esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contributions and Research Highlights  
The main contribution of this paper is in the use of a GPR model coupled with the core vector methodology which is used in forecasting GHI using South African data. To the best of our knowledge, this is the first application of GPR coupled with core vector regression in which the minimum enclosing ball is applied on GHI data. The other contribution is the application of MEB to select an appropriate kernel to be used coupled with GPR, it finds the ball that contains a given set of points with a minimum radius. The selection of kernels using MEB gives a description of the data domain that is accurate for a given data set. The CVM algorithm is much faster than the benchmark models, it can manage a bigger dataset than the SVM and GBM. The minimum enclosed ball is the computation of the smallest circle that accommodates the list of sets of points in the Euclidean plane. GPR takes into consideration real-time system forecasting and this improves the accuracy of the forecasts. This enables one to easily identify changes in the immediate, especially when dealing with weather conditions that are constantly evolving. This is an aspect that is ignored by most researchers when forecasting energy. The last contribution is that of combining forecasts, this was done by combining GPR and CVR. [PERSON] and [PERSON] (1998) analyzed the effects of combining forecasts and demonstrated that a combination of two coupled models improves forecasts since it reduces errors and this makes the forecasts more accurate.  
Lasso via hierarchical interactions was applied to select appropriate variables. Application of the GPR technique was used to determine the parameters of the models, which was done using the Bayesian approach to estimation. CVR is used for kernel selection by applying the concept of minimum enclosing ball to choose the appropriate kernel to use. The ratio between margin and radius of the minimum enclosed ball (MEB) is used to calculate a measure of goodness of a kernel to produce a new minimization formulation of kernel learning. After coming up with the Gaussian process regression model we then compared the results with two benchmark models, which are the gradient boosting method (GBM) and support vector modelling (SVM). The model evaluation was done using the following metrics: root mean square error (RMSE), mean absolute error (MAE), mean average percentage error (MAPE) and percentage bias (Pbias) RSME and the MAE for predictive models in each of the two different areas.  
The sections that follow are arranged as follows: in Section 2, models are presented starting with GPR modelling followed by the benchmark models, which are the GBM and SVR, respectively. In Section 3, empirical results are presented. Section 4 presents the discussions while Section 5 concludes the paper.
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 4 The Immersive Video ME App
### The i-EVS End-User App  
The i-EVS system provides two different interfaces to end users. One is based on web services, and is accessible from any browser; the other is thought to improve QoE, and has been developed as a mobile \"app\" (the i-EVS end-user app), which can be downloaded by consumers on their Mobile Terminals (two screenshots of the user management interface provided by the end-user app are shown in Figure 9).  
All the activities described above, related to the i-EVS ME app, can be accessed through this end-user app: registration, creation of groups, upload/download of contents, and video chatting.  
Figure 9: Screen shots of the i-EVS end-user app, showing the user management interface.
## 3 Test and Results
### Results
Figure 10: Ground distance D\({}_{60}\) line from TLS of a point 60 m high on the tower base during the deactivation of the turbine. The spectrum of vibration frequencies obtained by processing GB-RAR data acquired during the stop of the wind turbine is reported in Figure 13. The spectrum refers to a point located at a height H = 25 m above the tower base, corresponding to a slant distance R = 54 m from the radar. Two peaks are observed at 0.4 Hz and 3.1 Hz. It worth noting that besides the common peak at frequency f = 0.4 Hz, the two spectra had different characteristics. In particular, it should be noted that the peak at f = 0.4 Hz measured by GB-RAR in operating condition c was measured by TLS at H = 60 m but not at H = 25 m. Probably, this was due to the different amplitude of oscillations at the top and bottom of the tower.
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model  
In this section, we describe the details of the TRSA model. Its architecture is presented in Figure 3 and involves three modules: (1) a trust-aware evidence reasoning module for calculating the credibility of nodes in the information dispersion network based on a random walk model to discover high-quality evidence; (2) an evidence representation module with three units (temporal sequence representation unit, spatial structure representation unit, and fusion gate unit) for capturing the characteristics of high-level spatiotemporal structure and enhancing the semantic representation of evidence; and (3) an evidence semantic aggregation module for deepening the semantic interactions between evidence and source information, and modeling the implicit bias of evidence relative to source information based on a capsule network.
## 4 Results and Compilation
### Zonal Data  
Figure 7 demonstrates that the techniques employed in this study are able to capture data representing all six zones in the study area. We split the data on a yearly basis to illustrate the availability of data concerning the zonal trip production pattern. Each figure represents the vector of a zone to produce home-based trip for work purpose.  
Figure 7: Predicted Trip Lines for Each Production Zone of Serang City, 2018–2021. (**a**) _Z_-1:Cipocokjaya. (**b**) _Z_-2:Curug. (**c**) _Z_-3:Kasemen. (**d**) _Z_-4:Serang. (**e**) _Z_-5:Tkatakan. (**f**) _Z_-6:Walantaka.  
By presenting the predicted trip lines for each production zone in the years 2018, 2019, 2020, and 2021, we highlight the evolution of travel patterns and the effectiveness of our modeling approach. The yellow bullets on the maps represent home locations, while the green bullets indicate work locations in proximity to the respective zones. Among all six zones, Zone 1 emerges as the most productive zone in 2021, whereas Zone 3 exhibits the least productivity. Notably, Zone 3 shows minimal development from 2018 to 2021, whereas Zone 5 demonstrates significant development during this period.  
These maps serve as visual evidence that our study has successfully achieved its aim of developing trip production models using LBSN data.
## 4 Privacy and Security Enhanced Methods
### Privacy-Preserving Arbitration Mechanism
#### 4.1.3 Communication-Optimized Scheme  
The basic scheme introduces significant communication overhead, as it requires transmitting \(\binom{n}{2}\) difference ciphertexts for \(n\) executors. To address this issue, we propose a communication-optimized variant that aggregates homomorphic differences before transmission.  
Instead of sending individual difference ciphertexts, the arbitration and scheduling module computes an aggregated difference:  
\[D_{agg}=\bigoplus_{i=1}^{n-1}\bigoplus_{j=i+1}^{n}D_{ij}=Enc_{pk}\left(\sum_{ i=1}^{n-1}\sum_{j=i+1}^{n}(O_{i}-O_{j})\right). \tag{7}\]  
The cloud platform then decrypts this aggregated difference:  
\[\Delta_{agg}=Dec_{sk}(D_{agg})=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}(O_{i}-O_{j}). \tag{8}\]  
If \(\Delta_{agg}=0\), all executors have produced identical outputs, and the cloud platform can proceed with decrypting a single aggregated output. Only when \(\Delta_{agg}\
eq 0\), indicating inconsistency among executors, does the system revert to the basic scheme and transmit individual difference ciphertexts to identify the specific inconsistent executors.  
For scenarios involving large output values, we further optimize by employing cryptographic hash functions. Instead of calculating homomorphic differences over entire outputs, the system can compute  
\[D^{\prime}_{ij}=Enc_{pk}(H(O_{i})-H(O_{j})), \tag{9}\]where \(H(\cdot)\) represents a cryptographic hash function. This approach significantly reduces ciphertext size while maintaining the ability to detect inconsistencies.
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
## 5 Results & Discussion
### Application: Orthogonal Metal Cutting
#### 5.2.3 Effect of SPH Particles Size  
Before closing this section, the effect of resolution (i.e., the size of SPH particles) on the accuracy of the model is briefly discussed. For this purpose, a cutting geometry with \(\mu=0.65\) and the parameters listed in Table 1 is considered. Figure 9 shows the equivalent plastic strain distribution for two similar simulations in different resolutions. As seen in this comparison, the high-resolution model (i.e., \(\Delta x=0.008\) mm) performs better in capturing the shear bands and chip curling. It can be concluded that reducing the size of SPH particles increases the accuracy of the predicted chip shape.  
Previous studies have also investigated the influence of discretization size on SPH metal cutting results. For instance, the authors of [20] focused on this subject and presented a new framework for SPH metal cutting models with variable particle size. They showed that the size of SPH particles has a significant impact on the predicted chip shapes. Figure 10 demonstrates this finding, where the chip shape produced by smaller size SPH  
Figure 8: Distributions and contours of temperature obtained by SPH using the reference and proposed thermal model. An average of the rake face temperature in simulation results (considering the particles inside the black frames) is compared to the experimental measurement provided by [41]. The color bar is limited to 945 K for better visibility.  
particles (i.e., the high-resolution case with \(\Delta x=0.005\) mm) is much closer to the serrated chip shape observed in the experiment. This, in turn, serves as a qualitative measure of convergence of the method for chip prediction.
# Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
## 3 Results and Discussion
### Adsorption Ability and Selectivity  
The adsorption abilities of the Alg-Mel for heavy metals such as Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\), Co\({}^{2+}\) and Cr\({}^{3+}\) ions were also studied, and the results are listed in Table 1 (In an adsorption test, 100 mg of Alg-Mel was equilibrated with 40 mL of metal ions-containing solution at 25 \({}^{\circ}\)C for 6 h, and then filtered. The unextracted metal ions in the filtrate was measured by ICP-OES). As can be seen in Table 1, the Alg-Mel sorbent shows high affinity to Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\) and Cr\({}^{3+}\). It can uptake 95.4% of Pb\({}^{2+}\), 50.2% of Cd\({}^{2+}\), 59.3% of Cu\({}^{2+}\) and 67.0% of Cr\({}^{3+}\) from the corresponding solution, respectively. This may be ascribed to the coordination of the amino and carboxyl groups on the Alg-Mel with the heavy metals. Furthermore, the results of the competitive adsorption test indicate that the Alg-Mel  
Figure 5: Effect of contact time on Pb\({}^{2+}\) adsorption of the Alg-Mel. (\(\sim\)100 mg (**a**) and \(\sim\)20 mg (**b**) of Alg-Mel was equilibrated with 40 mL of 0.48 mM Pb\({}^{2+}\) at pH 5.0 and 25 \({}^{\circ}\)C, respectively).  
Figure 6: Effect of temperature on Pb\({}^{2+}\) adsorption of the Alg-Mel. (\(\sim\)100 mg (**a**) and \(\sim\)20 mg (**b**) of Alg-Mel was equilibrated with 40 mL of 0.48 mM Pb\({}^{2+}\) at 25 \({}^{\circ}\)C and pH 5.0 for 6 h, respectively).  
sorbent has higher adsorption capacity and selectivity to Pb\({}^{2+}\). The selectivity coefficient (\(k\)) values for Pb\({}^{2+}\)/Cd\({}^{2+}\), Pb\({}^{2+}\)/Cu\({}^{2+}\), Pb\({}^{2+}\)/Cr\({}^{3+}\), and Pb\({}^{2+}\)/Co\({}^{2+}\) are all over 7, which can be attributed to the similar hydrated radius of Pb\({}^{2+}\) (0.40 nm) and Ca\({}^{2+}\) (0.41 nm) and the higher binding energy of the carboxyl group with Pb\({}^{2+}\) than other metal ions [9; 14].  
The percentage of adsorbed metal ions (Adsorption%), adsorption capacity (Capacity), selectivity coefficient (\(k\)) and distribution coefficient (\(K_{\rm d}\)) of Alg-Mel for Pb\({}^{2+}\) were calculated as follows:  
\[\rm{Adsorption\%}=\frac{C_{i}-C_{f}}{C_{i}}\times 100\%Capacity=\frac{(C_{i}-C_{f} )\times 0.04}{MW}\]  
\[K_{d}=\frac{\Big{(}C_{i}-C_{f}\Big{)}\times 0.04}{C_{f}W}\qquad\qquad k=\frac{K_{d} \big{(}Pb^{2+}\big{)}}{K_{d}(metal\ ion)}\]  
where \(C_{i}\) represents the initial concentration of the metal ions in solution (mM), \(C_{f}\) represents the final concentration of the metal ions in solution (mM). M represents the molar mass of metal ion, \(W\) represents the mass of Alg-Mel sorbent (g). \(K_{d}\) represents the distribution coefficient (L/g), and \(k\) represents the selectivity coefficient.
# Steel Roll Eye Pose Detection Based on Binocular Vision and Mask R-CNN
## 1 Introduction
proposed a cooperative target attitude measurement method based on binocular stereo vision and validated the algorithm's effectiveness through a simulation system to meet the measurement requirements for tracking, approaching, and maintaining relative position [15; 16]. [PERSON] et al. designed a binocular vision-based missile assembly position recognition method to guide the docking mechanism in adjusting the compartment's position using compartment features to complete the docking process [17]. [PERSON] et al. proposed a collaborative spacecraft attitude measurement method based on three-view vision, which improves feature point localization accuracy, enhances system reliability, and addresses the challenge of measuring positional attitude parameters during the rendezvous and docking process [18]. Traditional machine vision position extraction methods face several challenges in steel coil end face inspection. First, the complex background and the interweaving of edge and mounting hole contours make image background segmentation difficult. Second, due to variations in packaging requirements and field environments, the steel coil end face images exhibit significant differences, necessitating the design of complex segmentation algorithms and intricate solution processes. Furthermore, traditional methods lack accurate positioning of contour regions and automatic feature extraction, which limits their portability and reusability. As a result, traditional target detection algorithms are increasingly being replaced by deep-learning algorithms. These deep-learning techniques have become the mainstream in target detection due to their ability to automatically learn features and classifiers, offering stronger robustness and adaptability [19; 20]. This paper introduces a novel method for detecting positional deviations in the steel coil end face, leveraging deep learning and binocular vision technologies, integrated with an inner corner guard butt joint installation scheme. The core of this approach is to transform the parameter information of the steel coil's roll eye from the image coordinate system to a 3D spatial pose in the world coordinate system. This transformation is accomplished through a series of steps involving image segmentation, feature extraction, and coordinate transformation techniques. Initially, the relationships between the four major coordinate systems of the camera are established based on visual sensing principles [21]. Subsequently, the Mask R-CNN architecture, combined with the RGB vector space segmentation method, is employed for image segmentation [22], producing a binarized image. Following this, the least-squares method is applied to extract elliptic parameters from the rolled eye parameters after performing Sobel edge detection. Finally, by integrating the camera's intrinsic parameters [23], the extracted elliptic parameter feature information, and the geometric positional relationship between the camera and the steel coil, the roll eye's positional attitude is accurately determined.
# The Future of Cybersecurity in the Age of Quantum Computers
## 6 Limitations and Caveats  
This paper provides theoretical support to two new hypotheses that are currently under investigation. These hypotheses have far-reaching implications for our understanding of solid-state electronics and computer hardware/software, in general, and for enhancing their security and resilience in building a robust Internet, in particular. As any hypothesis-generating research demands, great care is warranted in projecting the conclusions of this report to real-world scenarios for the following reasons:  
1. These hypotheses are formulated based on empirical data from a limited-use hardware wallet experiment [14] and need to be validated in diverse mainstream computing environments before any extrapolation to more complex computing environments;  
Figure 7: Quantum computer service provider (QCSP) providing quantum computing via 3 SoC Intranet to subscribers.  
* The ZVC/3 SoC research is ongoing, and the inferences drawn from the available data are preliminary and subject to updates as and when available;
* Currently, cloud services seem to be the only way quantum computing can become available to end users in the near future;
* Notably, 3 SoC devices inherently restrict the porting of generic or non-conforming third-party peripheral devices [27];
* Rigorous experimentation by peer researchers is warranted for testing and proving or disproving the ZVC/3 SoC hypotheses and replicating the conclusions;
* Appropriate key performance indicators (KPIs) should be framed to justify the quantity and quality of the case studies designed to investigate the formulated hypotheses.  
Despite its limitations, this study adds compelling evidence that quantum-safe security of computing devices is theoretically possible by using an encryption-agnostic approach to securing computing devices. ZVC is a novel encryption-agnostic method of securing future computers that can potentially render computers quantum-resistant. ZVC's 3 SoC abstraction also supports the feasibility of de-layering the legacy computer architecture for enhancing and replicating the robustness, energy efficiency, portability, and resilience of solid-state devices.
# Consumer Preferences for Wood-Pellet-Based Green Pricing Programs in the Eastern United States
## 4 Results and Discussion
### Introductory Questions  
Some findings about the trends in respondents' awareness, knowledge, and enrollment in wood pellets, co-firing, and GPPs from the introductory questions are summarized as follows. A total of 33.57% of respondents from the Eastern states were aware of the sources from which their energy provider generates energy. A closer state-wise analysis reveals that respondents from AL had the greatest percentage at 38.5% followed by NY (37.5%), VA (36.75%), PA (29.50%), and NJ (25.50%), respectively. In addition, 28.56% of the Eastern state respondents were aware of existing GPPs in their respective states. A state-wise comparison revealed similar trends as the levels of awareness of existing GPPs were highest in NY at 39.5% and the lowest awareness level was found in AL at 22.75%. The low levels of awareness of existing GPPs in Alabama could be due to the lack of state mandated RPS for energy providers, thus reducing the momentum for developing GPPs, coupled with subsequent marketing that often increases awareness [52]. Even so, out of all the respondents in the five states, only 13.42% were enrolled in a GPP offered by their energy provider. The rates of enrollment in a GPP were highest in NY (23.75%), followed by VA (11.75%), AL (10.75%), NJ (10.50%), and PA (9.25%). The high enrollment rates in NY correlate with the higher production of renewable energy found there as compared to the other states, where NY produces 9.4 million MWh, followed by AL (4.6 million MWh), PA and VA (2.3 million MWh), and NJ (0.5 million MWh) [53]. The presence of a source of renewable energy may push the momentum towards developing GPPs. A National Renewable Energy Laboratory (NREL) technical report by Bird and Swezey [54] showed that GPPs enrollment rates were 1.3% in the US, whereas, by 2018, there was a total of 885,000 customers purchasing 8.9 million MWh of green power from utility GPPs, indicating an increase of 55.26% [25]. We further assessed the respondents' level of awareness of existing co-firing plants in the Eastern states. Our results show that only 22.64% of respondents were aware of the existence of co-firing plants in their state of residence, from which only 14.81% of respondents were aware of energy generation plants using wood pellets in co-firing technology to generate electricity.
## 3 The Notification-Oriented Paradigm
```
1:fbe Sensor
2:private boolean atIsRead = false
3:private boolean atIsActivated = false
4:private method mtTrigger
5:assignment
6:this.atIsRead = true
7:this.atIsActivated = false
8:end_assignment
9:end_method
10:rule rI SensorTrigger
11:condition
12:subcondition
13:premise prIsReadFalse
14:this.atIsRead == false
15:end_premise
16:and
17:premise prIsActivated
18:this.atIsActivated == true
19:end_premise
20:end_subcondition
21:end_condition
22:action sequential
23:instigation sequential
24:call this.mtTrigger();
25:end_instigation
26:end_action
27:end_rule
28:end_fbe
```  
**Algorithm 1** Example of NOPL statements for _FBE_ Sensor and _Rule_ rITriggerSensor presented in Figure 1  
The core mechanism of the paradigm is the so-called NOP Inference Chain [63], which is modeled in Figure 2 and exemplified in Figure 3. Each NOP entity can send and/or receive precise and pertinent notifications, thereby allowing the evaluation of states only when a notification arrives. This collaborative notification-oriented behavior provides a new form to develop and execute software (and even hardware) applications based on small reactive notifiable entities [16; 21]. ```
1:/* Include the NOP Framework C++ 4.0 */
2:#include \"libnop/framework.h\"
3:
4:/* Create the class Sensor */
5:class Sensor
6:{
7:public:
8:
9:/* Create the Attribute atIsRead */
10:NOP::SharedAttribute\(<\)bool\(>\) atIsRead
11:{NOP::BuildAttribute\(<\)bool\(>\)(false) };
12:
13:/* Create the Attribute atIsActivated */
14:NOP::SharedAttribute\(<\)bool\(>\) atIsActivated
15:{NOP::BuildAttribute\(<\)bool\(>\)(false) };
16:
17:/* Create the Premise prlsRead */
18:NOP::SharedPremise prlsRead
19:{NOP::BuildPremise(this.atIsRead, false, NOP::Equal0) };
20:
21:/* Create the Premise prlsActivated */
22:NOP::SharedPremise prlsActivated
23:{NOP::BuildPremise(this.atIsActivated, true, NOP::Equal0) };
24:
25:/* Create the Condition cnRISensorTrigger */
26:NOP::SharedCondition cnRISensorTrigger =
27:NOP::BuildCondition\(<\)NOP::Conjunction\(>\)(prlsRead, prIsActivated);
28:
29:/* Create the Method mtTrigger */
30:NOP::Method mtTrigger{
31:this.AtIsRead = true;
32:this.atIsActivated = false;
33: };
34:
35:/* Create the Instigation inMtTrigger */
36:NOP::SharedInstigation inMtTrigger =
37:NOP::BuildInstigation(mtTrigger);
38:
39:/* Create the Action acMtTrigger */
40:NOP::SharedAction acMtTrigger =
41:NOP::BuildAction(inMtTrigger);
42:
43:/* Create the Rule rISensorTrigger */
44:NOP::SharedRule rISensorTrigger =
45:NOP::BuildRule(cnRISensorTrigger, acMtTrigger);
46:
47:/* End class */
48:];
```  
**Algorithm 2** Example of NOP Framework C++ statements for _FBE_ Sensor and _Rule_ rITriggerSensor presented in Figure 1