esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Focusing and Recombination of the EUV and Excitation Pulses  
For TR-Spin-ARPES measurements, the IR pump and EUV probe pulses are focused and overlapped spatially and temporally on the sample material, shown in Figure 5. The EUV monochromatized pulses are focused by a grazing-incidence toroidal mirror used in 1:1 configuration with 1 m input and output arms, in order to minimize aberration. The diameter of the focused EUV on the sample material could therefore be as small as the EUV source diameter, which is estimated to \(<\)30 \(\upmu\)m. In normal operation, in order to avoid space charge effects at the sample material, the EUV spot size on the sample should be much larger than source size (see Section 8.4). This is achieved by slightly de-focusing the toroidal mirror. The EUV focal spot on the sample material is measured on a Ce: YAG crystal which converts the EUV scattered radiation into visible green light. The excitation laser pulse is also focused on the sample material by a lens with 1.3 m focal length. The spot-size is adjusted to illuminate an area of about 1 mm \(\times\) 1.5 mm in order to provide the optimum excitation fluences on the sample material, typically 1-2 mJ/cm\({}^{2}\)/pulse.  
The vacuum in the focusing and recombination chamber is maintained at 10\({}^{-8}\) mbar and the ARPES chamber is operated in the 10\({}^{-10}\) mbar vacuum range. The differential pumping between the focusing and recombination chamber and the ARPES chamber is achieved by the three pinholes placed in the pipe between the two chambers.
## 2 Materials and Methods
### Methods: A Dynamic Column System  
The homogeneous columns were filled with granite samples, which were sampled from a small island near Kimmen, Fujian Province, China. For the porous media (i.e., crushed granite powders) used in this study, the hydraulic conductivity was found to be around \(10^{-6}\) cm s\({}^{-1}\)[14]. The bulk density (\(\rho\)) and the total porosity (\(\theta\)) of the granite columns are shown in Table 1. The ADE installation in the experiments comprised primarily a multi-channel water-supply peristaltic pump (MASTERFLEX L/S, Cole-Parmer Instrument Co., Chicago, IL, USA), PP (poly-propane) columns (modified type, Hsinchu ZeGi Industrial Co., Ltd., Hsinchu, Taiwan), the solution tanks (radiotracer and synthetic groundwater (GW)), and an auto-fraction collector, as shown in Figure 1. In addition, saturated granite columns with fixed diameters of 5 cm and various path lengths of 2, 4, and 8 cm were simulated at a fixed 2.0 \(\pm\) 0.2 mL/min flow rate for one-dimensional solute transport in homogeneous porous media. In fact, the three-to-two switch valves were open only to reservoir No. 2 (GW) to ensure water saturation in each pore space in the compacted granite before the ADE experiments. The packed column was slowly eluted to approximately a 5-10 pore volume (200-500 mL) with GW at a flow rate of 2.0 \(\pm\) 0.2 mL/min. During the water saturation process, 10 mL of effluent from each column was sampled every 30 min. The concentrations of Na, Mg, Ca, and K in the effluent were measured using inductively coupled plasma-optical emission spectrometry (ICP-OES, DV 7000, PerkinElmer, Waltham, MA, USA). The variations in the concentrations were found to be within 5% of the corresponding liquid phase concentrations, and it was determined that water saturation had been reached.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **No. 1** & **No. 2** & **No. 3** \\ \hline Length (cm) & 2 & 4 & 8 \\ Pore volume (mL) & 18.06 & 36.11 & 72.22 \\ \(\star\) Bulk density (\(\rho_{\text{b}}\), g/cm\({}^{3}\)) & & 1.45 & \\ \(\star\) Porosity (\(\theta\)) & & 0.46 & \\ \(\star\) Particle size (mm) & & \textless{}1 & \\ \(\star\) Flow rate (mL/min) & & 2.0 \(\pm\) 0.2 & \\ \(\star\) Radionuclide: & & HTO: 50 Bq/mL (V0 \(\rightleftharpoons\) 5000 mL) \\ HTO A0 (dpm/mL) & & Se(IV): \(1\times 10^{-4}\) M \\ Se and Cs Concentration C\({}_{0}\) (M) & & Cs: \(1\times 10^{-4}\) M \\ \hline \hline \end{tabular}  
* No. 1 and No. 3 have the same value as No. 2.  
\end{table}
Table 1: The ADE experimental conditions in this study.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 3 Hardware Design  
Section 3.1 presents an overview of the hardware components. Then, Section 3.2 describes the power consumption and optimization cost. Finally, Section 3.3 discusses alternative hardware setups.
## 4 Discussion
### Influence of Friction Coefficient  
The friction coefficient is set to be 0.01, 0.1, 0.5, 0.8, and 1 in five different numerical tests, and its influences on the average temperature change and peak stress are shown in Figure 11. Before the peak, the growing rate of average temperature change \(\Delta T_{ave}\) increases with the increase in friction coefficient. The peak values of \(\Delta T_{ave}\) and UCS both have a general trend of slight growth with the increasing friction coefficient, although there are some small fluctuations. On one hand, the higher friction coefficient causes a higher temperature rise induced by the friction effect. On the other hand, the higher friction coefficient leads to a higher UCS, and then the temperature will also increase under higher axial stress. Above all, the increasing friction coefficient does lead to an increase in average temperature, although this influence is not very significant. After the peak, the average temperature change \(\Delta T_{ave}\) decreases rapidly with the increasing axial strain.
# Quantum Natural Language Processing: Challenges and Opportunities
## 6 Quantum Computer Approaches
In order to minimize the impact of quantum noise, the experiments has been performed on a single run (100 iterations for the MC task with a exclusive access of ibmq_bogota with 12 h of runtime and 130 iterations for the RP task in a sharing mode with 72 h or runtime). Results demonstrate a test error of 16.7% for the MC task and 32.3% for the RP one, with F-score of 0.85 and 0.75. Although it is impossible to make an objective comparison with the simulator, these can be considered good results concerning the size of the datasets, factors affecting NISQ devices and different conditions in which experiments have been carried out. The simulation has been run on classical hardware without noise during many averaged runs, while quantum computation has been performed on a single run on a device affected by noise. In [65], a preliminary experiment focused on machine translation using DisCoCat has been proposed. The goal of the experiment is the possibility of a quantum-like approach for language understanding in different languages. This is the first work trying to use DisCoCat for a language other than English. In particular, simple, complex and negative sentences in two languages (Spanish and English) have been converted into DisCoCat diagrams, then into quantum circuits. Negative sentences do not succeed to be managed from the system even if DisCoCat model is capable of describing negative sentences and constructing diagrams. However, although some solutions have been proposed in the literature, the possibility of mapping negations using functors is still an open issue [107]. After that, these sentences have been encoded in an IBMQ quantum machine using python language. In particular, using an optimized model (IBM Q NISQ backend) has  
Figure 3: A simplified version of the pipeline proposed by [64]. demonstrated that it can answer factoid questions related to an input vocabulary. According to the theoretical approaches proposed in the literature [63], the score achieved by IBMQ is close to 1, indicating the correctness of the statement. The evaluation phase has shown that the system is able to give consistent answers handling simple sentences. The results are also quite good, as the complexity and length of the sentences increase. Evaluation phase show a sentence similarity between the two languages, reaching a value of 95%. Notice that, although the work claims to have used a NISQ device, no specific information is provided about the implementation, machine type or dataset in detail.
# Transcranial Doppler-Based Neurofeedback to Improve Hemispheric Lateralization
## 3 Results
### Results from the Questionnaire  
The questionnaire was administered only at the end of the first session to assess the appropriateness of the experimental setup. Most participants reported that the duration of both the training and testing phases was adequate, except for three individuals, who found the training phase too long. As a result, from the second session onward, participants were given the option to end the training phase earlier, if they wished.  
All participants stated that they put significant effort into achieving the objectives. They found the training period useful and appreciated the ball as a visual feedback tool, which they considered helpful for monitoring their progress.  
When asked whether they believed they had achieved good control of lateralization, three participants expressed confidence in their capacity, while the remaining participants stated that they tried but were not fully successful in lateralizing. This outcome is consistent with the results, presenting on average poor performance in the first session. Interestingly, the participants used various strategies to enhance lateralization. For right-hemisphere  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Session \#** & **1** & **2** & **3** & **4** \\ \hline Accuracy & 0.51 & 0.50 & 0.60 & 0.65 \\ ITR (bit/min) & 0.00012 & 0.010 & 0.036 & 0.084 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy and information transfer rate (ITR) of all subjects across each session.  
Figure 5: Results of the three-class classification based on adopting two thresholds for the lateralization index (LI). For each pair of thresholds, t1 and t2 varied through the maximum and minimum values of the LI, and the accuracy (**a**), information transfer rate (ITR) (**b**), and number of unclassified responses (**c**) are calculated.  
activation, they often combined emotional, cognitive, and motor approaches, often focusing on personal memories, emotions, or music, particularly instrumental melodies such as classical music or film scores. Relaxation techniques, including meditative states or clearing the mind, were also common. On the motor side, participants imagined left-sided movements like swimming, punching, or using the left hand, with some of them emphasizing the intention rather than visualization of these actions.  
For left-hemisphere activation, participants relied on linguistic, logical, and motor tasks linked to the right side of the body. These included imagining movements like playing sports or performing exercises with the right side, as well as engaging in cognitive tasks such as mental arithmetic, counting in foreign languages, or naming objects. Linguistic activities like tongue twisters, proverbs, and reciting texts were also frequently employed. Many participants reported that combining different approaches, like mixing mental calculations with imagined actions, was particularly effective for stimulating left-hemisphere activity.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion
### Evaluation Index  
To describe the performance of the GCN-GRU model, the following three indexes are proposed to evaluate the predicted results.  
\[\begin{split}\text{(1)}\quad\text{Root mean squared error (RMSE)}\\ \text{RMSE}=\sqrt{\frac{\sum_{t=1}^{m}\left(y_{t}-\hat{y}_{t} \right)^{2}}{m}}\\ \text{(2)}\quad\text{Mean absolute error (MAE)}\\ \text{MAE}=\frac{\sum_{i=1}^{m}\left|y_{t}-\hat{y}_{t}\right|}{m} \\ \text{(3)}\quad\text{Accuracy (Accuracy)}\\ \text{Accuracy}=1-\frac{\left\|y_{t}-\hat{y}_{t}\right\|_{F}}{ \left\|y_{t}\right\|_{F}}\\ \text{where }y_{t}\text{ and }\hat{y}_{t}\text{ represent the real traffic state value and predicted traffic state value,}\end{split} \tag{1}\]  
\[\text{RMSE}=\text{Var}\frac{\sum_{t=1}^{m}\left|y_{t}-\hat{y}_{t}\right|}{m} \tag{2}\]  
\[\text{(2)}\quad\text{Mean absolute error (MAE)}\\ \text{MAE}=\frac{\sum_{i=1}^{m}\left|y_{t}-\hat{y}_{t}\right|}{m} \\ \text{(3)}\quad\text{Accuracy (Accuracy)}\\ \text{Accuracy}=1-\frac{\left\|y_{t}-\hat{y}_{t}\right\|_{F}}{ \left\|y_{t}\right\|_{F}}\\ \text{where }y_{t}\text{ and }\hat{y}_{t}\text{ represent the real traffic state value and predicted traffic state value,}\end{split} \tag{3}\]  
where \(y_{t}\) and \(\hat{y}_{t}\) represent the real traffic state value and predicted traffic state value, respectively.  
_RMSE_ and _MAE_ can reflect the difference between the real value and the predicted value, and both two values fall into the interval [0, +\(\infty\)). These evaluation values are negatively correlated with the prediction effect, that is, the smaller the value is, the better the prediction effect is. _Accuracy_ is used to detect the precision of predicted results, that is, the larger the value is, the better the prediction effect is.
## 2 Materials and Methods
### ML Model Setting
#### 2.4.3 Support Vector Regression (SVR)  
A Support Vector Regression (SVR) is a non-parameter supervised machine learning model. The SVR function is derived based on training data to predict numerical values that find the best fitting line in the hyperplane with the maximum number of points [42]. The training attributes are defined by feature vectors \(x\) selected from the set of all features \(X\) (as in Table 1) and numerical label \(y\in\mathrm{R}\), which represents the prediction of TEC [43]. SVR is a regression function that is linear to the kernel function \(K(x_{i},x)\), trying to capture the non-linear relationship between selected features. Specifically, the prediction function is expressed as:  
\[\sum_{i\in SV}(\alpha_{i}-\alpha_{i}^{*})K(x_{i},x)+b \tag{5}\]  
where \(x\) denotes TEC, \(x_{i}\) represents the prediction variables in Table 1 (such as TP, AL, SL, Bu, and GDP), \(\alpha_{i}\) and \(\alpha_{i}^{*}\) are the Lagrange multipliers, and \(b\) is an intercept. This kernel function \(K(x_{i},x)\) was set up to map the nonlinear function to the flattest function in a feature space. We also applied the polynomial kernel, which is suitable for the normalized training dataset. The polynomial kernel is defined by:  
\[K(x_{i},x)=(1+(x_{i}\cdot x))^{d} \tag{6}\]  
where \(d\) is the polynomial degree, and the kernel degree parameter specifies it. We input the normalized variables into RapidMiner(r) Software for this study following five steps of building the prediction model. We applied the radial kernel with gamma = 0.001 and cache = 200. The hyperparameter C, which controls the relaxation of the error minimization problem, was set as 100. The convergence epsilon was set at 0.001 with a max iteration at 100,000. SVR constructed a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for regression prediction with a fast algorithm and sufficient results.
# A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model
## 2 The BWBN Model
### NSGA-II Algorithm  
Genetic Algorithm (GA) is a self-adaptive global search algorithm. It simulates the genetic and evolutionary process of organisms in the natural environment. A fitness function is introduced to measure the adaptability of individuals in the population. According to the fitness of the individual in the problem, the individual is selected according to the corresponding rules. With the help of the genetic operator of natural genetics, the population representing the new solution set is generated. The selection, crossover, mutation, and other operations are carried out in a probabilistic way, which increases the flexibility of the search process. This can converge to the optimal solution with a significant probability.  
The GA algorithm of the multi-objective optimization problem has been improved over recent years. [PERSON] and [PERSON] (1995) proposed non-dominated sorting genetic algorithms (NSGA) [65], which are based on the Pareto optimal concept. The main difference with the simple GA is that the NSGA are stratified according to the dominant relationship between individuals before the selection operator is executed. The selection operator, crossover operator, and mutation operator are not different from a simple genetic algorithm. Before the selection operation is executed, the population is sorted according to the dominant and non-dominant relationship among individuals.  
The computational complexity of the NSGA algorithm is \(O\big{(}mN^{3}\big{)}\), where \(m\) is the cardinality of objective function and \(N\) is the population size. When calculating the first Pareto front line, each solution must be compared with other \(N\)-1 solutions' m times. The total time of comparison is \(O\big{(}mN^{2}\big{)}=m(N-1)\)\(N\). In the worst case, only one solution is found in the search \(O\big{(}mN^{2}\big{)}\) times, and the rest \(N\)-1 times searches are needed, then the computational complexity is \(O\big{(}mN^{3}\big{)}=m(N-1)N^{2}\). Thus, the calculation is time-consuming when the population is large. [PERSON] (2000) further improved the algorithm by introducing the elite retention strategy [66]. A satisfactory solution is easily obtained by elitist retention strategy. The case of finding one solution in the search \(O\big{(}mN^{2}\big{)}\) times is avoided. The computational complexity is reduced to \(O\big{(}mN^{2}\big{)}\).
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### MANO Integration  
The coordinated and adaptive operations architectures integrate the CMS framework with MANO through standard OSS interfaces. Addressing the infrastructure and architecture challenges, this integration enables distributed core functionality deployments and SDN control by incorporating an SDN controller within the satellites, managed by MANO. From the perspective of integrated management challenges, this approach enables the seamless inclusion of satellite operations within broader network management. This is particularly evident in the adaptive architecture, where the NTN MANO leverages CMS data on upcoming satellite operations for optimized orchestration. Regarding operational dynamics challenges, the distributed SDN control mechanism effectively addresses key issues such as dynamic space routing, traffic offloading, unstable link management, and the deployment and maintenance of pVNF. Finally, in tackling technology maturation and deployment challenges, the integration of MANO within the framework using standardized and widely adopted interfaces enhances prototyping and real-world implementation. This is especially significant in the coordinated operations architecture, where a standard terrestrial MANO is employed, ensuring compatibility with existing network management practices.
## 4 Strategy Description
### Discussion on the Proposed Strategy  
**The efficiency of the proposed strategy.** The proposed strategy chooses the caching node that caches the least popular content for content placement, which can obviously increase the average popularity of the contents cached by the selected nodes. Average popularity refers to the average value of the popularity of different contents in the cache. Furthermore, the average popularity of cached contents on the transfer path has also been improved. As the least popular content is more likely to be replaced, the proposed strategy can increase the average popularity of cached contents on the path more significantly. Choosing to cache popular contents and using number-of-copies control can also reduce the number of cache placement, which in turn reduces the frequency of cache replacement. This saves the computing resources of the cache router and reduces power consumption. In addition, the proposed strategy is decoupled from content replacement policies, meaning that, when using PB-NCC, we can select different content replacement policies according to actual scenarios.  
**Path cooperation or regional cooperation.** The proposed strategy belongs to path cooperation. However, there may be caching nodes that cache less popular contents outside the path. For regional cooperation, there are usually two kinds of approaches. The first way is to adopt neighbor collaboration mechanisms to get the state of the neighbor nodes. The second way is to adopt centralized decision-making in order to search for appropriate caching nodes. These approaches are often used in off-path caching strategies, as discussed in Section 2, which inevitably brings communication overhead to the network. In addition, as the unpopular contents are frequently replaced in the cache, caching nodes also need to frequently exchange the content information with each other, and this takes up more computing resources for caching nodes. Based on these, we adopt path cooperation rather than regional cooperation.  
**The diffusion rate of popular contents.** Pushing popular contents to the caching nodes that are close to users will increase the cache hit ratio, as well as reduce the transfer latency of contents. However, on the other hand, the frequent content replacement will affect the serviceability of edge nodes. In the proposed strategy, content placement happens when the popularity of the transmitting content is higher than the least popular content cached on the path. This criterion prevents unpopular content from contaminating the cache near the edge, and the popular content can be progressively pushed to the edge. On the other hand, the content replica selection mechanism such as NRR in the ICN scenario with the NRS can further ensure the diffusion rate of popular contents, for the reason that users will access the nearest content replica. This means that popular content rarely travels over long paths.