esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accident Model with Considering Physical Processes for Indoor Environment Safety
## 4 Application of STPA
### Stpa  
The Systems-Theoretic Process Analysis (STPA) [2; 9] is a new hazard analysis technique based on the STAMP model. The goal is to identify causes that lead to hazards and result in losses so they can be eliminated or controlled. The STPA has three steps, the latter two of which are taken as the main steps.  
The first step is to establish the system engineering foundation. Three things should be done in this step. The first is to define the interested accident and its related system hazards. They should be specific and concise. Then, the system level safety requirements and design constraints need to be specified to prevent system hazards from occurring. The last is to define the safety control structure that takes the system level safety requirements and design constraints as inputs.  
The second step is to identify potentially unsafe control actions (UCAs). Every controller of systems usually has one or more control actions. System hazards thus result from inadequate control or enforcement of the safety constraints. Four taxonomies are provided to look for the UCAs:  
1. A control action required for safety is not provided or not followed;
2. An unsafe control action is provide;
3. A potentially safe control action is provided too early or too late (at the wrong time or in the wrong sequence);
4. A control action required for safety is stopped too soon or applied too long.  
Then, we translate the identified UCAs into safety requirement and designed constraints on system component behaviors.  
The third step determines how each UCA identified in step two could occur. For each UCA, examine the parts of the control loop as shown in Figure 4 to see if they can cause the UCA under some scenarios. Then, design controls and mitigation measures if they do not already exist or evaluate existing measures.
# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Impact of AutoML on Business Operations
#### 3.4.5 Access to Real-Time Insights  
AutoML's capability to deliver real-time insights is a game-changer for businesses looking to stay competitive in fast-paced markets. Traditional machine learning models often require significant time to develop, train, and deploy, which can delay the availability of actionable insights [65]. In contrast, AutoML platforms are designed to process and analyze data quickly, enabling businesses to generate insights in real-time. This immediacy is particularly valuable in industries where conditions can change rapidly, such as finance, retail, and logistics [20]. For example, in the financial sector, real-time insights can inform trading decisions, risk management, and fraud detection, helping companies respond to market fluctuations as they happen [21]. Real-time analysis of customer behavior and sales data in retail can enable businesses to adjust pricing, inventory, and marketing strategies quickly, maximizing revenue and customer satisfaction.  
AutoML's ability to continuously update models with new data ensures that the insights remain relevant and accurate. This allows businesses to maintain a dynamic and responsive approach to decision-making [66]. In addition, real-time insights from AutoML can support predictive maintenance in manufacturing, where early detection of potential equipment failures can prevent costly downtime and extend the life of machinery [22; 67]. By providing businesses with the tools to act quickly on the most current data, AutoML enhances their ability to navigate uncertainty and capitalize on emerging opportunities.
## 2 Experiment and Methods  
### Experiment  
The schematic of the experimental system shown in Figure 1 was composed of a gas distribution system, a reaction system and an analysis system. Figure 2 shows the two electrode structures of plasma reactors that were designated as coaxial cylinder dielectric barrier discharge (CC-DBD) and tubular multilayer dielectric barrier discharge (TM-DBD). The CC-DBD reactor consisted of an inner high-voltage electrode (stainless steel sheet), two quartz tubes (outer tube with 20 mm diameter and 200 mm length, inner tube with 12 mm diameter and 200 mm length), and an outer electrode (aluminum foil). The quartz tubes were coaxial cylinder in shape with 4 mm gap.  
The TM-DBD reactor consisted of three quartz tubes arranged in a row with 6.0 mm diameter inner electrode and 31 mm length. The stainless-steel electrodes were inserted into the quartz tubes. NO was prepared in cylinder containing very few N\({}_{2}\)O (1.98 \(\times\) 10\({}^{-3}\) ppm). The NO concentration was 345 ppm and gas flow rate was fixed at 6 L/min. Mass flow controller (MFC) (SEC-4400, Horbia, Kyoto, Japan) was used to adjust the flow of NO, O\({}_{2}\), N\({}_{2}\), and reactant gases.  
The final products of NO degradation were measured by FTIR spectroscopy (Nicolet Nexus 470, Thermo Fisher, Waltham, MA, USA) and the resolution of the FTIR is 1 cm\({}^{-1}\). The plasma reactor was driven by AC power supply (homemade power). The voltage was maintained at 5 kV (driven  
Figure 1: Flow diagram of experiment.  
Figure 2: Simplified geometric modeling of TM-DBD and CC-DBD reactor.  
frequency of 20 kHz) and input power was 128 W measured by power meter (PF9800, Everfine, Hangzhou, China).  
Destruction and removal efficiency (DRE) of NO and NO\({}_{\rm x}\) was calculated from the equations  
\[\text{DRE of }NO(\%)=\frac{\left[NO\right]_{in}-\left[NO\right]_{out}}{\left[NO \right]_{in}}\times 100\% \tag{1}\]  
\[\text{DRE of }NO_{\rm X}(\%)=\frac{\left[NO_{x}\right]_{in}-\left[NO_{x}\right]_{ out}}{\left[NO_{x}\right]_{in}}\times 100\% \tag{2}\]
# A Design for a Wide-Band Antenna Pair Applied for Mobile Terminals at the Sub-6 GHz Band
## 3 Evolution and Analysis of Antenna Pair
### Decoupling Structure  
The spacing between the two antenna elements within the proposed antenna pair is only 2 mm. In such a narrow configuration, the mutual coupling effect is inevitable. Figure 6 shows the models of the antenna pairs with different decoupling structures and the impact of the resonant frequency and isolation is depicted in Figure 7. Figure 7b shows that the isolation of antenna pairs without DGS is \(-8.3\) dB, which can be detrimental to antenna efficiency and port independence, resulting in a reduction in channel capacity.  
Figure 7b indicates that the worst isolation occurs in the 4-5 GHz band. By observing the surface current distributions in Figure 8, it can be seen that when Port 1 is excited, the propagation of the surface currents on the ground is the main reason for the interference of other antenna elements located on the common ground plane. Thus, the DGS is introduced on the ground plane to enhance the isolation. It can be seen from the S-parameters results of Figure 7 that adding the traditional strip DGS shifts the low-frequency resonance point to the left and improves impedance matching in the desired frequency band. Isolation is also reduced to \(-11.5\) dB. The traditional T-shaped DGS has a significant effect on the bandwidth expansion to lower frequencies, but the high-frequency impedance matching will deteriorate, and its isolation improvement is not obvious for the proposed antenna models. Thus, based on the strip DGS, a new DGS consisting of three interlaced rectangles has been designed. It is evident from Figure 8b that the new DGS can generate new resonant currents in the slots, which effectively block the surface current flow. After adding the proposed structures, the results in Figure 7 not only exhibit good impedance-matching characteristics for wide-band antennas but also effectively improve the isolation, which reduces 6.5 dB compared to isolation without the decoupling structures. Finally, antenna pairs with a wide bandwidth of 54% (3.3 GHz-5.9 GHz) and good isolation values lower than \(-14.8\) dB are achieved.  
Figure 5: Influence of the antenna pair size parameters on reflection coefficients: (**a**) \(l_{1}\) is the variable; (**b**) \(w_{1}\) is the variable; (**c**) \(l_{2}\) is the variable; (**d**) \(w_{2}\) is the variable.
## 6 Simulations and Result Analysis
### Conclusions  
As the number of cores increases, MCF-EONs with more cores can provide more spectrum resources for connection requests, and both metrics will be smaller. As the number of connection requests increases, the PFs obtained by the two algorithms become worse (away from the origin of coordinates), i.e., the blocking ratio and crosstalk ratio are getting higher.
# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 4 Data-Driven Decision Support in SaaS
### Main Focus
Specifically, this study investigated how customers switch from the activation phase to the retention phase in the AARRR (Acquisition, Activation, Retention, Revenue, Referral) model by identifying and examining behavior patterns of clients. In order to provide value to their customers, customer feedback must be taken into consideration, so that providers can offer a more personalized experience and cater to their clients' needs. The research presented in [35] explored structured approaches for collecting and integrating customer feedback in SaaS companies operating in the B2B market and ways of integrating customer knowledge into software development processes. SaaS pricing also takes its toll on user satisfaction as it affects SaaS users directly. The study presented in [36], however, showcased a data-driven framework integrating real-time usage tracking, machine learning for demand prediction, and customer-centric billing adjustments to optimize revenue for providers while enhancing user experience and customer loyalty. Additional factors influencing usage continuance in SaaS applications, particularly after the initial adoption phase were demonstrated in [37], in an attempt to improve user retention and reduce churn by enhancing usage penetration (the number of users actively engaging with the software). Finally, another problem that the research addressed was the Customer Lifetime Value (CLV) prediction. CLV prediction models predict the future revenue that customers may generate. The authors in [38] proposed a novel machine learning framework for predicting CLV in the context of B2B SaaS companies and described several business applications where CLV predictions were used to enhance marketing expenditures, improve Return on Investment (ROI), and provide essential insights for management decision-making in this context.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 3 Results
### Adapting the Change in Simulated Targets  
To show that ATTD generally works for different targets but is limited by experimental conditions, an image database (USC-SIP, containing 210 images [42]) was adopted. Each image was set as the target (**x**) to produce simulated BD signals under different noise levels. As Table 1 shows, the proposed ATTD showed a strong correlation of 0.97 with the clairvoyant benchmark reference at \(-\)70 dB, decreasing with increased noise level and reaching a moderate correlation of 0.47 at \(-\)20 dB. Fixed truncation started with a high correlation of 0.93 at \(-\)70 dB, but it decreased significantly as noise increased, with a low correlation of 0.14 at \(-\)20 dB. SURE began with a moderate correlation of 0.63 at \(-\)70 dB  
Figure 4: Adaptive performance comparison under varying fiber configurations (noise levels) using (**a**) clairvoyant benchmark \(k_{c}\), (**b**) ATTD \(k_{\text{d}}\), (**c**) fixed \(k_{i}\), and (**d**) SURE with estimated noise. The truncation threshold and Pearson correlation coefficients with the clairvoyant benchmark are marked in each image. Above each column, \((m,n)\) denotes the translation stage position (various noise levels).  
and decreased sharply, reaching a negligible correlation at higher noise levels (\(-35\) dB to \(-20\) dB) and indicating a decline in performance in the presence of noise.
## 3 Related Work  
There are previous surveys that provide a comprehensive overview of research on detection mechanisms for DDoS attacks in SDN as stated in [15; 19; 20; 21; 22; 23; 24; 25]. The authors of [15; 19; 20; 21; 22; 23; 24; 25] present several DDoS detection methods in SDN and illustrate some of the remaining frameworks for improving DDoS detection in SDN. [PERSON] et al. [15] analyzed and compared some DDoS detection mechanisms based on the characteristics of SDN and DDoS attacks. The mechanisms are categorized into five types, namely the statistical-based, machine learning-based, threshold-based, hybrid methods-based, and other method-based DDoS detection mechanisms. [PERSON] et al. [19] presented a classification approach for existing DoS/DDoS models in different types of networks: SDN, virtual networks, and traditional IP networks. They provided a thorough survey of existing attack models and quantified the damage of DoS/DDoS attacks in network recovery speed and network infection. [PERSON] et al. [20] focused on the analysis DDoS detection mechanisms and attacks in SDN and cloud computing architecture. [PERSON] et al. [21] presented an overview of detecting DDoS attacks on SDN controllers. They described DDoS detection techniques and classified them according to the techniques or methods used. [PERSON] et al. [22] provide some SDN-based mechanisms against DDoS attacks and machine learning-based DDoS detection methods in SDN and show the advantages of SDN over DDoS attacks in traditional IP networks. [PERSON] et al. [23] presented a systematic survey of link flooding attacks patterns on SDN architecture as a category of DDoS attacks in SDN. [PERSON] et al. [24] analyzed SDN security from a data perspective and surveyed some typical network attack detection methods, including statistical and machine learning methods. [PERSON] and [PERSON] [25] provide a systematic review of various SDN-based DDoS threats and the existing literature on fast DDoS detection and defense in SDN. They classified the mechanisms into four categories, namely: information theory-based methods, machine learning-based methods, artificial neural network (ANN)-based methods, and other miscellaneous methods.  
The surveys in [15; 19; 20; 21; 22; 23; 24; 25] mainly focus on high-rate DDoS attacks detection mechanism. None of them focus on addressing current detection approaches based on machine learning for low-rate DDoS attacks in SDN. Table 1 summarizes a comparison of this survey work with the existing surveys in the security of SDN. The purpose of our survey article is to provide a complete and up-to-date review of LDDoS detection in SDN by presenting LDDoS detection mechanisms based on machine learning in SDN and explanations linked to the SDN layers, which are control layers, application layer, and data layer. Additionally,we demonstrate an inclusive classification of LDDoS attacks targeting SDN three-layer architecture.
## 4 Clustering and Classification  
In general, the intersection is considered as the most complex road traffic environment. Furthermore, each vehicle on the road shows very different driving characteristics depending on the driving style or physical/mental conditions of the driver in the vehicle. Hence, the movements of vehicles approaching an intersection to cross are very different from each other and are affected by various factors. Thus, for safer intersection traffic through traffic light control, it is not enough to identify which vehicle is likely to be an RLR. To determine the length of the all-red signal appropriately, it is also necessary to identify the characteristics of the vehicle movement and determine the necessary  
Figure 1: The proposed dynamic all-red signal control architecture.  
all-red signal time for the vehicle accordingly. Our approach to addressing this issue is to utilize techniques for time-series clustering for characterization of RLRs into several clusters according to their movements. Then, the identified groups of clusters are used as labels for the generation of the traffic dataset to be used for training the MC-DCNN classifier.  
Figure 2 shows the overall procedure for dataset generation. The data collected from the traffic environment includes traffic signal data, vehicle movement data, and also whether each vehicle is RLR or non-RLR. In the collected raw traffic data, RLR vehicles are not distinguished according to their characteristics. Therefore, clusters for each RLR characteristic are generated through Dynamic Time Wrapping (DTW) and Hierarchical Clustering Analysis (HCA) processes. After this process, a dataset for training MC-DCNN is constructed based on the traffic data together with RLR cluster labels so that each vehicle in the dataset is now labeled with a cluster ID according to its driving characteristics.
## 4 Discussion  
As is known, data alignment has always been an important topic in crop phenotyping research, with many studies [31; 32; 33] focusing primarily on algorithmic research. However, there are fewer reports on improving alignment efficiency through hardware solutions. Aiming to address the problem of asynchronous acquisition time of parameters in the crop phenotyping platform, which hinders the application of multi-sensor technology, this study, with low cost as the goal, introduces PTP high-precision time synchronization technology into the parameter acquisition system of the crop phenotyping platform for the first time, realizing the synchronous acquisition of crop RGB images, infrared thermal images, and laser point cloud images, which provides a reliable data support for the subsequent data alignment and intelligent analysis. The paper analyzes the synchronous acquisition performance, system stability, and dynamic performance of the system in detail, and the results are as follows:  
The synchronous triggering of each timing board is the core foundation of this study. In this paper, inexpensive timing boards and gateways were selected, and the PTP was applied at the physical layer. The root mean square value of the synchronization time error between each timing board was controlled within 130 ns. This synchronization error is much smaller than that of NTP technology (50-200 ms) and approaches the theoretical synchronization time error of PTP (approximately 20 ns) [16], reaching the ns level. Compared with [PERSON] et al. [19], who built the PTP on the application layer and controlled the time synchronization error of the displacement sensor within 20 us, although the hardware cost has increased slightly, the software cost has been greatly reduced, and the accuracy has  
Figure 9: Scatter plot of data acquisition time of laser point clouds, visible light images, and thermal infrared images of Guofu 909 peppers.  
been improved from the microsecond level to the nanosecond level, which can better serve the fast mobile crop phenotype platform in the agricultural field.  
During the acquisition of visible light images and thermal infrared images, due to the shift in sunlight, the brightness changes in the visible light images and thermal infrared images in the early and late stages of acquisition were relatively obvious, resulting in a relatively low calculation result of the structural similarity SSIM. Therefore, we proposed the Difference Structural Similarity Index Measure (DSSIM) indicator, taking the first acquired file as the benchmark, and calculating the similarity between the first file and the remaining files. The DSSIM value between each two visible light images and thermal infrared images was as high as above 0.95, indicating that there was no obvious difference in structure and contrast between each two images, and the acquired data had good stability. In addition, the maximum value and mean absolute error of the pixel error between each two images were only about 3%, again indicating that the system had a high degree of stability in the acquisition of visible light and thermal infrared images.
# Support of Advanced Technologies in Supply Chain Processes and Sustainability Impact
## 4 Research Methodology
### Research Design and Data Collection  
The design of this research is illustrated in Figure 2. Firstly, a literature review of advanced technologies supporting the digitalization of SCs, the SCOR Model and the performance of SCs in terms of sustainability was performed. From the literature, the research questions were formulated and an adequate methodology to achieve the research objective chosen. The case study methodology was used, based on virtual interviews with managers from manufacturing companies located in different countries. To collect data, an interview protocol was developed. The protocol was pre-tested with a senior manager of an automotive company. The pre-test consisted of first mailing the protocol and then interviewing this individual by phone and in person regarding the appropriateness and clarity of the questions. The individual provided written and verbal comments which helped to validate the protocol. Subsequently, a few structural changes were made to the protocol. Based on the analysis of results some conclusions were drawn and the practical implications of this study to professionals from manufacturing companies belonging to SC were suggested.  
The data collection was carried out through virtual (by email) semi-structured interviews, launched with professionals from manufacturing companies around the world. Multiple interviews were used to provide a broader view of the variables under study. Two set of variables were assessed: (i) advanced technologies adopted by companies belonging to different SCs in supporting the different SCOR processes; and (ii) Performance indicators to assess the level of performance of SC4.0 in terms of sustainability, considering the three dimensions of sustainability: economic, social and environmental. The same structured interview protocol (Appendix A) was used at each session to avoid interviewers' bias. In all cases, the company names are withheld in accordance with the general request for confidentiality.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 6 Discussion
### Mineral Effect  
Rock surface temperature variation is not controlled only by pores, but also by the macroscopically occurring mineral phases. The \"mineral effect\" can be well observed in thermograms belonging to holocrystalline rocks, where crystals can be distinguished according to their different surface temperature. In granite, for example, micas are characterized by the highest surface temperature (approximate estimated \(\varepsilon\) = 0.97-0.98), while feldspars own intermediate values (approximate estimated \(\varepsilon\) = 0.85-0.89) and quartz is the coldest phase (approximate estimated \(\varepsilon\) = 0.77-0.80) (Figure 6a,b). This proves the heterogeneity of such rock types with respect to emissivity, supporting the need of weighting the representative rock emissivity value on the whole framed surface. This outcome paves the way for further studies aimed at shedding light on a new potential IRT application as a tentative mineral mapping tool for crystalline rocks.  
A kind of mineral effect can be also hypothesized for sedimentary rocks, although no visible crystals occur, where the quartz content of quartzarenite and sandstone could be responsible for their lower emissivity if compared to limestone and calcisiltite of the same rock group.