esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[12] proposed integrating the shop scheduling scheme, operation sequencing, and transportation task assignment of vehicles into genetic factor coding, and designed a neighborhood searching strategy for critical paths to improve the effectiveness of local path search on AGV. In autonomous underwater vehicle (AUV) path planning, [PERSON] et al. [13] proposed an adaptive GA for AUV global path planning, which is used to optimize the global path based on the collision detection mechanism. [PERSON] et al. [14] proposed a path planning cost function based on the ocean current model to improve GA for the shortest time required to guide AUVs. In autonomous mobile robot path planning, [PERSON] et al. [15] proposed an enhanced GA, a linear rank-based, or clearance-based, probabilistic road map (CBPRM) technique that overcomes these shortcomings. The new model guides the population initialization process by using the fitness score of each cell in the environment, lowering the number of infeasible pathways created. [PERSON] et al. [16] proposed a pathfinding optimization process using GA, and its evaluation criteria for the path are path length and travel time. [PERSON] et al. [17] opposed a path planning algorithm based on the Markov decision process, which is used for the point-to-point path planning of wheeled robots. Among the existing agricultural robot or unmanned vehicle full coverage path planning algorithms, the template method, bio-inspired neural network algorithm, and GA have advantages, but shortcomings limit their applicability and efficiency in different operating areas. In the application of GA, the traditional GA performs well in various optimization problems, but has limitations in solving complete coverage path planning problems, such as slow convergence speed, easily getting stuck in local optima, sensitive parameter settings, and low efficiency when dealing with large work areas. Therefore, based on the traditional GA, the paper introduced the state transition matrix of Markov chains that can constrain random methods to guide population individuals to mutate, thereby improving the individual quality of the population, which will enhance the search ability and convergence speed of the algorithm, and improve the applicability and efficiency of path planning for different work areas. Section 2 is materials and methods including the work area modeling analysis, the algorithm improvement based on Markov chain, the path evaluation function analysis, and the design of the path planning algorithm. Section 3 is the simulation analysis and field experiments. Section 4 is the conclusion.
## 2 Problem Formulation
### Reduced Dimensional Search  
Based on the analysis above, the sensor position information in a 2D target space can be denoted by \((c_{x},c_{y},\varphi)\), where \((c_{x},c_{y})\) represents the sensor position and \(\varphi\) represents the azimuth angle. This positional information determines sensor coverage and the reconstruction error of the target point covered by this sensor. Therefore, in the study of the sensor layout problem, the rational planning of this parameter combination is the key to solve it.  
However, most environments have restrictions on the location of sensors, such as in our article, where sensors can only be deployed on four sides of a rectangle. Therefore, in this paper, we design a method to search for sensor location information using one-dimensional parameters to represent sensor location information in the solution space dimensionality reduction search method.  
We set the origin of the rectangular coordinate system as the starting point, and move counterclockwise along the boundary of the target space; then, each discrete point on the boundary uniquely corresponds to moving forward distance \(d\). With the previous direction distance representing the position information of the sensor, 2D position parameters can be reduced to 1D distance parameters. This method can reduce optimization parameters and improve running speed. In addition, since the sensor position is defined by only one parameter of the forward distance, for an irregularly shaped space, as long as the mathematical expression of the space boundary is known, the dimensionality reduction technique can take effect as well.
# Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals
###### Abstract  
This paper presents a method of ground target detection using reflected signals of BeiDou satellites. The phase difference information, which is the output of the phase-lock loop (PLL) in the tracking process, is an important observation in this technique. The geometric relationships between the specular point of different BeiDou satellites and the target are established. In addition, the detection and false alarm probability are also analyzed. In order to verify the reliability of the method, an experiment in the suburb area of Beijing was completed. The target was placed in the coverage area of the left-handed circular polarization (LHCP) antenna for two time periods (10-20 s and 40-55 s). By observing the phase difference in BeiDou reflected signals in the presence of a target, it was found that the changing trend was in good agreement with the target placement time periods. In the second experiment, the target moved east and west at a speed of 0.5 m/s, and the range of motion was 6 m. During the acquisition of the BeiDou reflection signal, the target passed through the antenna 14 times. The performance of target detection with different parameters was observed by extracting in-phase (I) branch component data, phase difference information, and the carrier-to-noise ratio (CNR) of five BeiDou reflected signals. The experimental results allowed three conclusions to be drawn as follows: (1) The target detection performance of the three parameters has a certain relationship with the altitude angle and the azimuth angle of the satellite; (2) target motion direction information can be reflected in the change of the satellite I branch component data; (3) The CNR information of different satellite reflected signals varies greatly when the target moves, which is quite different from that of the first experimental target when it is stationary. Thus, the feasibility of target detection using BeiDou reflection signal was demonstrated through these two experiments.  
BeiDou; reflected signals; target detection; phase difference information; I branch component; CNR
## 3 Experimental Results
### Improvement in Classification Outcomes by DL Optimizers  
In this work, a performance enhancement of the CNN architectures has been attempted by training the best model (obtained from the prior phase of algorithm comparison) over several deep learning optimization functions. Table 7 resumes the outcomes found by using different optimization algorithms. Some significant observations can be stated as following:  
Noticeable variations were seen in training/validation accuracy, loss, recall, precision, and F1 score when training the InceptionV3 model using various deep learning optimizers. SGD and Adam were the most performing optimizers for the chosen architecture.  
The InceptionV3 architecture trained with the SGD optimizer reached the highest validation accuracy and F1 score of 87.60% and 0.8941, respectively, which clearly shows the effectiveness of the proposed optimizer. Therefore, it could be used for various other agricultural operations. Yet, a decrement in performance was also noticed once the optimization functions were switched from SGD to Adadelta and Adagrada, as illustrated in Figure 18 where the optimizer showed its lowest validation accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activation Function** & **Accuracy** & **Precision** & **Recall** & **F1-score** \\ \hline Adadelta & 80.50\% & 81.74\% & 81.38\% & 81.25\% \\ Adagrada & 86.00\% & 88.50\% & 88.31\% & 88.33\% \\ Adam & 86.80\% & 89.60\% & 89.45\% & 89.47\% \\ RMSprop & 86.30\% & 85.75\% & 81.78\% & 81.03\% \\ SGD & 87.60\% & 83.86\% & 80.06\% & 89.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of the applied deep learning optimizers to train the InceptionV3 model.  
Figure 17: ROC and AUC curves of tested DL and ML models.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
###### Abstract  
Recent material science advances have resulted in the use of High-Performance Concrete (HPC) and Ultra-High-Performance Concrete (UHPC) in superstructures, which were chosen for their superior strength. However, under cyclic loads, these materials frequently show fatigue. Carbon-Fibre-Reinforced Polymer (CFRP) rods are replacing steel rebars due to their corrosion resistance and excellent strength-to-weight ratio and are thus gaining popularity in both infrastructural and superstructural design. However, due to a lack of understanding of their bond mechanics, modelling the interaction between CFRP rods and these advanced concretes in finite element simulations remains complex, particularly under cyclic loading. The bond behaviour of CFRP rods and both standard Grade 40 concrete and Ultra High-Performance Fibre-Reinforced Concrete (UHFFRC) under cyclic stresses is investigated in this work. A finite element model of connected concrete cube samples was built and analysed under cyclic stress, combining these concretes with CFRP rods. Furthermore, these samples were subjected to dynamic actuation testing to develop a traction-based constitutive model for the CFRP-concrete interface. In finite element models, an interface element devised for this study effectively approximated the binding, matching experimental data. The new analytical interface element improved simulation precision by 19% in displacement and 49% in pull-out force, resulting in a significant improvement in predicting the performance of the CFRP-UHFFRC bond under cyclic loading. The improved performance of the CFRP-UHFFRC bond under cyclic loading is attributed to the optimised interface model that enhances the bond integrity between CFRP rods and concrete.  
CFRP-UHFFRC interface; FEA of CFRP embedded in concrete; cyclic bond strength enhancement; cyclic load response modelling; interface element development for bond simulation; durability characteristics under cyclic loading  
19 December 2023  
12 January 2024  
Accepted: 22 January 2024  
Published: 29 January 2024
Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information  
Zhengwei Huang  
conversation is also reflected in the affective interaction between the speakers. For instance, customers express strong dissatisfaction (i.e., the sixth utterance) when they perceive the agent's response as perfunctory (i.e., the fifth utterance). The agent provides a more positive response (i.e., the seventh utterance) after sensing a very negative customer sentiment. Thus, modeling the affective interaction information between speakers can improve the accuracy of the sentiment recognition model to a certain extent.  
For the characteristics of customer service conversation, this paper proposes a conversational sentiment analysis model based on contextual semantics and affective interaction information, which fully exploits the value of contextual semantics and affective interaction information between speakers. Specifically, to extract the contextual semantic information, this paper uses different BiGRU networks to contextually encode the customer utterance text and agent utterance text. Then, the two are fused into the global dialogues to encode the global contexts and achieve a deep and refined mining of different context types. To realize the modeling of affective interaction information between speakers, we construct a directed graph structure to portray the affective interaction dependencies between interlocutors and use graph convolutional neural networks to extract the affective dependency interaction features between speakers and themselves and interlocutors.  
This paper has three main contributions. Firstly, we propose a model that considers contextual semantics and affective interaction information between speakers to recognize customer sentiment in conversation, which can effectively use the relevant information of the utterance. Second, we propose a contextual semantic encoder and affective interaction encoder to capture different speakers' contextual information and inter-speaker affective interaction information, respectively. Third, the experiment results demonstrate that the performance of our model is superior to the baseline models for a sentiment analysis of the JDDC dataset.
## 2 Modeling of PMSG
### Wind Turbine Model  
The mechanical power from wind captured by the wind turbine is given by  
\[P_{m}=\frac{1}{2}\rho\pi R^{2}V_{wind}^{3}c_{p}(\lambda,\beta) \tag{1}\]  
where \(\rho\), \(R\), \(V_{wind}\), \(c_{p}\), \(\lambda\), and \(\beta\) are the air density, rotor radius of the wind turbine, wind speed, power coefficient, tip speed ratio, and pitch angle, respectively. The tip speed ratio \(\lambda\) is the ratio of the blade tip speed to the wind speed. As in [11], the power coefficient \(c_{p}\), which is a function of the pitch angle and tip speed ratio, can be expressed as  
\[c_{p}=0.580000\left(\frac{119.400}{\lambda_{i}}-6.18500\right)e^{\frac{-21.4 (000)}{\lambda_{i}}}+0.00680000\;\lambda \tag{2}\]  
with  
\[\frac{1}{\lambda_{i}}=\frac{1}{\lambda+0.0800000\beta}-\frac{0.0350000}{\beta^ {3}+1.00000} \tag{3}\]  
The mechanical drivetrain of the wind turbine is represented as a two-mass shaft model, as shown in Figure 2. The wind turbine side torque, \(T_{m}\), which is the aerodynamic torque from the wind turbine rotor, and the generator side torque, \(T_{g}\), from the permanent magnetic synchronous generator act in opposition to each other. The torque equations of the mechanical drivetrain are:  
\[T_{m}=J_{r}\ddot{\theta_{m}}+C_{shaft}\left(\omega_{m}-\frac{\omega_{g}}{N} \right)+K_{shaft}\left(\theta_{m}-\frac{\theta_{g}}{N}\right) \tag{4}\]  
\[-T_{g}=J_{g}\ddot{\theta_{r}}+\frac{C_{shaft}}{N^{2}}\left(\omega_{g}-N\omega _{m}\right)+\frac{K_{shaft}}{N^{2}}\left(\theta_{g}-N\theta_{m}\right) \tag{5}\]  
where \(J_{r}\), \(J_{g}\), \(\omega_{m}\), \(\omega_{g}\), \(\theta_{m}\), \(\theta_{g}\), \(C_{shaft}\), \(K_{shaft}\), and N are the moments of inertia of the wind turbine rotor and generator, wind turbine rotor and generator speed, angular position of rotor and generator, equivalent damping and stiffness of the two-mass model, and gear ratio, respectively.  
Figure 1: Configuration of the PMSG-type wind turbine.
### Adapting to Weather  
Adverse weather conditions, particularly those that are not present in the training dataset, present a real challenge for deployed computer vision systems that are exposed to the weather. Typically, approaches to concept drift rely on detecting drift, and then adapting accordingly [15; 16]. With deep-learning-based approaches, this typically means a general system will be trained to establish a baseline, and this would then subsequently be exposed to unseen data. Depending on the task, an evaluation metric will be used as a method to detect drift [17]. When adapting to weather-related drift, some have tried to remove the distracting elements directly [18; 19; 20; 21; 22]; train weather-agnostic models by simulating various weather conditions and including that in the training loop [23; 24; 25; 26]; or train several models in an ensemble and leveraging a weighted approach to determine the final prediction [27; 28; 29; 30]. Moreover, in situations where an unsatisfactory amount of variation can be captured in the training data, continual-learning [16; 31] or domain-adaptation [15; 16] approaches are often leveraged in an attempt to obtain a consistent performance as the visual appearance of the context changes.
## 3 Results
### Box Plots of Project Time Interval and Area Extrapolations  
Time and area comparisons for NEE are provided in Figure 2 shown as box plots for the full record of no exit, 5 and 20 year exits, and for the CO\({}_{2}\) isotopocules for \({}^{ISO}\)NEE t\({}^{13}\)C\({}^{16}\)O\({}_{2}\) and \({}^{ISO}\)NEE t\({}^{12}\)C\({}^{18}\)O\({}^{16}\)O isoflaves. Figure 2A shows that the no exit 28-year project was more volatile considering values for first and third quartiles and outliers, than for a 20-year exit (median values: $79.5 and $21.5 ha\({}^{-1}\) year\({}^{-1}\) respectively, in a $10 tCO\({}_{2}\)e pricing scenario), and that carbon isotopocule isoflav products would command higher per-ton price levels than non-isotopic counterparts, in line with enhanced partition values for \(R_{eco}\) and R\({}_{\rm a}\). Figure 2B.  
Figure 2: Yearly financial CO\({}_{2}\) and \({}^{13}\)CO\({}_{2}\) (or \({}^{12}\)C\({}^{18}\)O\({}^{16}\)O) box plots with median (black line), first and third quartiles (box), 5 and 95 percentiles (whiskers) and outliers (circles) per ha (**A**), extrapolated to Prospect Hill area (300 ha; (**B**)) and extrapolated to 40,468 ha (100,000 acres; (**C**)), based solely on numerical projection to illustrate potential project revenue with increasing land area, in logarithmic scale. The red points are accumulated values over the whole period; blue points are mean accumulated values at the end of exited projects (5 or 20 years, respectively). Left and right \(y\)-axis show different pricing scenarios (left: a ton of CO\({}_{2}\) is worth $10 and a ton of \({}^{13}\)CO\({}_{2}\) (or \({}^{12}\)C\({}^{18}\)O\({}^{16}\)O) $50; right: a ton of CO\({}_{2}\) is worth $30 and a ton of \({}^{13}\)CO\({}_{2}\) (or \({}^{12}\)C\({}^{18}\)O\({}^{16}\)O) $150).  
Figure 2C shows the potential magnitude of pre-tax gross revenue for 300 and 40,468 ha, based solely on numerical projection to illustrate potential project revenue with increasing land area, $813 thousand and -$109 million, respectively, priced at $10 tCO\({}_{2}\). The financial values presented in Figure 2A-C and Figure 1D assumes that carbon storage is permanent, an assumption that does not apply to forests.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Data Collection and Preprocessing  
To fine-tune and evaluate the proposed real-time fall detection and response system, a comprehensive dataset was created using custom-recorded video sequences and voice interaction scenarios.  
1. Video Data Collection  
Video recordings were conducted using two cameras placed at different angles, positioned to capture the full body of participants in various household settings. The dual-camera setup allowed for multi-view analysis, reducing occlusions and improving pose estimation accuracy. Volunteers of diverse ages, genders, and physical characteristics were recruited to perform a series of predefined actions that simulate typical daily activities and potential fall scenarios, including the following:  
* _Intentional fall scenarios_: forward falls, backward falls, and side falls;
* _Fall-like actions_: sitting abruptly, stumbling, or bending to pick up objects;
* _Normal activities_: walking, standing, sitting, and lying down.  
Each video frame was annotated with the following:  
* Pose keypoints, including head, shoulders, elbows, hips, knees, and feet;
* Fall event labels, categorized as \"Fall detected,\" \"Sitting,\" \"Bent over,\" or \"Standing\";
* Additional annotations, such as movement types.
2. Voice Interaction Data  
Since the system also includes a voice-based assistant, interaction testing was integrated into the data collection workflow. This assistant is triggered after a detected fall event, prompting the user with a spoken message (e.g., \"Do you need help?\") and awaiting a verbal response.  
The system matches the transcribed text against these lists to determine the appropriate response:  
* If any help-related phrase is detected, an immediate alert is sent via the Telegram Bot API to notify caregivers;
* If a reassuring phrase is detected, the fall is logged as a non-critical event and no alert is sent;
* If no response is detected within a timeout (typically 15 s), or the confidence in recognition is low, the system assumes the user is incapacitated and sends an automated alert.  
The voice assistant uses a local speech-to-text (STT) and text-to-speech (TTS) pipeline, leveraging the following:  
* speech_recognition for converting speech to text;
* yyttsx3 for converting system prompts to speech.  
This entirely on-device local processing approach was chosen to preserve user privacy and reduce latency, ensuring a responsive system.
## 3 DQN-Based Solution
### State, Action and Reward Specification  
The state _s(t)_ is a vector that includes the following components for a given \(u\)-th UE:  
* Requirement of the \(u\)-th UE: \(R_{u}\).
* Spectral efficiency per RAT/cell \(\{S_{m,n}\}\) of the \(u\)-th UE.
* Current configuration \(\mathbf{B_{u}}=\{\beta_{m,n}\}\), which corresponds to the configuration applied at time \(t\)-1 to the \(u\)-th UE.
* Fraction of occupied bandwidth resources by the \(u\)-th UE in each RAT/cell \(\{\gamma(\beta_{m,n})\}\).
* Fraction of total occupied bandwidth resources in each RAT/cell \(\{\rho_{m,n}(\beta_{m,n})\}\).  
All the values \(S_{m,n}\), \(\gamma(\beta_{m,n})\) and \(\rho_{m,n}(\beta_{m,n})\) are average values measured during the last time window of duration \(\Delta T\), i.e., between discrete times \(t\)-1 and \(t\). Notice that the state has a total of \(1+4\cdot N\cdot M\) components.  
Each action _a(t)\(\in\mathcal{A}\)_ represents a matrix \(\mathbf{B_{u}}=\{\beta_{m,n}\}\) that corresponds to the MC configuration to be applied during the next time window \(\Delta T\). The action space \(\mathcal{A}\) includes all the possible MC configurations, and is defined considering that the possible \(\beta_{m,n}\) values are discretized with granularity \(\Delta\beta\) and that the aggregate of all \(\beta_{m,n}\) values in matrix \(\mathbf{B_{u}}\) equals 1. Moreover, the action space also considers that a UE can be connected to a maximum of \(N_{max}\) cells. Therefore, at most \(N_{max}\) values of \(\beta_{m,n}\) can be different from 0 in a certain action.  
The reward \(r(t+1)\) measures how good or bad was the performance obtained by the last action _a(t)_ for the state _s(t)_ in relation to the target and constraints of the optimization. Then, considering the optimization problem (2), and that the last action _a(t)_ is given by MC configuration \(\mathbf{B_{u}}=\{\beta_{m,n}\}\), the reward is defined as:  
\[r(t+1)=\left(1-\frac{1}{w_{max}}\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N} \gamma(\beta_{m,n})\right)\cdot min\left(1,\frac{T_{u}(\mathbf{B_{u}})}{R_{u}} \right)\cdot\prod\limits_{\begin{subarray}{c}m,n\\ p_{m,n}>0\end{subarray}}min\left(1,\frac{\rho_{max}}{\rho_{m,n}(\beta_{m,n})}\right) \tag{3}\]  
The first multiplicative term in \(r(t+1)\) captures the total bandwidth assigned to the \(u\)-th UE in all the cells/RATs, so the lower the amount of bandwidth assigned the higher the reward, and this reflects a better fulfilment of the optimization target in Equation (2). The second term multiplicative represents a penalty introduced when the achieved throughput \(T_{u}(\mathbf{B_{u}})\) is lower than the minimum requirement \(R_{u}\), corresponding to the first constraint in Equation (2). The last multiplicative term introduces a penalty for each cell/RAT in which the UE has transmitted traffic (i.e., \(\beta_{m,n}>0\)) and the cell is overloaded, thus capturing the second constraint in Equation (2). Note that the values of \(\gamma(\beta_{m,n})\), \(\rho_{m,n}(\beta_{m,n})\) and \(T_{u}(\mathbf{B_{u}})\) correspond to the averages obtained during the time window \(\Delta T\) between discrete times \(t\) and \(t+1\).