esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Study
#### 3.2.1 Impact of MB Initial Concentration (C\({}_{0}\)) and Equilibrium Time  
The impact of C\({}_{0}\) (varied from 5-40 mg/L) on percentage removal was investigated at 25 \({}^{\circ}\)C, with a pH of 8 and an nZVISLP dosage of 1.2 g/L. The effect of different C\({}_{0}\) values of MB uptake obeyed an inverse exponential form (see Figure 5a), i.e., rapid in the initial stages and steady in the later stages. This signifies the possibility of monolayer adsorption taking place on the surface of nZVISLP. The increasing trend in MB removal continued until it reached an equilibrium state (\(\sim\)180 min). This may have been due to the fact that the vacant sites on the surface of nZVISLP were only available in the early stages of the adsorption process. Typically, 80% of the adsorbate (with C\({}_{0}\) = 10 mg/L) was removed within 30 min of the adsorption process (see Figure 5a), and the process then proceeded at a slow pace, reaching a maximum removal of 91% in 300 min. However, an increase in C\({}_{0}\) resulted in a decrease in removal concentration. Upon increasing dye concentration, the adsorption site became saturated, which limited the removal of MB [36]. As a result, q\({}_{t}\) increased from 8.20 \(\pm\) 0.01 to 32.6 \(\pm\) 0.02 mg/g with a rise in C\({}_{0}\) from 10 mg/L to 40 mg/L. The decrease in q\({}_{t}\) with the increase in C\({}_{0}\) potentially occurred as a result of the initial concentration providing the driving force to overcome the mass transfer resistance [37].  
Figure 5: Influence of various parameters on MB removal: (**a**) C\({}_{0}\); (**b**) pH; (**c**) adsorbent dosage; (**d**) process temperature.
## 2 Related Work
### Domain Transferability Estimation (DTE)  
DTE is an unsupervised source selection technique that iterates through domains to identify more-beneficial domains and, at the same time, discards less-beneficial domains by evaluating the transferability across multiple domains [10].  
\[\mathbb{S}_{i}=\left\{X_{S}^{(i)},y_{S}^{(i)}\right\}_{i}^{\mathcal{Z}}=1 \tag{2}\]  
In this case, Equation (2) represents the number of labelled source domains depicted by \(\mathbb{S}_{i}\), with the label vector being denoted by \(\mathbb{y}_{S}^{(i)}\), while the feature matrix acquired from the source domains is denoted by \(X_{S}^{(i)}\). Moreover, each individual-source domain is depicted by \(i\), while the overall number of domains is denoted by \(\mathcal{Z}\)[10].  
\[DIS(\mathbb{S}_{i})=\left\|S_{b}^{\mathbb{S}_{i}}\right\|_{1} \tag{3}\]  
The discriminability of individual-source domains is denoted by \(DIS(\mathbb{S}_{i})\) and computed utilizing Equation (3), with \(\mathbb{S}_{i}\) depicting the number of labelled source domains and the between-class scatter matrix being denoted by \(S_{\text{b}}\).  
\[DIF(\mathbb{S}_{i},\mathbb{T})=\left(\left\|S_{\text{b}}^{\mathbb{S}_{i}, \mathbb{T}}\right\|_{1}\right) \tag{4}\]  
Variations across the source domains and the target domain are computed using Equation (4) and represented by \(DIF(\mathbb{S}_{i},\mathbb{T})\), whereby \(S_{\text{b}}\) represents the between-class scatter matrix, \(\mathbb{S}_{i}\) represents the labelled source domains, and the unlabelled feature matrix is denoted by \(\mathbb{T}\)[10].  
\[r(\mathbb{S}_{i},\mathbb{T})=\frac{DIS(\mathbb{S}_{i})}{DIF(\mathbb{S}_{i}, \mathbb{T})} \tag{5}\]  
The discriminability of individual-source domains depicted by Equation (3) and the differences between the source domains and the target domain depicted using Equation (4) are utilized to form Equation (5). In this instance, Equation (5) is utilized to compute the transferability of the source domains, denoted by \(r(\mathbb{S}_{i},\mathbb{T})\), with \(DIF(\mathbb{S}_{i},\mathbb{T})\) representing the variation between the source and target domains, while \(DIS(\mathbb{S}_{i})\) represents the discriminability of the \(i\)-_th_ SD, \(\mathbb{T}\) represents the target domain, and the labelled source domains are denoted by \(\mathbb{S}_{i}\)[10].
## 1 Introduction  
The human population is growing at an alarming rate. This rapidly growing population has resulted in new health problems due to various factors such as demographic aging, rapid urbanization, and the spread of unhealthy lifestyles. According to the World Health Organization (WHO), non-communicable diseases, such as cardiovascular disease, cancer, diabetes and chronic lung diseases,have overtaken infectious diseases as the world's leading cause of mortality [1]. Innovations in the health-care industry are continuously emerging to monitor, treat and reduce the death and disability caused by such non-communicable diseases. With the advent of nano-technology, medical sensors and devices are becoming highly miniaturized [2]. The size of the sensor nodes is also being reduced, which allows them to be implanted inside the body using minimal invasive surgery. The advantage of being closer to the cause of a disease will increase the understanding of the pathological symptoms of the disease [3]. In general, the advantages of miniaturized implantable sensors over external sensors include (i) their accurate diagnosis of a symptom; (ii) being unobtrusive compared to the body-worn sensors; and (iii) having minimal impact on the quality-of-life of a patient. While some implantable medical devices, such as pace-makers, are known already for more than five decades, many new implantable devices, such as drug-delivery devices, neural- and deep-brain simulators have only been introduced in the last two decades. It is important to note that the traditional implantable devices had neither complex computational operations nor the demanding RF-communication capabilities. These additional functionalities of the advanced medical implants can cause bio-compatibility issues such as tissue burns due to thermal overloading [4]. Also, usual bio-compatibility problems, such as fibrous encapsulation, and calcification of implants can have a negative impact on the performance of advanced implantable devices [4]. Similarly, continuous exposure of tissues to radio waves can cause surface heating of the tissues around the implant [5]. Although there are different biochemical ways to overcome the bio-compatibility problems [4], it is crucial to handle the problems with engineering solutions. For example, multi-sensor arrays can be used to reduce the measurement errors caused by bio-fouling of sensor nodes [6]. These methods not only improve the accuracy of the measurements, but also eliminate the need of complex processing of the data; doing so will prevent over-heating of the sensor nodes. While, traditionally, many implantable medical devices have operated in isolation, recently some attempts have been made to connect them and form a network of implantable medical devices and sensors, the so-called IBSN. An IBSN is very different from the network of body-worn sensor nodes because of its special characteristics and requirements. Table 1 shows the typical differences between IBSN and BSN.
## 4 Numerical Results and Discussion
### Bottom Hole Pressure  
Bottom hole pressure is one of the most important indexes in oil and gas extraction. With the injection of fracturing fluid, the bottom hole pressure rises approximately linearly until it reaches the extreme point, which represents the breakdown pressure. A new fracture surface is formed after fracture initiation, and fracturing fluid fills the new fracture space, which results in a temporary decrease in bottom hole pressure. When the cohesive element at the fracture tip is completely damaged, the fracture propagates forward, and the pressure reaches a second peak. With the increase in fracture length, the fracturing fluid  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\sigma_{t}\)**(MPa)** & \(\sigma_{c}\)**(MPa)** & \
\begin{tabular}{c} **Length of Main** \\ **Fractures (m)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum Aperture** \\ **(10\({}^{-3}\) m)** \\ \end{tabular} \\ \hline
3 & 1.0 & 40.4 & 8.442 \\
4 & 1.0 & 38.6 & 8.601 \\
5 & 1.0 & 37.9 & 8.900 \\
6 & 1.0 & 37.7 & 8.815 \\
7 & 1.0 & 35.5 & 8.896 \\
8 & 1.0 & 36.2 & 9.242 \\
9 & 1.0 & 35.1 & 9.418 \\
3 & 0.5 & 41.1 & 8.265 \\
3 & 1.5 & 39.6 & 8.532 \\
3 & 2.0 & 39.1 & 8.682 \\
3 & 2.5 & 37.3 & 8.735 \\
3 & 3.0 & 35.5 & 8.808 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fracture parameters under different tensile strengths of rock and cementing strengths of natural fractures.  
Figure 19: Fracture length under different cementing strengths of a natural fracture.  
leak-off into the rock increases, and the pressure at the bottom of the well remains stable, finally. Bottom hole pressure curves under different in situ stress differences are shown in Figure 20. With the increase in in situ stress difference, the breakdown pressure decreased. Figures 21 and 22 are bottom hole pressure curves under different rock tensile strengths and natural fracture cementing strengths, respectively. As can be seen from Figure 21, when the tensile strength of rock was 9 MPa, the initiation pressure was obviously higher than that of 6 MPa and 3 MPa. This is mainly because the greater the tensile strength of rock, the more energy is needed for fracturing the rock. As shown in Figure 22, the breakdown pressure did not change much when the cementing strength of natural cracks changed, because hydraulic fracture initiates in the rock and the breakdown pressure mainly depends on the mechanical parameters of reservoir.  
Figure 21: The bottom hole pressure under different tensile strengths of rock.  
Figure 20: The bottom hole pressure under different in situ horizontal stresses.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)
### Time-Domain Methods  
The time-domain HRV parameter extraction methods comprise the geometrical and statistical methods [6]. The statistical methods provide the parameters derived either explicitly from the RR intervals or indirectly after the alteration in the length of the RR intervals [29]. Parameters like standard deviation (SD) of the heart rate (HR SD), average heart rate, mean of the NN intervals (or RR intervals), and standard deviation of the NN intervals (SD NN) can be attained straight from the RR intervals. In this instance, the constraints HR SD and SD NN reveal details related to the dispersion of the HR and NN intermission values from their respective mean values. Conversely, the statistical constraints RMSSD, NN50, and pNN50 offer additional material about the high-frequency aberration in the HR. These parameters are based on the variances amid the RR intervals. The root-mean-square standard deviation, or RMSSD, is the average variation in the intermission between beats and is represented by the square root of the consecutive variance involving the RR intervals. The value of NN50 reveals data regarding the number of successive NN interval differences with more than 50 milliseconds. The value of the parameter pNN50 can be calculated by dividing NN50 by the overall number of NN intervals that are longer than 50 milliseconds. TINN, which stands for \"triangular interpolation of NN interval histogram,\" and the HRV triangular index, are examples of time-domain geometrical parameters. TINN denotes the reference size of the RRI (or NN intermission) histogram when it is calculated using triangular interpolation [30]. The HRV triangular index is obtained from the RRI histogram (Figure 2) by computing the proportion of the entire amount of RRIs to the stature of the RRI histogram [6].
## 5 Statistical Algorithm for S Turns  
From Criterion 3 and the concept of S turns, it can be seen that \(S\) is actually a mark set for S turn maneuvers in which the change in yaw angle is less than 360\({}^{\circ}\) (or \((360-c)^{\circ}\) when considering the threshold). Similar to set \(H\), if the number of elements in set \(S\) is \(N_{S}^{0}\), then the aircraft takes S turns \(N_{S}^{0}\) times near the median \(\overline{\Psi}_{0}\) of \(\Psi_{0}\), where the superscript or subscript \"0\" represents the S turn number obtained from the original signal \(\Psi_{0}\).  
However, in addition to S turns near \(\overline{\Psi}_{0}\), an aircraft also turns in an S shape near other yaw angles, as shown in Figure 1a, where the aircraft performs an S turn near a yaw angle of 50\({}^{\circ}\). Thus, statistical algorithms for the S turns of an aircraft near other angles rather than \(\overline{\Psi}_{0}\) will be studied in this section. The general thinking is that, with the help of PSD splitting the signal into halves, the original signal \(\Psi_{0}\) is divided into several layers; then, the S turns of each layer can be are cyclically counted, and at the end, statistical algorithms for S turns of an aircraft in a flight profile were developed.
## 1 Introduction
Very often, it relies on RTK receivers [12] to compensate for LiDAR locations in obtaining detailed point clouds. It is necessary to assume that the position deviation affects the distortion of the resulting point cloud objects composed of frames and, thus, limits the classification capability. LiDAR imaging is very fast and accurate. However, the limited position correctness of LiDAR for compensation during frame merging under large robot accelerations (e.g., UAV) requires very smooth and probably slow robot control. RTK receivers to guide the tractor, possibly supplemented by the collection of data on land or crops, were described, for example, in references [4; 13]; they make very efficient use of satellite signal corrections for autonomous tractor guidance and compare them with conventional correction reception. Cooperation between robots and humans can be defined at four points from the supervision of routine tasks, where robots perform a limited series of actions, to remote-controlled machines for dealing with unpredictable situations where a person is in the position of a passenger or supervisor [14]. The multi-robot tractor system in reference [15] relied on the coordinated guidance of the tractor fleet to the position obtained from the RTK receiver. In Farm Management Information Systems (FMIS), the RTK method can help increase the precision of tracking agricultural machines connected with other systems and, as a result, optimise the process chains, build smart farming systems and network various sets of data from diverse sources [16]. For most of the above applications, as well as countless others, a positioning accuracy equivalent to the accuracy of the acquired model or the precision of the intervention was required. All the above-mentioned studies used RTK receivers to obtain position information, but they assumed an error of its measurement almost always in the range from 0.01 m to 0.03 m.
## 5 Tracking Experiment  
In this paper, a DeepSORT tracker based on the YOLOv11-ECA detector is added to implement a multi-target tracking system. The detector improves the small target detection accuracy by a parameter configuration of 640 \(\times\) 640 input resolution, 0.4 confidence threshold and 0.5 NMS IoU threshold combined with a dynamic adaptive anchor frame. The tracker uses Kalman filtering with the Mars-smallCNN feature extraction model (128-dimensional features) and sets the maximum track survival time to 30 frames (max_age) and the track confirmation threshold to 5 frames (n_init) to cope with the occlusion problem. The experiments were run on NVIDIA RTX 3060 GPUs, and the system performance was verified using a 1920 \(\times\) 1080 resolution Yoga Action dataset. The system achieves 58.3% MOTA vs. 62.1% IDF1 in the test, with a bounding box positioning error (MOTP) of 0.213 and a real-time processing speed of 45.7 FPS. As shown in Figure 7, the target ID and bounding box remain stable in the regular scene, but there is a 12% drop in MOTA in the extremely dense region (target spacing \(<\) 10 pixels). Experiments show that the dynamic anchor frame design improves the IDF1 of 20 px spaced targets by more than 60%, while the frequency of ID switching due to occlusion increases by a factor of 2.3 in dense regions. In the future, it is proposed to optimize the feature matching module through the spatio-temporal attention mechanism to improve the complex occlusion processing capability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experimental** & **Model Structure** & **mAP** & **FPS** & **Params/FLOPs** \\ \hline Group 1 & Original model YOLOv11 (SE & 0.977 & 85.99 & 2.59 M/3.21 G \\ Group 2 & Improved model YOLOv11 (ECA & 0.986 & 85.79 & 2.35 M/3.11 G \\ Group 3 & Removal of the PSA module & 0.979 & 89.88 & 2.34 M/3.11 G \\ Group 4 & Remove space and channel & 0.977 & 70.41 & 2.59 M/3.11 G \\ \hline \hline \end{tabular}
\end{table}
Table 5: Masking experiment results.
## 1 Introduction
### Research Gaps Identified  
* From the research in the Indian context, using lithium-lon (Li-Ion), lead-acid (LA), and nickel-iron (Ni-Fe) batteries for the off-grid microgrids has not been thoroughly explored. To gain an understanding of the advantages of these technologies in off-grid microgrids, further research is necessary. This should include an analysis of cost effectiveness, reliability, long-term performance, environmental impact, and a cost-benefit assessment. Additionally, safety, usability, scalability, and potential integration with other energy sources should also be considered;
* Further, the majority of studies carried out a techno-economic feasibility analysis with one or two types of battery technologies to provide a continuous power supply. This often fails to take into account the unique requirements and characteristics of different battery technologies. Given that lithium-ion (Li-Ion), lead-acid (LA), and nickel-iron (Ni-Fe) each have distinct advantages and disadvantages, it is important to explore the potential for utilizing these technologies for the off-grid microgrids in order to create an optimal balance between cost, performance, and reliability. Furthermore, there is a need for further research into the design of these combinations and their potential for scalability and integration with other energy sources;
* Further research can be conducted to compare the proposed algorithm with other algorithms in terms of robustness, efficiency, and scalability. Such studies would help identify the best algorithms for different conditions, such as complexity, grid constraints, load profiles, and cost considerations. Additionally, other approaches, such as meta-heuristics and optimization techniques, could also be applied to the off-grid microgrid-sizing problem. This would enable a more comprehensive comparison between various algorithms in terms of performance and implementation;
* Demand side management (DSM) strategies have the potential to play a major role in achieving better energy balance and overall efficiency of the off-grid microgrids. However, there is still a need for more studies to explore the potential benefits of different DSM approaches for off-grid microgrids. Research should focus on the assessment of individual DSM techniques, such as time-of-use pricing, demand-response programs, dynamic pricing, and the potential for their combinations with other distributed energy resources, such as solar PV, storage, and wind turbines. Further, there is an urgent need to investigate the potential of energy-conservation DSM strategies for off-grid microgrids, such as energy-efficiency measures, demand management, and load shifting. Such research could help to identify cost-effective approaches that can improve the operational efficiency and overall economic viability of the off-grid microgrids.
## 4 Flexible Route Planning Method Combining Q-learning and Graph Search
### Combination of \(A^{*}\) and \(Q\)-learning  
#### 4.2.1 A* Search Algorithm  
A* algorithm is an algorithm for the graph search problem of finding a path from an initial position to a destination on a graph, using a heuristic function \(h(n)\), which serves as a guidepost for the search. \(h\) is a function that returns some reasonable estimate of the distance from each node \(n\) to the destination and is solved by various \(h\) that can be designed depending on the type of graph search problem. In this paper, the heuristic function is defined as the Manhattan distance between the current position and the destination position. The evaluation value \(f\) is the sum of the distance \(g\) from the initial position to the current point and the heuristic value \(h\). The priority was determined at random if the evaluation values matched at several nodes.
# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 2 Related Work  
Non-verbal behaviors, such as eye and head movements, are essential components of human communication, providing insights into emotions, personality, and mental state [4; 13]. Eye blink rate (EBR), in particular, has been widely studied for its connection to cognitive functioning. Spontaneous EBR varies with cognitive activities, increasing during tasks like speaking, memorizing, or emotional expression, and decreasing during visual tracking or reading [14; 15; 16; 17]. These changes are linked to brain activity and are influenced by factors such as aging, environmental conditions, and cognitive health [18; 19]. Clinically, EBR has shown promise as a biomarker for cognitive conditions, including dementia and mild cognitive impairment (MCI). For instance, studies have found that individuals with MCI exhibit a higher EBR than individuals without MCI, and further increases may signal progression to dementia [5; 20]. Similarly, reduced EBR has been observed in [PERSON]'s disease, which can also lead to cognitive decline [21]. In addition to EBR, head movements have been investigated as potential indicators of cognitive impairment. Several studies have shown that people with MCI or dementia tend to exhibit an increase in head turns, which may reflect changes in attention or spatial awareness [6; 22]. These findings highlight the potential of EBR and head movements as non-verbal cues for the early detection and monitoring of cognitive disorders.  
As interest in automated methods for early dementia detection grows, researchers have investigated visual hand-crafted features (HCFs) to enhance diagnostic accuracy [7; 8; 10; 12; 23; 24; 25]. However, research on dementia detection using visual cues remains limited, with only a few studies exploring features such as smile expressions [7; 10], facial action units, and eye gaze patterns [8]. Advanced techniques, including neural networks (NNs), have also been employed to enhance the accuracy of dementia detection [11; 26]. While previous work has predominantly relied on data recorded in controlled laboratory environments, our study utilizes in-the-wild data, which better reflect real-world variability and challenges. Only a few techniques, such as HCFs and NNs, have been explored for dementia detection in previous studies [7; 8; 10; 12; 23; 24; 25]. In this work, we focus on HCFs (specifically EBR and HM) as they are based on clinically validated cues and do not require large amounts of training data, unlike NNs. These features are automatically extracted to evaluate the system's performance in distinguishing between NDs, MCI, FMD, and HCs. This approach aims to provide a more accessible and objective tool for early diagnosis, addressing the limitations of traditional methods that rely on controlled settings and subjective assessments.
## 5 Experiment  
In this section, we present a comprehensive evaluation of the performance of our proposed DeepChaos+ framework in removing chaos from simulated original signals in long-haul WDM optical communication systems utilizing the DP-16 QAM modulation scheme. We conduct experiments under various settings to assess the effectiveness of DeepChaos+. We begin by describing the experiment setup, which includes the selection of hyperparameters, dataset description, and comparison methods. Hyperparameters such as the learning rate, batch size, and network architecture are carefully chosen to ensure the optimal performance of DeepChaos+. Additionally, we compare the performance of DeepChaos+ with traditional methods to establish its superiority.