esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
###### Abstract  
The autonomic composition of Virtual Networks (VNs) and Service Function Chains (SFCs) based on application requirements is significant for complex environments. In this paper, we use graph transformation in order to compose an Extended Virtual Network (EVN) that is based on different requirements, such as locations, low latency, redundancy, and security functions. The EVN can represent physical environment devices and virtual application and network functions. We build a generic Virtual Network Embedding (VNE) framework for transforming an Application Request (AR) to an EVN. Subsequently, we define a set of transformations that reflect preliminary topological, performance, reliability, and security policies. These transformations update the entities and demands of the VN and add SFCs that include the required Virtual Network Functions (VNFs). Additionally, we propose a greedy proactive heuristic for path-independent embedding of the composed SFCs. This heuristic is appropriate for real complex environments, such as industrial networks. Furthermore, we present an Industrail Internet of Things (IIoT) use case that was inspired by Industry 4.0 concepts, in which EVNs for remote asset management are deployed over three levels; manufacturing halls and edge and cloud computing. We also implement the developed methods in Alevin and show exemplary mapping results from our use case. Finally, we evaluate the chain embedding heuristic while using a random topology that is typical for such a use case, and show that it can improve the admission ratio and resource utilization with minimal overhead.  
NFV; SFC; VNE; IIoT Article
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 2 Theoretical Background
### Cloud Storage  
Data storage includes recording, transportation, replication, compression, cleaning, indexing, stream processing, integration, and transformation of data. Given the increase in data volume (big data), the main question is how to store all data and where. Data warehouses and centers are the most often used. A data warehouse (DW) is an integrated collection of subject-oriented decision support data [29]. Data warehousing (DW) is a specific type of information system and should enable the acquisition of business data, its transformation into appropriate strategic business information, and subsequent storage of data in a format that facilitates business analysis [30]. The cloud is currently the hardware and software solution of the data center providing the services [31]. The cloud is a parallel and distributed computing system consisting of a collection of interconnected and virtualized computers that are dynamically delivered and presented as one or more unified computing resources based on service level agreements negotiated between a service provider and a customer [32]. Such resources are dynamically transformed to adapt to variable load, enabling the optimal use of resources [33]. Clouds are hardware services offering computing, networking, and storage capacity [34]. Mostly, clouds are operated on a cloud deployment models basis [35]: public cloud, private cloud, hybrid cloud, and community cloud [36; 37; 38]. The cloud inherently includes the concept of cloud computing, based on the provision of services or programs stored on servers and the internet. Cloud computing distinguishes three types of distribution models [22]: IaaS (infrastructure as a service), PaaS (platform as a service), and SaaS (software as a service). For the purposes of data storage, there are IaaS services, i.e., the use of virtualization, providing only infrastructure and hardware. Block chain technology [39] is emerging significantly in the architecture of the internet and is pushing out the standard model of client-server architectures. The point is that individual transactions between different entities are transparent to everyone, but no one other than the two entities that took the action can influence and change this record. Block chains remove the third-party distribution of information flow [40]. Block chain is basically a data structure that is used to create a digital transaction ledger that is not stored by a single provider but is shared across a distributed network of computers. Block chain is, therefore, a special type of distributed decentralized database in which records are stored.
## 3 Experimental Program
### Materials' Properties' Subsection  
Like in the previous research stages [24; 25; 26], the steel fibers had an ultimate tensile strength of 1100 MPa, a length of 50 mm, and a diameter of 1 mm. The number of fibers per 1 kg is about 3150.  
The SFHSC concrete mixture consisted of the following:  
- Portland cement CEM I 52.5 with a density of 3.1 kg/dm\({}^{3}\);  
- Fly ash with a density of 2.3 kg/dm\({}^{3}\);  
- Poly-carboxylic ether-based superplasticizer with a density of about 1.07 kg/dm\({}^{3}\);  
- Natural sand with a fraction size of 0 to 2 mm;  
- Two different types of gravel with fraction sizes of 2 to 8 mm and 8 to 16 mm.  
Sand and gravel had specific densities of 2.66 kg/dm\({}^{3}\) and 2.64 kg/dm\({}^{3}\), respectively. For NSC, Portland cement CEM II 42.5 with a density of 3.085 kg/dm\({}^{3}\) was used. The SFHSC and NSC mixture composition is given in Table 1. Dried aggregates were used for all mixtures.  
For beams' reinforcement, steel bars with an ultimate strain of 25 % and modulus of elasticity, yield strength, and ultimate strength of 200 GPa, 500 MPa, and 525 MPa, respectively [31], were used. Only two longitudinal reinforcement steel bars with a diameter of 8 mm were placed at the bottom of the beam. There were no stirrups in the tested beams in order to avoid the effect of compressed and shear reinforcements on the obtained experimental data. The depths of the SFHSC and NSC layers were 5 and 10 cm, respectively. A scheme of the tested specimens was like in [32] and it is given in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{2}{c}{**Quantity, kg/m\({}^{3}\)**} \\ \cline{2-3}  & **SFHSC** & **NSC** \\ \hline Cement (C) & 400 & 300 \\ Water (W) & 152 & 180 \\ Fly ash (FA) & 100 & 0 \\ Superplasticizer & 15.4 & 0 \\
0/2 sand & 675.43 & 659.36 \\
2/8 gravel & 429.71 & 560.91 \\
8/16 gravel & 618.78 & 654.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete mixture compositions.  
Figure 1: Dimensions and loading scheme of the tested TLBs [32].
## 2 Materials and Methods
### Methodology Design and Implementation
#### 2.2.4 Machine Learning Models
* CatBoost CatBoost, short for Category Boosting, as with XGBoost and LightGBM, is based on a Gradient Boosting framework that uses the Decision Tree learning technique. Developed in 2017 [39], CatBoost attempts to solve categorical features using permutation techniques. * AdaBoost AdaBoost or Adaptive Boosting is the first practical boosting algorithm by [PERSON] et al. [40]. This is an ensemble machine learning algorithm in which weights are allocated adaptively, with greater weights assigned to incorrectly classified instances. When the subsequent learners are grown from the previously grown learners, the weak learners are transformed into strong learners. * LogitBoost LogitBoost is a variation of AdaBoost. The LogitBoost method was developed as an alternative to AdaBoost in order to solve the shortcomings of AdaBoost in dealing with noise and outliers.
###### Abstract
Several works have found that the optimal active layer thickness in MAPI is around 300 nm [23; 24]. However, in [25], the authors demonstrated that using a hot casting method improves both light absorption and charge transport, developing high-efficiency devices (over 19%) with good thickness insensibility from 700 nm up to 1150 nm active layers. In this work, C\({}_{0.15}\)FA\({}_{0.85}\)PbI\({}_{2.85}\)Br\({}_{0.15}\) PSCs with different active layer thicknesses (350, 500 and 650 nm) were characterized in DC (current density-voltage, \(J\)-\(V\)) and AC (impedance spectroscopy, IS). Since the contacts and layer structure are the same in all cells, any change in the performance can be directly related to the perovskite thickness. Devices showed a dramatic burn-in degradation that increases with active layer thickness. After several minutes of continuous illumination, the efficiency of thinner and medium thickness devices stabilizes. However, thicker samples show no stabilization of the efficiency. In order to study in detail the dynamical processes occurring in the device, DC and AC characterization were also performed and thoroughly analyzed during degradation under 1-sun illumination. The complete layer structure was ITO/PTAA/Cs\({}_{0.15}\)FA\({}_{0.85}\)PbI\({}_{2.85}\)Br\({}_{0.15}\)/PCBM/BCP/Cu and the device effective area was 16 mm\({}^{2}\). In addition, 2D numerical simulations using SILVACO ATLAS TCAD were performed, including two species of mobile ions in the active layer. In the presence of mobile ions, simulations show that the internal electric field at 1-sun illumination in short circuit conditions is smaller for the thickest active layer devices. This decrease in the electric field for the 650 nm devices implies a reduction in the carrier drift and, therefore, of the effective charge extraction. In [26], the authors showed, for similar devices, that hysteresis increases with thickness. Moreover, the occurrence of hysteresis requires the presence of mobile ionic species [27], and the accumulation of these mobile species at the interface can transiently affect charge extraction in the device. In summary, this more pronounced ion accumulation in thicker devices is responsible for partially screening the electric field, which, in turn, hampers charge extraction and ultimately reduces the cell efficiency.
## 4 Discussion
The data acquisition time of HJ on April 7 th and of the radar data on March 29 th resulted in a reduction in the estimation accuracy of LAI and biomass. Therefore, we think that growth stages impacted the predictive power of the indices, as the plants show very different optical-chemical and structural properties at different growth stages. These differences should influence the optical and SAR signals during different growth stages. Therefore, the small difference in the HJ and radar data acquisition time should be considered to better estimate LAI and biomass in future research. The establishment of regression equations and experimental field data are necessary, and some important factors should be carefully considered in the data analysis. For example, the incidence angle of SAR largely influenced the vegetation backscattering information [27,36] and SAR backscattering information was influenced by the amount of precipitation. These factors may result in some errors in the estimation of water-related crop or soil parameters. The results of the MSR and PLSR methods show that the COSVI-RPPs were highly related to LAI and biomass of winter wheat (Table 9). The estimation accuracy of LAI and biomass was higher with the PLSR than the MSR method. Previous studies have shown that PLSR outperforms MSR in estimating biophysical and biochemical parameters [11,59]. This may be because the MSR method can be used to concentrate on some spectral band features with known links to the variables of interest [59]. In comparison, the PLSR method fully considered the relationships between the covariance of spectral band features and biophysical variables by applying data compression into regression factors. Therefore, the PLSR obtained the best estimation accuracy of LAI and biomass. However, the MSR also has merit, particularly when taking into consideration the simplicity of its application. But the MSR and PLSR regression models are quite unstable when they are applied to the larger region even though the calibration results look good in our study. Therefore, the performance of MSR and PLSR regression models have to be carefully verified by using sufficient independent datasets of different crops and ecological regions, as this study was limited to winter wheat in the Yangling district of Shaanxi, China.
## 4 Experiments
### Dropout Effects  
In addition to varying the number of topics, we conducted experiments by fixing the dropout rate by 0.1. This allowed us to evaluate potential differences in model performance associated with changes in the dropout rate. The dropout range was alternated from 0.1 to 0.5, with experimental results shown in Figure 4.  
For the MRD dataset, ANTM achieved a performance peak at a dropout rate of 0.2, as well as relatively poor performance at dropout rates of 0.4 and 0.5. In contrast, ENTM exhibited its peak performance at a dropout rate of 0.3.  
For the 20 Newsgroups dataset, both ANTM and ENTM performed significantly worse as the dropout rate changed from 0.1 to 0.5. For the YELP dataset, performance was  
Figure 4: Visualizing the effect of dropout.  
relatively low for dropout rates of 0.4 and 0.5, with ENTM performing the worst at a dropout rate of 0.5.  
As was the case with the number of topics, a significant degradation in performance was noted with certain datasets owing to changes in the dropout rate. Moreover, the lowest performance was often observed at a dropout rate of 0.5 for all three datasets, indicating that an increase in the proportion of dropouts to prevent overfitting may degrade learning performance.  
Ultimately, a comprehensive evaluation of the dropout rate's impact on model performance suggests that selecting an appropriate dropout rate is crucial for optimizing performance, with excessively high dropout rates having an especially detrimental effect on model accuracy.
# Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data
## 3 Arm Gesture Recognition
### Gesture Recognition  
The proposed set of features, is partially inspired to those proposed by [PERSON] [24], whose work extended the one of [PERSON] [25] from the 2D to the 3D space. More specifically, let \(J\) denote a given joint. Then, by \(J_{c}\) and \(J_{p}\) we denote its child and parent joints, accordingly. Also let \(F_{i},\ i=1,2,\ldots,N\) denote a given video frame and \(\mathbf{v}_{i}^{(J)}=\left(v_{x,i}^{(J)},v_{y,i}^{(J)},v_{z,i}^{(J)}\right)\) a vector corresponding to the 3D coordinates of \(J\) at frame \(F_{i}\). Also, let \(\mathcal{V}^{(\mathcal{J})}\) be the set of all \(\mathbf{v}_{i}^{(J)}\). By \(B(\mathcal{V}^{(\mathcal{J})})\) we denote the 3D bounding box of \(\mathcal{V}^{(\mathcal{J})}\), by \(a_{B(\mathcal{V}^{(\mathcal{J})})}\) and \(b_{B(\mathcal{V}^{(\mathcal{J})})}\) the two different lengths of its sides. The extracted features are depicted in Table 1. Note that each feature is calculated for a given joint \(J\) and may involve a different subset of frames. The notation is summarized in Table 2.  
More specifically, we extracted the spatial angle between the first two frames (i.e., \(F_{1}\) and \(F_{2}\)), the last two frames (i.e., \(F_{N-1}\) and \(F_{N}\)) and the first and the last frame (i.e., \(F_{1}\) and \(F_{N}\)), in all cases using the inner product of the corresponding vectors. Moreover, we extracted the total and the squared total vector angles, between \(F_{1}\) and \(F_{N}\) using the inner product of the corresponding vectors, i.e., \(\mathbf{v}_{1}^{(J)}\) and \(\mathbf{v}_{N}^{(J)}\). The total vector displacement is the magnitude of the difference of the vectors corresponding to the initial and the final positions of \(J\), while the total displacement is the sum of the magnitudes of all differences between  
Figure 1: (**a**) Extracted human skeleton 3D joints using the Kinect software development kit (SDK). (**b**) Visual representation of a given node \(J\), its parent \(J_{p}\) and its child \(J_{c}\); \(a,b,c,d\), used in Equations (2)–(6) and the reference point \((v_{x,i}^{J_{p}},0,0)\). the positions of \(J\) within any two given consecutive frames. Similarly, the maximum displacement is the magnitude of the maximum difference between the positions of \(J\) within any two given frames. Finally, we built a 3D bounding box for the set of all positions of the joint within all frames and estimate accordingly the length and the angle of its diagonal. Note that from the skeletal data we used only a subset of joints (i.e., their 3D coordinates). Also, note that, \(J\in\{\) ElbowLeft, ElbowRight, HandLeft, HandRight, WristLeft, WristRight\(\}\). Features marked with \(*\), are calculated using only HandLeft and/or HandRight.
## 4 Experiment
### Outdoor Test  
In order to further verify the system performance, we tested the communication distance of the system in a pool with high green algae content, and the test environment is shown in Figure 16. It can be seen that the chlorophyll content in the pool was relatively high, and there were many impurities in the water.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rate** & **BER** \\ \hline
1.25 Mbps & 0 \\
2 Mbps & 0 \\
3.125 Mbps & 0 \\
5 Mbps & \(7.53\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of the bit error rate at different transmission rates.  
Figure 15: The BER at different transmitter offset angles.  
When the transmitter and receiver were aligned (the offset angle did not exceed \(10^{\circ}\)) and the RS (255,239) error correction code was added, the results of the maximum communication distance without errors for each transmission rate are shown in Table 2:  
The results show that the maximum communication distance of the system in the turbid pool was much shorter than that of the indoor pool. According to the paper [29], chlorophyll has the most serious attenuation to visible light with a wavelength of about 430 nm. Further, considering the impurities in the water, it is normal for the visible light to attenuate seriously.  
In this turbid pool, we set the transmission rate to 3.125 Mbps, and collected the voltage strength of the received signal (the average peak-to-peak value of the received signal voltage) at different distances. The results are shown in Figure 17. The curve in the figure was fitted according to the optical power attenuation formula (4) at the receiving end in seawater.  
\[P_{r}=P_{t}\times e^{-c(\lambda)d} \tag{4}\]  
where \(P_{t}\) is the transmitted signal power, \(P_{r}\) is the received signal power, \(d\) is the communication distance, and the attenuation coefficient of 450 nm wavelength visible light in the turbid pool is about \(c(\lambda)=0.3299\). According to the paper [10], the attenuation coefficient in deep-sea clear water is 0.151, and the error-free communication distance of the system at a communication rate of 3.125 Mbps can reach 11.8 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rate** & **Distance** \\ \hline
1.25 Mbps & 6 m \\
2 Mbps & 5.8 m \\
3.125 Mbps & 5.4 m \\
5 Mbps & 3.6 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: The maximum transmission distance without errors at each transmission rate.  
Figure 16: Outdoor pool environment.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Short Range Communication Technologies
#### 4.3.4 Wi-Fi (IEEE 802.11)  
This is a wireless communication technique is based on IEEE 802.11 standard. Similar to other short-range communication technologies discussed earlier, it uses the ISM band. The two frequencies most often used are 2.4 GHz and 5 GHz, depending on the requiredapplication. A new version of Wi-Fi, known as Wi-Fi 6, uses the 6 GHz frequency. In telemedicine applications, the Wi-Fi network usually comprises multiple access points, with medical sensor nodes moving freely within access points [107]. Wi-Fi or WLAN can be used along with WBAN to transmit biosignals to a nearby server and then to the internet.  
The authors of [31] demonstrated a WLAN/IEEE 802.11n standard transmission scheme for wireless telemedicine applications. Their WLAN system transmits measured medical data using digital bits, ECG signals, case histories, and audio/video signals for telemedicine purposes.  
Drawbacks:  
A major drawback of Wi-Fi is the issue of data security, as data are broadcast wirelessly to nearby compatible devices. According to research, several security threats are associated with Wi-Fi networks due to their use of an open medium to transmit and receive data. Threats which can be detrimental in medical diagnosis contexts include denial of service, eavesdropping, and services theft [31].
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 2 Methodology
### Research Flow  
The overall flowchart of this study is shown in Figure 3. First, several virtual forests were created in step 1. Parameters other than canopy shape (e.g., TH and CC) were obtained from point clouds acquired by UAV-SfM. The canopy shapes used were cylinder, ellipsoid, half ellipsoid, and inverted half ellipsoid, as shown in Figure 4. The tree model parameters TH, canopy length (CL), canopy radius (CR) and diameter at breast height (DBH) were also referenced to the UAV-SfM results. We used Phantom 4 for UAV observation and flight condition, as shown in Table 1. Details on the parameters used for the virtual forests can be found in Section 2.5. In Step 2, CSRS was used to simulate the reflectance of each virtual forest under several SZA conditions. The aim of the step 2 was to analyze the effect of canopy shape on simulated reflectance and to determine the SZA to be employed when finding the optimal canopy shape in the next step. Therefore, the analysis in step 2 could not be performed using a specific satellite image. Then, we used the 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\) SZAs in this step based on the SZAs checked in Section 2.2 for satellite overpasses over a one-year period. For the SAA, we assumed that the effect of the SAA was less and set it to 0\({}^{\circ}\) because of the symmetrical canopy shape. The description of CSRS is in Section 2.4, and the detail simulation conditions are described in Section 2.5. To investigate the significant difference between each simulated reflectance, we applied [PERSON]'s honestly significant difference (HSD) test. After that, in step 3, the optimal canopy shape for estimating the shadow fraction was determined. We determined the optimal canopy shape by minimizing the discrepancy between the simulated reflectance from the virtual forest and the reflectance observed by satellite sensor. Based on the results of step 2, Sentinel-2 images were selected that satisfy the SZA, which causes significant differences among canopy shapes in reflectance. Finally, the shadow fractions calculated from virtual forests were compared with those calculated from the 3D model acquired by UAV-SfM in target forest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification and Parameters** & **Value** \\ \hline Date & 30 September 2018 \\ Average Ground Sampling Distance & 4.5 cm \\ Flight elevation & 80 m \\ Number of images & 806 \\ Type of sensor onboard the UAV & Optical \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the UAV and flight conditions.  
Figure 2: Sentinel-2A/B local overpass time in 2019 (**left**) and Sun zenith angle and Sun azimuth angle during the time of Sentinel-2A/B overpass in the target area (**right**).  
Figure 3: Overall flowchart of this study.