esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
While working on the tracking mode, based on the stabilizing loop, the miss distances of the target are obtained by analyzing the vision images. Then, motor rotations will be controlled to keep the LOS always aligned with the target. Specifically, the target is always kept in the center of FOS. While working on the positioning mode, the real-time positions of the frames are obtained by position sensors, and then the motors will be controlled to rotate to the specified positions. Obviously, the stabilizing function is the core of the whole system, and the basis of further applications such as tracking and positioning. Therefore, stability accuracy of the stabilizing function is the key performance index, and it is treated as the focus of this research. Compared to unpredictable factors, the assembly errors of system components will also affect that index greatly. Those errors can be controlled relatively easily. The relative analysis will provide valuable guidance for the design, development, and manufacturing process. In this research, a method to analyze assembly error effect on stability accuracy is proposed. Stability accuracy is comprehensively modeled and simulated. The system's general kinematics, compensation principle, and major components are studied. Assembly errors of axis perpendicularities, run-outs, and gyroscopes are defined, modeled, and introduced into the overall model. The effects caused by assembly errors under different disturbance frequencies are studied. Error sensitivity is proposed by using standard deviation analysis. The most sensitive assembly errors are identified, and the results can be used to determine standards and priorities of improvements in the future. The modeling and simulations are all carried out in MATLAB R2019b Simulink. The MATLAB codes of the simulation works are available online at Supplementary Materials.
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### Network Functions Virtualization
Failures like this could also be related to inadequate resource slicing or temporal interference, wherein co-located services shared infrastructure may lead to performance unpredictability due to shared contention [37]. \(\bigcirc\) _Proposed solution--Hierarchical Real-Time CPU Scheduling_: In the work proposed by [37], this solution is introduced based on real-time CPU scheduling techniques. This method, integrated within the Linux kernel itself, allows for precise CPU resource allocation to each container (VNF), ensuring that each service receives a defined share of CPU time regardless of the activities of other containers. * **Management and Orchestration (MANO) Security**: The MANO layer orchestrates NFV services and manages their lifecycle. Since it has a comprehensive view of the network functions, it becomes a critical security concern. Compromising the MANO layer can lead to widespread network disruption [38]. \(\bigcirc\) _Proposed solution--Security Framework:_ The SecMANO framework proposed by [38] is a security-oriented enhancement of the existing MANO framework. It incorporates security by design from the initial stages of network service and throughout the service lifecycle. It enables adaptive deployment and management of security functions according to real-time demands and threats and utilises a policy-based approach to ensure consistent and effective security measures across all network functions. * **Integrity of NFVs**: The NFV environment presents a challenge in ensuring the integrity and authenticity of the network functions due to its reliance on virtualization and cloud technologies. The complexity of establishing trust in such a dynamic and distributed environment has been emphasised in [39; 40], with concerns about the integrity and privacy of virtual instances hosted on multi-tenant platforms. \(\bigcirc\) _Proposed solution--Remote Attestation and OpenCIT_: In the work of [40], a combination of solutions is proposed, namely the use of Remote Attestation workflows which are used to allow external verification of the system's integrity. These workflows involve the Trusted Third Party verifying the integrity measures reported by the Trusted Platform Module (TPM) against a known configuration. This TPM is present within OpenCIT, an Intel framework combining hardware elements (TPM) and software elements to establish a Chain of Trust. This approach verifies the integrity of each system component from the hardware level up to the software stack.
# Susceptibility to Expansive Reactions of a _Greener_ Uhpc: Micro to Macro-Scale Study
## 4 Conclusions and Final Remarks  
A greener UHPC was developed using locally available materials and a local industrial waste material from the oil refinery. The new UHPC evidenced ultra-high compressive strength even though lower cement and silica fume dosages were employed. The current work evaluated the susceptibility of new UHPC against expansive reactions, ASR and external sulphate attack, from micro to macro scale. The following conclusions can be drawn:  
* UHPC presented very low porosity (4.8%), with no capillary pores, and the average pore size diameter was 8.8 nm;
* The ASR test revealed the extraordinary resistance of UHPC (no expansion) against very harsh rich alkalis conditions during 14 days of the standard ASTM 1260 test;  
Figure 11: SEM images and XRD diffraction analysis: (**a**) site of ettringite formation over a pore; (**b**) location of zones Z1 (ettringite); and (**c**) XRD diffraction analysis of material in zone Z1.  
* The ASR test was extended until the UHPC specimens reached 0.1%, which took half a year;
* After half a year of immersion in harsh rich alkalis conditions, the specimens did not present visual damage; however, the ASR gel was found by SEM observations;
* The external sulphates also seemed not to be a concern in UHPC since after 26 weeks of immersion in sodium sulphate solution, specimens did not present expansion or damage;
* Even though UHPC specimens did not expand or present visual damage, ettringite was found by SEM observations.  
Further validation of UHPC performance against expansive reaction requires structural level research and considering \"in service\" conditions, i.e., in cracking conditions.  
This work was financially supported by: Base Funding--UIDB/04708/2020 and Programmatic Funding--UIDP/04708/2020 of the CONSTRUCT--Instituto de I&D em Estruturas e Construcoes--funded by national funds through the FCT/MCTES (PIDDAC); by the project POCI-01-0145-FEDER-031777--'UHPGRADE--Next generation of ultra-high performance fibre-reinforced cement-based composites for rehabilitation and strengthening of the existing infrastructure\" funded by FEDER funds through COMPETE2020--Programa Operacional Competitividade e Internacional-izacao (POCI) and by national funds (PIDDAC) through FCT/MCTES; and by FCT--Fundacao para a Ciencia e a Tecnologia through the Scientific Employment Stimulus 2021.01765.CEECIND (individual call).  
Not applicable.  
The materials supply and collaboration of Sines Refinery/Galp Energia, Secil, Omya, Sika, and EUROMODAL is gratefully acknowledged.  
The authors declare no conflict of interest.
## 4 Model Implementation and Evaluation  
### Model Evaluation Metrics  
The proposed models were evaluated using accuracy, precision, recall, and the F1-score. The used measured metrics are defined in Equations (20)-(23).  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{20}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{21}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{22}\]  
\[\text{F1-score}=2\ \times(\text{Precision}\ \times\text{Recall})/(\text{ Precision}+\text{Recall}) \tag{23}\]  
TP, TN, FP, and FN represent true positive, false negative, false positive, and false negative, respectively. The degree to which the result corresponds to the actual value that we were aiming for is called accuracy. The proportion of positive samples correctly predicted by the model is what is meant by the term precision. The ratio of the number of actual positives to the number of true positives is what is meant by the term recall. The harmonic average of recall and precision is known as the F1-score.
## 4 Interference Monitoring
### Correlator Output
#### 4.3.2 Correlator Output Power Standard Deviation  
The standard deviation of correlator output power (\(COP\sigma\)) can be also utilized to analyze the extent of impact on navigation signals from interferences. The \(COP\sigma\) is the measures of dispersion of \(COP\), which is defined by:  
\[\begin{split} COP\sigma&=\sqrt{E[COP-E(COP)]^{2}}\\ &=\sqrt{E(COP^{2})-[E(COP)]^{2}}\end{split} \tag{21}\]  
When the input signals are constant, the peak value of \(COP\) is relatively stable. Thus the \(COP\sigma\) is relatively small. However, when the unstable interferences exist, the correlation peak jitters increase and the value of \(COP\sigma\) gets larger.
# Design and Implementation of Inverse Kinematics and Motion Monitoring System for 6 DoF Platform
## 2 Inverse Kinematics Design  
For six-axis motion platforms with a parallel structure, when the upper platform changes directions, the crank arm mechanism can bear the evenly divided load. That is, the highly rigid structure has a high load-bearing capacity. For six-axis motion platforms with a serial design, a structure resembling a cantilever beam is formed when the connecting rod is fully extended, and each joint connected in series accumulates errors (which reduces accuracy), possesses a low bearing capacity, and easily wobbles. Accordingly, a parallel structure has greater rigidity and stability and can achieve high-precision positioning in a complex working environment. Kinematics is divided into forward and inverse kinematics. Forward kinematics refers to the rotation angles of the servo motors of a six-axis platform, and numerical methods are used to determine the position, speed, and acceleration corresponding to the platform [19]. Inverse kinematics involves the use of platform position, speed, and acceleration to calculate the rotation angle of servo motors. This is the method adopted in the current study [20]. Figure 1 presents the physical model of the six-axis crank arm motion platform, which comprises six crank arm mechanisms, an upper platform (moving platform), a lower platform (fixed platform), and six support arms. The coordinates of the upper platform, lower platform, and rotation axis of the drive shaft are (xpi, ypi, zpi), (xbi, ybi, zbi), and (xb_Mi, yb_Mi, zb_Mi), respectively, where i = 1, 2-6. Traditional inverse kinematics methods and the proposed inverse kinematics method are explained in the section below.
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
###### Abstract  
A method of establishing a prediction model of the greenhouse temperature based on time-series analysis and the boosting tree model is proposed, aiming at the problem that the temperature of a greenhouse cannot be accurately predicted owing to nonlinear changes in the temperature of the closed ecosystem of a greenhouse featuring modern agricultural technology and various influencing factors. This model comprehensively considers environmental parameters, including humidity inside and outside the greenhouse, air pressure inside and outside the greenhouse, and temperature outside the greenhouse, as well as time-series changes, to make a more accurate prediction of the temperature in the greenhouse. Experiments show that the R\({}^{2}\) determination coefficients of different prediction models are improved and the mean square error and mean absolute error are reduced after adding time-series features. Among the models tested, LightGBM performs best, with the mean square error of the prediction results of the model decreasing by 18.61% after adding time-series features. Comparing with the support vector machine, radial basis function neural network, back-propagation neural network, and multiple linear regression model after adding time-series features, the mean square error is 11.70% to 29.12% lower. Furthermore, the fitting degree of LightGBM is the best among the models. The prediction results of LightGBM therefore have important application value in greenhouse temperature control.  
time series; nonlinear change; boosting tree model; greenhouse temperature prediction 2023
# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 4 Experimental Results
### Experimental Results  
The experimental results are shown in Figure 5, which depicts the results in terms of accuracy using the aforementioned AL techniques in an FL context. In the first 50 iterations, there is no significant difference in all six AL techniques, but afterwards, there is substantial variation in terms of accuracy utilizing these techniques. The performance of each approach stagnated after the 50 th iteration. In particular, QBC approaches tend to acquire relatively fair higher accuracy rather than uncertainty approaches. The AL techniques are utilized to achieve better accuracy despite smaller sample sizes. Therefore, the QBC technique outperformed the uncertainty methods by achieving a relatively high accuracy despite the reduced training instances. In the majority of instances, with the exception of queries by the committee using consensus entropy and entropy-based disagreement strategies, the performance of FL is somewhat inferior to that of centralized learning.  
Figure 5: Comparison of FL versus centralized learning contexts based on six participating nodes in various AL techniques. (**a**) Human sentiments; (**b**) Human physical activities.  
The assessment of the above techniques is based on basic ML metrics, such as the F1-score, precision, recall, and test accuracy. These metrics are useful for assessing the above methods accurately, keeping in mind the imbalances in datasets in general and in implementing AL techniques. As shown in Tables 5 and 6, the values of the accuracy, recall, and F1-score follows a largely comparable pattern. Moreover, there is variation in these matrices, which motivated us to present the standard deviation among them to better understand the impact of AL in FL and centralized learning contexts.
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 3 Read Algorithm
### Read Failure Improvement  
As previously described in the program algorithm, various solutions have been proposed to solve the cell reliability and variation problems caused by the geometry of the process integration technology of 3D NAND in the read operation.
## 2 Literature Review  
Table 1 summarizes the leaching conditions used in various studies on Ag leaching from PV panels. These conditions refer to low-temperature acid leaching; there are no reported studies on hydrothermal leaching from waste PV panels. The most common method for Ag recovery from solar cells was etching/leaching in acidic solution using nitric acid (HNO\({}_{3}\)). Various pretreatment techniques were applied for EVA removal, followed with milling and sieving. As seen in Table 1, the optimized HNO\({}_{3}\) concentration varies from 2.3 to 14 N, the temperature ranges from 25 to 80 \({}^{\circ}\)C and the time varies from 2 h to complete dissolution.  
After successful leaching of Ag, the recovery of metallic Ag was achieved with electrochemical precipitation when the concentration in the solution was high enough [9], or with chemical precipitation and subsequent reduction of the silver. For the electrochemical silver precipitation, [PERSON] et al. [5] processed 50 mL of leached solutions using a platinum plate (7 cm \(\times\) 2 cm), a steel plate (7 cm \(\times\) 2 cm) with a density of 60 A/cm\({}^{2}\), and a space between the electrodes of 30 mm for 1 h. For the chemical precipitation of Ag, 37% HCl was used. Then, AgCl was converted to Ag metal powder, first reacting with an aqueous NaOH solution (2% wt.) while stirring at 200 rpm for 1 h, followed with the addition of H\({}_{2}\)O\({}_{2}\) (30% wt.) and a further reaction for 0.5 h. The recovered Ag metal powder was manufactured into an ingot using a thermal torch [22]. Alternatively, high purity Ag powder can be obtained with ammonia dissolution using a hydrazine hydrate reduction of the AgCl precipitate [28].  
Various Al leaching conditions are also found in the literature. [PERSON] et al. [20] reported that the use of 3 N HNO\({}_{3}\) for 2 h at 25 \({}^{\circ}\)C could quantitatively leach Al from PV powder and flakes. However, [PERSON] et al. [21] leached 76% of Al using 5 N HNO\({}_{3}\) for 1 h at 80 \({}^{\circ}\)C, as the complete dissolution of Al was not possible due to Al-Si alloy formation. Since HNO\({}_{3}\) can also leach Ag, if Ag is to be electron, the simultaneous extraction of Al and Ag should be avoided. In this case, selective dissolution of Al can be accomplished with H\({}_{2}\)SO\({}_{4}\) (12 M, 3 h, 25 \({}^{\circ}\)C) [20; 29].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Leaching Agent** & **Temperature (\({}^{\circ}\)C)** & **Time (h)** \\ \hline
[9] & 3 N HNO\({}_{3}\) & 50 & 3 \\
[22] & 90:10 methanesulfonic acid (99 wt.\%):H\({}_{2}\)O\({}_{2}\) (30 wt.\%) & 25 & 4 \\
[5] & 2.3 N HNO\({}_{3}\) & 55 & 2 \\
[21] & 5 N HNO\({}_{3}\) & 80 & 1 \\
[13] & 13.8 N HNO\({}_{3}\) & 25 & 2 \\
[8] & 2.6 N HNO\({}_{3}\) & 60 & until complete dissolution \\ \hline \end{tabular}
\end{table}
Table 1: Leaching conditions for Ag dissolution from PV panels.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 3 Proposed Approach
### Productivity-Safety Effectiveness Index  
In economics, the efficiency indexes are widely used to understand the value-based performance of targets and thus, the fewer inputs are, the better performances are concerning relative value. From the perspective of efficiency between productivity and safety,  
Figure 3: Research procedure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Manufacturing Industry** & **IR** & **FR** & **SR** & **LP** & **PC** & **OR** \\ \hline
1. Food and Beverage & 7.82 & 3.50 & 1.58 & 98.45 & 101.30 & 101.53 \\
2. Textile (except apparel) & 3.60 & 1.62 & 0.83 & 98.25 & 99.70 & 95.95 \\
3. Wood & 17.36 & 7.79 & 4.13 & 97.73 & 101.20 & 100.45 \\
4. Pulp and paper & 8.91 & 4.01 & 2.28 & 97.15 & 98.15 & 100.13 \\
5. Coal and petroleum products & 4.49 & 1.97 & 3.72 & 105.05 & 103.70 & 105.13 \\
6. Chemicals & 5.92 & 2.64 & 1.58 & 105.13 & 103.58 & 101.53 \\
7. Rubber and plastic products & 9.49 & 4.25 & 1.97 & 97.53 & 100.03 & 98.85 \\
8. Non-metallic mineral & 14.25 & 6.43 & 4.06 & 106.50 & 103.63 & 103.90 \\
9. Basic metal & 2.00 & 0.89 & 1.00 & 102.20 & 100.27 & 101.30 \\
10. Metal fabrication & 10.53 & 4.67 & 3.26 & 101.18 & 100.20 & 101.81 \\
11. General machinery and equipment & 7.39 & 3.33 & 1.77 & 105.50 & 100.30 & 99.63 \\
12. Precision instruments & 2.45 & 1.11 & 0.51 & 104.63 & 106.57 & 98.60 \\
13. Electrical equipment & 2.94 & 1.32 & 0.61 & 100.53 & 100.87 & 102.23 \\
14. Automobile & 6.00 & 2.70 & 1.16 & 98.49 & 100.15 & 90.98 \\
15. Transportation equipment & 5.95 & 2.67 & 1.58 & 98.57 & 92.37 & 88.43 \\ \hline Average & 7.27 & 3.26 & 2.00 & 101.13 & 100.80 & 99.36 \\ Standard deviation & 4.35 & 1.96 & 1.23 & 3.41 & 3.15 & 4.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean of productivity and incident indexes (2015–2018).  
the lower the safety performance, the better the productivity, because the higher value of efficiency presents good condition. We developed a new index of industries in South Korea, the _Productivity-Safety Effectiveness Index_ (PSEI), to measure the efficiency relationships between productivity and safety performance of industries. The relationship between inputs of safety performance and outputs of productivity can be presented as Equation (3).  
\[\text{Productivity}-\text{Safety Effectiveness}=\frac{f(LP,PC,FI)}{f(IR,FR, SR)}. \tag{3}\]  
When PSEI is measured from the DEA model, we determine the \(x_{0}\) values of the inputs and the \(y_{0}\) values of the outputs in the objective function, respectively. Thus, three safety performance indexes are coded as inputs, and the remaining three productivity indexes are set as outputs. The \(z\) of the objective function indicates the PSEI score of each industry. Thus, we obtain the relative value of industries, comparing productivity and safety performance. The policymakers of government can obtain an understanding of the relationships between productivity and safety for managing the industries' economy based on PSEI results, as shown in Table 4.
# Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems +
## 3 HULK: Hyper-Heuristic Based on ACO and Local Search Operators
### Choosing and Applying Local Search Operator
#### 3.2.1 Lin-Kernighan Local Search Operator  
The LK heuristic performs a series of \(k\)-opt moves to transform a TSP tour into a shorter one [33], where \(k\)-opt move consists of the exchange of a set of \(k\) tour arcs by a set of \(k\) new arcs keeping the tour feasibility. The algorithm has an efficient implementation [34] with open code but with so many modifications over time that are very difficult to use and adapt; however, here we successfully made it also include the ability to deal with asymmetry. The LK heuristic starts with two empty arc sets: \(X\) (i.e., _out_-arcs) and \(Y\) (i.e., _in_-arcs). At each step, one arc that currently belongs to the tour will be added to \(X\) and a new arc that does not belong to the tour will be added to \(Y\). After the first step, the LK heuristic will favour arc insertions that result in a shorter complete TSP tour. When a new complete tour is achieved, the algorithm will begin a new phase of arc exchanges and this process will continue until there is no further improvement.  
LK establishes a series of rules to be followed, aimed at enhancing performance. These rules can be summarized as follows:  
* Each arc removed must share a node with its added counterpart. After the first arc exchange in each cycle, each arc being removed must also share a node with the previously added arc. Figure 1 illustrates an example of a 2-_opt_ move, where in the first step arc (\(V_{1}\),\(V_{2}\)) is removed and arc (\(V_{2}\),\(V_{4}\)), which shares the node \(V_{2}\) with its removed counterpart, is added. In the second step arc (\(V_{3}\),\(V_{4}\)), which shares node \(V_{4}\) with the previously added arc, is removed and arc (\(V_{3}\),\(V_{1}\)) is added, closing the tour.
* No exchanges that result in the tour being broken into multiple closed circuits are allowed. An example of this type of exchange is shown in Figure 2, where arcs (\(V_{1}\),\(V_{2}\)) and (\(V_{3}\),\(V_{4}\)) are removed and arcs (\(V_{4}\),\(V_{1}\)) and (\(V_{3}\),\(V_{2}\)) are added. In this case, the addition of any arcs would not be accepted because it would result in a segment of tour forming a cycle.
* Each pair of arcs exchanged must be gainful, meaning that each arc being added must be shorter than its removed counterpart. If the problem is asymmetric, both orientations of the resulting tour must be analysed.
* Once an arc is removed, it cannot be reinserted until the tour is closed.  
Although LK was originally designed for symmetric problems [33], it can easily be modified to address asymmetric problems by transforming an asymmetric weight matrix into a symmetric, normally by doubling the graph nodes [34; 42].
## 2 Materials and Methods
### Raw Data Collection and DSM Generation  
There are many topographic surveys to collect DSM original image data. In this paper, UAV oblique photogrammetry is used for data collection. After that, using terrain processing software to extract the elevation points from the original image and generating the DSM, as shown in Figure 2. Save the elevation points information of DSM in the following matrix:  
\[E=\left[\begin{array}{ccc}e_{11}&\cdots&e_{1n}\\ \vdots&\ddots&\vdots\\ e_{m1}&\cdots&e_{mn}\end{array}\right], \tag{1}\]  
where \(e_{ij}\) is the corresponding coordinate of elevation points.