esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 UAV Navigation and Control
### Path Planning and Trajectory Design
#### 4.2.1 Two-Dimensional Path Planning  
Basically, in the 2D path planning, the optimization variables are the UAV-BS or RISeUAV waypoints at consecutive time samples (slots) to maximize the achievable rate and throughput of UEs. Therefore, based on the achievable rate formula in (9) and (10), the optimization problem is defined as follows:  
\[\mathcal{P}1:\] \[\text{OF1.}\quad\underset{\text{P}^{RU}}{\text{max}}\quad\log_{2} \left(1+\underbrace{\left\|p^{RU}-p^{BS}\right\|_{2}}_{d\otimes RU}\underbrace{ \left\|p^{RU}-p^{UE}\right\|_{2}}_{d\otimes RUU}\right)\] \[\text{s. t.}\quad\left\|p^{RU}[k+1]-p^{RU}[k]\right\|_{2}^{2}<D\]where \(\text{P}^{RU}=\left\{p^{RU}[k],\ \forall k\in\left\{1\sim K\right\}\right\}\) denotes the path including waypoints corresponding to discretized samples times from 1 to \(K\); \(D=\delta V_{max}\) denotes the maximum distance the UAV can fly at each sample time based on its maximum linear speed \(V_{max}\).  
Based on Remark 3, the objective function OF1 is nonconvex, which is studied in Section 6. Constraint C1\({}_{1}\) imposes the speed constraints. However, as can be seen in \(\mathcal{P}1\), the UAV motion constraints, including velocity/acceleration/nonholonomic constraints, as well as obstacle-free navigation, have not been considered. Moreover, an extra control loop is required for path tracking. Therefore, the produced path may not be feasible for practical applications, unless it is for long obstacle-free paths.  
The 2D trajectory optimization of the RISeUAV has been studied in [48] by considering power transmit and RIS phase shift as joint objectives. The joint optimization problem is solved with iterative optimization and decomposing the OF into three optimization problems. Only UAV speed has been considered as a constraint, and motion constraints and obstacle-free navigation have not been considered. The finite flying time has been discretized into \(N\); some time slots and the waypoints of the 2D trajectory are obtained for each sample time that maximizes the objective function, which is the throughput of UEs. It is almost the case in many relevant works in 2D path planning, see Table 1.
## 2 Materials and Methods
### Data Processing Flow Chart  
Figure 2 presents the methodology used for the data processing, which is revealed in four steps. In the data preparation step, the SAR image was selected, downloaded, and pre-processed in SNAP 7.0. Parallel to this, the historical data of earthquakes that occurred in the central region of Chile were downloaded from the CSN. Second, the processing of the images used SNAP and the Stanford method for persistent scatterers (PStaMPS) [52]. Then, to obtain the deformation maps, a spatial statistical analysis was carried out using geographic information systems (QGIS open-source program ([[https://qgis.org/](https://qgis.org/)]([https://qgis.org/](https://qgis.org/)), accessed on 10 January 2022) and system for automated geoscientific analyses (SAGA) GIS ([[http://www.saga-gis.org/](http://www.saga-gis.org/)]([http://www.saga-gis.org/](http://www.saga-gis.org/)), accessed on 10 January 2022) using the final PS candidates. Finally, to obtain the trend deformation, the validation process was performed using Matlab 2021 and Origin software ([[https://www.originlab.com/](https://www.originlab.com/)]([https://www.originlab.com/](https://www.originlab.com/)), accessed on 12 January 2022). Only the Matlab license is required for working with STAMPS/MTI, while the other software mentioned (QGIS, SAGA) could be downloaded freely, thus proving to be cost efficient in comparison to other DInSAR tools, such as Gamma software and SARscape [53]. A more detailed description of each step is provided below.
## 4 Methodology
### Data Split  
Once the data from the Teatro Real building had been pre-processed, we defined the forecast horizon of the energy consumption of the HVAC system in one day (short-term) since this allows for anticipating the energy that these systems will use; therefore, it will help building managers/administators to design and implement optimal control strategies over these systems and access optimal energy rates in the continuous electricity market [70; 71; 8]. Therefore, the one-day forecast horizon for the building is a 96 samples/time step.  
Subsequently, we divided the data into three subsets (training-set, validation-set, and testing-set) because the division of these subsets was essential for the accuracy of the model [72]. From this perspective, the pre-processed data were divided into 80% for training and 20% as unseen data to be used in the final evaluation (testing) of the model. It should be noted that 20% of the data from the training-set we used for validation during model training (see Figure 3).  
Because the different variables were recorded as a time series, it is of great importance that these data sets were kept in order and preserved the temporal behavior. In addition, we had multivariate data, so it was more difficult to analyze them using classical techniques [73]. Therefore, we applied the sliding window method [74; 75; 76; 77], which allows transforming time series data into supervised learning. To achieve this, the method maps the past observations (time lags) as input variables. The time lag can be 15 min., one hour, one day, or more than one day. Subsequently, it uses the time steps after this time lagged (one day of samples for the case study) as forecast output, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stats** & **Outside Temperature** & **Indoor Temperature** & **Heat Pump Power** \\ \hline Mean & 8.717577 & 20.942005 & 80.989117 \\ Std & 3.214003 & 1.715789 & 50.924245 \\ Minimum & \(-\)0.768667 & 16.068332 & 0.48 \\
25\% & 6.5815 & 19.617981 & 26.405333 \\
50\% & 8.534667 & 21  
It should be noted that the input variables included the three variables presented in Section 4.2. and the output was the daily energy consumption of the HVAC system to be forecasted.
## 1 Introduction
[34] modified oil sludge pyrolysis residue with SCA and found that the surface of the oil sludge pyrolysis residue became rough after treatment with KH550, forming strong physical and chemical adsorption between the oil sludge pyrolysis residue and asphalt. Currently, most research on the interface between RCA/asphal is conducted at a macroscopic level. However, the interaction between RCA and asphalt is a complex process that is easily influenced by interactions between different molecular groups. With current experimental techniques, it is still very difficult to understand the impact of SCA modification on the interaction for the RCA/asphal interface [35]. It is now possible to know how SCA modification affects the interaction between RCA and the asphalt interface, as molecular dynamics simulation techniques have been widely used to investigate the interaction between asphalt and aggregates. Previous studies have used molecular dynamics simulations to establish molecular models of asphalt, aggregates, and C-S-H and have studied their interactions [36; 37]. [PERSON] et al. [38] explored the impact of interface water on the structure of asphalt and the interaction energy of the asphalt/aggregate interface and discovered that the existence of interface water altered the nanostructure of the asphalt and diminished the interaction energy of the interface. [PERSON] et al. [39] revealed that the anisotropy of aggregate surfaces significantly affects the interaction energy with asphalt and the water stability of the interface. [PERSON] et al. [40] investigated the effect of asphalt aging on the damage behavior of the asphalt/aggregate interface and found that asphalt aging reduced the thermodynamic properties of asphalt, such as surface free energy and cohesive energy. However, it also enhanced the adhesion strength between the asphalt/aggregate interface. The above study primarily describes the influence of SCA modification on the interfacial performances through experimental methods. It also investigates the interaction relationship between the asphalt and aggregate using molecular dynamics. However, the influence of SCA modification on the interfacial performances and interaction relationship between RCA and asphalt has not yet been resolved. Therefore, the objective of this study is to explore the multi-scale enhancement mechanism of SCA modification on the interface between RCA and asphalt. First, at the macro-scale, the adhesion behavior and tensile strength of the interface between RCA and asphalt were determined before and after SCA modification using a boiling water test and direct tensile test. Second, on a micro-scale, the impact of SCA modification on the microstructure of the RCA/asphal interface was observed through scanning electron microscopy. Finally, at the nanoscale, the main component of mortar, C-S-H, was taken as the research object to analyze the hydrogen bonding, interaction energy, and interface transition zone of the C-S-H/asphal interface system using the molecular dynamics method.
## 5 Results and Discussion
### Relationship between Outsole Pattern and COSF  
Four outside patterns, namely flat, rectangular, hexagon, and zigzag with shore hardness A25 (as shown in Figure 10), were employed to investigate the relationship between the geometric parameters of slip specimens and the COSF. From Figure 11, it is evident that the smallest COSF value of 0.58 was observed for the flat pattern in wet conditions, while the largest COSF value of 0.85 was obtained for the hexagon pattern in dry conditions. There were no significant differences in COSF values among the outside patterns in the dry working condition (0.8 for flat and rectangular, 0.85 for hexagons, and 0.81 for zigzag). However, these patterns had a strong impact on the wet working condition, with COSF values of 0.58 for flat, 0.66 for rectangular, 0.76 for hexagons, and 0.73 for zigzag. This can be attributed to the fact that patterns with open contours, which facilitate drainage, result in an enhanced COSF. Interestingly, the rectangular pattern did not provide the highest  
Figure 9: Relationship between shore hardness of slip specimens and COSF in dry/wet working conditions.  
COSF as expected, suggesting that the ratio between the contact area and the open area of the outside pattern may influence the COSF. Additionally, the COSF for the zigzag pattern exhibited a slight decrease from 0.81 to 0.73, representing a minimal decrease of 9.9%. This suggests that the zigzag pattern could be a suitable candidate for the outside pattern.
## 3 The Proposed Conversational System: CORES
### The Context Dimensions in CORES  
In CORES, context is categorized into four key areas: _User_, _Item_, _Store_, and _Social_, as depicted in Figure 2. To offer relevant recommendations, CORES analyzes users' requests to understand their intentions and shopping preferences, while also considering their emotions and environmental factors, such as available items and delivery times. The system incorporates several profiles and registers to effectively manage and process this contextual information.  
* **User context:** A successful recommendation system must address users' diverse and evolving needs by considering both their current and long-term preferences. CORES achieves this by analyzing users' shopping needs, social conversation histories through interactive queries, historical interactions (e.g., users' past purchases), and explicit preferences (e.g., product type, size, color) to analyze past social interactions and conversations using emotion-driven AI models, like RoBERTa [41, 42], and contextual  
Figure 1: The general architecture of CORES using VR.  
Figure 2: The context dimensions in CORES.  
factors such as delivery location and time via GPS to consider users' proximity to stores, delivery locations, or preferred delivery times. By integrating preferences like item durability, quality, price, services, and style, CORES ensures personalized recommendations that enhance user satisfaction.
* **Store context:** it consists of context information related to each store, highlighting key features such as selling domains, store location, delivery policy, etc.
* **Item context:** Each item in CORES includes rich contextual details like price, color, size, and material, along with highly detailed 3D models for realistic visual representation. The 3D reconstruction process involves data capture using LIDAR and cameras to scan items from multiple angles, generating a point cloud for detailed modeling. MeshLab refines and textures the resulting 3D mesh, which is exported in OBJ format for visualization in Unreal Engine, enhancing the immersive VR experience (see Figure 3).
* **Social context:** CORES integrates the user's social media data, such as Facebook activity, to incorporate friends' emotions and interactions with items. Recommendations consider factors like price, quality, and social history, promoting customer-centric e-commerce. By analyzing emotional expressions such as admiration, anger, and  
Figure 3: Three-dimensional mesh generation.  
gratitude, CORES tailors recommendations to provide both immediate and long-term satisfaction. This social dimension ensures personalized emotionally relevant suggestions, aligning with user satisfaction and fostering long-term success in e-commerce.
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 2 Preparation and Modification of Biochar
### Modification of Biochar
#### 2.2.1 Chemical Oxidation  
Chemical oxidation refers to the oxidation of the biochar surface to increase the oxygen-containing functional groups such as -OH, -COOH, etc., thereby its hydrophilicity is increased. At the same time, the pore size and structure of the biochar would be changed, and, finally, its adsorption capacity for the polar adsorbate would be enhanced. The commonly used oxidants are HCl, HNO\({}_{3}\), H\({}_{2}\)O\({}_{2}\), H\({}_{3}\)PO\({}_{4}\), etc. [56; 57; 58; 59; 60]. Although the specific surface area of biochar modified by HCl, HNO\({}_{3}\), and H\({}_{2}\)O\({}_{2}\) has little difference, compared with biochar modified by HCl, the biochar modified by HNO\({}_{3}\) contains more acidic oxygen-containing functional groups [61] and has stronger adsorption capacity for NH\({}_{3}\)-N. Compared with other acids, biochar modified by H\({}_{3}\)PO\({}_{4}\) has more advantages in removing Pb pollution. The increased specific surface area and pore volume, as well as the role of phosphate precipitation, increase the biochar adsorption capacity of Pb [60].
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 3 Dynamic RID Model with Sliding Time Window
### Evolutionary Search  
An evolutionary search is applied to find the optimised disaster regions sequence for the distribution schedule. Chromosome structure (representing each gene as a disaster region), encoding and decoding are applied as:  
Coding and Decoding  
In this paper, a disaster region-based representation is applied to generate a chromosome where the genes of the chromosomes describe the disaster regions sequence that needs relief from the nearest supply points. For the solution space, the greedy search strategy starts from the first gene of the chromosome with the local search where the disaster region finds the nearest supply point for the dispatch of relief items. When any vehicle tour is planned with extra relief items, the demand of the individual demand region is updated accordingly in the sequence.  
A sample chromosome representation of demand sequence:  
12, 19, 1, 8, 13, 4, 28, 23, 17, 20, 26, 11, 29, 6, 27, 14, 24, 22, 3, 7, 10, 25, 15, 18, 5, 9, 16, 21  
For the evolutionary search, the population size is set with 60 solutions (population) for each generation. Random sequences of disaster regions are generated as parent chromosomes in the population pool at the initial state. After the first generation, the population pool is filled with solutions with multiple criteria. Elitism is used for selecting the best 10% of solutions based on distribution time and operational cost ranking from the current generation to the next generation. The remaining individuals are decided based on tournament selection. Two-point crossover with a repair is applied to avoid any possible conflict in gene exchange during the crossover. This allows removing chromosomes with the faulty gene regarding repetition of the same gene or missing any gene in the chromosome encoding. This multi-criteria population's selection gives diversity in the population set. Different mutation rates ranging from 0.01 to 0.1 are applied during the implementation of the evolutionary algorithm. After several simulations run, a 0.05 value is set as the mutation rate for this evolutionary search. In each iteration of the evolutionary search, for each chromosome encoding, the greedy heuristic search is applied to find the nearest demand region from the selected supply source on each round of relief item scheduling-based distance matrix.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 2 Background and Related Work
### KPI Anomaly Detection Algorithm
[19] studied application monitoring problems based on VAE. In 2021, [PERSON] et al. [25] designed a novel unsupervised temporal autoencoder architecture based on convolutional neural networks (TCN-AE). It can utilize the information from different time scales in the anomaly detection process. Then, [PERSON] et al. [20] developed two different schemes to detect abnormalities in energy consumption. These are an unsupervised abnormality detection based on one-class support vector machine (UAD-OCSVM) and a supervised abnormality detection based on micro-moments (SAD-M2). In the same year, [PERSON] et al. [21] proposed a clustering-based approach to detect anomalies concerning the amplitude and the shape of multivariate time series. They generate a set of multivariate subsequences by setting the sliding window. To improve the detection efficiency, [PERSON] et al. [22] proposed FluxEV, a fast and effective unsupervised anomaly detection framework. It can extract appropriate features to indicate the degree of abnormality, and make the features of anomalies as extreme as possible. Recently, [PERSON] et al. [23] presented a framework Neural Contextual Anomaly Detection (NCAD) that scales seamlessly from the unsupervised to supervised setting. It is a window-based approach which can facilitate learning the boundary between normal and anomalous classes by injecting generic synthetic anomalies into the available data. Moreover, it adopted the moments method to speed up the parameter estimation in the automatic thresholding. Although they achieved good performance, the defect is also unsuitable for intricate KPI profiles due to lack of significant seasonality in original data. In order to solve the above problem, [PERSON], [PERSON] et al. [7] devised a periodic adaptable algorithm Period, to enhance the accuracy of KPI anomaly detection. The authors of this work assumed that the intricate KPI profiles had a 1-day seasonal length and split the KPI data. However, this strategy is not universal, because not all intricate KPI profiles have a 1-day seasonal length. In fact, KPIs may show distinct patterns in different time intervals, which are referred to as KPI profiles, such as weekly, quarterly or other imperfect or complex periodicity. To deal with the situation described above, a new algorithm to recognize intricate KPI profiles with uncertain seasonal lengths must be developed. Therefore, this study proposes an adaptive seasonality anomaly detection algorithm under intricate KPI profiles. The notations list of our research is shown in Table 2.
## 4 Simulation Results
### Open Sky with Four SVs  
Scenarios where only four satellites are visible represent the minimum condition for a GPS receiver to compute a PVT solution. Therefore it is interesting to assess the performance of the TCAPLL in such conditions. We assume that 500 s after beginning the trajectory, the receiver starts to see only four SVs from the sky plot in Figure 7. The new constellation chosen to be investigated consists of SVs 1, 3, 6 and 19. Its corresponding position solution is shown in Figure 15, and the standard deviation and mean of the position and velocity errors are shown in Table 4. It can be noted that the  
Figure 14: Estimated accelerometer turn-on to turn-on constant bias (**left**) and gyroscope bias drift (**right**).  
Figure 13: PLI output when \(C/N_{0}=32\) dB-Hz, \(T_{int}=5\) ms, and \(B_{n}=4\) Hz.  
GPS solution is much noisier than the TCAPLL solution. The latter reduces the position error by 86%, and the velocity error by 92% compared to the GPS.  
Moreover, Table 4 shows that decreasing the number of visible satellites to four has only a small impact on the velocity solution, which is less sensitive than the position to the change in visibility from seven to four SVs, and from one constellation to another. This is seen in the uncertainty of the aiding frequency of the feed-forward component in Figure 16, where \(e_{f-f}\) is slightly increased. For example, the average frequency error for the non-visible SV 11 is increased to roughly 3 Hz, which allows maintaining a code phase of less than 0.5 chips for 256 s using the feed-forward component only for tracking. If the satellite re-appears within this period, then no re-acquisition is needed.  
In summary, when the number of visible satellites decreases to four, the TCAPLL improves the navigation solution compared to the GPS. The position error increase is higher than the velocity error  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Velocity Error [m/s]**} & \multicolumn{2}{c}{**Position Error [m]**} \\ \hline  & GPS & TCAPLL & GPS & TCAPLL \\ \(\sigma\) & 5.095 & 0.376 & 40.557 & 5.571 \\ mean & 0.567 & 0.098 & 20.317 & 4.615 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard deviation and mean of the velocity and position errors when only 4 SVs are visible.  
Figure 16: Feed-forward frequency uncertainty when four SVs are in view.  
Figure 15: Position solutions when four SVs are visible.  
increase, which is less dependent on the number of visible satellites. The quality of Doppler aiding slightly decreases, but is still largely acceptable to maintain robust carrier and code tracking.
# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
###### Abstract  
Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.  
ambient backscatter communication; sensor network; ultralow power communication; sensor tag; IoT +
Footnote †: journal: Sensors