esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 2 Kinematic Modeling of the Mechanism  
### Kinematical Modeling  
The adjusting mechanism is a parallel mechanism consisting of a fixed platform, a moving platform, and four struts connecting the moving platform and the fixed platform, each strut containing a moving sub (P), a universal sub (U), and a rotating sub (R). The sketch of this adjustment mechanism is shown in Figure 1. In the figure, A\({}_{1}\), A\({}_{2}\), A\({}_{3}\), and A\({}_{4}\) are the intersection points of the four moving subs and the fixed platform. B\({}_{1}\), B\({}_{2}\), B\({}_{3}\), and B\({}_{4}\) are located on the R sub at the intersection point of the fixed platform and the four branch chains. C\({}_{1}\), C\({}_{2}\), C\({}_{3}\), and C\({}_{4}\) are at the center of the four U subs. The quadrilateral A\({}_{1}\)A\({}_{2}\)A\({}_{3}\)A\({}_{4}\) and B\({}_{1}\)B\({}_{2}\)B\({}_{3}\)B\({}_{4}\) are both squares, and O and O' are the center points of the squares A\({}_{1}\)A\({}_{2}\)A\({}_{3}\)A\({}_{4}\) and B\({}_{1}\)B\({}_{2}\)B\({}_{3}\)B\({}_{4}\), respectively. A fixed coordinate system O-xyz is set up in the A\({}_{1}\)A\({}_{2}\)A\({}_{3}\)A\({}_{4}\) plane with the point O at the center, where the x- and y-axes point to a branch chain, and the z-axis points vertically upwards. A fixed coordinate system O'-x'y'z' is established in the B\({}_{1}\)B\({}_{2}\)B\({}_{3}\)B\({}_{4}\) plane with the point O' at the center, the x'- and y'-axes pointing to a branch chain and the z'-axis pointing vertically upwards.  
A fixed angle transformation is applied to describe the position of the dynamic coordinate system O'-x'y'z' about the fixed coordinate system O-xyz. Where \(\psi\) is the angle of rotation around the x-axis, \(\theta\) around the y-axis, and \(\varphi\) around the z-axis, these angles develop the attitude matrix as follows:  
\[\begin{array}{l}\mathbf{R}=\mathbf{R}_{z}(\varphi)\mathbf{R}_{y}(\theta) \mathbf{R}_{x}(\psi)\\ =\begin{bmatrix}c\varphi c\varphi\delta\varphi s\theta s\psi-s\sigma c\psi&c \varphi s\delta c\psi+s\varphi s\psi\\ s\varphi s\theta&s\varphi s\theta s\psi+c\sigma c\psi&s\varphi s\theta c\psi-c \varphi s\psi\\ -s\theta&c\theta s\psi&c\theta c\psi\end{bmatrix},\end{array} \tag{1}\]  
Figure 1: Sketch of the adjustment mechanism.  
where \"\(s\)\" and \"\(c\)\" denote sine and cosine, respectively.
## 3 Results
### Evaluation Using Independent Datasets
#### 3.2.1 Evapotranspiration and GPP
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean of the**} & \multirow{2}{*}{**Experiments**} & \multirow{2}{*}{**RMSD**} & \multirow{2}{*}{**R**} \\  & **Evaluation Data Set** & & & \\ \hline \multirow{2}{*}{Evapotranspiration} & \multirow{2}{*}{1.46 kg/m\({}^{2}\)/d} & Open-loop & 0.87 kg/m\({}^{2}\)/d & 0.80 \\ \cline{3-5}  & & Analysis & 0.85 kg/m\({}^{2}\)/d & 0.81 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Gross Primary \\ Production \\ \end{tabular} } & \multirow{2}{*}{1.76 g(C)/m\({}^{2}\)/d} & Open-loop & 2.20 g(C)/m\({}^{2}\)/d & 0.74 \\ \cline{3-5}  & & Analysis & 1.91 g(C)/m\({}^{2}\)/d & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical scores from the evaluation of both the open-loop and the analysis with respect to evapotranspiration and gross primary production (GPP) averaged over 2010–2016. RMSD—root mean square difference. Figure 6: Top row: (**a**) normalized root mean square difference (RMSD) (blue colours indicate an improvement) and (**b**) normalized information contribution (NIC) applied on correlations values (red colours indicate an improvement) for evapotranspiration from the analysis with respect to the model. Bottom row: same as top row for gross primary production. Units are percentages.
## 2 Materials and Methods
### SoH Prediction in Electric Vehicles  
SoH is defined based on the LIB's nominal capacity, internal resistance, energy storage capacity, and overall performance. In EVs, LIBs are one of the most important energy sources, and the health of these batteries has a direct impact on the vehicle's performance, range, and safety. Therefore, it is important to estimate correctly. SoH prediction is a study that needs to be understood to predict how the performance of a battery can change over its lifetime. Formulations and calculation methods are often associated with indicators such as the ratio of the nominal capacity of the battery to its measured capacity or the increase in the internal resistance of the battery. Mathematically, the SoH has been modeled, as given in Equation (1).  
\[SoH=\frac{Q_{Max}}{Q_{Rated}}\times 100\% \tag{1}\]  
\(Q_{max}\), given in Equation (1), represents the measured capacity, and \(Q_{Rated}\) represents the nominal capacity value. This value refers to the designed or specified maximum capacity of the battery. The nominal capacity specified by the manufacturer is the amount of capacity that the battery should carry under ideal conditions. By proportioning these two values, Equation (1) expresses the ratio of the measured capacity of the battery to the nominal capacity. This ratio is usually expressed as a percentage and shows the current capacity of the battery as a percentage of the specified maximum capacity. This ratio is an important metric that shows how much performance the battery loses or maintains over its life cycle. Therefore, the accurate monitoring and prediction of battery health in EVs are critical elements that affect the range, performance, and safety of vehicles.
## 2 Materials and Methods
### Optimization Criterion  
Only the directional kinematic capabilities of the robot will be considered in this study. In the case of surface machining, the machining path consists of a set of waypoints, which the robot has to follow, and are known a priori. To find the optimum placement of the workpiece with regard to the proposed machining requirements, the following optimization criterion will be expressed as:  
\[F(x,y,\varphi)=V_{\text{max}}^{path}. \tag{17}\]  
To determine the kinematically optimal placement of the workpiece, the optimization problem can be formulated as maximizing the objective function \(F(x,y,\varphi)\):  
\[\max_{x,y,\varphi}F(x,y,\varphi) \tag{18}\]  
subject to:  
* The joint angles of the robot should be bounded between its positional joint limits: \[\theta_{i,\text{min}}\leq\theta_{i}\leq\theta_{i,\text{max}},\ i=1,\dots,6,\] (19)
* The workpiece should be located within a working area, which is defined relative to the robot's base frame {B} within the range: \[\begin{array}{l}X_{\text{min}}\leq X\leq X_{\text{max}},\\ Y_{\text{min}}\leq Y\leq Y_{\text{max}},\end{array}\] (20)  
Figure 3: URSe collaborative robot on a mobile platform and presentation of the coordinate system (red-green-blue arrows refer to x-y-z Cartesian coordinate system axes).  
The rotation of the workpiece should be limited between:  
\[\varphi_{\min}\leq\varphi\leq\varphi_{\max}, \tag{21}\]  
All waypoints on the robot's path should be reachable, which means that an inverse kinematics (IK) solution exists for all waypoints on the robot's path.
## 3 Application Areas of IoT and UAV in Smart Farming
### Monitoring  
Monitoring refers to timely sensing of different parameters of a farm. Automatic monitoring is one of the preliminary features of smart agriculture. Strategically placed sensors can automatically sense and transmit data to a gateway for further analysis and processing. Sensors are used to monitor crop parameters such as leaf area index [36], plant height, colour, shape and size of leaves [37]. They are also useful in monitoring soil moisture [38; 39]; irrigation water parameters such as salinity and pH level [40]; and weather parameters such as air pressure, temperature, relative humidity, wind speed, wind direction, rainfall, radiation, etc. [41; 42]. Furthermore, remote sensing is also being used very effectively. Due to the light weight of sensors, remote sensors are installed in lower altitude UAVs and consequently can monitor crops effectively and cost-efficiently. Thus, high-resolution data are being obtained by eliminating different types of constrained such as weather condition. For instance, authors in [43] conducted an experiment to collect 3D data from lightweight snapshot cameras mounted in aerial vehicles.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 5 Security Evaluation
### Burrows-Abadi-Needham (BAN) Logic
#### 5.1.3 Protocol Goal  
The goal of the 5 GSBA is to achieve mutual authentication between two sides (UE and CN). Hence, we need to create a mutually trusted common session key after the execution of the protocol. We can express the goal with these four equations:  
\[U\mid\equiv U\stackrel{{ K_{\textit{IUE}}}}{{ \leftrightarrow}}C \tag{15}\] \[C\mid\equiv U\stackrel{{ K_{\textit{IUE}}}}{{ \leftrightarrow}}C\] (16) \[U\mid\equiv C\mid\equiv U\stackrel{{ K_{\textit{IUE}} }}{{\leftrightarrow}}C\] (17) \[C\mid\equiv U\mid\equiv U\stackrel{{ K_{\textit{IUE}} }}{{\leftrightarrow}}C \tag{18}\]
## 6 Comprehensive Analysis of Hydrocarbon Source Rock Development Factors
### Distribution Characteristics of Hydrocarbon Source Rocks  
Through a comparative analysis of the Silurian hydrocarbon source rock characteristics in North Africa, the Middle East, and South China, and in conjunction with the background of plate tectonic activities, ancient climate, and paleogeography, it is believed that the developmental pattern of hydrocarbon source rocks predominantly exhibits characteristics of shallow marine shelf facies. Based on the thicknesses of hydrocarbon source rocks revealed by drilling in different regions, a comprehensive distribution map of hydrocarbon source rocks (Figure 13) was compiled. This map shows that the original distribution of hydrocarbon source rocks in North Africa, the Middle East, and South China was extensive. However, due to the influence of ancient sedimentary geomorphology, there are variations in the characteristics of hydrocarbon source rocks in different regions. In the low-lying areas of the shelf terrain, specifically the \"deep shelf stagnant basins\", the kinetic energy of the water is low, creating an anoxic environment conducive to the preservation of organic matter. These conditions result in high-quality hydrocarbon source rocks. In contrast, elevated areas of the shelf terrain, namely, \"elevations within or between deep shelf stagnant  
Figure 12: Distribution model for the development of Silurian hydrocarbon source rock shelf facies in North Africa, the Middle East, and South China.  
basins\", represent shallow marine shelves where water is more active. This leads to slightly poorer preservation of organic matter, resulting in an inferior quality to the former. The Hercynian orogen caused the original \"Pan-Cratonic Basin\" to uplift unevenly, leading to topographic differentiation [83]. Hydrocarbon source rocks in tectonically uplifted areas were eroded, reducing their distribution range. This resulted in the absence of Silurian hydrocarbon source rocks in regions such as the Ougarta Uplift in western North Africa, the Hasa-Mesopotamia uplift in the Triassic Basin, the peripheral areas of the Arabian Shield in the western Middle East [84], and the central Sichuan and Qianzhong Uplifts in the Yangtze Platform of South China's Sichuan Basin.
## 3 Problem Statement
### Bandwidth Competition  
Multipath transmission has been proposed and applied. Content can be forwarded and transmitted from multiple paths to achieve a better overall transmission rate. Although we expect the differences in the paths traveled by different subflows of the same transmission to be as large as possible, there will always be subflows passing through the same bottleneck link, known as shared bottlenecks. It is difficult for sending nodes to identify shared bottlenecks accurately. In shared bottleneck links, the transmission of multiple subflows will have more substantial bandwidth competitiveness, leading to bandwidth encroachment of other transmissions.
# Person Re-Identification Based on Attention Mechanism and Context Information Fusion
## 3 Experiment
### Experimental Details  
Our person ReID models are implemented on PyTorch framework. All experiments are performed under the hardware environment of Intel i9-10900k 3.7 GHz CPU and single NVIDIA GeForce RTX3090 GPU.  
Before training the model, the image was preprocessed according to [7]. The size of the image was adjusted to 256 \(\times\) 128 using interpolation method, and pad the image 10 pixels with zero values. Then the image is cropped to 256 \(\times\) 128 at random. Moreover, each pedestrian image has a 0.5 probability of being flipped horizontally, we also normalize RGB channels by subtracting 0.485, 0.456, and 0.406 and dividing by 0.229, 0.224, and 0.225, respectively. In order to solve the overfitting problem caused by a small scale of data, the method called Random Erasing Augmentation (REA) [31] is also used in our experiment to process the image, the probability of Random Erasing is 0.5. In addition, the batch size is set to 32, the dropout probability is 0.5, and the epoch is 60. Learning rate also plays an important role in the training of person ReID model. We apply a warmup strategy [7] to train our network. Formula (14) shows the relationships between learning rate \(lr(t)\) and epoch \(t\). When the epoch is less than or equal to 10, we make the learning rate increase linearly. At the 40 th epoch, the learning rate decays to 0.000035.  
\[lr(t)=\left\{\begin{array}{cc}3.5\times 10^{-5}\times\frac{t}{10}&t\leq 10\\ 3.5\times 10^{-4}&10<t\leq 40\\ 3.5\times 10^{-5}&40<t\leq 60\end{array}\right. \tag{14}\]  
Take the Market1501 dataset as an example, we draw the training and validation loss curve and the top1 error curve. From Figure 6, it can be seen that the training effect is basically optimal when the epoch reaches 40 times. Local optimization has been achieved before the 40 th epoch. At the 40 th epoch, the learning rate is reduced by 10 times, and the reduced training step size make loss decrease rapidly.
## 3 Test and Results
### Ablation Test  
An important aspect to analyze is the contribution of the main components established in the proposed architecture: the dynamic kernel convolution operation, the combined loss function, and the optimizer adjustment. To investigate this, an ablation study was conducted to compare our model with a simplified version that omits one of these components. Additionally, since this model is built upon a U-Net architecture, the ablation study is also performed using the original architecture. This comparison allows for the evaluation of the real contribution of the proposed architecture compared to other configurations with the same elemental structure. The results of the ablation study are shown in Table 3.  
The results of the ablation study demonstrate the significant contributions of the proposed components in the architecture. Removing the dynamic kernel convolution operation (DKC) led to the largest decrease in performance, with a drop in OA, IoU, and F1, while the BER increased. The combined loss function (CLF) also contributed to improved performance, as removing it caused a minor drop in metrics. The optimizer adjustment (OAd) had a slight effect on overall performance, but it was crucial for training efficiency, as its absence resulted in longer training times. Notably, the increase in training time for several models, despite the removal of certain components, can be attributed to the implementation of early stopping, which triggers when there is a change in the optimizer, thereby preventing the model from training too long and enhancing efficiency. The U-Net baseline, without any of the proposed components, exhibited the poorest performance, confirming the value of the added features in enhancing model capabilities.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Version** & **DKC** & **CLF** & **OAd** & **UNet** & **Time** & **OA (\%)** & **IoU** & **F1** & **BER (\%)** \\ \hline
**Full Model** & Yes & Yes & Yes & Yes & 1980.07 & 96.48 & 0.8548 & 0.9217 & 5.02 \\ \hline
**No DKC** & No & Yes & Yes & Yes & 1682.77 & 95.74 & 0.8251 & 0.9042 & 6.51 \\ \hline
**No CLF** & Yes & No & Yes & Yes & 2399.78 & 95.37 & 0.8412 & 0.9106 & 5.96 \\ \hline
**No OAd** & Yes & Yes & No & Yes & 2309.45 & 96.3 & 0.8502 & 0.92 & 5.36 \\ \hline
**U-Net** & No & No & No & Yes & 1653.2 & 94.86 & 0.7960 & 0.8864 & 7.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the ablation study comparing the proposed model with simplified versions that omit one or more key components.
# Relaxed Data Types as Consistency Conditions +
## 5 Consistency Condition to Relaxation  
Thus far, we have shown that we can convert familiar relaxations to consistency conditions. The interest in relaxed data types is largely founded on their ease of use and understanding, compared to consistency conditions. Ideally, then, any consistency condition would be representable as a relaxed data type. This does not seem to be true, at least for our current understanding of relaxed data types, as relaxed data type specifications are sequential, while consistency conditions may be inherently concurrent, either with certain operations only available to certain processes, or by allowing different behavior in the presence of concurrency. Sequential specifications do not have any notion of processes or concurrency, so such conditions cannot be represented as a sequential relaxation.  
For example, sequential consistency requires that there exist a permutation of all operation instances that is legal, and in which all instances invoked at a particular process appear in the order in which they were invoked. Because a sequential specification does not know about multiple processes, it is not well-defined for one to require or guarantee that all instances invoked at a single process have some desired relation.  
Despite this conclusion that the sets of relaxations and consistency conditions are not equivalent, in this section we will show that at least one known consistency condition can be equivalently expressed as relaxed data types. We consider a well-established consistency condition from the literature, and define a generic data type relaxation equivalent to it.
## 3 Data Assimilation
### Data Assimilation Platform  
OpenDA is an open-source tool, enabling the incorporation of random numerical models and observations through assimilation algorithms. It can be utilised to minimise the programming effort by enabling flexible software implementation among users ([[http://www.openda.org](http://www.openda.org)]([http://www.openda.org](http://www.openda.org)) (accessed on 1 December 2020)). The OpenDA framework is validated by performing both real and synthetic DA that supports the potency of this relatively new framework launched in 2010 (OpenDA). Three main data assimilation components in OpenDA include: (i) a stochastic observer for specifying observation data used in the application and information about data uncertainty; (ii) an algorithm that itemizes the input parameters required by DA; and (iii) a stochastic model in which users identify model-related information. The complete source code is accessible via GitHub ([[https://github.com/OpenDA-Association/OpenDA](https://github.com/OpenDA-Association/OpenDA)]([https://github.com/OpenDA-Association/OpenDA](https://github.com/OpenDA-Association/OpenDA)) (accessed on 1 December 2020)).  
This environment supports assimilation of the available observations through the use of filtering techniques such as EnKF and particle filters. These algorithms have been successfully applied in different areas like DA of currents and salinity profiles [37], flood forecasting [38] and more recently in DA for accurate estimation of sea level anomalies (SLA) and residual currents [39; 40]. These diverse applications demonstrate the efficacy of OpenDA as a generic toolbox for DA.
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 4 Results
### On-Site Implementation and Effectiveness of the Case Study
#### 4.1.2 Automatic Exhibition Guide System  
This study utilizes the GPS of electronic devices to obtain location coordinates, calculate the distance to exhibits, and identify adjacent exhibits. The system then compares the direction indicated by the electronic compass with the display direction of the exhibits to determine the matching exhibits and play their explanatory content. This system can be executed by mobile devices such as smartphones or tablets. The instructions for executing the above method can be included in a computer program product configured to be executed by one or more processors. Compared to traditional technology, this electronic device and its automatic guide method can instantly detect the location coordinates and direction of the electronic device and automatically select and play the matching exhibit explanatory content based on this information, thereby solving the problem of visitors manually entering exhibit numbers and the unavailability of guide audio equipment.  
According to Figure 5, the structural block diagram of the best implementation of the electronic device of this invention is shown. In this implementation, the electronic device includes a storage device, a Global Positioning System (GPS), an electronic compass, a network module, a display screen, and a processor, all connected via a data bus. The storage device stores the exhibition's automatic guide system. This system can instantly detect the location coordinates and direction of the electronic device and automatically select and play the matching exhibit explanatory content based on this information. The flowchart of the preferred implementation method for conducting automatic exhibition guidance using an electronic device is specifically illustrated in Figure 6.  
Figure 5: Structural block diagram of the best implementation of the electronic device.
## 3 Structural Characteristics of Ginger Polysaccharides
### Monosaccharide Composition  
Polysaccharides are polymers of monosaccharides linked by glycosidic bonds. Polysaccharides can be divided into homopolysaccharides and heteropolysaccharides according to whether they are composed of the same monosaccharide [53]. Many studies have shown that ginger polysaccharides are heteropolysaccharides and different studies have significant differences in the monosaccharide composition and molar ratio of polysaccharides extracted from ginger. However, most polysaccharides are composed of galactose (Gal), glucose (Glu), mannose (Man), arabinose (Ara), rhamnose (Rha), and xylose (Xyl). Interestingly, uronic acid, including glucuronic acid (GlcA), and galacturonic acid (GalA) have been detected in some polysaccharides. The diversity of monosaccharide composition and molar ratio in these polysaccharides is affected by many factors. For example, the monosaccharide composition of ginger polysaccharides HWE-GSLP extracted from ginger stems and leaves by the HWE method is Man:Rha:Glu:Gal:Xyl:Ara:Glu:A:GalA = 1.95:17.224.69:38.88:5.66:28.42:1.81:1.34. The monosaccharide composition of ginger polysaccharide GP extracted from ginger rhizome is Man:Rha:Gcl:AGal:AGlcGal:Xyl:Ara:Fuc = 0.170:130.210.120.08:10.160.640.18. The monosaccharide composition of ginger polysaccharides extracted from ginger stems and leaves by EAE is Man:Rha:Glu:Gal:Xyl:Ara:Glu:A:GalA = 1.47:9.63:3.84:15.31:27.94:35.55:3.682.586 [29; 32; 46]. The difference in monosaccharide composition and molar ratio between these polysaccharides may be caused by the raw materials, extraction methods, separation, and purification methods, etc.