esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Verification Using the Real Data
### Parallelism Between T/T and B.B Rotating Shafts Axes  
In Figure 8, the coordinates of two check points (\(d1\), \(d11\)) are:  
\[d1(55.39,24,353.63) d11(55.39,-24,353.63)\]  
The coordinate of middle point \(d5\) located between \(d1\) and \(d2\) is:  
\[d5(55.39,0,353.63)\]  
The X-axis and Y-axis coordinates of axis point \(d4\) at the T/T rotating shaft are transferred to d5 to form \(d5^{\prime}\). Therefore, the vector formed by \(d4\) and \(d5^{\prime}\) is \(\overrightarrow{d5^{\prime}d4}\).  
\[d5^{\prime}=(59.1395,0,360.12772) \tag{28}\]  
In Figure 7,  
\[\overrightarrow{a2a1}=(0,48-(-333.5),0)=(0,381.5,0) \tag{29}\]  
The parallelism angel (\(\theta\)) between \(\overrightarrow{a2a1}\) and \(\overrightarrow{d5^{\prime}d4}\) can be calculated as:  
\[\cos\theta=\frac{\overrightarrow{d5^{\prime}d4}\cdot\overrightarrow{a2a1}}{ \overrightarrow{d5^{\prime}d4}\left|\overrightarrow{a2a1}\right|}=\frac{(0,24,0)\cdot(0,381.5,0)}{\left|(0,24,0)\right|\cdot\left|(0,381.5,0)\right|}= \frac{9156}{9156}=1 \tag{30}\]_Appl. Sci._**2019**, \(9\), 5355  
As above, the T/T and B.B rotating shafts axes are confirmed parallel.
# Properties of Vector Embeddings in Social Networks
## 5 Experiments
### Quantitative Results
#### 5.3.2 Approximating Centrality Values  
We aim to compute network centralities in an efficient time complexity. We report the results in terms of Mean Squared Error, hence smaller error gives better approximation. To approximate each centrality, we randomly selected 70% of nodes in the Facebook graph as training set and the rest as a test. Table 4 demonstrates average value and standard deviation of centralities as well as errors where  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Weight** & **DeepWalk** & **LINE** & **Loc** & **Node2 vec(1)** & **Node2 vec(2)** \\ \hline \multirow{4}{*}{Facebook} & \(w_{DC}\) & \(0.09\pm 0.02\) & \(-0.15\pm 0.05\) & \(\mathbf{0.92\pm 0.06}\) & \(\mathbf{0.82\pm 0.01}\) & \(-0.15\pm 0.07\) \\  & \(w_{CC}\) & \(-0.01\pm  
we feed the model with different embeddings. The Normalized Root Mean Squared Error (RMSE) is a standard statistical metric to measure model performance.  
We also report results in terms of Normalized Root Mean Squared Error (NRMSE) and Coefficient of Variation of the RMSE:  
\[NRMSE=\frac{RMSE}{y_{max}-y_{min}},\]  
\[CV(RMSE)=\frac{RMSE}{\overline{y}},\]  
where \(\overline{y}\) is the average of target values on test set. \(y_{max}\) and \(y_{min}\) are maximum and minimum of target values on test set [44].  
It can be seen that, for closeness centrality, the error range is quite low compared to the average value, hence the regression model approximates closeness sufficiently well. Although closeness centrality has not been a relevant factor for explaining the ranking of nodes in our previous learning-to-rank experiment, a nonlinear mapping as provided by the neural network can estimate closeness values. A possible interpretation is the existence of local manifolds in embedding space containing nodes with similar closeness values. Since closeness represents the average distance of a node to all other nodes in the graph, it also hints that graph distances are preserved in embeddings space (in a nonlinear manner). However, for the other centrality measures, RMSE values are in the range of the average value or even higher. Hence, it seems that embeddings alone are not strong enough features to approximate these centralities.
## 3 Computation Model  
### Generation of Airfoil, Judgment of Grid Point Type  
As shown in Figure 2, we select the NACA0012 airfoil as the computation object. The airfoil consists of a series of line segments connected by scatter points. After partitioning the Cartesian grid, a known point A is determined inside the airfoil, and, in turn for each grid point, a type judgment is made. For the grid point B to be judged, it is only necessary to calculate whether the line segment AB intersects the line segment constituting the airfoil. If it does intersect, point B is then considered an external point, i.e., it is a fluid point. If it does not intersect, then point B is an internal point, i.e., a solid point. After finding all solid points, a circle of grid points around the solid point is considered the boundary point.  
Figure 1: The two dimensional and nine velocity (D2Q9) model.  
Figure 2: NACA0012 airfoil grid point type judgment diagram.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Dynamic Bandwidth
#### 5.2.4 Verification of Zero Packet Loss  
We evaluate the packet loss under burst flow conditions. The experimental scenario is similar to that of the previous experiment, with the priority set as \(p_{1}=p_{2}<p_{3}<p_{4}\). Consumers begin sending Interest packets at moments 0 s, 1 s, 3 s, and 5 s. In this setup, Flows 3 and 4 are burst flows that significantly increase the number of forwarded packets, potentially causing packet loss. We assess the packet loss for the CCLF and PCON methods on Router 2 and Router 3. The packet loss statistics are illustrated in Figure 19.  
Figure 19 demonstrates that the PCON mechanism fails to quickly alleviate congestion during a sudden traffic surge, resulting in packet losses across multiple routers. In this experiment, the PCON method leads to a total of 1592 packet losses within 12 s. By contrast, the CCLF method ensures zero packet losses across all routers.  
This is because PCON requires consumers to gradually reduce the sending rate of Interests and cannot immediately decrease data traffic to a congestion-free level, making packet loss unavoidable during the adjustment period. In contrast, the CCLF method enables each router to directly control the forwarding of Data packets. When burst traffic arrives, Data  
Figure 19: Validation of packet loss free of CCLF with time-varying bandwidth.  
Figure 18: Total throughput of the congested link with dynamic bandwidth.  
packets can be buffered in the CS to ensure that no packets are discarded. By avoiding packet loss, data retransmissions are eliminated, reducing flow completion time.  
Notably, under conditions of extreme congestion or network failures, the CS reaches its capacity and results in overflow. The CCLF method cannot guarantee lossless transmission in such scenarios. However, NDN features proximity data retrieval, meaning that upon recovery from network failures, users can send Interest packets to retrieve Data packets from upstream routers that have stored these packets in their CS, thereby avoiding fetching them from the original producer. This significantly reduces content acquisition delay. This paper emphasizes that the CCLF method guarantees lossless forwarding at the network layer. As long as link connections are intact, Data packets can be forwarded without loss.
## 1 Introduction  
Nowadays, the need to integrate modern technologies in conventional electrical distribution systems is of crucial importance in terms of optimization, security, confidence, reliability and energy efficiency [1]. One of the critical issues in power distribution systems is the uncontrollable increase in demand. This is mainly due to the increase in consumers and the increasingly high dependence on electricity for heating and cooling. Therefore, these factors are enablers of significant fluctuations in the rate of consumption of electrical energy. With the increase in demand at peak hours, there is a need for more generation plants to avoid voltage drops and the decrease in the quality of the electrical energy. As a result, institutions should encourage Demand Side Management (DSM), which becomes viable by implementing robust bidirectional communication systems [2]. These systems need appropriate hybrid topologies to allow the communication network to provide the user with reliability and safety on the use of information [3]. This approach opens a path toward the existence of an Intelligent Electric Network (IEN). An IEN is possible thanks to the use of communications to obtain data on the intrinsic components of a network (data obtained from producers to consumers). This contributes to our economic and environmental health [4]. The information obtained from the network will be collected by Smart Meters (SMs) [5] spread over the area of interest, and their locations will be fixed [6]. The conventional electricity meters must necessarily be replaced by SMs, since they will be able to communicate with diverse types of electronic devices [7] distributed in the conventional network. Each SM will not only be able to receive and transmit information of electrical parameters as active and reactive power, but will also have the ability to run events, such as reconnection, disconnection, sensing the theft of electricity supply, integration of Distributed Renewable Energy Sources (DRES) and the proper management of energy resources in each individual household. The measurements can be collected without the need to visit the facilities of the customer. This may be carried out in intervals of time of 15, 30 or 60 min. The analysis of these data supports analysts to improve the operation, planning, control and supervision of the conventional electric network [8; 9]. Additionally, this analysis gives us sufficient data to contribute to forecasting home loads and the grouping of each load profile [10].
## 2 Experimental Part
### Method
#### 2.2.2 Measurement of Magnetically-Induced Optical Birefringence  
Optical linear birefringence was measured using a laboratory made polarimeter set-up depicted schematically in Scheme 1. The details are reported in ref [22]. Briefly, the sample was held in a glass cell between poles of iron-core electromagnet (B \(\leq\) 2 T, length of the cell 130 mm). Magnetic field induction was measured by a teslameter placed in the middle of the optical path. The magnetic field intensity distribution along the optical path was uniform (within 1.5%) except for the very ends of the poles (5 mm from each side). The probe light from He-Ne laser (\(\lambda\) = 632.8 nm) was firstly linearly polarized by high quality Glan-Thompson polariser (P) whose transmission axis was oriented at 45\({}^{\circ}\) azimuth angle with respect to the magnetic field direction. After turning the magnetic field on, the sample becomes axially anisotropic and thus birefringent. Consequently, light beam passing through the sample changes its polarization state from linear into the elliptical one. The role of the quarter-wave plate (\(\lambda\)/4) is to restore the linear polarization state. For this purpose, its optical axis is oriented along the azimuth angle of polarizer P. In such an arrangement, any polarization ellipticity (being the signature of birefringence) is converted into the rotation of the plane of polarization. To find the angle of rotation, \(\alpha\), the light is passed through another linear polarizer, working as an analyzer (A). The angle \(\alpha\) is found from the position of the analyzer for which light intensity reaching the photomultiplier is at its minimum. The value of induced birefringence can be easily calculated from the measured value of \(\alpha\):  
\[\Delta n=\alpha\frac{\lambda}{\pi L} \tag{1}\]  
For a given laser wavelength, the uncertainty of \(\Delta n\) measurement depends on the optical path \(L\) and precision of estimation of angle \(\alpha\). For our system where \(L\) = 0.13 m and \(\Delta\alpha\) = \(\pm\)2 \(\times\) 10\({}^{-5}\) rad, uncertainty of \(\Delta n\) is estimated to be about \(\pm\)10\({}^{-10}\).  
All measurements were performed at a constant temperature of 25 \({}^{\circ}\)C.  
It must be noted that, during the experiments, the liquid was kept in a sealed cuvette and the whole of its volume was exposed to uniform magnetic field. No liquid could flow in or out of the cell. For this reason, any anisotropy generated by directional flow or liquid shearing can be disregarded.
## 2 Materials and Methods  
### Study Design and Population  
PRATER was a non-interventional, single-country, multicentre, retrospective 2-part study based on medical chart review (Figure S1) carried out at 16 major public hospital clinics.  
The primary part focused on individual patient-level data planned to be collected for a sample of approximately 300 patients diagnosed with ES-NSCLC (stage IA-IIIC) between 1 January 2018 and 31 December 2021 (index period) at a 1:1:1: ratio across NSCLC stages I, II and III in order to achieve a meaningful number of patients in each study subpopulation (for more details, see section 'Statistical methods'). Patients were eligible if diagnosed with histologically or cytologically confirmed ES-NSCLC during the index period; aged \(\geq\)18 years at initial NSCLC diagnosis; and with sufficient medical records for data abstraction to meet the study objectives. Patients were selected using consecutive sampling from the end of 2021 backwards, until the target sample was achieved. The aforementioned allocation ratio was monitored through the electronic case report form (eCRF) during the patient accrual period across the study sites. The retrospective data collection period extended from the visit that led to the suspicion of the disease or diagnosis of NSCLC (hereinafter referred to as 'visit that led to diagnosis') until the end of the patient's observation period (EOP); the latter was the earliest date of initial therapeutic strategy (ITS) completion, last contact with the patient, death or study end (31 December 2022). ITS was defined as the sequence of all different treatment modalities (i.e., surgery, RT, ST; excluding supportive treatments) administered immediately after initial diagnosis until earliest date of next treatment initiation or EOP. Data were collected through the eCRF.  
In the exploratory part, physicians provided high-level cumulative aggregate information on the number of new NSCLC diagnoses and ITS by year of index period and disease stage, considering all patients diagnosed with NSCLC of any stage (I-IV) at their site during the index period. This information was collected via an electronic survey, in which 14 of the 16 study sites participated. Eligible patients should have been diagnosed with histologically or cytologically confirmed stage I-IV NSCLC during the index period and be \(\geq\)18 years of age at initial diagnosis.  
Participation in an interventional trial in the context of ITS for NSCLC served as exclusion criterion for the primary part only.  
The study conformed to the principles of Declaration of Helsinki, international guidelines for Good Pharmacoepidemiology Practice (GPP) [50], the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [51], the European Union General Data Protection Regulation (GDPR), and local rules and regulations. The protocol was approved by the Institutional Review Boards of the participating sites before any study-related procedures. Informed consent was not required.
## 2 Investigation of Temperature Dependence of Current and Luminous Flux
### Study of Temperature Dependence of Current and Luminous Flux of LED Spotlight SDO 06-20
The external view of the spotlight LED matrix with electronic components was presented in Figure 19:3  
The spotlight contains eighteen SMD 2835 LEDs with direct voltage 12 V, nominal current 75 mA and power 1 W [17]. Current stabilization is provided by high-voltage direct current driver BP2335. Principle electric scheme of the spotlight LL6020 is shown in Figure 27, and functional diagram of BP2335 chip is in Figure 28 [20; 21]. The voltage after the circuit diode bridge VDS is fed to DRAIN input of BP2335. Figure 19:4 Principle electric scheme of LL6020 spotlight. Figure 19:5 An external view of LL6020 spotlight LED matrix. Figure 19:6 Functional diagram of BP2335. Figure 19:7 Voltage formation at CS output of BP2335. LL6020 spotlight has small low-frequency pulsation of the current through LEDs, on which the harmonics of high-frequency component are covered (Figure 31). Dependencies of the relative current values of \(I_{inp}/I_{inp25}\) of LL6020 spotlight on temperature \(T_{c}\) at supply voltage 198 V, 220 V and 242 V are presented in Figure 19:8  
Dependencies of the relative values luminous flux \(F\)/\(F_{25}\) of LL6020 spotlight on temperature \(T_{c}\) at supply voltage 198 V, 220 V and 242 V were shown in Figure 19:9  
If the input current of the spotlight (Figure 33) changes within small limits with increasing temperature at different values of the supply voltage, then the luminous flux  
Figure 18:0 Current pulsation of LL6020 spotlight. Figure 18:1 Dependencies of the relative current values \(I_{inp}/I_{inp25}\) of LL6020 spotlight on temperature \(T_{c}\) at 198 V, 220 V and 242 V supply voltage. within the temperature range +15... +60 \({}^{\circ}\)C decreases by more than 10%, which is due to the effect of temperature on LEDs parameters.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Random Forest Classifier
#### 4.2.2 WT-RF
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Class** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{5}{*}{**WT-RF 256 Variables**} & **Irrigated** & 0.94 & 0.81 & 0.87 \\  & **Non-Irrigated** & 0.90 & 0.97 & 0.94 \\  & **OA** & & 91.4\% & \\  & **Kappa** & & 0.81 & \\  & **F-score** & & 0.91 & \\ \hline \multirow{5}{*}{**WT-RF 18 Important Variables**} & **Irrigated** & 0.89 & 0.78 & 0.83 \\  & **Non-Irrigated** & 0.89 & 0.95 & 0.92 \\ \cline{1-1}  & **OA** & & 89.1\% & \\ \cline{1-1}  & **Kappa** & & 0.75 & \\ \cline{1-1}  & **F-score** & & 0.89 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The values of the overall accuracy, Kappa, F-score, precision, and recall obtained from the RF classifier using the 256 wavelet coefficients and the 16 important wavelet coefficients.
## 3 Data Collection and Methods
### Flash Flood Hazards Assessment Parameters  
In this work, the Hydrologic Modeling System (HEC-HMS) was adapted to provide a hydrological simulation in order to complete the hydrologic characteristics of the catchments and sub-catchments. Following recent hydrological models [36], the loss estimation techniques, in addition to the lag-time estimation method, are the most used methods to extract the hydrological parameters for the rainfall-runoff relationship.  
Many factors were calculated to estimate and evaluate the flood risks, including lag time (T\({}_{\text{Lag}}\)), time of concentration (T\({}_{\text{c}}\)), flow velocity (V), peak discharge value (Qp), and flood volume value (Qt).  
Main flash flood parameters were calculated based on the formula stated by [45; 46], and they are listed in Table 3.
## 3 Principles of Operation
### Fuel Cells
#### 3.3.5 Fuel Cell Efficiency  
The efficiency of a fuel cell operated at different loads can be expressed in terms of the ratio of the actual output voltage to the ideal cell voltage. The actual output voltage is lower than the ideal cell voltage owning to the voltage drop.  
\[cell\,efficiency=\frac{E^{cell}}{E^{0}}\cdot 100\% \tag{26}\]  
Figure 4: Cell potential as a function of the current density.  
where the actual output voltage \(E^{cell}\) at different current density levels can be obtained from Equation (26).
# Temporomandibularibular Disorders and Serological Tests in Patients with Rheumatoid Arthritis
## 3 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{2}{c}{**Click**} \\ \hline
**Characteristic** & **No, N = 9 1** & **Yes, N = 8 1** & **_p-Value 2_ \\ \hline Gender & & & 0.99 \\ Female & 8.0 (88.9\%) & 7.0 (87.5\%) & \\ Male & 1.0 (11.1\%) & 1.0 (12.5\%) & \\ Age & 46.00 (12.00) & 56.00 (17.50) & 0.16 \\ Hypertension & 6.0 (66.7\%) & 6.0 (75.0\%) & 0.99 \\ Diabetes & 4.0 (44.4\%) & 0.0 (0.0\%) & 0.08 \\ Hepatic Steatosis & 1.0 (11.1\%) & 0.0 (0.0\%) & 0.99 \\ Hyperuricemia & 1.0 (11.1\%) & 0.0 (0.0\%) & 0.99 \\ Lung Disease & 1.0 (11.1\%) & 2.0 (25.0\%) & 0.58 \\ Atherosclerosis & 0.0 (0.0\%) & 2.0 (25.0\%) & 0.21 \\ ESR & 19.00 (30.00) & 25.00 (19.50) & 0.99 \\ CRP & 1.00 (1.04) & 0.36 (1.24) & 0.63 \\ RF & & 0.99 \\ Negative & 2.0 (22.2\%) & 1.0 (12.5\%) & \\ Positive & 7.0 (77.8\%) & 7.0 (87.5\%) & \\ ACPA & & & 0.64 \\ Negative & 5.0 (55.6\%) & 3.0 (37.5\%) & \\ Positive & 4.0 (44.4\%) & 5.0 (62.5\%) & \\ ANA & & & 0.99 \\ Negative & 5.0 (55.6\%) & 5.0 (62.5\%) & \\ Positive & 4.0 (44.4\%) & 3.0 (37.5\%) & \\ \hline \hline \end{tabular} \({}^{1}\) Median (IQR) or Frequency (%). 2 [PERSON]’s exact test; [PERSON] rank-sum test.  
\end{table} 
Table 4: Association between possible predictor variables and click. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{2}{c}{**Arthralgia**} \\ \hline
**Characteristic** & **No, N = 6 1** & **Yes, N = 11 1** & **_p-Value 2_ \\ \hline Gender & & 0.99 \\ Female & 5.0 (83.3\%) & 10.0 (90.9\%) & \\ Male & 1.0 (16.7\%) & 1.0 (9.1\%) & \\ Age & 53.50 (14.50) & 46.00 (13.50) & 0.76 \\ Hypertension & 3.0 (50.0\%) & 9.0 (81.8\%) & 0.28 \\ Diabetes & 0.0 (0.0\%) & 4.0 (36.4\%) & 0.24 \\ Hepatic Steatosis & 0.0 (0.0\%) & 1.0 (9.1\%) & 0.99 \\ Hyperuricemia & 0.0 (0.0\%) & 1.0 (9.1\%) & 0.99 \\ Lung Disease & 2.0 (33.3\%) & 1.0 (9.1\%) & 0.51 \\ Atherosclerosis & 1.0 (16.7\%) & 1.0 (9.1\%) & 0.99 \\ ESR & 25.50 (37.00) & 21.00 (15.50) & 0.76 \\ CRP & 0.61 (0.96) & 0.50 (1.52) & 0.48 \\ RF & & & 0.99 \\ Negative & 1.0 (16.7\%) & 2.0 (18.2\%) & \\ Positive & 5.0 (83.3\%) & 9.0 (81.8\%) & \\ Negative & 1.0 (16.7\%) & 7.0 (63.6\%) & \\ Positive & 5.0 (83.3\%) & 4.0 (36.4\%) & \\ ANA & & & 0.99 \\ Negative & 4.0 (66.7\%) & 6.0 (54.5\%) & \\ Positive & 2.0 (33.3\%) & 5.0 (45.5\%) & \\ \hline \hline \end{tabular} \({}^{1}\) Median (IQR) or Frequency (%). 2 [PERSON]’s exact test; [PERSON] rank-sum test.  
\end{table} 
Table 3: Association between possible predictor variables and arthralgia. In Table 5, it can be seen that only three RA patients presented a crepitus, but they were all RF-positive. The statistical significance was demonstrated between crepitus and ANA positivity (\(p\) = 0.05). Both ACPA and ANA were related to clinical TMDs (myalgia and crepitus). Negative ACPA had a significant association with myalgia (\(p\) = 0.03), and positive ANA was significantly associated with crepitus (\(p\) = 0.05).