the scenarios, where the nodes were placed in the area of 2 km \(\times\) 2 km. AODV had a constant network load for the same number of UAVs, despite the fact of changing speeds and area dimensions of the UAVs. OLSR consistently had the minimum network load in all scenarios, due to the advantage of the multi-point relay mechanism.  
Figure 5: Delay simulation results (**a**) 30 nodes with 25 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**b**) 30 nodes with 60 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**c**) 100 nodes with 25 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**d**) 100 nodes with 25 m/s and 1 km \(\times\) 1 km speed and area, respectively; (**e**) 100 nodes with 60 m/s and 2 km \(\times\) 2 km speed and area, respectively; (**f**) 100 nodes with 60 m/s and 1 km \(\times\) 1 km speed and area, respectively.
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 3 The Proposed Model
### Text Pre-Processing  
Text pre-processing is the second step in the model. The problem that we are trying to solve in this step is the fact that machine learning models only accept numerical data and cannot deal with textual data. Therefore, the objective is to transform the textual data collected from short messages into an understandable format which can be interpreted by machine learning algorithms. For this task we propose two separate methods due to the different nature of the machine learning algorithms that were used in this approach.
## 3 Proposed Model
### Multi-BB Parallel Model  
The sequential BB algorithm uses a breadth search to traverse the binary tree and calculate the optimal solution. This algorithm progresses level by level, calculating all the subsets generated. Therefore, to explain the proposal of the parallel model, the following example is presented. The subset [2, 4] is supposed to be the optimal solution to the problem. The subset [2, 4, 5] is at level 3, as is shown in the tree in Figure 2, where elements 2 and 4 are in the first locations. Thus, the algorithm will have to reach level 3 to calculate the solution. In the worst case, the algorithm will require \(2^{0}+2^{1}+2^{2}+2^{3}\) subsets, so it will also be necessary to perform calculus for the subsets of level 4 (\(2^{4}\)) to determine that the solution is optimal. These subsets are stored in a list and are resolved sequentially. In the worst case, the calculated subsets will be 31.  
When at the start of the problem, element 3 is fixed and removed instead of 1, the subsets by level are level 0 [\(\Phi\), [1,2,3,4,5]]; level 1 [[3], [1,2,3,4,5]] and [\(\Phi\), [1,2,4,5]]; and level 2 [[1,3], [1,2,3,4,5]], [[3], [2,3,4,5]], [[1], [1,2,4,5]], and [\(\Phi\), [2,4,5]]. The solution is in the last subset. The number of subsets needed to reach the solution is \(2^{0}+2^{1}+2^{2}\), and the subsets of level 3 (\(2^{3}\)) will be required to determine that the solution is optimal. In the worst case, 15 subsets will be calculated, which is \(2^{4}\) operations less than in the previous case.  
Because the fixed or removed element that helps determine the optimal solution in fewer iterations is not known in advance, we propose generating different binary trees. In each tree, a different element is fixed or removed. When the element is fixed, that element is forced to be part of the solution, and when it is removed in another tree, that element will not be part of the solution, meaning that other elements must form the solution. In each binary tree, the process described previously is followed. The processing units share their best solution to accelerate the calculation, thereby increasing the number of branches that are pruned in each tree. Consequently, it is possible to reduce the parallel computation time. Communications among the processing units influence the synchronization of the processing units, an aspect that is resolved, as indicated in the following paragraphs. The ideal number of binary trees is equal to twice the number of elements of the problem, because in one-half of the trees, a different element is fixed, and in the other half, each element is removed.  
Therefore, we propose generating different trees to increase the spaces of the feasible solutions, with the possibility that in some binary trees, the optimal solution is found in its first levels.  
An outline of the algorithm is shown in Figure 3, where the trees that are generated do not constitute a forest [24] because they share the same leaves.  
Figure 3: Multi-BB (branch-and-bound) tree scheme.
# Recent Trends on UV filters
## 3 Results
### Association of Organic/Inorganic UV Filters in Adults' and Children's Sunscreens  
The existence of sunscreens with only organic UV filters, with only inorganic UV filters, or with both organic and inorganic filters was also explored (Figure 3). The association of diverse organic UV filters in the same product, such as octocylene and avobenzene, has been confirmed as beneficial regarding stability, safety, and effectiveness [37]. Clinical studies performed to understand the effects of the combination of octocylene with avobenzene demonstrated that it is non-phototoxic and non-photo-allergenic and provided evidence of broad photoprotective activity [38]. Regarding the combination of UV filters in adults' sunscreens, the use of only organic UV filters (234 of 379) is more common, with a percentage of 61.7%, followed by photoprotectors containing both organic and inorganic filters (122 of 379) with 32.19%. Generally, organic and inorganic UV filters are used in association to boost photoprotective action, thus ensuring protection against UVA and UVB radiation and higher SPF. Regarding children's sunscreens, among all the products marketed for paediatric use, photoprotectors containing only organic filters (30 of 65) represented 46.1%. A significant increase in the use of organic-inorganic filters (28 of 65) in children's sunscreens (43.1%) was observed compared to their usage in adults' sunscreens (32.2%). This variation could be justified by the ability of the inorganic filters to absorb, reflect, and disperse UVR, thus promoting higher SPF values [39]. The existence of  
Figure 2: Frequency of use for the different UV filters in sunscreens for children. (BMDBM: butyl methoxydibenzoylmethane, EHT: ethylhexylt triazone, EHS: ethylhexylt salicate, Tinosorb S\({}^{\otimes}\): bisethylhexyloxyphenol methoxyphenyl triazine, OC: octocrylene, DHHB: diethylamino hydroxybenzyl hexyl benzoate, DBT: diethylhexyl butamido triazone, PBSA: phenylbenzimidazole sulfonic acid, DTS: dometrizole trisiloxane, HMS: homosalate, EHMC: ethylhexylt methoxycinnamate, MBC: 4-methylbenzylidene camphor, TDSA: terephthalyludidene dicamphor sulfonic acid, PS15: polysiliconne-15, Tinosorb M\({}^{\oplus}\): methylene bis-benzotriazolyl tetramethylbutylphenol, and DPDT: disodium phenyl dibenzimidazole tetrasulfonate.)  
products incorporating only inorganic UV filters is also higher among children's sunscreens (10.8%) than in adults' photoprotectors (6.07%). As mentioned before, this finding could be explained by the need to afford higher photoprotection to children, which requires the use of sunscreens with higher SPF values. Some studies have also reported that the use of organic UV filters in skin care products from an early age increases the probability of developing sensitization reactions [40].
## 3 Results and Discussion
### Testing of Mechanical Properties of Composite Materials  
#### 3.2.1 Tensile Properties  
Tensile tests were carried out on the pure carbon-fiber-reinforced polyetheretherketone composites and MWCNT/HA/CF/PEEK composites using an electronic universal material testing machine. The loading speed of the specimens was 2 mm/min, and the tensile properties of the MWCNT/HA/CF/PEEK composites represent the average data collected from the five specimens. The tensile properties are mainly characterized by tensile strength and tensile modulus. In particular, the tensile strength can be calculated using Equation (1):  
\[\sigma_{t}=\frac{F}{b\times d} \tag{1}\]  
The tensile modulus can be calculated using Equation (2):  
\[E_{t}=\frac{\sigma^{\prime\prime}-\sigma^{\prime}}{\epsilon^{\prime\prime}- \epsilon^{\prime}} \tag{2}\]  
According to the tensile sample dimensions, the analytical model in ANSYS can be modeled with a shell model, which is more simplified than a solid model. The length and width of the model are 180 mm and 20 mm, respectively, with 20 mm extended reinforcement sheet areas at the top and bottom ends for applying constraints and loads. In the resultant output, stress-strain curves are output by adding equivalent stresses and displacements. Fixed support (U1 = U2 = U3 = UR1 = UR2 = UR3 = 0) is applied to the lower-end stiffener region, and constraints (U1 = U2 = URI = UR2 = UR3 = 0) are applied to the upper-end stiffener region, whereby the translational degrees of freedom are retained in the z-direction only. In the analysis step, displacement loads are applied in the z-direction. Finite element analysis requires meshing of the model, and after meshing, dynamic display operations are performed using the ANSYS module.  
The tensile test simulated damage that occurred at 0.05 s of the analysis step, and the stress-strain curve was calculated according to the established model, as shown in Figure 3c. The strength of MHCP2 obtained in the experiment was 123.5 MPa, which was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Form**} & \multicolumn{2}{c}{**Contact Angle (\({}^{\circ}\))**} & \multirow{2}{*}{\(\gamma\)} \\ \cline{2-3} \cline{5-5}  & **Water** & **Glycerol** & **(m/m2)** \\ \hline CF & 47.2 & 80.5 & 210.00 \\ CF-D & 132.2 & 129.5 & 2.11 \\ CF1 & 57.7 & 83.5 & 152.81 \\ CF2 & 19.6 & 102 & 754.37 \\ CF3 & 56.4 & 51.6 & 43.99 \\ CF4 & 31.6 & 79.5 & 319.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results for contact angle and surface energy of carbon fiber under different treatments.  
17.62% higher than the tensile strength of CP and 75.82% higher than that of pure PEEK. The elastic modulus of the MHCP2 composite was 11.73 GPa, which is close to the elastic modulus of human cortical bone, and the tensile strength of MHCP2 obtained from ANSYS simulation was 153.22 MPa, which is close to that of the test result at 123.5 MPa.
# A Voxel Generator Based on Autoencoder
## 4 Performance Evaluation  
In this paper, we have implemented VoxGen and undertaken a series of performance evaluations. The implementation language was Python3.7 [30], the applied deep learning framework was TensorFlow 2.8 [31], and the accelerator function library was CUDA 11.3. The experimental environment platform was a personal computer with Intel(r) Xeon(R) Silver 4210 CPU @ 2.20 GHz CP, Nvidia GeForce(r) GTX 3090 GPU, 64 GB DDR4 2933 MHz 288P Reg. ECC RAM and 1 TB SSD. The operating system was 18.04.5 LTS (GNU/Linux 4.15.0-177-generic x86_64). The performance evaluation index was a volumetric Intersection over Union (volumetric IOU). The formula of IOU is as follows:  
\[\text{IOU}\big{(}V_{gen},\ V_{gt}\ \big{)}=\left|\frac{V_{gen}\cap\ V_{gt}}{V_{ gen}\cup\ V_{gt}}\right|\,\]  
Figure 7: Structure of mixed-multiple convolution decoder.  
where \(V_{gen}\) is the generated voxel model and \(V_{gt}\) is the ground-truth voxel model.  
In our experiment, the 3D data output by the voxel generator was a floating point number representing the probability of that point. In this study, the threshold was set to 0.5. If the probability was above the threshold, the data output would be 1; otherwise, it would be 0, and then the IOU value could be calculated. In this study, we used four classes, i.e., 3389 cars, 2199 chairs, 2318 lamps, and 2539 tables, in ShapeNet as the datasets for our performance evaluation. We randomly selected 80% of them as the training set and used the remaining 20% as the test set. The total numbers of training and test data were 481,896 and 120,164, respectively. The number of training epochs (Epoch) of the voxel generator network was 500, the batch size (Batch Size) was 128, and the optimizer (Optimizer) used Adam.  
In the following subsections, we will discuss the impact of different encoders, flattening layers, and decoders on the IOU value of the generated voxel model. Finally, we will compare the performance of VoxGen with those of other related methods.