esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 1 Introduction
### Research Objectives  
Aimed at the path planning requirements for terrestrial target movement in spatiotemporal big data application scenarios, this study conducts comprehensive research on multi-source data-driven terrestrial multi-algorithm fusion path planning technology to satisfy diverse application demands. By integrating multi-source data (e.g., terrestrial elevation data and road network data) with multi-algorithm collaboration (Dijkstra, A*, ant colony optimization, and deep reinforcement learning), a dynamic adaptive path planning model is constructed to address the limitations of traditional single-source data perception in complex environments, delayed responses to dynamic obstacles, and conflicts in multi-objective optimization. The proposed technology enhances the global robustness, real-time performance, and multi-constraint coordination capabilities of path planning, thereby providing efficient and reliable comprehensive path decision-making support for terrestrial targets. To summarize, the main contributions of this study are as follows:  
* We construct a standardized multi-source geospatial database by integrating ASTER GDEM terrain data with OpenStreetMap (OSM) road network information, enabling comprehensive terrain and traffic awareness.
* We propose a hybrid path planning framework that combines global A* search with local Deep Q-Network (DQN) optimization, achieving both global feasibility and local adaptability in dynamic environments.
* We introduce a dynamic multi-objective weighting mechanism tailored to diverse travel scenarios, balancing efficiency, safety, congestion, and route constraints in real time.
* We validate the effectiveness of the proposed method through extensive experiments on realistic urban road networks, demonstrating superior performance in risk control, route efficiency, and obstacle handling compared to traditional methods.
## 2 Materials and Methods
### Plant Growth Measurements  
The general appearance of the plants was monitored qualitatively throughout the entire experiment and recorded in a logbook daily. At 28 days after planting (DAP), plants were harvested, and plant growth measurements were recorded. Measurements included shoot fresh mass, shoot height and diameter, leaf area, relative chlorophyll, and anthocyanin content index. Leaf area measurements were made using a Li-Cor area meter (LI-3100C, Lincoln, NE, USA), relative chlorophyll estimates were made using a SPAD-502 DL meter (Konica Minolta Sensing, Osaka, Japan), and anthocyanin content index was measured using an ACM-200 Plus meter (Opti-Sciences, Inc., Hudson, NH, USA).
## 2 Materials and Methods  
### The Formulation of ASK, FSK, and PSK  
A type of digital transmission in which the digital baseband signal is changed into a band-restricted high-frequency passband signal is known as a digital passband modulation. Transition bands may be modulated using _ASK_, _FSK_, or _PSK_.  
* Amplitude-shift keying: The identical frequency carriers in A\({}_{1}\) and A\({}_{2}\) carry bits 0 and 1 of the baseband signal.
* Frequency-shift keying: Bits 0 and 1 of the baseband signal are modulated using the frequency-shift keying (_FSK_) technique. Two frequencies with the same amplitude are used in _FSK_.
* Phase-shift keying: _PSK_ modulation uses phase variations of the same amplitude and frequency to modify baseband signal bits 0 and 1.  
The basic modulation formulae for _ASK_, _FSK_, and _PSK_ are listed here:  
\[X_{m_{ASK}}(t)=A_{m}\cos\omega_{c}t \tag{1}\]  
\[X_{m_{PSK}}(t)=A\cos(\omega_{c}t+\theta_{m}) \tag{2}\]  
\[X_{m_{FSK}}(t)=A\cos(\omega_{c_{m}}t) \tag{3}\]  
The data has been distorted in accordance with the SNR ratio for binary-10110100, which is data with a decimal value of 180, as illustrated in the diagrams for binary-10110100 digital modulation (Figure 1).
## 2 Description of the Origami Twisted Tower
### Kinematics of the Twisted Tower
#### 2.2.1 Axial Extension/Contraction Motions  
Firstly, the axial motion of the one-layer octagonal origami structure is analyzed. In Figure 3, \(h\) is the distance between the top and bottom plates, \(\theta\) is the rotation angle around the \(z\)-axis, and \(s\) is the length of the link plate [31].  
When the width of the unfolded segment paper is set to \(L\), and the length of the unfolded segment paper is set to 2 \(L\), the length s of the link plate can be represented as follows:  
\[s=L-L\text{sin}(\pi/8) \tag{1}\]  
The original distance between the top and bottom plates \(h_{0}\) can be represented as follows:  
\[h_{0}=s=L-L\text{sin}(\pi/8) \tag{2}\]  
The rotation angle \(\theta\) around the z-axis ranges from \(-4\pi\) to \(4\pi\). When a rotation force is applied on the top plate, the distance \(h\) between the top and bottom plates can be represented as follows:  
\[h=s\sqrt{1-sin^{2}(\theta/2)/sin^{2}(\pi/8)} \tag{3}\]  
The geometric model of a two-layer twisted tower is demonstrated in Figure 4. The coordinate system is set up in the center of the bottom plate of the first one-layer structure. The vertical line segment represents the link plate of the structure. The motion trajectory of the top plate can be considered as an arc on the circumscribed circle of the octagon.  
Figure 3: Schematics of axial motion with rotation angle \(\theta\).  
There is an offset angle \(\alpha\) between the link and bottom plates. The definition of offset angle \(\alpha\) for clockwise and counterclockwise twisting is as shown in Figure 5. The relationship between offset angle \(\alpha\) and rotation angle \(\theta\) can be represented as follows:  
\[h=s\cdot\sin\alpha \tag{4}\]  
\[\alpha=\sin^{-1}\biggl{(}\sqrt{1-sin^{2}(\theta/2)/sin^{2}(\pi/8)}\biggr{)} \tag{5}\]  
Therefore, the motion of a multilayer twisted tower is the superposed motions of several one-layer twisted towers. The change in the overall structure can be calculated by measuring the offset angle \(\alpha\) of each layer.
## 4 Experimental Evaluation of Performances
### Community Anomaly Detection in Synthesis Data
#### 4.3.3 Dissection of Results  
From the analysis of these results, and after applying the CAnom model on synthesized data and comparing it with CODA and ConSub, it can be concluded that the proposed model gives the best result. The model achieves good-quality results and high scalability with the attribute number increasing, as it defines the relevant attributes of the network instead of considering the full attribute space. Therefore, the model is suitable for today's applications, where the number of attributes is rising. The ConSub model achieves better results than CODA, as it also defines the context of the network, while CODA considers all of the network attributes.
# Isocyanide Multicomponent Reactions on Solid Phase: State of the Art and Future Application
## 2 Isocyanide-Based Multicomponent Reactions (IMCRs)  
The discovery and course of IMCRs is explained in detail and comprehensively illustrated by [PERSON] and [PERSON] [22; 23]. Briefly, isocyanides were discovered in 1859 by [PERSON], who prepared them by the random reaction of allyl iodide and silver cyanide [24]. The term \"isonitrile\" was introduced by [PERSON] and simultaneously by [PERSON], who prepared these molecules by reacting primary amines with chloroform and alkali [25]. Note that we used the term \"isocyanide\" within this text because it is preferred by IUPAC (International Union of Pure and Applied Chemistry).  
Approximately a half-century later, in 1921, [PERSON] introduced the first MCRs of isocyanides. Passerini three-component reaction (P-3 CR) of a carbonyl compound, isocyanide, and carboxylic acid afforded \(\alpha\)-acyloxy carboxamide **4** (Scheme 1). Since the 1950s, attempts to prepare isocyanides by a more convenient method have appeared in the literature [26]. Finally, the beginning of U-4 CR dates to 1959, when [PERSON] (1930-2005) performed a one-pot reaction of amine, carbonyl compound, isocyanide, and acid, leading to \(\alpha\)-acylamido carboxamide **9**[7; 27; 28]. Since 1962, the reaction is referred to as the Ugi reaction (U-4 CR). Approximately twenty years later, the transformation was also performed on a solid phase [29]. The first stereoselective Ugi reaction on the solid phase was accomplished in 2000 by [PERSON] et al. [19]. Finally, the newest MCR is the Groebke-Blackburn-Bienayme three-component reaction (GBB-3 CR, product **13**) [30; 31; 32].  
Figure 1: Types of isocyanide-based multicomponent reactions (IMCRs) described in this review.
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.1 FluxCom  
The FluxCom dataset (www.fluxcom.org/, 1 February 2021) is used to evaluate the impact of the new developments on the energy and carbon fluxes. This dataset uses machine learning techniques, namely Random forests (RF), Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Splines (MARS), to upscale FLUXNET in-situ flux observations in space and time and integrate them with remote-sensing and meteorological forcing data. It uses the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data for the FluxCom-RS version and additionally uses reanalysis meteorological forcing in the FluxCom-RS + METEO version. FluxCom provides fluxes for the RS + METEO version at 0.5\({}^{\circ}\) spatial resolution and daily temporal resolution for the 1979-2013 period and the RS version is available at 8-day temporal resolution with 0.0833\({}^{\circ}\) spatial resolution for the 2001-2015 period. Further details of the FluxCom data is available in [PERSON] et al. (2017).
## 2 Materials and Methods
### Clinical Periodontal Inflammatory Parameters  
Intra-oral examination was performed by a calibrated examiner (SA) to evaluate the periodontal parameters PPD, CAL, and BOP at six sites per tooth except for the 3 rd molars. The examiner was calibrated with a trained periodontitis on 10 patients, the readings were taken by both the examiners, and inter-examiner reliability was calculated using Kappa Statistics and the agreement was found to be excellent (94.3%). PPD (rounded off to the nearest mm) was measured as the distance from the cementoenamel junction to the bottom of the pocket (mm). BOP was assessed as present or absent. These measurements were performed in all the quadrants using a UNC 15 probe in six sites per tooth (mesiobuccal, mid-buccal, distobuccal, distolingual/distopalatal, mid-lingual/mid-palatal, mesiolingual/mesiopalatal). For classification of periodontitis, the criteria proposed by the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC/AAP) was used [33; 34]. Severe periodontitis was defined as at least 2 sites with CAL of \(\geq\)6 mm (on 2 different teeth) or at least 1 site with PPD \(\geq\) 5 mm. Moderate periodontitis was indicated by the presence of CAL of \(\geq\)4 mm or at least 2 sites with PPD of \(\geq\)5 mm (on 2 different teeth). Mild periodontitis was indicated in cases with at least 2 sites with a CAL \(\geq\) 3 mm and PPD \(\geq\) 5 mm (on 2 different teeth). Healthy patients were categorized by the presence of BOP in less than 6 sites or completely absent, PPD less than 4 mm, and CAL 0-2 mm at less than 6 sites. Fractured teeth or broken-down roots were not assessed.  
PESA was calculated using equations for each tooth type with CAL and PPD. PISA is the sum of PPD on BOP positive sites. Both parameters were calculated using MS Excel Spreadsheet from a previous study (www.parsprototo.info) (accessed on 20 June 2021) [35; 36] and the mean values were then entered in SPSS (Version 22, IBM, NY, USA) for analysis.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 3 Preliminaries
### Definitions  
Let \(\mathcal{U}=\{u_{i}\mid i=1,2,\ldots,n\}\) and \(\mathcal{S}=\{s_{j}\mid j=1,2,\ldots,m\}\) represent the collections of \(n\) passengers and \(m\) stations. Specifically, \(u_{i}\) represents the \(i\) -th passenger and \(s_{j}\) represents the \(j\)-th station.  
Definition 1: Passenger Behavior. The passenger behavior is defined as an interaction \(e_{k}=(u_{k},s_{k},l_{k},t_{k})\) where the passenger \(u_{k}\) enters or leaves the subway station \(s_{k}\) at timestamp \(t_{k}\).  
Definition 2: Dynamic Graph. A dynamic graph can be represented as a monotonically non-decreasing sequence of interactions denoted by \(\mathcal{G}^{t}=\{e_{1},\cdots,e_{k}\}\), where \(0<t_{1}\leq\cdots\leq t_{k}\leq t\). In the graph, each passenger node \(u_{i}\) is associated with a node feature \(x_{u_{i}}\in\mathbb{R}^{d_{N}}\), and the feature of each station node \(x_{s_{j}}\in\mathbb{R}^{u_{i}}\) also carries the same meaning. Additionally, we defined corresponding features for the edges \(x_{e_{k}}^{t_{k}}\in\mathbb{R}^{d_{E}}\), where \(d_{N}\) and \(d_{E}\) denote the dimensions of the node feature and the edge feature.
## 2 Literature Review
### Optimization Methods and Techniques  
To effectively leverage a PHM model, acquiring the Weibull parameters and their corresponding covariate weights is crucial. While these parameters are typically known when dealing with synthetic data, real-world applications often require expert judgment to gather this information. Without such expertise, implementing the PHM model becomes challenging. Hence, optimization techniques like minimizing the log-likelihood prove invaluable in determining these parameters [3]. However, solving nonlinear problemscan be complex, and traditional solver software may struggle due to its limitations in handling optimization problems, such as local domain restrictions and rigid formulations [19]. One ML tool suitable for solving these types of problems are GAs, which offer a flexible approach to optimization by representing solutions as chromosomes and using selection, crossover, and mutation operators to find better solutions across a wider solution domain [20]. Therefore, GAs excel in optimizing complex problems, often outperforming traditional solvers [21].  
Recent studies have applied GAs to maintenance challenges. For instance, Ref. [22] used GA to address aircraft maintenance planning, handling the problem's complexity that traditional solvers struggle with. In another application, the authors of Ref. [23] combined a GA with an arithmetic optimization algorithm to improve feature selection in survival analysis, achieving better results than other methods. Furthermore, the authors of Ref. [3] used a GA alongside a traditional solver to optimize covariate weights for a PHM. While the conventional solver performed better on the primary score index, its impact on reliability metrics and maintenance policy remains to be explored. This study aimed to investigate these effects.
## 2 System Model
### Cognitive Radio Wireless Sensor Network Energy Consumption Model  
Network energy consumption for CR-WSNs mainly comprises of energy consumptions for vacant channels detection, event sensing, data processing, and communication. The energy consumption for detecting vacant channels is the energy consumed for cooperative channels sensing. The event sensing energy consumption is attributed to energy consumed for the sensing event while the data processing energy is attributed to energy consumed for data logging. The energy consumption for data transmission is attributed to energy consumed for intra-cluster and inter-cluster data communications.  
Several attempts have been made to model wireless sensor network energy consumptions. The most widely adopted models [40; 41; 42] have combined the impact of the external radio environment and the sensor node's communication hardware together. According to these models, energy consumption for transmitting a unit bit of data depends largely on the distance between the transmitting and the receiving nodes. However, the outcome of experimental measurements conducted in [43] is inconsistent with the widely adopted simplified models. In addition, it is revealed that separating energy consumption of each hardware component from the external radio environment may lead to a more realistic energy consumption model [44]. The authors in [45] proposed a comprehensive node power consumption model which considered other energy consumption sources that were ignored in the previous models such as sensor sensing and sensor logging. However, these approaches are specifically developed for conventional sensor nodes without due consideration of the cognitive radio aspect. Therefore, accurate estimation of CR-WSN life expectancy requires a realistic network energy consumption model that incorporates channel sensing energy consumption as well.  
The main components of a cognitive radio sensor node are event sensing, data processing, communication and cognitive radio units as shown in Figure 4. The event sensing unit monitors the environment and generates signal traffic whenever an event is occurred. The processing unit processes the data while the communication unit transmits the data to desire sink over a free licensed channel and also receives data. The cognitive radio module detects set of unused licensed channels and then accesses the most suitable channel to communicate the data.
## 3 Results and Discussion
### Correlations of TP, TF, CGA, and Caffeine and Antioxidant Activities  
Correlations between the contents of TP, TF, CGA, and caffeine and antioxidant activities were analyzed with Pearson's correlation tests. In the espresso, the DPPH radical scavenging activity was positively moderately correlated with the contents of TP (r = 0.664) and CGA (r = 0.679), whereas the caffeine content showed a moderately negative correlation with the DPPH radical scavenging activity (r = \(-\)0.691) (Table 6). In particular, high positive correlations between the FRAP and the contents of TP (r = 0.723), TF (r = 0.751), and CGA (r = 0.843) were found in the espresso, while the caffeine content negatively correlated with the FRAP (r = \(-\)0.761) (Table 6). In the drip, we also found moderately positive correlations between the contents of TP (r = 0.563), TF (r = 0.674), and CGA (r = 0.686), but the caffeine content had negative correlation with the DPPH radical scavenging activity (r = \(-\)0.638) (Table 7). Moreover, the FRAP in the drip was found to be highly positively correlated with the contents of TP (r = 0.878), TF (r = 0.915), and CGA (r = 0.885), whereas the content of caffeine was negatively correlated (r = \(-\)0.777) (Table 7). A previous study has reported an interaction between caffeine and polyphenols in black tea [55]. They observed a strong positive relationship between DPPH radical scavenging activity and levels of EGCG, flavonoids, and total phenolics, but a very weak correlation between caffeine content and DPPH radical scavenging activity [55]. They have also reported that releasing gallic acid, EGCG, and caffeine into the tea brew is faster than polyphenols and flavonoids [55]. [PERSON] et al. have shown a greater extraction efficiency of caffeine than that of the polyphenols, indicating that the release of polyphenols could be partially blocked by caffeine [56]. Therefore, TP, TF, and CGA contents might well account for the antioxidant capacity of both coffee extracts.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
###### Abstract
[PERSON] proposed a \"grey model\" that integrates the finite element method (FEM) with the ANNs [32], aiming for a more accurate forecast of PWP variations. [PERSON] et al. (2005) [33] conducted a comparative analysis between FEM and ANNs for flow seepage through the Jeziorsko earth-fill dam in Poland. Their discussion touched upon the suitability and competitiveness of ANNs against FEM in predicting seepage through the dam. [PERSON] et al. [3] showcased the enhanced precision and robustness of gated recurrent unit (GRU) and Long Short-Term Memory (LSTM) models in predicting slope seepage/stability under rainfall compared to the standard RNNs. RF has been applied to the prediction of daily pore water pressure in the embankment [34; 35], and MLP has been used in the case of concrete dam seepage behavior [9]. RNNs and GRU are applied in pore water pressure prediction of a tunnel boring machine [36] and slope [3]. These elements have been added to the end of the introduction section for each method. In the previous studies mentioned above, most of the research was limited to the ANN method but not various ML methods for predicting PWP in earth dams. Moreover, these studies did not discuss the time effect and irreversible effect, which are important factors in the safety of earth dams. This paper deals with the use and comparison of several machine learning methods for analyzing monitoring data and predicting PWP in earth dams. This type of approach can be useful for detecting pathology or abnormal dam behavior. The aim of this study is to forecast PWP in embankment dams using several ML techniques, such as random forest (RF) with simulated annealing (SA), multilayer perceptrons (MLP), traditional recurrent neural networks (RNNs), and gated recurrent unit (GRU). This research marks the initial effort to use diverse static and dynamic neural networks to analyze monitoring data and to compare their accuracy in predicting the evolution of PWP in earth dams. The case study of this paper is a 36 m high earth dam. It is equipped with 45 piezometers (PWP cells), and four represented sensors monitoring data are chosen to build and validate the prediction models. Prediction models show the powerful capacity to predict PWP, which could make a reference for applying ML methods in non-linear and time series hydraulic problems in geotechnics.