esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Research Methodology  
### Analysis of Methods for Obtaining Data on Free-Flowing Vehicle Speeds  
Since, as mentioned above, FFS plays an important role in the planning, operational analysis, and modeling of traffic, there are requirements for the use of reliable methods for determining this parameter. However, the determination of FFS in real traffic conditions is time consuming because it requires obtaining a sufficient sample size of vehicle speeds at appropriate times when flow intensity is minimal and at appropriate locations with uniform geometric, traffic, and regulation characteristics.  
Published scientific papers use different methods of measuring vehicle speeds, which differ in both speed recording devices and data processing technology. Their comparative analysis is given in [29]. Data from traffic monitoring or control systems can be used to obtain data on car speeds, but this requires the use of additional equipment and software for data processing, such as in most cases in which scientists are dealing with one-time measurements on specially selected sections of streets and roads carried out by a small team of researchers.  
Therefore, the use of portable radar-type measuring instruments may be a suitable method for measuring velocities. They allow you to measure the speed of a single vehicle and a vehicle moving in a group. The error in measuring speed with modern radars usually does not exceed 1 km/h. One of the disadvantages of using such devices is that they cannot reliably record the speed of several vehicles moving in different lanes; however, when measuring the speed of a single vehicle in free conditions, this limitation does not matter.  
Recording speed directly by a competent investigator using handheld radar has another advantage over other known methods such as the use of detectors, video surveillance, or GPS track data. The researcher is able to more accurately select cars moving in free conditions, and he can also exclude unique cases from measurements. These include cars whose drivers choose or are forced to choose speed due to special circumstances. For example, cars that are moving with technical faults, carrying oversized cargo, police, or emergency service vehicles.  
After carrying out the required number of measurements, a table of the results is formed. Standard or specialized software, such as Excel, Matlab, and Statistica, is used for further data processing.
## 5 Discussion
### VI SOS Spatial Patterns  
Average PPI-derived SOS occurs in the beginning and middle of May at southern latitudes but is delayed towards the beginning of June with an increase in latitudes (Figure 3a). EVI and especially NDVI have showed up to 20-40 days earlier SOS than PPI-derived SOS (Figure 3B,C). These differences in timing are likely to be caused by EVI and NDVI responding to variations in the snow season, rather than vegetation phenology. The greatest differences have been observed in the southern latitudes of the study area. The same pattern was observed in [48] where PPI phenology was compared to EVI phenology from MCD12Q2 land surface phenology product and showed that EVI detected SOS as much as one month earlier than PPI. This pattern was also explained by a larger temporal lag between the canopy growth onset and the snow melting time in the south than in the north [48]. The high latitude boreal zones are located within regions of subarctic and cold continental climate, which is defined by long and severe winters and short summers, limited by 50-100 frost-free days [78]. The short summer periods result in strong vegetation sensitivity to air temperature, leading to an almost immediate start of the growing season after snow melt, in order to ripen seeds successfully [78; 79; 80]. Hence, an overlap of the timing of snowmelt and the onset of canopy growth at the high latitudes is likely to cause the convergence of PPI, NDVI and EVI SOS estimates (Figure 3).
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases
### DOCK Family of RHOGEFs  
The 11 members of the DOCK family can be categorized into four subfamilies: DOCK-A, DOCK-B, DOCK-C, and DOCK-D [81; 102]. DOCK GEFs have two conserved domains: lipid-binding DOCK homology region 1 (DHR-1), which facilitates DOCK localization to membrane compartments, and catalytic DOCK homology region 2 (DHR-2), which induces the GDP-GTP exchange reaction [81; 102; 103; 104]. It has been proposed that DOCK GEFs activate RAC1 and CDC42 proteins, but not other RHO proteins [105; 106].  
DOCK proteins orchestrate important processes in brain development including neuron, microglial, and Schwann cell development and functions [102; 107]. DOCK2 and DOCK8 play significant roles in immune responses such as the chemotactic responses of T cells and B cells, ROS production in neutrophils, and migration of mature dendriticcells [81]. [PERSON] et al. demonstrated that DOCK1 forms a complex with ELMO1, RAC1, RAC2, and Gai2, which initiates actin polymerization in breast cancer cells [108].  
DOCK2 has been indicated to increase amyloid beta plaque formation, which makes this protein a potential [PERSON]'s therapeutic target [109; 110]. [PERSON] et al. showed that in T cells, DOCK8 can form a complex with WASP and ARP2/3 and link TCR to the actin cytoskeleton to form a synapse for T cell responses [111].  
Overall, DOCK GEFs play pivotal roles in different biological processes that can be dependent or independent of their GEF activity.
## Appendix A Parameters of Conventional AGC Algorithms  
* **PI**, two-area: proportional \(k_{\rm P}=-0.882\), integral \(k_{\rm I}=-0.10\); three-area: \(k_{\rm P}=-402.63\), \(k_{\rm I}=-747.86\); _China Southern Power Grid_: \(k_{\rm P}=-2527.60\), \(k_{\rm I}=-559.42\);
* **PID**, two-area: \(k_{\rm P}=-0.584\), \(k_{\rm I}=-0.275\), derivative \(k_{\rm d}=-0.01\); three-area: \(k_{\rm P}=-415.12\), \(k_{\rm I}=-780.46\), \(k_{\rm d}=-0.001\); _China Southern Power Grid_: \(k_{\rm P}=-6640.60\), \(k_{\rm I}=-658.77\), \(k_{\rm d}=-0.005\);
* **SMC**, switch on/off point \(k_{\rm P}=\pm 0.3\) Hz; two-area: output when on/off \(k_{\rm v}\pm\) = 1000 (MW); three-area \(k_{\rm v}\pm\) = 45,000 (MW); _China Southern Power Grid_: \(k_{\rm v}\pm\) = 40,000 (MW);* **ADRC**, extended state observer \(A=\left[\begin{array}{cccc}0&0.0001&0&0\\ 0&0&0.0001&0\\ 0&0&0&0.0001\\ 0&0&0&0\end{array}\right]\), \(B=\left[\begin{array}{cccc}0&0\\ 0&0\\ 0.0001&0.0001\\ 0&0&0\end{array}\right]\), \(C=\mathrm{diag}\left(\begin{array}{cccc}0.1&0.1&0.1&0.1\\ 0.1&0.1&0.1\\ \end{array}\right)\), \(D=\mathrm{0_{4}}\times\mathrm{2}\), \(k_{4}=1\), two-area: \(k_{1}=-1040\), \(k_{2}=1\), \(k_{3}=10\); three-area: \(k_{1}=-668.11\), \(k_{2}=734.67\), \(k_{3}=-374.53\); _China Southern Power Grid_: \(k_{1}=-851.95\), \(k_{2}=161.64\), \(k_{3}=428.63\);
* **FOPID**, two-area: \(k_{\mathrm{P}}=-0.89639\), \(k_{\mathrm{I}}=-0.2071\), \(k_{\mathrm{d}}=0.33636\), \(\lambda=-0.52061\), \(\mu=0.51401\); three-area: \(k_{\mathrm{P}}=-83500\), \(k_{\mathrm{I}}=-0.005\), \(k_{\mathrm{d}}=0\), \(\lambda=-0.1\), \(\mu=0.1\); _China Southern Power Grid_: \(k_{\mathrm{P}}=-6.7573\), \(k_{\mathrm{I}}=-6.7002\), \(k_{\mathrm{d}}=-9.7831\), \(\lambda=0.86527\), \(\mu=0.61834\);
* **FLC**, \(X\) (input, \(\Delta f\)) 21 grids from \(-0.2\) to 0.2 (Hz), Y (input, \(\int\Delta f\)) 21 grids from \(-1\) to 1 (Hz), two-area: \(Z\) (output, \(\Delta P\)) is 441 grids from \(-256\) to 256 (MW); three-area: \(Z\) from \(-91\),285 to 91,285 (MW); _China Southern Power Grid_: Z from \(-58\),527 to 58,527 (MW);
* **ANN**, layer size \(L=8\), epochs \(E=2\);
* **Q learning**, learning rate \(\alpha=0.1\), the constant of probability distribution method \(\beta=0.05\), the discounted rate of future reward \(\gamma=0.9\), state set \(S=\{\underbrace{-\infty,-0.50,-0.46,...,0.46,0.50,\infty}_{33}\}\), two-area: action set \(A=\{\underbrace{-100,-93.75,...,100}_{33}\}\); three-area: \(A=\{\underbrace{-3000,-2812.5,...,3000}_{33}\}\); _China Southern Power Grid_: \(A=\{\underbrace{-1020,-956.25,...,1020}_{33}\}\);
* **Q(\(\lambda\)) learning**, \(\lambda=0.9\), \(\alpha\), \(\beta\), \(\gamma\), \(A\) are the same as that of Q learning algorithm;
* **R(\(\lambda\)) learning**, \(\lambda=0.9\), \(R_{0}=0\), \(\alpha\), \(\beta\), \(\gamma\), \(A\) are the same as that of Q learning algorithm.
## 3 Results and Discussion
### Gauge Trace Analyses
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test No.**} & \multirow{2}{*}{
\begin{tabular}{c} **Impact Velocity** \\ \(\mathbf{v_{\mathrm{imp}}}\) **(m/s)** \\ \end{tabular} } & \multicolumn{2}{c}{**Thickness (mm)**} & \multicolumn{2}{c}{**Diameter (mm)**} \\ \cline{3-6}  & & **Specimen** & **OFHC** & **Specimen** & **OFHC** \\ \hline
001 & 194 & 2.960 & 1.497 & 14.99 & 34.97 \\
002 & 409 & 3.051 & 1.480 & 14.95 & 34.95 \\
003 & 410 & 3.055 & 1.480 & 14.99 & 34.95 \\
004 & 598 & 3.030 & 1.479 & 15.02 & 35.02 \\
005 & 782 & 3.051 & 1.487 & 15.01 & 35.01 \\
006 & 858 & 3.040 & 1.497 & 15.00 & 34.96 \\
007 & 938 & 3.050 & 1.488 & 15.02 & 34.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental details of the plate impact test. Figure 4 presents the measured back-free surface velocity curves for all specimens at flyer impact velocities ranging from 194 to 938 m/s, each of which indicates the transition from elastic to plastic deformation. As can be seen, the free surface velocity at the HEL varies very little, ranging from approximately 65.5 to 74.0 m/s. Unlike HEL, the trend in pullback velocity is more complex, and the details of spall strength are quantified and discussed in Section 3.3. It should be noted that the slope curve increases significantly after velocity pullback, which is believed to be related to the change in the failure mode of the material from brittle to more ductile [22]. It can be also observed in Figure 4 that the change in particle velocity curves at higher impact velocities (782 m/s, 858 m/s and 938 m/s), i.e., another step change in particle velocity occurred at the peak platform. As the peak stress (\(\sigma_{\text{peak}}\)) increased to the phase transition stress threshold, the \(\alpha\)\(\rightarrow\)\(\varepsilon\) phase transition occurred and a phase-transition pulse was generated, resulting in a transformation of the particle velocity profile from a two-wave structure to a three-wave structure, consisting of elastic, plastic and phase transition waves. Referring to the method used by [PERSON] and [PERSON] [23], the peak stress was determined using the peak free surface velocity (\(u_{\text{pfs}}\)), the shock velocity (\(U_{\text{s}}\) for elastic waves and \(U_{\text{p}}\) for plastic waves) and density. According to the Rankine-Hugoniot relationships, the stress threshold for the \(\alpha\)\(\rightarrow\)\(\varepsilon\) phase transition was calculated as 13.51 GPa. Figure 3: Representative free surface velocity in test No. 004 under impact loading.
## 2 Research Motivation
### International Standards and Frameworks  
The International Organisation for Standardisation (ISO), in its ISO 22301:2019 standard, specifies requirements to implement, maintain, and improve a management system to protect against, reduce the likelihood of the occurrence of, prepare for, respond to, and recover from disruptions when they arise [9].  
The Capability Maturity Model Integration (CMMI) v2.0 represents a collection of best practices that empower businesses to enhance their performance while also providing a framework for planning mitigation strategies to handle substantial disruptions in business operations [18].  
In the BC scope, the primary goal of Control Objectives for Information and Related Technology (COBIT) 2019, a framework designed for the governance and management of enterprise ICT, is to furnish a strategy that allows both business and ICT organisations to effectively respond to incidents and swiftly adapt to disruptions [19].  
The Information Technology Infrastructure Library (ITIL) 4, provides guidance to address service management challenges and utilise the potential of modern technology. It introduces service continuity management practices to guarantee the availability and performance of services in the event of a disaster [20].  
The National Fire Protection Association (NFPA) 1600 standard establishes essential criteria for readiness and resilience through a program that encompasses prevention, mitigation, response, continuity, and recovery [21].  
These frameworks are organised into activities, objectives, or practices that, when combined with their overarching vision, provide a structured approach to effectively address, guide, diminish, mitigate, manage, or respond to disasters, incidents, or disruptions in business operations or service delivery.
## 2 Materials and Methods
### Data Analysis  
Prior to statistical analysis, the values of the effect of _B. bassiana_ on _Z. mays_ plant germination percentage and the effect of _B. bassiana_ treated plants on the percentage of each developmental stage of the larvae were arcsine transformed for normalization. Data from three biological replicates were subjected to one-way ANOVA using the software IBM SPSS Statistics, version 21. Before ANOVA, all data were tested for homogeneity of variance using [PERSON]'s test. When a significant F value was obtained after ANOVA, post-hoc [PERSON]'s multiple range tests were performed. Considering that there were only two groups to analyze the germination data of field trial Experiment 1 and corn cob data from field trial Experiment 2, Independent sample _T_-test analysis was used. Significance levels were calculated by [PERSON]'s test for equality of variance. To interpret the putative changes in the frequency of _S. frugiperda_ larva found in the first field trial, an analysis with a Generalized Linear Model (GLM), fitted with a negative binomial distribution, was used. Pearson chi-square value > 0.05 indicated goodness of fit of the data. Omnibus test with \(p\) < 0.05 indicated significant differences. The results of the Test of Model effects Type III were reported.
## 4 Results and Discussion  
### Finite Element Model Results  
The finite model displacement results were derived by the dynamic loading conditions applied in the same conditions with field measurement site. Analysis result of vertical rail displacement according to train speed (85, 100, 155, 185, 195, 205 km/h) is shown in Table 8, where the displacement minimum and maximum displacement from the field measurement data are outlined.
# Sign Language Interpreting System Using Recursive Neural Networks
## 3 The Proposed System
### Dataset Construction  
As discussed above, we use a custom dataset consisting of the spatial coordinates of the body parts to train the model. To achieve this, we utilize the Mediapipe model to  
Figure 1: Block diagram of the proposed system.  
Figure 2: Workflow to develop the proposed system.  
compute the landmarks of the involved body parts; thus, in this section, we detail the general procedure to construct the patterns of this dataset.  
To delve into the details of how to compute keypoints using the Mediapipe model, let us define \(\mathbb{V}\) as the set of frames that comprises a video sequence; each frame \((\mathbb{V}_{i})\) of \(\mathbb{V}\) is feed into the Mediapipe model and it outputs a tuple, which we call \(\mathbb{K}_{i}\), the first three members of which are the \(x\), \(y\), and \(z\) spatial coordinates of the main landmarks of hands, face, and torso. Examples of these landmarks include knuckles, wrists, eyes, mouth, shoulders, and other important landmarks. Besides the spatial coordinates, the Mediapipe model outputs a score called visibility, which is unnecessary for the model we propose and thus we discard it; that is to say, we assemble a tuple in this way: \((x,y,z,visibility)\rightarrow(x,y)\) for each landmark. Next, we append this tuple to a time series; the resulting time series \(\mathbb{S}_{i}\) is made of the spatial coordinates of body landmarks, which serves as patterns for the NN model we propose.  
We detail the entire process in Algorithm 1 and Figure 3, which explains the use of the keypoints computed by the Mediapipe model.  
```
Data: Input video sequence(\(\mathbb{V}\)). // Setup Mediapipe library parameters // Let:  mp_drawing = mp.solutions.drawing_utils  mp_holistic = mp.solutions.holistic // Compute keypoints thought the video sequence for each frame(\(\mathbb{V}_{i}\)) in \(\mathbb{V}\)do //Compute Keypoints ( \(\mathbb{K}_{i}\)) from \(\mathbb{V}_{i}\). // \(\mathbb{V}_{i}\rightarrow\mathbb{K}_{i}\), where \(\mathbb{K}_{i}=(x,y,z,visibility)\) if\(\mathbb{K}_{i}\) is a keypoint of hand, face or torso then  Discard the \(z\) coordinate and visibility.  Append the point \((x,y)\) into the time series \(\mathbb{S}_{i}\)  end for  end for Result: Time series \(\mathbb{S}_{i}\) containing spatial position of body parts involved with the signaling of words.
```  
**Algorithm 1**Procedure for computing the coordinates of landmarks using the Mediapipe model.  
Figure 3: Tracking the spatial position of keypoints leads to a pattern related to the word signaled.
## 4 TSN Integration with Edge Computing for Enhanced Reliability
### Single Application Instance Handling End Device Capability  
In this case, the industry end device is capable to handle only a single application instance (e.g., the device can process only those frames that come from a given MAC/IP  
Figure 8: TSN FRER in the DC GW/TSN FRER in the edge cloud domain.  
Figure 9: Robustness provided by per-application instance TSN streams using different transport and cloud resources.  
address). Typically, this is the case in brownfield deployment when a device control application functionality is offloaded to the edge. The main advantage is that the existing application software can be reused (at least on the device side), so backward compatibility can be provided. To ensure reliability, multiple virtualized application instances are running in the edge domain; however, the end device is still capable of handling a single application instance. Therefore, it is required to hide the multiple application instances from the device by emulating a single application instance and related TSN FRER function. In order to fulfill the above requirement, the FRER functionality, as well as the TSN talker/listener entities, should also be virtualized and moved into the cloud domain to handle the TSN and the application instance deployments in a coordinated way. Figure 10 shows an illustrative deployment example in the (edge) cloud domain for this case, as well as how the emulation should look like for the end device.  
The upper part of the figure shows a possible edge computing deployment: The edge domain is connected to other domains via multiple DC GW nodes, and it is also assumed that the underlying network between the data center nodes can offer redundant paths. To increase deployment flexibility, the required TSN functions are virtualized and comprised of a TSN interworking functionality (TSN IWF). It includes the talker/listener functions as well as the TSN FRER functionality. Furthermore, TSN IWF compromise the features needed for emulating a single application instance towards the device--the details will be discussed in Sections 4.2.1 and 4.2.2. As it can be seen in the deployment view, the multiple application instances are running on different server nodes, and in order to avoid a single point of failure, multiple TSN IWF instances are also deployed. However, since the device is not capable of handling multiple application instances, a single application and a single TSN-IWF entity has to be emulated towards the device, as shown in the lower part of the figure.
## 2 Materials and Methods
### Evaluation of Current Intakes for Total Fat and Specific Fatty Acids
#### 2.2.4 Data Assessment  
Mean or median fatty acid intake data were evaluated against the FAO/WHO and EFSA recommendations using the same segmentation as outlined above: 1-2 years, 2-3 years, 3-5 years and 5-7 years [17; 18; 19; 20]. Data evaluated included the fats for which intake recommendations were found, namely total fat, total SFA, LA, ALA, DHA, and the sum of EPA and DHA. The comparison with the recommendation was done using the following units: contributions to total daily energy intake (%E) for total fat, SFA, LA and ALA; absolute values (mg/day) for DHA and EPA. Because the age range classification differed between papers, we included some of the reported data in more than one of the four age groups of interest (e.g., intake data for children 1 to 3 years of age were evaluated against the recommendations for children 1 to 2 years and that of children 2 to 3 years). For studies where the age range extended beyond our inclusion criteria, data were reported only for our relevant ages (e.g., studies reporting intake for children age 2-8 years were included in the 2-3 years, 3-5 years and 5-7 years groups). The number and proportion of studies with intake outside the recommendations were then calculated. In studies that reported data for two groups (e.g., mean for boys and mean for girls) each of the two sub-groups were counted as half a study. Due to the substantial heterogeneity of the included studies, meta-analysis of the data was not possible.
## 5 Discussion
### Interplay between the Various Techniques Used in the Proposed Approach
For example, large drones with a large payload carrying heavy items to multiple destinations could probably afford more computationally involved algorithms than lighter drones. Thus, the proposed approach complements, instead of competing with, other encryption techniques. To provide more experimental data supporting this fact, in Appendix A, we provide the MATLAB code for an example where the method of [29] (AES-256 algorithm with CBC for internal encryption of the 256-bit data blocks) is implemented internally inside each \"Encryption\" block of Figure 3, while our CBC blockchain-like approach is implemented externally to link the different ciphertexts of the various transactions. The symmetric encryption keys were encrypted and decrypted using RSA. We used this code to perform an experiment where we measured the time needed for each of the encryption/decryption operations. The results were averaged over 1000 iterations, performed on a laptop with an Intel Core i7 1.7 GHz processor with 16 GB RAM and running the Windows 11 operating system. They are displayed in Table 1 for AES and Table 2 for RSA. In the approach of Figure 3, the encryption of a given transaction consists of: (i) XOR-ing the input with the previously encrypted block, and (ii) performing symmetric encryption (in this case using AES) of the result. Table 1 shows that the XOR operation is orders of magnitude faster than the encryption operation, which nevertheless takes, on average, 32.24 ms. The decryption takes nearly double that time. However, the decryption operation is not performed by the drone's processor. It is performed at the Control Center after the UAV's return. In RSA, the public and private keys are generated from two prime numbers \(p\) and \(q\). The key generation operation is known to be the most computationally intensive phase [28]. Therefore, in Table 2, we performed the experiment with two scenarios: in the first one, the prime numbers were relatively small (\(p=13\), \(q=113\)), while they were significantly larger in the second scenario (\(p=5381\), \(q=9319\)). Table 2 shows that the time for all three operations (key generation, encryption, and decryption) increases when larger primes are used. Although the time roughly doubles for encryption and decryption, it increases by orders of magnitude for key generation. Given that the RSA key generation and decryption operations are performed at the Control Center, whereas only encryption is performed by the UAV, the proposed approach does not impose a high computational burden on the drone. The Control Center is expected to have ample resources to handle the computationally intensive operations.