esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and State-Of-The-Art Technologies
### State-Of-The-Art Technologies
#### Message Channel--Redis  
Another key part of a computational scientific workflow framework is related to the question of how can the information flow between workflow tasks be organized and coordinated. Since tasks are executed in distributed containers, for coordination and message exchange between the tasks, an adequate distributed technology is needed. Such communication and coordination services are often based on key-value databases as storage (e.g., etcd [38], Zookeeper [39], et al.) and eventually use a message protocol as communication interface (e.g., Redis, REmote DIctionary Server). As presented in [40], Redis is an in-memory data structure store, used as a key-value non-relational database, cache and message broker. Due to its in-memory nature, Redis boasts high performance for read and write operations. Besides, to allow users form high-level data structures as values Redis provides five abstract data types for values: strings, lists, sets, hashes and sorted sets from which more complex objects can be constructed. Redis is written in ANSI C and lightweight with no external dependencies. Furthermore, Redis offers atomic operations for read/write actions in order to allow using data structures for coordination, e.g., for the implementation of a simple and thread-save implementation of a distributed counter for instance. Such counters will maintain consistency when manipulated by multiple parallel operating tasks.  
By integrating Redis into the presented framework as a communication and coordination interface and using its practical publish/subscribe functionality, information exchange between distributed tasks and their coordination can be implemented to be very efficient, fast and failure-tolerant.
## 5 Discussion
Worthy of note is the fact that subsidence clustered only within the Sarno basin, thus enforcing the idea that subsidence occurred where unconsolidated deposits were present in the subsurface of the Sarno basin. Furthermore, Figure 14 depicts the groundwater withdrawal rates at SF, MP, and SM well-fields (refer to Figure 7 for locations), located within Sarno Mountains karst aquifer, and monthly rainfall during the 2003-2020 period. The temporal variations of both hydrogeological parameters did not show significant anomalies but, rather, smooth trends. Consequently, we reasoned that the observed vertical ground deformation was not triggered by a decrease in the natural groundwater recharge of the plain aquifer because the precipitation regime did not display any substantial change during the same interval of time. This field evidence presents new results that were not reported by or discussed in previous studies [21; 49; 83]. These findings allow for a better understanding of the anthropogenic cause of the ground deformation mechanisms observed in the plain aquifer. Figure 14: (**a**) total monthly groundwater pumping rate (L/s) at SF, MP, SM well fields, located within the Sarno Mountains karst aquifer, and (**b**) monthly precipitation (mm) for the period of 2003–2020. The dotted red line indicates the mean long-time trend. Indeed, even in the absence of updated monitoring data on the groundwater table and pumping rates from the alluvial aquifer, these results indirectly confirmed that the land subsidence observed in the period of 1993-2020 (Figure 13) was mainly attributable to the widespread and uncontrolled groundwater abstraction, for industrial and irrigation use, within the plain aquifer. In a retrospective analysis, this hydrogeological interpretation is reasonably consistent with a strong link between the trend of ground deformation and groundwater table depletion observed in the period of 1992-2003. Our results suggested that a positive feedback exists between fault activity and the distribution of subsidence. In fact, fault activity caused the formation of the Sarno basin and the consequent accumulation of unconsolidated deposits. The presence of these deposits amplified the deformation effect induced by groundwater pumping during the past 30 years, thus resulting in the significant subsidence rate detected by geodetic data, which is clustered within the Sarno basin.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 4 Results and Discussion
### Novelty and Limitations  
This study has proposed several machine-learning-based approaches for the detection of potential icing clouds, and compared their performance to the existing physical-theory-based models. The proposed models produced better performance than the existing models, which are based on simple rule-based approaches for the reference data available in this study. The superiority of the proposed models is partly due to the uncertainty of the satellite-derived products used in the existing physical-theory-based models. For example, the fraction correct for COMS CPH was reported as 0.642 when compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data [66]. Similarly, the COMS COT yielded a root mean square error (RMSE) of 3.45 when compared to the MODIS COT [67]. Using such products as inputs for the threshold-based rules may result in a significant increase in uncertainty. The simple threshold-based rules used in the existing models might not be sufficient to model the complex characteristics of potential icing areas. By using additional input variables other than those used in the existing models, the proposed approaches were able to provide more accurate results with more sophisticated modeling techniques. The stable distribution of potential icing time-series confirmed the robustness of the proposed approaches.  
However, there are several limitations to this study. First of all, the proposed models are heavily dependent on the reference data (i.e., PIREPs). Although careful quality control was conducted to secure accurate icing and non-icing samples, there is no guarantee that the selected icing and non-icing samples cover all possible icing and non-icing clouds. In particular, it is possible that potential icing areas may be missed simply because there is no visually identifiable icing phenomenon, which often leads to the underestimation of icing regions [68]. Not only the quality of the PIREPs, but also the number of PIREPs, is important to develop successful machine-learning-based icing detection models. For example, there are many PIREPs (more than 20,000) over CONUS for winter from 2006-2008 [15], which is ideal for the development of machine-learning-based models. However, only a small number of icing PIREPs are available in East Asia, which is a major limitation of this research. Such a small sample size often results in overfitting [62; 63]. Since satellite-derived products typically provide information on the characteristics of cloud tops, it should be noted that PIREP-based icing information collected far below the cloud tops is not always closely related to satellite-derived cloud top products.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Hansen Solubility Parameter
The use of a second solvent, solubilizing partially or poorly PCL, requires the adjustment of its volume fraction in the whole system to fit with the Hansen solubility parameters of the binary solvent system, and therefore ensuring the good solubility of PCL. According to [PERSON] et al. [123], the proportions of the solvents mixed were determined geometrically based on the Teas graph using a method similar to the lever rule. The solubility results of mixed solvents using this method were compared with predictions drawn from the Teas graph. After that, the corresponding Hansen solubility parameters of binary solvent systems can also be calculated by this lever rule. The other criteria to consider are based on toxicity and environmental. For example according to the direction of REACH (Registration, Evaluation, and Authorization of Chemicals) and the regulation of European Pharmacopoeia, anisole, acetic acid, and ethyl acetate are nontoxic and good solvents for dissolving PCL [99].
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 3 Proposed Method for Signal Detection
### Proposed OAOR Based Method  
The AOR method has been utilized widely in solving various linear system problems [48]. However, it is highly dependent on the relaxation parameters selection [39]. In this section, we will provide a detailed derivation of the proposed OAOR iterative method for signal detection in large antenna scenarios. These relaxation parameters play an important role in the convergence and stability of the algorithm. Proper tuning of these parameters improves detection performance and reduces computational complexity. Instead of using fixed values, we will employ the optimized approach [49] so that the algorithm can adjust these values as per changing system configurations and channel conditions. We will utilize a meta-heuristic approach of a novel PSO to iteratively solve the linear system arising from the MMSE criterion without directly computing the matrix inverse. The MMSE estimate of the transmitted signal vector \(\mathbf{x}\) is given by  
\[\mathbf{\hat{x}}=(\mathbf{G}^{H}\mathbf{G}+\beta\mathbf{I})^{-1}\mathbf{G}^{H} \mathbf{r} \tag{4}\]  
where \(\mathbf{G}\in\mathbb{C}^{N\times K}\) is the channel matrix, \(\mathbf{r}\in\mathbb{C}^{N}\) is the received signal vector, and \(\beta\) is a regularization parameter. The direct calculation of this inverse is computationally expensive, especially, for large \(N\) and \(K\), which is the case in massive MIMO systems. Now we formulate the iterative equation to solve (3) without computing the direct matrix inversion. The key to our approach lies in the decomposition of the matrix \(\mathbf{G}^{H}\mathbf{G}\). We express this matrix as  
\[\mathbf{G}^{H}\mathbf{G}=\mathbf{D}-\mathbf{L}-\mathbf{U} \tag{5}\]  
where \(\mathbf{D}\) is the diagonal component of \(\mathbf{G}^{H}\mathbf{G}\), \(\mathbf{L}\) is the strictly lower triangular component, and \(\mathbf{U}\) is the strictly upper triangular component. This decomposition allows us to reformulate the problem into an iterative form. The OAOR method updates the estimate \(\mathbf{x}^{(k)}\) at the \(k\)-th iteration using the following formula:  
\[\mathbf{x}^{(k+1)}=\mathbf{x}^{(k)}+\omega^{*}(\mathbf{D}^{-1}\mathbf{G}^{H} \mathbf{r}-\mathbf{D}^{-1}\mathbf{G}^{H}\mathbf{G}\mathbf{x}^{(k)}+\gamma^{*} (\mathbf{D}-\mathbf{L}-\mathbf{U})\mathbf{x}^{(k)}) \tag{6}\]  
where \(\omega^{*}\) and \(\gamma^{*}\) are the optimal relaxation parameters that control the convergence behavior of the OAOR method. These parameters are crucial in accelerating convergence compared to traditional methods.
# An Improved Robust Fractal Image Compression Based on M-Estimator
## 3 Robust M-Estimator  
The method of least squares is a prototypical M-estimator [57] since the estimator is defined as a minimum of the sum of squares of the residuals. However, the ordinary least square method is very sensitive to outlying observations. There are several approaches to deal with this problem. One approach is robust regression, which is to employ a fitting criterion that is not as vulnerable as least squares to unusual data. Statisticians have developed many robust methods based on different techniques. The most common general method of robust regression is M-estimation, introduced by [PERSON]. Consider the general regression model  
\[y_{i}={x_{i}}^{T}\beta+\varepsilon_{i},i=1,2,\ldots,n \tag{9}\]  
for the \(i\)-th of \(n\) observations, where \(x_{i}^{T}=[1\ x_{1}\ x_{2}\ \cdots\ x_{ik}]\) and \(\beta^{T}=[\alpha\ \beta_{1}\ \beta_{2}\ \cdots\ \beta_{k}]^{T}\). Here, \(\varepsilon_{i}\) is an unobserved random variable, which is added to the linear relationship between the dependent variable \(y_{i}\) and the vector of regressors \(x_{i}\).  
The general M-estimator is defined to minimize the objective function  
\[E=\sum_{i=1}^{n}\rho(\varepsilon_{i})=\sum_{i=1}^{n}\rho(y_{i}-x_{i}^{T}\beta) \tag{10}\]  
where the function \(\rho(\cdot)\) is a symmetric positive definite function with a unique minimum at zero and is chosen to be increasing slower than quadratically.  
Let \(\psi=\rho^{\prime}\) be the derivation of \(\rho\). Differentiating the objective function with respect to the coefficients \(\beta\), and setting the partial derivatives to 0, produces \(k+1\) a system of estimating equations for the coefficients  
\[\sum\limits_{i=1}^{n}\psi(y_{i}-x_{i}^{T}\beta)x_{i}^{T}=0 \tag{11}\]  
Define the weight function \(w(x)=\psi(x)/x\), and let \(w_{i}=w(\epsilon_{i})\). Then the estimating equations can be written as  
\[\sum\limits_{i=1}^{n}w_{i}(y_{i}-x_{i}^{T}\beta)x_{i}^{T}=0 \tag{12}\]  
This equation is a weighted least-squares problem, aiming at minimizing \(\sum\limits_{i=1}^{n}w_{i}\epsilon_{i}\). An IRLS (iteratively reweighted least-squares) algorithm can be used.  
At the iteration \(t\), the new weighted-least-squares estimates are computed by  
\[\beta^{(t)}=[X^{T}W^{(t-1)}X]^{-1}X^{T}W^{(t-1)}Y \tag{13}\]  
where  
\[X=\begin{bmatrix}x_{1}^{T}\\ x_{1}^{T}\\ \vdots\\ x_{n}^{T}\end{bmatrix}=\begin{bmatrix}1&x_{11}&\cdots&x_{1k}\\ 1&x_{21}&\cdots&x_{2k}\\ \vdots&\vdots&\ddots&\vdots\\ 1&x_{n1}&\cdots&x_{nk}\end{bmatrix}\]  
and \(W^{(t-1)}=diag\Big{\{}w_{i}^{(t-1)}\Big{\}}\) is the current weight matrix.
###### Abstract  
Assessment of cerebrovascular function is crucial for managing neurological disorders, with cerebral blood flow (CBF) measurement being key. Single photon emission computed tomography (SPECT), a traditional method, uses radiation exposure. Blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) with carbon dioxide (CO\({}_{2}\)) is a non-invasive cerebrovascular reactivity (CVR) alternative, but direct SPECT-MRI CO\({}_{2}\) comparisons for MRI's replacement potential are limited. This study directly compared CVR from SPECT and MRI CO\({}_{2}\) in nine healthy participants. Delay-based MRI (tcMRI) with stimulus timing correction was analyzed alongside conventional MRI. Results showed no significant CVR differences between SPECT and tcMRI (\(p\) = 0.688) or SPECT and conventional MRI (\(p\) = 0.813), indicating comparable overall CVR. However, tcMRI significantly differed from conventional MRI (\(p\) = 0.016) and showed a greater similarity to SPECT. Regionally, the largest CVR differences were observed between tcMRI and conventional MRI, particularly in the cingulate cortex, frontal lobe, and basal ganglia. These discrepancies suggest that tcMRI may capture subtle CVR abnormalities not detected by conventional MRI. The findings support the clinical utility of CO\({}_{2}\)-MRI, especially with stimulus timing correction, as a safe, repeatable, and radiation-free alternative to SPECT. In particular, tcMRI may offer advantages for repeated CVR assessments in long-term clinical monitoring.  
c pacs: 15, 5352 +
Footnote †: journal: Applied sciences  
_Article_
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Cross Database Experiments  
Cross-database experiments were also carried out to investigate the generalization ability and robustness of BIQA-SC. In the experiments, we used one database as training set and the other databases as testing sets. In Table 7, we list the SROCC of the competing algorithms when these algorithms were trained on LIVE database and tested on the other databases. The two best algorithms are highlighted in boldface. It can be seen in Table 7 that the performance of all the competing algorithms decreased significantly compared with their performance on individual databases. The main reason is that there are only five distortion types in LIVE database while the testing databases contain many other types of distortions, such as contrast distortion, multiple distortions, color distortions, etc. Moreover, only TID2013 and LIVE databases contain partial images of the same scene. The images in other databases are completely different from those in LIVE database. Nevertheless, BIQA-SC still performed well compared to the competing algorithms. Besides, we also trained the competing algorithms on the entire TID2013 database, and tested them on the other databases. The experimental results shown in Table 8 demonstrate that BIQA-SC is database independent and outperformed the other competing algorithms.
## 3 Results and Discussion
### Developed Protocol for ABC Determination  
Regarding all the above, a protocol for ABC determination based on an original methodology including the presence of woody material (chips) in the titrated system has been defined. The developed protocol comprises four steps: (1) preparation of material, (2) chip suspension mixing until stabilized pH, (3) pH-metric titration of chip suspension, and (4) calculations. The presence of chips in the system and the avoidance of boiling are what makes the difference between the new method and the classic one involving aqueous extracts [9].  
Thus, the procedure can be delineated as follows: 20 g chips of known moisture content and defined size range as used in a given particleboard production process are added to 200 g distilled water. (The amount of these can be changed as long as the weight ratio of 1:10 is kept.) The suspension needs to be mixed (160 rpm) at room temperature for 30 min or until a stabilized pH is reached. Do not filter the chips prior to titration. Then titrate the suspension with 0.025 N HCl or H\({}_{2}\)SO\({}_{4}\) (1.0 mL titrant per minute) to pH 4. Use Equation (3) for the calculation of the acid buffering capacity per gram:  
\[\beta_{pg}=\frac{\beta}{m}=\frac{\Delta n}{\Delta pH\times m}=\frac{\Delta V \times n}{\Delta pH\times m}, \tag{3}\]  
where \(\beta_{pg}\) is the acid buffering capacity per gram, \(\beta\) is the acid buffering capacity, \(m\) is the dry chip mass (g), \(\Delta n\) is the amount of added acid, \(\Delta pH\) is the resulting change in pH, \(\Delta V\) is the titrant amount in 1.0 liter, and \(n\) is the mmol titrant per liter.  
After the substitution of the fixed values, \(m=20\) g, \(n=50\) mmol/L, Equation (4) can be calculated:  
\[\beta_{pg}=\frac{\Delta V\times n}{\Delta pH\times m}=\frac{\Delta V}{\Delta pH \times 2.5}, \tag{4}\]  
Figure 4: pH drift of pine chips in water suspension for different chip size.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Analysis of Results  
During the experiment, to simulate the malicious attack of the nodes in the satellite network, the route nodes can be randomly selected to become malicious nodes. The performance of the design scheme is tested by different performance indicators, and the average end-to-end delay, packet loss rate, and network throughput of the network are analyzed, respectively.  
1 Average end-to-end delay: The time taken from the source end node where the data packet starts to the destination node is calculated to completely accept the total time spent on the packet [18]:  
\[t_{delay}=t_{end}-t_{start} \tag{22}\]  
2 Package rate: The packet loss rate refers to the ratio of the number of packets lost in the process of data transmission to the number of packets sent [18]:  
\[t_{plr}=\frac{n_{loss}}{n_{send}} \tag{23}\]  
3 Network throughput: Network throughput refers to the amount of node transmission data during the unit time without data packet loss [20]:  
\[th=\frac{S_{data}}{U_{t}} \tag{24}\]
## 4 Results: The Usage of CARIMO
### Results of the CARIMO Usage Data Analysis
#### 4.5.2 User Groups of CARIMO: Intensity of Use  
Table 5 shows that, on average, about two-thirds of the trial participants used the body-related features of CARIMO on a frequent or regular basis. Toward the end of the field trial (month 8), the use decreased and the share of non-users increased. A comparison between Austria and Italy revealed that, on average, the usage of CARIMO in Austria was about 30% higher than in Italy. Furthermore, the proportion of frequent users over time was higher in Austria than the proportion of regular users. In Italy, this was only the case in months one, two, and seven. In general, there were more non-users for body-related features in Italy than in Austria.  
With respect to mind-related features, Table 5 indicates that between 52% and 66% of the users used these features on a frequent or regular basis. In months six and seven, usage increased, which reflects the rollout of three new games in month six. In addition, for mind-related features, the share of non-users increased toward the end of the field trial (month 8). At the beginning of the trial, Italians used these features less often than Austrians. Starting with month four, the use in Italy approached the Austrian level. For the \"information and entertainment\" function in both countries, the proportion of regular users over time was higher than the proportion of frequent users.  
The body- and mind-related features of CARIMO were perceived equally well by its users. However, body-related features were more frequently used (between 62% and 75% frequent and regular users) than mind-related features (between 52% and 66% frequent and regular users).
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 2 Materials and Methods
### Pollution Measurement  
For street-level PM\({}_{2.5}\) pollution mapping, a central area (approximately 2.5 km \(\times\) 2.8 km), containing busy traffic avenues, a main city highway, secondary residential streets, and two parks was chosen. PM\({}_{2.5}\) concentration scans were performed over four days (30 May and 3, 12, and 18 June, 2019) during the morning rush hours (8:00-10:00). It is considered that these data would contribute to building the most conservative and representative model for the worst air quality conditions, which is important for health implications in urban areas. A portable real-time CEL-712 Microdust Pro monitor [29] was paired with a GPS device [30]. The Microdust Pro sensor, based on a near forward angle light scattering technique, was calibrated before the experiment by using zero-air and a known concentration filter (164 mg m\({}^{-3}\)). In addition, the validity of this portable particle sensor was confirmed by collocating it for 8 h (battery life) with the Environmental Protection Agency approved method (EQPM-1102-150) showing a good correlation (R = 0.86) [31]. This automated Thermo Scientific 5014i Beta Continuous Ambient instrument forms a part of the air quality and meteorological (Vaisala WXT536) monitoring station (Belisario) in the center of the experimental site (Figure 1c). The background concentrations of PM\({}_{2.5}\) and meteorological data were downloaded from this station, previously described elsewhere [10; 28]. The portable PM\({}_{2.5}\) and the GPS equipment were synchronized to function at a 5-s time step. Both instruments were held at the height of 1.5 m and faced the particle inlet forward while walking on a sidewalk following the traffic flow. The approximate speed of sampling was 4 km/h.  
Figure 1: Location of the study area in the center of Quito, Ecuador (**a**). The upper right panel (**b**) indicates the locations of the air quality network stations, and the lower right panel (**c**) shows the study area in the urban infrastructure.