esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Flow-Statistical-Feature-Based Methods  
This method classifies traffic by analyzing statistical features like packet size distribution, or inter-arriving time [14]. This method allows large-scale data analysis. Monitoring large enterprise networks can effectively identify abnormal traffic and optimize resource allocation. AppScanner [15] used a support vector classifier (SVC) and random forest (RF) to classify traffic based on 54 features of mobile traffic [3]. Similarly, this method also performs well in network environments like the Internet of Things (IoT) with known malicious traffic characteristics. [PERSON] et al. designed a feature-selection metric framework, selected the statistical features of IoT traffic, and used multiple machine learning algorithms to detect malicious traffic [16]. [PERSON] et al. centered around the problem of classifying network traffic elephants flows based on the first packet 5-tuple header field using a tree-based classifier [17] along with other classifiers [18] to improve traffic engineering and QoS applications. However, statistical feature analysis requires a large amount of calculation, making it unsuitable for real-time scenarios. Moreover, it needs complex feature engineering. This makes it difficult for small enterprises or organizations with limited resources to implement, as it requires strong hardware and professional expertise.  
Sequence feature-based methods view network traffic as time-series data and achieve traffic classification by analyzing the time patterns and behavioral trends [19]. Early research on sequential features focused on Markov chains. [PERSON] et al. classified SSL/TLS encrypted applications by combining the message type and packet length in the HTTPS handshake stage to build a second-order Markov chain model [20]. [PERSON] et al. transformed the original packet length sequence into the length block sequence and trained the Markov model by combining the message type sequences [9]. Recent research has focused on the representation of sequential features and used machine learning methods for classification. [PERSON] et al. proposed FS-Net, extracts feature from the original flow sequence using a multi-layer encoder-decoder structure and introducing a reconstruction mechanism [21]. [PERSON] et al. proposed an ETC-PS method [7], introducing the traffic path and analyzing sequence features that can effectively classify encrypted traffic without decryption. [PERSON] et al. proposed NetTiSA [22], which uses time-series analysis inside the IP flow exporter to generate 13 known and novel features in the extended IP flow. These methods rely on a substantial number of packets arriving at the station prior to classification, which hinders their application in real-time scenarios. The method proposed in this paper is based only on a small number of the first few packets (less than 10). We also explored the feasibility of using local features for classification in Appendix A.
## 3 The Adaptive Attitude Control Strategy of the MAV with Actuator Dynamics
### The Online Parameter Estimation Method for the MAV
According to linear system theory, Equation (14) can then be approximately represented as:  
\[T_{a}\ddot{e}_{f}+a_{q}\dot{e}_{f}+a_{q}\dot{e}_{f}=-\Delta a_{q}\dot{q}_{f}- \Delta a_{q}q_{f}+\Delta b_{g_{\mu\alpha}}\delta_{p\mu i}+\Delta b_{u}u_{df}+ \Delta b_{w}u_{df}+\Delta b_{p}\,p_{df}+\Delta b_{g_{\mu\alpha}}\delta_{rold}+ \Delta b_{g_{\mu\alpha}}\delta_{rold}+\Delta b_{e} \tag{16}\]  
Note that \(e_{f},p_{df},q_{f},u_{df},w_{df},\delta_{p\mu i},\delta_{rold}\) and \(\delta_{rold}\) become easily computable with a proper \(G_{f}\left(s\right),\) and we return to this point later. Consider the Lyapunov function candidate:  
\[V=T_{a}\dot{e}_{f}^{2}+a_{q}e_{f}^{2}+\Delta^{\mathrm{T}}A\Delta \tag{17}\]  
where \(\Delta=\left[\Delta a_{q},\Delta a_{q},\Delta b_{g_{\mu\alpha}},\Delta b_{w},\Delta b_{w},\Delta b_{g_{\mu\alpha}},\Delta b_{g_{\mu\alpha}},\Delta b_{g_{ \mu\alpha}},\Delta b_{e}\right]^{\mathrm{T}},\)\(A=\mathrm{diag}\left(\lambda_{q},\lambda_{q},\lambda_{g_{\mu\alpha}},\lambda_{w},\lambda_{w}, \lambda_{p},\lambda_{g_{\mu\alpha}},\lambda_{\delta_{r\alpha}},\lambda_{e} \right).\) We can therefore conclude that \(\dot{V}\) is negative definite only if:  
\[\Delta^{\mathrm{T}}\left(\mathbf{R}_{f}\dot{e}_{f}+\mathbf{\Lambda}\dot{\mathbf{k}}\right)\leq 0 \tag{18}\]  
where \(\mathbf{R}_{f}=\left[-\dot{q}_{f},-q_{f},\delta_{p\mu i},u_{df},w_{df},p_{df}, \delta_{rold},\delta_{rold},1\right]^{\mathrm{T}}\) and \(\mathbf{K}=\left[a_{q},a_{q},b_{g_{\mu\alpha}},b_{u},b_{w},b_{p},b_{g_{\mu\alpha}},b_{\delta_{r\alpha}},b_{e}\right]^{\mathrm{T}}.\) In fact, a similar reasoning can be made for the system given by Equation (14), and the global asymptotic convergence of the error \(e\) is guaranteed under the constraint in Equation (18). In particular, the Constraint (18) holds if the parameter regulation algorithm is represented as:  
\[\dot{\mathbf{k}}=-\mathbf{I}\mathbf{R}_{f}\left(\dot{e}_{f}+\varepsilon_{f}\right) \tag{19}\]  
where \(\mathbf{\Gamma}=\mathbf{\Lambda}^{-1}\overset{\mathrm{def}}{=}\mathrm{diag}\left( \rho_{\dot{q}},p_{q},\rho_{\dot{g}_{\mu\alpha}},p_{u},p_{w},p_{p},\rho_{\dot{g} _{\mu\alpha}},p_{\dot{g}_{\mu\alpha}},p_{\dot{g}_{\mu\alpha}},p_{e}\right);\)\(\varepsilon_{f}\) is optional and satisfies the constraint as follows:  
\[\mathrm{sgn}\left(\varepsilon_{f}\right)=\mathrm{sgn}\left(T_{a}\ddot{e}_{f}+a _{q}\dot{e}_{f}+a_{q}\varepsilon_{f}\right) \tag{20}\]  
The identification parameters can therefore quickly converge to the corresponding parameters of the plant in Equation (9), because \(\varepsilon_{f}\) is optional and can largely contribute to the convergence. To suppress the possible oscillations of identification parameters, the variation rate of \(\mathbf{\kappa}\) is assumed to be bounded by the optional upper bound \(\tau\):  
\[\left\|\dot{\mathbf{\Lambda}}\right\|_{\infty}<\tau \tag{21}\]
## 7 Experimental Results
### Performance Comparison of EAR2M, EAR2M_R, and EAR2M_E  
We extend the proposed scheme by allowing the relay between flooding points of the expected area in order to transfer data packets to multiple flooding points in Chapter VI. In this subchapter, we compare the extended schemes of EAR2M with four flooding points.  
Figure 9a indicates the DSR according to the end-to-end distance of the proposed schemes (EAR2M, EAR2M_R, and EAR2M_E). In this experiment, we fix the moving speed at 20 m/s. As the end-to-end distance increases, the DSR decreases. In contrast to EAR2M, EAR2M_R and EAR2M_E show a lower DSR because their unicast distance is longer than that of EAR2M and higher data delivery speeds are required. However, the difference is very trivial as the extended distance is extremely small compared to the end-to-end distance.  
Figure 9b shows the EC according to the end-to-end distance. As the end-to-end distance becomes larger, more nodes should participate in a path and the energy consumption increases. In the extended schemes, multiple paths for multiple flooding points are merged. Due to the merged path, the cost for unicasting toward flooding points can be reduced. EAR2M_E shows a lower EC than EAR2M_R because EAR2M_E allows the multiple flooding points to relay toward other flooding points.  
Figure 7: Performance comparison of EAR2 with EAR2M according to sink speed: (**a**) deadline success ratio and (**b**) energy consumption.  
To simulate the impact of sink speed, we fix the end-to-end distance between the source and the sink at 300 m. Figure 10a shows the DSR according to the moving sink speed. The proposed schemes determine the expected area for moving the sink. The expected area is exponentially proportional to the sink speed. Due to the size of the expected area, all values in all schemes decrease. The results of both EAR2M_R and EAR2M_E are slightly  
Figure 8: Performance comparison of EAR2 with EAR2M according to end-to-end distance: (**a**) dead-line success ratio and (**b**) energy consumption.  
lower than that of EAR2M because EAR2M_R and EAR2M_E require a higher data delivery speed for a longer distance than EAR2M.  
Figure 10b indicates the energy consumption of the proposed schemes according to the sink speed. The extended schemes, EAR2M_R and EAR2M_E, reduce the energy consumption by merging the paths for unicasting and allowing relay via the flooding  
Figure 9: Performance comparison of the extended schemes according to end-to-end distance: (**a**) deadline success ratio and (**b**) energy consumption.  
points. Although the moving sink speed is faster, the schemes always show a lower energy consumption than EAR2M.
# Vibration Analysis and Empirical Law Definition for Different Equipment in a Civil Construction
## 3 Results and Discussion
### Historical Site Analysis
\[v_{max}(100)=v_{Bmax}+n_{Ex}v_{Exmax}+n_{R}v_{Rmax}+n_{VR}v_{VRmax}+n_{BD}V_{BDmax} +n_{CR}V_{CRmax} \tag{6}\]  
\(V_{Bmax}\): background maximum velocity;  
\(V_{Exmax}\): excavator maximum velocity;  
\(V_{Rmax}\): ripper maximum velocity;  
\(V_{VRmax}\): vibrating ripper maximum velocity;  
\(V_{BDmax}\): bulldozer maximum velocity;  
\(V_{CRmax}\): vibrating roller maximum velocity;  
\(n\): number of equipment. If Equations (3)-(5) are applied for a distance r = 100 m from the vibration source, the maximum velocities are: excavator V\({}_{\rm Exmax}\) = 0.0012 mm/s, ripper V\({}_{\rm Rmax}\) = 0.0012 mm/s, vibrating ripper V\({}_{\rm VRmax}\) = 0.015 mm/s, bulldozer V\({}_{\rm BDmax}\) = 0.0043 mm/s and vibrating roller V\({}_{\rm CRmax}\) = 0.015 mm/s. These values, multiplied by the type and number of machines working each day and the background constant velocity V\({}_{\rm Bmax}\) = 0.005 mm/s give the potential velocity estimated, as in Figure 11, validating the accuracy of the equations proposed for the purpose of the study. Figure 12 displays the comparison between the actual values and values using Equation (6). As it can be seen, the equipment with the highest influence are the vibration ripper and vibrating roller, while the other type of machine has an impact between 10 and 15 times lower. \begin{table}
\begin{tabular}{c c c c c} \hline
**V Bucket** & **Ripper** & **Vibration Ripper** & **Bulldozer** & **Vibrating Roller** & **Velocity** \\ \hline
1 & 0 & 1 & 1 & 0 & 0.015 \\
0 & 0 & 0 & 0 & 0 & 0.008 \\
0 & 0 & 0 & 0 & 0 & 0.005 \\
1 & 0 & 1 & 1 & 0 & 0.015 \\
1 & 0 & 1 &It can be observed from the previous figure that the values obtained by Equation (6) slightly overestimate the velocity, allowing a certain safety factor in real situations and ensuring no affectation to the infrastructure or construction. The empirical laws proposed in this section can be used to assess the ground vibration level and solve complex problems in a practical way. Moreover, the influential variables are easy to understand and measure in real situations. Equations (3)-(6) are also useful in many different scenarios in construction works. However, their applicability should be limited to similar conditions to those found in the case study, either rock mass or machinery. Figure 11: Maximum filtered daily velocities, in blue, and estimated values using Equations (3)–(5), in red. Figure 12: Correlation between actual velocities and velocities estimated by Equation (6).
## 3 Results and Discussion
### ATR-FTIR Analysis of the Electrospun Nonwoven Fabrics  
The functional groups of HPO, PLA, and HPO/PLA electrospun nonwoven fabrics were determined by ATR-FTIR spectroscopy (Figure 2).  
Figure 1: SEM images and fiber diameter distributions of the electrospun nonwoven fabrics obtained from (**a**) PLA, (**b**) 3 HPO/PLA and (**c**) 5 HPO/PLA (\(\times\)10,000) (average \(\pm\) standard deviation).  
The characteristic peaks of the HPO observed at 3308\({}^{-1}\), 2853\({}^{-1}\), 1743 cm\({}^{-1}\), and 844 cm\({}^{-1}\) were assigned to aromatic C-H stretching, aliphatic C-H stretching, C=O stretching, and aromatic rings, respectively [34,41]. The signal at 1237 cm\({}^{-1}\) typically overlaps, most notably C-O ester, which is found in oils [77]. The characteristic peaks of the PLA electrospun fabrics observed at 2994 cm\({}^{-1}\), 2944 cm\({}^{-1}\),1745 cm\({}^{-1}\), 1182 cm\({}^{-1}\), 1085 cm\({}^{-1}\), and 1045 cm\({}^{-1}\) were assigned to C=O groups, asymmetric and symmetric stretching vibrations of the C-O-C group, and C-CH\({}_{3}\) stretching, respectively [78,79]. The characteristic peaks of the 3 HPO/PLA electrospun fabrics observed at 2995 cm\({}^{-1}\), 2926 cm\({}^{-1}\), and 2854 cm\({}^{-1}\), assigned to -CH\({}_{3}\) asymmetric, -CH\({}_{3}\) symmetric, C-H stretching. The characteristic peaks of the 5 HPO/PLA electrospun fabrics observed at 2997 cm\({}^{-1}\), 2925 cm\({}^{-1}\), and 2854 cm\({}^{-1}\), were assigned to -CH\({}_{3}\) asymmetric, -CH\({}_{3}\) symmetric, C-H stretching. A small shift from 2994 cm\({}^{-1}\) (PLA) to 2995-2997 cm\({}^{-1}\) (3 HPO/PLA-5 HPO/PLA) was observed. This shift in the absorption peak shows the miscibility and interaction of HPO and PLA. With the addition of HPO to PLA fibers, the spectra of PLA electrospun nonwoven fabrics changed and the adsorption bands in the frequency range 3000-2800 cm\({}^{-1}\) were broadened due to CH stretching vibrations of different groups in HPO. The peak intensity of the stretching of CH bands, which corresponds to the varying concentration of the HPO compound, shows that HPO was successfully incorporated into the PLA fibers.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 4 Discussion
### Current Use of Information  
The users in both C-ITS field studies are familiar with digital information devices _in their current use of information systems while driving_. The overlap in the subsequent numbers indicates that many drivers even use multiple navigation systems at once. In ESRIUM, roughly 80% of the respondents reported using a navigation app (e.g., Waze, Google Maps) on their smartphone. Meanwhile, ca. 75% used their vehicle's built-in navigation system, and ca. 45% relied on a separate navigation device (e.g., TomTom). In the ASFINAG study, the results indicate that navigation apps for traffic information are the most popular, with 74.5% of respondents using them. Built-in navigation systems are used by 42.0% of respondents. In contrast, separate navigation devices (20.0%) and speed camera warnings (6.5%) are far less common (see Figure 13).  
Also, the literature shows that digital information is consumed whilst driving, evidence has shown that drivers see the mobile phone as their primary system for navigation and In-Vehicle Information Systems (IVIS) as secondary [29; 30; 31]. Even though IVIS is designed to reduce unsafe mobile phone use while driving, some drivers experience mobile phones as more intuitive for navigation and communication [29]. Also [PERSON], [PERSON], [PERSON] found that mobile phones are often preferred over IVIS while driving because IVIS are perceived as having lower usability and lower ease of use [29].  
Figure 12: Heat map of test drivers.  
In summary, the results of the field studies and literature research confirm that smartphone navigation apps are the preferred source of information. The studies indicate that smartphone apps are perceived as more intuitive for both navigation and communication, whereas IVIS are associated with lower usability. This may be because IVIS with C-ITS features are relatively new to the commercial market, while smartphones are already widely used. Additionally, IVIS user interfaces vary between vehicles, whereas smartphone apps maintain a consistent look and feel across different platforms.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## Materials and Methods  
### Study Area: Nunavik  
The Nunavik region, Quebec, Canada was chosen as study area. This large region is very interesting for permafrost research since it shows high comprehensiveness and diversity by crossing several permafrost zones.  
The climate of Quebec varies highly depending on latitude, topography, and marine influence. The main types of climate in Nunavik are subarctic in the south and arctic in the north, both with long, very cold winters and short, cool summers with long hours of daylight. By crossing the borderline from the Arctic to the sub-Arctic, changes in vegetation cover and permafrost distribution can be observed, mainly reflected by the northern timberline and the substitution of discontinuous permafrost for continuous permafrost. In general, the climate in Nunavik can be characterized by low annual solar radiation, low temperature, low air humidity, and little precipitation [33]. Under the background of global climate change, the climate in Nunavik is also transforming. Slight cooling was measured from 1950 until the 1990s. According to [PERSON] et al. (2007) [34], as of 1995, temperatures rose above the long-term average, and they have remained at elevated levels since the early 2000s [33].  
In Quebec, permafrost extends from the sporadic discontinuous zone to the continuous zone (Figure 1). With general climate warming, ground temperature warming has occurred since the early 1990s with an increasing warming rate [35]. Over time, this has resulted in a warmer vertical temperature profile in the permafrost and a greater depth of summer thaw [36].  
This region has attracted much research focused on permafrost studies, coastal geology, and geomorphological characterization [38; 39; 40; 41; 42; 43; 44; 45]. Research by the CEN (Centre d'Etudes Nordiques) has been conducted here since 1980.  
The concrete test sites were scattered over the large Nunavik region and were carefully selected with consideration of the data availability and computation efficiency. The variability over the region of interest was retained as much as possible.
## 3 Results and Discussion
### Cloud Connectivity Scheduling
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cloud Workflow**} & \multicolumn{5}{c}{**Algorithms**} \\ \cline{2-6}  & **Series (1)** & **Series (2)** & **Series (3)** & **Series (4)** & **Series (5)** \\ \hline i & 0.615 & 0.954 & 0.905 & 0.945 & 0.961 \\ ii & 0.917 & 0.932 & 0.876 & 0.929 & 0.954 \\ iii & 0.822 & 0.863 & 0.871 & 0.935 & 0.949 \\ iv & 0.716 & 0.365 & 0.762 & 0.787 & 0.847 \\ v & 0.408 & 0.652 & 0.634 & 0.785 & 0.846 \\ vi & 0.269 & 0.2 & 0.502 & 0.697 & 0.782 \\ vii & 0.944 & 0.945 & 0.915 & 0.947 & 0.96 \\ viii & 0.721 & 0.637 & 0.916 & 0.966 & 0.978 \\ ix & 0.429 & 0.928 & 0.813 & 0.934 & 0.961 \\ x & 0.126 & 0.398 & 0.117 & 0.519 & 0.519 \\ xi & 0.2 & 0.595 & 0.391 & 0.722 & 0.762 \\ xii & 0.279 & 0.2 & 0.347 & 0.82 & 0.837 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Workflow comparative studies of existed algorithms. Table 1 and Figure 4 show five series of workflow cloud comparison studies, where the series-5 algorithm attracts attention through providing the best performance, higher than other four series as a workflow approach for a cloud apps environment. On other hand, the series-5 also gives a promising result as seen through the best speed of runtime for cloud workflows (Table 2 and Figure 5), with the respective datasets of: (i) cybershake-30, (ii) cybershake-50, (iii) cybershake-100, (iv) inspiral-30, (v) inspiral-50, (vi) inspiral-100, (vii) montage-25, (viii) montage-50, (ix) montage-100, (x) sight-30, (xi) sight-60, (xii) sight-100. such application connected to cloud intelligent technology related to the surveillance and monitoring of COVID19 spread.
## 2 Materials and Methods
### Rice Data Set  
Rice samples were collected across different seasons from local millers, and from four regions in Vietnam. Authentic samples were high-quality rice (namely J85), while adulterated samples were obtained by the addition of two different percentages (5% and 10%, given in w/w%) of lower quality rice (namely DT8).  
Jasmine 85 (J85) is an aromatic rice variety developed in Thailand by the International Rice Research Institute from a cross between IR262-43-8-11 and KHAO DAWK MALI 4-2-105. J85 was imported to Vietnam in 1993 by the Cuu Long Delta Rice Research Institute, and mostly cultivated in 13 provinces in the Mekong Delta. This variety has many outstanding characteristics, such as a good-looking grain, and amylose content (around 18-20%). When cooked, the rice has a medium grain cooking quality, soft texture, and is cohesive.  
Dai Thom 8 (DT8) is a rice variety cultivated in different places, from the Mekong Delta to the Central Highlands and Central Coast. This variety produces long, clear, silver-brown rice, with an incredibly low amylose content (around 16.29%). When cooked, the rice has a light, soft, fragrant aroma.  
Authentic samples (J85) were collected from 4 provinces in the Mekong Delta: Can Tho, An Giang, Dong Thap, and Hau Giang from 2 factories during 3 seasons (Monsoon, Summer-Autumn, Winter-Spring), for a total of 24 lots. Each lot was further sampled three times; therefore, there were 72 high-quality rice samples (Authentic rice).  
DT8 samples were obtained from the same provinces and factories as the authentic ones, and were collected across the same three seasons. For the preparation of the adulterated samples, each J85 rice resulting from the combination of a particular region and factory was cross-mixed with any of the region/factory combinations of DT8 rice at both 5% and 10% ratios. Despite the season not being explicitly considered as an additional combinatorial factor, care was taken that all the three seasons were fairly represented in the adulterated samples. Accordingly, 64 adulterated samples at 5% addition, and 64 adulterated samples at 10% addition, were available for the analysis.  
As the main purpose was to discriminate high-quality rice from adulterated rice, the two groups with 5% and 10% addition were collected together into a single class of adulterated samples; consequently, 128 adulterated samples (Adulterated rice) were used for this study. Prior to the creation of the classification models, replicated measurements were averaged, obtaining a data matrix of dimensions 200 \(\times\) 331.  
The spectra were split by the Duplex algorithm Duplex (1993) into a training set, used for the calibration of the models, and a test set, which was only used for their external validation. The most appropriate data-preprocessing approach, and the number of latent variables (LVs) to be extracted, were chosen in a 5-fold cross-validation procedure on the calibration samples only.
## 4 Implementation
### GPSR_DA Algorithm  
In the greedy forwarding process of GPSR, the nearest node to the destination node is selected as the next hop for forwarding. However, this approach may lead to two issues: (i) there may exist a shorter overall path between the source and destination nodes; (ii) nodes along a routing path may be excessively utilized, leading to high-frequency consumption of certain nodes in the network. Based on these issues, we design the GPSR_DA algorithm to select the next-hop forwarding node. Specifically, the forwarding node calcu  
Figure 2: The process of SD-GPSR handling packets.  
lates the distances between itself and all neighboring nodes towards the destination node. Then, it selects all nodes that are closer to the destination node than itself and calculates the angles formed by these nodes with respect to the forwarding node and the destination node. Assuming the set of neighboring nodes of a forwarding node is denoted as \(N=\{N_{i},i\in N^{*}\}\), with \(S\) representing the forwarding node, \(D\) representing the destination node, and \(R\) representing the communication range of node \(S\). The specific calculation methods for distance and angle are as follows.