esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cost Components  
In the past, the calculation of the life-cycle cost of expressway preventive maintenance mainly included agency costs and user costs. This paper also includes environmental costs in the scope of cost accounting. Among them, the agency cost includes the initial construction cost, maintenance cost, and salvage value of the expressway; the user cost includes time cost, vehicle operating cost, safety cost, etc.; and the environmental cost mainly includes energy consumption and emission costs. During the pavement analysis period, from the beginning of the design to the end of the analysis period, the specific cost composition is shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Cost Classification** & **Cost Components** \\ \hline \multirow{4}{*}{Agency Cost} & Initial construction cost \\  & routine maintenance costs \\  & Preventive maintenance costs \\  & Cost of major and medium maintenance \\  & salvage value \\ \hline \multirow{4}{*}{Road User Cost} & \multirow{4}{*}{Normal operating user cost} & Fuel consumption cost \\  & & Tire Wear-and-Tear Cost \\  & & Repair and Maintenance Cost \\ \cline{3-3}  & & User Travel Time cost \\  & & accident cost \\ \cline{2-3}  & work zone user cost & Additional vehicle fuel consumption cost \\  & & time delay cost \\ \hline \multirow{4}{*}{environment cost} & Normal operating environmental costs & energy consumption costs \\  & costs & Environmental emission costs \\ \cline{2-3}  & Environmental costs of construction activities \\ \cline{1-1} \cline{2-3}  & Environmental emission costs of construction activities \\ \cline{1-1} \cline{2-3}  & Environmental costs of work zone & Energy Consumption cost of work Zone \\ \cline{1-1} \cline{2-3}  & Environmental emission costs of work zone \\ \hline \end{tabular}
\end{table}
Table 1: Cost components in the pavement life cycle.
## 4 Implementation
### Objectness Clarification Process
#### 4.4.3 Inference Time  
We measured the model's inference times for videos by applying two methods to enhance the objectivities of vehicles described above. The length of each video is 240 frames, and the RPN score threshold of the model was configured with the default value of 0.05. Figure 12 illustrates the measurement results.  
Figure 11: Bitrate on video transmission with ROI expansion and background coloring. Because both bitrates of the white and black background cases appeared very similar, we presented result of only black cases in the figure.  
Figure 10: Two images before and after ROI expansions: (**a**) example of DSSIM filtered image which was damaged in objectness; (**b**) processed image with dilate method for restoring objectness.  
As shown in Figure 12, the model's inference time slightly increased when the ROI expansion method was applied. For the colors of non-ROI areas, the inference time was shorter in the order of white, black, and green. Since shorter inference time implies that the areas proposed by the RPN had decreased, we can assure that the green color is more appropriate for our background coloring method to enhance the model's performance.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
###### Abstract
To the best of our knowledge, this is the first of its kind that studies encrypted QUIC multiclass classification using state-of-the-art award-winning machine learning techniques. In the literature, other methods may need more requirements to achieve higher results, such as a greater amount of data, more features, or more complex model architecture. These requirements may not be available at some networks. On the other hand, our results are directly comparable with the related works in the literature, achieving distinguishable results compared to [5; 7; 8], and closely comparable results with [9; 10] with much simplistic model architecture. The highest accuracy and F1-score we achieved is 99.40%. We summarize the contribution as follows: We investigated ensemble machine learning for QUIC traffic classification using a different number of packets and fewer features. Our experimental results show that XGBT and LGBM are the best models for encrypted traffic classification even considering very few packets (15 packets). Additionally, we also investigated training and testing time and model size. The training and testing time for LGBM is 69 s and 0.017 s, respectively, which shows LGBM can be deployed in a real-time scenario. In comparison with other works in the literature, our model shows the highest performance in terms of accuracy and F1-score. Furthermore, we made our models available ([[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) NKJCWY3F-j2 CWDqKAJ4D5 Pi4 azV8 zHBh?usp=share_link]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) NKJCWY3F-j2 CWDqKAJ4D5 Pi4 azV8 zHBh?usp=share_link) accessed on 20 February 2023), and they can act benchmarks in inferencing on other data or in comparisons with other techniques. The remainder of the paper is structured as follows. Section 2 provides a short background on QUIC protocol. While Section 3 discusses the previous works related to the topic followed by methodology in Section 4, Section 5 elaborates the details about the experiments. While we discuss and analyze the experimental results in Section 6, before concluding the paper in Section 7.
## 2 System Model
### Clipping  
Before amplification through the PA, the signal is clipped as per saturation levels of the PA. The operation of clipping is defined mathematically as  
\[x_{c}^{p}[n]=\left\{\begin{array}{ll}-A,x^{p}&x^{p}[n]\leq-A\\ x^{p}[n],&\left|x^{p}[n]\right|<A\\ A,&x^{p}[n]\geq A\end{array}\right. \tag{11}\]  
The system model shown in Figure 1 is explained mathematically in the above equations. Here, \(x_{c}^{p}[n]\) represents the clipped signal for the \(p^{th}\) OFDM signal; \"A\" determines the level of clipping amplitude level. Clipping is the most basic reduction technique for reducing PAPRs. However, it has a major drawback of in-band and out-band distortion, which causes the performance of the BER in the overall system. When we clip the input signal to the PA, we should make sure that the signal is passing through a linear amplification region so that PAPR performance is improved.
## 2 Materials and Methods
### Full 3D Model of the Image  
There are two photographs of the vault (Figure 2) that contain the original image before the fire (taken with a camera whose parameters are unknown). Since a photograph is a 2D document, it will be necessary to project the image on the 3D model of the surface to obtain a 3D image. For this, it is necessary to establish a relationship of each pixel of the image with its corresponding spatial position on the surface. First, it is necessary to establish the spatial coordinates of the shooting points of both photographs. To do this, identifiable existing points on the surface are identified in the photograph (it is only possible on an identifiable construction element: corners and edges). Selecting the points on straight lines in the ceiling, these lines must also be rectilinear in the image (1):  
\[x_{i}=\frac{a*X+b*Y+c*Z+d}{a_{2}*X+b_{2}*Y+c_{2}*Z+d_{2}}\ ;\ \ y_{i}=\frac{a_{1}*X+b_{1}*Y+c_{1}*Z+d_{1}}{a_{2}*X+b_{2}*Y+c_{2}*Z+d_{2}} \tag{1}\]  
where  
\(a\), \(b\), \(c\), \(d\): projective parameters including Euler matrix angles and the scale factor between object space and image space;  
(\(X\), \(Y\), \(Z\)): coordinates of a point in space;  
(\(x_{i}\), \(y_{i}\)): coordinates of the same point in the image. As images were taken with old cameras that used a photographic plate (it is a photographic support made of a glass sheet covered with a light-sensitive emulsion) they were more stable and did not warp or distort (especially in large formats for wide field imaging). Therefore, radial distortion is negligible and does not introduce any error in the process. While in oblique photography, there are enough identifiable points for the determination of the coefficients (Figure 2b), in zenith/nadir photography, it will be more difficult to find valid points, so the fit may be inconsistent. In this situation, it is necessary to add more condition equations to the system. The verticality of the lamp chain, located on one side of the vault, provides an equation of direction. The dimension of its vectors in space and in the image defined by \(\partial\) and \(\partial^{\prime}\) is given by Equation (2):  
\[\partial=\sqrt{\Delta X^{2}+\Delta Y^{2}+\Delta Z^{2}}\qquad\qquad\partial= \sqrt{\Delta x_{im}^{2}+\Delta y_{im}^{2}} \tag{2}\]  
where  
(\(\Delta X\), \(\Delta Y\), \(\Delta Z\)): vector in space;  
(\(\Delta x_{im}\), \(\Delta y_{im}\)): vector in the image. Figure 2: (a) Original photograph (nadir). (b) Original photograph (oblique). Equation (2) particularized for a vertical element is Equation (3):  
\[\Delta X=0,\ \ \ \Delta Y=0,\ \ \ \ \Delta Z=\partial \tag{3}\]  
Known in space (\(\Delta\)X, \(\Delta\)Y, \(\Delta\)Z), we can calculate that dimension in the image by Equation (4):  
\[ \begin{array}{l}x_{i}+\Delta x_{i}=\frac{a*(X+\Delta X)+b*(Y+\Delta Y)+c(Z+ \Delta Z)+d}{a_{2}*(X+\Delta X)+b*(Y+\Delta Y)+c_{2}*(Z+\Delta Z)+d_{2}}\\ y_{i}+\Delta y_{i}=\frac{a_{1}*(X+\Delta X)+b_{1}*(Y+\Delta Y)+c_{1}(Z+\Delta Z )+d_{1}}{a_{2}*(X+\Delta X)+b_{2}*(Y+\Delta Y)+c_{2}*(Z+\Delta Z)+d_{2}}\end{array}
## 2 Data Collection and Processing  
The IoT system we developed, shown in Figure 1, comprises three core components: (a) Data acquisition devices are used capture energy consumption, temperature, and humidity data from office appliances. We utilized the AM2302 sensor to collect temperature and humidity data. This sensor provides an accuracy of 2-5% for humidity readings and \(\pm\)0.5 \({}^{\circ}\)C for temperature measurements. For energy data collection, we measured voltage and current using the ZMPT101B voltage sensor and the SEN0211 current sensor. Energy consumption was calculated using current and voltage through mathematical equations. The data collection spanned a period of 18 months, from 4 October 2022 to 25 March 2024, resulting in a dataset of size 44,075,808 \(\times\) 10. The dataset contains energy used by the device per minute and environmental data at each minute. The data were organized in a time series fashion. After processing, the dataset was aggregated to calculate the daily energy consumption for each appliance, resulting in 513 rows of data per appliance. Similarly, the daily averages for room temperature and humidity were computed, also covering 513 days. (b) Edge IoT systems were used to process the data locally and run AI models for anomaly detection. Finally, (c) a centralized IoT system was used for comprehensive data analysis and AI model training.  
The architecture, depicted in the figures, illustrates the use of MQTT and HTTPS protocols for secure data transmission from data acquisition devices to edge systems and further to the central cloud system. Edge IoT systems employ local routers and MQTT brokers for data collection and processing, storing data in local SQL databases, and utilizing IoT infrastructure for real-time analysis. Cloud IoT systems receive data via global MQTS connections, store the data in centralized SQL databases, and train AI models, which are then deployed back to edge systems. The term \"cloud database\" describes the centralized database used to store processed and aggregated data from edge devices that are housed in the cloud. On the other hand, a global database offers a unified picture of data from many geographical locations by logically integrating several cloud databases. The global dashboard presents a comprehensive picture of data collected from all areas, whereas the cloud dashboard gives targeted insights based on data stored in a particular cloud region. In our system, MQTT facilitates lightweight, real-time communication between edge IoT devices and brokers, ensuring efficient local data collection, while HTTPS enables secure  
Figure 1: Data collection system for power forecasting.  
interactions with the cloud system. The system's security is ensured using Transport Layer Security (TLS) encryption and self-signed certificates generated with OpenSSL, protecting data integrity and authenticity across the network. This comprehensive setup enables real-time monitoring, anomaly detection, and secure communication, accessible globally via public Internet Protocol (IP) addresses [4].
## 2 Materials and Methods
### MISTiC Instrument Design and Sensitivity Performance
cooling is used for its IR spectrometer, as was done for AIRS. However, in order to be efficient, the passive cryo-radiator must have a relatively clear view to deep space, and this is best accomplished in a sun-synchronous orbit, with the additional constraint that the host spacecraft not have any structure within the cryo-radiator field of view. An additional challenge for MISTiC, and precision measurements generally aboard a micro-satellite, is that the thermal environment is quite variable over the orbit, yet the spacecraft and instrument resources available to manage these challenges are severely constrained. This challenge was assessed through thermal modeling under the IIP, and key results are summarized in Figure 9.  
For MISTiC, the worst-case thermal condition is encountered by constellation members in the sun-synchronous 1:30 orbit. The spacecraft passes into and out of the earth's shadow during the orbit. A small spacecraft lacks a large heat capacity, so its temperature-response to changing solar illumination can be quite pronounced. Special thermal design measures for MISTiC, including an adiabatic interface with the spacecraft, are employed to stabilize the spectrometer warm interface temperature, further stabilizing spectral calibration. With these measures in place, detailed thermal modeling shows that the spectrometer temperature variation over the orbit, presented in Figure 9, is very small, and consistent with MISTiC requirements for spectral calibration stability.
# Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things
## 4 Results Analysis
Figure 4: Jain’s Fairness Index for the proposed strategies: MaxBattery, DRM and MinHop. In Figure 6, the overhead introduced by the various strategies is shown. Specifically, the results were obtained using the three different packets shown in Table 1.  
It can be noticed that the DRM approach is slightly higher than the ones requested by the HopMin and MaxBattery solutions. This is due to the more complex control operated by the SDN Controller in order to modify the node flow table and, hence, increasing the overall network lifetime. From the previous analysis, it can be intuitively argued that the proposed DRM approach achieves a better trade-off between lifetime and latency. Specifically, DRM can reach a network lifetime comparable with the MaxBattery approach, by maintaining low latency and overhead, which represent two of the most important QoS parameters for WSNs in IoT domains. Therefore, its performance is further compared against existing techniques (ACO [29], CSO [37], cooperative PSO [31; 38] and LOA [23]) in terms of average latency and average energy consumption, as shown in Figures 7 and 8. In particular, in Figure 7, the average latency values achieved by DRM approach and the above strategies are compared varying the network size from 25 to 225 nodes. The results show that the DRM approach outperforms all other strategies when the number of nodes is greater than 175. This minimal latency value is obtained by an adaptive use of the weight \(w_{\tau}\) within the DRM, as shown in Algorithm 1. Figure 8: Average energy consumption comparison. Figure 7: Average latency comparison for different network size. In Figure 8, the comparison of DRM approach against the existing techniques in terms of average energy consumption is depicted for the worst case scenario of a network comprised of 225 nodes. It is possible to notice that the residual energy of all approaches monotonically decreases. The LOA algorithm outperforms all other solution most of the time, but the best asymptotic performance is achieved by the DRM solution. It mainly depends on the activation of energy saving procedures operated by the DRM approach, after which it is more focused on the network lifetime rather than the hops optimisation, thus reducing its decreasing slope. Finally, Figure 9 highlights the objective function values for the optimisation problem (1) achieved by all approaches. Specifically, the results were obtained with a network topology of 225 nodes. Although the LOA strategy initially presents the better performance, DRM is able to attain the best asymptotic performance.
# An Interface Pressure Detection Method of Cable Silicone Rubber-XLPE Based on Nonlinear Ultrasound
###### Abstract  
The interface pressure between the cable attachment and the body is crucial for the stable long-term operation of the cable. To solve the issue of interface insulation characteristics' damage caused by the pressure transducer measurement method, a non-destructive testing method of silicone rubber interface pressure using nonlinear ultrasound is presented. Initially, the study analyzes the propagation characteristics of ultrasonic waves at the interface of cross-linked polyethylene and silicone rubber. The study also establishes the relationship between the nonlinear coefficient and the interface pressure. Subsequently, a nonlinear ultrasonic test platform is constructed using the pulse reflection method to measure the interface pressure of flat silicone rubber and cross-linked polyethylene through nonlinear ultrasonic testing. Theoretical and experimental results indicate that the fundamental amplitude of the frequency domain of the interface reflection wave decreases, and the second harmonic amplitude and nonlinear coefficient both increase as pressure increases. These results demonstrate that the nonlinear ultrasound, non-destructive testing method can accurately evaluate the interfacial pressure state of the cable accessories.  
cable; silicone rubber; nonlinear ultrasound; interfacial pressure; nonlinear coefficient Article An Interface Pressure Detection Method of Cable Silicone Rubber-XLPE Based on Nonlinear Ultrasound  
1
## 5 Calculation Results  
### Results of Delphi Survey  
The risk identification step aimed to record the details of the uncertainties before the occurrence of risks. In the present study, after identifying the various risks of CRCBPs based on the study of research literature (e.g., [5; 16; 24; 31; 64; 70; 76]), the relevance of the identified risks was evaluated and monitored. Finally, based on three rounds of the Delphi technique, 82 risks of CRCBPs were identified and recorded; see Tables 3 and 4, respectively, for internal and external risks. The agreement of experts in the Delphi method was investigated using Kendall's coefficient of concordance (W); the experts rank several categories based on their importance in a similar manner by using, essentially, the same judgment criteria of importance for each category. This Kendall's coefficient of concordance has a range from zero to one, indicating the degree of consensus between individuals (with W > 0.9 indicating very strong consensus; W > 0.7 strong consensus; W = 0.5 average consensus; W = 0.3 weak consensus and W = 0.1 very weak consensus). Furthermore, the significance of W is not enough for stopping the Delphi panel since for panels with more than 10 members, even small values of W are sometimes significant [65; 66]. In this study, Kendall's coefficient of concordance was computed using SPSS computer software. According to the calculations, Kendall's concordance coefficient of the current study was equal to W = 0.734, which indicates a strong consensus and favorable agreement between respondents in identifying risks. The results of calculating the Kendall's coefficient of concordance are reported in Table 5. In addition, based on Formula (1) the value of W has been provided. \[\text{W}=\frac{\text{S}}{\frac{1}{12}\text{ k}^{2}\Big{(}\text{N}^{3}-\text{N} \Big{)}}=\frac{\sum\Big{[}R_{j}-\frac{\sum R_{j}}{N}\Big{]}^{2}}{\frac{1}{12} \text{ k}^{2}\Big{(}\text{N}^{3}-\text{N}\Big{)}}=\frac{11092347}{\frac{1}{12} \text{ (30)}^{2}\Big{(}\big{(}82\big{)}^{3}-82\Big{)}}=\frac{30182908}{41121128}=0.734\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Code** & **Social Risks** \\ \hline R\({}_{1}\) & General dissatisfaction with the project’s location \\ R\({}_{2}\) & Sabotage \\ R\({}_{3}\) & Cultural difference between people \\ R\({}_{4}\) & Regional and ethnic limitations \\ \hline
**Code** & **Economic Risks** \\ \hline R\({}_{5}\) & Exchange rate fluctuation \\ R\({}_{6}\) & Inflation fluctuation \\ \hline R\({}_{7}\) & Bank interest fluctuation \\ R\({}_{8}\) & Change in duties of imported equipment \\ R\({}_{9}\) & Law changes and economic policies of materials \\ \hline
**Code** & **Political Risks** \\ \hline R\({}_{10}\) & Government internal policies contradiction \\ R\({}_{11}\) & Foreign threats \\ R\({}_{12}\) & Inappropriate work relation of government organizations \\ R\({}_{13}\) & Government instability \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The identified risks of CRCBPs based on literature review and the Delphi survey technique—internal risks.
# Securing IPv6 Neighbor Discovery Address Resolution with Voucher-Based Addressing
## 3 Voucher-Based Addressing  
### Terminology  
A glossary of terms and acronyms related to Voucher-Based Addressing is necessary to index, organize, and comprehend the many different aspects of its proposal. To acquire more prerequisite context, please see Section 2.1 of the NDP specification in RFC 4861 [3] for the definitions of the following terms: neighbor, node, interface, link, address, router, host, on-link, off-link, IP, ICMP, packet, target, Neighbor Unreachability Detection (NUD), Neighbor Cache (NC), and all well-known NDP ICMP packet types (Redirect, RS, RA, NS, and NA).