esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Upper-Level Multiple Resources Coordinated Optimization Modeling and Solving
### Mathematical Model for Multiple Resources Coordinated Optimization
#### 3.2.4 Control Resources Amount Constraints  
The control amount of HVDC emergency power support, ELS, and VEPR falls into the following ranges:  
\[P_{\text{dc},i}\leq P_{\text{dc},i}^{0}+k_{\text{dc},i}P_{\text{dc},i}^{0},i=1,\cdots,N_{\text{dc}} \tag{12}\]  
\[P_{\text{td},j}^{\min}\leq P_{\text{td},j}\leq P_{\text{td},j}^{\max},j=1, \cdots,N_{\text{td}} \tag{13}\]  
\[P_{\text{vpp},k}^{\min}\leq P_{\text{vpp},k}\leq P_{\text{vpp},k}^{\max},k=1, \cdots,N_{\text{vpp}} \tag{14}\]  
where \(P_{\text{td},j}^{\min}\) and \(P_{\text{td},j}^{\max}\) are the minimum and maximum response amount of \(j\)th ELS station, respectively. \(P_{\text{vpp},k}^{\min}\) and \(P_{\text{vpp},k}^{\max}\) are the minimum and maximum response amount of \(k\)th VPP, respectively.  
To calculate the ELS amount faster, the ELS variables are treated as continuous in this work. However, in the actual power grid, as the ELS trips the outlet or substation, these variables actually consist of a series of discrete values. Therefore, treating them as continuous variables can only obtain the approximate optimal values of the load shedding. In engineering practice, the optimal combination of loads needs to be found to make the shedding amount of the ELS station require the control amount with the minimum excess of shedding. The DER equipment power in VPP is very small. For example, the rated power of slow-charging EV, AC, and WH are usually only a few kW, so it is reasonable to treat the response variables of VPP as continuous values.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 3 Experimental Results
### Case Study
The relationship between relative wave height and PS of dDDM is presented in Figure 11. The variability of WH can be classified into three stages: (1) the WH changes rapidly from dDDM 1 to dDDM 18 and (2) a relatively slow change from dDDM 19 to dDDM 38 and (3) a decrease from dDDM 38 to dDDM 56. Only the large variability in PS and PN from dDDM 1 to dDDM 18 shows agreement with the changing of WH. Therefore, the variability should be affected by the ocean surface (e.g., wave height, wind speed, sea state etc. ), but it is difficult to make any specific conclusions or models about the ocean surface. Figure 11: The relationship between PS and WH-ECMWF of dDDM 1 to dDDM 56. Four groups of PS results (\(DDM_{T}\) = 0.36, \(DDM_{T}\) = 0.38, \(DDM_{T}\) = 0.40, \(DDM_{T}\) = 0.42) is presented. The values of WH are scaled down as we just concentrate on the variability. Figure 10: Significant Wave Height data from the European Centre for Medium-Range Weather Forecasts (HW-ECMWF) over high latitude area of the north hemisphere (Latitude: 56\({}^{\circ}\) N–84\({}^{\circ}\) N, Longitude: 44\({}^{\circ}\) W–40\({}^{\circ}\) E) on 26 October 2015. The red line is SP ground-track of DDMs (DDM 1 to DDM 57). There is no valid wave height from DDM 58 to DDM 131.
## 2 Theory and Modeling
### The Reflection Matrix of Wave Facets
For the water-incident case (see Figure 3a), the light is reflected toward the water onto the under-side of the sea surface (reflection \"sea to sea\"). Its surface reflection matrix \(\mathbf{rww}(\mu^{\prime},\;\varphi^{\prime},\mu,\varphi)\) is expressed as follows:  
\[\mathbf{rww}(\mu^{\prime},\;\varphi^{\prime},\mu,\varphi)=\;S(\mu,\mu^{\prime})\, \frac{\pi p(\mu_{n},\varphi_{n})}{4\big{|}\mu\big{|}\mu^{\prime}\big{|}\mu_{n}} \mathbf{R}(\pi-\chi_{2})\mathbf{RF}_{WW}\big{(}\theta_{i}^{w}\big{)}\mathbf{R}(-\chi_{1}). \tag{12}\]  
The water-water Fresnel reflection matrix \(\mathbf{RF}_{WW}\big{(}\theta_{i}^{w}\big{)}\) is a function of the incidence angle \(\theta_{i}^{w}\) in the reflection plane. \(\mathbf{RF}_{WW}\big{(}\theta_{i}^{w}\big{)}\) is expressed in the same way as Equations (10) and (11), but \(\tau_{l}^{w}\big{(}\theta_{i}^{w}\big{)}\) and \(\tau_{r}^{w}\big{(}\theta_{i}^{w}\big{)}\) are different from \(\tau_{l}^{n}\big{(}\theta_{i}^{u}\big{)}\) and \(\tau_{r}^{n}\big{(}\theta_{i}^{u}\big{)}\). \[\left\{ \begin{array}{l}\tau_{l}^{w}\Big{(}\theta_{i}^{w}\big{)}=\frac{\cos \theta_{i}^{w}-m\sqrt{1-m^{2}sin^{2}\theta_{i}^{w}}}{\cos\theta_{i}^{w}+m\sqrt {1-m^{2}sin^{2}\theta_{i}^{w}}}\\ \tau_{r}^{w}\Big{(}\theta_{i}^{w}\Big{)}=\frac{m\,\cos\theta_{i}^{w}-\sqrt{1- m^{2}sin^{2}\theta_{i}^{w}}}{\cos\theta_{i}^{w}+\sqrt{1-m^{2}sin^{2}\theta_{i}^{w}}} \end{array} \right.. \tag{13}\]  
Figure 3: Geometry of the propagation of the lidar light across a rough sea surface for the water-incident case. (**a**) Reflection “sea to sea”. (**b**) Refraction “sea to air”. All notations are as follows: \(\theta_{i}^{w},\;\theta_{r}^{w},\;\theta_{t}^{w}\) and \(\theta_{t}^{w}\) are respectively the incidence angle, reflection angle, and refraction angle relative to the wave facet normal vector \(\overrightarrow{n}\) in the scattering plane; \(\overrightarrow{s},\;\overrightarrow{v},\;\text{and}\;\overrightarrow{t}\) are the unit vectors of the incident, reflected, and transmitted light respectively; \(\theta^{\prime}\) is the zenith angle of the incident light, \(\theta\) is the zenith angle of the exit light (reflected light for the reflection matrix and transmitted light for the transmission matrix), and \(\varphi\) is the azimuth angle. Note that the total reflection happens beyond the critical angle in water (\(\theta_{lim}=\arcsin(1/m)\)); thus, one has  
\[\left\{ \begin{array}{l}r_{11}^{w}\big{(}\theta_{i}^{w}>\theta_{lim}\big{)}=1\\ r_{12}^{w}\big{(}\theta_{i}^{w}>\theta_{lim}\big{)}=0\\ r_{33}^{w}\big{(}\theta_{i}^{w}>\theta_{lim}\big{)}=1\\ r_{34}^{w}\big{(}\theta_{i}^{w}>\theta_{lim}\big{)}=0\end{array} \right.. \tag{14}\]
## 4 Experiments  
### Datasets and Evaluation Metrics  
NTU RGB+D 60 dataset [35] is one of the most widely used large-scale action recognition datasets, consisting of 40 subjects aged between 10 and 35 years performing 60 types of actions, with a total of 56,880 videos captured by 3 Microsoft Kinect v2 cameras. The actions are categorized into three classes: (1) 40 daily actions; (2) 9 health-related actions; (3) 11 interactive actions. The dataset follows two different benchmarks: (1) Cross-Subject (X-Sub) includes 40,320 training samples and 16,560 testing samples, divided by separating the 40 subjects into two groups. (2) Cross-View (X-View) uses videos collected from cameras 2 and 3 as training samples (37,920 videos) and videos from camera 1 as testing samples (18,960 videos). A total of 302 samples with missing or incomplete skeleton data are ignored [15]. The dataset provides RGB images, depth images, and skeleton information simultaneously. In our experiments, we used 3D skeleton sequences provided by the Kinect v2 camera and 2D human pose results extracted from RGB videos. The 2D human pose estimation was mainly achieved by the Faster-RCNN [36] with a ResNet50 backbone network and the HRNet-w32 [37] single-person pose estimation algorithm.  
NTU RGB+D 120 dataset [38] is presently the most extensive dataset available for indoor action recognition and serves as an advanced iteration of the NTU RGB+D 60 dataset. It comprises 114,480 videos that are categorized into 120 action classes. The dataset also follows two benchmarks: (1) Cross-Subject (X-Sub) includes 630,226 training samples and 50,922 evaluation samples; (2) Cross-Setup (X-Set) includes 54,471 training videos and 59,477 evaluation videos, which are divided based on the distance and height between the subject and the camera. Additionally, 532 samples with missing or incomplete skeleton data are excluded from the dataset.  
Evaluation metric: We report the Top-1 accuracy for action recognition datasets. Additionally, we measure the model efficiency by considering the number of trainable parameters (Params) and floating point operations (FLOPs). A higher number of FLOPs indicates that more computational operations are needed during the neural network inference process, which usually means greater computational complexity and longer execution time. In our ablation studies and comparative experiments on the NTU RGB+D 60 dataset, we included Params and FLOPs as further evaluation metrics.
## 4 Fall Detection Machine Learning Techniques  
Machine learning is usually classified into unsupervised learning and supervised learning. Supervised learning is increasingly being utilized for fall detection in two stages: training and validation. The following are some of the most popular machine learning methods to detect a fall:  
* Artificial Neural Network (ANN): Its general architecture includes three main layers, namely inputs, a hidden layer, and outputs. The hidden layer links between the inputs and outputs layers. It consists of multi-internal layers. Training of this algorithm is complex due to the nature of hidden layers as a black box. It becomes unobvious to clarify how a fall event occurred [67; 68]. The ANNs have a high tolerance for heterogeneous data with precise results, making them suitable for detecting falls, particularly in the fog-computing layer used on smart devices to save the required high computing resources, e.g., graphical processing units (GPUs).
* Support vector machines (SVMs) are algorithms that divide multidimensional data into two categories. SVM is simpler than ANN for small and high dimensionality datasets. It is recommended for the edge-computing layer, specifically on fall postures [67; 69; 70].
* Decision Trees: This algorithm divides data into two classes of either fall or non-fall, according to the path taken through a tree-like graph according to a set of conditions. Learning is clear and simple in this algorithm, making it suitable for investigating the causes of falls, particularly the physiological data to classify the patterns and possible risk of falls [71; 72]. For instance, changes in blood pressure or oxygen levels are correlated to the falling.
* Naive Bayes: It is a relatively straightforward algorithm with high accuracy and performance. It needs less memory and time requirements to train the classifier. As a result, it can be used to detect falls directly on edge devices [73].
* Deep Learning: The Internet of Things (IoT) includes several sensors that can continuously generate big data with velocity, volume, and variety [74]. The ANN with many hidden layers was developed as a deep learning method to process large amounts of data [75]. Unlike traditional machine learning algorithms, deep learning does not require external feature extractors. However, it takes a long time to train a big dataset, while a short time for the process of prediction. Similar to neural networks, processing still appears to be a black box and is difficult to comprehend [76].
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 6 Simulation Performance and Analysis
### Result Discussion  
To test the validity of DHBP in the LSN, simulations of DHBP, DBPR [4], OSPF [43], and SALB [8] are carried out under different traffic patterns. The traffic patterns adopted in this paper include constant bit rate traffic, Poisson traffic, and bursty traffic.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 4 Technical Requirements: from Automotive to Network Perspective
### 5 GCAR Use Case Technical Requirements
#### 4.3.4 High Definition Local Map Acquisition (HDLM)  
Figure 4 illustrates the high definition local map acquisition use case, which depicts an example of a layout for the dynamic map. Polygons are used to organize and divide the information, the off-board system (so called application server) is used to gather and process all of the information collected from different sources available on and along the road. Finally, the roadside infrastructure provides vehicles with information collected from the connected sensors. Moreover, the host vehicles receiving the HD-maps also aggregate and process the information received from the server.  
Several automotive as well as network specific requirements are analyzed for this use-case. The most critical automotive requirements are: (1) relevance area, which is at least five seconds horizon or 250 m and up to 10 km, since it can be used for both short term and long term route planning, respectively; (2) localization, which requires 15 to 25 cm accuracy as sufficient for driving automation of Level 3 cars, but 5 cm at best to detect half the width of lane marking for fully autonomous cars; (3) overtake time of 10 s is needed for stopping the autonomous vehicle safely in case of any hazardous situation. The inter-vehicle distance requirements are similar to the ones derived for the see-through use case. (4) A relevance area equivalent at least to five seconds horizon (approximately 250 m) is needed. On the network side, the basic requirement is coverage availability of 99% due to the redundant information included in the trajectory messages and the high reliability requirement of 99.99% that guarantee the quality of the information. A data rate of around 2 Mbps is required if an update frequency of 50 ms is used for the objects located within 100 m radius; and, a data rate of around 1 Mbps is required if an update rate of 100 ms is sufficient for objects that are located more than 100 m away. Both flows will be sent simultaneously, so the overall data rate for the downlink would be 2.88 Mbps per vehicle, in order to build a continuous electronic horizon. Finally, the end-to-end latency must be below 30 ms for the network communication layer. In fact, an end-to-end application layer represents the end-to-end latency from a Local Dynamic Map (LDM) object report via an LDM application server to the vehicle while using the LDM object report in Sensor Fusion (SF). The SF function calculates the weight of sensor value relevance based on age of information received from the detection or the report, from a 100 ms \"old\" report the weight will get lower, and reaching 150 ms \"old\", the weight will become zero and the value be discarded. In the end to end latency, three elements are taken into account: the uplink latency, server processing latency, and the downlink latency. If between 50 and 100 ms are reserved for the server, then around 25 ms would be required for the maximum communication latency.
## 1 Introduction
They used root mean square error (RMSE) and mean relative error (MRE) for estimation of the predictive accuracy of the four models. They have obtained better accuracy and generalization than BPNN and RBFNN methods [15]. In this study, the drying performances between the drying under the sun and the AHSC drying system were compared. Pears were dried in both drying methods. The \(h_{c}\) values of pears were calculated in the AHSC drying system and under the sun drying. The SVM regression was applied for the calculated \(h_{c}\) coefficients in AHSC drying system. The aim of this study was to establish a predictive model for \(h_{c}\) values and to determine which drying method of pears would be faster.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.2.4 Classification Based on SVM  
In this subsection, the condition patterns are identified using SVM based on the normalized feature set. The performance of classification highly depended on the features set. Consequently, the time-domain features and the frequency-domain features were combined to construct the input data vectors. Then, a normalized 10-dimension input vector was fed into SVM to train for the downstream classification processing. Figures 12-14 show the recognition results based on the features of different domains.  
Figure 14: Recognition results of SVM classifier based on T-F features.  
Figure 12: Recognition results of SVM classifier based on time-domain features.  
Figure 13: Recognition results of SVM classifier based on frequency-domain features.  
The quantitative classification performance of the SVM is compared in Table 5, which shows the classification results of the condition patterns using the features of different domains.  
The classification accuracy of individual time-domain features or individual frequency-domain features is poor as compared to the time-frequency domain features. These results suggest that either time domain or frequency domain alone cannot fully capture the condition patterns. To tackle this problem, it would be effective to make use of each of the domain features to comprehensively describe all patterns.  
In terms of the computational complexity [50; 51] and the number of sample points \(N_{d}\) with \(d\) dimensions to be trained, the computational complexity of the training phase required could be \(\mathrm{O}\big{(}N_{d}^{2}d\big{)}\). In the test phase, the computational complexity required \(\mathrm{O}(MN)\) operations, where \(M\) and \(N\) are the kernel operations and support vectors, respectively. The computational complexity of SVM in this example, specifically the 400 points with 50 dimension T-F features to be trained, required the computational complexity to be \(\mathrm{O}\big{(}400^{2}\times 50\big{)}\). The RBF kernel was evaluated for 200 operations requiring 307 support vectors. Therefore, the computational complexity required \(\mathrm{O}(200\times 307)\) for the test phase.
## 2 Materials and Methods
### VCCs  
Each sample (10 g) was homogenized in 90 mL of sterile peptone saline (0.1% polypeptide, 0.85% NaCl) for 2 min. The samples were then diluted in series using a saline solution. Plate count agar (PCA) was used to detect general viable bacteria [16]. Streptomycescinthallus acetate-acidione agar (STAA) was used for _Brochotrix thermosphacta_[18]. De Man, Rogosa, Sharpe agar (MRS, Merck KGaA, Darmstadt, Germany) was used for mesophilic LAB, and Violet Red Bile Glucose agar (VRBG, Merck KGaA) for Enterobacteriaceae. For these bacteria, 1 mL of the diluted solution was mixed with each medium. _Aeromonas_ isolation agar (Sigma-Aldrich, St. Louis, MO, USA) with 5.0 mg/L of ampicillin (AIA) for _Aeromonas_ spp., marine agar (Condalab, Madrid, Spain) for heterotrophic marine bacteria, cephaloridine fucidin cetrimide agar (Merck KGaA) with 1.0% (\(v/v\)) glycerol (CFC) for _Pseudomonas_ species, and black colonies of iron agar (Condalab) with 5.0% NaCl for H\({}_{2}\)S-producing bacteria were used. A dilution (0.1 mL) of bacterial suspension was added to the medium. The incubation conditions were as follows: PCA at 30 \({}^{\circ}\)C for 72 h, STAA and VRBD at 25 \({}^{\circ}\)C for 48 h, MRS at 30 \({}^{\circ}\)C for 48 h, AIA and Marine agar at 20 \({}^{\circ}\)C for 72 h, CFC at 35 \({}^{\circ}\)C for 72 h, and iron agar at 25 \({}^{\circ}\)C for 96 h. After incubation, the colony-forming units in the samples were counted, resulting in VCC values (Log CFU/g). Microbial populations of 7 Log CFU/g have been consistently identified as the threshold for spoilage of fish products across various studies [19; 20; 21].
## 4 Mmpl3 Transporter: The Other Hot TB Target of the Moment
### MmpL3: The Achilles Heel of Mycobacterium Tuberculosis
Analysis of the transmembrane region has shown the existence of two pairs of hydrophilic residues (D256-Y646 and Y257-D645) on TM4 and TM 10 which links these two helices by forming hydrogen bonds. These residues are similar to the Asp-Asp-Lys triad found in AcrB and the Asp-Asp-Thr triad in SecDF, which are known to be involved in the proton relay pathway [106; 107]. Consistently with this, three out of four of these residues have been shown to be essential for MmpL3 activity [81]. Furthermore, this catalytic tetrad is conserved in most MmpL transporters, with the exception of MmpL7, so it is likely that all MmpL transporters use the same mechanism to drive its substrate translocation [81]. The crystal structure of MmpL3 bound to SQ109, Au1235 and ICA38 has shown that these MmpL3 inhibitors bind to the same pocket in the center of the transmembrane region of the protein (Figure 3A), inducing conformational changes which in turn disrupt the interaction of the two Asp-Tyr pairs involved in proton translocation (Figure 3B,C). Furthermore, molecular docking assays with six other compounds (BM212, NITD-349, GSK2200150A, C215, PIPD1 and HC2091) showed that they can fit the same binding pocket within MmpL3. Thus, MmpL3 inhibitors seem to have an identical mechanism of action, binding to the same pocket and blocking the proton translocation relay pathway that is essential for substrate transport. This is also supported by the fact that the majority of resistance mutations found in MmpL3 lie in the TM region, close to the binding pocket. [PERSON] et al. purified 12 different mutant versions of MmpL3 and showed that most of the mutations affected the binding of the inhibitors to the pocket by disrupting hydrophobic/electrostatic interactions, inducing conformational changes or causing steric hindrance which leads to a decrease or abolition of the binding. On the other hand, there is still no clear evidence as to how mutations located further for the binding pocket induce resistance. Although MmpL3 is the only essential MmpL protein in _M. tuberculosis_, several other MmpL proteins have been shown to be required for virulence and/or drug efflux [108; 109]. The fact that all inhibitors act by disrupting the proton relay pathway, a conserved feature of all MmpL transporters, opens the avenue for the development of molecules that target not only MmpL3 but all the MmpL family of transporters and thus affect virulence and resistance to other drugs. In summary, these findings will lead to a new era in MmpL3 inhibitor drug discovery since structure-guided molecules can now be designed with better anti-tubercular efficacy and pharmacokinetic/pharmacodynamic (PK/PD) properties.