esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Approach
### Thermogram Characteristics and Measurands
The resulting intensity gradient in the thermogram begins at the leading edge and extends over a height \(h_{\text{grad}}\). The remaining flow region to the natural transition line, with the height \(h_{\text{lam}}\) has an almost constant intensity in the thermographic image. Since the intensity gradient near the leading edge reduces the image contrast of the turbulence wedge's tip, the accurate detection of the wedge's tip is challenging. In order to verify and characterize the functionality of a wedge-detection algorithm, a ground truth needs to be known. Therefore, simulated thermograms are necessary, which emulate the characteristic features of real thermograms for given values of the measurement quantities. A simulated thermogram is shown in Figure 1 (right), which is not an exact replication of the real thermogram to its left but models all important characteristics. It shows only a single wedge with a different angle \(\alpha_{i}\) compared to the real thermogram to illustrate the relation between the angle and the tip position of the wedge. Furthermore, the intensity gradient in the simulated thermogram is exaggerated compared to the real thermogram for the sake of visibility. A detailed description of the simulation of thermograms follows in Section 3. Simulated thermograms are already utilized in the subsequent description of the functionality of the algorithm principle for the wedge detection.
## 2 Materials and Methods
estimation using the orbit initial conditions are performed iteratively until the satellite orbit accuracy meets the requirements. For the current BeiDou regional navigation system, GEO satellites remain in an area with a small range, whereas IGSO satellites fly within a specific longitude range. The solution of the long arcs of the BeiDou satellite can be stable. Therefore, unlike the GPS one-day arc orbit determination, this study uses the three-day arc for BeiDou satellite orbit determination. The computation process of the three-day solution of the BeiDou satellite orbit is slightly different from that of the one-day solution. The three-day solution needs to combine the observation data of ground tracking stations and broadcast ephemeris over three consecutive days, whilst the other computation steps are identical to those of the one-day solution. A total of 124 stations were selected for POD to conduct a comprehensive analysis of the three types of satellite orbits of BeiDou, GEO, IGSO, and MEO. The 124 BDS/GPS stations include 24 CNCORS and 100 MGEX stations. The observation period was from 16 July 2016 to 2 August 2016. Eight types of GNSS receiver equipment at the 124 ground stations are listed in Table 3. Of this number, 24 CNCORS stations are equipped with the Trimble NETR9 receiver and the chock-ring antenna, which are able to receive observation data from both GPS and BDS satellites. This study adopts the following four computation strategies to conduct the BeiDou orbit determination and analyze the influence of ground station distribution and multi-system joint processing on the accuracy of BeiDou orbit determination. The specific strategies are as follows:  
Strategy 1: BDS/GPS joint orbit determination method. The ground station uses observation data from the 124 stations, including the CNCORS and MGEX stations. The ground station distribution is shown in Figure 1.  
Strategy 2: BDS/GPS joint orbit determination method. The ground station uses data from the MGEX stations only, which means that the CNCORS stations are excluded. Strategy 3: BDS Single-system orbit determination method. The ground station distribution is the same as that in strategy 1. Strategy 4: BDS Single-system orbit determination method. The ground station distribution is the same as that in strategy 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Receiver Type** & **Number** \\ \hline
**1** & Trimble NETR9 & 86 \\
**2** & Septentrio POLARX4 TR & 7 \\
**3** & Septentrio POLARX4 & 9 \\
**4** & Septentrio POLARX3/XS & 2 \\
**5** & LEICA GR25 & 10 \\
**6** & LEICA GR10 & 6 \\
**7** & LEICA GRX1200+ & 1 \\
**8** & JAVAD TRE\_G3 TH DELTA & 3 \\ Sum & & 124 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tracking station receiver type and number.
## 1 Introduction
It is essential to analyze the hydraulic characteristics inside the fishway in more detail for passage efficiency. Therefore, two-dimensional (2D) analysis is sufficient for attraction efficiency, whereas the passage efficiency requires three-dimensional (3D) analysis. Hydraulics in fish passage structures have been investigated for more than 30 years. Most numerical simulations have focused on the flow field inside a fishway according to its type. However, it is surprising to find that there is lesser published research on numerical simulation of flow fields around fishways than those inside [1]. Hereby, the following is a brief look at the previous studies that analyzed the flow velocity structure inside the fishways in three dimensions. [PERSON] et al. [8] conducted a three-dimensional numerical  
Figure 1: Attraction (**a**) and passage efficiency (**b**) according to fishway type [5]. simulation on a small-scale fishway (1.13 m in width) installed in the laboratory to identify the flow field in the vertical slot, and drew results that were relatively consistent with the observed flow field. [PERSON] et al. [9] used the three-dimensional flow simulation result in the design of appropriate spacing between the slot and the weir of the vertical slot type fishway. [PERSON] et al. [10] represented the flow pattern in a fishway with a turning pool using a three-dimensional numerical model. If the obstacle's height to be overcome is high, the length of the fishway becomes longer. In the case where the fishway is rotated in the form of a 180-degree curve is called a tunning pool. Studies on this have continued until recently, and [PERSON] [11] analyzed the characteristics of the flow field by 3D simulation while changing even the positions of orifices and notch in a fishway with a tunning pool. In this study, we propose a hydraulic analysis method that can evaluate attraction and passage efficiencies separately. For attraction efficiency analysis, the River-2D model with a fish physical habitat module was used, and the weighted usable area is suggested as an indicator of efficiency. For passage efficiency analysis, the FLOW-3D model, which has a free surface and can analyze the fluid behaviors in hydraulic structures, was used. For the target fishway for evaluation, we selected the ice-harbor type fishway at Dalseong weir in the Nakdong River of Korea.
## 4 Discussion and Conclusions  
This pilot study explored the experimental and technical solutions for the analysis of gait-event-related brain potentials (gERPs) during treadmill walking based on gait markers taken from pressure plates implemented in the treadmill. Although the attempt to evaluate the neural correlates of human gait by means of EEG is not new, the approach of the present study aimed to investigate open key questions that will now be discussed in detail.  
The technical solution investigated in this study allows for the synchronization of gait and EEG data from different recording devices and from different recording software installed on different computers. While parallel ports are the best way to avoid jittering and produce precise triggering, the problem is that today these ports are no longer available in customized computers and are instead replaced by serial ports or by Bluetooth. These solutions, however, do not reach the accuracy of data transfer of parallel ports. As observed in the validation tests performed in the present study, the custom-made USB synchronization interface--connecting the stimulation unit and the pressure plate--produced a temporal delay of the trigger signal of >2 ms. This temporal delay is intolerable for ERP analysis because ERPs are measured time-locked to the onset of the event of interest. Therefore, the data streams recorded need to be synchronized, such that the gait events of interest can be used as reliable markers for EEG epoching and ERP averaging. In the present solution, the custom-built USB interface was developed. The interface controls signal transfer from one device to the other and allows for determining the temporal delay elicited during data transfer and uses this information for data synchronization. Whereas the first step included a hardware solution, the second step included a software solution using MATLAB as a programming platform. Moreover, a parallel port was used for sending triggers from the stimulation unit to the EEG recording unit. These triggers were used as jitter-free references to which triggers sent from the stimulation unit to the force plate were later aligned.
## 4 Simulation Results and Performance Analysis
### Comparison Charts
Figure 12: (**a**) Cluster head density (**b**) Cluster state distribution. Figure 13: Clustering performance. Figure 15 compares the delay incurred in receipt of the packet from the source vehicle to all the vehicles in the 5 km range, that is the vehicles in the region of interest by CDS_SC along with DTA and DWCM. The delay is lesser in CDS_SC than the other two because of the faster dissemination due to reduction in broadcast storm and efficient clustering mechanism. Figures 16 and 17 shows the Packet Collision Ratio (PCR) and Packet Loss Ratio (PLR) when the number of vehicles per km increases. Due to the reduction in number of rebroadcasting vehicles both PCR and PLR are negligible and comes around maximum 2% only. Figure 14: Packet delivery ratio versus vehicle density. Figure 15: End-to-end delay versus vehicle speed.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 3 Results  
### Grid Convergence Index (GCI)  
The GCI was calculated in order to minimize the discretization error. The grid refinement factor r was calculated based on the representative mesh size \(h\) (1) as:  
\[r=\frac{h_{coarse}}{h_{fine}}, \tag{6}\]  
Moreover, the following assumption was made:  
\[h_{1}<h_{2}<h_{3};\;\;r_{21}=\frac{h_{2}}{h_{1}};\;\;r_{32}=\frac{h_{3}}{h_{2 }}, \tag{7}\]  
The order of convergence p was calculated based on Equation (8) [30]:\[p=\frac{\left|ln\Big{|}\frac{\varepsilon_{32}}{\varepsilon_{21}}\Big{|}+ln\Bigg{(} \frac{r_{21}^{p}-1\cdot sgn\Big{(}\frac{\varepsilon_{32}}{\varepsilon_{21}} \Big{)}}{r_{21}^{p}-1\cdot sgn\Big{(}\frac{\varepsilon_{32}}{\varepsilon_{21}} \Big{)}}\Bigg{)}\right|}{ln(r_{21})}, \tag{8}\]  
where:  
\[\varepsilon_{32}=\phi_{3}-\phi_{2};\ \ \varepsilon_{21}=\phi_{2}-\phi_{1}, \tag{9}\]  
\(\phi_{k}\) denotes the value of the variable important to the objective of the simulation study for solution obtained with the \(k\)th mesh. The average relative \(X\) velocity calculated for line X\({}_{12}\) was selected as the above-mentioned variable (Figure 7 describes the location of the X\({}_{12}\) line).  
The approximate relative error was calculated on the basis of Equation (10).  
\[e_{a}^{21}=\left|\frac{\phi_{1}-\phi_{2}}{\phi_{1}}\right| \tag{10}\]  
Finally, the GCI was determined using Equation (11):  
\[GCI_{21}=\frac{1.25\cdot e_{a}^{21}}{r_{21}^{p}-1} \tag{11}\]  
All the values of the above-defined quantities are listed in Table 1.  
The obtained results indicate the mesh convergence in case of all analysed mesh types: HEX, POLY and TETRA.
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
## 5 Conclusions  
In response to the problem of communication quality degradation or even interruption caused by downlink jamming in satellite communication earth stations, this paper constructs a reflector antenna array model, derives the corresponding MVDR beamforming algorithm, and applies the algorithm in the reflector antenna array of satellite communication earth stations. Finally, the algorithm is simulated and analyzed using Matlab 2016. The simulation results show that using multiple satellite communication earth stations to form an array, and using the MVDR beamforming algorithm to assign certain weights to signals from different directions, can improve the directional gain of satellite signals, effectively suppress jamming, and enhance the SINR of the array output. Furthermore, the jamming suppression effect gradually improves with an increase in snapshots. However, the proposed algorithm has poor anti-jamming performance at some angles, and further optimization of the algorithm and array layout is needed to improve the range of jamming suppression for the array.  
Software, J.L.; Formal analysis, Y.X.; Investigation, W.Z.; Writing--original draft, Y.X. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Natural Science Foundation of China (No. 62271500), Natural Science Research Fund Program of Shaanxi (No. 2020 JM-344), and Graduate Innovation Practice Fund of Air Force Engineering University (No. CXJ2022024).  
Data Availability Statement: Data is unavailable due to privacy.  
We appreciate [PERSON]'s professional guidance during the writing and revision of the paper. The authors thank the anonymous reviewers for offering extraordinary advice.  
The authors declare no conflict of interest.
# Dynamic Cost-Aware Routing of Web Requests
## 2 Literature Review
### Routing Work Loads to Renewable Energy Powered DCs  
A global load balancing of web requests to DCs where the renewable energy is maximum or the energy is cheaper was proposed [PERSON] (1976). Renewable energy can be stored using uninterrupted power supplies (UPS) to be later used during power fluctuations to reduce energy costs. The power stored in UPS can be also used to reduce the peak power demand cost--known as peak shaving during the peak utilization hours [PERSON] (1976). Two algorithms were proposed [PERSON] (1976) to reduce the use of brown energy to zero by shifting workloads to the DCs in regions where the energy cost is low and by switching off servers at lightly loaded DCs. The receding horizon control (RHC) algorithm for homogeneous settings and the averaging fixed horizon control algorithm (AFHC) for heterogeneous settings were proposed.  
A fuzzy logic-based load balancing method was proposed to route the workload among geographically distributed datacenters to reduce electricity costs by considering renewable energy availability, brown energy consumption and average electricity cost at DC locations [PERSON] _et al._ (2010). A reactive global load balancing and auto scaling of servers were proposed to reduce electricity cost and brown energy usage by routing requests to sites with higher renewable energy and lower costs [PERSON] _et al._ (2010). A cost function that comprises a linear combination of energy cost and lost revenue due to SLA violation is used to route the job requests. A distributed geographical load balancer was proposed [PERSON] (1976), in which the jobs are routed to DCs using \"follow the renewable routing\" policy to DCs where the proportion of brown energy to total energy production is at a minimum.  
A two-time-scale load balancing algorithm, TLB-ARMA, was proposed to route web requests to a DC where the electricity price is minimum and powered by wind energy [PERSON] _et al._ (2010). The air temperature was measured at the beginning of scheduling intervals to compute cooling efficiency and to predict the wind power. A constant number of servers were maintained to avoid power wastage in frequent _on/off_ of servers. The most recent work [PERSON] (2016) proposes a renewable aware load balancing policy using a combination of Reinforcement Learning and Neural Networks to optimize Big Data Analytic jobs across geographically distributed datacenters.  
An energy-aware pricing in the three-tiered cloud service market was proposed [PERSON] (2016) to minimize the total energy cost of the system by promoting integration of renewable energy in the system. The square root staffing law policy was used to maintain required number of servers in the _on_ state to provide better QoS and to increase the revenue to the Infrastructure-as-a-Service (IaaS) providers. A Global load balancing policy was used to distribute workloads of software-as-a-service providers (SaaS) to IaaS providers across spatially different electricity markets while reducing the cost incurred to SaaS providers, eventually to reduce prices of cloud services to end users.
###### Abstract  
Background: Patellar instability can cause pain and disability. A finite element model of a healthy human knee was performed to analyze the role of quadriceps and medial retinaculum imbalance in patellar instability. Methods: The model was created by matching magnetic resonance and computed tomography images of a normal adult patient's knee. Muscle force intensities were calculated by static optimization, considering the lower limb muscles, knee movement and the ground reaction during walking. Patellar instability was experimentally generated by progressively uncoupling muscular forces, (90 N versus 110 N), while at the same time, the load derived from the quadriceps was gradually reduced by 20%. Results: This loss in force symmetry of 10 N on the retinaculum may produce a displacement of approximately 7 mm, with an increase in patellar contact forces of approximately 44%. When the quadriceps force is reduced by 10% and the unbalanced medial retinaculum acts together, the displacements are in the order of 14 mm, and the patellar contact forces increase by 84%. Conclusion: A reduced quadriceps force alone is not able to cause significant patellar instability, while an imbalance of forces at the level of the retinaculum could lead to patellar instability, especially when the two effects are combined. A better understanding of joint relationships and muscle synergies can help to improve clinical approaches to patella instability  
finite element modelling; knee flexion; medial retinaculum; muscular unbalancing; patellar instability
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 3 Proposed Methods
### Method 2: Drowsiness Detection and Prediction Using Deep Learning
#### 3.2.1 Phase 1: Training  
Step 1: Pre-Processing  
In order to create a good training dataset, we pre-process a set of drowsy and non-drowsy images extracted from videos. In this step, we perform the extraction of images from the input videos. The extraction rate of images from videos is 25 frames per second.  
Figure 10: Model of the proposed method 2 for detecting drowsiness using deep learning.  
We first detect faces in these images using the SSD network (Single Shot MultiBox Detector) with ResNet-10. Then, the facial images are normalized to the size of 224 \(\times\) 224 to create the training dataset.  
Step 2: Feature Extraction and Training  
In order to extract features and train, the training dataset is passed through deep neural networks (DNNs). From the advantages of the neural networks described in Section 2.3, we develop two adaptive networks for drowsiness detection, inspired by ResNet-50V2 and MobileNet-v2, by making improvements in some layers of these networks to adapt the drowsiness detection. We construct an adaptive network by adding layers such as Flatten, Relu (Densen), Dropout, Dense (Sigmoid) and by suppressing the AveragePooling2D layer of the MobileNet-v2 network, as shown in Figure 11. It aims at solving the problem of overfitting and helps the model to converge faster. Additionally, we propose another adaptive network by adding layers such as Flatten, two Relu (Densen), Dropout, Dense (Sigmoid) to the ResNet-50V2, as shown in Figure 12.  
Figure 11: The network architecture of the proposed method 2 developed from MobileNet-v2.  
Figure 12: The network architecture of the proposed method 2 developed from ResNet-50V2.  
Taking advantage of the transfer learning approach, the proposed networks are pre-trained on datasets of Bing Search API, Kaggle, and RMFD. We use the pre-trained weights and re-train them on our training dataset to fine-tune the parameters of these networks. This leads to faster learning, a shorter training time, and no requirement for large training datasets. During the training process, we decide to stop the training phase when the Loss_value is not reduced.
## 3 Numerical Example 1
### Impact of Primary System Mistuning
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**MTMD**} & \multirow{2}{*}{**No.**} & \(\mathbf{m_{k}}^{t}\) & \(\mathbf{k_{k}}^{t}\) & \(\mathbf{\zeta_{k}}^{t}\) & \(\mathbf{\beta_{k}}\) \\ \cline{3-6}  & & [kg] & [kN/m] & [–] & [–] \\ \hline
1 TMD & 1 & 2000 & 1.960905 & 0.068462 & 0.990178 \\ \hline \multirow{2}{*}{2 TMDs} & 1 & 1000 & 0.895498 & 0.041605 & 0.946307 \\  & 2 & 1000 & 1.096614 & 0.044381 & 1.047193 \\ \hline \multirow{4}{*}{4 TMDs} & 1 & 500 & 0.419478 & 0.025349 & 0.915945 \\  & 2 & 500 & 0.470454 & 0.025186 & 0.970004 \\ \cline{1-1} \cline{3-6}  & 3 & 500 & 0.526416 & 0.025998 & 1.026076 \\ \cline{1-1} \cline{3-6}  & 4 & 500 & 0.595890 & 0.028082 & 1.091687 \\ \hline \multirow{4}{*}{2 TMDs} & 1 & 250 & 0.200353 & 0.016536 & 0.895215 \\  & 2 & 250 & 0.214738 & 0.014990 & 0.926796 \\ \cline{1-1} \cline{3-6}  & 3 & 250 & 0.228431 & 0.014593 & 0.955889 \\ \cline{1-1} \cline{3-6}  & 4 & 250 & 0.242203 & 0.014669 & 0.984282 \\ \cline{1-1} \cline{3-6}  & 5 & 250 & 0.256858 & 0.015086 & 1.013624 \\ \cline{1-1} \cline{3-6}  & 6 & 250 & 0.273079 & 0.015356 & 1.045139 \\ \cline{1-1} \cline{3-6}  & 7 & 250 & 0.291974 & 0.015877 & 1.080691 \\ \cline{1-1} \cline{3-6}  & 8 & 250 & 0.316299 & 0.017789 & 1.124809 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal \(k_{k}{}^{t}\), \(\zeta_{k}{}^{t}\), \(\beta_{k}\) values obtained for the J1 objective function.
###### Abstract  
A novel method for the determination of lipid oxidation using Raman microscopy was developed. A home-made surface-enhanced Raman spectroscopy (SERS) substrate based on silver nanoparticles deposited on a glass Petri dish was used. The degradation of ground pork stored at 5 \({}^{\circ}\)C was monitored for 16 days. Two packages were considered: an active packaging containing an oregano extract with antioxidant properties and a conventional one consisting of a low-density polyethylene (LDPE) film. The lipid fraction of the ground pork was extracted with a mixture of diethyl ether/n-hexane (1:1). A remarkable maximum signal enhancement factor of 1.64 \(\times\) 10\({}^{7}\) at 1439 cm\({}^{-1}\) shift (and up to 8.58 \(\times\) 10\({}^{6}\) at 1655 cm\({}^{-1}\), chosen for fat oxidation assessment) was obtained with SERS compared to conventional Raman. In addition, SERS provided better discrimination among samples than the results obtained by the thiobarbituric acid reactive substances (TBARS) method. The experimental conditions for SERS were optimized and discussed.  
lipid oxidation; surface-enhanced Raman spectroscopy (SERS); silver nanoparticles; Raman microscopy +
Footnote †: journal: Applied Sciences