esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Pointing Direction and Its Blockage by UAVs
### Fluctuation of the Antenna's Drive Angle due to Turning  
The fluctuations of the antenna's driving angles during turns in flight are calculated by using Equation (1). A result of antenna driving angle in elevation is shown in Figure 6, with \(\alpha\) = 0.15, \(\beta\) = 7.5, and \(\phi\) as parameters. By defining the area blocked by the UAV as \(\theta_{\rm GC}<0^{\circ}\), the area can be determined, as marked in yellow in Figures 8 and 9. In short, as \(\phi\) increases, is more likely to block the antenna's direction.  
The fluctuation of the antenna's elevation angle when \(\phi=10^{\circ}\) is expressed with respect to the heading and position of the UAV, as shown in Figure 9. As the UAV tilted left in the same direction as the target antenna, blocking occurred at \(48^{\circ}<\psi_{\rm P}<132^{\circ}\).  
Figure 8: Fluctuation in gimbal elevation angle.  
Figure 9: Fluctuation in gimbal elevation angle when \(\phi=10^{\circ}\).
## 3 Applications of Single Molecule Measurement: Disease Diagnosis, Monitoring
### Virus Detection  
It has become an important target to diagnose the presence or absence of viral infection cheaply, quickly and accurately (Figure 5) due to the recent COVID-19 pandemic having raised interest in on-site sequencing to monitor mutant strains. Up to now, there have been several reports on virus detection by single-molecule detection methods [53; 64; 88; 89; 90; 91; 92; 93; 94]. They are mainly categorized into two methods. The first is the detection of viral RNA sequences, and the second is the detection of viral particle shapes. For instance, the first method successfully detected influenza A [89], HPV [64], Lhasa fever [110], and Ebola [85] by on-site sequencing with Minion. The second method is the direct detection of differences in the three-dimensional structure and shape of viruses. Using this method, various types of viruses, such as Influenza A and B, Coronavirus, Adenovirus, and Respiratory Syncytial virus [88], along with HCOV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2 [53], have been successfully identified.
# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 4 Methodology  
### Calculation of the Food Accessibility Indicator  
#### 4.1.1 Travel Time Calculation  
The rate of private car ownership in Chongqing is low. The undulating terrain also makes travel by bicycle uncommon. Access to public transport varies greatly between regions, complicating the accurate reflection of the food accessibility index. Walking is a common mode of travel and is more suitable for analyzing the accessibility of food necessities in Chongqing. Within the use of walking for analyzing food accessibility in Chongqing, categories of 15, 20 and 30 min walks can be applied. The present study applied a walk duration of 20 min as input to the model.  
Traditional methods for calculating travel cost are usually based on complex road networks. Clearly, the representation of a large road network in a model is a huge barrier to calculating the time cost distance. With the calculation results considering a large road network, it can be difficult to consider uncertainties such as the real-time road network, road congestion, and road and bridge maintenance.  
Baidu mapping services provide a real-time point-to-point time travel cost and path distance calculation service. Users of the service can rapidly calculate travel time and distance by setting a community center as the starting point and a shopping point as the target point through a Rest service request. This allows users to achieve real-time travel mode, travel time, and distance between any two points without the need to construct an urban road network model.  
The present study used the Baidu mapping services to identify food stores within a 20 min walk (usually less than 1.350 m) of each community and calculated the time cost between the communities and the POI points of seven food types.
###### Abstract  
Aiming at the problem of autonomous decision making and trajectory planning (ADMTP) for launch vehicles under power failure conditions, the target degradation order strategy (TDOS) and the trajectory online planning method were studied in this paper. Firstly, the influence of power failure on the orbit-injection process was analyzed. Secondly, the TDOS was proposed according to the mission attribute, failure mode, and multi-payload combination. Then, an online planning method based on the adaptive target update iterative guidance method (ATU-IGM) and radial basis neural network (RBFNN) was proposed, where the ATU-IGM adopted the basic TDOS criterion for generating optimal orbit-injection samples and online guidance instructions, and the RBFNN was used for orbit-injection samples training and online generation of orbital missions. Finally, the comparative simulation analysis was performed under multi-failure conditions. The results showed that the TDOS proposed in this paper could meet the requirements of the mission decision making under different failures, target orbit types, orbit-injection methods, and payload compositions. The online trajectory-planning capability deviation was less than 5%, and the mission decision-making and trajectory-planning time were less than 10 ms. This study provides theoretical support for autonomous decision making and planning of space launch missions.  
launch vehicle; power failure; target degradation; orbit-injection strategy; online trajectory planning 2022  
2
# Guineensine: Isolation, Synthesis, and Biological Activity
## 1 Introduction
### Alkaloids and Alkamides in Piper Species  
Studies on the phytochemical profile of _Piper_ spp. indicate the presence of a variety of bioactive compounds such as flavonoids and other phenolic compounds, tannins, saponins, glycosides, terpenoids, amides and alkaloids [1; 2; 4; 12; 13]. Most apparent is the presence of amide alkaloids, also called piperamides, and alkalamides for which extensive research is dedicated to their biological activity [3; 14; 15; 16; 17]. Alkalamides are natural derived compounds, and products of the secondary metabolism of several medicinal plants of the Asteraceae, Solanaceae, Rutaceae, and Piperaceae families [18; 19]. The alkaloid piperine is the representative compound of the genus _Piper_, and it is found in remarkable abundance in _P. nigrum_ and _P. longum_ fruits [20; 21]. Guinea, although found in minor quantity with respect to piperine, is an alkamide that has gained popularity among the scientific community, as is discussed in the following paragraphs.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Model Selection and Prediction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Class Name** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{Multinomial Logistic Regression} & High & 0.73 & 0.48 & 0.58 \\ \cline{2-5}  & Low & 0.75 & 0.55 & 0.64 \\ \cline{2-5}  & Moderate & 0.65 & 0.85 & 0.74 \\ \hline \multirow{3}{*}{Decision Tree} & High & 0.88 & 0.65 & 0.75 \\ \cline{2-5}  & Low & 0.88 & 0.92 & 0.90 \\ \cline{2-5}  & Moderate & 0.88 & 0.89 & 0.88 \\ \hline \multirow{3}{*}{Random Forest} & High & 0.88 & 0.65 & 0.75 \\ \cline{2-5}  & Low & 0.93 & 0.94 & 0.94 \\ \cline{2-5}  & Moderate & 0.90 & 0.94 & 0.92 \\ \hline \multirow{3}{*}{K-Nearest Neighbors} & High & 0.77 & 0.43 & 0.56 \\ \cline{2-5}  & Low & 0.82 & 0.86 & 0.84 \\ \cline{2-5}  & Moderate & 0.79 & 0.82 & 0.80 \\ \hline \multirow{3}{*}{XGBoost} & High & 0.71 & 0.52 & 0.60 \\ \cline{2-5}  & Low & 0.89 & 0.65 & 0.75 \\ \cline{2-5}  & Moderate & 0.70 & 0.91 & 0.80 \\ \hline \multirow{3}{*}{Gradient Boosting} & High & 0.87 & 0.57 & 0.68 \\ \cline{2-5}  & Low & 0.86 & 0.84 & 0.85 \\ \cline{2-5}  & Moderate & 0.81 & 0.88 & 0.84 \\ \hline \multirow{3}{*}{Gaussian Naive Bayes} & High & 0.62 & 0.57 & 0.59 \\ \cline{2-5}  & Low & 0.73 & 0.60 & 0.66 \\ \cline{2-5}  & Moderate & 0.66 & 0.77 & 0.71 \\ \hline \multirow{3}{*}{Support Vector Machine (SVM)} & High & 0.65 & 0.57 & 0.60 \\ \cline{2-5}  & Low & 0.85 & 0.55 & 0.67 \\ \cline{2-5}  & Moderate & 0.66 & 0.89 & 0.76 \\ \hline \multirow{3}{*}{Ensemble} & High & 1.0 & 0.52 & 0.69 \\ \cline{2-5}  & Low & 0.95 & 0.89 & 0.92 \\ \cline{2-5}  & Moderate & 0.84 & 0.97 & 0.90 \\ \hline \multirow{3}{*}{AdaBoost} & High & 0.62 & 0.35 & 0.44 \\ \cline{2-5}  & Low & 0.73 & 0.67 & 0.70 \\ \cline{1-1} \cline{2-5}  & Moderate & 0.67 & 0.78 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance Metrics of The Deployed Models. A comparison of the accuracy obtained by several machine learning algorithms used in the classification of student adaptability levels is shown in Figure 3. The accuracy of an algorithm is represented by each bar, which is measured as the proportion of test cases that are correctly classified, with Random Forest proving the overall best with 91%, followed by Neural Network and CatBoost with 90% each. Performance metrics and confusion matrices highlight how crucial model selection is in the field of educational data analytics. In particular, the Random Forest, CatBoost, and Neural Network models have demonstrated encouraging possibilities for use in learning environments with the goal of predicting and improving student adaptability.
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 3 Method
### Statistical Analysis Based on Edge-Density  
For statistical analysis of the nodes, we apply the nodes' mean centrality and standard deviation for a respective layer to derive a specific score, as discussed below. We summarize basic notions according to the standard formalizations in the following.  
In terms of arithmetics, the mean or average is usually denoted by \(\bar{x}\) or \(\mu\), considering the sum of the sample values divided by the number of items in the sample, i.e.,  
\[\mu=\frac{1}{n}\left(\sum_{i=1}^{n}x_{i}\right)=\frac{x_{1}+x_{2}+\cdots+x_{n} }{n}\,,\]  
as the sum of the sampled values \(x_{i}\) divided by the number of items \(n\) in the sample.  
The standard deviation  
\[\sigma=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(x_{i}-\mu)^{2}}\,,\]  
measures the spread of the sample compared to the mean. Thus, it can be interpreted as how the measurements for a group are spread out from the average (mean) or expected value. A low standard deviation means that most of the numbers are close to the average. A high standard deviation means that the numbers are more spread out. Furthermore, we include another parameter, i.e., the edge density, which measures the fraction of present to possible edges. In a dense graph, the number of edges is close to the maximal number of edges. In contrast, a graph with only a few edges is then a sparse graph. For undirected simple graphs, the (graph) density is defined as \(D=\frac{2|E|}{|V|(|V|-1)}\,.\) For a directed graph, this graph density is divided by two in order to take the directionality of edges into account.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Machine Learning Methods for Yield Prediction
#### 2.3.2 Decision Tree Models and Their Extensions  
This study also utilized various decision tree-based models to capture the nonlinear characteristics of winter wheat yield, including the decision tree regressor, extra tree regres  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Variables** & **Spatial Resolution** & **Temporal Resolution** & **Sources** \\ \hline \multirow{2}{*}{Statistical data} & Yield & \multirow{2}{*}{County-level} & \multirow{2}{*}{Yearly} & \multirow{2}{*}{Statistical Yearbook [39]} \\  & Wheat area & & & \\ \hline Vector data & Wheat & County-level & Yearly & Sentinel 2 Image Extraction \\ \hline \multirow{3}{*}{Vegetation index} & EVI & \multirow{3}{*}{500 m} & \multirow{3}{*}{8-day} & MOD09A1 \\  & NDVI & & & MOD09A1 \\  & FPAR & & & MOD15A2H \\  & LST & 1 km & & MOD11A2 \\ \hline \multirow{2}{*}{Ecological data} & GPP & \multirow{2}{*}{500 m} & \multirow{2}{*}{8-day} & MOD17A2H \\  & LAI & & & MOD15A2H \\ \hline \multirow{2}{*}{Hydrological data} & ET & \multirow{2}{*}{500 m} & \multirow{2}{*}{8-day} & MOD16A2 \\ \cline{1-1} \cline{5-5}  & PET & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of winter wheat production forecast data.  
sor, and random forest regressor. The decision tree regressor recursively partitions the data space and makes predictions within each partition, making it particularly suitable for noisy data [44]. To mitigate the risk of overfitting, the absolute error criterion (criterion = \"absolute_error\") was employed and a random state was set. The extra tree regressor enhances model robustness by introducing randomness during node splitting [45], using a random splitter (splitter = \"random\") and the squared error criterion (criterion = \"squared_error\"). The random forest regressor reduces model variance by integrating multiple decision trees, showing excellent performance, especially in handling high-dimensional and missing data. In this study, we used 100 trees (n_estimators = 100) and full feature selection (max_features = 1.0) to ensure the model's robustness.
# Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems
## 6 Conclusions  
In this paper, we derived the CSI error bounded in the case of UE location uncertainty, with the help of the Taylor approximation, when using the estimated UE position to obtain the CSI in UWB wireless communication systems. Then, to resist the impact of the position error on the beamforming design, we proposed a robust beamforming optimization problem to minimize the total transmit power. To efficiently solve this nonconvex problem, we proposed an SDR-based algorithm and evaluated the performance of the UE in terms of SINR, scene size, and the antenna number. The simulation results verify the theoretical derivation of the CSI error bound, and the robustness of the method was verified by its comparison with the worst-case and non-robust methods. Therefore, the beamforming scheme proposed in this paper can have a good application prospect in promoting the development of location information-assisted UWB communication.  
Conceptualization, L.Y. and S.L.; software, Z.Z., X.F., and Z.S.; writing--review and editing, L.Y. and S.C.; formal analysis, S.L.; funding acquisition, S.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 under Grant 61771474.  
Not applicable.  
The authors would like to thank the editors and the reviewers for their helpful suggestions and constructive comments.  
The authors declare no conflict of interest.
# Magnetic Localization Method for Vehicles Based on Transformer
## 3 Methods
### System Architecture  
Based on the abovementioned vehicle characteristics, we design the architecture shown in Figure 3, which consists of an offline phase and an online phase. During the offline phase, the three-axis magnetic field vectors within a certain distance are collected and divided into equal distance intervals based on their mileage, which ensures that the sequence is independent of the vehicle's speed and magnetometer's sampling rates. Moreover, the true positions are collected as the labels of the training set, which are normalized to 0-1 to easily train the Transformer model. Thus, using multiple collected data, a training set that includes numerous sequences consisting of the magnetic field and the corresponding positions of a fixed number of points is generated. During the online phase, the trained Transformer model predicts the position coordinates with equidistant magnetic sequences obtained in the same way as those acquired in the offline phase. Notably, the mean magnetic field within a sequence must be subtracted to prevent the influence of carrier interference.  
According to the above procedure, the input of the proposed method is a fixed-length three-axis magnetic sequence whose size is \(W\times 3\), where \(W\) is the number of points in the sequence, which is calculated by the whole length of the sequence \(L\) and the spatial interval \(d\):  
\[W=L/d \tag{1}\]  
where \(L\) is related to the size of the target area. As a matter of experience, short sequences (approximately ten meters) can achieve accurate results for a small area; conversely, a larger area requires longer sequences (tens to thousands of meters) to distinguish different positions. This is because the larger the area is, the greater the possibility of short sequences with similar shapes, making it easier to cause mismatches. In addition, \(d\) represents the spatial resolution, which is related to the fluctuation degree of the magnetic field in the environment. For an indoor or a densely built environment, the magnetic field varies greatly in the spatial domain, so \(d\) needs to be set to a small value (0.5 m to 1 m).  
Regarding the output, the proposed method aims to estimate the 2-dimensional coordinates of the current position (\(x\),\(y\)), which is the last point in the sequence based on the current and past magnetic field values.
## 5 Discussion
### Evaluation Metrics  
In order to compare works among themselves, a common evaluation metric is needed. Most works report their results in terms of the average positioning error in different evaluation points, which is the positioning error defined as the Euclidean distance between the actual and estimated positions (_Mean Error_ on the table). Among these, most report the Root Mean Squared Error (RMSE) as well. Other metrics used are the Mean Squared Error (MSE) and the Median Error. Another important metric is the percentage, which is used in one way or another in 36 papers [22; 24; 25; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65]. Unfortunately, two articles do  
Figure 5: Evolution of the types of signal used.  
not show their results clearly; papers [66; 67] only show a graph, however, it is difficult to determine the obtained results from the image.  
In analyzing those papers that use metrics recommended by ISO/IEC 18305 [68] (the standard methodology to evaluate indoor localization systems), it can be seen that all of the articles (except those that do not show results) comply with this standard, specifically, mean error, accuracy in one zone or floor, root mean square error, and standard deviation. Figure 6 shows how many papers used each metric.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Demand Response/Load Management  
The demand response and load management mechanisms are related to the rational distribution of electrical loads on the network, taking into account the requirements of the users [58; 60]. Therefore, these two mechanisms are directly related to the smart metering system, the automation of home energy management, and especially to the distributed energy production.  
Regarding distributed energy production, it should be emphasized that it is a rather complicated process in comparison to the easily regulated production of electricity from measurable natural resources (fossil fuels, nuclear fuel, etc.). The energy production from renewable sources (solar, wind, hydroelectric, etc.) is not entirely predictable as it includes several uncertainty factors. Given also that the worldwide share of the regulated power is constantly decreasing, the challenges in the management system create a new specialized area of study and research. In any case, the management mechanisms that will emerge from the research in this area will benefit from a highly reliable and high-speed telecommunication system.  
In conclusion, an optimal load management process requires the modeling of energy data which must be detailed and continuously available including load profile information, production information, etc. [7; 61]. These requirements are met by IEC 61968 and IEC 61850-7-420, which support all TCP/IP-based wired and wireless communication technologies including IPv6. A Distributed Energy Management System and a Home and Building Electronic Systems/Building Automation and Control System (HBES/BACS), respectively, are supported by a wide set of standards (ISO 16484 series, ISO/IEC 14543-3, EN 13321 series, EN 50090 series, EN 50428, EN 50491 series, China: GB/Z 20965, USA: ANSI/ASHRAE 135) which require upgrades/modifications in order to enable their interoperability through appropriate interfaces [62].
# Performance Modeling and Analysis of LTE/Wi-Fi Coexistence
## 2 Related Work
Therefore, they assumed in this technique a shared access point between both technologies. Based on this, a mixed-integer non-linear programming problem is formulated and solved. As per the provided numerical results, it is shown that power saving can be improved using the virtualization technique, which is the main objective of the present work. The authors in [22] emphasized the fact that, while LTE and Wi-Fi coexist, multiple issues and challenges might appear and, therefore, the authors investigated those issues and studied the performance of both technologies via simulations. As per the given performance results, it is shown by the authors that the coexistence deployment harms Wi-Fi performance more than LTE performance. In addition to coexistence, when authors consider the reuse principle of sub-frames in LTE, which is referred to as blank sub-frames, where LTE is not transmitting, it is shown that the Wi-Fi throughput improves 50 times more with this principle. The coexistence of the IEEE 802.11n standard of Wi-Fi with LTE, with FTP traffic under the Time Division Duplex (TDD), is investigated in [23] through a multi-cell scenario. The authors conducted an analysis to study the performance of the network while both technologies were coexisting. The performance results show that the performance of Wi-Fi is negatively affected, while LTE performance is not. Linked to other people's works, the following points demonstrate the author's contributions:  
* The proposed model in this research demonstrates the fact that although the integration between both technologies might have a negative effect on the LTE performance, it improves the performance of the network by balancing the load over the whole network. * In this current research, we propose an interoperability model of coexistence among LTE and Wi-Fi. The cell and network performance are studied based on the consequences of integrating the two technologies. Moreover, comparative results are prepared and demonstrated to show the effect of this integration and coexistence on LTE performance, because some authors' results in the literature reflect the positive effect of this integration on LTE performance, while other results alternatively reflect the negative effect. * The main goal of the current research is to invent a new methodology to help mobile operators to estimate the proper performance measures that help to compare different interoperability models under various deployment and integration scenarios. * As per the team effort and experience in developing and solving the suggested model, numerically by the simulation and analytically by the mathematical solution, this new methodology can be used as a new tool that has an important commercial value for mobile operators. Therefore, the proper utilization of the above methodology and associated tool can help in proposing new deployment and integration scenarios to accomplish better network performance by maximizing the capacity of the network as well as achieving better QoS for the end-user.