esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### ICCD Diagnostics with Exposure Time of 100 \(\mu\)s  
IV curve of 4.4 mm rf-DBD at atmospheric pressure is shown in Figure 2. The voltage from point 1 to point 2 (138-208 W) basically remained unchanged, because the plasma area increased during this process. From point 2 (208 W) when the plasma filled the discharge gap, the voltage and current show a linear relationship. From point 8 to point 9 (624-686 W), the linear relationship suddenly changed, which is preliminarily assumed to be due to the change of plasma impedance after the switch from \(\alpha\) mode to \(\gamma\) mode [6,18].  
Figure 3 shows photos of two discharge modes. In \(\alpha\) mode at 138 W (Figure 3a), the luminous intensity seems to be distributed evenly along the gap. At the highest power (836 W, Figure 3b), it is possible to observe longitudinal luminous intensity profile typical for \(\gamma\) mode, with dark spaces and strongly shining negative glow near surfaces [6,13].  
In order to obtain the relationship between the luminescence intensity at different longitudinal positions of the plasma and the discharge power, ICCD photos were analyzed and the relational graph is shown in Figure 4. The coordinates represent the position between  
Figure 3: ICCD photos: (**a**) and (**b**) correspond to point 1 and point 12 in Figure 2 (exposure time: 100 \(\mu\)s).  
Figure 2: IV curve of 4.4 mm DBD.  
the gaps (0-4.4 mm), discharge power (100-900 W) and luminous intensity (0-40,000), respectively. As we can see, the luminous intensity of the plasma increases with the power at all positions. At the beginning, the luminous intensity at different positions of the discharge gap is basically the same. As the power increased, the intensity of plasma on dielectric surface is obviously higher than that at the middle position. However, throughout the process, there is no obvious turning point from which it can be considered as the change of discharge mode. Therefore, the spatiotemporal characteristics within one cycle (73.7 ns) of rf-DBD were studied.
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 2 Analysis of the Characteristics of Customers
### Characteristics of Customers  
First, we arbitrarily selected one normal electricity consumer and one electricity theft consumer and drew their electricity consumption bar graphs for the whole year from 1 January 2015 to 31 December 2015, as shown in Figure 1. In this figure, we can see that both normal and electricity theft consumers had obvious seasonal characteristics during the year. As shown in Figure 1a, January to May was the low period of electricity consumption and June to October was the peak period of electricity consumption. In Figure 1b, there was a small peak in February, a peak in August, and troughs from March to May and from September to December. Customers' electricity consumption was mainly affected by seasonal factors, such as hot weather in the summer when customers used air conditioners or other cooling measures, which resulted in a surge in electricity consumption. Electricity consumption was also affected by personal, work, rest, and economic conditions, resulting in differences in customers' electricity consumption. Thus, it would be very difficult to judge whether customers were thieving electricity only from their daily electricity consumption for a year.  
Further, the annual electricity consumption of a normal customer and an electricity thief was plotted month by month, as mentioned above. Figure 2a shows the 12-month daily electricity load curve of a normal customer, while Figure 2b shows the 12-month daily electricity load curve of an electricity thief. It can be observed that in the monthly load curve of the normal customer, the electricity consumption curve of most months was relatively flat. However, in the monthly load curve of the electricity theft customer, the electricity load curve often fluctuated greatly during the peak period of electricity consumption, from May to October, which raised suspicion.  
August, the peak month of electricity consumption, was chosen for the study, and daily electricity consumption load curves were plotted for normal and electricity theft customers to better observe their daily characteristics. As shown in Figure 3a, the daily electricity consumption curves in August for a normal customer, and Figure 3b, the daily electricity consumption curve in August for an electricity theft customer, it can be clearly observed from the graphs that the daily electricity consumption load of the normal customer in August was very flat most of the time, whereas the electricity consumption load of the electricity theft customer in August fluctuated drastically.  
Figure 1: Bar chart of yearly electricity consumption of normal customers and electricity theft customers. (**a**) Normal customer; (**b**) Electricity theft customer.
###### Abstract  
Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 \(\times\) 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SG\({}_{\text{lumber}}\)) and 25-mm block (SG\({}_{\text{block}}\)) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SG\({}_{\text{block}}\) gave the best results, followed by SG\({}_{\text{lumber}}\), MOE, and MOR. The NIR-HSI SG\({}_{\text{lumber}}\), MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.  
bending stiffness; bending strength; NIR; nondestructive testing; _Pseudotsuga menziesii_; wood and fiber quality
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Data Plane
#### 4.3.2 Service Function Forwarder  
The architecture reuses the principles from the BGP control plane protocol RFC draft [10] to let Service Function Forwarders (SFFs) be responsible for forwarding SFC packets and to announce their connected Service Functions. This RFC suggests using one SFF per Compute Node, but, since the architecture includes an additional SFC header, the SFF must also be able to read double-tagged SFC headers. Therefore, it is suggested to extend the RFC draft to include a hierarchy of SFFs per Compute Node. This is achieved by having one SFF to handle SFC header 1 forwarding decisions and one SFF to handle the forwarding of SFC header 2. Figure 14 refers to these two components as SFF1 and SFF2. Both SFFs send and receive SFC routes from a network controller over BGP and is responsible for forwarding packets containing SFC headers according to the revised SFC routes. This enables the SFFs to make dynamic forwarding decisions based on the SFC routes received from BGP. The hierarchy of  
Figure 14: Services on the dataplane.  
SFFs on the same Compute Node implements the requirement [16] that SFC encryption services must be co-located with the VNFs. This makes it secure that encryption services for a VNF cannot be moved to a different Compute Node without also moving both the VNF and the encryption service together.
## 3 Numerical Example
### Example 3
The comparison results are shown in Figure 14, and it can be seen that the difference of results with and without adaptive DOFs is small, which indicates that it is feasible to use ADOF-FEM to solve the simulation problem of the transient temperature field during the construction period of the asphalt-concrete core wall. Figure 14: The temperature at typical point positions of Example 3. Figure 13: The temperature contour in vertical and cross sections of Example 3. The computational time and the number of DOFs for Example 3 are shown in Figure 15, which vary similarly to Example 2. By using ADOF-FEM, the DOFs of Example 3 can be saved up to 70% and the computational time can be saved by approximately 40%. When using ADOF-FEM to condense the DOFs, all of the elements and DOFs in the model exist objectively and are assembled into the global conductivity matrix in a cyclic traversal. ADOF-FEM only changes the assembly process of DOFs based on the transformation matrix, so the time to assemble the global conductivity matrix cannot be saved, and the only time saved by ADOF-FEM is the matrix factorization and equation solution. Even so, ADOF-FEM is helpful when using a large 3D finite element model to simulate the temperature field for the whole construction process of the asphalt-concrete core wall. Example 3 is used to simulate the temperature change during the construction of a 16-layer core-wall dam under the same conditions and processes and to verify the applicability of the ADOF-FEM. Since the relevant conditions and processes are generally constant throughout the construction of the core-wall dam, the results of the calculations will have similar characteristics if we continue to extend the construction of the 17 th, 18 th, and other layers of the core wall. ADOF-FEM can be used naturally in a similar way and eventually solve the problem of the high cost of temperature simulation during the construction of the entire asphalt-concrete core-wall dam.
# Quantitative Analysis of Caffe in Roasted Coffee: A Comparison of Brewing Methods
## 1 Introduction
The brewing method may have a particularly significant impact on the various factors influencing caffeine content. In recent years, a literature review was conducted on the effects of different brewing parameters on the caffeine content of green and black coffee [27]. The authors analyzed the influence of several factors, including brewing time, amount of coffee and water used, type of water, cup volume, brewing temperature, and pressure, as well as the type and variety of coffee, its origin, and the degrees of roasting or grinding of the beans. Although the literature review details the influence of individual brewing parameters, it does not consider the interaction between factors within a single brewing method. The conclusions point out that to reliably compare individual factors' effects on caffeine content, other factors should remain constant. The review also takes into account certain limitations, such as the different ways of expressing the results, the various origins of the coffee, and the methods used to determine the caffeine content. In view of the above conclusions, it was decided to update and extend the state of the knowledge in this area. The aim of this review article was to analyze the effects of different brewing methods on the caffeine content of roasted coffee, taking into account the method of caffeine determination. Recalculated caffeine values focused on one type of coffee (roasted coffee) to reduce the variables influencing the results. It also presented the characteristics of coffee and brewing methods. In addition, different methods for determining caffeine (inter alia liquid chromatography, spectrophotometry) in coffee were considered, taking into account the analytical parameters used to present the current knowledge in this area and verify their impact on determining this component. As the health effects of caffeine are dose-dependent, it is necessary to consider as many factors as possible when comparing study results. This review attempts to address these variables comprehensively. The summary of the results of this study could have significant practical applications. First, analyzing the impact of different coffee brewing methods on caffeine content provides valuable insights that can help optimize brewing techniques. As a result, consumers will be able to make informed decisions on how to prepare coffee, potentially enhancing the health benefits of its consumption. Second, understanding the factors influencing caffeine levels in various brewing methods allows for refining the brewing process. Furthermore, the findings of this study could form the basis for developing health guidelines that establish safe and beneficial coffee consumption levels, customized to the needs of different social groups.
## 4 Results and Findings
### Experimental Analysis  
The questions of the research survey were structured to respond to the following primary objectives of the paper:  
2.1 To Identify Areas of Weakness in the Cybersecurity Framework in the E-Government Operations of Saudi Arabia  
A majority of the respondents of the research indicated that they viewed no significant weaknesses in the cybersecurity environment of Saudi Arabia. Most of the respondents indicated that they had confidence in the capabilities of the government of Saudi Arabia to respond to any cybersecurity issues arising within the e-government framework of the country. The respondents further indicated that they believed that the government of the country had taken enough measures to ensure that the country did not face any adversities within the cybersecurity of e-government operations in the long run. This research results are indicative of a relatively safe cybersecurity environment for the e-government infrastructure of Saudi Arabia.  
2.2 To Establish The Level of Strength in the Cybersecurity Framework of the E-Government Operations of Saudi Arabia  
A significant proportion of the respondents of the study indicated that they believed the cybersecurity framework of the country was strong. Most of the respondents intimated that the country had taken sufficient measures to ensure that the country procured some of the most robust systems to safeguard the interests of its citizenry. The respondents indicated that they believed the information of the citizenry of Saudi Arabia was secure and that external threats would not easily compromise the system of service delivery through e-government platforms. The respondents seem to suggest that the cybersecurity environment of e-government in Saudi Arabia is quite robust given the risks that face them in the present.  
Figure 2: Distribution of years of experience among the respondents.  
2.3 To Compare the Strength of Cyber Security Framework of the E-Government of Saudi Arabia to Other Countries of the World  
A majority of respondents who took the survey indicated that the e-government framework of Saudi Arabia compares relatively well with those of other countries of the world. The respondents also indicated that the cybersecurity framework of e-government platforms in Saudi Arabia compares quite competitively with those of major countries of the world. The respondents further noted, however, that the cybersecurity training within different workforce cadres of Saudi Arabia were still very low and needed corrective action from government. The respondents indicated that Saudi Arabia needs to be more proactive in improving the cybersecurity framework from a capacity building point of view.
## 1 Introduction
[PERSON] [12] established an event tree and failure tree to describe the relationship between the equipment and used the minimum cut set theory to identify the most critical and vulnerable equipment in the substation system. [PERSON] et al. [23] proposed a new probability-based method, the state tree method, to evaluate the seismic reliability of complex systems. Compared with the original fault tree, the state tree features the addition of several fictional events (FEs). The establishment of a state tree is actually the establishment of a successful path [24], so the time of the evaluation process is shorter than that of establishing the fault tree. [PERSON] et al. [25; 26] developed a rapid assessment framework for earthquake recovery that integrates network models with time-varying functional characteristics. They proposed a bi-dimensional functional network model and a post-earthquake multi-objective optimization algorithm to enhance the recovery efficiency of power substations. The research indicates that equipment-level repairs can more rapidly restore the functionality of substations after an earthquake. The research focus, methodology, and key indicators of the relevant literature are shown in Table 1.  
In summary, most existing research on the seismic reliability of substation systems has focused on modeling the prior evaluation process. However, a post-evaluation process has not been constructed due to the extensive calculations required. To address the current issues, the main tasks undertaken in this project are as follows:  
(1) Proposes a post-assessment process using the quasi-Monte Carlo method (QMC) for analyzing substation systems; (2) emphasizes considering interactions among all facilities during the evaluation process; (3) advocates for the appropriate use of functional units to simplify the system model and enhance the reliability of QMC simulation results; (4) fully describes the state of each unit, including the equipment, structures, and their combinations, to accurately calculate system reliability; and (5) introduces the proposed processing steps and validates its effectiveness using a typical 220 kV substation in China with 6 incoming and 10 outgoing lines as a case study. The remainder of this paper is as follows. Section 2 introduces the system functionality definition, QMC simulation method, and solution algorithm. Section 3 presents a case study on a typical substation. Section 4 provides an analysis of the results and reinforcement recommendations. Section 5 offers concluding remarks and outlines future research directions.
###### Abstract  
This article investigates the dynamic topology control problem of satellite cluster networks (SCNs) in Earth observation (EO) missions by applying a novel metric of stability for inter-satellite links (ISLs). The properties of the periodicity and predictability of satellites' relative position are involved in the link cost metric which is to give a selection criterion for choosing the most reliable data routing paths. Also, a cooperative work model with reliability is proposed for the situation of emergency EO missions. Based on the link cost metric and the proposed reliability model, a reliability assurance topology control algorithm and its corresponding dynamic topology control (RAT) strategy are established to maximize the stability of data transmission in the SCNs. The SCNs scenario is tested through some numeric simulations of the topology stability of average topology lifetime and average packet loss rate. Simulation results show that the proposed reliable strategy applied in SCNs significantly improves the data transmission performance and prolongs the average topology lifetime.  
Keywords:topology control strategy; reliability assurance; satellite mobility; satellite cluster networks; earth observation +
Footnote †: journal: Sensors
## 1 Introduction
[8] built a 3D model for complex structures using terrestrial laser scanning and photogrammetry. A geometric model for a complex structure was constructed by combining various measuring instruments, such as an commercially-available digital camera, laser scanner, and total station, and the results were expressed with a commercial photogrammetry program [8]. Photogrammetric techniques were used to perform the material testing and structural monitoring of civil structures as well as complex structures produced in a lab [9; 10; 11; 12]. In this study, considering the features of a VLBI antenna surface providing mm-level position accuracy, a precise close-range photogrammetry algorithm was applied to multiple feature points on the VLBI antenna surface without installing a separate marker. A displacement monitoring target was selected in line with the features of the VLBI structure, which corresponded to a complex structure. To monitor the main reflector of the VLBI antenna, 3D measurements of each feature point were made after determining the interior and exterior orientation parameters. Many studies have been conducted for accurate 3D measurement with photogrammetry, with various mechanisms for verifying the accuracy [13; 14; 15]. In this study, a close-range digital photogrammetry algorithm with a collinearity equation was applied to determine 3D position coordinates with mm-level accuracy for each monitoring object of the stereo images used for static displacement monitoring. To maintain the performance and position measurement accuracy of the VLBI antenna, a program was developed that can constantly monitor the deformation rate of a VLBI antenna surface with minimum manpower and cost.
# Efficient Robot Skills Learning with Weighted Near-Optimal Experiences Policy Optimization
## 3 Methods  
Our goal was to train a policy network and use it to control the robot to realize the walking skill. The WNEPO proposed in this paper can improve the sample efficiency for robot skill learning, as shown in Figure 1. In this section, we will introduce WNEPO in detail.  
To achieve end-to-end control of the robot, the policy \(\pi_{\theta}(u_{t}|x_{t})\) is expressed as:  
\[\pi_{\theta}(u_{t}|x_{t})\;\sim N(\mu^{\pi}(x_{t}),\Sigma^{\pi}(x_{t})) \tag{5}\]  
We use a neural network to model \(\pi_{\theta}(u_{t}|x_{t})\). \(\mu^{\pi}(x_{t})\) and \(\Sigma^{\pi}(x_{t})\) are the outputs of the neural network, and the two respectively represent the mean and variance used to determine a normal distribution. That is, when the robot is in the state \(x_{t}\), the action \(u_{t}\) output by the policy network obeys the normal distribution. The state \(x_{t}\) of the robot can be measured directly by sensors, and this paper considers the sensor observation \(o_{t}=x_{t}\) and the action \(u_{t}\in R^{E}\) to be the torque applied to the joints.  
Since the policy \(\pi_{\theta}(u_{t}|x_{t})\) is modeled with a neural network, it is usually necessary to construct a large-scale training set first to update the parameters \(\theta\). However, in the case of unknown environment dynamics, the sample size obtained from the collection is small and insufficient to train a good performance neural network. To address this issue, we divided the optimization of the policy network into two stages: the imitation phase and the practice phase. In the imitation phase, the policy network learns in a supervised learning manner from the near-optimal trajectories generated by optimizing the linear Gaussian controller. In this way, a policy network with good performance can be trained in the case of small sample size. When the performance of the policy network is close to the linear Gaussian controller, the training process will switch to the next phase. In the practice phase, the network interacts with the environment in a self-exploratory manner. With the help of the weighted replay buffer, the update of the policy network will be more stable.  
In the two phases above, a mini-batch of MDUs is sampled randomly from the weighted replay buffer to train the network. Weighted replay buffer plays a key role in the fast and stable learning of a policy network. The following will first discuss the idea of the weighted replay buffer. Then, the details of policy network learning in the imitation phase and the practice phase are analyzed respectively.