esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 3 Model Design
### Data Notation  
We use \(D=\{(x_{i},y_{i})\}_{i=1}^{N}\) to represent a time-series database, where \(N\) represents the number of records, \(x_{i}=\{x^{1},x^{2}\cdots x^{m}\}\) represents the \(i^{\text{th}}\) record, \(x^{j}\) represents the ordered observation value of the record in the whole time-series \(m\), and \(y_{i}\in\{1,2\cdots C\}\) represents the category of the \(i^{\text{th}}\) record, where \(C\in Z^{+}\) is the number of classes. Generally speaking, the goal of TSC is to learn a mapping function from \(x_{i}\) to \(y_{i}\), as shown in the following formula:  
\[y_{i}=f\left(x_{i}\right). \tag{1}\]  
where \(f\left(\cdot\right)\) is the mapping function we aim to learn. The following two sections describe how to extract sequence features and visualization features through LSTM-A and CNN-A, respectively.
# Multi Stage Retrieval for Web Search During Crisis
## 2 Related Works
### Retrieval in Crisis Management  
Crisis situations like natural disasters, industrial accidents, and public health emergencies require fast and accurate information retrieval to support decision-making. Social media platforms, including Twitter, Facebook, and Reddit, have become crucial real-time information sources [34]. However, the high volume of unstructured, redundant, and often contradictory data presents significant challenges for retrieval systems. Another challenge in crisis information retrieval is the presence of misinformation and redundant content.  
CrisisFACTS [35, 36] provides the first attempt at creating a corpus tailored for crisis management with all the mentioned challenges. During the two editions of the challenge, various solutions were proposed to solve a retrieval and summarization task, ranging from extractive [37, 38], reinforcement-learning-based [39], and LLM-based [11, 12] methods. The first edition highlighted the need to improve the recall of the designed systems [35]. While abstractive solutions can improve the quality of the writing, the retriever should be able to recover all relevant documents to avoid definitively losing important facts during a crisis. This work focuses on a full extractive solution that can surpass even the most recent abstractive solutions thanks to a retrieval pipeline tailored to recover as many relevant documents as possible with a multi-stage approach.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 2 Performance of the Navigation Signal  
Three kinds of measurements can be used for positioning: Doppler, pseudorange, and carrier phase. If the carrier phase is required for positioning, the signal must be continuous. Burst signal can only supply Doppler and pseudorange measurements.  
If the signal is continuous, delay lock loop (DLL) can be used to get the pseudorange measurement [15], and frequency lock loop (FLL) can be used to get the Doppler measurement. If the signal is burst, which is true for method 2, only open loop estimation is possible for the corresponding measurements including pseudorange and Doppler.
## 4 Experimental Implementations
The secret key generation rates obtained with a system implementing this scheme, operating at a 1-MHz repetition rate, are shown in Figure 2 as a function of distance. Secret key generation is possible in this case at distances as long as 80 km with a data block size of \(10^{9}\)[64]. These results correspond to the current state-of-the-art in communication range for the continuous-variable QKD technology. In the same figure, we include some representative results of implementations of other CVQKD protocols. We note in particular a recent implementation involving the use of squeezed states and homodyne detectors with the goal of demonstrating composable security against arbitrary attacks (see Table 1), albeit at short distances [62]. Composable security against this type of attack has yet to be shown with coherent states. In this case, as was discussed in Section 3, a security proof is available for heterodyne detection [24], but this setting was only been studied experimentally a few years ago and, therefore, does not take finite-size effects into account [65]. Finally, we note an early implementation of the protocol employing Gaussian post-selection [66], whose security proof was extended later in [51,52]. A few remarks are in order on possible improvements for practical CVQKD implementations. First, it is important to emphasize that thanks to the aforementioned advances, the distance limitation is no longer determined by the efficiency of the post-processing algorithms, but rather by the excess noise present in the setup and especially by the capacity to properly estimate the relevant experimental parameters, as explained above. In terms of reducing the excess noise, recent protocols based on  
Figure 2: Experimental results obtained for various CVQKD protocols offering different levels of security. The distance for the results of [62] and [65] has been calculated from data obtained with free-space experiments (expressed in dB) assuming an optical fiber with an attenuation coefficient of 0.2 dB/km, which is standard at telecommunication wavelengths. the so-called \"noiseless amplification\" [52; 67; 68] might be promising. Efficient parameter estimation over large data blocks, which requires a very stable experimental setup, plays a role not only for the distance, but also for achieving composable security, as discussed in Section 3, and for increasing the secret key generation rate. Indeed, in the implementation of the GG02 protocol described above, a big fraction of the light pulses was used for this process [64]; this fraction can be reduced by improving the hardware stability and, hence, by enabling the estimation of experimental parameters over larger blocks. Furthermore, the secret key generation rate may be increased by increasing the initial repetition rate of the experiments. This necessitates shortening the pulse duration and the time-multiplexing data sampling period, increasing the homodyne detection bandwidth [69] and performing faster error correction on multiple devices, up to the capacity of the network link used for the transmission of the classical data.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 3 Proposed Visibility Estimation Methodology
### Road Visibility Adjustment in Night Scenes  
Although the methods described in Sections 3.1-3.3 might be used to estimate the visibility, they do not generally produce accurate results because the DCP algorithm does not consider the visibility in other driving conditions such as during night time. For example, as shown in Figure 4, from daytime to evening and to late-night, as the ambient brightness decreases, even when the air is clear, the information about the road ahead that people can recognize during driving is less and less. Night road visibility should be based on the driver's identification of road information such as road directions, road boundaries, road signs, etc., so we refer to the actual ambient brightness to estimate night road visibility.  
In the same context, we use the average brightness of the image as the current ambient brightness. First, the ambient brightness \(g_{0}\) of the clear day in this context is to be obtained. The ambient brightness of the current image is \(g\). The adjustment parameter \(\alpha\) at this time is:  
\[\alpha=\frac{g(x)}{g_{0}(x)} \tag{21}\]  
Night road visibility is:  
\[d=\alpha*d_{0},\alpha\in(0,1] \tag{22}\]  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Variable** & **Description** \\ \hline Atmospheric transmissivity \(\tau\) & Atmospheric transmissivity obtained according to the theory of laser atmospheric transmission \\ \hline Road image transmittance \(t_{R}\) & Image characteristic: road image transmittance obtained according to the DCP algorithm \\ \hline Target image transmittance \(t_{T}\) & Image characteristic: target image transmittance obtained according to the DCP algorithm \\ \hline Target image low brightness values \(t_{l}\) & Image characteristic: low brightness area pixel value of the image \\ \hline Target image high brightness values \(t_{h}\) & Image characteristic: high brightness area pixel value
## 2 Materials and Methods
### Characterization of Multilayer Sheets
#### 2.3.2 Optical Evaluation  
The internal transmittance (\(T_{i}\)) was determined for ascertaining the transparency of the monolayer and multilayer samples by the application of the Kubelka-Munk multiple-scattering theory of the reflection spectrum [25]. This theory is based on the fact that light incident on a translucent material can be absorbed or scattered depending on the material's absorption (K) and scattering (S) coefficients. The reflection spectrum (\(R\)) of the samples from 360 nm to 700 nm was obtained on white (\(R_{g}\)) and black (\(R_{0}\)) backgrounds using a MI-NOLTA spectrocolorimeter (CM-3600d, Minolta Co., Tokyo, Japan). Equations (1) and (2) were used to determine the \(a\) and \(b\) parameters, whereas the reflectance spectra of a sheet with an infinite thickness (\(R_{\infty}\)) were calculated according to Equation (3) and \(T_{i}\) was determined using Equation (4). Three measurements were taken on each sample, at different points, from the glossy side of the paper samples.  
\[a=\frac{1}{2}\cdot\left(R+\frac{R_{0}-R+R_{g}}{R_{0}\cdot R_{g}}\right) \tag{1}\]  
\[b=\left(a^{2}-1\right)^{\frac{1}{2}} \tag{2}\]  
\[R_{\infty}=a-b \tag{3}\]  
\[T_{i}=\sqrt{\left(a-R_{0}\right)^{2}-b^{2}} \tag{4}\]  
Film color coordinates \(L^{*}\) (\(L^{*}\) = 0: black, \(L^{*}\) = 100: white), \(a^{*}\) (\(a^{*}\) > 0: red; \(a^{*}\) < 0: green), and \(b^{*}\) (\(b^{*}\) > 0: yellow; \(b^{*}\) < 0: blue) were determined from \(R_{\infty}\) spectra, using 10\({}^{\circ}\) observer and D65 illuminant as reference system. The chroma or color saturation (\(C_{ab}\)*), expressed from 0 to 100, was determined from Equation (5). The tone or hue angle (\(h_{ab}\)*) was calculated from the \(a^{*}\) and \(b^{*}\) coordinates, which are expressed in degrees from 0 to 360\({}^{\circ}\), using Equation (6). Finally, Equation (7) was used to determine the color difference (\(\Delta E_{ab}\)*), where \(\Delta L^{*}\), \(\Delta a^{*}\), and \(\Delta b^{*}\) correspond to the differences between the color parameters of each multilayer compared to the uncoated paper.  
\[C_{ab}*=\sqrt{a*^{2}+b*^{2}} \tag{5}\]  
\[h_{ab}*=arctg\left(\frac{b*}{a*}\right) \tag{6}\]  
\[\Delta E*=\sqrt{\left(\Delta L*\right)^{2}+\left(\Delta a*\right)^{2}+\left( \Delta b*\right)^{2}} \tag{7}\]  
Figure 1: (**a**) Hydraulic press used for the heat-sealing process; (**b**) scheme of the developed multilayers based on paper sheets and films of poly(3-hydroxybutyrate-_co_-3-hydroxyvalerate) (PHBV) and polyethylene terephthalate (PET).  
The color change was evaluated according to the following criteria: negligible (\(\Delta E_{ab}\)* \(<\) 1), only an experienced observer can tell the difference (\(\Delta E_{ab}\)* \(\geq\) 1 and \(<\)2); an inexperienced observer notes the difference (\(\Delta E_{ab}\)* \(\geq\) 2 and \(<\) 3.5); there is a clear notable difference (\(\Delta E_{ab}\)* \(\geq\) 3.5 and \(<\)5); and the observer notices different colors (\(\Delta E_{ab}\)* \(\geq\) 5) [26].
# Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
### Measurement Parameters in Eye Tracking Technology  
As mentioned above, eye tracking technology facilitates the collection of different metrics. First, it enables the recording of the learner's eye movement or eye tracker while performing an activity. In addition, the use of eye tracking technology allows the definition of relevant vs. non-relevant areas (AOI) in the information being learned [17]. Within these metrics, different parameters can be studied, such as the fixation time of the eye on the part of the stimulus (interval between 200 and 300 ms). In this line, recent studies [18] indicate that the acquisition of information is related to the number of eye fixations of the learner. Similarly, another important metric is the saccade, which is defined as the sudden and rapid movement of a fixation (the interval is 40-50 ms). [PERSON] et al. [18,19] found differences in the type of saccade depending on the phase of information encoding the learner was at. Another relevant parameter is the scan path or tracking path. This metric collects, in chronological order, the steps that the learner performs in the resolution of the learning task within the AOI marked by the teacher [18,19]. Likewise, eye tracking technology allows the use of supervised machine learning techniques to predict the level of learners' understanding, as this seems to be related to the number of fixations [20]. Recent studies indicate that variability in gaze behaviour is determined by image properties (position, intensity, colour and orientation), task instructions, semantic information and the type of information processing of the learner. These differences are detected using AOIs that are set by the experimenter [21]. In summary, eye tracking technology records diverse types of parameters that provide different interpretations of the underlying cognitive processes during the execution of a task. These parameters fall into three categories: fixations, saccades and scan path. The first one, fixations, refers to the stabilisation of the eye on a part of the stimulus during a time interval between 200 and 300 ms. In addition, eye tracking technology provides information about the start and the end time in x and y coordinates. The meaning of the cognitive interpretation is related to the perception, encoding and processing of the stimulus. The second ones, saccades, refers to the movement from one fixation to another, which is very fast and in the range of 40-50 ms. The third ones, scan path, refers to a series of fixations in the AOIs in chronological order of execution. This cognitive metric is useful for understanding the behavioural patterns of different participants in the same activity. Furthermore, each of these metrics has its own measurement specifications. Table 1 below shows the most significant ones and, where appropriate, their relationship with information processing.
## 2 Materials and Methods
### Material Characterization  
1. FTIR spectra of the membrane films were obtained with an FTIR Spectrometer (Spectrum 100, PerkinElmer, Waltham, MA, USA) in ATR mode using a diamond crystal (scan number = 4, resolution = 4 cm\({}^{-1}\)).
2. The H-NMR spectrum was determined by dissolving 0.05 g sPEEK in 1 mL of deuterated DMSO. The H-NMR instrument (Bruker Avance III HD 400, Kontich, Belgium) was operated at 400 MHz. The degree of sulfonation was determined via the method of [PERSON] et al. [31]: \[\frac{n}{12-2n}=\frac{H_{E}}{H_{A}+H_{B}+H_{C}+H_{D}+H_{A^{\prime}}+H_{B^{ \prime}}}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Membrane**} & **Sulfonation** & **Sulfonation** & **Crosslinking** & **Crosslinking** \\  & **Temperature (\({}^{\circ}\)C)** & **Time (h)** & **Temperature (\({}^{\circ}\)C)** & **Time (h)** \\ \hline sPEEK & 60 & 24 & / & / \\ sPEEK/DCX & 60 & 24 & 140 & 15 \\ sPEEK/DCX-s90\({}^{\circ}\)C & 90 & 24 & 140 & 15 \\ sPEEK/DCX-CX24h & 60 & 24 & 140 & 24 \\ sPEEK/DCX-CX190\({}^{\circ}\)C & 60 & 24 & 190 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications for the synthesis of the considered proton exchange membranes.  
Figure 1: Schematic of the different steps for the membrane preparation. (**a**) Sulfonation of PEEK, (**b**) preparation of casting solution, (**c**) crosslinking of the membrane material.  
Degree of sulfonation (DS) = \(n\cdot 100\%\) in which H\({}_{\text{A}}\), H\({}_{\text{B}}\), H\({}_{\text{A}^{\prime}}\), H\({}_{\text{B}^{\prime}}\), H\({}_{\text{C}}\), H\({}_{\text{D}}\) and H\({}_{\text{E}}\) refer to the area below the peaks on the H-NMR spectrum corresponding to the aromatic hydrogens on position A, B, A\({}^{\prime}\), B\({}^{\prime}\), C, D and E, respectively. The identification of the aromatic hydrogen peaks is given in Figure 2[31]. 3. X-ray photoelectron spectroscopy (XPS) was carried out on a Kratos Axis SUPRAX-ray photoelectron spectrometer equipped with a charge neutralizer and a magnetic focusing lens, using Al K monochromated radiation (1486.7 eV). The surface charging of these samples was neutralized using a flood gun. All the spectra were fitted using CasaXPS, with energy referencing the C 1s peak. The background was corrected using a Shirley-type function. 4. Scanning electron microscopy (SEM): To visualize the membrane structure and pore sizes, the membranes were analyzed using an SEM ULTRA (Zeiss). Membrane samples of approximately 1 cm\({}^{2}\) were placed on an SEM holder and sputter coated with a gold layer of 1.5 to 2 nm (BALZERS UNION FL 9460 BALZERS SCD 030). A voltage of 5 kV was used to obtain an image of the membrane surface. To evaluate the cross-section of the membrane, the membranes were repeatedly dipped in deionized water and consecutively immersed in liquid nitrogen. At these low temperatures, the membranes became brittle and could be broken without deforming the cross-section. A gold coating was also applied to these samples before the SEM analysis [32].
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
###### Abstract  
Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA-VMD-LSTM-MLR-FE (SVLM-FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA-VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACP). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM-FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM-FE has high accuracy and stability.  
short-term power load forecasting; variational mode decomposition; sparrow search algorithm; hybrid forecasting; feature selection +
Footnote †: journal: Applied Sciences  
doi:10.3390/app13116845
## 3 Modeling
### Validation of Model  
To evaluate the performance of the simulation model of the direct operating solenoid valve, the laboratory testing is conducted to make the comparison with simulation results. Figure 6 provides the schematic diagram of the test rig 12. It is comprised of a heat exchanger 1 manufactured by Siemens(Berlin, Germany), an oil filter 2, a motor (also by Siemens), an oil pump 3 whose rated power is 13.6 kW, a reservoir, a TCU 5, an external computer 4 and a fixture 11 where the direct operating solenoid valve inserted in. The pressure of the control port is measured by a pressure sensor 9 (Sensata, Attleboro, MA, USA) which has a measuring range of 0-2.5 MPa. And the flow rate is obtained by a flow meter 6 (Hydrotechnik, Limburg, Germany) ranging from 7.5 L/min to 30 L/min. The automatic transmission fluid used in the test is Dexron IV from local supplier.  
Figure 5: Modeling of the direct operating solenoid valve.  
Figure 6: Schematic diagram and photos of experimental test rig.  
The principle of the hydraulic circuit can be simply described as follows. High pressure hydraulic fluid was supplied from the mechanic pump driven by the motor to the direct operating solenoid valve 10 installed in a tailor made fixture 11. The control pressure of the solenoid valve spool was controlled by the input signal sent by the computer and transferred by the transmission control unit (TCU) to the electromagnetic unit. The control pressure, which is a most important feature to verify the simulation model as well as analyze the valve dynamic characteristics, was displayed in the computer collected by the pressure sensor and converted by TCU.  
Comparison was made between the measured and simulated pressure at the control port. The input current which is extracted from the actual control signal is shown in Figure 7. The current begins with a peak up to 600 mA to activate the spool within 0.01 s. After 0.14 s, the electric current returns to 300 mA and holds for 1 s. From 1.16 s to 11.16 s, the electric current increases linearly to 1000 mA and maintains for another 1 s. Finally, the electric current declines to 0 from 12.16 s to 28.29 s.  
Both the simulated and measured control pressure at the control port is illustrated in Figure 7. It is obvious that the maximum pressure in the simulation result is the same with that in the experimental one. And there is a good agreement between the two curves with about only 4% error of the slope. Moreover, the pressure response time and the time when the pressure disappears are completely consistent. Therefore, the experimental result confirms the correctness of the model. The pressure difference is within 0.1 MPa, which is probably caused by the equipment error.
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 5 Application Areas
### Disinfection of Food Contact Surfaces  
In dairy technology, cross-contamination of micro-organisms between machines and products is a major concern [10]. The growth of bacteria, fungi, and yeasts is easily obtainable in every area [76]. Surfaces of conveyors as well as other devices being used during preparing, processing, and storage regions may cause contamination and it can be disinfected with UV light [10]. UV light is a dry and biologically inert process that decreases the microorganism count by around 99.9% with minimum heating of the packing material [25]. Between the UV lamp and the area to be disinfected, there should be no obstruction. Since dirt absorbs radiation and thereby protects bacteria, the effectiveness of this application is therefore dependent on the sanitation of the material surfaces. As a result, it is possible to conclude that UV light should be used during the washing of dairy appliances [10].