esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background on Technologies for RSBD Processing and Related Works
### Cloud Computing for Scalable Big Data Processing: OpenStack  
OpenStack is a free and open-source software package for cloud computing; it is under the Apache License. The OpenStack framework administers big pools of computing, storage, and networking materials through a datacenter within a dashboard or via the OpenStack Application Programming Interface (API). OpenStack runs with prevalent enterprise and open-source technologies, making it perfect for mixed infrastructure [11]. Several of the world's largest brands trust OpenStack to manage their businesses, reduce costs, and optimize performance. OpenStack has a robust system constructed by a prosperous community of developers.  
OpenStack is a group of package tools used for building and handling cloud computing platforms for public and private clouds. The OpenStack cloud operating system supervises all hypervisors in a data center or across numerous data centers into pools of resources consumed from a single place, a dashboard. Administators and users can easily manage the cluster via a dashboard, create Virtual Machines (VMs), configure networks, and set up volumes AQ [12]. It computes a server that can deliver a central processing unit (CPU), storage, network, and memory resources; therefore, it significantly affects the cloud deployment model's use and performance. OpenStack is composed of many components, which are listed and explained in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citeria/** & **Horton** & **Cloudera** & **MR** & **IBM Insights** & **Pivotal HD** \\ \hline Centralized & + & + & \(-\) & \(-\) & \(-\) \\ Cloud services & + & + & + & + & \(+\) \\ Data Access & + & + & + & + & \(+\) \\ Data Analysis & + & + & + & + & \(+\) \\ Data Ingestion Batch & + & + & + & + & \(+\) \\ Data Ingestion & \(-\) & \(-\) & + & + & \(+\) \\ Data Analysis & + & + & + & + & \(+\) \\ Data warehousing & + & + & + & + & \(+\) \\ Distributed & \(-\) & \(-\) & + & + & \(+\) \\ ETL & + & + & + & + & \(+\) \\ Machine learning & + & + & + & + & \(+\) \\ Management tools & + & + & + & + & \(+\) \\ MR & + & + & + & + & \(+\) \\ Parallel Query & + & + & + & + & \(+\) \\ Replication Data & + & + & + & + & \(+\) \\ Scripting platform & + & + & + & + & \(+\) \\ \hline \hline \end{tabular}  
* Cell with (+) means that the option is supported; however, (\(-\)) means the absence of the option.  
\end{table}
Table 1: Technical comparison among the five distributions of Hadoop.
Besides, it is possible to integrate Hadoop as a component of OpenStack; all OpenStack schedulers are used to control how schedules compute, network, and volume requests; and Memcached service, which stores data in memory to reduce the rate with which an external database must be requested.
# Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss
## 6 Discussion  
There has been a significant improvement in finger and hand prostheses from passive hooks and shoulder harnesses to myoelectrically controlled prostheses [34; 43; 45; 46]. At present, there are various myoelectrically controlled commercial prosthetic hands in the markets and they offer a range of individual hand/finger movements [11; 12; 13]. Some commonly used models include the i-Limb, Vincent Hand, i-Limb Pulse, SensorHand, BeBionic, BeBionic V2, and Michelangelo models [43; 44; 45; 46; 47]. Still, the precise control of prosthetic hands or fingers is a problem.  
The myoelectric activities of the residual limb for phantom limb movements help for the pattern recognition regarding myoelectric control and intelligent interfaces can control prosthetic hands and fingers, which can suggest finger and hand movement. The signals are assigned to control the movement of the finger/hand prosthesis. The muscle's electrical activity can be interpreted by the sEMG signal [16; 17]. Several factors (pre-processing, net architecture, and parameters for optimization) are important for the analysis of sEMG data. More accuracy on computer vision and object recognition tasks is seen with larger networks [20].  
Ossoseintegrated implants in retained finger prosthesis offer better retention and help in different functions [52]. In addition, osseointegrated implants improve functionality, durability, and freedom of movement in the prosthesis, as well vibrotactile and pressure feedback secondary to osseoperception [54; 55; 56; 57]. An example of this is grip control and motor coordination in three transhumeral amputees with the e-OPRA device [60]. The synergy between bone-anchored devices, advanced neuromuscular interfaces, and targeted muscle reinnervation can significantly improve the sensory and motor function of advanced prostheses.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 10 cm Depth in Bangladesh
#### 5.1.3 Five-Day Lead-Time Forecast  
Similar to Sections 5.1.1 and 5.1.2, we compare the forecast performance of five tree-based models under different input scenarios and time windows on the testing dataset. In order to avoid the repetition of similar tables, we only report the main results here. The DT model exhibited good performance in forecasting soil temperature, and the combination of the MEAN-MAX-MIN input scenario, and four-day time window demonstrated the lowest average RMSE of 1.995 \({}^{\circ}\)C and the highest average R value of 0.902. Although a one-day time window always exhibited lower accuracy with mean input parameters, the largest time window did not produce the best accuracy. For RF forecasts, the sixth input scenario performed the best under all time windows; in particular, the lowest average RMSE (1.910 \({}^{\circ}\)C) and highest average R (0.911) were reached under a four-day time window. The GBT model with the eighth input scenario achieved the highest accuracy under various time windows. Among them, the use of a four-day time window yielded the lowest average RMSE (1.915 \({}^{\circ}\)C) and the highest average R (0.910). In the forecast performance of the hybrid DT-GBT, the use of the sixth input scenario and a four-day time window achieved the lowest average RMSE (1.936 \({}^{\circ}\)C) and the highest average R (0.908) values. The best hybrid RF-GBT forecast consistently adopted the eighth input scenario for all time windows. The combination of the eighth input scenario and four-day time window achieved the lowest average RMSE value (1.908 \({}^{\circ}\)C) and the highest average R value (0.911).  
**Best combination for five-day lead-time forecast:** The hybrid RF-GBT, utilizing all the input factors under a four-day time window, emerged as the optimal performing model with the largest average R value (0.911) and the lowest average RMSE value (1.908 \({}^{\circ}\)C). Figure 5 demonstrates that the forecast of the hybrid RF-GBT under a four-day time window offered a close fit with the observed values and superior accuracy at the Dinajpur, Bora, Mymensigh, and Bariasal stations. The hybrid RF-GBT model properly captured the oscillation patterns of soil temperature, but it provided a clear underestimate of extremely high soil temperatures. At the same time, some unusually high observed soil temperatures in Mymensigh were caused by measurement errors.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
## 2 Methods
### Measurements of the Polarized Upwelling Radiance Distribution
For in situ radiometric measurements, instrument self-shading must be considered [24]. In our case, instrument self-shading may also affect the _DOLP_ measurements due to camera geometry. The physical displacement of the multiple lenses used means that the images through different polarizers will look into the instrument shadow in slightly different directions. When one lens views a portion of the radiance distribution with less shadow, it will appear brighter in that image. In the algorithm from which the polarization information is derived, for the scene to be unpolarized requires that region of the three camera images to be closely matched in intensity. If the region in one camera is less shaded, it will cause the algorithm to assume that the light field is polarized along the direction of the linear polarizer in that camera. Thus shadowing, which is not symmetric in all the images, will appear as an increase in the _DOLP_, and therefore, a negative model-data _DOLP_ difference. This effect should be greater in turbid water and for the PolRADS instrument (because of its larger size) than in clear water or with the DPOL instrument. In our analysis, the area of direct shadow (the anti-solar point) was excluded from the data set. More subtle shadow areas may exist and could cause the measured _DOLP_ to be larger than the modelled _DOLP_ (as will be seen, this was not generally the case in our data set).
# Risk Assessment of Bisphenol A in the Korean General Population
## 2 Literature Search for Safety Levels of BPA
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Agencies** & **Endpoint** & \begin{tabular}{c} **Point of Departure** \\ **(Uncertainty Factor)** \\ \end{tabular} & \begin{tabular}{c} **Tolerable Daily** \\ **Intake** \\ \end{tabular} \\ \hline US EPA 1 & Reduced body weight & NOAEL 5 mg/kg bw/day & 0.05 mg/kg bw/day \\  & Reduced body weight and liver effects & NOAEL 5 mg/kg bw/day & 0.005 mg/kg bw/day \\ US FDA 2 & \begin{tabular}{c} Irreversible reproductive effects \\ \end{tabular} & NOAEL 50 mg/kg bw/day & 0.05 mg/kg bw/day \\  & \begin{tabular}{c} Reversible reproductive effects \\ \end{tabular} & NOAEL 50 mg/kg bw/day & 0.5 mg/kg bw/day \\  & Body weight & NOAEL 5 mg/kg bw/day & 0.05 mg/kg bw/day \\ Japan 3 & \begin{tabular}{c} Reproduction \\ \end{tabular} & NOAEL 50 mg/kg bw/day & 0.5 mg/kg bw/day \\  & Liver effects & NOAEL 23 mg/kg bw/day & 0.046 mg/kg bw/day \\  & \begin{tabular}{c} Relative kidney weight increase \\ \end{tabular} & \begin{tabular}{c} BMDL\({}_{10}\) \\ (500) \\ \end{tabular} & \
\begin{tabular}{c} 0.5 mg/kg bw/day \\ (00) \\ \end{tabular} &
\begin{tabular}{c} 0.02 mg/kg bw/day \\ (0.016 mg/kg bw/day \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Various international health-based guidance values of bisphenol A (BPA). EFSA set a temporary TDI for BPA of 4 \(\mu\)g/kg bw/day [20] which was recently reduced to 0.04 ng/kg bw/day [19]. The process for the re-evaluation of BPA by EFSA is a reasonable approach and will be helpful for addressing various questions with the current risk assessment. In the benchmark dose (BMD) analysis, the benchmark dose lower confidence interval (BMDL)\({}_{20}\) values for cellular immunity based on Th17 cells were the lowest BMDL\({}_{20}\) (0.93 \(\mu\)g/kg bw/day) compared to other endpoints, such as metabolic effects, neurotoxicity, and reproductive and developmental toxicity [19]. In Republic of Korea, the TDI of BPA was assessed twice, in 2010 and 2016, by the NIFDS [17; 18]. The last assessment set the TDI at 0.02 mg/kg bw/day, which was derived by applying an uncertainty factor of 500 to a BMDL\({}_{10}\) 10.5 mg/kg bw/day based on [PERSON] et al. [47; 48].
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 4 Security Analysis
### Collusion Attack  
In traditional attribute encryption schemes, a single AA combines parts of a user's private key (corresponding to different attributes) through key randomization. This randomization makes the elements of different keys of one user compatible with each other, but cannot be combined with the key elements of other users. This key randomization technique is not suitable for decentralized attribute encryption, because no CA can combine these elements, and each key comes from a different AA. In order to overcome this difficulty, this article uses different global identities to combine different user key parts to meet the needs of anti-collusion. Consumer users base this solution on the CP-ABE access control method to defend against conspiracy attacks. Suppose the number of permissions involved in ciphertext is \(n\), and the number of collusion permissions is \(m\). If \(m=n\), intuitively, these permissions can get all the keys that can be used to decrypt the content of ciphertext. If \(m\leq n-1\), there is at least one privilege that the key cannot be obtained. Therefore, ciphertext cannot be decrypted. This scheme implements a collusion attack of up to (\(n\)\(-\)1) privilege.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 2 Disclose the Valuable Information
### Transformations  
It is well established that the frequency characteristics of voltage and current profiles change dramatically on the occurrence of a fault. However, if the fault is identified and investigated accurately, it can help to protect the affected transmission line (TL) to a great extent [12]. Numbers of methods are available to analyze the frequency characteristics of time-domain signals but wavelet transform, Fourier transform and S-transform have frequently employed techniques in fault identification systems/protective relays.
# Aerodynamic Characteristics of a Square Cylinder with Vertical-Axis Wind Turbines at Corners
## 3 Results and Discussions  
### Mean Pressure Coefficients  
#### 3.1.1 Stationary Wind Turbines at Cylinder Corners  
Comparisons of mean pressure coefficients (\(\overline{Cp}\)) around the cylinder surface among the four cases with stationary wind turbines at the cylinder corners and the plain cylinder are provided in Figure 5.  
On the windward face, \(\overline{Cp}\) at the stagnation point is approximately 1.0 for all cases. \(\overline{Cp}\) gradually decreases from the stagnation point to two corners and \(\overline{Cp}\) on two side faces are symmetrically distributed as expected. Because of the flow separation, the value of \(\overline{Cp}\) on two side faces is negative. Compared with the cases with turbines at corners, \(\overline{Cp}\) on the side faces of the plain cylinder has the lowest value. It means that the existence of stationary wind turbines in the corners reduces the suction on the side faces. Furthermore, \(\overline{Cp}\) on the side faces has the largest value when \(\theta\) is \(\pm 45^{\circ}\) (\(S2\)), which means that the suction on the side faces of \(S2\) is lower than the other cases.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Cases** & **Plain** & **\(\mathbf{S1}\)** & **\(\mathbf{S2}\)** & **\(\mathbf{S3}\)** & **\(\mathbf{S4}\)** \\ \hline \(\theta\) & - & \(0^{\circ}\) & \(45^{\circ}\) & \(90^{\circ}\) & \(135^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 4: Stationary test cases.  
Figure 4: Simulation cases: (a) Stationary cases; (b) Grid surrounding square cylinder.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Cases** & **Plain** & \(\mathbf{R0}\) & \(\mathbf{R1}\) & \(\mathbf{R2}\) & \(\mathbf{R3}\) & \(\mathbf{R4}\) & \(\mathbf{R5}\) & \(\mathbf{R6}\) & \(\mathbf{R7}\) & \(\mathbf{R8}\) \\ \hline \(V_{t}/f_{s}\) & - & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1 & 2 & 3 & 4 \\ \(V_{t}\) (r/s) & - & 0 & 2.38 & 4.75 & 7.13 & 9.50 & 11.88 & 23.76 & 35.64 & 47.52 \\ \hline \end{tabular}
\end{table}
Table 3: Rotating test cases.  
On the leeward face, the suctions for cases with stationary wind turbines are significantly lower than that of the plain cylinder. Similar to the observations on the side face, the stationary wind turbines weaken the suction on the leeward face and it becomes more obvious when \(\theta\) is \(\pm\)45\({}^{\circ}\), i.e., S2.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 5 Simulation Results
### Performance Evaluation for PAR  
The optimal PAR was estimated based on the joint texture/depth power allocation method [29]:  
\[\text{PAR}=\frac{\sqrt{M+\sum_{i=1}^{L}\alpha_{i}}\sqrt{\sum_{m=1}^{M}(\sum_{j= 1}^{N}\sqrt{\lambda_{m,j}^{t}})^{2}}}{\sqrt{\sum_{i=1}^{L}\beta_{i}}\sqrt{ \sum_{m=1}^{M}(\sum_{j=1}^{N}\sqrt{\lambda_{m,j}^{d}})^{2}}} \tag{30}\]  
where \(M\) is the number of reference viewpoints transmitted in a 3D video SoftCast transmission, \(L\) is the number of virtual viewpoints to be synthesized at the receiver, \(t\) and \(d\) represent the original texture video and depth map, respectively, and \(\alpha_{i}\) and \(\beta_{i}\) represent the parameters of the distortion model of both the transmitted reference views and the synthesized virtual views. To reach the global optimal PAR, a full search was conducted iteratively, with the search step set to 0.05 [29]. Although PAR is not theoretically a discrete variable, simulations proved that the 0.05 step was small enough to indicate any changes. The estimated PAR and the global optimal PAR results for the simulation video sequences are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence** & **3D Scene** & **Reference** & **Synthesized** & **Resolution** \\  & **Nature** & **Viewpoints** & **Viewpoints** & \\ \hline Balloons & real & 1 and 5 & 2, 3,4 & 1024 \(\times\) 768 \\ Kendo & real & 1 and 5 & 2, 3,4 & 1024 \(\times\) 768 \\ Newspaper & real & 2 and 5 & 2,5, 3, 3.5 & 1024 \(\times\) 768 \\ Dancer & computer- & 1 and 5 & 2, 3, 4 & 1920 \(\times\) 1088 \\ getFly & computer- & 1 and 5 & 2, 3, 4 & 1920 \(\times\) 1088 \\ PoznanHall2 & real & 5 and 7 & 5.5, 6, 6.5 & 1920 \(\times\) 1088 \\ PoznanStreet & real & 3 and 5 & 3.5, 4, 4.5 & 1920 \(\times\) 1088 \\ Shark & computer- & 1 and 5 & 2, 3, 4 & 1920 \(\times\) 1088 \\  & generated & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: 3D video sequences used in the experiments [29].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Estimated PAR** & **Global Optimal PAR** \\ \hline Balloons & 2.23 & 2.05 \\ Kendo & 2.55 & 2.60 \\ Newspaper & 3.32 & 2.85 \\ Dancer & 3.08 & 3.65 \\ gtfly & 5.78 & 7.00 \\ PoznanHall2 & 2.84 & 3.45 \\ PoznanStreet & 2.24 & 2.25 \\ Shark & 4.50 & 4.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power allocation ratio of the 3D video sequences [29].
## 3 Results and Discussion
### Characteristics of Large Spherical Au Nanoparticles by TEM and XRD  
Typical TEM images of different-sized gold spherical nanoparticles resulting from the gold nanodots via a seed-directed growth approach are presented in Figure 3. Figure 3a-d show that the Au nanoparticles prepared at a growth/seed ratio of 2, 4, 6, and 8 produced nanoparticles with mean diameters of 10.0 \(\pm\) 2.8 nm, 10.7 \(\pm\) 2.2 nm, 14.3 \(\pm\) 1.6 nm, and 23.9 \(\pm\) 3.4 nm, respectively.  
Figure 1: Transmission electron microscope (TEM) and high resolution transmission electron microscope (HRTEM) images of gold round nanodots (**a**); short nanobars (**b**); long nanorods (**c**); and silk-like ultralong and ultrathin nanowires (**d**) prepared at [TPS];[Au];[OA];[OAm] = 3:1:0.8:3.8, 3:1:48:45.6, 3:1:24:45.6, and 3:1:24:114, respectively.  
Figure 2: X-ray diffraction (XRD) pattern of silk-like ultralong and ultrathin nanowires.  
Obviously, the size of the gold nanoparticles was controlled by the ratio of the seed to the growth precursor. The gold nanoparticles increased in size with an increase in the growth/seed ratio, as shown in Figure 4a. The increasing tendency in the size of the gold nanoparticles was also confirmed by dynamic light scattering (DLS) measurements. The mean diameters of the Au nanoparticles were measured to be 14.8 \(\pm\) 2.4 nm, 17.0 \(\pm\) 1.2 nm, 21.2 \(\pm\) 3.9 nm, and 37.2 \(\pm\) 9.3 nm, respectively, at the growth/seed ratios of 2, 4, 6, and 8, as shown in the Supplementary Material Figure S2. By carefully adjusting the growth/seed ratio, spherical gold nanoparticles in the size of 10-25 nm could be obtained. The crystallographic structure of the largest Au nanoparticles (growth/seed ratio = 8) was also confirmed by X-ray diffraction pattern (Figure 4b). The diffraction peaks at 20 = 38.1\({}^{\circ}\), 44.2\({}^{\circ}\), and 64.7\({}^{\circ}\) were respectively ascribed to (111), (200), and (220) planes of fcc Au, which agree well with the diffraction standard of fcc Au.  
Figure 3: TEM images of the hydrophobic gold spherical nanoparticles resulting from the growth of seed gold nanodots at growth/seed ratios = 2:1 (a); 4:1 (b); 6:1 (c); and 8:1 (d).
## 2 Problem Formulation
### OPF Constraints
#### 2.1.1. OPF Equality Constraints  
Load dispatch equations present these constraints of grid powers according to Formulas (6) and (7). The real and reactive power supply constraints of network buses are defined as follows [9]:  
\[P_{Gi}-P_{Di}=V_{i}\sum_{j=1}^{NB}V_{j}\big{(}G_{ij}\cos\delta_{ij}+B_{ij}\sin \delta_{ij}\big{)} \tag{6}\]  
\[Q_{Gi}-Q_{Di}=V_{i}\sum_{j=1}^{NB}V_{j}\big{(}G_{ij}\sin\delta_{ij}-B_{ij} \cos\delta_{ij}\big{)} \tag{7}\]  
In the above equations, \(i\) and \(j\) are the indices of bus numbers; \(V_{i}\) and \(V_{j}\) represent the voltage magnitudes; \(P_{Gi}\) and \(Q_{Gi}\) are the real and reactive power outputs of power generators, respectively; \(Q_{Di}\) and \(P_{Di}\) are the reactive and real load demands, respectively; \(B_{ij}\), \(G_{ij}\) are the susceptance and conductance of the branch between buses \(i\) and \(j\), respectively; \(\delta_{ij}\) is the difference between the phase angles of voltages between these buses; and \(NB\) denotes the number of buses.
# An Optimized Load Balance Solution for Multi-homed Host in Heterogeneous Wireless Networks
## 3 Proposed System
### Stage 2: Connection Ranking Using the MADM Method  
As seen in Figure 2, a MADM method is required to determine the priority vector as an initial ranking weight of the available multiple connections after which the rule of the utility connection equation is to distribute the application's data in accordance to the determined weights in unequal cost load balance scheme. The main duty of Stage 2 is ranking the available multiple connections in accordance with the application class and the attributes of the connection.  
The hierarchy process is beneficial for the proposed solution. That process conducts a relationship between the applications, the connection attributes influencing the applications, and the available multiple connections which have a different measurement with attributes. As stated in Stage 1, every application has a different dependent attribute for which its performance perform. We use the relationship between the applications, attributes, and available multiple connections to construct the required hierarchy process of MADM method for which it will do the required ranking. As shown in Figure 4 the analysis relationship is illustrated in a hierarchy form. The ranking process yields a weighted metric which will be used by the utility connection equation next to distribute the application data classes over the available multiple connections.  
As seen in Figure 2, the optimization process is the last stage of the proposed system model. The application traffic is distributed among available multiple connections by applying overall traffic utilization through the connection utility equation (17). Every application will have a different profile of its own data distribution based on the weighted criteria. The connection utility equation is applying the optimization of the selected MADM ranking process in order to maintain the distribution of the application's data in accordance with the weighted round robin in adaptive form based on application weighted criteria.  
The utility connection equation will use its good state form represented in Equation (18) to distribute the application's data over available multiple connections, while the system power is in its normal cases. However, it will transit to use its low power state form represented in Equation (19) to offload the application's data over the less power consuming connection among the available multiple connections, which is determined through Stage 2 of the proposed system.