esa-sceva/satcom-chunk-collection huggingface dataset:
# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 4 Algorithm Design  
We propose SLA-DQTS, an intelligent decision algorithm based on double DQN (DDQN) [6] to solve the multi-objective optimization online task scheduling problem under SLA constraints. All of the Q values in DQN are obtained by the greedy rule, i.e., during training, the max method is used to take the action and make the Q value corresponding to the action the largest. Although this can quickly bring the Q value closer to the optimization goal, it can easily cause unequal sampling. Overestimation makes the Q value greater than the actual value, and the model is more likely to oscillate and diverge. DDQN uses two Q-value networks, for normal updates and independent delayed updates, for action selection and calculation of Q values to reduce the impact of overestimation, respectively, reducing the possibility of training shock divergence, and ensuring more stability than DQN.  
Aiming at the multi-objective optimization problem of online task scheduling, a new model is designed through the state, action, and reward of the reinforcement learning model.  
Most DRL schemes used in cloud computing scheduling use the approach shown in Figure 3(a) [35]. The input vector is generally the feature of the task plus the feature of each VM as the state space, so its input dimension changes with the environment. The change, i.e., the dimension of the action space, generally equals the number of VMs, which makes it necessary to readjust the model and retrain according to the environment when the number of VMs changes. For this reason, we adopt the design plan of Figure 3(b) to take the state and action as input, and use the feature engineering method in ML to use the mean and standard deviation of the VM features as the state, which is the Gaussian distribution of the relevant features. This can reflect the state of the environment in a fixed dimension, the VM-related features to be allocated are taken as actions, and the output is their matching score. The input and output dimensions of the model are fixed. As the number of VMs changes, we do not need to modify the model structure to adapt to the changes in the state space caused by the change in the number of VMs; we need to add the same scheduling model when scheduling.
## 3 Numerical Modeling Process
### Train Load Simulation  
CRH380A series high-speed trains were used on the Da'xi passenger-dedicated line. The maximum axle load of the train was 15 t, and the static load on one side was 75 kN.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **Unit Weight** \\ \(\gamma\)**/kN-m\({}^{-3}\)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ \(E\)**/MPa** \\ \end{tabular} & **Poisson Ratio \(\mu\)** & **Cohesion \(c\)**/kPa** & **Friction Angle \(\varphi^{c}\)** \\ \hline Rail & 78 & \(2.1\times 10^{5}\) & 0.15 & — & — \\ Sleeper & 25 & \(3\times 10^{4}\) & 0.20 & — & — \\ Ballast & 20 & 200 & 0.25 & 0 & 40 \\ Surface layer of the & 19.5 & 190 & 0.3 & 80 & 30 \\ subgrade bed & 19 & 120 & 0.3 & 70 & 28 \\ Embankment & 18.5 & 60 & 0.35 & 60 & 25 \\ Concrete base plate & 25 & \(3\times 10^{4}\) & 0.16 & — & — \\ CFG pile & 20 & \(2\times 10^{4}\) & 0.2 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subgrade parameters [9].  
Figure 3: Standard section of the CFG pile in the composite foundation (unit: m).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition Number** & **Intersection Angle \(\theta^{c}\)** & **Foundation Form** & **Train Direction** \\ \hline \hline \multirow{3}{*}{\(\tt\backslash\)} & \multirow{3}{*}{30\({}^{\circ}\)} & Composite foundation & \multirow{3}{*}{From the hanging wall to the footwall} \\  & & & Natural foundation & \\ \cline{1-1}  & & & Composite foundation & \\ \cline{1-1}  & & & Natural foundation & \\ \cline{1-1}  & & & Composite foundation & \\ \cline{1-1}  & & & Natural foundation & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical simulation conditions.  
The train was composed of eight carriages. The length of each carriage was 25 m, the center distance between bogies was 17.5 m, the fixed wheelbase was 2.5 m, and the total length was 203 m. Table 4 shows the train parameters.  
Considering the dynamic effect of wheel load, the calculation formula for exciting force load was as follows [38]:  
\[P_{d}=P_{s}(1+\alpha v) \tag{1}\]  
where  
\(P_{s}\)--wheel static load (kN);  
\(\alpha\)--velocity amplification factor (about 0.004);  
\(v\)--train speed (km/h)  
The time-history curve of excitation force load at a 250 km/h train speed was calculated (Figure 4), and the moving load was set according to it.
## 3 System Model Description
### Signal and System Model  
The system model is depicted in Figure 1. It consists of two single-antenna BSs and \(N\) single-antenna users designated to each (\(N\) equals 4 in this figure, and it is not limited to 4 in real scenarios). These users are paired and mapped onto \(K\) subcarriers for uplink transmission. For example, User 1 located at base station A is paired with User 4 in base station B and the two users are mapped on the subcarrier denoted by a blue line. In this model, backhaul resource is defined \(C_{BH}\) bits per channel use (bpcu). It means cooperation between BS A and BS B is under the constraint of \(C_{BH}\) bits for lossless transmission.  
The channel model in frequency domain is illustrated as follows:  
\[\mathbf{y}_{A}=\mathbf{H}_{A}\mathbf{s}+\mathbf{n}_{A} \tag{4}\]  
\[\mathbf{y}_{B}=\mathbf{H}_{B}\mathbf{s}+\mathbf{n}_{B} \tag{5}\]  
\(\mathbf{y}_{A}(\mathbf{y}_{B})\sim C^{K\times~{}1}\) is the received signal of BS A (or BS B), \(\mathbf{s}\in C^{2K\times 1}\) is the transmitted symbol vector with the \(k-\)th element, \(s_{k}\) is denoted as user information modulated on subcarrier \(k\). \(\mathbf{H}_{A(B)}=diag(h_{A(B),k})\in C^{K\times 2K}\) describes the channel matrix, where the diagonal element \(h_{A,k}(h_{B,k})\) is the channel response from users mapped on subcarrier k to BS A (or BS B). The noise vector \(\mathbf{n}_{A}(\mathbf{n}_{B})\in C^{K\times 1}\) is a realization of a zero-mean circularly symmetric complex Gaussian random process: \(\mathbf{n}_{A}\left(\mathbf{n}_{B}\right)\sim\mathcal{NC}\left(0,\sigma^{2}\mathrm{I}_ {K}\right)\), where \(\sigma^{2}\) is noise variance.  
The compression and decompression process is similar to the scalar case:  
\[\hat{\mathbf{y}}_{B}=\mathbf{y}_{B}+\mathbf{q}\quad\mathbf{q}\sim\mathcal{NC}\left(0,\Phi\right) \tag{6}\]  
\(\mathbf{q}\in C^{K\times~{}1}\) is the compression noise vector with zero mean and \(\Phi\in C^{K\times K}\) is covariance variance matrix of compression noise. In the following part, the modeling and formulation will be expressed.  
Figure 1: Cooperative Scenario with capacity constraint backhaul system. Users located at different base stations will transmit uplink information and base stations will receive information from every users.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 4 The Design of a Multi-Line Prefetch Covert Channel with Huge Pages
### The Fine-Grained Decoding Mechanism  
The receiver measures the timing of PREFETCHW operations across all \(n\) cache lines and decodes the message by comparing the measured latencies to pre-calibrated thresholds. For example,  
If the measured timing exceeds \(T1\) but is less than \(T2\), it corresponds to Message 1 (one cache line accessed).  
If the timing exceeds \(T2\) but is less than \(T3\), it corresponds to Message 2 (two cache lines accessed).  
This fine-grained decoding allows the receiver to infer multi-bit data, improving the efficiency and accuracy in covert communication.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 3 Methodology  
We develop a system named NARO Agent that works as an intermediate layer between AmGSD and the client. It translates gridded climate data into point-based data and then responds to clients'requests through SOS API. NARO Agent absorbs the complicated structure of AmGSD with SOS API and internal components. NARO Agent consists of several components, as shown in Figure 2. Each component supports each other to provide sufficient SOS API for agricultural applications. To efficiently serve data for agricultural applications, several internal components, such as caching, mesh system conversion, controlling multi-process and threading access modes, are implemented.  
NARO Agent is written in Python programing language in the Flask framework [17]. It has been deployed on Conoha [18], an OpenStack cloud platform, for easy maintenance, scalability and future migration purposes.  
The detail of each component is described consequently as follows.
# A Multimode Fusion-Based Aviation Communication System
## 3 Hardware Design
### Ad Hoc Network Communication Module  
The ad hoc network communication link uses point-to-point broadband networking data communication technology to support up to 32 nodes. Figure 3 shows that the link is based on the LTE wireless communication standard and adopts OFDM key technology. It also supports multiple bandwidth allocation, which flattens the system architecture design and effectively reduces system delays while improving the data transmission capacity. The ad hoc network communication data link has a long transmission distance, large data throughput, and strong anti-interference ability, which allows it to perform the following communication functions:  
1. The optical lens data collected by the aircraft is transmitted to the ground control center via an ad hoc network link. The optical payload image and video are analyzed and displayed with the ad hoc network communication support 4K HD video transmission, transmission rate up to 20 Mbps.
2. The aircraft control computer transmits the flight status data to the ground control center through an ad hoc network link for analysis and display.
3. The aircraft control computer processes ground control command data uploaded via an ad hoc network link through the aviation multimode fusion communication terminal.
4. The ground control center can communicate bidirectionally with up to 32 aviation multimode fusion communication terminals using an aircraft ground ad hoc network communication function.  
Figure 2: Aviation multimode fusion-based communication composition diagram.  
The ad hoc network communication module, public network communication module, and BeiDou satellite communication module are integrated on one board, and the block diagram is shown in Figure 4.  
This technology relies on the creation of high-performance software radio SoC chips. To ensure secure communication, the SoC chips have implemented encryption protocols to protect data transmitted across the network. The RF chip uses a broadband-integrated chip that has high integration. As a result, the system's power consumption decreases, and the module size decreases. The module's power consumption is around 5 W.
## 3 Methodology
### Data Processing  
Quality control of the images was first executed on a visual level using the image quality index (IQI). Images that were shaken or blurred were excluded from the image-based 3D modeling process. The selected images were then aligned using the structure-from-motion algorithm, creating a sparse point cloud. Then the dense point cloud was created by applying the multi-view stereo algorithm. The triangulation irregular network (TIN) method was then applied to the dense point cloud through which the points were joined together to create a 3D digital object in the mesh form (Figure 5). Then, the pixels of the high-resolution images collected by the UAS to generate the orthomosaic were orthorectified. The processing of data were the same in all data sets, and the same settings were used.  
The 3D digital object was then rendered and wrapped with a photorealistic texture from the high-resolution images collected during the data acquisition. The next stage in data processing was the creation of the 360\({}^{\circ}\) panorama. Initially, a camera group was created to add all the photos captured from the Litchi mode panorama [67]. The group of photos was then defined as a camera station and the alignment of the images was performed. The photos needed to overlap to perform this specific process (alignment) to create a complete panorama. The editing process proceeded by stitching the spherical panoramas generated from multiple images taken from the same camera position. Figure 6 shows the set of panoramic images (60 m) stitched according to the spherical model and the camera's angle used to create the panorama. The panorama borders were adjusted so that there were no empty sections at the top and bottom of the st  
Figure 5: Data processing using SfM and MVS.  
photograph was placed in the central position of the panorama. The software used for data processing was Agisoft's Metashape [68].
## 4 Results  
### Frequency for Geophysical Surveying  
Frequency for geophysical surveying and subsequent groundwater monitoring was calculated through Equation (1). Geological information deduced from geotechnical soundings S1 to S3 (Figure 1d) after [40] and hydrogeological data compiled from [7; 10] were used for this attempt. Taking average \(b\) = 1.1 for predominant longitudinal groundwater flow paths [55] such as in aquifer H, \(A\) = 1.46 km\({}^{2}\) (Figure 1b), \(S\) = 0.05 [40], and \(T\) = 300 m\({}^{2}\) day\({}^{-1}\) and 800 m\({}^{2}\) day\({}^{-1}\), measured respectively in dry and rainy phases by [10], and \(G\) varied in the 3.2-8.9-month range.  
All geophysical surveys were performed during 2014 (Figure 3). Long-term significance of the selected year 2014 was evaluated from the analysis of the global North Atlantic Oscillation index [NAO website: [[http://www.cpc.ncep.noaa.gov/](http://www.cpc.ncep.noaa.gov/)]([http://www.cpc.ncep.noaa.gov/](http://www.cpc.ncep.noaa.gov/))] (Figure 3a). The NAO index controls long-term precipitation and temperature regimes in southern Portugal [56], and therefore actual evapotranspiration (E) and stream flow rates [41]. The selected year 2014 fits well to the average hydrological condition deduced from the long-term P (Figure 3b) and E (Figure 3c) time series from Sines meteorological station (Figure 1a). Thus, geophysical and hydrogeological interpretations should be framed into the context of an average hydrological condition in the area.  
In this context, three months was the optimal frequency adopted for ERT surveying (Table 2) and subsequent groundwater monitoring (Table 3) at sites 1, 2, and 3 (Figure 1d). This frequency covers adequately the about four-month long predominately rainy, moderately rainy, and dry phases taking place in the area, as shown graphically in Figure 4. Only one MASW survey was performed at sites 1 and 3 (Table 2).
## 5 Recent Technologies Used in Ammonia Production
### Conventional Ammonia Production  
In this way, ammonia is produced by the Haber-Bosh process. Hydrogen used in this process is produced using a catalyst at high temperatures and pressures from hydrocarbon sources. Recent developments in catalyst technology have improved the efficiency of the Haber-Bosch process, enabling lower operating temperatures and pressures. The production of ammonia, which remains the most widely used technique, involves the utilization of several catalyst types with unique properties, as illustrated in Figure 18 and Table 9 [93].  
Figure 18: Catalysts used in the Haber–Bosh process [94].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Value** \\ \hline Physical state & Liquid \\ \hline Melting point & \(-78\,^{\circ}\)C \\ \hline Boiling point & \(-33.4\,^{\circ}\)C \\ \hline Critical temperature & \(133\,^{\circ}\)C \\ \hline Critical density & \(0.23\) g/cm \\ \hline Critical volume & \(4.225\) cm\({}^{3}\)/g \\ \hline Critical thermal conductivity & \(0.522\) kJ/K h m \\ \hline Heat of fusion at (1 bar) & \(332.3\) kJ/kg \\ \hline Vapor pressure at 20 \({}^{\circ}\)C & \(861\) kPa \\ \hline Boiling point (at 1 bar) & \(239.72\) K \\ \hline Heat of vaporization (at 1 bar) & \(1.37\) MJ/kg \\ \hline Standard enthalpy of formation (gas at 298 K, 1 bar) & \(-46.22\) kJ/mol \\ \hline Standard entropy (gas at 298 K, 1 bar) & \(192.731\) J/mol.K \\ \hline Free enthalpy of formation (gas at 298 K, 1 bar) & \(-16.391\) kJ/mol \\ \hline Net heating value (LHV) & \(18.57\) MJ/kg \\ \hline Vapor density at 20 \({}^{\circ}\)C & \(0.656\) kg/m\({}^{3}\) \\ \hline Liquid density at 20 \({}^{\circ}\)C & \(610\) kg/m\({}^{3}\) \\ \hline Solubility in water & \(51\)–\(53\) g NH3/100 mL water \\ \hline Autoignition temperature & \(651\,^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 8: Anhydrotus ammonia characteristics [92].  
Boron Radical for Ammonia Synthesis  
University Paul Sabatier researchers discovered new methods that can give a kinetically and thermodynamically advantageous to produce ammonia at room temperature without metals or hydrogen gas using reactive boron compounds. These boron-centered radicals were produced by combining organic boron halides with a strong reducing agent that transformed molecular nitrogen into borylamines. This, when combined with aqueous acid, yielded ammonium chloride by decomposing the stable three bounds of molecular nitrogen. Figure 19 presents this novel strategy that expands the potential for ammonia production without relying on fossil-based basic sources [98].
## 5 Conclusions  
This paper investigates the transmission characteristics of a high-temperature superconducting magnetically coupled resonant wireless energy transmission system under coaxial and lateral misalignment conditions, focusing on the effect of azimuthal misalignment. The experimental results demonstrate that when azimuthal deflection angle increases under coaxial alignment, the transmission efficiency and load output power of the system gradually decrease with a flat slope period, and the turning point of the sharp decrease of the power lags behind the transmission efficiency, which is beneficial for practical applications. Under lateral misalignment at a distance of 4 cm, the PTE of the system initially increases and then decreases, while the load output power is stable. Under transverse misalignment at a distance of 8 cm, both the PTE and load output power of the system increase with the increase in azimuthal deflection. By adjusting the lateral misalignment distance and azimuthal deflection angle simultaneously, the transmission efficiency and load output power of the high-temperature superconducting WPT system can be improved. Moreover, using high-temperature superconducting coils instead of copper coils under different azimuthal misalignments can significantly enhance the PTE and load output power of the MCR-WPT system by 6.1%-12.3% for PTE and 60.2%-84.3% for LOP. These properties predict that magnetically coupled resonant superconducting wireless power transmission systems have good prospects for practical applications in wireless power transmission scenarios for devices such as electric vehicles or AUVs. Since the experimental power selected in this paper is low, we will use Leeds wire to make coils with higher current-carrying capacity to build the prototype in the future. Further study is needed of the transmission characteristics of the superconducting MCR-WPT system at kilowatt-level high-power conditions.  
Conceptualization, Z.Y. and T.Z.; methodology, M.C.; software, D.Z.; validation, M.C., S.Z. and Y.G.; formal analysis, Z.Y.; investigation, M.C.; resources, Y.G.; data curation, M.C.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y.; visualization, Y.G.; supervision, Y.G.; project administration, S.Z.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDB25000000, National Natural Science Foundation (52172271).  
The authors declare no conflict of interest.
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 3 Materials and Methods  
### Overview  
Figure 1 shows the overall flow of the proposed system. The proposed system is divided into three components: real-time data gathering, composite statistical analysis, and prediction of PQP using LSTM.  
In Figure 1, the first step is to perform real-time data collection. Generally, during the manufacturing of products, machines in a factory generate data of different types, such as machine status, production process, and images of testing results. Among them, production process data (PPD) represent the condition values of the machines and the results of the manufactured product. In this paper, we first collect PPD from machines while they are in the production process. Here, to transmit the PPD in real-time to a server system over a wireless or wired network, an Apache Kafka streaming improvement system will be used, as proposed by [PERSON] et al. [24].  
Second, a composite statistical analysis of PPD is proposed to detect abnormal processes in machines, where each machine is manufacturing products periodically. To provide better analysis, four main issues are introduced, including manufacturing cycle time analysis (MCtA), process trajectory of abnormal detection (PTAD), statistical process control  
Figure 1: Overall flow of the proposed system.  
analysis (SPCA), and process capability control analysis (PCCA). Here, MCtA is defined as the time it takes to make a product; PTAD identifies abnormal processes when machines are in production. In SPCA [25], the main parts are only the control charts of type X and R, used to control the quality of processes. Lastly, Cpk of PCCA is applied [12] to measure the quality of processes and to measure how well the processes are running. The details of this phase will be described in Section 3.2.  
Third, prediction of PQP using LSTM is performed. For this, we first perform a data processing procedure on the dataset obtained from previous composite statistical analysis of the PPD step. Specifically, we perform data splitting, feature scaling, and categorical data encoding procedures. Further, the extracted features are used for training LSTM. Lastly, the Softmax activation function is selected to work with the last layer in the prediction model to classify and output PQP results from 0.00 to 1.00. Here, higher results indicate superior process quality, which might have zero or fewer defective products output. The details of this phase will be provided in Section 3.3.