esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Estimation of Logistic Models with Banking Data Using Particle Swarm Optimization
## 8 New Logistic Model  
In the section on logistic model reviews (Section 2), it is known what all logistic models have in common, e.g., that the growth rate \(r\) is a constant, except that only the [PERSON] and [PERSON] generalization model has a time-varying-growth rate as \(r(t)=a_{1}+a_{2}t+a_{3}t^{2}\).Now, we propose a new logistic model by generalizing the Tsoularis model with time-varying-growth rate \(r(t)=a_{1}+a_{2}t+a_{3}t^{2}\) as follows:  
\[\frac{\mathrm{d}y}{\mathrm{d}t}=(a_{1}+a_{2}t+a_{3}t^{2})y^{\beta}\Big{(}1- \Big{[}\frac{y}{K}\Big{]}^{\alpha}\Big{)}^{\gamma}. \tag{23}\]  
This model cannot be solved analytically. Consequently, numerical methods are required to estimate the parameters of the model. In this instance, we employ the Runge-Kutta method. We compare it with the outcomes from the Tsoularis model for commercial banks' data and the Verhulst model for rural banks' data. We have estimated all seven parameters of the new model (23), but we have that the result is superior compared to the benchmark models. The problem may arise from the new model having an increased number of parameters, and the nonautonomous nonlinear differential equation complicates precise numerical solutions. Therefore, we made a strategy, as follows. We use the obtained value of the Tsoularis model's parameters \(\alpha\), \(\beta\), \(\gamma\), and \(K\) from Tables 3 and 4, and we only estimate the parameters \(a_{1}\), \(a_{2}\) and \(a_{3}\). The parameter estimations utilize the optimal PSO parameters derived from the Taguchi method in the preceding section. The outcome is displayed in Table 7. When compared to the results of the Tsoularis and Verhulst models, the newly proposed logistic model produces less MAPE training but higher MAPE validation. The new model demonstrates improved accuracy in capturing model fitting; however, it is unable to predict subsequent data for validation purposes.
## 2 Topics and Results
### Changes in the Endothelial Cells of the Vascular Walls
The peptide vascular vasoconstritor known as endothelin (ET) was discovered in 1985 [93]. After its discovery, [PERSON] et al. (1988) [94] concluded that this cellular factor comprises of 21 amino acids arranged in a strictly defined sequence. Another group of authors proved that ET contains three isoforms--Endothelin-1 (ET-1), endothelin-2 (ET-2) and endothelin-3 (ET-3), which are encoded by three different genes. ET-1 is responsible for cardiovascular changes [95] and is secreted by cardiac muscle cells (cardiomyocytes), cardiac endothelial cells and cardiac fibroblasts [96]. Regarding the fact that physical activity changes blood flow to different organs (active or inactive), this physical activity is considered the most significant factor for the secretion of these isoforms (hormones). In the study by [PERSON] et al. (1994) [97] monitored the plasma concentration of ET-1 in young athletic men after exercise on an ergometric bicycle (a 30-min session). Based on the results of this study, it has been shown that the plasma concentration of ET-1 increases after physical training. The experimental evaluation in seven patients with cardiovascular syndrome by [PERSON] et al. (2012) [98] also showed that after a 6-min walk, left ventricular volume and ET-1 levels increased. Author collective [PERSON] et al. (2012) [99] found that after 3 months of aerobic exercise in elderly women, blood pressure and ET-1 level normalized. The authors report a positive training effect for problems with the cardiovascular system. Moreover, [PERSON] et al. (2004) [100] investigated the effects of endurance training on plasma ET-1 levels in young men aged 25-27 years. Participants were enrolled in an endurance training program for 8 weeks and 3 days. The results show that despite the lack of difference between blood pressure, heart rate and maximal oxygen consumption, the plasma level of ET-1 decreases significantly. [PERSON] et al. (1996) [101] found that plasma ET-1 levels did not change after a 15-min cycle ergometer exercise (of moderate intensity) in young athletes. Based on the results of [PERSON] et al. (1998) [102] suggested that 8 weeks of aerobic exercise had no significant effect on ET-1 levels in patients with cardiovascular problems. Due to the conflicting results of the above studies, it can be summarized that more in-depth future research is needed in this direction. In a study by [PERSON] et al. (2015) [90] the authors compared the effect of eight weeks of moderate aerobic exercise on two groups of participants: a control group of healthy individuals and a group of patients with coronary artery disease. The same author team proved that there were no statistically significant changes in the plasma levels of ET-1 in both studied groups. The researchers concluded that it is likely that changes in plasma ET-1 level occur when exercise exceeds a certain threshold of intensity and duration of impact. These threshold levels probably also depend on many individual factors, different for each participant in the experiment.
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 5 Discussion  
In this section, additional details are discussed.  
There is some evidence that downsizing the image causes some loss of detail, especially in the most distant areas of the scene. This results in the worse coding or reconstruction of the relevant elements and somewhat lower correlation coefficients. However, the higher inference rate achieved compensates for this deterioration, allowing small continuous corrections to be made to the steering angle. This provides a better overall driving result.  
Including the semantic segmentation phase in an end-to-end model provides several advantages. The results show that a more robust model is obtained, as it can remove redundant elements from the scene. It also provides a simple mechanism to deliver an interpretation of the scene. For a given driving scene, the full Autoencoder can be used to obtain the semantic segmentation performed by the encoder. This facilitates the detection of causes of a driving failure, which is not so easy in end-to-end models.  
A further advantage of the proposed model concerns the decomposition of the learning task into two tasks: feature extraction and driving learning. The development of the system is more flexible, making it possible to approach the work incrementally and reduce the  
Figure 18: Different input images and output reconstruction of the Autoencoder with different difficulties: (**a,b**) shadow, (**c,d**) light reflection, and (**e,f**) partial occlusion.  
required training time. Once the Autoencoder has been trained, training the steering network is a faster process. In this way, it is possible to train different driving styles more quickly. An approach based on a single task training a convolutional neural network necessitates retraining the entire network.  
The advantage of image superposition is that it allows the generation of larger datasets, by combinatorics, than those that can be obtained from plain supervised driving.  
The supervised training of an autonomous driving system based on semantic segmentation has the disadvantage of requiring a dataset with accurate semantic interpretation of the scenes and their elements. As this is difficult to achieve in the real world, an initial phase of training using simulators is needed, which, in turn, has the disadvantage of lacking realism in the generated images. Our proposal has the advantage of performing the same process of semantic interpretation in an indoor environment using chroma key techniques.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
## Preliminaries  
### The Principle of the H-Infinity Filter  
Ho filtering, whose another name is the minimax filter, does not make any assumptions about the noise, and it minimizes the worst-case estimation error (hence the term minimax) [15]. The mathematical model of Ho filtering is as follows:  
\[x_{k}=f(x_{k-1},u_{k})+w_{h,k}\approx F_{k-1}x_{k-1}+w_{h,k} \tag{2}\]  
\[y_{k}=h(x_{k},u_{k})+v_{h,k}\approx H_{k}x_{k}+v_{h,k} \tag{3}\]  
\[z_{k}=L_{k}x_{k} \tag{4}\]  
where \(x_{k}\) and \(y_{k}\) are the state vector and the measurement vector at time \(k\), respectively. \(f\) and \(h\) are the system dynamics and observation functions, respectively. \(w_{h,k}\) and \(v_{h,k}\) are noise terms, which are assumed to be independent and identically distributed with zero mean and covariance matrices \(Q_{h,k}\) and \(R_{h,k}\), respectively. \(z_{k}\) is a linear combination of various components of the system state, which is to be estimated. \(L_{k}\) is a custom matrix and is usually assumed to be fully-ranked.  
The Ho filter must ensure that the upper bound of its estimation error is limited, and at the same time minimizes the upper bound. The cost function is as follows:  
\[J=\sup_{\{x_{0},x_{h,k}x_{h,k}\}}\frac{\sum\limits_{k=0}^{N-1}\left\|z_{k}- \hat{z}_{k}\right\|}{\left\|x_{0}-\hat{z}_{0}\right\|_{p_{0}^{-1}}^{2}+\sum \limits_{k=0}^{N-1}\left(\left\|w_{k}\right\|_{Q_{h,k}^{-1}}^{2}+\left\|v_{k} \right\|_{R_{h,k}^{-1}}^{2}\right)}\leq\gamma^{2} \tag{5}\]where \(x_{0}\) and \(P_{0}\) are the initial state vector and its covariance matrix, respectively. \(\gamma\) is a positive scalar parameter that bounds the uncertainties [13]. \(J\) is a threshold value used to evaluate the performance index of the H\(\infty\) filtering. H\(\infty\) filtering aims to minimize the maximum value of \(J\) as follows [12]:  
\[J^{*}=\min_{\hat{x}_{k}}\max_{x_{0},x_{0},x_{0}}J \tag{6}\]  
The steps of H\(\infty\) filtering are as follows [16]:  
\[\hat{z}_{k|k}=L_{k}\hat{x}_{k|k} \tag{7}\]  
\[\hat{x}_{k|k-1}=f(\hat{x}_{k-1|k-1}) \tag{8}\]  
\[P_{k|k-1}=F_{k-1}P_{k-1|k-1}F_{k-1}^{T}+Q \tag{9}\]  
\[\hat{x}_{k|k}=\hat{x}_{k|k-1}+K_{k}(y_{k}-h(\hat{x}_{k|k-1})) \tag{10}\]  
\[K_{k}=P_{k|k-1}H_{k}^{T}(R+H_{k}P_{k|k-1}H_{k}^{T})^{-1} \tag{11}\]  
\[P_{k|k}=P_{k|k-1}-P_{k|k-1}[H_{k}^{T}L_{k}^{T}]R_{e|k}^{-1}\left[\begin{array} []{c}H_{k}\\ L_{k}\end{array}\right]P_{k|k-1} \tag{12}\]  
where \(F_{k-1}\) and \(H_{k}\) are the Jacobian matrices of the nonlinear functions \(f\) and \(h\). The matrix \(R_{e,k}\) is given by  
\[R_{e,k}=\left[\begin{array}{cc}R&0\\ 0&-\gamma^{2}I\end{array}\right]+\left[\begin{array}{c}H_{k}\\ L_{k}\end{array}\right]P_{k|k-1}\left[\begin{array}{cc}H_{k}^{T}&L_{k}^{T} \end{array}\right] \tag{13}\]
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 5 Discussion and Conclusions
### Theoretical Integration: From Technology Adoption to Trust in a Converging Ecosystem  
The co-citation and factorial analyses (Figure 9) reveal that traditional Technology Acceptance Models (TAM, UTAUT) have evolved into more expansive frameworks that integrate trust, risk perception, and security--particularly in the context of mobile and decentralized platforms. These findings support and extend prior works (e.g., [28; 29]), highlighting the increasing importance of multi-dimensional trust and contextual security in influencing user adoption.  
The factorial analysis further distinguishes two interrelated drivers of e-payment adoption:  
1. User perception--trust, satisfaction, loyalty;
2. System acceptance--ease of use, intention, and infrastructural readiness.  
This dual-path structure suggests that technology adoption is no longer a purely cognitive evaluation but is increasingly shaped by affective, behavioral, and contextual factors. These findings align with post-TAM perspectives and underscore the blurring boundaries between technical design and user psychology, particularly in a globally interconnected financial environment.
### Third-Generation (3G) Technology  
The third generation of mobile communications is known as 3G. With EDGE, high-volume data transfer is now possible, but because the packet transfer over the air interface behaves like a circuit's switch call, some of the packet connection efficiency in the circuit switch environment is lost. The 3G network was created in response to the requirement for a network with universally standardized performance. On 1 October 2001, NTT DoCoMo in Japan introduced the first commercial W-CDMA network, with the exception of the United States, where carriers were able to run 3G service on the same frequencies as other services, due to the fact that 3G networks utilized different radio frequencies than 2G. Three large businesses received licenses to run the 3G network on various protocols after China's 3G debut in 2009. These companies were China Mobile (Beijing, China)for TD-SCDMA, China Unicom (Beijing, China) for WCDMA, and China Telecom(Beijing, China) for CDMA2000. Some of the main features of 3G are third-generation (3G) features, including GPRS, with a maximum downlink speed of 144 kbps, EDGE, with a maximum downlink speed of 384 kbps, UMTS WCDMA, with a maximum downlink speed of 1.92 Mbps, and HSDPA, with a maximum downlink speed of 14 Mbps. Despite its advancements, 3G networks have several drawbacks. Firstly, the infrastructure costs for deploying 3G networks are high, which made the initial rollout expensive for service providers. Secondly, while 3G offers better data speeds than its predecessors, it still falls short compared to the capabilities of newer 4G and 5G technologies, leading to slower internet experiences for users. Additionally, 3G networks tend to have higher latency, affecting real-time applications such as video calls and online gaming. Lastly, the battery consumption of devices using 3G is generally higher, due to the increased data processing requirements, leading to shorter battery life for mobile devices [4].
## 4 Conclusions  
The effects of spatial variability in the hydraulic conductivity on the consolidation process are explored in this study. Based on a parametric study for the binary mixing soils and spatially correlated random soils using the finite element method, the following conclusions can be drawn:  
* For the binary mixture cases, the excess pore water dissipation is significantly affected by the distance from a free-drainage boundary to permeable layer, and relative boundary conditions formed by relative hydraulic conductivity k difference among layers. Smaller layer thickness ratios and lower magnitude differences reduce those specific patterns, yet initial heterogeneity configurations are quite different from the equivalent homogeneous one during dissipation of excess pore pressure.
* The greater the heterogeneity in the hydraulic conductivity distribution that is applied, the more time will be taken for excess pore water pressure to dissipate. This is because higher variance enhances impermeability among the layers, delays the pore water pressure dissipation, and forms the bottleneck.
* When spatially varying layered soil has statistically identical consolidation properties, the consolidation process by the single drainage perpendicular to the layers can yield clear differences with respect to the heterogeneity configurations. However, consolidation patterns of  
Figure 8: Effect of dimensionless correlation length in spatially varying hydraulic conductivity k on consolidation times t\({}_{50}\) and t\({}_{90}\) of heterogeneous soils with different drainage conditions; (**a**) No vertical drainage; (**b**) Vertical drainage.  
different heterogeneous media become homogenized when radial drainage is allowed together with vertical drainage.
* The more dominant the radial drainage by shorter spacing between the vertical drains is, the more homogenized and the closer to the homogeneous soils the consolidation process of the spatially varying soils will be.  
Sites characterized with spatially varying consolidation properties induce specific characteristics of consolidation [6]. Thus, it should be taken into consideration, spatial variability that reflects the location of very thin and very impermeable layers to capture the observed field response.
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 5 Experimental Evaluation
### Prediction of Image Privacy Level
#### 5.2.1 Image Privacy Level Dataset  
In order to evaluate our methods, explained in Section 4, we used the _PicAlert_ ([[http://l3s.de/picalert/](http://l3s.de/picalert/)]([http://l3s.de/picalert/](http://l3s.de/picalert/)), accessed on 20 May 2021) dataset made available by [PERSON] et al. [12]. The dataset contains Flickr images and metadata, including the privacy level of each image, that can be: _public, private_, or _undecidable_. The privacy level was manually assigned by individuals inspecting each image and evaluating how much its release may harm the privacy of the owner. More details about the labeling procedure and dataset may be found in the original paper [12]. For the sake of our study, we considered two privacy levels: public and private. The dataset contains only the url of pictures, not the pictures themselves. Hence, we downloaded from Flickr all those pictures that were still available online at the time of writing. We collected a dataset of about 28,000 images, in which the distribution of public and private ones was homogeneous.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Data Type** & **Precision** & **Recall** & **F\({}_{1}\)** & **Accuracy** \\ \hline Contact & 0.495 & 0.213 & 0.298 & 0.985 \\ Contact address book & 0.736 & 0.299 & 0.425 & 0.988 \\ Contact city & 0.768 & 0.606 & 0.677 & 0.997 \\ Contact email address & 0.837 & 0.794 & 0.815 & 0.972 \\ Contact password & 0.797 & 0.671 & 0.729 & 0.993 \\ Contact phone number & 0.856 & 0.772 & 0.812 & 0.986 \\ Contact postal address & 0.741 & 0.476 & 0.579 & 0.982 \\ Contact ZIP & 0.9 & 0.686 & 0.778 & 0.997 \\ Demographic & 0.805 & 0.842 & 0.823 & 0.995 \\ Demographic age & 0.914 & 0.741 & 0.819 & 0.994 \\ Demographic gender & 0.922 & 0.809 & 0.862 & 0.996 \\ Identifier & 0.701 & 0.353 & 0.47 & 0.993 \\ Identifier AD ID & 0.838 & 0.4 & 0.542 & 0.990 \\ Identifier cookie & 0.873 & 0.892 & 0.883 & 0.982 \\ Identifier device ID & 0.83 & 0.771 & 0.8 & 0.986 \\ Identifier IMEI & 0.946 & 0.897 & 0.921 & 0.999 \\ Identifier IMSI & 1 & 0.786 & 0.88 & 1.000 \\ Identifier IP address & 0.952 & 0.829 & 0.886 & 0.990 \\ Identifier MAC & 0.958 & 0.827 & 0.888 & 0.998 \\ Identifier mobile carrier & 0.833 & 0.495 & 0.621 & 0.996 \\ Identifier SIM serial & 0.762 & 0.533 & 0.627 & 0.999 \\ Identifier SSID BSSID & 0.636 & 0.389 & 0.483 & 0.999 \\ Location & 0.833 & 0.796 & 0.814 & 0.978 \\ Location Bluetooth & 0.795 & 0.682 & 0.735 & 0.995 \\ Location cell tower & 0.792 & 0.421 & 0.55 & 0.994 \\ Location GPS & 0.871 & 0.706 & 0.78 & 0.994 \\ Location IP address & 0.719 & 0.403 & 0.516 & 0.992 \\ Location WiFi & 0.779 & 0.595 & 0.674 & 0.993 \\ SSO & 0.508 & 0.23 & 0.317 & 0.982 \\ Facebook SSO & 0.606 & 0.417 & 0.494 & 0.989 \\ Aggregate contact & 0.766 & 0.565 & 0.639 & 0.988 \\ Aggregate location & 0.798 & 0.601 & 0.678 & 0.991 \\ Aggregate demographic
## 3 Results and Discussion
### Method Development and Optimization
#### 3.2.4 Optimization of Extraction Temperature  
Within a certain temperature range, an increase in temperature aids the volatilization of olfactory substances and improves the extraction efficiency of the extraction fiber. However, for some volatile low-molecular-weight organics, the adsorption of the extraction fiber coating is an exothermic process. As the temperature increases, the thermal desorption process of volatile organics becomes stronger; therefore, an excessively high temperature will reduce the efficiency of the extraction fibers [29]. Therefore, it is necessary to select an appropriate extraction temperature to achieve better extraction efficiency for each analyte. In this study, we selected the conditions of 20% ion concentration and 40 min extraction time, and used 50/30 \(\upmu\)m DVB/CAR/PDMS extraction fibers to extract 200 ng/L of the mixed standard solution to investigate the extraction effect of the three DMSs at extraction temperatures of 35 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 65 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C. The extraction of the three DMSs at different temperatures is shown in Figure 2c, which shows an analytical plot of the effects of different extraction temperatures on extraction efficiency.  
As shown in Figure 2c, the areas of the three DMS peaks varied with increasing extraction temperature, and the differences were obvious. A linear fitting was performed for DMS, and the fitted curve showed that the peak area of DMS decreased linearly with an increase in the extraction temperature. The linear relationship between the two was good, with R\({}^{2}\) reaching 0.9997. It indicated that the adsorption of DMS by the 50/30 \(\upmu\)m DVB/CAR/PDMS extraction fiber was an exothermic process, and the efficiency of the DMS being extracted decreased with increasing temperature. The fitted DMDS curve showed that the peak area of DMDS exhibited a roughly parabolic trend with the increase in extraction temperature, and reached the maximum value when the extraction temperature was 55 \({}^{\circ}\)C. The curve fitting of DMTS showed that the DMTS peak area increased logarithmically with an increase in the extraction temperature, the growth rate increased, and the effect of the extraction temperature on the peak area of DMTS was most significant. The extraction temperature should not be too high, or it will cause water vapor to attach to the extraction fiber, affecting the extraction and adsorption of the target analytes [20]. In this study, the optimal extraction temperatures for DMS and DMDS were not excessively high. Therefore, the trend of the three DMSs peak areas with the change in extraction time was integrated, and the extraction temperature condition where the DMDS peak area reached its maximum (50 \({}^{\circ}\)C) was taken as the baseline, and 5 \({}^{\circ}\)C before and after it was selected as the temperature gradient, i.e., 45 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 55 \({}^{\circ}\)C were selected as the three extraction temperatures in the orthogonal optimization experiments.
###### Abstract  
Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term _in situ_ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.  
drought monitoring; VCI; VIUPD; NDVI; MODIS +
Footnote †: journal: Remote Sensing