esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.1 Heterogeneous Cloud Resource Integration Framework  
The heterogeneous cloud resource integration framework addresses decentralized management across multiple platforms by creating a unified cloud management engine for resource interoperability and centralized control. It includes a standard cloud API, user and resource management, a monitoring system, and a portal API, ensuring compatibility and facilitating resource exchange among platforms. Figure 2 shows the architecture of the heterogeneous cloud management engine, which consists of five components.  
The unified standard cloud API is the core of the heterogeneous cloud management engine, enabling the control of multiple cloud platforms through a single command. This simplifies operations, ensures compatibility, and facilitates future extensions. The unified user management module centralizes user account and permission control, enhancing security and accountability. The unified resource management module integrates resources from different platforms for centralized management and dynamic resource allocation, optimizing efficiency and load balancing. The unified monitoring management module allows for the real-time monitoring of cloud resources and devices, enabling timely alerts and quick responses to ensure stability and security. Finally, the portal API module provides a user-friendly interface for managing heterogeneous cloud resources.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Neural Network Structure and Function
#### 3.1.1 Artificial Neurons  
Artificial neurons [32] generate an output value representing their activity by applying a nonlinear activation function. In this process, this study assumes that the neuron receives \(n\) input signals \(X=(X_{1},X_{2},\cdots,X_{n})\). These input signals are weighted and summed up, represented by \(a\) state variable \(z\). Finally, the output value of this neuron, namely its activity \(a\), is calculated based on the state \(z\) through an activation function. Figure 3 illustrates the model of an artificial neuron.  
The most commonly used activation function in traditional neural networks is the sigmoid function. The sigmoid function refers to a class of S-shaped curve functions, with commonly used sigmoid functions including the logistic function \(\sigma(x)\) and the tanh function.  
\[\sigma(x)=\frac{1}{1+e^{-x}} \tag{32}\]  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{33}\]
## 3 Results and Discussion
### Influence of Coating Processes on the Performances of Waterborne Coatings Containing Microcapsules
#### 3.2.1 Influence of Coating Processes on Optical Performances of Waterborne Coatings
The change of liquid resistance gloss is shown in Table 7. It was illustrated that the gloss changes were not obvious after the four solutions were tested for the liquid resistance test. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples (\#)**} & \multicolumn{4}{c}{**Liquid Resistant Chromatic Aberration**} \\ \cline{2-5}  & **NaCl** & **Ethanol** & **Detergent** & **Red Ink** \\ \hline
1 & 3.04 & 2.12 & 2.75 & 45.21 \\
2 & 2.57 & 1.85 & 3.06 & 40.15 \\
3 & 2.10 & 2.25 & 1.95 & 43.02 \\
4 & 3.99 & 2.56 & 2.18 & 39.12 \\
5 & 1.93 & 1.40 & 2.67 & 34.79 \\
6 & 1.88 & 1.24 & 1.83 & 35.31 \\
7 & 1.32 & 1.88 & 1.81 & 36.25 \\
8 & 1.88 & 1.59 & 1.24 & 37.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Influence of coating processes on liquid resistant chromatic aberration of waterborne coating containing microcapsules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples (\#)**} & \multirow{2}{*}{**Adhesion**} & \multirow{2}{*}{**Hardness**} & **Impact Resistance** & **Elongation at** \\  & & & **(kg\_cm)** & **Break (\%)** \\ \hline
1 & 1 & 5H & 5 & 10.42 \\
2 & 1 & 5H & 5 & 19.70 \\
3 & 0 & 4H & 5 & 22.56 \\
4 & 1 & 5H & 5 & 24.46 \\
5 & 0 & 4H & 7 & 18.48 \\
6 & 0 & 4H & 10 & 30.90 \\
7 & 0 & 4H & 7 & 26.82 \\
8 & 0 & 4H & 10 & 18.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Influence of coating processes on mechanical performances of waterborne coating containing microcapsules. The effect of coating processes on liquid resistant grade is shown in Table 8. All samples had good liquid resistance after being soaked with colorless liquid, which was grade 1. There was no obvious trace on the surface. However, there was difference in the liquid resistance grade after red ink infiltration. In addition, compared with the coating process of microcapsules added in the finish, the coating process of microcapsules added in the primer impacted the liquid resistance in a more remarkable way. The liquid resistance of coatings prepared by the coating process of a microcapsule added into the primer is better.
## 2 Theoretical Background
### CS Based on ADMM + K-SVD  
The mathematical model of the K-SVD dictionary learning algorithm is as follows:  
\[\|\mathcal{Q}-\mathbf{DX}\|_{F}^{2}=\left\|Q-\sum\limits_{j=1}^{k}d_{j}\mathbf{X}_{T} ^{j}\right\|=\left\|\left(Q-\sum\limits_{j\
eq k}d_{j}\mathbf{X}_{T}^{j}\right)-d _{k}\mathbf{X}_{T}^{(k)}\right\|_{F}^{2}=\left\|E_{(k)}-d_{(k)}\mathbf{X}_{T}^{(k)} \right\|_{F}^{2}, \tag{9}\]  
where \(Q\) is the sample signal, \(\mathbf{D}\) is the original dictionary, \(\mathbf{X}\) is the sparse vector matrix and \(E_{(k)}\) is the residual. The de-zero contraction of \(\mathbf{X}_{T}^{(k)}\) yields \(E_{(k)}^{R}\), then performs singular value decomposition, yielding the following:  
\[E_{(k)}^{R}=\mathbf{U}\Delta V^{T}. \tag{10}\]  
The dictionary training process uses the elements in the \(\mathbf{U}\)-matrix obtained by decomposition to assign values to the atomic training in dictionary \(D\) one by one.  
It has been proven in the literature that the CS method based on K-SVD has the best reconstruction performance using an empirical dictionary as the initial dictionary [62; 63]. The steps of the CS reconstruction signal method based on ADMM + K-SVD algorithms are shown in Table 1.
## 4 Conclusions and Future Prospects  
This paper addresses the robotic arm docking scenarios in certain specialized operations, designing corresponding visual guidance methods based on the characteristics of the docking interfaces. It combines the YOLOv8 target detection algorithm with the PnP pose estimation method to directly recover the position and attitude of the target from images. An experimental platform was set up in the laboratory to compare the target positioning methods based on OpenCV and YOLOv8. The experimental data shows that when the target object undergoes only positional changes without attitudinal changes, both methods can meet the docking requirements within the given range, but the YOLOv8-based target positioning method has higher accuracy. When both position and attitude change simultaneously, the OpenCV-based recognition method has larger positioning errors. In 10 sets of experiments, 3 sets could not complete the docking successfully, whereas the YOLOv8-based target positioning method had smaller positioning errors, and all 10 sets of experiments successfully completed the docking. Additionally, the average time for the robotic arm to complete docking operations using the YOLOv8-based method was 9.89 s, compared to 10.575 s using the OpenCV-based method, representing an average reduction of 6.9% in docking operation time using the YOLOv8-based method.  
The results indicate that in such environments, using a combination of the YOLOv8 and PnP algorithms as the visual guidance method for the robotic arm is highly applicable and can effectively reduce the time taken for the robotic arm to complete docking operations. Future research could further expand the field of robotic arm docking by exploring a wider range of application scenarios and more complex operational environments. Firstly, there is potential to extend visual guidance methods to encompass various types of robotic arms and docking scenarios, including industrial production lines, aerospace applications, and medical robotics. Secondly, there is an opportunity for in-depth investigation and optimization of deep learning algorithms for robotic vision guidance, aiming to enhance the recognition and localization capabilities for targets, thereby achieving more precise and stable docking operations. Additionally, the exploration of machine learning and autonomous control techniques in the docking process could enable robotic arms to make flexible decisions and motion planning based on real-time environmental information and task requirements, thereby enhancing the autonomy and intelligence level of docking operations. Through these endeavors, novel breakthroughs and advancements in robotic arm docking technology and its applications are anticipated.  
Conceptualization, M.Y. and J.L.; methodology, J.L. and M.Y.; software, J.L.; validation, J.L.; resources, M.Y. and J.L.; data curation, J.L.; writing--original draft preparation, J.L. and M.Y.; writing--review and editing, M.Y.; supervision, M.Y.; project administration, J.L. and M.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Time comparison between two methods.
## 4 Methods
### Inter-Channel Spatial/Temporal Correlation  
A target is expected exhibiting different dielectric and geometrical properties if sensed with distinct SAR configurations. Thus, a pixel based spatial correlation image, \(r_{\mathrm{sp}}\), was evaluated for each plot using a moving kernel. A plot representative spatial correlation, \(r_{\mathrm{s}}\), was obtained by averaging values of \(r_{\mathrm{sp}}\) co-localized to in situ measures. The calculation is extended to all available channel pairs and acquisition times, thus, generating an \(r_{\mathrm{s}}\) domain cube. Because the moving kernel is applied on spatially aggregated images, a minimum kernel size (3 \(\times\) 3) was chosen to avoid exceeding plots' dimensions. A total of 45 channel pairs were analyzed; the \(r_{\mathrm{s}}\) cube was evaluated for each of the 9 monitored plots.  
The dynamic between \(r_{\mathrm{s}}\) and SVV was analyzed via a single regression over 9 selected pairs of channels. The analysis determines in what extent \(r_{\mathrm{s}}\) could be useful to assess SVV changes; thus, results are shown in terms of plot temporal correlation coefficients, \(r_{\mathrm{t}}\), out-coming from the \(r_{\mathrm{s}}\) vs. SVV regressions (Figure 4).  
Finally, errors associated to best regressions (both \(\sigma^{\circ}\) vs. SVV and \(r_{\rm s}\) vs. SVV) are computed in terms of mean absolute errors (MAE).
# Effect of Preheating Treatment before Defatting on the Flavor Quality of Skim Milk
## 2 Results and Discussion
### Sensory Descriptive Analysis (DA)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Descriptor Sample** & **RSM** & **PSM** & **USM** \\ \hline Overall liking & 1.66 \(\pm\) 0.44 a & 2.50 \(\pm\) 1.22 a & 3.54 \(\pm\) 0.65 b \\  & Aroma & & \\ Milk aroma & 1.66 \(\pm\) 0.74 a & 2.75 \(\pm\) 0.76 b & 3.75 \(\pm\) 0.53 c \\ Cooked/sulfurous & 2.06 \(\pm\) 0.86 a & 2.35 \(\pm\) 1.37 a & 3.06 \(\pm\) 0.86 a \\ Caramelized & 0.75 \(\pm\) 0.65 a & 1.28 \(\pm\) 0.88 a & 2.94 \(\pm\) 0.18 b \\ Fishy & 2.39 \(\pm\) 0.86 a & 0.89 \(\pm\) 0.60 b & 0.45 \(\pm\) 0.49 b \\  & Flavor/Taste & & \\ Milk flavor & 1.60 \(\pm\) 0.66 a & 2.54 \(\pm\) 0.95 b & 3.50 \(\pm\) 0.93 c \\ Sweet taste & 2.04 \(\pm\) 1.36 a & 2.19 \(\pm\) 1.00 a & 2.63 \(\pm\) 0.88 a \\ Umami & 1.64 \(\pm\) 0.58 a & 1.80 \(\pm\) 0.85 a & 1.80 \(\pm\) 1.00 a \\ Sour & 0.38 \(\pm\) 0.44 a & 0.31 \(\pm\) 0.37 a & 0.25 \(\pm\) 0.38 a \\ Salty & 1.79 \(\pm\) 1.19 a & 1.45 \(\pm\) 0.68 a & 0.95 \(\pm\) 0.64 a \\ Astringency & 1.00 \(\pm\) 0.93 a & 0.88 \(\pm\) 1.09 a & 0.69 \(\pm\) 0.53 a \\  & Mouthfeel & & \\ Residual mouthfeel & 1.81 \(\pm\) 0.30 a & 2.40 \(\pm\) 1.03 a & 3.29 \(\pm\) 0.67 b \\ \hline \hline \end{tabular} a-c Significant (\(p<0.05\)) difference between samples. RSM, skim milk obtained by centrifugation of raw milk; PSM, skim milk obtained by centrifugation of raw milk after preheating treatment at 85 \({}^{\circ}\)C, 15 s; USM, skim milk obtained by centrifugation of raw milk after preheating treatment at 137–141 \({}^{\circ}\)C, 4 s.  
\end{table} 
Table 1: Sensory evaluation results of three different kinds of skim milk samples.
## 2 Computation of the Effective Length of Type-A Grounding System According to IEC 62305-3
### Definition of Effective Length of the Grounding System Used in This Paper  
The effective length of the grounding electrode can be defined in various ways. According to [4], the effective length of the grounding electrode represents the optimal length of the electrode whereby increasing its length does not significantly lower the impulse  
Figure 1: Observed type-A grounding system as prescribed in [16].  
impedance of the grounding electrode. This impulse impedance is a well-known parameter [3], which is defined as the ratio between the peak values of the scalar potential at the injection point and the transient current at the injection point of the grounding system.  
In the scope of this paper, we have selected the following condition to define the effective length of each electrode for the observed grounding system: when increasing the length of the grounding electrode does not produce a significant decrease in impulse impedance, then the effective length of the grounding electrode \(\ell_{eff}\) is considered to be found. Or put more mathematically, the condition is satisfied when the rate of change (ROC) of impulse impedance \(Z\) relative to the increasing length of the grounding system \(\ell\) becomes sufficiently small [4]:  
\[\arctan\left(\frac{\Delta Z}{\Delta\ell}\right)<5^{\circ} \tag{1}\]  
where \(\Delta Z\) is the change in the impulse impedance and \(\Delta\ell\) is the change in the grounding electrode length. The \(5^{\circ}\) angle of the slope taken from [4] is an often-used cut-off angle value, after which one can safely assume that the impulse impedance will not decrease in a significant manner when increasing the grounding electrode length.
## 3 Results and Discussion
### Kinetics Analysis of Oxidation

Table 4: Kinetics parameters (\(a\) and \(b\)—adjustable coefficients, \(\text{{E}a}\)—activation energy, \(Z\)—pre-exponential factor, \(k\)—reaction rate coefficient) of oxidation of oils extracted from quinoa and amaranth seeds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Oil** & _Ea_ **(kJ/mol)** & \(\text{{Z}}\) **(1/min)** & \(\text{{k}}\) **ange** & **References** \\ \hline quinoa seeds oil & \(77.75\pm 3.24\) & \(1.44\times 10^{7}\) & \(1.89\times 10^{-4}\)–\(2.14\times 10^{-3}\) & this study \\  & & (at 100–140 \({}^{\circ}\)C) & \\ amaranth seeds oil & \(80.82\pm 2.87\) & \(5.13\times 10^{7}\) & \(2.48\times 10^{-4}\)–\(3.10\times 10^{-3}\) & this study \\  & & (at 100–140 \({}^{\circ}\)C) & this study \\  & & (at 100–140 \({}^{\circ}\)C) & this study \\  & & & (at 90–130 \({}^{\circ}\)C) & [42] \\  & & & (at 90–130 \({}^{\circ}\)C) & [10] \\  & & & (at 100–140 \({}^{\circ}\)C) & [39] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of oxidation parameters (activation energy—\(\text{{E}a}\), pre-exponential factor—\(Z\), and reaction rate coefficient—\(k\)) of pseudo-cereal oils with camelina, linseed, hazelnut, rapeseed and olive oil.
# Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoders
## 3 Proposed Low-Complexity Multi-Size Circular-Shift Network for 5G New Radio LDPC Decoder
### Fine-Coarse Structure for Circular-Shift Network  
The fine-coarse structure for circular-shift network is designed to support different block sizes, in which the maximum block size is defined by \(P\). Let \(Z_{p}\) denotes the GCD between all block sizes, and \(Z_{m}=P/Z_{p}\). To support multi circular shifts with low complexity, the proposed low-complexity MSCS network for 5G NR LDPC decoders is decomposed into two parts: the fine part and the coarse part. More precisely, the MSCS network architecture comprises of \(Z_{m}\)\(Z_{p}\)-input pre-rotators, plus an additional stage of \(Z_{p}\)\(Z_{m}\)-input main  
Figure 5: **(a)** FRCS network structure; **(b)** An example of FRCS network with \(Z=8\), \(P=12\) and \(c=3\).  
rotators. Let \(Z\) denotes the block size of the circular-shift message and \(c\) denotes the circular-shift value which is distributed over the range from 0 to \(Z-1\). The fine-coarse structure decomposes the circular shift into a pre-circular shift \(c_{p}\) and a main circular shift \(c_{m}\). The circular-shift amount \(c\) can be described by the sum of two terms as \(c=c_{p}+c_{m}\times Z_{p}\), where \(0\leq c_{p}<Z_{p}\). In the fine-coarse structure, the output data are generated in two steps, which are listed below:  
* _Step 1. Pre-rotator_: Each sub-block of \(Z_{p}\) input data are processed by a pre-rotator subnetwork to generate the pre-circular shift by performing \(c_{p}\) shift operation.
* _Step 2. Main rotator_: The \(k\)-th ouput datum of each pre-rotator network is distributed into the \(k\)-th main rotator subnetwork, where \(0\leq k<Z_{p}\). The main circular shift is generated by performing \(c_{m}\) shift operation on each main rotator subnetwork. To overcome the inside-group rotation at the pre-rotator network, the cross-group movement is performed by the \(c_{m}+1\) circular shift at the \(k\)-th main rotator subnetwork where \(k+c_{p}\geq Z_{p}\).  
As shown in Figure 6, the fine-coarse structure has two components, i.e., the pre-rotator network, and the main rotator network, which corresponds to step 1, and 2 described above. In this structure, the support of multiple sizes is achieved by rearranging the system into smaller subnetwork with fixed block sizes.
## 5 Publicly Available Datasets and Benchmarks
### Places
DRD attempts aims to address the problems caused by the pooling regions in SPL for example, loss of certain salient features caused during the division of regions into several fragments. DRD focuses on the regions of interest. Using the deformable part-based models [90] with some latent SVM training, aids in discovering the common visual structures that helps in capturing the continual visual elements alongside salient objects. The Object bank [70], on the other hand, sits atop the response maps and contains several object-sensing filters that are pretrained on generic-labeled objects and integrates the local semantic meanings into a complete image representation. The increase in the number of detected objects from scene images, results in an increase in the dimensionality of the response vector, and therefore, a regularized logistic regression can be used for activating several instances belonging to each class. * OCA models the relationship among the diverse assignment of objects and scene categories and is considered the most challenging tasks among several scene recognition approaches. Here, the discriminative patch identification serve as the first step in carrying out the subsequent correlation analysis and this depends either on the pre-trained object detectors or on other practical region proposal methods. To analyze and understand the relationship between the diverse assignment of objects and scene categories, several probability models have been introduced in the literature. With the humungous information, OCA achieves a moderate recognition accuracy, and is considered the slowest in terms of the inference speed because of heavy computational load. It is to be noted that OCA can serve as an alternative in situations when object detection is needed for the task. * HDMs are considered as effective approaches to scene recognition. The intermediate layers in the CNN, on the one hand, capture the local features whereas the top layers capture the holistic features. In the end-to-end networks, multi-stage convolutional features should be considered for example the DAG-CNNs (directed acyclic graph CNNs) [75]. In general, features from layers close by contains unwanted information that is correlated, and hinders the scene recognition performance. These hybrid models though exhibit considerable advantages, but suffer from similar issues as with codebook learning. Also, codebook learning and deep models together consume huge memory and computational resources. Therefore, by combining the codebook approaches (existing) with deep networks serve as a potential solution and is considered an open issue.
## 2 Context and Research Objectives
### Introduction to Industry 4.0
The integration of Information Technology (IT) and Operational Technology (OT) into an Industrial Internet of Things (IIoT) underpins the concept of Factory of the Future (FoF) where everything is permanently connected and integrated into one system [17]. Other characteristics are the use of autonomous machines with learning ability [18], collaborative robotics [19], additive manufacturing [20], advanced HMIs [21], augmented reality [22], big data analytics [23], Cloud Manufacturing (CMfg) [24], IIoT and meshed networks [25].
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 4 Evaluation Indexes  
With respect to our specific application in PV power forecasting, although several evaluation indexes with different meanings are adopted in the literature, their trends are usually highly correlated, as they are calculated on the common basis of the hourly error \(e_{h}\):  
\[e_{h}=P_{m,h}-P_{p,h} \tag{4}\]  
which is the difference between the measured value of the output power \(P_{m,h}\) and the forecasted one \(P_{p,h}\) in the same hour h. Therefore, we now consider the normalized mean absolute error \(NMAE_{\%}\) as a reference for evaluating the performance of the forecasts:  
\[NMAE_{\%}=\frac{1}{N\cdot C}\sum_{i=1}^{N}\left|P_{m,h}-P_{p,h}\right|\cdot 100. \tag{5}\]  
The normalized mean absolute error \(NMAE_{\%}\) is based on the net capacity of the plant \(C\). \(N\) is the number of time samples (hours) considered in the evaluated period (i.e., \(N=24\) in a daily error basis calculation).  
This index was calculated for the output \"test set\" of each individual network's performance. As the \(NMAE_{\%}\) value is a random variable, its trend could be analyzed by means of the theory of parametric estimation. Therefore, when looking for the settings which on average minimize \(NMAE_{\%}\) values, the FFNN will be analyzed as a function of:  
1. a single layer number of neurons;
2. a double layer number of neurons; and
3. trials in the ensemble forecast.  
To proceed with this analysis, these steps are followed:  
1. one or more ANN parameters are kept constant;
2. the free parameter to be inspected (i.e., number of neurons within a layer) is varied within a specific range; and
3. the \(NMAE_{\%}\) values of similar networks are calculated.  
Under appropriate conditions, a range of values of the \(NMAE_{\%}\) can be calculated, assuming that the unknown mean of all of the possible \(NMAE_{\%}\) values, with those ANN settings, is within that interval of confidence.