esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### 2006/2007 Flood Event Assessment  
The 2006/07 flood was caused by three violent precipitation events on (1) 17-20 December 2006; (2) 24-28 December 2006; and (3) 11-14 January 2007. These violent precipitation events were brought about by the strong northeasterly winds that interacted with the Madden-Julian Oscillation [19]. The highest total amount of rain during these 13 rainy days was recorded at station 48679 as 1187 mm, with a maximum amount of 264 mm on 11 January 2007. The capability of the different SPPs was assessed from 1 December 2006 to 31 January 2007 and for eight rain gauges, which received a daily rainfall amount of more than 100 mm/day (Table 6).  
The temporal evolution of the observed and estimated daily precipitation from December 2006 to January 2007 is shown in Figure 8. Most of the products performed well over the period under study, with the best performances occurring in southern PM, where most of the intense precipitation  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & & **3B42 RT** & **3B42V7**  
occurred. For example, the daily precipitation R\({}^{2}\) validation of the 3B42V7 for the station 48616 improved from 0.57 for the entire period (January 2003 to December 2007) to 0.8 for the 2006/2007 flood event (December 2006 to January 2007). This result is similar to that of [PERSON] _et al._[45], where R\({}^{2}\) increased from 0.5 (2005 to 2010) to 0.7 (2010 flood period). Overall, for the different regions and throughout the study duration, APHRODITE, 3B42 RT, and 3B42V7 showed the best performance, as suggested by higher R\({}^{2}\) and lower RB values (Table 6).  
A systematic underestimation of the highest daily volumes occurred for most of the SPPs, except TRMM. GPCP-1 DD and CMORPH performed the worst, with underestimations up to 60% (Table 6). This might be due to the SPPs' weakness for point-to-grid comparisons, because SPPs provide area rainfall (grid cell) while rain gauges provide point-based rainfall [60]. With a maximum R\({}^{2}\) of 0.86 and 0.88, respectively, the 3B42 RT and 3B42V7 appeared to have the best potential for analyzing abnormal precipitation events over Malaysia. Moreover, our results are in agreement with the findings of [PERSON] _et al._[21], who evaluated the performance of previous version (V6) TRMM products at four rain gauges over south, east, west and highland areas of PM with R\({}^{2}\) ranging from 0.63 to 0.96, but better performances of version 7 than version 6 had been previously reported [65]. Our findings confirm that the 3B42V7 and 3B42 RT are reliable and suitable for heavy precipitation estimation and monitoring (3B42 RT is a real-time product) in Malaysia.  
Figure 8: Comparison of daily precipitation series between precipitation products and selected rain gauges (highly affected area) for the 2006/2007 flood event.
## 2 Materials and Methods
### Evaluation of Image Performance (Noise Characteristics)  
The performance of the designed algorithm for CLSM was evaluated in terms of the NNPS, CNR, and COV, which are defined as  
\[\begin{split}\text{NNPS}(\text{u},\text{v})=\frac{\text{NPS}(\text{ u},\text{v})}{\text{(mean signal of average ROI)}^{2}}\\ (\text{NPS}(\text{u}_{\text{n}},\text{v}_{\text{k}})=\lim_{\text{ N}_{\text{n}},\text{N}_{\text{y}}\to\infty}(\text{N}_{\text{x}}\text{N}_{\text{y}} \Delta\text{x}\Delta\text{y})<|\text{FT}_{\text{nk}}|(\text{x},\text{y})-\text{ S}(\text{x},\text{y})|^{2}>\\ =\lim_{\text{N}_{\text{x}}\text{N}_{\text{y}}\to\infty}\lim_{ \text{M}\to\infty}\frac{(\text{N}_{\text{x}}\text{N}_{\text{y}}\Delta\text{x} \Delta\text{y})}{\text{M}}\sum_{\text{m}=1}^{\text{M}}|\text{FT}_{\text{nk}} \text{I}(\text{x},\text{y})-\text{S}(\text{x},\text{y})|^{2}\\ =\lim_{\text{N}_{\text{x}}\text{N}_{\text{y}},\text{M}\to\infty} \frac{\Delta\text{x}\Delta\text{y}}{\text{M}\text{N}_{\text{x}}\text{N}_{\text {y}}}\sum_{\text{m}=1}^{\text{M}}<|\sum_{\text{i}=1}^{\text{N}_{\text{x}}} \sum_{\text{j}=1}^{\text{N}_{\text{y}}}\left(\text{I}(\text{x}_{\text{i}}, \text{y}_{\text{j}})-\text{S}(\text{x},\text{y})\right)\exp\left(-2\pi\text{i}( \text{u}_{\text{n}}\text{x}_{\text{i}}+\text{v}_{\text{k}}\text{y}_{\text{j}}) \right)|^{2}\right)\end{split} \tag{3}\] \[\text{CNR} = \frac{|\text{O}_{\text{T}}-\text{O}_{\text{B}}|}{\sqrt{\sigma_{ \text{T}}^{2}+\sigma_{\text{B}}{}^{2}}}\] (4) \[\text{COV}=\frac{\sigma_{\text{T}}}{\text{O}_{\text{T}}} \tag{5}\]  
where, \(u\) and \(v\) are the spatial frequency conjugates in the X and Y directions, respectively; \(N_{x}\) and \(N_{y}\) are the number of pixels in the X and Y directions, respectively; \(\Delta x\) and \(\Delta y\) are the pixel spacings in the X and Y directions, respectively; \(I(x_{i},y_{j})\) is the image intensity at the \((x_{i},y_{j})\) pixel location; \(S(x,y)\) is the mean intensity; \(\text{O}_{\text{T}}\) and \(\sigma_{\text{T}}\) are the mean and standard deviation, respectively, of the target region of interest (ROI); \(\text{O}_{\text{B}}\) and \(\sigma_{\text{B}}\) are the mean and standard deviation, respectively, of the background ROI. The established/selected ROIs represent the early carious lesions in the acquired image.
## 4 Data Set Generation and Processing
### GNSS Signal and Playback with Specific Bit-Depth
The negative numbers are represented with the two's complement by taking the absolute value of the number (e.g., 10), converting it to 16-bit binary (e.g., 0000 0000 0000 1010), inverting each bit (e.g, 1111 1111 1111 0101), and adding 1 to generate the final binary representation (e.g., 1111 1111 1111 0110). The key point is that the Most Significant Bit (MSB) of a two's complement number determines its sign: 0 for positive, 1 for negative. Therefore, discarding less significant bits in two's complement representation preserves the sign bit, maintaining both positive and negative values. In the algorithm implemented in this study, to achieve bit-depths lower than 16 (e.g., 4), the discarding of the Least Significant Bit (LSB)s does not impact the sign information. If we take, for example, the decimal number \(-\)15,234, represented in binary as 1100 0100 0111 1110, discarding the four LSBs generates 1100 0100 0111. During playback, the digital content is converted back to I16 by treating the discarded bits as zeros: 1100 0100 0111 becomes 1100 0100 0111 0000. This results in the decimal value \(-\)15,248, which is very close to the original one, as expected. Although the precision is reduced, the sign bit (1) is preserved, reflecting the negative nature of the original value. In general, the error introduced by discarding the four LSBs ranges from 0000 to 1111 (15 dB), causing a minor loss in precision. To enhance the realism of the interference simulation, the continuous interference signals generated in the L1 band were transformed into burst transmissions. This intermittent behavior, where the jammer alternates between one-minute 'on' and 'off' periods, better reflects the characteristics of certain real-world interference sources. This intermittent effect was created on the continuous data stream generated by the chirp signal process (involving the Signal Source and Voltage-Controlled Oscillator (VCO) blocks) by segmentation using the stream to tag the stream block (see Figure 4). The burst shaper block was employed to selectively manipulate the tagged packets. This block was configured to create alternating one-minute intervals where the interference signal is passed through ('on' state) and suppressed ('off' state). These intermittent values were also used to define the label information. Each interference condition is generated using a specific power level of the interferer, which may change due to the type of interference in the attempt to make the impact visible in the \(C/N_{0}\) equivalent (e.g., a wideband chirp interferer has a larger impact than a narrowband chirp interferer at the same level of dB). The power levels of the interference are defined by setting the attenuation (using the variable attenuator shown in Figure 3) to the values presented in Table 2 and represented in the rest of this paper as \(L_{I}\) = L (low), \(L_{I}\) = M (medium), and \(L_{I}\) = H (high),
# Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach
## 3 Results and Validation
### Field Data Testing  
To test the program and the robust joint inversion algorithm, we performed the inversion of preliminary field data from Los Contreras phreatomagmatic maar (Figure 11), located at the San Luis Potosi volcanic field, Central Mexico [84,85]. Gravity data were obtained at 50 m through walks using a CG-5 Autograv gravity meter with a reading resolution of 0.001 mGal. Measurement of the Earth's magnetic field was realized at the same spots using a GSM-19 high-sensitivity Overhauser effect magnetometer with 0.01 nT resolution and 0.2 nT absolute accuracy. The magnetic field in the area is characterized by B = 41,922.8, I = 50.86, and D = 4.51.  
After corrections were performed to the raw data, the residual gravity anomaly was calculated by subtraction of the regional components from the observed complete Bouguer anomaly, and simultaneously, the regional magnetic field was subtracted from the observed magnetic anomaly data. After data reduction, we statistically interpolate the data using variograms to obtain better coverage of the study area and resample a regular mesh at 100 m of spacing, obtaining 128 data points for each (gravity and magnetic) method (Figure 12).  
Figure 11: Topography of the maar Los Contreras, SLP. Acquired gravity and magnetic data location in black marks.  
Figure 10: Model convergence (Equation (18)) and data misfit (Equation (19)) achieved in inversion of test data 2 at each iteration, (**a**) for standard separate inversion and (**b**) for our robust joint inversion.  
We performed a joint inversion with a smoothness regularizer for 20 iterations with a stopping condition of convergence between models less than 1%. No _a priori_ information, structural direction, variable standard deviation to data or models was introduced due to better results according to the area geological-structural context, which are beyond the scope of this article. A domain of 21 \(\times\) 11 \(\times\) 10 (2310) voxels was automatically generated based on the spatial limits of the input data and the voxel size of 100 m per side. Results are shown in Figure 13, slicing density and magnetization domains from top to bottom at 300 m until no more physical property contrast is visible. Additionally, the convergence and RMS graphs are also displayed in Figure 13.  
Figure 12: Observed geophysical data in Los Contreras area. (**a**) Residual complete Bouguer gravity anomaly and (**b**) Residual magnetic anomaly.  
Figure 13: Inversion results of preliminary field data, slicing the 3-D view each 300 meters from top to bottom until no more physical property contrast is visible, (**a**) Density contrast, (**b**) Magnetization contrast and (**c**) Convergence and RMS plots.
## 3 Modified Algorithm Implementation
### The Proposed MGTO  
Like other swarm intelligence algorithms, GTO still falls into local optimum and suffers from slow convergence easily. To overcome these shortcomings and further enhance the performance of the GTO, a modified MGTO is proposed that introduces QIBAS, TLBO and QRBL into the GTO. First, the QIBAS algorithm is employed to enrich the initial position of the silverback. Then TLBO is hybridized with the stage involving following the silverback into the exploitation phase. In this stage, the silverback can be considered as a teacher, and other gorillas learn from the silverback. Through this operation, the search ability of gorillas is enhanced, and the differences between the silverback and gorillas are reduced. Thirdly, QRBL is adopted to update the position of the silverback at the end of the stage, thus facilitating the quality of the optimal position. In the initialization phase, the MGTO generates the random population, \(X_{i}\), and initializes the position of the silverback. Then the QIBAS algorithm is used to calculate the search positions on both sides of the silverback. The _NewPosition_ can be calculated by Equation (28). The quadratic interpolation function is employed to generate _NewPosition_1. _NewPosition_1 can be expressed by Equation (29). To choose an optimal position, the fitness values of these two positions are compared, and then, if the fitness value of the \"new position\" is better than the previous one, it will replace the previous position:  
\[ \begin{array}{l}Newposition=x_{t}-s\times b\times sign(f(x_{l})-f(x_{r}))\end{array}  \tag{28}\]  
\[ \begin{array}{l}Newposition1=\\ \frac{1}{2}\frac{(x_{h-Silverback}^{2})f(x_{r})+(Silverback^{2}-x_{R}^{2})f(x_ {l})+(x_{h-}^{2}-x_{R}^{2})f(Silverback)}{(x_{h-Silverback})f(x_{r})+(Silverback -x_{R})f(x_{l})+(x_{rh}-x_{h})f(Silverback)}\end{array}  \tag{29}\]  
In the exploitation phase, the TLBO algorithm is adopted to update the behavior of following the silverback with 50% probability. The first step is calculating the mean, \(M\), of the population. Secondly, the teach factor, \(F\), and the difference, _Difference_, between gorillas and the silverback are calculated. In addition, the third step is to update the position, as shown below:  
\[ \begin{array}{l}GX(i)=X_{old,i}+Difference\_Mean_{i}\end{array}  \tag{30}\]  
In the final phase, in order to get a new position of the silverback, QRBL is used to generate the quasi-reflection position \(X_{Silverback}\)[47]. By comparing the fitness values of \(X_{Silverback}\) and \(X_{Silverback}\)[47], the position is selected from two positions as the final optimal position:  
\[ \begin{array}{l}Positions\_ROL=rand\bigg{(}\frac{lb_{i}+ub_{i}}{2},\ x_{i} \bigg{)}\end{array}  \tag{31}\]  
Eventually, repeating the steps mentioned until the maximum value of iterations is reached. The pseudo-code is represented below. And the flowchart of MGTO is shown in Figure 3.  
Figure 3: The flowchart of MGTO. ```
% MGTO setting Inputs: the size of population, \(N\); the maximum number of iterations, \(T\); and parameters \(\beta\), \(p\), \(W\), \(d\) and \(s\).
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 4 Results
### Performance Evaluation  
The performance evaluation of the ML model candidates was based on classification score, inference time, model size, and power consumption. The measurement for each performance parameter was taken for an average of 10 iterations. This approach was performed to ensure that any external factors would not influence the consistency of the results.  
This work evaluated a performance using F-score metrics from the classification report. F-score or F-measure represents the harmonic mean of the precision and recall scores from the predicted label to the actual label [37]. Precision is the number of true positives (\(tp\)) divided by the total number of positive classifications, including false positives (\(fp\)). Recall is the number of true positives divided by the total number of positives, including false negatives (\(fn\)). In binary classification, recall is usually referred to as sensitivity. The F-score (\(F_{\beta}\)) is formulated as follows:  
\[F_{\beta}=\left(1+\beta^{2}\right)\times\frac{Precision\times Recall}{\left( \beta^{2}\times Precision\right)+Recall}\enspace, \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Optimized Parameters** & **Trials** & **Best Value** \\ \hline Windowed Averaging & window size & 10–300 & 50 \\ PyOD Models & outlier fraction & 0.05–0.2 & 0.16 \\ kNN & neighbors & 10–300 & 255 \\ LOF & neighbors & 10–350 & 280 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 12: The first two principal component plots after pre-processing.  
or  
\[F_{\beta}=\frac{\big{(}1+\beta^{2}\big{)}tp}{(1+\beta^{2})tp+\beta^{2}fn+fp}. \tag{7}\]  
The F1-score (\(\beta=1\)) is selected as the minimum number of false positives, and false negatives are paramount for future onboard implementation. Further, the evaluation used F-score metrics considering the binary classification and imbalance class problem on the available dataset [38].  
The inference time evaluation was measured directly by the standard package provided by the Jupyter Notebook. It measures the processing time of the CPU for specific instructions. The pickle library estimated the model size from a converted python object into a byte stream. Then, the power consumption was measured for the Raspberry Pi experiment, which considered the pre-processing stage and model inference.
## 4 Results and Discussion
### Aerosol Droplets Tracking  
Figure 8 shows the aerosol droplet time tracking with the time course for the different ventilation rates.  
Figure 7: Pathlines emerging from the ceiling diffuser above the instructor (Run R2-Time 180 s).  
Figure 9 presents the time residence of the dispersed aerosol droplets after 180 s from the instructor's start for the different ventilation scenarios. It is noted that the velocity field for scenarios R3 and R4 is remarkably different from scenarios R1 and R2.  
As the ventilation rate increases, the aerosol droplets' upward entrainment (induction) due to the generated swirl air flow by the swirl diffuser becomes more apparent and more robust. The entrainment process that is caused by the swirl diffuser was previously discussed by other researchers [17]. This entrainment process will help in reducing the risk of student exposure to harmful aerosol droplets.  
Figure 10 shows an example of the students' exposed areas to the traces of aerosol droplets colored by the normalized concentration under no ventilation conditions. Some students were exposed to aerosol droplets, whereas others were not. Exposed students differed in the body's exposed location and whether it was the face, the body, or both, as shown in Figure 10.  
Figure 8: Time tracking of aerosol droplets with the time course and different ventilation rates (scenarios R1–R4).  
Figure 10: Students exposure areas after 180 s (no ventilation case-R1). (**a**) Full class. (**b**) Zoom-in of the dashed window shown in Figure 10a.  
Figure 9: Time residence for dispersed aerosol droplets after 180 s.
## 3 Results  
### Isolation and Identification of Staphylococcus spp  
After analyzing the phenotypic features of bacterial colonies grown on selective microbiological media (Bird-Parker and Chapman), bacterial strains showing phenotypic features characteristic of the _Staphylococcus_ genus were isolated from 93 out of 100 cow milk samples. On Bird-Parker medium, the colonies were convex, black, or grey, with a diameter of 1-1.5 mm after incubation for 24 h and a diameter larger than 2.5 mm after 48 h. In 30 cultures, colonies with a typical opalescent ring were observed, in 26, a transparent ring around the colony. On Chapman's medium (7% NaCl content), mannitol-refmenting coagulase-positive staphylococci (CPS) grew as cream to orange colonies, turning the medium yellow. Coagulase-negative staphylococci (CNS) grew as pink colonies, and the medium did not change its original color.  
After analyzing Gram-stained microscopic slides and determining the ability to produce catalase and coagulase, 56 bacterial strains isolated from 28 bulk tank milk samples were determined to belong to _Staphylococcus_ spp.  
The CPS group included 28 isolates showing a positive reaction to coagulase production. All of them were identified as _S. aureus_ because they had a fragment of 235 rRNA (_Stuart_) and the _coa_ gene in the isolated genetic material. The remaining 28 were considered CNS due to showing a negative reaction to coagulase production. Among the isolated CNS strains, 18 strains were identified as _S. aureus_ based on the presence of a 235 rRNA fragment in the genome, while the remaining (10 strains) belonged to other species belonging to _Staphylococcus_ spp. (Table 2).  
Among CPS, 9 strains showed \(\alpha\) hemolysis and the remaining strains on blood agar caused \(\beta\) hemolysis. The vast majority of CNS strains (17) caused \(\alpha\) hemolysis (11 strains _S. aureus_ and 6 strains _Staphylococcus_ sp.), only a few (3) \(\beta\) hemolysis (respectively 2 and 1), and 8 strains (respectively 5 and 3) did not show it at all (Table 2).
## 2 Related Works
### Deep Learning Approaches  
The field of HAR has displayed significant interest in EMG information due to its ability to record muscle activation patterns during various movements. Systems for recognizing human movements based on EMG data can offer thorough insights into muscle involvement, fatigue, and coordination. Therefore, they play a crucial role in various applications, such as monitoring fitness progress, rehabilitation progress, and recognizing gestures [4].  
Recent advancements in deep learning methodologies have revolutionized the methodology of recognizing human movements using EMG data. Deep learning models like CNNs and recurrent neural networks (RNNs) have demonstrated outstanding proficiency in extracting distinctive features from unprocessed EMG data and precisely classifying human actions [11].  
CNNs have shown exceptional efficacy in collecting both spatial and temporal patterns in EMG data. CNNs have the ability to acquire hierarchical features that remain unaffected by translation and scaling by using convolutional filters on the unprocessed EMG signals [12]. The capacity to extract resilient features has resulted in significant improvements in the accuracy of EMG-based HAR compared to conventional machine learning techniques that depend on manually designed features [9].  
On the other hand, RNNs have demonstrated notable promise in effectively capturing the temporal connections observed in EMG data. LSTM networks and gated recurrent units (GRUs) are popular designs of RNNs that have proven effective in grasping long-term dependencies and adeptly managing EMG sequences of varying lengths [13]. By leveraging temporal context, RNNs can improve their comprehension of muscle activation patterns and refine the recognition of intricate human movements [10].  
Researchers have also investigated hybrid deep learning architectures integrating CNNs and RNNs for EMG-based HAR. This study seeks to use the advantages of both architectures by first extracting spatial characteristics via CNNs and then modeling the temporal relationships using RNNs [14]. Hybrid techniques have shown enhanced efficacy in collecting the spatio-temporal attributes of EMG data and improving the resilience of HAR systems [5].
# Evaluation of Deviations Produced by Soft Tissue Fitting in Virtually Planned Orthopathic Surgery
## 2 Materials and Methods
### Final Measurements and Variance Evaluation  
Measurements of surgical outcomes were performed based on postoperative radiological explorations. Postoperative CT scans were compared with preoperative CT scans, and the distances and movements of many facial landmarks were recorded. The postoperative CT was registered with the preoperative CT based on cranial anatomical landmarks. Once registered, both CTs were superimposed, and the positions of the pogonion, glabella, and superior incisor were marked on both CTs. In conclusion, the distance between each marker was measured in both radiological images and decomposed into vertical and anteroposterior distances. Based on CT image resolution, the measurement reliability was \(\pm\) 0.556 mm for anteroposterior observations and \(\pm\)0.896 mm for vertical ones.  
The parameters considered in the study were the anteroposterior and vertical displacements of the superior incisive (SI), pogonion (Pg), and glabella-Pg and glabella-SI. These measurements were summarized in combination with the parameters recorded from the surgical plan, and the two sets of measurements were then compared.  
Deviations from the surgical plan and the results were calculated using the surgical plan data and the preoperative and postoperative CT measures. Deviation summaries were also calculated considering deviation in the vertical and anteroposterior axes. Finally, deviation ratios were calculated for performance revision according to surgeon feedback records from surgery.