esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
As no works on the combination of eye-tracking method and pseudoisochromatic tests have been published so far, it is difficult to compare specific research results.  
Based on our results an effective way of evaluating the multiple or cross-classified contingency tables is loglinear regression; however, this method shows only which parameter is significant and which is not [47]. In case the conditions of the parametric statistical tests are not appropriate (data type, homogeneity of variance and normal distribution), we can either perform transformation on our data or apply the corresponding non-parametric method. The Scheirer-Ray-Hare (SRH) test is a non-parametric pair of ANOVA; therefore, it is an appropriate statistical method to describe and compare the parameters of eye-tracking [48; 49].  
Survival analysis, used primarily in clinical research, has been used successfully in several cases, in combination with eye-movement tracking in relation to consumer choices [50]. It can be well observed that each discipline adapts different methodologies.  
The aim of our first test series, the colored test series, was to compare our results with the classical color-vision testing methods. The survival analysis of the results of the colored test series showed that normal trichromats performed more successfully, which corresponds well with the literature regarding loss of chromatic discrimination ability of anomalous trichromats [51].  
For further analysis, we applied the second test series, achromatic test series, measuring luminance contrast sensitivity. Even though our analysis did not show significant evidence on superior achromatic discrimination of anomalous trichromats compared to normal trichromats, the results of survival analysis suggested that they performed better. Similar results can be found in the current literature [52].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Achromatic Images**} \\ \hline  & \(\chi^{2}\) & **df** & \(\mathbf{p}\) \\ \hline Log Rank (Mantel–Cox) & 1.531 & 1 & 0.216 \\ \hline Breslow (Generalized Wilcoxon) & 1.514 & 1 & 0.218 \\ \hline Tarone–Ware & 1.599 & 1 & 0.206 \\ \hline  & \multicolumn{3}{c}{**Colored Images**} \\ \hline Log Rank (Mantel–Cox) & 84.706 & 1 & \textless{}0.001 \\ \hline Breslow (Generalized Wilcoxon) & 83.086 & 1 & \textless{}0.001 \\ \hline Tarone–Ware & 91.747 & 1 & \textless{}0.001 \\ \hline df, degree of freedom. & & & \\ \end{tabular}
\end{table}
Table 17: Comparison of the survival functions of achromatic- and colored-image series.
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 4 Discussion
### Datasets
#### 4.4.3 Ablation Studies  
In this section, we systematically investigate the impact of three enhancement techniques on the network model. The experimental results are shown in Table 4. Our model is based on the Faster-RCNN network. First, we evaluate the effectiveness of the Consistency Learning backbone network on the entire network. Second, we verify the impact of LightImg on the network. Finally, we examine the combined effect of Consistency Learning, LightImg, and Label Randomization on the network.  
Figure 4: On the ExDark dataset, we compare the performance of the original images, TDD, 2 PCNet, and our method. Unlike other methods, our approach is capable of detecting small nighttime objects and minimizing additional false positive predictions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AP** & **APs** & **APm** & **API** \\ \hline TDD [33] & 33.2 & 10.1 & 29.2 & 37.2 \\
2 PCNet [41] & 44.7 & 11.3 & 28.0 & 39.6 \\ UMT [47] & 32.4 & 9.1 & 24.8 & 29.3 \\ NUDN (ours) & 39.6 & 16.2 & 30.4 & 45.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: This paper reports the comparative results of TDD, 2 PCNet, UMT, and our Method in cross-domain adaptation using the nighttime dataset from SHIFT.  
Consistency Learning: As shown in Table 4, there is a significant difference before and after adding Consistency Learning (CL) to the base model, with an improvement of +5.1 AP (16.09%).  
LightImg: This method transforms nighttime images into daytime images. As shown in Figure 1, the addition of LightImg (I) not only converts nighttime objects into daytime objects but also increases the detection rate of small objects by 6%.  
Label Randomization: This module scales pseudo-labels and images processed by LightImg (I). It is particularly beneficial for small object detection, showing an improvement of 4.7 AP (50%) over Faster-RCNN + Consistency Learning + LightImg.  
Further analysis revealed that LightImg not only directly improves the quality of low-light images but also indirectly enhances the effectiveness of consistency learning by increasing the reliability of input data, thereby improving the efficiency of label randomization. This indicates that LightImg serves not only as an independent image enhancement module but also as a means to optimize the overall synergy of various components, significantly enhancing the model's detection capability under low-light conditions at night.
## 4 Model Validation
### Model Result and Analysis
#### 4.3.3 Statistical Relevance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Basis for Grouping**} & \multicolumn{1}{c}{\multirow{2}{*}{**Basis for Grouping**}} & \multicolumn{1}{c}{\multirow{2}{*}{**B\({}^{2}\) of**}} & \mul  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Basis for Grouping**} & \multicolumn{1}{c}{\multirow{2}{*}{**Basis for Grouping**}} & \multicolumn{1}{c}{\multirow{2}{*}{**B\({}^{2}\) of**}} & \mul \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Basis for Grouping**} & \multicolumn{3}{c}{**Number of Projects with BAC in the**} & \multicolumn{3}{c}{**Number of Projects with PD in the following Ranges**} \\  & & \multicolumn{3}{c}{**R\({}^{2}\) of**} & \multicolumn{3}{c}{**R\({}^{2}\) of**} & \multicolumn{3}{c}{**Following Ranges (Tes0 (Unic G)**} & \multicolumn{3}{c}{**(Test0 (Unic Days)**} \\ \cline{3-11}  & **Percentage of** & **Percentage of** & **SIP(Io)** & **SIP(Io)** & **SIP(Io)** & **(50 w w)** & **(100 w)** & **(300 w)** & **(600 w)** & **(100, 200)** & **(200, 300)** & **(300, 1000)** \\ \cline{2-11}  & **Training Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** & **Testing Sets (\%)** \\ \hline
91 & & & 0.987 & 0.996 & 57 & 40 & 55 & 38 & 43 & 55 & 42 & 50 \\
92 & & & 0.987 & 0.991 & 59 & 40 & 53 & 38 & 46 & 54 & 41 & 49 \\
93 & & & 0.987 & 0.993 & 59 & 41 & 53 & 37 & 46 & 55 & 41 & 48 \\
94 & & & 0.995 & 0.987 & 62 & 38 & 55 & 35 & 47 & 54 & 42 & 47 \\
95 & & & 0.987 & 0.994 & 61 & 39 & 52 & 38 & 46 & 55 & 40 & 49 \\
96 & & & 0.992 & 0.988 & 58 & 42 & 56 & 34 & 45 & 56 & 44 & 45 \\
97 & & & 0.987 & 0.991 & 59 & 41 & 54 & 36 & 44 & 57 & 42 & 47 \\
98 & & & 0.987 & 0.987 & 57 & 41 & 55 & 37 & 42 & 57 & 43 & 48 \\
99 & & & 0.987 & 0.997 & 56 & 40 & 56 & 38 & 43 & 55 & 43 & 50 \\
100 & & & 0.994 & 0.987 & 58 & 39 & 57 & 36 & 44 & 54 & 44 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._As evidenced by the data presented in Table 6, the R\({}^{2}\)-value of both the training set and the testing set in 100 experiments is approximately 1. This suggests that irrespective of the number of samples included in the training set and the testing set, the forecast values of the model exhibit a high degree of agreement with the actual observed values, thereby validating the model's robust performance. Therefore, despite the relatively limited sample data employed in this study, the constructed RBF neural network demonstrates robust performance in project performance forecast, with the capacity to be applied to the forecast of project portfolios of varying sizes. The resulting forecast outcomes are found to be satisfactory.
## 5 Experimental Results
### Cascade Approach  
In this section, the cascade approach proposed in Section 4 is evaluated. Firstly, a hierarchical dataset is elaborated from existing datasets. Secondly, the best performing audio features are selected. Finally, the classifiers are evaluated for the cascade approach and for an equivalent non-hierarchical dataset.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 1 Introduction
### Efficiency Assessment of the Oil Companies Using Data Envelopment Analysis (DEA) and Inverse DEA  
The energy sector faces pressure to improve operational efficiency, manage resources, remain competitive, and reduce environmental impact. To optimize the performance of oil companies while mitigating their adverse effects, it is important to evaluate how effectively these companies utilize their resources. One analytical method that can assess the efficiency of these companies is the Data Envelopment Analysis tool (DEA). Data Envelopment Analysis (DEA) was first proposed by [38] and is a quantitative technique used to evaluate the efficiency of decision-making units (DMUs) within a set using input and output data. These DMUs can be organizations, departments, firms, or other entities that change inputs into outputs. It is commonly employed as a benchmarking tool to enhance organizational performance [39]. Benchmarking involves evaluating and analyzing companies' processes, products, services, or various aspects to compare and use this data as reference points for future strategic decision-making [40]. The goal is to gain insights from the experiences of others in the sector to enhance the performance of the unit being assessed. Different studies, such as those by [41; 42; 43], have extensively utilized DEA to address pollution control, water treatment, waste management, and environmental efficiency. On the other hand, the inverse DEA is the reverse process of the conventional DEA. It was first developed by [PERSON] et al. (2000) [44]. The DEA model calculates a DMU's efficiency score. In contrast, the inverse DEA model employs a predetermined DMU efficiency score to calculate the best variations in input and output data. The inverse DEA model has greater flexibility than the standard DEA approach [45]. Different studies, such as [45; 46; 47; 48], have been conducted on using inverse DEA to reduce CO\({}_{2}\) emissions.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results
### Link Budget  
There are four studies that were done on link budget analysis, which are mentioned below:  
* The effect of weather attenuation on the received power at different elevation angles of a UAV receiver: Weather attenuation significantly impacts the quality of the communication link. By evaluating how weather conditions affect the received power at different elevation angles, the research provides insights into the robustness of  
Figure 4: Visualization of the elevation mask on the drone.  
the communication link across diverse atmospheric conditions. This is essential for designing UAV communications that remain reliable in adverse weather.
* The effect of the altitude of a UAV on the link parameters: The altitude of a UAV directly influences several link parameters, such as the path loss, delay, and signal strength. Higher altitudes may facilitate a better line of sight with satellites, but they could also result in increased path losses with terrestrial base stations, as reported in [26]. Evaluating this effect allows for the optimization of UAV operating altitudes to balance the benefits of improved satellite visibility against the potential disadvantages of increased path losses with terrestrial links.
* Path loss vs. elevation angle: The path loss varies with the elevation angle due to the changing distance and propagation environment between the transmitter and receiver. This evaluation helps in understanding how the geometric configuration of a satellite and a UAV impacts the efficiency of the communication link. Optimizing the elevation angle can minimize the path loss, thereby enhancing the overall performance of the network.
* Link margin vs. elevation angle: The link margin, the difference between the received signal strength and the minimum required signal strength for acceptable performance, is a critical parameter in ensuring communication reliability. By studying how the link margin varies with elevation angle, research can determine the most reliable operational angles for UAVs. This is crucial for ensuring that UAVs maintain sufficient link margins to handle unexpected variations in signal strength due to dynamic changes in their operating environment.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 1 Introduction
First, this paper presents a 1-40 GHz analysis in a series of sub-bands covering the complete band. Secondly, a specific analysis in the main WRC'15/19 bands is presented. The core microwave bands were selected in WRC'15 to fulfill the increase in traffic requirements for usual dense urban networks. The 24.25-27.5 GHz and 37-43.5 GHz bands were identified by the WRC'19. These two low-mmW bands provide an additional 3.25 GHz and 6.5 GHz of contiguous blocks of spectrum to enable the 5G enhanced mobile broadband (eMBB) experience. Thus, the selected bands are the microwave band between 3.3-4.99 GHz, which includes the C-band and the 4.8-4.99 GHz band, and the low-mmW 24.25-27.5 GHz and 37-43.5 GHz bands. Moreover, following our previous work [23], we show models of the RMS DS versus the transmitter-receiver distance in logarithmic units which can be useful to predict the time dispersion in indoor environments. To fulfill the first two objectives a frequency based wideband measurement system was used; a calibration step was performed to guarantee the precision in all measured positions and the antenna patterns were carefully measured to extract their effect from the channel parameters. Finally, the third principal aim of the work is to show, thanks to an accurate ray-tracing tool, what are the main propagation mechanisms contributing to the radio channel. A tunning phase was applied to find the best diffuse scattering model values and appropriate permittivity values were assigned to the materials present in the scenario to achieve the desired accuracy. The paper is organized as follows. Section 2 describes the experimental setting. Section 3 presents PL and K-factor. Section 4 shows the RMS DS analysis. Section 5 shows the comparison with ray tracing simulations; the simulation results for WRC bands are included in Appendix A. Finally, in Section 6 the conclusions are explained. Notations: all magnitudes are written in italics. The symbol denotes average, the symbol \(\langle\,\rangle\) represents the expectation operator and the symbol \(|\,|\) denotes absolute value. The base-10 logarithm is represented as log10. The PL and relative received power are expressed in dB, the frequency in GHz, the K factor in dB and the delay spread parameters in nanoseconds.
# Study on the Interference Process of Liquid Radial Reflux on the Stability of a Shaped Charge Jet
## 5 Conclusions  
Considering the interference of liquid radial reflux driven by the bottom plate reflection shock wave on the jet and the change in sound velocity when the liquid is compressed, the process of jet penetration in the LFCS is divided in this study into the following five stages: jet penetration in the cover plate, jet reaming in the liquid, side-wall reflection interference, bottom plate reflection interference, and liquid spraying and side-wall secondary reflection interference. The theoretical calculation model of the velocity interval of the disturbed jet and residual penetration depth of jet is established, and the conclusions are as follows. 1. The interference of liquid radial reflux in the SCJ mainly comprises three parts: side-wall reflection interference, bottom plate reflection interference, and side-wall secondary reflection interference. The bottom plate reflection interference has observed interference on the tip of the SCJ. 2. The interference jet velocity interval during jet penetration in the LFCS is \(\left[v_{j\mu 2}|_{h_{x}=0},~{}v_{j\mu 3}\right]\cup\left[v_{j\mu 4},~{}v_{j 46}\right]\) based on the theoretical model, and the residual penetration depth can be calculated through its combination with the stand-off curve of the jet. 3. Theoretical analysis and experimental results show that the LFCS has interference on the head of the SCJ. Thus, applying the LFCS to extreme protection fields, such as ammunition safety protection, is possible. 4. The theoretical model established in this study is suitable for calculating the interference process of the LFCS with a Newtonian liquid in the SCJ to predict the anti-jet capacity of the LFCS. \begin{table}
\begin{tabular}{c c c} \hline
**Data Source** &
\begin{tabular}{c} **Interference Velocity** \\ **Interval (m/s)** \\ \end{tabular} & **Residual Penetration Depth (mm)** \\ \hline Theoretical calculation results & [3131, 3813] \(\cup\)[3916, 5321] & 76.3 \\ Experimental result & [3389.9, 5456.7] & 81 & 79.5 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison between the theoretical calculation results and the experimental results. **Author Contributions:** Conceptualization, Y.T. ; methodology, Y.C. ; validation, X.Z. ; writing--original draft preparation, Y.C. ; writing--review and editing, X.Z. and Z.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Jiangsu Province Graduate Research and Practice Innovation Program (grant no. KYCX20_0308) and the National Natural Science Foundation of China (grant no. 11602110). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within this article; more detailed data can be obtained from the corresponding author. **Conflicts of Interest:** The authors declare no conflict of interest.
# Review of Intelligent Methods and Their Potential Use towards a Smart Grid Negotiation Framework
## 1 Introduction
### Contribution of the Review  
The transition to a green energy model necessitates the integration of RES and Electric Vehicles (EVs) into the system. However, the inherent variability and unpredictability of these resources make the flexibility of the system essential for maintaining stability and security while ensuring efficient energy supply to all users. To address these challenges, the present review focuses on intelligent methods to manage variability and enhance system flexibility through demand-shifting policies.  
Lower energy costs can be achieved when consumers act as prosumers forming energy communities of microgrids. These prosumers can participate collaboratively in energy exchange or trade energy directly among themselves, reducing electricity costs through local trading at mutually beneficial prices [5]. There are three primary scenarios where negotiation occurs in the SG context:  
1. Energy Market Transactions: The case where prosumers sell surplus energy to the grid.
2. Peer-to-Peer (P2P) and Local Transactions: In this case, the energy transactions occur directly between prosumers.
3. Retail Market Tariff Selection: Consumers choose tariffs that best suit their needs among various options offered by retailers, as part of RTP strategy.  
The first two scenarios involve direct negotiation of energy amounts and prices, while the third scenario, though not purely a negotiation, involves continuous DM as consumers evaluate and select the most suitable tariffs [13].  
This review highlights the significant potential of AI techniques in optimizing these negotiation processes. By reviewing the existing literature on negotiation cases and DM scenarios using AI, including applications of q-ROFS combined with MA, the present study provides insights into effective strategies for managing complex DM processes in SG. This integration of AI enhances the robustness and reliability of negotiation processes, helping to cope with the inherent variability and uncertainty of SG where RES plays a significant role.  
In summary, this review contributes to the understanding of intelligent negotiation methods in SG, emphasizing the integration of advanced AI techniques to improve DM and transaction outcomes in the evolving energy market landscape.
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 1 Introduction
Although the device exploited more advanced modulation formats and thus augmented the overall capacity, it does not constitute a complete transceiver solution. In this paper, we present a high-speed VCSEL-based optical transceiver tailored for intra-datacenter connectivity. We describe the architectures and assembly processes of all the modules that comprise the optical transceiver, namely (1) high-speed single-mode VCSELs, (2) the high-speed VCSEL driver, and (3) the linear receiver. Moreover, we provide the experimental results from (1) a high-speed single-mode VCSEL able to transmit data at 80 Gb/s for up to 500 m of a standard single-mode fiber (SSFM) and (2) a high-speed optical transceiver with a 64 Gb/s per lane capacity suitable for high-speed intra-datacenter interconnects. Materials, methods, and processes used for the development of the structures along with the respective experimental setups are described in Section 2. In Section 3, we present the experimental results that were acquired and in Section 4, we summarize the key points of the manuscript.
## 2 Background of Mobile _Ad Hoc_ Network Routing
### DSDV Routing Protocol  
DSDV is based on the Bellman-Ford routing algorithm [5; 14]. The MANETs that use this protocol try to make all the nodes store the routes to all the other nodes. Therefore, it requires the participating nodes periodically to broadcast maintenance information to keep the other nodes updated. Each node in the network has own routing table for all reachable nodes. Each entry of the routing table has corresponding sequence number and the number of hops to reach the destination node. The sequence number is used for identify obsolete routing information so that the algorithm can discard loops as well as diversions. During the updating process, each node periodically submits its own routing table to the neighbor nodes. Whenever the table is drastically modified, it sends the table to the neighbor nodes as well. Each _route update packet_ has a sequence number assigned by the source node as well as the routing table information so that the latest route information can be identified and used. When two different routes have the same sequence number, the shorter one is stored in the routing table.