esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Dose Rate (\(D_{r}\)s), OSL Signal and \(D_{e}\)s
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **238-U** & **232-Th** & **40-K** & **F (w = 40\%)** & **Dr (Gy/ka)** \\ \hline MERO-1 & 16 \(\pm\) 9 & 29 \(\pm\) 4 & 226 \(\pm\) 20 & 7 \(\pm\) 1 & 1.50 \(\pm\) 0.25 \\ \hline MERO-2 & 21 \(\pm\) 12 & 21 \(\pm\) 3 & 82 \(\pm\) 16 & 8 \(\pm\) 2 & 1.83 \(\pm\) 0.14 \\ \hline MERO-3 & 23 \(\pm\) 11 & 37 \(\pm\) 6 & 113 \(\pm\) 15 & 16 \(\pm\) 4 & 1.21 \(\pm\) 0.20 \\ \hline MERO-4 & 19 \(\pm\) 9 & 13 \(\pm\) 6 & 91 \(\pm\) 15 & 16 \(\pm\) 4 & 0.90 \(\pm\) 0.13 \\ \hline MERO-5 & 21 \(\pm\) 10 & 27 \(\pm\) 7 & 127 \(\pm\) 16 & 13 \(\pm\) 4 & 1.15 \(\pm\) 0.07 \\ \hline MERO-6 & 9 \(\pm\) 1 & 10 \(\pm\) 7 & 78 \(\pm\) 17 & 8 \(\pm\) 2 & 0.68 \(\pm\) 0.10 \\ \hline MERO-7 & 26 \(\pm\) 2 & 26 \(\pm\) 1 & 115 \(\pm\) 79 & 11 \(\pm\) 2 & 1.37 \(\pm\) 0.27 \\ \hline MERO-8 & 29 \(\pm\) 13 & 53 \(\pm\) 2 & 126 \(\pm\) 8 & 8 \(\pm\) 2 & 1.64 \(\pm\) 0.16 \\ \hline \end{tabular}
\end{table} 
Table 1: Radioisotopic activity (Bq/kg) from 238 uranium, 232 thorium and 40 potassium. Dose-rate (\(D_{r}\)) and saturation fraction (F) from water content (w = 40 \(\pm\) 4%) following [20]. (50%) and Mero-8 (70%). The proportion of rejected aliquots because they did not meet the SAR recycling test is below 30%, except for Mero-2 (70%). Recovery tests showed ratios between 0.9 and 1.1, with low overdispersion values (_OD-recovery_ < 5%) [11]. All samples show highly dispersed distributions (Figure 3) and high _OD_ values from central dose (_D\({}_{e}\)_) (Table 2), except for sample Mero-7 (_OD_ = 13 \(\pm\) 5%). However, these distributions are non-skewed and unimodal (Figure 3). Normality tests indicate a good fit to a normal distribution (_p_ < \(\alpha\) = 0.05), with the exception of the Mero-6 sample, which shows a highly skewed distribution [11]. Figure 3: OSL signal and _D\({}_{e}\)s_ distributions. (Up) Examples of OSL decay curves and growth curves of Mero-4 and Mero-8 samples. (Down) Abanico plots of samples Mero-1, 3, 4, 5, 7 and 8: standard estimate (s.e) and (\(\pm\)2\(\sigma\)) standard error (grey area), relative error (r.e), equivalent dose (_D\({}_{e}\)_) and accumulate density (n) from each aliquot (black dots).
# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 2 Materials and Methods
### Methodology
#### 2.3.3 Bayes Method (BMA)  
The Bayes technique, also known as Bayesian Model Averaging (BMA), employs Bayesian statistics to address the tasks of model selection and averaging. The incorporation of the Bayes factor (BF) and the Bayesian Information Criterion (BIC) in this approach allows for the consideration of the trade-off between model complexity and predictive performance. These components aid in the assessment and choice of models that strike a balance between simplicity and complexity, hence enhancing the ability to generalize to new data [55,56].  
Bayesian Model Averaging (BMA) effectively addresses the problem of duplication in multivariable linear regression by objectively identifying the variables that make a meaningful contribution to the model. By excluding factors that do not have a significant effect, the model's accuracy and interpretability are enhanced. The methodology utilizes probabilistic frameworks to calculate the average of many models, taking into account the uncertainty related to the parameters of each model [46].  
If we have two models M\({}_{1}\) and M\({}_{2}\) and assume that one of them is true, the posterior probability of M\({}_{1}\) is as follows:  
\[P_{j}(M_{1}|y)=\frac{P(\gamma|M_{1})P(M_{1})}{P(y|M_{1})P(M_{1})+P(y|M_{2})P(M _{2})} \tag{4}\]  
In fact, we can also compare the two models M\({}_{1}\) and M\({}_{2}\) through real evidence:  
\[\frac{P(M_{1}|\gamma)}{P(M_{2}|\gamma)}=\frac{P(\gamma|M_{1})}{P(\gamma|M_{2}) }\times\frac{P(M_{1})}{P(M_{2})} \tag{5}\]  
This ratio is called the Bayes factor (BF). In the above interpretation, BF gives us information that the data are toward M\({}_{1}\) or M\({}_{2}\). With the BMA, each study does not have only one model, but there can be many models that can also explain \(\gamma\).  
Various elements, such as climate and meteorology, influence the outcomes of calculating the drought index, known as the Standardized Precipitation Evapotranspirationindex (SPEI). This study employed the Bayesian method to discover influential elements that significantly impact the Standardized Precipitation Evapotranspirationindex (SPEI) in the Mekong Delta, in order to establish the typical parameters for machine learning models. The application of the Bayesian method (BMA) in statistical analysis enables the identification of influential elements that significantly impact the SPEI value. Consequently, these factors may be determined as the primary parameters affecting the SPEI and can then be utilized as input parameters for the SPEI.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 4 Results and Discussion
### Results for Only Voice Streams
#### 4.1.1 Results from the TCP SYN Attack  
During this attack, Asterisk was flooded with TCP SYN packets [57]. Figure 5 represents the number of different TCP packets. As can be seen from the graphs, the number of TCP SYN and the TCPSYNACK packets have increased many times, which is normal for this attack. Asterisk responds to the TCP requests with TCP SYNACK, even generating  
Figure 4: Instantaneous values of the jitter for the voice stream between VM_4 and Asterisk (**a**) and between VM_3 and Asterisk (**b**) during normal operation mode.  
Figure 3: Summarized results for the main parameters of the voice stream between VM_3 and the Asterisk (**a**) and between VM_4 and the Asterisk (**b**) during normal operation mode.  
TCP RST packets to terminate the problematic session. However, terminating the session was impossible. Asterisk is accessed through a browser. The proof for this statement is the presence of TCP FIN packets, which means that there was a successful TCP session that was terminated.  
Figure 6 presents the summarized results for the voice traffic that was exchanged in both directions between VM_4 and VM_3 during the attack. Figure 6a shows the summarized results for the voice stream that was exchanged between VM_4 and Asterisk, and Figure 6b shows the summarized results for the voice stream that was exchanged between VM_3 and Asterisk. An increase in the maximum jitter value due to the attack was observed. The average jitter values in both streams were elevated but were within the norm. There were no packet losses.  
Figure 7a,b shows how the jitter values of the voice stream between VM_4/VM_3 and Asterisk changed in both directions. As can be seen from the graphs, excluding  
Figure 5: Number of different TCP packets sent during the TCP SYN attack.  
Figure 6: Summarized results for the main parameters of the voice stream between VM_3 and the Asterisk (**a**) and between VM_4 and the Asterisk (**b**) during the TCP SYN attack.  
individual spikes, the jitter varied within the acceptable range, but the values were still higher compared to the time when Asterisk was not being attacked.
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Mathematical Models
#### 2.1.2 Attenuation Due to Clouds and Fog  
Wireless signal attenuation as it propagates through clouds or fog is mainly caused by dispersion and absorption [19], which result from interactions with their constituent particles. These particles, which can range in size from tens to several hundred micrometers [20] and may vary in composition, may consist of water, ice, or a combination of both particles. The absorption and dispersion properties in clouds and fog differ significantly from those of water vapor in the atmosphere, primarily due to the distinct nature of the present particles. These differences in particle composition are key factors influencing the signal attenuation in such environments. As such, attenuation due to clouds and fog is governed by a different model than the one used for gases, and is given by [7]:  
\[\gamma_{c}=K_{l}(f,T)\rho_{l} \tag{3}\]  
where \(\gamma_{c}\) is the specific attenuation of cloud, \(K_{l}\) is the cloud liquid water specific attenuation coefficient, \(f\) is the operating frequency, \(T\) is the cloud liquid water temperature, and \(\rho_{l}\) is the liquid water density of cloud/fog.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Water Vapor** & **Nitrogen** & **Oxygen** & **Argon** \\ \hline
1\% & 77.30\% & 20.70\% & 0.92\% \\
2\% & 76.52\% & 20.53\% & 0.91\% \\
3\% & 75.74\% & 20.32\% & 0.90\% \\
4\% & 74.96\% & 20.11\% & 0.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major atmospheric gases, including water vapor.  
Further, the value of \(K_{L}(f)\) is given by  
\[K_{L}(f)=K_{l}(f,T)\cdot\left(A_{1}e^{-\frac{(f-f_{1})^{2}}{\sigma_{1}}}+A_{2}e^{ \frac{(f-f_{2})^{2}}{\sigma_{2}}}+A_{3}\right) \tag{4}\]  
where \(A_{1}\)= 0.1522, \(A_{2}\) = 11.51, \(A_{3}\) = \(-10.4912\), \(f_{1}\) = \(-23.9589\), \(f_{2}\) = 219.2096, \(\sigma_{1}\) = 3.2991 \(\times\) 103, and \(\sigma_{2}\) = 2.7595 \(\times\) 106.  
Attenuation resulting from clouds/fog along a specified line-of-sight path is subsequently determined by substituting the value of \(\gamma_{i}\) in Equation (1) with the value of \(\gamma_{c}\) for the specific path segment.
## 6 Performance Evaluation
### Simulation-Based Performance Analysis  
To further validate the practical performance of Bio-2 FA-IoD in a realistic IoD environment, we conducted a comprehensive simulation analysis using the Contiki OS and Cooja simulator [16; 17]. This simulation aims to evaluate key metrics such as average end-to-end latency, packet delivery ratio (PDR), and average radio ON time (as an indicator of energy consumption).
## 2 Study Area
### Geological and Hydrogeological Setting  
The study area belongs to the easternmost outcrop of the Hercynian basement of the Axial Zone of the Pyrenees. The Axial Zone consists of an elongated structure along the Pyrenean chain. In this Axial Zone, together with the North Pyrenean massifs, rocks from the Hercynian basement outcrop due to both Hercynian and Alpine tectonic uplift [40]. Locally, two geological units can be differentiated in the study area: (1) Palaeozoic basement includes (i) schists, slates, and phyllites and (ii) amphybolites all affected by the Hercynian regional metamorphism, and (2) Pos-Hercynian sedimentary cover includes (i) Upper Pleistocene deposits of alluvial and aeolian origin and (ii) Holocene alluvial deposits [41].  
In hydrogeological terms, lithological units can be classified also into two groups according to their permeability [42]: (1) Cambro-Ordivician schists and phyllites of low-hydraulic conductivity forming the impervious basement of the alluvial aquifer; (2) quaternary deposits of gravel and sand with variable silt content (water stream alluvial deposits) and sand and clay with pebbles of high hydraulic conductivity acting as an unconfined aquifer (Figure 3). The contact between the two formations was identified at depths ranging from eight to 14 m in the Port de la Selva site, but there is a lack of additional data in the overall studied area (\(1\cdot 10^{8}\) m\({}^{2}\)) as the exploration boreholes (Figure 4) are mainly clustered around the SAT facilities (\(3\cdot 10^{5}\) m\({}^{2}\)).  
Figure 3: (**a**) Hydrogeological setting of Port de la Selva; (**b**) simplified geological cross-sections of Port de la Selva coastal area. Modified from References [42; 43].  
Groundwater flow through the metamorphic substrate is controlled by faults and joints (secondary porosity) of the otherwise impermeable rock. For this reason, the flow and water storage capacity of this considered secondary aquifer is limited to discrete planes. Then, for practical purposes, the water resources of the secondary aquifer are irrelevant, except for the most important faults.  
The alluvial aquifer is located at the bottom of the valley, over the metamorphic bedrock. It has a surface of 0.66 km\({}^{2}\) and could reach 30 m in thickness [43]. The hydraulic conductivity values range from 50 to 250 m/day. The relatively small total volume and high topographic gradient at the top of the basin mean that it does not have a very high storage capacity [35]. It is assessed as moderate to high on groundwater pollution vulnerability [43]. Recharge to alluvial aquifer comes from direct rainfall and runoff from intermittent water streams of Riera de la Selva and Riera de Romanyac and water outflows are due to pumping and discharge to the Mediterranean Sea. Water supply wells operate in this section of the alluvial aquifer with estimated groundwater withdrawn of 500-600 m\({}^{3}\)/day in winter and 2500 m\({}^{3}\)/day in summer.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## 4 Experimental Results and Analysis
### Evaluation of Robustness  
The normalized correlation (NC) is applied to calculate the similarity of two images and further evaluate the robustness of the watermarking scheme, which is defined as Equation (17):  
\[\text{NC}=\frac{\sum\limits_{i=0}^{n-1}\sum\limits_{j=0}^{n-1}W_{0}(i,j)\times W _{\text{e}}(i,j)}{\sum\limits_{i=0}^{n-1}\sum\limits_{j=0}^{n-1}W_{0}(i,j) \times W_{0}(i,j)}, \tag{17}\]  
where \(W_{0}\) is the original watermark and \(W_{\text{e}}\) is the extracted watermark. The size of the watermark is \(n~{}\times~{}n\).  
The watermark is embedded into host images with different scaling factors, as shown in Figure 10. To resolve the trade-off between the robustness and invisibility of watermarking, the scaling factor is set as 0.5.  
Table 1 gives the results of test images in number of keypoints and NC value (host images are tested with watermark 1).  
\begin{table}
\begin{tabular}{c c c} \hline
**Images** & **Number of Keypoints** & **NC** \\ \hline Airplane & 2037 & 0.9970 \\ Lena & 1233 & 0.9964 \\ Barbara & 1590 & 0.9969 \\ Bank & 948 & 0.9973 \\ Announcement & 973 & 0.9975 \\ Fishing Boat & 1943 & 0.9978 \\ Milkdrop & 272 & 0.9972 \\ Baboon & 3264 & 0.9976 \\ Boat & 2881 & 0.9971 \\ Bridge & 3813 & 0.9974 \\ Einstein & 812 & 0.9977 \\ Peppers & 780 & 0.9967 \\ Goldhill & 1947 & 0.9973 \\ Model & 593 & 0.9965 \\ Mountain & 2442 & 0.9964 \\ Zelda & 880 & 0.9969 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental results on test images.  
Figure 10: Structural similarity index (SSIM) and normalized correlation (NC) values of the proposed method with different quantization steps.
# Computer Architecture for Industrial Training Evaluation
## 5 Architecture Validation
### Parametric Method by Hypothesis Testing
#### 5.1.3 Mean Comparison  
With the information collected and descriptively analyzed, the next step was to consolidate the indicators of the two groups into a single database. With this data, a Student's \(t\)-test was carried out. The test variables were Efficiency, Effectiveness, and Satisfaction. The selected grouping variable was \"Group\". SPSS version 27 software was used to process the 272 observations (Table 12).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & \multicolumn{2}{c}{**Statistic**} & \multicolumn{2}{c}{**Std. Error**} \\ \hline \multirow{4}{*}{Time} & Mean & \multicolumn{2}{c}{658.15} & \multicolumn{2}{c}{4.024} \\  & 95\% Confidence Interval for Mean & Lower Bound & 650.10 & \\  & 5\% Trimmed Mean & \multicolumn{2}{c}{666.19} & \\  & Median & \multicolumn{2}{c}{658.47} & \\  & Variance & \multicolumn{2}{c}{1004.093} & \\  & Std. Deviation & \multicolumn{2}{c}{31.687} & \\  & Minimum & \multicolumn{2}{c}{588} & \\  & Maximum & \multicolumn{2}{c}{724} & \\  & Range & \multicolumn{2}{c}{136} & \\  & Interquartile Range & \multicolumn{2}{c}{46} & \\  & Skewness & \multicolumn{2}{c}{\(-\)0.136} & 0.304 \\  & Kurtosis & \multicolumn{2}{c}{\(-\)0.590} & 0.599 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental group descriptive statistics.  
Figure 36: Experimental group histogram.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{2}{c}{**Kolmogorov–Smirnov a**} & \multicolumn{2}{c}{**Shapiro-Wilk**} & \multicolumn{2}{c}{**Sig.**} \\  & **Statistic** & **df** & **Sig.** & **Statistic** & **df** & **Sig.** \\ \hline Time & 0.073 & 62 & 0.200 * & 0.988 & 62 & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Kolmogorov test experimental group.  
As illustrated in Table 12, the standard deviation metrics of the three indicators (Efficiency, Effectiveness, and Satisfaction) in Group 1 exceed the values reported by Group 2. This discrepancy can be attributed to the elevated variability in Group 1, signifying that the data points are more dispersed around the mean for each indicator.  
According to Table 13, Efficiency in the Student's \(t\)-test gives a value less than 0.001 which indicates that we must reject the null hypothesis, thus that there is a difference in means between the control and experimental groups. That is, Group 2 that used the mobile application for training, was more efficient in carrying out the process.  
In the Effectiveness variable, the significance value is less than 0.001, indicating that Group 2 had a better performance compared to the average.  
Satisfaction had a significance level less than 0.001. For this indicator we also rejected the null hypothesis. However, this variable is not a decisive statistic, because the satisfaction survey was parameterized according to the context of the training; therefore, the results are not comparable in a standard way.
# Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks
## 2 Observed Data Analysis and Processing
### Analysis of Traffic Congestion Characteristics  
Traditional OD estimation methods in congested networks assume that link travel time increases monotonically with link flow (as shown in Figure 1a). However, in actual urban networks, link travel time is a non-monotony and non-convex function of link flow [18], and each link flow value corresponds to two different road conditions (as shown in Figure 1b). Therefore, traditional methods using link characteristic functions, which are increasing monotonical, is not suitable for OD demand estimation in congested networks; function errors might propagate to output results, which reduces the reliability of traffic network planning and design. There is a one-to-one correspondence between road travel time and traffic flow; therefore road travel time can be used as an effective observed variable for traffic demand estimation in congested networks.
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 3 Results and Analysis
### Free Liquid Level Analysis  
During the maneuvering process, the fluctuations of the fuel free liquid levels monitored by eight sensors are shown in Figure 5. Overall, liquid-level sensors 1-4 detected a relatively calm liquid level, while the liquid level at the location of sensors 5-8 experienced more violent surges and back-and-forth oscillations. The fuel free liquid level motion characteristics at different sensor locations are listed in Table 2.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Sensor** & **Characteristics** \\ \hline Sensor 1 & The immersion height of Sensor 1 fluctuates in a “double-peak” pattern, and eventually approaches zero, indicating that there is no fuel present at this location in the later stage of the maneuver \\ Sensor 2 & Minimum position, and then remains at a stable high level, indicating that the fuel at this location occupies the entire region between the bottom and top of the tank \\ Sensors 3, 4, and 6 & The immersion heights of Sensors 3, 4, and 6 are mostly below 0.05 m, indicating that there is little or no fuel at these three measurement locations during the maneuver. \\ Sensor 5 & The trend of the immersion height of Sensor 5 is similar to that of Sensor 1. \\  & The free liquid level at this location has rapidly and complexly changing characteristics. Within the first 1.5 s of the maneuver, the free liquid level oscillates through approximately six peaks/valleys with an average height of 0.07 m. In the latter 1.5 s of the maneuver, the liquid level gradually decreases after experiencing three high-level oscillations of peaks/valleys, with the liquid level reaching a maximum of 0.23 m and a minimum of 0.02 m during the oscillation process. \\ Sensor 8 & The immersion height of Sensor 8 remains below 0.05 m in the early stage of the maneuver, and rapidly increases to and stabilizes at 0.22 m at \(t=1.1\) s, indicating that this location has been rapidly filled with fuel. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Free liquid level characteristics at sensor locations.  
Figure 5: Graph of the liquid level change monitored by the sensors. (**a**) Sensor 1–4 data; (**b**) Sensor 5–8 data.
# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
###### Abstract  
Allocating bandwidth guarantees to applications in the cloud has become increasingly demanding and essential as applications compete to share cloud network resources. However, cloud-computing providers offer no network bandwidth guarantees in a cloud environment, predictably preventing tenants from running their applications. Existing schemes offer tenants practical cluster abstraction solutions emulating underlying physical network resources, proving impractical; however, providing virtual network abstractions has remained an essential step in the right direction. In this paper, we consider the requirements for enabling the application-aware network with bandwidth guarantees in a Virtual Data Center (VDC). We design GANA-VDC, a network virtualization framework supporting VDC application-aware networking with bandwidth guarantees in a cloud datacenter. GANA-VDC achieves scalability using an interceptor to translate OpenFlow features to prompt fine-grained Quality of Service (QoS). Facilitating the expression of diverse network resource demands, we also propose a new Virtual Network (VN) to Physical Network (PN) mapping approach, Graph Abstraction Network Architecture (GANA), which we innovatively introduce in this paper, allowing tenants to provide applications with cloud networking environment, thereby increasing the preservation performance. Our results show GANA-VDC can provide bandwidth guarantee and achieve low time complexity, yielding higher network utility.  
cloud datacenter; application-aware networking; bandwidth guarantee; virtual network; resource allocation +
Footnote †: journal:  
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