esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 2 Related Work
For example, the control theoretic approach and optimization approach work well in analysis, but the complexity of their implementation design makes them impractical to use in the router [10]. Any new AQM implementation drastically impacts the router architecture design and the software [30]. The primary objective of this paper is to design a simple and practical RED variant that needs minimal adjustment to the original RED algorithm and is also able to maintain the RLC buffer in the LTE network at the predictable value under a wide variety of traffic load. Figure 1 shows the architectural view of the RLC entity in the LTE network [3]. The information about congestion measured by our approach can be fed as cross layer information to the PDCP layer to adjust the discard timer accordingly. In the LTE network, there is a discard function in the PDCP layer which is upon RLC layer. Each Service Data Unit (SDU) that comes from the higher layer matches a discard timer in the PDCP layer, and when the PDCP layer receives a SDU, the matched timer starts. If the timer expires and the SDU is still in the buffer, then the SDU will be discarded. The discard timer value can be set according to the congestion level of the RLC buffer. However, this adjustment according to the congestion level also depends on the specific requirements of the QoS of the corresponding bearer and is an open research problem [2; 3].
## 4 Model Establishment and Evaluation
### Model Evaluation Indicators  
In order to comprehensively and objectively evaluate the performances of the models, we selected the root mean square error (RMSE), mean relative error (MRE), and predictive error variance (PEV) as the evaluation indicators of the models.  
The RMSE is calculated as Equation (13):  
\[\text{RMSE}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\left(y_{i}- \hat{y}_{i}\right)^{2}} \tag{13}\]  
The MRE is calculated as Equation (14):  
\[\text{MRE}=\frac{1}{n}\underset{i=1}{\overset{n}{\sum}}\frac{|y_{i}-\hat{y}_ {i}|}{|y_{i}|} \tag{14}\]  
The PEV is calculated as Equations (15)-(17):  
\[\text{PEV}=\frac{\underset{i=1}{\overset{m}{\sum}}\left(e_{i}-\overline{e} \right)^{2}}{m} \tag{15}\]  
\[e_{i}=y_{i}-\hat{y}_{i} \tag{16}\]  
\[\overline{e}=\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}e_{i} \tag{17}\]  
where \(y_{i}\) is the real value, \(\hat{y}_{i}\) is the predicted value, \(e_{i}\) is the prediction error, and \(\overline{e}\) is the mean of the prediction errors.  
The RMSE can visually express the error of the model. However, it is greatly affected by outliers and needs to be comprehensively evaluated in combination with other indicators.  
The MRE can avoid the error amplification problem caused by the large gap between the predicted value and real value to a certain extent.  
The PEV refers to the variance of the prediction error, which can not only measure the size of the prediction error as a whole to judge whether the model algorithm is scientifically feasible, but also measure the stability and dispersion of the prediction error. Generally speaking, more robust models have smaller PEV values.
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 2 Materials and Methods
### Coastline Extraction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The First Type** & **The Secondary Type** & **Classification Definition** \\ \hline \multirow{4}{*}{Natural boundary line} & Gravelly boundary line & The intertidal sediment of sandy coastline is mainly gravel, which is a relatively straight \\  & Silty boundary line & coastline formed by sediment such as sand and gravel under the long-term action of waves. \\  & Bedrock boundary line & A sea-land boundary consisting of silt or silty muddular materials. \\  & Breeding boundary line & It is the demarcation line between land and sea on the bedrock coast. \\  & Salt field boundary & The sea-land dividing line for saline-shell drying. \\  & Port and what boundary line & The demarcation line between land and sea for the functional use of port terminals. \\ Artificial boundary line & Construction of coastline near the sea & The demarcation line between land and sea for the development of coastal industry and other \\  & Road boundary line & The land-sea dividing line of the road constructed artificially. \\  & Protective engineering & It refers to the boundary line formed by the tidal tidal skies, wave walls and slope protection \\  & coastline & structures built to prevent the erosion of sea waves and coastal currents and the invasion of \\  & Other artificial boundary lines & Animal disasters such as typhons, cyclones, and cold waves and gales. \\ Estuary boundary line & Other artificial boundary lines & Artificial boundary lines that do not belong to the above classification. \\  & The boundary between estuary and ocean, and between river and ocean. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The coastline classification system of Yantai. aquaculture ponds, salt fields, or other water-retaining structures. An estuary boundary line is the demarcation line between an estuary and the ocean. Figure 2 shows the results of various types of coastline extraction methods.
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 1 Introduction  
Recently, cognitive radio technology has been regarded as a promising solution for solving spectrum resource shortages due to its characteristic of efficient spectrum utilization [1; 2; 3]. In addition, satellite communications have received significant attention due to their widely applicability in emergency rescues, environmental monitoring, and network access in unpopulated areas [4; 5; 6; 7; 8; 9; 10; 11; 12]. The spectrum management of most commercial geostationary orbit communication satellites follows fixed channel authorization allocation modes. With the increase in message volume and the number of users, the frequency band is becoming more and more crowded, and the spectrum resources are increasingly scarce. However, satellite communication channels are not occupied by users all the time, resulting in a large number of spectrum holes in the spectrum of satellite communication. In a report written by the Spectrum Policy Task Force [13], the FCC noted that the spectrum utilization in the vast majority of the authorized spectrum allocated is only 15 to 85%. The report points out the prominent contradictions in spectrum management: spectrum resources are scarce, but the spectrum utilization rate is very low.  
NC-OFDM uses cognitive radio technology to discover the spectrum holes in the communication environment and to obtain access to them in the form of non-continuous subcarriers, which avoids the interference from cognitive users to authorized users [14; 15]. Other multiple access methods, such as spread spectrum, cannot use spectrum holes due to their continuous spectrum characteristics. The NC-OFDM-related research mainly focuses on spectrum forming [16], channel estimation [17], synchronous [18], peak ratio suppression [19], etc., but there is no detailed solution for NC-OFDM satellite communication proposed. OFDM technology is proposed to be applied to the field of satellite communication, and OFDM communication satellites will be developed [20; 21]. Recently,in [22], the authors proposed using NC-OFDM in satellite communication to improve the spectrum efficiency. However, the cost of launching OFDM communication satellites is high. An NC-OFDM system based on cognitive radio can achieve spectrum sharing with authorized users, so it can greatly improve the spectrum utilization. If NC-OFDM can be combined with satellite communication, the spectrum utilization efficiency of satellite communication will be greatly improved, and the shortage of spectrum resources will be alleviated.  
The most valuable resource in wireless communication is frequency spectrum, and the rational utilization, sharing, and protection of the radio spectrum has become a growing requirement. It is obviously an economical and feasible means to apply NC-OFDM to satellite communication, which will improve the spectrum utilization efficiency of satellite communication.
## 4 Results and Analysis
### Dynamic Load Balancing and QoS Improvement
The reduction in jitter confirms the improvement in QoS in a network by implementing traffic classification-based load balancing in a multi-controller SDN environment. According to Table 6, the jitter in this research work is nearly identical for two-controller-based and three-controller-based implementation. This may be the result of the fact that, despite the  
Figure 11: Comparison of packet rate for different conditions. Figure 12: Comparison of average jitter for different conditions. presence of two controllers, neither controller experienced congestion during our testing, resulting in the same jitter result for both circumstances.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 4 Battery and Energy Harvesting Technologies for Sensing and Monitoring in Buildings
### Commercial Building Energy Harvesting Solutions  
From a commercial perspective, there are a number of energy harvesting solutions available off-the-shelf at present for potential use in buildings. EnOcean, its white label brand Dolphin, and the EnOcean wireless standard (discussed in Section 3), has enabled manufacturers to develop energy harvesting solutions for use in building management systems. Although EnOcean and NISSHA appear to be the two prominent manufacturers currently offering turnkey solutions; combining energy harvesting, sensors and data transmission in one pre-packaged unit, there are an increasing number of component manufacturers offering energy harvesting S \({}^{\circ}\)Cs and hardware. Table 6 highlights and compares some of these commercial energy harvesting solutions based on their respective energy conversion technologies, relevant characteristics and cost. The data has been extracted or calculated from various manufacturer datasheets and user manuals available online at the time of publication.  
Figure 4: (**a**) Floor plan of a one-bedroom apartment in Southampton, UK. (**b**) Typical apartment living room configuration in San Sebastian, Spain [115].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Energy Harvesting Source** & **Typical Range of Ambient Energy Levels** & **Estimated Electrical DC Power** & **Calculated Power Density** \\ \hline Photovoltaic & Light intensity: 100–3700 1x & 25–1149 \(\mu\)W & 9–399 \(\mu\)W/cm\({}^{3}\) \\ \hline Thermal & Thermal gradient: 10–40 \({}^{\circ}\)C & 1–10 mW & 0.7–7.1 mW/cm\({}^{3}\) \\ \hline Kinetic & Acceleration: 0.0245–2.82 m/s\({}^{2}\) Frequency: 43.1–162.3 Hz & 0.008–68.97 \(\mu\)W & 0.05–459.8 \(\mu\)W/cm\({}^{3}\) \\ \hline RF & EM wave strength: \(-\)74 to \(-\)29 dBm & 0.028–944 nW & 0.00169–57.37 nW/cm\({}^{3}\) \\ \hline Airflow & Airflow speed: 1–10 m/s & 0.9–324 mW & 0.017–6.0 mW/cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of recorded measurements and the overall power performance of five main energy harvesting technologies available in buildings ([PERSON] et al. [115]).
## 5 Results and Discussion
### Computational Complexity  
Computational complexity measures the proportion of processing assets (work and area) that a certain algorithm consumes as it executes when it is being performed. When using a UVLC system, the computational complexity is enormous. Table 3 indicates the comparison of Computational complexity (%). The computational complexity of the suggested and existing approaches is shown in Figure 7. This demonstrates that the proposed method has a 48% lower level of computational complexity. The formula for computational complexity can be determined as follows:  
\[T(n)=c,\ S(n)=c \tag{15}\]  
Here, \(T(n)\) denotes the runtime, \(S(n)\) indicates the space usage, and \(c\) is a constant.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & **Distortion Rate (\%)** \\ \hline APNN [33] & 91 \\ TFCNN [34] & 65 \\ DSANet [35] & 85 \\ CE-LSTM [36] & 72 \\ AFL-DLE [Proposed] & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of distortion rate (%).  
Figure 6: Distortion rate of the proposed and existing techniques, namely APNN [33], TFCNN [34], DSANet [35] and CE-LSTM [36].
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 1 Introduction
Such environment-dependent functionality can also be enabled by the deep learning approach, which has been proven effective in solving different types of problems and applied in many research of GNSS field over the recent years [6; 34; 35; 36]. CNN, as one of the most popular deep learning algorithms, is inspired by human brain neurons and has the significant advantage of capturing complex patterns and spatial information through functions such as filtering and pooling. Moreover, for the same dimension of inputs, CNN contains a fewer total number of trainable parameters than the conventional fully connected neural network, thus appearing to be less prone to overfitting [37; 38]. Motivated by strengths brought from the FGO algorithm and CNN, the proposed AFGO approach first extracts some features from GNSS measurements and then predicts the GNSS solution uncertainty used in the adaptive GNSS/INS integration with the trained CNN model. Moreover, it is considered that the carrier phase measurement is popular in precise positioning, but its performance can be significantly degraded in urban areas due to cycle slips and the poor measurement quality of consumer-grade devices [39]. Thus, this study was focused on using the code measurement rather than the carrier phase measurement for positioning. The contribution of this paper is fourfold: (1) we developed an adaptive GNSS/INS integrated positioning algorithm implemented by MATLAB based on the FGO architecture to achieve better accuracy and robustness; (2) we employed deep learning to predict the GNSS solution uncertainty during its integration with INS; (3) we assessed and compared the performance of conventional KF and FGO with the open-source large-scale datasets, UrbanNav [40] in terms of different grades of receivers; and (4) we validated the feasibility of the proposed algorithm using the preceding large-scale datasets with different environments (i.e., different urbanization levels) and receiver grades. The rest of this paper is structured as follows: Section 2 demonstrates the mechanisms of GNSS/INS coupling methods, while Section 3 will discuss how the adaptive functions operate in multi-sensor coupling. The validation of the proposed algorithm will be presented using experiments in Section 4, along with a further evaluation of results in Section 5. Section 6 will finally itemize some limitations of current work, summarize this study, and propose prospective research areas.
## 1 Introduction  
The Internet of Things (IoT) phenomenon plays an important role in many areas today. Every year, the number of devices that we could consider as elements of the Internet of Things is growing. The Internet of Things can be understood as a large network with different connected types of objects, able to communicate with each other and exchange information, regardless of whether they belong to the same group. The network creation consisting of mutually communicating devices provides the user with the possibility of more effective management of all connected devices. The Internet of Things allows objects to be captured and controlled remotely over existing network infrastructures, creating opportunities for further direct integration of the physical world into computer systems, resulting in increased efficiency, accuracy and economic benefits [1]. IoT is augmented by sensors and actuators, making it a cyber-physical system that includes technologies such as smart grids, smart homes, intelligent transport, smart cities, and also smart education [2, 3].  
The choice of measurement methods and sensors is a complex process in which a large number of questions are asked. Physiological parameters can be measured in the same way as the physical principles of signal acquisition. The measurement technology in relation to the individual sensors creates a huge number of choices.  
Common devices connected to the Internet provide us with a wealth of information and data. Such devices include, for example, ordinary smart wristbands, smart mobile devices, eye tracking cameras, etc. These devices were used to look for relationships between the physiological state of the student and the impact on his results.  
The aim of the article is to point out the relationship between the concentration we measured with eye tracking technology and the excitement we measured with heart rate (HR) as a stress measurement tool and how it all affects the results of the study. We are looking for dependencies (connections) between physiological functions and students' knowledge. In this article, we try to find the relationships between the values of test results in students and their heart rate during testing. We assume that during testing, the level of stress may increase and thus the heart rate will also increase. The dependence of heart rate and stress has already been studied by many authors [4; 5; 6; 7; 8; 9]. We also reveal the dependence of the student's heart rate before learning that they would be tested and during testing, and we monitored this change. We have formulated all these questions into research questions, which are defined in the article.
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 6 Conclusions  
NC-OFDM communication has advantages of flexible and controllable subcarriers, and it is a promising technique to improve satellite communication spectrum efficiency. The key issue of whether NC-OFDM can be applied is its compatibility with authorized satellite communication users.  
Based on the compressed spectrum sensing of a real satellite channel, a subcarrier distribution scheme for NC-OFDM satellite communication is proposed in this paper. Moreover, a hybrid genetic particle swarm optimization algorithm is proposed for NC-OFDM sub-carrier power control, which maximizes the throughput of the NC-OFDM system. Through the effective utilization of the satellite spectrum holes, the extra data transmission of 228 Mbps is achieved, which is impressive for commercial communication satellites with a bandwidth of 500 MHz. The concept of the spectrum overlap ratio is defined to describe the interference of NC-OFDM signals to authorized signals. The BER performance of authorized system under interference condition is studied. Fast and efficient spectrum sensing and flexible power allocation of subcarriers are effective ways to solve the compatibility problem. The NC-OFDM satellite communication method can significantly improve the spectrum efficiency of satellite communication. This work is an extended version of the previously published conference paper [22].  
Writing--original draft preparation, Y.W., Q.Z. and L.W.; writing--review and editing, H.N.; supervision, Y.G.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (61901490 and 61671454) and the Research Plan Project of NUDT (ZK21-33). The authors declare no conflict of interest.
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 5 Other Algorithms for the ARP
### Random Search (RS)  
The basic random search procedure, referred to as RS, serves as a baseline method in this study. RS generates a specified number of permutation solutions uniformly at random by employing the well-known Fisher-Yates algorithm [40]. After generating these solutions, it evaluates the objective function for all of them and selects the best one.  
Despite its simplicity, RS represents a noteworthy baseline method due to its ability to evaluate all solutions in parallel (at least theoretically) [41; 42].
# Maximizing Test Coverage for Security Threats Using Optimal Test Data Generation
## 1 Introduction
#### Research Significance  
Our study's research significance is:  
1. Firstly, it will help in designing consistent acceptance test cases for security threats (authentication and authorization) through structured misuse case descriptions for early-stage mitigation of security threats. This will help us to overcome the challenge of inconsistent acceptance test case design due to its reliance upon human judgment.
2. Secondly, by comparing two state-of-the-art approaches that maximize test coverage through optimal test data generation for structured misuse case descriptions. It was evident from our results that modified condition decision coverage (MC/DC) maximizes test coverage through optimal test data generation with minimum test conditions in contrast to decision coverage (D/C) through structured misuse case descriptions.
## 3 Methodology
### Goal and Scope  
The primary objective of this research is to calculate the carbon footprint of the EPAL pallet management strategy employed by Nolpal in comparison to standard exchange models.  
The functional unit, serving as the basis of comparison, is defined as 1000 cycles traveled by lorries. In this context, a \"cycle\" refers to the trips made within a supply chain, starting from the trademark industry to a specific retailer and to the nearest Nolpal Point. This functional unit, frequently used in similar studies, was selected because it provides a consistent framework for comparing the studied scenarios [15].  
The calculations were conducted using the globally recognized IPCC 2021 methodology. The IPCC regularly publishes assessment reports that include emissions metrics for Global Warming Potential. Among the impact categories assessed, this study focuses on the 'Climate Change, Global Warming Potential (GWP100)' category, which measures the Global Warming Potential over a 100-year time horizon. This impact category encompasses emissions from biogenic, fossil, and land use sources.  
The analysis was conducted by following the requirements stated in the ISO 14040 (EN ISO 14040:2006) and ISO 14044 (EN ISO 14044:2006) [16,17]. The Ecoinvent 3.10 database was used for data input during the calculations.  
The lifecycle of EPAL pallets includes the production of hardwood and softwood lumber, pallet manufacturing, sanitary treatment, use and transportation, and end-of-life treatment [18]. However, as shown in Figure 4, this study specifically focuses on the transportation and use phases of pallets' lifecycle, excluding other stages from the system boundary. This exclusion is primarily justified by the purpose of the LCA, which aims to inform the carbon footprint impacts of pallet management decisions. Additionally, the transport phase represents a significant environmental impact for EPAL pallets. Indeed, even if the use phase of wooden pallets is characterized by high variability and uncertainty, numerous studies, including those conducted by the United States Department of Agriculture, highlight that this phase contributes the most to the overall carbon footprint of a pallet's life cycle [19]. Moreover, since our study focuses on EPAL pallets rather than one-way pallets, the raw material sourcing and manufacturing phases have a relatively lower impact on the pallet's lifecycle.  
Figure 4: Life cycle of EPAL pallet and system boundaries of this study.  
The geographical scope of this study is limited to Italy, where the Nolpal network operates.