esa-sceva/satcom-chunk-collection huggingface dataset:
# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
## 3 Model Description
### GANA Model
Obviously, we have veb(VS\({}_{im}\)) = vib(VS\({}_{jn}\)) and veb(VS\({}_{jn}\)) = vib(VS\({}_{im}\)). Figure 1 shows the GANA model for a simple VDC sample with a multi-tier network oversubscribed. In this example, as noted, the labels <B1, B2> and <B3, B4> correspond to the bidirectional edges linking the aggregation layer and the core layer. A similar labeling scheme is applied for the edges connecting the access layer and the aggregation layer. To reduce the cost and excessive use of bandwidth, the proper send and receive rates for tenants control the VM in the VDC; therefore, they direct the traffic between uplink and downlink. The main objective of GANA is to provide online VN to PN algorithm for VDC, which can map VN requests with node and link constraints on the premise of maintaining virtual network topology:  
Server Mapping denoted as \(\overline{VM}\to M\) \begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Description** \\ \hline PG (H, S, E) & The physical network infrastructure \\ \hline VG (VM, VS, VE) & The VDC network infrastructure \\ \hline DFS & Depth First Search \\ \hline LLDP & Link Layer Discovery Protocol \\ \hline PAM & Pod Allocation Manager \\ \hline GL & Global Leader \\ \hline \end{tabular}
\end{table} 
Table 1: Notations description. \begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Description** \\ \hline GVMG & Green VDC Mapping Graph \\ \hline S\(ij\) & The /th port of /th physical switch {core, aggregation, edge} in S \\ \hline VS\(ij\) (VCS, PAS, VES) & The /th port of /th virtual switch {core, aggregation, edge} in VS \\ \hline Interface (Si) & The port number of switch \(i\). \\ \hline Interface (VS\(i\)) & The port number of virtual switch \(i\) in VDC \\ \hline Ib (S\(ij\)) & Physical ingress bandwidth of S\(ij\) \\ \hline Eb (S\(ij\)) & Physical egress bandwidth of S\(ij\) \\ \hline Rib (S\(ij\)) & Residual ingress bandwidth of S\(ij\) \\ \hline Reb (S\(ij\)) & Residual egress bandwidth of S\(ij\) \\ \hline vib(VS\(ij\)) & The ingress bandwidth requirement of VS\(ij\) \\ \hline veb(VS\(ij\)) & The egress bandwidth requirement of VS\(ij\) \\ \hline rvib(VS\(ij\)) & Residual ingress bandwidth of VS\(ij\) \\ \hline rveb(VS\(ij\)) & Residual egress bandwidth of VS\(ij\) \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Switch Mapping denoted \(VS\to S\)  
Link Mapping denoted as \(VE\to E\)  
Subject to  
\[\left\{ \begin{array}{l}ib\big{(}S_{p,q}\big{)}\leq rib\big{(}S_{p,q}\big{)}, \quad\forall VS_{i}\in VS,VS_{i,j}\to S_{p,q}\\ eb\big{(}S_{p,q}\big{)}\leq reb\big{(}S_{p,q}\big{)},\quad\forall VS_{i}\in VS,VS_{i,j}\to S_{p,q}\end{array} \right. \tag{1}\]
###### Abstract
The vulnerability threat caused by vegetation degradation has been studied in many regions, and RS and GIS are important methods for studying vegetation vulnerability in areas with limited data. The studies on vegetation in the MRB mainly focus on the mapping of vegetation types [23], the response of vegetation to climate change [24], and the calculation of vegetation ecological water demand [25], trying to understand the changes in vegetation under natural and socio-economic changes [26; 27]. However, there are relatively few studies studying vegetation vulnerability. Therefore, taking 2010-2019 as the base period, evenly divided the remaining years of the 21 st century as near future period (2020-2059) and distinct future period (2060-2099), vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) were used to assess vegetation vulnerability in the MRB. Then the spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the stochastic matrix was used to analyze the transition of vegetation vulnerability among the base period, near future period, and distinct future period. This study can provide a comprehensive understanding of vegetation vulnerability and point out the areas with severe vulnerability in the future in the MRB. It can also provide a further understanding of the relationship between vegetation protection and geographic location under different SSPs for scientific support and comprehensive decision-making for vegetation protection for the local government.
## 4 Experimental Setups and Data Description
### Data Description  
Figure 4 shows a three-story building structure is made of aluminum columns and plates assembled using bolted joints with a rigid. The building structure has three floors where the top and bottom of the building (30.5 cm \(\times\) 30.5 cm \(\times\) 2.5 cm) structure are connected by utilizing four aluminum columns (17.7 cm \(\times\) 2.5 cm \(\times\) 0.6 cm) to form a four-degree of freedom system. Moreover, a center column is installed on the top floor, shown in Figure 3(b), which can change the degree of nonlinearity via changing the distance between the column and the bumper [33]. To collect data from the structure, the structure has five channels, including a force transducer and four acceleration sensors obtaining vibration signals. The sampling frequency is set to 322.58 Hz. Because temperature has a significant influence on the dynamic parameters of the structure monitored, considering that the three-story frame structure was tested in the LOS ALAMOS national laboratory, the temperature should be constant. Thus, the temperature generating influence can be ignored.  
Figure 4: Experimental setup of the building structure.  
According to different damage and nonlinear degree in Table 1, there are some 13 structural conditions. Then, each scenario is repeated ten times, and these data sensors are recorded by sensors. Structural conditions (1-5) represent structural damage caused by nonlinearity, which imitates effects from crack opening and closing. Structural conditions (6-7) are designed as mass and nonlinearity changes, which represent impact-induced damage. Structural conditions (8-13) denote stiffness changes, where structural conditions (11-13) are more severe than (8-10) due to more stiffness reduction of structure.  
It can be seen from Table 2 that 13 structural conditions can be divided into six scenarios that represent a different degree of structural damage, namely 10% damage condition, 20% damage condition, 30% damage condition, and 40% damage condition. Then, these four damaged conditions can be divided into the training set, testing set, and validation set with radio 6:2:2. In addition, every state includes 8192 time domain data points. The sampling time is 25.44 s because the sampling frequency is set to 322.58 Hz.
# Evaluation of Coastal Sea Level Offshore Hong Kong from Jason-2 Altimetry
## 5 Results
### Sea Level Trend  
#### 5.2.1 A regional View  
We first used the gridded CCI sea level data to obtain a regional picture of the sea level trends from July 2008 to December 2014 (Figure 8a). To compare the gridded CCI sea level with the coastal sea level estimated in this study (see below), we interpolated the CCI trend grid along the Jason-2 pass at 1-Hz resolution (~7 km). Corresponding CCI-based along-track sea level trends are shown in Figure 8b. The uncertainty associated with the trend estimation is also provided (blue bars). These error bars are rather large because the time span of analysis is short and the inter-annual variability is large. Trend estimates using the whole CCI altimetry record (i.e., 23-years, from January 1993 to December 2015) display much smaller errors, but a behavior similar to Figure 8b is also observed, i.e., larger errors around 16\({}^{\circ}\)N than near the coast.  
Figure 8: \(Cont.\)  
Figure 7: Percentages of waveform categories in the 3 cases.  
In parallel, in order to see the impact of the different altimetry products used here (thus of the different data processing) on the results, the linear 6.5-year trends were also estimated along the Jason-2 pass from both the raw and filtered versions of the X-TRACK SLA time series. Results obtained from the filtered X-TRACK SLA are shown in Figure 9 as a function of latitude.  
From Figures 8 and 9, we observe a significant spatial variability in the regional sea level trends. The CCI map (Figure 8a) displays values of \(-6\) to \(-4\) mm/yr over the open ocean which tend to increase to \(+2\) to \(+4\) mm/yr near the coast. Figure 8b illustrates how Jason 2 pass 153 captures part of this regional trend pattern, with values varying significantly from \(-2\) mm/yr to \(+4\) mm/yr along the track. In X-TRACK (Figure 9), around \(20^{\circ}\)N, the linear trend is also relatively small (\(-2\)-\(3\) mm/yr), in agreement with the CCI data. A change is observed near \(19^{\circ}\)N, corresponding to the along-track influence of a narrow trench (the bathymer rapidly decreases to \(\sim\)\(-2500\) m and then recovers to \(-100\) m). As in the CCI data, the X-TRACK trends increase towards the coast but the values obtained in the vicinity of HK are much larger than for the CCI trends: \(+8\) to \(+9\) mm/yr against \(+2\) mm/yr. The sea level trend at the last point of the filtered X-TRACK SLA is: \(+8.6\)\(\pm\) 2.2 mm/yr. However, gridded altimetry products, such as in the CCI data set, have too low resolution to capture near-shore sea level variations. This also explains why the trends of the gridded CCI data have larger uncertainty than the unsmoothed X-TRACK data.  
Figure 8: Regional 6.5 yr-long trends in sea level derived from the CCI sea level data. (**a**) using gridded data and (**b**) interpolated along the Jason-2 pass 153. Vertical blue bars in Figure 8b represent the uncertainty on the trend estimation.
## 4 Numerical Example
### Step 2: Maintenance Strategy  
We assumed that our system has a degradation law characterized by a Weibull distribution. The Weibull scale and shape parameters are \(\beta\) = 50 and \(\alpha\) = 2. The preventive and corrective maintenance costs are respectively \(M_{p}\) = 220 mu and \(M_{c}\) = 1000 mu. In order to solve the maintenance cost, we use the software MATHEMATICA (11.1.1, Wolfram Research, Oxfordshire, UK).  
Figure 2 illustrates the total cost of the maintenance according to each number of preventive maintenance actions. We notice that the optimal number of preventive maintenance actions \(P\) is equal to three. Thus, the optimal period of intervention \(T^{*}\) is equal to three. Therefore, the total cost of the maintenance policy is equal to 1638 mu.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Period** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Demand & 25 & 17 & 35 & 35 & 22 & 25 & 16 & 20 & 17 & 25 \\ Distribution plan & 20 & 17 & 20 & 20 & 20 & 20 & 16 & 20 & 17 & 20 \\ Allocation to UDC\({}_{1}\) & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 \\ Quantity subcontracted to UDC\({}_{1}\) & 0 & 0 & 1 & 7 & 2 & 0 & 0 & 0 & 0 & 1 \\ Allocation to UDC\({}_{2}\) & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 \\ Quantity subcontracted to UDC\({}_{2}\) & 0 & 0 & 3 & 8 & 1 & 0 & 0 & 0 & 0 & 0 \\ Allocation to UDC\({}_{3}\) & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\ Quantity subcontracted to UDC\({}_{3}\) & 5 & 0 & 8 & 0 & 2 & 5 & 0 & 0 & 0 & 2 \\ Delayed quantity & 0 & 0 & 3 & 3 & 0 & 0 & 0 & 0 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Distribution plan with the subcontracting strategy using the maximal capacity.  
Figure 2: Average total cost of the maintenance according to \(P\).  
Now, let \(L_{p}=1\) (\(a=6\%\)) and \(L_{r}=5\) (\(b=35\%\)) define capacities lost with respect to the delivery rate due respectively to the preventive maintenance actions and minimal repair actions. Table 7 shows the available delivery rate in each period taking into account the maintenance actions' effects.
## 4 Signal Processing Algorithms
### Locate Signal Peak(s)  
The signal peak(s) which have the largest magnitude will correspond to regions from the sample that the light signal has been backreflected or backscattered from. Thus, these frequencies correspond to the depth values at areas of interest on the sample. A straightforward method of finding the peak(s) within a signal is through use of the MATLAB function \"findpeaks\". The resolution of the measurement can be increased by fitting a 2 nd order polynomial to datapoints surrounding the peak location relating to the measured sample offset distance as shown in Figure 9b. The obtained peak frequency can then be converted to a spatial depth as described in Section 2.2.
## 2 Background and Related Work
### Blockchain and Smart Contract  
From the system architecture aspect, a typical Blockchain system consists of three essential components: a distributed ledger, a consensus protocol, and smart contracts. Essentially, distributed ledger technology (DLT) is a type of distributed database that is shared, replicated, and maintained by all participants under a P2P networking environment. Each participant maintains a local view of the distributed ledger in the context of a distributed computing environment, and a well-established consensus allows all participants to securely reach an agreement on a global view of the distributed ledger under the consideration of failures (Byzantines or crash faults). Given different consensus algorithms and network models, distributed consensus protocols are categorized into Nakamoto consensus protocols [13] or Byzantine fault-tolerant (BFT) consensus protocols [14]. From the topology aspect, Blockchains can be classified into three types: public (permissionless) Blockchains, private (permissioned) Blockchains, and consortium Blockchains [15].  
Thanks to the cryptographic and secure computing schemes, a _smart contract_ (SC) brings programmability to the Blockchain by integrating protocols with user interfaces to formalize and secure the relationships of participants over computer networks [16]. Essentially, SCs are programmable applications containing predefined instructions and data, and they are compiled and saved in the addressable storage of the Blockchain. Through exposing a set of public functions or application binary interfaces (ABIs), an SC acts as the autonomous trusted agent between parties to perform predefined business logic functions or contract agreements under specific conditions. Owing to the secure execution of the predefined functional logic, global unique address, and open-access ABIs, the SC is an ideal candidate to implement decentralized applications (Dapps) under dynamic, heterogeneous, and distributed network environments.
## Materials and Methods
### Meteorological Data  
Meteorological data measured at the Jiangyin, Wuxi, Suzhou, and Dongshan meteorological stations near Lake Taihu (Figure 1), including precipitation and wind speed data, were obtained from the China Meteorological Data Sharing Service System ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). Significant correlations between the meteorological data from the four meteorological stations were observed (hourly wind speed: \(r>0.59\), \(p<0.01\); daily precipitation: \(r>0.85\), \(p<0.01\)). Thus, to analyze the variation of \(K_{\mathrm{d}}\)(490), we used the mean hourly wind speed and daily precipitation of the four stations to characterize the meteorological data for the entire lake.
## 4 Evaluation and Discussion  
In this paper, we have described the development of an automatic visual inspection system for the location and classification of defects on wood veneer surfaces. Each layer of the veneer has to be individually inspected before gluing them all together into plywood. The defective regions of each layer have to be replaced with non-defective pieces of wood. Therefore, the visual inspection system has to output the location of the defective region and the type of defect. In comparison with the results of other authors working on the topic of wood surface defect recognition (see Table 5), our approach works well, achieving an average accuracy of 80.6% with a high performance in terms of defect recognition. The best performance, achieved by the AlexNet architecture (6.76 ms), allows the developed automatic visual inspection system to be used on a wood veneer sorting conveyor line in a real-world industrial wood veneer processing facility. The results can be explained by the combination of the fully connected and convolutional layers, which ensures high recognition performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network Model** & **Precision** & **Recall** & **F-Score** \\ \hline AlexNet & 0.7999 & 0.8001 & 0.7998 \\ VGG16 & 0.7804 & 0.7774 & 0.7716 \\ BNInception & 0.7778 & 0.7806 & 0.7754 \\ ResNet152 & **0.8053** & **0.8065** & **0.8010** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean precision, recall, and f-score values of AlexNet, VGG16, BNInception, and ResNet152 neural network models used for transfer learning. The best results are shown in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Neural Network Model** & \multicolumn{5}{c}{**Average Accuracy of Classes, \%**} & **Grand Average** & **Performance,** \\ \cline{2-6}
**AlexNet** & **Branch** & **Scratch** & **Stain** & **Core** & **Background** & **Accuracy, \%** & **ms** \\ \hline
**AlexNet** & \(\mathbf{82.7\pm 1.8}\) & \(81.7\pm 1.7\) & \(\mathbf{66.0\pm 1.2}\) & \(82.7\pm 1.7\) & \(88.1\pm 1.9\) & \(80.0\pm 1.7\) & **6.76** \\ VGG16 & \(86.2\pm 2.1\) & \(81.7\pm 1.9\) & \(52.0\pm 1.4\) & \(81.9\pm 1.9\) & \(88.1\pm 2.1\) & \(78.0\pm 1.9\) & 23.04 \\ BNInception & \(\mathbf{91.9\pm 2.3}\) & \(\mathbf{86.5\pm 2.1}\) & \(56.0\pm 1.5\) & \(64.6\pm 2.1\) & \(92.4\pm 2.3\) & \(77.7\pm 2.1\) & 13.12 \\ ResNet152 & \(88.5\pm 1.7\) & \(72.1\pm 1.6\) & \(59.0\pm 1.1\) & \(\mathbf{89.6\pm 1.6}\) & \(\mathbf{94.6\pm 1.7}\) & \(\mathbf{80.6\pm 1.6}\) & 48.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy classification when using AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The best results are shown in bold. \begin{table}
###### Abstract  
This study investigated the aerosol particle properties and light absorption properties of brown carbon (BrC) by utilizing a seven-wavelength aethalometer, and analyzed NH\({}_{4}\)\({}^{+}\), NO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{-}\), K\({}^{+}\), K, organic carbon, elemental carbon, levoglucosan, and mannosan in PM\({}_{2.5}\). The research was conducted in a rural area of Jeonnam, South Korea, during the winter season. In addition, the dithiothreitol assay-oxidative potential normalized to 9,10-phenanthrenequinone (QDTT-OP) was investigated throughout the study period. The absorption coefficient was found to be 2.6 to 5.6 times higher at 370 nm compared to 880 nm, suggesting the presence of light-absorbing substances in addition to black carbon (BC) particles. The estimated absorption coefficient of BrC\({}_{370}\) was 29.9% of the total light absorption coefficient at 370 nm. Furthermore, BrC\({}_{370}\) exhibited a strong affinity with levoglucosan while showing a weak correlation with K\({}^{+}\), confirming the suitability of levoglucosan as a tracer for biomass burning. The QDTT-OP was 5.3 nM m\({}^{-3}\), and highly correlated with the carbonaceous components levoglucosan and mannosan, suggesting a relatively high contribution of biomass combustion emissions to oxidative potential. Further research should be conducted to assess the health risks associated with future PM\({}_{2.5}\) exposure related to biomass burning in the atmosphere.  
black carbon; brown carbon; light absorption; biomass burning; DTT-OP +
Footnote †: journal: Applied Sciences
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 8 Digital Management
### Supervisory Control and Data Acquisition  
Supervisory control and data acquisition (SCADA) monitors and manages the Smart Infrastructure equipment and assets, including industrial and manufacturing processes, via Programmable Logic Controller (PLC), Remote Terminal Units (RTU) and Remote Inputs Outputs (RIO). Originally SCADA was based on proprietary protocols such as Modbus, Conitel although newer versions IEC and DNP3 are standardized and vendor agnostic based on Transmission Control Protocol / Internet Protocol (TCP/IP).
## 3 Results and Discussion
### Selection of Oceanic-Atmospheric Indices
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & **Climatic Indices** & **Correlation Coefficient** & \(p\)**-Value** & **VIF** \\ \hline Jan & TNA(10), ERSST(3) & \(-\)0.67, 0.61 & 0.001, 0.005 & \textless{}1.0 \\ Feb & WP(4), MEI(8) & \(-\)0.67, 0.68 & 0.001, 0.001 & \textless{}1.3 \\ Mar & EA(4), AO(5), AMM(3), ERSST(3) & \(-\)0.71, \(-\)0.64, \(-\)0.59, 0.45 & 0.0007, 0.002, 0.007, 0.05 & \textless{}1.7 \\ Apr & NINA3(10), SOI(5), EAVR(6), PDO(8) & 0.83, \(-\)0.57, 0.44, 0.66 & 0.0001, 0.009, 0.05, 0.001 & \textless{}4.1 \\ May & EA(9), AO(9), AAO(12), ERSST(3) & 0.67, 0.45, \(-\)0.47, 0.47 & 0.001, 0.04, 0.03, 0.03 & \textless{}1.3 \\ Nov & TNA(8), ERSST(3) & \(-\)0.54, 0.75 & 0.01, 0.0002 & \textless{}1.1 \\ Dec & NAO(8), NINAO3(5), ERSST(3) & 0.46, 0.57, 0.66 & 0.04, 0.01, 0.002 & \textless{}1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of teleconnection indices used for each month (the number in the ( ) means the months lagged). Figure 5: Moving correlations (years 1998–2016) between the monthly reservoir inflow and the climatic indices.
# A Novel Soft Robotic Exoskeleton System for Hand Rehabilitation and Assistance Purposes
## 2 Development of the SEG Device
### Development of the SEG Device's Software
The PC, along with the installed GUI application, connects with the SEG device via a simple USB type B cable. Once all components have been connected, the GUI application can be executed smoothly. The GUI application was developed to function in two different modes. The first mode follows a patient-friendly protocol with five distinct steps. The first step is to register the patient's code in order to follow the international standards for personal data and the  
Figure 6: (**a**) Flowchart of the control glove; (**b**) Flowchart of the SEG control software; (**c**) Experimental set-up of the control and functionality testing coupled with the response diagram of the control configuration. number of exercise cycles that the patient or healthcare professional desires (Figure 7a). Then, as is shown in Figure 7b, the main window appears with the motion bars for each finger, the protocol's steps, the iterations number, and some generic information about the device, such as the battery condition of the control glove, the connected network, and the version of the application. The next step is the capture of the desired finger motion. Hence, the control glove is activated, and when the motion is completed, the glove transmits the motion data to the SEG device, and the positions are shown on the GUI's motion bar with white dots. Afterward, the patient performs the same movement without any assistance, and the achieved finger positions are represented on the screen with blue dots. Then, the motion is repeated with the assistance of the SEG in order to achieve the desired positions and activate the muscle memory. This procedure is repeated for the times that have been set at the start of the session. Finally, when the treatment has been completed, the goals and the motion data coupled with the best performance of the patient, in terms of the minimum distance from the goals, are stored on cloud storage with the patient's code in order to be examined by a healthcare professional and assesses the patient's progress. On the other hand, the second mode allows the user to operate the SEG manually, setting the desired goals for the motion without the need for the control glove. In this scenario, the graphical environment is the same as the one presented in Figure 7; however, the motion goals are selected with the assistance of a computer mouse. This mode is used for testing purposes and specific movements.