esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Patient satisfaction is an important humanistic testimony to determine the outcome, serves and sustainability of any health care system [1]. The evidences promulgate that satisfied patients uphold good relations with their health care providers and adhere to treatment that ultimately lead to better health outcomes. More satisfied patients are persistent in using health care services and values [2].  
Patient satisfaction is also a key indicator to comparing the quality of services in different patient care services, systems, and programs. These indicators are helpful for improving healthcare and for ensuring higher compliance [3]. It is also a vital tool to monitor the advancement andquality improvement in health care delivery systems [4]. Patient satisfaction studies are useful for drawing a baseline when launching new strategies [5]. An appraisal of patients' satisfaction is necessary to optimize resource utilization [6]. Many studies have been conducted worldwide to evaluate patient satisfaction toward community pharmacy services. As this indicator become pivotal marker in developed countries, interest in patient satisfaction assessment is growing in developing countries to analyze the services of community pharmacies [2,6]. Due to difference in performance of community pharmacies, the patients' need, perception and satisfaction level also varied in developing countries [5,6].  
Similarly, the structure and operating standards of community pharmacies are at an early transition state in Pakistan. About 80% of medicines are distributed to patients through these channels [7]. There is a shortage of community pharmacists [8]. In 2011, the International Pharmaceutical Federation (FIP) and World Health Organization (WHO) jointly recommended Good Pharmacy Practice (GPP) guidelines. Together, they advised all countries to endorse GPP in community pharmacies for patients' benefit by economical, rational, and quality use of medicines [9]. After the recommendations offIP and WHO, patient counseling services were started to some extent but GPP remained absolutely unnoticed; additionally, there was an urgent need for more patient-based services [10]. Doctors also have low regard for the quality of patient care in the pharmacies of Pakistan [11]. Patients are not aware about the role of pharmacists in the healthcare system [8]. Some institutional-based studies about the services of hospitals indicate that patients were not satisfied with their pharmacy services [12,13]. To the best of our knowledge, no evidence exists with regard to patient satisfaction with community pharmacy services in Pakistan. Moreover, no tool exists to analyze the patient views according current domestic situation. We designed this study to assess pharmacies services with regard to patient's need. This work signifies the first effort in the field to evaluate patient's satisfaction with practices and services. In addition, the current study serves as baseline data and evidence for the need to improve services and practices.
## 3 Results and Discussion
### Adsorption Kinetics and Equilibrium Models
The complexity of the adsorption mechanisms can also be observed from the results of the intra-particle diffusion model (Table 3). The model provided insights into the diffusion mechanisms but did not fully describe the adsorption process, as indicated by the lower R\({}^{2}\) values. The non-zero intercept 'C' obtained from this model suggests that surface adsorption and boundary layer diffusion may contribute to the overall rate of adsorption, yet they are not the sole controlling steps. This implies that MB adsorption on the studied biosorbents is a complex process, potentially involving multiple concurrent or sequential steps.
## 3 Numerical Solution: The Temperature Particles Method (TPM)
### Turbulence Modeling
In Figure 4, for every time stepping, \(4^{l}\) boxes are constructed to delimit the fluid domain containing the vorticity and heat, \(l\) being the box levels definition. In all test cases, \(l=8\) was adopted for the box levels. As a result of adopting \(l=8\), the fluid domain was divided into 16 boxes in the x-direction and 16 boxes in the y-direction, generating a total of 65,536 boxes. For the level 0, \(L_{x}\) and \(L_{y}\) define the lengths of each side; in consequence, the length of each box for a given level is defined by \(\Delta x=L_{x}/2^{l}\) and \(\Delta y=L_{y}/2^{l}\). It is important to note that for the level zero (\(i=1\) and \(j=1\)), it is required to sweep all vortex blobs to compute the eddy viscosity coefficient and the turbulent thermal diffusivity coefficient, which implies a higher computational cost. The present numerical strategy becomes efficient when searching the set of vortex blobs occupying a circular crown just in the neighboring boxes of a particle under analysis. As a criterion, the neighboring boxes are distanced approximately \(r_{ext}\) from the particle under analysis. For example, consider the computation of the local turbulent thermal diffusivity coefficient for a group of temperature particles instantaneously placed at box 2-3 when the level 2 is chosen (see also the region marked in grey in Figure 4). It is remarkable that the intended computation is efficiently accelerated, since only the contribution of vortex blobs placed in the region marked with grey is considered. This computation also depends on the solution of Equation (17) adapted for every temperature particle. The localization of the \(k\)-th particle from the origin, \((x_{0},y_{0})=(0,0)\), is related to the corresponding box in accordance with the following equations:  
\[i=\left(\frac{y_{k}-y_{0}}{\Delta y}\right)+1 \tag{19}\]  
\[j=\left(\frac{x_{k}-x_{0}}{\Delta x}\right)+1 \tag{20}\]
## 3 Development and Experimentation
Unlike laboratory-based or post-processed methods, our approach collects EM field data directly under real flight conditions, addressing dynamic environmental factors in situ--something not explored in the analyzed publications. The stability of the UAV during flight can influence the accuracy of the measurements. Turbulence or slight movements can cause variations in the recorded data. But also, in this case, it cannot impact just one channel. The drone itself can act as a physical obstruction, reflecting or blocking signals. This can lead to multipath propagation, where signals take multiple paths to reach the receiver, causing variations in the measured data. Drones flying close to the ground can exacerbate ground reflection effects. Signals bouncing off the ground can interfere with the direct signals, leading to inaccuracies in the measurements. The presence of a drone can alter the local environment, affecting factors like air pressure and temperature, which, in turn, can influence signal propagation.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 3 Preliminary
### System Model  
To further analyze the deployment challenges of emergency communication networks, it is essential to systematically quantify and model key environmental indicators. The core environmental factors affecting hub element deployment can be categorized into the following aspects.  
* Terrain. In this problem, the geographical environment is the core factor determining the feasibility of site selection for large mobile equipment. Therefore, terrain features need to be evaluated: areas with mobility advantages \(D\) such as roads, flat terrains, or grasslands are assigned a reward value of +1, while complex terrains or ecologically sensitive zones are penalized with \(-1\) through spatial constraint algorithms to improve deployment efficiency.  
Figure 1: Topological structure of emergency communication network.  
\[R_{1}=\begin{cases}1,&a_{t}\in D\\ -1,&a_{t}\
otin D\end{cases} \tag{1}\]
* Deployable range. As presented in Figure 2, the spatial distribution of elements must satisfy dual constraints, avoiding exceeding the maximum communication distance \(d_{max}\) while preventing signal interference caused by overly close proximity \(d_{min}\). To address this, a dynamic reward function is constructed using a Gaussian formulation. \[R_{2}=e^{-\frac{\left(d-\frac{d_{max}-d_{min}}{2}\right)^{2}}{2\epsilon_{1} ^{2}}}\] (2)
* Distance from optical cable access point. To minimize the potential damage to exposed optical cables from secondary disasters and ensure stable optical communication, the optical terminal unit should be deployed in close proximity to the optical cable access point. The corresponding reward function is formulated as follows: \[R_{3}=e^{-\frac{\mu^{2}}{2\epsilon_{2}^{2}}}\] (3)
* Microwave direction. By employing the free-space path loss model for microwave transmission, the transmission distance \(d\) between microwave transceiver sites is leveraged to compute the propagation loss using the electromagnetic attenuation formula. This loss metric is subsequently transformed into a dynamically adjustable reward signal, which guides the optimization of transceiver deployment positions. \[R_{4}=\alpha_{4}\frac{G_{t}G_{r}C^{2}}{16\pi^{2}d^{2}f^{2}}\] (4)
* Radio coverage. To ensure full communication coverage for rescue personnel at the disaster site, both 5G and cluster base stations should be deployed in a manner that maximizes their signal reach over the affected area. The rationality of their placement can be assessed by computing the Intersection over Union (IoU) between the communication coverage area of each base station and the disaster-affected region. \[R_{5}=\alpha_{5}\frac{|A_{com}\cap A_{dis}|}{|A_{com}\cup A_{dis}|}\] (5) The frequently used notations are listed in the Table 2:  
Figure 2: Diagram of distance range between different elements.
## 2 Materials and Methods
### Evaluation Metrics
The effectiveness of each ML model is measured using three common evaluation metrics: accuracy, F1-score, and area under the curve (AUC). Accuracy represents the percentage of correctly classified samples out of the total samples, providing a straightforward measure of overall model performance. The F1-score balances precision and recall, especially important when dealing with imbalanced classes often present in medical datasets, and AUC measures the ability of the model to distinguish between classes, with higher AUC values indicating better class separability. To evaluate the quality of our UMAP-based synthetic data generation algorithm, the obtained fidelity and utility metrics are compared with the analog measures for three widely used state-of-the-art methods: CTGAN, CopulaGAN, and TVAE. These methods are commonly applied in healthcare data generation [38; 39; 40] and provide effective benchmarks for assessing the performance of our algorithm. CTGAN [41], or Conditional Tabular GAN, uses a Generative Adversarial Network framework designed specifically for synthetic tabular data generation. It addresses several challenges in GAN-based data generation, such as mixed data types, complex multi-modal distributions, and class imbalance in categorical variables. CTGAN incorporates mode-specific normalization to capture patterns without mode collapse and employs a conditional generator to better represent minority classes in categorical variables, allowing it to closely reproduce real-world data distributions. This architecture includes separate pre-processing steps for continuous and discrete columns and uses fully connected networks in both the generator and discriminator, enabling CTGAN to handle intricate relationships among data variables. In contrast, CopulaGAN [42] combines GANs with copula functions [43], which are mathematical models used to capture dependencies between variables independently of their marginal distributions. By leveraging Gaussian copulas, CopulaGAN transforms data distributions into Gaussian-like forms, simplifying the learning process for complex tabular data. This approach allows CopulaGAN to better represent non-linear dependencies in datasets, making it particularly effective for applications requiring the preservation of inter-variable relationships. The TVAE, also referred to as a tabular variational autoencoder, firstly proposed in [41], extends the Variational Autoencoder (VAE) framework for tabular data. Unlike traditional autoencoders, which map input data to deterministic latent representations, VAEs use a probabilistic latent space, representing data as distributions rather than fixed points [15]. This enables sampling from the latent space to generate new data points consistent with the input distribution. The TVAE adapts this approach to handle the unique challenges of tabular data, such as mixed data types and feature dependencies.
## 4 Regulatory Mechanisms of Lily Floral Scent Components
### Other Factors  
In addition to light, circadian rhythm, and hormones, many other factors can influence the release of lily floral scent. For example, 2-(aminooxy) acetic acid (AOA), an inhibitor of phenylalanine ammonia-lyase, significantly reduces volatile emissions when sprayed continuously during the bud stage, without affecting flower opening [66]. The concentration of floral volatiles in liliies is altered by the nitrogen and potassium nutrients provided during cultivation. Nitrogen fertilization increases the concentration of terpenoids such as linalool, while low potassium levels increase the concentration of methyl benzoate [66]. Researchers have also found more refined regulatory mechanisms in lily floral scent formation. Increased light intensity triggers Ca\({}^{2+}\) influx into the cytoplasm, activating the downstream gene expression of monoterpene synthases, thereby regulating the biosynthesis and release of monoterpenoids [67]. Hexokinase (LoHXK) and fructkinase (LoFRK) in liliies significantly alter the release of \(\beta\)-ocimene and linalool by regulating the expression of the volatile synthesis genes _LoTPS1_ and _LoTPS3_[68]. Additionally, temperature is a crucial factor affecting the synthesis and release of volatiles [69]. At 30 \({}^{\circ}\)C, the release of terpenoids, alcohols, aldehydes, ketones, and esters in _Lilium_ 'Siberia' reaches its maximum, while the release of alkanes is minimal [62].
## 1 Introduction
[24] combined the proportional-derivative type iterative learning controller (PD-ILC) and the adaptive sliding mode controller (ASMC) and applied it to a two-degree-of-freedom (2-DOF) rotary pendulum, which improved the system's convergence speed and tracking performance. Based on the above research and considering the particularity of FTS system processing, we introduced exponential convergence iterative learning control based on adaptive sliding mode control to compensate for the impact of periodic disturbances such as cutting force on the FTS system [25]. In this manuscript, we developed the iterative learning with adaptive sliding mode control for fast tool servo systems to solve the impact of disturbance factors such as model uncertainty, cutting force, mechanical vibration, and temperature changes on the tracking accuracy of the fast tool servo system during the machining process of the fast tool servo system. In particular, ASMC overcomes the chattering problem through adaptive switching gain based on tracking error, adjusts its switching gain online, and resists uncertainty in model parameters and sudden external disturbance. ILC solves the impact of periodic disturbance such as spindle vibration and cutting force during the periodic machining process of the fast tool servo system, and improves the convergence speed and tracking accuracy of the fast tool servo system. According to the real-time experimental results on the FTS prototype, the developed control method demonstrated has a good suppression effect on disturbance and it realizes the optimization of the tracking motion of the fast tool servo system in a complex processing environment. The main contribution of the paper can be succinctly summarized as follows:  
1. An improved adaptive sliding mode control is proposed to adjust the control gain online improved system anti-disturbance and robustness. 2. The exponential convergence iterative learning control is employed to inhibit the periodic disturbance. Combined with the adaptive sliding mode control, the optimization of the tracking motion of the fast tool servo system in complex processing environments is achieved. The remainder of this article is organized as follows. Section 2 introduces the mathematical modeling of the piezoelectric-driven FTS system. Section 3 proposes adaptive SMC for non-periodic disturbances such as uncertainty in the FTS system. Section 4 designs a PD-type ILC to compensate for the periodic disturbance of the FTS system. Section 5 presents real-time experiments conducted to verify the performance of the proposed control structure. Section 6 gives some conclusions.
###### Abstract  
Existing seismic evaluations of electrical equipment in substations mainly focus on post-type equipment, with few studies addressing the suspended equipment that exhibits significant geometric nonlinearity. Most of the vulnerability analyses on substation equipment consider only ground motion uncertainty, not processing other uncertainties including material properties. Thus, this paper investigates the seismic responses of an \(\pm\)800 kV suspended filter capacitor using a simulation model. A new approach for vulnerability assessment based on an adaptive boosting (AdaBoost) regressor is proposed considering the uncertainties of multiple material parameters of the suspension insulators. It is applied to the filter capacitor and validated by conventional incremental dynamic analysis (IDA). In addition, the influence of the pre-tension force of the bottommost suspension insulators is also discussed. The results indicate that increasing the pre-tension force can avoid pressure generation in the insulators and reduce the maximum displacements of the filter capacitor. The failure probability will significantly increase when the pre-tension force increases from 20 kN, although the growth rate continues to fall. The established AdaBoost regressors substantially lower the calculational cost while maintaining an accurate vulnerability prediction, compared to IDA. The proposed method is endorsed due to its high accuracy and low calculation cost, although its feasibility is validated by only one suspended filter capacitor in this paper.  
seismic response; seismic vulnerability; suspended filter capacitor; geometric nonlinearity; material uncertainty; adaptive boosting regressor
# Analysis and Suppression of Self-Excited Oscillations in Pressure Servo Valve System
## 4 Oscillation Suppression of PSVS
### Verification of Oscillation Suppression Method  
#### 4.2.1 Simulation Analysis  
When a step and ramp current signal of 40 mA is given, the output pressure of PSVS is shown in Figure 18.  
It can be seen that, compared with the prototype system, the output pressure of the PSVS does not oscillate under either the step signal or the ramp signal input, which means that the self-excited oscillation of PSVS is effectively eliminated.
## 5 Results and Discussion
On the other hand, as \(N\) increases, the total energy dissipated increases, because the total energy dissipated is linearly proportional to \(N\). Nevertheless, the two proposed ENO schemes show significantly lower dissipated energy levels. Figure 5: (a) Average achievable rate in cluster and (b) total energy dissipated in network vs. number of clusters (\(K\)) when \(N\) = 300 and \(W\) = 30 m.  
Figure 6: (a) Average achievable rate in cluster and (b) total energy dissipated in network vs. number of nodes in network (\(N\)) when \(K\) = 10 and \(W\) = 30 m.  
Figure 7 shows the average achievable rate in cluster and the total energy dissipated in network versus the width of the square network (\(W\)) when \(N\) = 300 and \(K\) = 10. As the network area increases, the average achievable rate decreases because the average distance between nodes increases. On the other hand, the total energy dissipation decreases as the network area increases because the amount of energy harvested in all of the nodes becomes smaller. Likewise, the proposed ENO schemes show better performances than the conventional schemes according to the change in network size.
## 4 Results Analysis
### Land-Use Rising/Falling Tupu Analysis
#### 4.3.1 Land-Use Rising Tupu
Table 7 and Figure 4b further demonstrate that during the period from 2010 to 2020, the area affected by land-use changes (LUCs) in Putian City was less than that of the preceding period from 2000 to 2010, amounting to a total of 168.88 km\({}^{2}\). The BL saw the most significant increase, with a new addition of 90.04 km\({}^{2}\). This expansion was predominantly a result of the rapid urbanization and industrialization processes, which led to the conversion of CL, FL, and GL into BL areas due to the pressures of development. The increase in CL was 23.82 km\({}^{2}\), which constituted 14.10% of the total area that experienced an increase. In contrast, there was a decrease in the area of newly added FL and GL by 2.64 km\({}^{2}\) and 2.90 km\({}^{2}\), respectively, when compared to the earlier period from 2000 to 2010. Figure 4: Land-use rising Tupu in Putian City from 2000 to 2020. Sources: Drawings from this study. This suggests a trend of land-use conversion from these natural land types to other uses, particularly towards urban and industrial development. When examining the data from a district and county perspective, Xianyou County had the largest expansion in CL, with a new increase of 12.61 km\({}^{2}\), followed closely by Hanjiang District with an additional 5.08 km\({}^{2}\). Across all districts and counties, there was a notable decrease in the area of newly increased BL when compared to the period from 2000 to 2010, with Licheng District recording the most significant reduction of 16.20 km\({}^{2}\). Furthermore, with the exception of Licheng District and Xiuyu District, the area of newly increased FL saw a decline in all other districts and counties relative to the previous period, with Xianyou County experiencing the most substantial decrease. The newly added OL was predominantly situated in Xianyou County (2.15 km\({}^{2}\)), followed by Xiuyu District (0.63 km\({}^{2}\)), Chengxiang District (0.60 km\({}^{2}\)), and Licheng District (0.01 km\({}^{2}\)).
# On the Octonion Cross Wigner Distribution of 3-D Signals
## 6 OWD and Analytic Signals
### Octonion Wigner-Ville Distribution  
One of the important trends in research using Wigner distributions is considering the distribution of analytic signal associated with a real-valued function, i.e., signal with the single-orthant spectrum obtained by canceling the part at negative frequencies and doubling the part at positive frequencies [6]. The distributions of such functions are commonly called Wigner-Ville distributions [55].  
The symmetry property mentioned in the previous section plays a vital role in these considerations. The research conducted both on the subject of Wigner distributions and analytic signals [26] raised hopes that similar properties would also be possible in the case of hypercomplex setup. Furthermore, while it is, of course, possible to define the Wigner-Ville octonion distribution of a real-valued signal as the Wigner distribution of the related octonion analytic signal, unfortunately, the lack of relation (5) means that these considerations do not lead to too optimistic conclusions. This remains within the scope of future work when (perhaps) other properties of the OFT and OWD become known.