esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 2 Materials and Methods
### Experiment Setup and Reference Data  
Measurement stations were planned at locations with different horizon exposure conditions, so that the test would be reliable and different results could be obtained. A sketch of the measured points (Figure 2) shows approximately the actual conditions in the field. Measurements were made in May, when trees already start to gather leaves and constitute a barrier for a satellite signal. Results of these observations are presented in Table 1. Station 1 had a perfect exposure of the horizon, stations 2-4 had the horizon covered only from one side, station 5 had in addition tree branches directly above it, station 6 was surrounded by trees and a nearby hill, while station 7 was located in an \"urban canyon\".
###### Abstract  
The issue of water pollution is one of the hot topics of global concern, which requires us to efficiently treat pollutants in water, especially printing and dyeing sewage. There are varieties of dyestuffs and intermediates, which are complex and difficult to degrade, and they even contain heavy metals. In this study, a bacterial strain named Q3-6 with potential for sewage treatment was isolated and its physiological, biochemical, and genomic characteristics, and potential application value, were further investigated. The genome sequence confirmed that it belongs to _Bacillus thuringiensis_. Strain Q3-6 has a significant decolorization effect on the dyes. The decolorization rate for Brilliant blue G-250 (0.1 g/L) and Congo Red (0.1 g/L) can reach 93.9% and 91.9%, respectively. In addition, strain Q3-6 is resistant to many kinds of antibiotics and heavy metals. Further, it has strong heat resistance, and heating at 80 \({}^{\circ}\)C can promote the biomass of the strain. Genomic analysis revealed the presence of genes related to heat shock proteins (GroES, GrpE, Dna), GroEL, DnaK, ClpB, and ClpA) in strain Q3-6. These results suggest the strain's exceptional resilience and adaptability to intricate environments with heavy metals, antibiotics, or high-temperature environments, suggesting its pivotal role in the bioremediation of complex contaminated effluents.  
bioremediation; sewage treatment; dyeing effluents; stress-resistant; _Bacillus_ Article
## 3 Numerical Examples
### Slu-Osler Problem  
This problem verifies the dissipative properties of the scheme. The initial condition is [5]:  
\[(\rho,u,p)=\begin{cases}(3.857143,2.629369,10.33333),&x\leq 1,\\ (1+0.2\sin(5x),0,1),&1<x\leq 10.\end{cases} \tag{49}\]  
The computational domain spans from \([0,10]\) in the x and N = 201 uniform mesh points are used. Application of non-reflecting boundary conditions and the ultimate simulation time set to \(t=1.8\). A fixed-time step size \(DT=1\times 10^{-4}\). Since this problem has no exact solution, the result obtained by ROE with N = 4001 is used as the exact solution. As can be seen from Figure 11, the proposed scheme exhibits excellent shock wave capture and high wavenumber density fluctuation capabilities, outperforming the LF scheme in these key areas. The ability to resolve density peaks more efficiently is crucial in simulations involving strongly compressible flows and discontinuities. The scheme's high-resolution nature enables it to handle complex phenomena.  
Figure 10: Density distributions of the Sod problem at t = 0.25 were obtained using the LF, ROE, and URLBFS schemes compared to the exact solution. The results illustrate the differences in accuracy and resolution between these schemes. Panels (**a,c**) show the results using the WENO5 and WENO7 reconstruction methods, respectively, while panels (**b,d**) provide partial zoom\(-\)ins of critical areas to visualize the differences better.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 7 Proposal for Security Incident Simulation Method
### Security Incident Scenario Service  
This service involves building a system that generates security incident scenarios and enables their prediction and management. The system operates through an engine that determines the likelihood of all types of security incidents at the current time based on both collected and input information within the organization. The process of generating security incident scenarios consists of four steps: data collection, analysis, profiling, and scenario generation. Figure 13 below shows an example of classification and determination for different types of security incidents [37].  
The virtual security incident scenarios are automatically collected and mapped onto the internal system diagram, analyzing each security incident's vulnerabilities and malware characteristics. This provides a visualized UI depicting the potential flow of actual security incidents. Scenario-based penetration tests are conducted, helping even general administrators easily understand the flow of each security incident [38].  
Additionally, by incorporating vulnerabilities specific to each endpoint, virtual scenarios like the one shown in Figure 14 are generated, offering predictions of potential incidents and suggesting appropriate countermeasures.
## 4 Proposed Method
### Morphological Restoration  
The final step of our suggested method is the process of filling small gaps or holes that arise from the distorted foreground mask (image) caused by shadow removal. It is generally accepted that shadow removal is a destructive process. This happens because of the similarity of the pixel intensities in the foreground object and corresponding background. Therefore, it has some negative effects, such as removing key parts of an object's shape after considering them as shadows. As a result, the original shapes of moving objects are distorted. To handle this issue, we employed a morphological operator to the foreground binary mask for restoration after the distorting shadow removal process.  
Using appropriate thresholds is necessary for the foregoing geometry-based removal process to assure that all shadow pixels are removed [26]. As a consequence, only the regions not affected by noise and those clearly free of shadows are subject to the shape reconstruction process performed using Equation (9), which is represented as follows:  
\[R=M_{s}\cap(\widetilde{M}\oplus SE) \tag{9}\]  
where \(M_{s}\) is the mask image, \(\widetilde{M}\) is the restored image, and \(SE\) is a structure element, whose size usually depends on the size of the objects of interest, although a \(3\times 3\) square element proved to work well in all of our tests. Furthermore, we use a manual image superimposing tool to superimpose the extracted object on the foreground image for the performance testing of our proposal.
## 4 Numerical Simulation Results and Discussions
### Laser Dynamic States under Single External Cavity
As found in Figure 3(b1), RIN shows a sharp peak at PO frequency \(f_{PO}\) = 3.221 GHz and its higher harmonic peaks, which represent uniform periodic oscillations with the relaxation frequency \(\sim\)\(f_{r}\) = 3.22 GHz. The RIN power spectrum in the PD dynamic state is distinguished by two sharp peaks at PD frequency \(f_{PD}\) = 3.2 GHz and its half harmonic, as shown in Figure 3(b2). An unequal time type of \(S(t)\) variations represents the chaotic operation as displayed in Figure 3(b3). These inequalities in the output of the LD are created due to operating the LD unstably with two conditions: one of them identifies the LD with a frequency of \(f_{Laser}\) = 3.4 GHz and the other to the compound cavity with a frequency of \(f_{Comb}\) = 4.6 GHz [9]. Figure 3: Dynamics states of the LD under single OFB \(\psi_{1}\) = 0.0 (red color) and \(\omega\tau_{1}\) (black color): (**a1**,**b1**) PO with \(K_{\text{ex1}}\) = 0.02, (**a2**,**b2**) PD with \(K_{\text{ex1}}\) = 0.025, and (**a3**,**b3**) chaos with \(K_{\text{ex1}}\) = 0.04.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Method
#### 3.2.1 Ship Tracks  
The first step is to estimate the tracks of the studied ships. Taking into account a lifetime of NO\({}_{\text{x}}\) equal to a few hours, we studied the track of the ship starting from two hours before the moment of the satellite overpass. We distinguished two types of ships' tracks: the _ship track_, obtained based on resampling and interpolation of the AIS data, and the _wind-shifted ship track_. For the calculation of the _wind-shifted ship track_, we assumed that the plume emitted by a ship has moved in accordance to the wind direction by a distance \(d=\
u\times|\Delta t|\), where \(\
u\) is the speed at a given location at 10 m above sea level from ECMWF for 12:00 UTC, and \(|\Delta t|\) is the time difference between the time of the satellite overpass and the time of a given AIS ship position. Both wind speed and wind direction are assumed to be constant for the whole time during which we study the plume. Such an assumption may create uncertainties in the expected position of the plume of the ship. However, with the methodology presented further in this section, such uncertainties will not affect the correctness of the ship-plume allocation.  
Summing up, the _ship track_ provides us the information on the position of the ship from where the studied ship plume was emitted. The _wind-shifted ship track_ indicates the expected position of the center line of the NO\({}_{2}\) plume after displacement driven by local wind conditions. Figure 4a,b give examples of the _ship track_ and its corresponding _wind-shifted ship track_, respectively.  
Figure 3: A list of days used for the dataset creation and the number of ships per day studied.
# Thermal Comfort Applied in Hospital Environments: A Literature Review
## 5 Final Considerations  
It was found in the articles analyzed that there are still some little explored topics, such as productivity in hospital environments. Comfort conditions in specific patients, such as patients with chronic diseases and children, are also little explored. Only two articles were identified, one of which studied pregnant women [18] and another premature babies [36]. Regarding the comfort of specific patients, given the importance of the theme, it is necessary that research of this nature be performed in order to define standards for these types of patients for different types of environments.  
The review [22] makes two criticisms addressed here: (a) Studies in the area were conducted in only one hospital and (b) focused on only one group of people. Through the analysis of the current articles, this research adds, relative to criticism (a), that most of the new studies have been carried out in more than one hospital, thus overcoming this judgment; and to criticism (b), that there was no significant improvement since 15 articles focused on more than 1 group while 11 focused on only 1. It should be noted that the universe of thermal comfort in hospital environments is very little explored, especially relative to PMV.  
This article proposes four research questions. When answering RQ1, it is shown that around 81% of the published studies deal with the health/wellbeing aspect, 17% deal with energy savings, and around 2% address the productivity aspect. The response from RQ2, on the other hand, shows that the PMV model was not effective when applied in hospital environments since 10 of the 12 articles that relate PMV to TSV pointed out that the Fanger index does not represent the real thermal sensation felt by people in hospital environments. RQ3 shows that the groups \"staff\" and \"patients\" are the most studied, as can be expected; however, only the staff group has well-defined specifications, and the medical team is the most approached type. In RQ4, results showed that the IEQ parameter in addition to thermal comfort is IAQ, very important today due the pandemic caused by coronavirus.  
Finally, it is important to emphasize the importance of the relationship between buildings and the thermal comfort of the occupants, since discomfort can affect not only patients, but the entire staff. It is believed that this relationship has so far been little explored and that there are still questions to be answered through further studies conducted in these environments.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Volatile Compounds Evaluation  
#### 3.6.1 Preliminary Identification of Volatile Compounds  
A total of 60 volatile compounds were identified via mass spectral matching (NIST 14), retention indices (RIs), and characteristic \(m/z\) fragment ions (Table S4). These included 48 hydrocarbons (80.0%), 4 alcohols (6.7%), 5 ketones (8.3%), 2 esters (3.1%), and 1 aldehyde (1.7%), with terpen comprising 89.6% of all hydrocarbons. The number of identified compounds varied across drying methods: ND (58), HD (56), VFD (54), VD (54), and MD (48).  
PCA and HCA confirmed significant differences in volatile composition (Figure 2). Both results, based on the normalized relative peak areas of 60 volatile compounds (\(n=3\)), explained 64.2% of the variance (PC1: 35.9%, PC2: 28.3%), with ND and VFD forming distinct clusters, indicating effective volatile compound retention. HCA (Figure 3) further validated this clustering, grouping ND and VFD together, while HD and MD clustered separately, reflecting the greater impact of high-temperature drying on volatile degradation. These findings underscore the importance of optimizing drying strategies to preserve the aroma and flavor profile of AS fruits. A study reported a 59% decrease in total volatiles in dried mangoes, particularly monoterenes and sesquiterpenes, due to thermal degradation, oxidation, and evaporation [45].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Amino Acid (mg/g)** & **HD** & **VD** & **ND** & **MD** & **VFD** \\ \hline Asp & 0.54 \(\pm\) 0.02 b  & 0.48 \(\pm\) 0.01 c  & 0.14 \(\pm\) 0.01 e  & 0.31 \(\pm\) 0.01 d  & 0.57 \(\pm\) 0.02 a  \\ Thr & 0.95 \(\pm\) 0.03 c  & 1.83 \(\pm\) 0.06 b  & 0.72 \(\pm\) 0.02 d  \\ Ser & 0.62 \(\pm\) 0.02 b  & 0.55 \(\pm\) 0.02 c  & 0.17 \(\pm\) 0.01 e  & 0.26 \(\pm\) 0.01 d  & 0.71 \(\pm\) 0.02 a  \\ Glu & 0.58 \(\pm\) 0.02 b  & 0.31 \(\pm\) 0.01 ee  & 0.49 \(\pm\) 0.02 c  & 0.37 \(\pm\) 0.01 d  & 0.90 \(\pm\) 0.003 a  \\ Cys & 0.082 \(\pm\) 0.011 c  & 0.090 \(\pm\) 0.003 b  & 0.094 \(\pm\) 0.005 b  & 0.088 \(\pm\) 0.007 b  \\ Val & 0.34 \(\pm\) 0.01 b  & 0.36 \(\pm\) 0.01 a  & 0.13 \(\pm\) 0.02 c  & 0.12 \(\pm\) 0.01 c  \\ Met & n.d. & n.d. & n.d. & n.d. & 0.040 \(\pm\) 0.001 a  \\ Ile & 0.19 \(\pm\) 0.01 c [FOOTNOTE:b]Footnote b: Results are expressed as mean \(\pm\) standard error (SE). Mean values within the same column followed by different letters (a–e) indicate statistically significant differences (\(p\leq\) 0.05) among
## 5 Discussion  
### Model Parameter Comparison  
The comparison results in parameter counts with classical methods are shown in Table 4; only the average instance accuracy was chosen here as the evaluation metric. The comparative results demonstrate the remarkable efficiency and effectiveness of our proposed models, MSSAViT-TV and MSSAViT-SV, in multi-view 3D object classification tasks. Both models outperformed the majority of existing approaches across the ModelNet10 and ModelNet40 datasets while maintaining a significantly lower parameter count. For instance, MSSAViT-SV achieved an accuracy of 98.57% on ModelNet10 and 96.11% on ModelNet40, surpassing state-of-the-art methods such as MVMSAN (98.57% on ModelNet10, 96.96% on ModelNet40) despite utilizing only 6.1 M parameters, compared to MVMSAN's > 8.8 M. Furthermore, in comparison to methods like FusionNet (93.11% on ModelNet10, 90.80% on ModelNet40, 118 M parameters) and MVCNN (90.10% on ModelNet40, >130 M parameters), our models achieve superior performance with drastically reduced computational costs. This efficiency stems from the integration of lightweight MobileNet backbones with our novel structural and temporal self-attention mechanisms, which effectively capture critical spatial and sequential dependencies. The results validate the potential of our ap  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Representation** & **Backbone** & \multirow{2}{*}{**Total Parameters**} & \multirow{2}{*}{**ModelNet10**} & \multirow{2}{*}{**ModelNet40**} \\  & **Type** & **Network** & & & \\ \hline SV2 SL [1] & SeqView & VGG & \textgreater{}130 M & 94.60 & 93.31 \\
3D2 SV [2] & SeqView & Restnet101 & \textgreater{}45 M & - & 93.40 \\ MVCNN [4] & View & VGG & \textgreater{}130 M & - & 90.10 \\ MVPointNet [38] & Points + View & VGG & \textgreater{}130 M & 95.20 & - \\ PVRNet [40] & Point + View & AlexNet & \textgreater{}60 M & - & 93.60 \\ FusionNet [31] & Voxel + View & - & 118 M & 93.11 & 90.80 \\
3 DGAN [20] & Voxel & - & 11 M & 91.00 & 83.30 \\ MVDAN [44] & View & VGG & \textgreater{}130 M & - & 96.60 \\ MVMSAN [39] & View & ResNet14d & \textgreater{}8.8 M & 98.57 & 96.96 \\ MVContrast [45] & View & ResNet18 & \textgreater{}11.7 M & - & 92.54 \\ SelectiveMV [48] & View & Restnet152 & \textgreater{}60 M & - & 88.13 \\ MSSAViT-TV (ours) & SeqView & MobileNet & 6.1 M & 98.45 & 96.07 \\ MSSAViT-SV (ours) & SeqView & MobileNet & 6.1 M & 98.57 & 96.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results in parameters counts.  
Figure 7: Confusion matrices of **(a)** MSSAViT-TV and **(b)** MSSAViT-SV on Modelnet40.  
proach to deliver high accuracy with minimal model complexity, making it well suited for real-world applications where computational resources are limited.
## 3 Network and Partial Networking Coding Models
### Packet Delivery Probability  
During the initialization phase, each sink node broadcasts information including its transmission power \(f\) to all sensor nodes, which then each calculates its distance to the sink node based on the received signal strength. In the attenuation model of underwater acoustic signal, if the distance between a transmitting node and a receiving node is \(x\) and the geometry of signal propagation is described using spreading factor \(k\) (_k_ = 1.5 for a practical scenario), then the attenuation factor is calculated by:  
\[A(x)=x^{k}a^{x} \tag{1}\]  
where \(a=10^{a(f)\ln 0}\) and \(a(f)=\frac{0.11f}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 10^{-4}f^{2} +0.003\).  
Built on the foundation of a typical underwater acoustic channel model described by Equation (1), we use Rayleigh fading to model small-scale fading of signal propagation and Binary Phase Shift Keying (BPSK) to calculate the average Bit Error Rate (BER). If a symbol Signal-to-Noise ratio (SNR) is _r\({}_{s}\)_ and a bit SNR is _r\({}_{b}\)_, then _r\({}_{s}=r_{b}\)_ if using BPSK. If we further define \(\overline{r_{s}}=10^{\gamma_{s}/10}\), then BER is calculated by:  
\[p_{b}(r_{b})=\frac{1}{2}(1-\sqrt{\frac{\overline{r_{s}}}{1+r_{s}}}) \tag{2}\]  
The following passive sonar equation gives each bit SNR of the underwater signal at a receiving node:  
\[r_{b}=SL-k\times 10\times\log d-a(f)\times d\times 10^{-3}-50+18\times\log f \tag{3}\]  
where _SL_ (typically 118 dB) is the sound intensity level.  
Finally, for any pair of nodes with the distance of \(d\), the delivery probability of a packet with the size of \(m\) bits is given by:  
\[p(d,m)=(1-p_{b}(r_{b}))^{m} \tag{4}\]
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 4 The Dataset  
The dataset used in this work was obtained from the Kaggle website under the title of Traffic, Driving Style and Road Surface Condition [24]. Two cars were used to collect the dataset, a Peugeot 207 1.4 HDi and an Opel Corsa 1.3 HDi. The dataset was collected from the vehicles On Board Diagnostics port (OBD) by using an OBD device that can be paired with a smartphone. [PERSON] et al. [25] used this dataset to propose machine learning models in Internet of Things (IOT).  
The dataset includes 14 input features. They are summarized as follows:  
* Altitude change, calculated over 10 s.
* Current speed value, which is the average speed in the last 60 s.
* Speed variance in the last 60 s.
* Speed variation for every second of detection.
* Longitudinal acceleration.
* Engine load, expressed as a percentage.
* Engine coolant temperatures in degree celsius.
* Manifold Air Pressure (MAP), a parameter used by the internal combustion engine used to compute the optimal air/fuel ratio.
* Revolutions Per Minute (RPM) of the engine.
* Mass Air Flow (MAF) Rate measured in g/s. This reading is used by the engine to set fuel delivery and spark timing.
* Intake Air Temperature (IAT) at the engine entrance.
* Vertical acceleration.
* Average fuel consumption, calculated as liters per 100 km.  
The dataset was labeled to three sub-problem categories, i.e., road surface conditions, road traffic conditions and driving style. The road surface condition was labeled as smooth, even or full of holes. The road traffic condition was labeled as low, normal or high, and the driving style was labeled as normal or aggressive style. The dataset includes 24,957 data points. Table 1 summarizes the input features, the categories and the labels for each category of the dataset.  
Figure 1: The figure shows the system architecture for road conditions and driving style prediction.  
The number of labels for each category are different. Smooth roads, normal traffic conditions and normal driving style represent the majority of the labels of each category. This is due to the nature of the roads used for data collection. Figure 2 shows the distribution of the labels for the classification categories we considered in this study.
## 3 Results and Discussion
### Multivariate Analysis
#### 3.2.2 Partial Least Square Discriminant Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pre-Processing Method** & **Modeled Class** & **Cherimoya** & **Soursop** & **Pineapple** \\ \hline \multirow{6}{*}{**Mean centering**} & Sensitivity (Cal) & 0.959 & **1.000** & **1.000** \\ \cline{2-4}  & Specificity (Cal) & 0.929 & 0.949 & **1.000** \\ \cline{2-4}  & Sensitivity (CV) & 0.939 & **1.000** & **1.000** \\ \cline{2-4}  & Specificity (CV) & 0.909 & 0.949 & **1.000** \\ \cline{2-4}  & Class. Err (Cal) & **0.0557617** & 0.0252525 & 0 \\ \cline{2-4}  & Class. Err (CV) & **0.0760668** & 0.0252525 & 0 \\ \cline{2-4}  & RMSEC & **0.31426** & 0.273395 & 0.217022 \\ \cline{2-4}  & RMSECV & **0.333692** & 0.289513 & 0.229203 \\ \cline{2-4}  & Bias & **1.66533 × \(10^{-16}\)** & **1.66533 × \(10^{-16}\)** & 1.11022 × \(10^{-16}\) \\ \hline CV Bias & **0.00127205** & \(-0.00184987\) & 0.000577824 \\ \hline R\({}^{2}\) Cal & 0.554066 & 0.662499 & **0.78946** \\ \hline R\({}^{2}\) CV & 0.501205 & 0.622938 & **0.766066** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of the PLS-DA model with different pre-processing methods. We also analyzed the classification power of the PLS-DA model through the receiver operating characteristic (ROC) curves (Figure 6). The ROC curves of cherimoya, soursop, and pineapple were located closer to the upper left corner of the ROC space, which indicates that the first derivative of the SG pre-processing method produces a PLS-DA model with optimal performance, since it can correctly classify the tropical fruits in the classes to which they belong. In all three fruits, the value of the area under the curve (AUC) was one. These results confirm the ROC curves.