esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 HTOPSIS for Solving the HMADM Model with Deviation and Fixation Attributes  
### New Normalization Approach for Heterogeneous Attributes  
The heterogeneous attributes entailed by high-speed press structural optimization are incommensurable due to the difference in their metrics. Thus, the normalization of heterogeneous attributes is of great importance in the implementation of HTOPSIS. As concluded by [PERSON] and [PERSON] [24], vector transformation can retain the differences among all the attribute values after normalization, and it is frequently applied to the normalization of different attributes in ranking. However, all of the currently available vector transformation methods focus only on the normalization of benefit and cost attributes, and cannot handle the deviation and fixation ones, especially when they are described in different mathematical forms.  
In order to solve the HMADM model describing the high-speed press optimization problem (Section 2), the deviation and fixation attributes are firstly transformed into benefit and cost attributes; then, the equivalent and normalized attribute values of all the alternatives are calculated based on the possibility degree method and relative preference relation. Specifically, a deviation attribute is firstly transformed into a benefit one with zero NIS; then, attribute values smaller than 0 are symmetrically mapped about 0 to ensure that the attribute values of all the alternatives are nonnegative; and finally, the deviation attribute can be normalized in the same way as a benefit attribute. The normalization approach for a fixation attribute is similar. The only difference is that the fixation attribute should be firstly converted into a cost attribute with zero PIS before normalization.  
Without loss of generality, the deviation attribute is taken as an example, here, to explain the normalization approach when the attribute value is described as a real number, interval number and TFN. Supposing that the normalized decision matrix of the HMADM problem is described as \(\mathbf{R}=\left[r_{ij}\right]_{m\times m}\), where \(r_{ij}\) is the \(j\)th normalized attribute value of the \(i\)th alternative configuration, the derivations for the normalization of heterogeneous attributes are given in detail hereinafter.  
Figure 2: TFNs of fuzzy linguistic terms.
## 2 Problem Description  
The proposed flow-shop group-scheduling problem can be stated as follows. A set of \(G\) groups of jobs has to be processed in a manufacturing cell with \(M\) machines arranged in a flow-shop layout. Each group \(g=1\), \(2\),..., \(G\) holds \(n_{g}\) jobs; the total amount of jobs to be processed along the system is equal to \(\sum\limits_{g=1}^{G}n_{g}=N\). The setup time of each \(j\)-th job pertaining to group \(g\) (\(j=1\), \(2\),..., \(n_{g}\)) on machine \(i\) (\(i=1\), \(2\),..., \(M\)) is included in its runtime \(t_{ig}\) and does not depend on the preceding job. On the other hand, a sequence-dependent setup time \(S_{ihg}\) is necessary when switching from a group \(h\) to group \(g\) on a given machine \(i\). All jobs and groups are processed in the same sequence through each workstation. Group setup operations are assumed to be anticipatory, as they can be performed even if the first job belonging to the group is still unavailable. Pre-emption is not allowed, _i.e._, when a job starts to be processed, it must be completed before leaving the machine. All jobs are ready to be worked at the beginning of the scheduling period, being their release time equal to zero. Machines are continuously available during the whole production session. No precedence relationship exists either among groups or among jobs belonging to the same group. The objective function to be minimized is the total flow time, _i.e._, the sum of completion times of all jobs.  
For sake of clarity, Tables 1-2 reports setup and processing times for an example problem in which \(M=2\), \(G=2\), \(n_{1}=3\), \(n_{2}=2\). It is worth noting that symbol \(S_{i0g}\) denotes the time required to setup a group of jobs \(g\) whether it is the first group to be processed in machine \(i\). An illustrative example is reported in the following paragraph to clarify what stated before. It refers to Table 1 where two groups with two and three jobs have been recognized, respectively. Let suppose to process a sequence of groups 2-1, while the corresponding job schedules for each group are: 1-2 for group two and 2-3-1 for group one. The related Gantt is shown in Figure 1, the total flow time value associated to such a schedule is \(F_{11}+F_{12}+F_{13}+F_{21}+F_{22}=25+17+20+9+11=82\).
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Ionic Liquids (ILs) and Deep Eutectic Solvents (DES)  
Ionic liquids (ILs) are organic salts, composed by an anion and a cation, that remain liquid near room temperature. The term \"designer solvents\" is frequently applied to these compounds, due to their particularly versatility: through the combination of different cations and ions, ILs can be obtained with very different physical and chemical properties, such as melting point, viscosity, density, solvation ability, and hydrophobicity [53]. ILs also have typically excellent thermal stability and are non-flammable [54]. One of the most important characteristics of ILs is their negligible vapor pressure, which means that they are essentially non-volatile, and can be contained, completely recycled, and used several times, which is a great advantage over volatile organic compounds [55].  
However, their high cost is still an issue, and also, the toxicity effect on humans/environment of most ILs is still unclear [56; 57], which leads to the question, how green are these solvents? Nevertheless, this drawback can be overcome by choosing an infinite variety of cations and anions, enabling the specific design of less toxic ILs.  
Since the nineties, the number of publications in this area increased exponentially [58] with applications in many different fields, including the production of MIPs, wherein ILs started to be used as solvents [59; 60; 61; 62; 63; 64; 65], and further with other functionalities, such as templates (Figure 2) [66; 67; 68; 69], monomers [70; 71; 72], crosslinkers [73] or additives to polymer reactions.  
It has already been shown that the use of ILs in MIP synthesis can accelerate the process, also improving the selectivity and adsorption of the template [74; 75].  
Figure 1: Scheme for the production of MIP-layered silica beads: (a) 3-(Trimethoxysilyl) propyl methacrylate (MPS)-functionalization in scCO\({}_{2}\); (b) plasma functionalization of silica beads, in which \(R\) are the radicals produced; (c) MIP-layered core-shell beads produced in scCO\({}_{2}\). Reproduced from reference [52] with permission from Elsevier.
# Hg\({}^{2+}\) Optical Fiber Sensor Based on LSPR with PDDA-Templated AuNPs and CS/PAA Bilayers
## 3 Results
### Refractive Index Response  
The refractive index sensing performance was tested by immersing the sensor probe in a series of sucrose solutions with different concentrations of 4.8 wt%, 11.4 wt%, 18.00 wt%, 24.00 wt%, 29.00 wt%, and 35.00 wt%. The external refractive index of the sucrose solutions can be estimated to be 1.3400,  
Figure 4: SEM images of AuNPs immobilized on optical fibers (**a**) before and (**b**) after the flame-brushing process.  
Figure 3: LSPR spectra evolution (**a**) absorption spectra of gold nanoparticles (AuNPs) assembled films at different assembly time; (**b**) absorption spectra after flame brushing and coating multiple CS/PAA (chitosanan/poly acrylic acid) bilayers.  
1.3500, 1.3605, 1.3706, 1.3794, and 1.3903, according to the empirical formula at room temperature [33, 34]. Figure 5a,b illustrates the spectra evolution and the wavelength shift of the LSPR absorption peak. The results exhibit a nonlinear response, and the sensitivity is around 257.8 and 821.3 nm/RIU and at the RIU of 1.34 and 1.39, respectively. The full width at half maximum (FWHM) of the sensor is around 93 nm, and hence the Q-factor is calculated to be the ratio between the resonance wavelength and the FWHM, i.e., 6.1. The figure of merit (FOM) can be calculated as the quotient between the FWHM and the sensitivity, and hence the FOM of the sensor is expected to be 2.7 to 8.8 RIU\({}^{-1}\).
## 2 Materials and Methods  
### Reagents and Materials  
Chemical standards hexanal, \(N\)-ethylpyrrole, 2-methylpyrazine, heptanal, 2,5-dimethylpyrazine, 2-acetylfuran, dimethyl trisulfide, 1-octen-3-ol, methylheptenone, octanal, (_E,E_)-2,4-heptadieneI, 2,2,6-trimethyl-cyclohexanone, benzeneacetaldehyde, (_E_)-2-octenal, 2-acetylyrrole, 3,5-octalationi-2-one, methyl benzoate, linaol, nonanal, 6-methyl-3,5-heptadiene-2-one, phenylethyl alcohol, (_E_)-2-nonenal, benzyl acetate, methyl salicylate, (_E,E_)-2-A-nonadenal, indole, hexyl hexanoate, (_E_)-2-hexenyl hexanoate, \(\alpha\)-cedrene, \(\alpha\)-ionone, coumarin, (_E_)-\(\beta\)-famesene, _trans_-\(\beta\)-ionone, dihydroactinidiolide, limonene, linaol, \(\alpha\)-terpineol, (_E_)-neridol, and (_Z_)-neridol were purchased from J&K Scientific Ltd. (Beijing, China), Sigma-Aldrich Corp. (Shanghai, China), Innochem Science & Technology Co., Ltd. (Beijing, China), and Aladdin Corp. (Shanghai, China). _Trans_-linalo oxide (furanoid), _cis_-linalo oxide (furanoid) and _trans_-\(\beta\)-ocimene were purchased from Toronto Research Chemicals Inc. (Toronto, ON, Canada). Calamenene was purchased from Purchem-Standard Biotechnology Co. Ltd. (Chengdu, China). The standards were diluted with ethanol (PR grade; Beijing, China) for chemical structure identification. \(N\)-alkanes mixed standards (C8-C40) for linear retention index (RI) determination were purchased from J&K Scientific Ltd. (Beijing, China), and purified water, which was used as the tea solvent, was purchased from Wahaha Group Company (Hangzhou, China).  
Divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, 2 cm) purchased from Supelco (Bellefonte, PA, USA) was used as the HS-SPME fiber for extracting volatiles in teas. Thread headspace bottles (20 mL) were purchased from Agilent Technologies, Inc. (Santa Clara, CA, USA).
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 4 Results  
The proposed hybrid deep learning framework yielded robust classification performance and provided valuable insights into the neurophysiological patterns associated with ADHD. The results are organized into exploratory EEG signal analysis, model evaluation metrics, and interpretability findings using SHAP values. Emphasis is placed on both diagnostic accuracy and the clinical relevance of key EEG features contributing to model predictions.
### Observational Data
#### 2.1.2 In Situ Observation Data  
In situ observation data are collected directly from the ocean using a variety of instruments deployed in the water, providing highly accurate and localized measurements. These instruments include buoys, floats, underwater cameras, tide gauges, and research vessels, each offering unique capabilities [48]. Specifically, buoys, such as those operated by the National Data Buoy Center (NDBC), are stationary platforms equipped with sensors to measure wave height, SST, and atmospheric pressure, providing insights into oceanographic and meteorological processes [49]. Floats, such as Argo floats, are free-drifting devices that move vertically through the water column, gathering data on temperature, salinity, and other ocean parameters at varying depths to support studies on ocean circulation, ecosystem dynamics, and climate-driven changes. Underwater cameras capture visual data on marine ecosystems and biodiversity, offering direct observations of subsurface habitats [50]. Tide gauges, positioned along coastlines, monitor changes in sea level and tidal patterns, contributing to research on coastal dynamics and sea level rise. Research vessels play a vital role in in situ ocean observation by offering high-resolution, multiparameter measurements [51]. Equipped with advanced instruments such as Conductivity-Temperature-Depth profilers and rosette samplers, these ships can collect detailed data on physical (e.g., temperature, salinity), chemical (e.g., dissolved oxygen, pH), and biological (e.g., chlorophyll concentration, plankton abundance) parameters. Unlike autonomous platforms such as buoys or floats, research vessels allow adaptive mission planning and direct human supervision, valuable for validating satellite observations and supporting interdisciplinary research. Notable programs include the Global Ocean Data Analysis Project (GLODAP), which provides high-quality, ship-based measurements of the ocean carbon system, and the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP), which offers physical and biogeochemical ocean data to support global climate research.  
While ocean remote sensing provides broader spatial coverage, in situ observation data complement it by delivering ground-truth measurements that validate remote observations, improving the accuracy and reliability of oceanographic information.
## 3 Parameter Analysis and Optimization Design
### Parameter Analysis
#### 3.1.6 Impact of Inter-Story Displacement  
This section explores the variation law of \(f_{d}\) and \(f_{E}\) with the inter-story displacement \(U_{1}\). \(U_{1}\) affects \(f_{d}\) and \(f_{E}\) by means of changing the loss stiffness \(k_{d}^{\prime\prime}\) of VD. As is shown in Figure 11(a), when increasing \(U_{1}\), \(f_{d}\) gradually increases, and \(f_{E}\) increases first and later decreases. Increasing the lever's amplification coefficient \(\chi\) can increase \(f_{E}\), which has been discussed in Section 3.1.3. As is shown in Figure 11(b), when \(U_{1}\) is larger, increasing \(\chi\) has a significant improvement on \(f_{E}\). However, when \(U_{1}\) is small, increasing \(\chi\) does not have a good effect on improving \(f_{E}\). In Figure 11(c), \(i_{b1}\) refers to the beam's bending line stiffness with the section of 400 mm \(\times\) 800 mm. As is shown in Figure 11(c), when \(U_{1}\) is small, increasing \(i_{b}\) has a significant improvement on \(f_{E}\). However, when \(U_{1}\) is larger, increasing \(i_{b}\) will lead to a decrease in \(f_{E}\). Meanwhile, increasing \(i_{b}\) causes the peak energy-consumption efficiency \(f_{PE}\) to decrease first and then increase. In Figure 11(d), \(c_{1}=5(\text{kN}/(\text{mm}/\text{s})\,^{0.5})\). As is revealed in Figure 11(d), when \(U_{1}\) is larger, increasing the damping coefficient \(c\) has a significant improvement on \(f_{E}\). However, when \(U_{1}\) is smaller, a higher damping coefficient \(c\) leads to a decrease in \(f_{E}\). Meanwhile, increasing the coefficient \(c\) does not change the magnitude of the peak energy consumption coefficient \(f_{PE}\). Moreover, it will cause \(f_{PE}\) to move towards larger displacements.
## 4 Test Scenarios
### Scenario III: Maritime Navigation under the Influence of Jamming on GNSS L5/E5a/ E5b Signal  
The wideband L5 jammer was used in this scenario to impair the L5/E5a/E5b signals. Once the jamming was applied on L5/E5a/E5b signals, other signals were unaffected, as expected. As seen in Figure 8, with the variation in J/S power, the L5/E5a/E5b signal exhibits a somewhat similar average C/N\({}_{0}\) disparity pattern to that in Scenario II.  
Figure 8: Average C/N\({}_{0}\) of tracked GNSS signals during the presence of jamming on L5/E5a/E5b signals.  
Figure 7: Average C/N\({}_{0}\) of tracked GNSS signals with the presence of jamming on L1/E1/B1 signals.
# A TCP Acceleration Algorithm for Aerospace-Ground Service Networks
## 3 Transmission Acceleration Using BoostTCP
### Improved Congestion Judging and Handling Mechanism
#### 3.1.2 Prevent Congestion Deterioration  
Congestion may also occur in an ultra-long-distance special-line optical fiber transmission network due to a large number of networks and relays. In addition, congestion may worsen if not handled properly, causing two problems. First, the time required for retransmission and hole filling will be extremely long due to the high packet loss rate, and as a result, the TCP transmission window will become stuck for an extended period, and transmission will become slower or even fail. Second, retransmission is required due to increased packet loss; the retransmission rate increases while the effective data rate declines.  
Therefore, users will notice that although online traffic increases, the actual application rate does not change.  
BoostTCP determines the congestion degree in real time and slows it down to prevent congestion from deterioration and reduce the number of lost packets, resulting in faster and smoother transmissions with an effective data rate.  
In summary, the BoostTCP congestion judgment algorithm is an automatic state machine that considers various network characteristics along the transmission path. Its function is to learn and improve congestion judgment skills intelligently in a connection-by-connection manner. Since learning the network characteristics requires data accumulation, and the BoostTCP algorithm allocates resources to each TCP connection, the optimal application scenario for the BoostTCP algorithm is a long-connection scenario rather than a high-concurrency scenario, which is satellite payload data in this article.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 4 Experimental Results
### Experimental Set-Up
The first one is recovery angular error (recovery AE) [9; 18], which is defined as the angle in degrees between the illumination's actual 3D-chromaticity \(\mathbf{e}_{a}\) and its estimated 3D-chromaticity \(\mathbf{e}_{c}\): \(\gamma(\mathbf{e}_{a},\mathbf{e}_{c})=\cos^{-1}\left(\frac{\mathbf{e}_{a} \mathbf{e}_{c}}{\|\mathbf{e}_{a}\|_{\mathbf{e}_{c}}\|}\right)\). Here the symbol of \(\cdot\) denotes the scalar product (dot product), and the symbol of \(\|\cdot\|\) indicates the Euclidean norm of a vector. The second metric is called reproduction angular error (reproduction AE) [53; 54], which usually is more efficient than the traditional recovery AE. The reproduction AE is defined as the angle in degrees between the true color of whitesurface and the reproduced color of white surface: \(\gamma(\mathbf{e}_{c},\mathbf{e}_{u})=\cos^{-1}\left(\frac{\mathbf{e}_{c}-\mathbf{e }_{u}}{\|\mathbf{e}_{c}\|\|\mathbf{e}_{u}\|}\right)\). Here \(\mathbf{e}_{c}\) denotes the white light source (true color of white surface), \(\mathbf{e}_{c}=[1,1,1]^{T}\), and \(\mathbf{e}_{u}\) indicates the reproduced color of white surface, \(\mathbf{e}_{u}=\mathbf{e}_{a}/\mathbf{e}_{e}\), which is the actual 3D-chromaticity \(\mathbf{e}_{a}\) divided by the estimated 3D-chromaticity \(\mathbf{e}_{e}\). Since both recovery and reproduction AEs are not normally distributed, the Mean and Median values along with the Trimean value are used to assess the statistical performance of color constancy algorithms in this study. Trimean is defined as a weighted average \(\mathit{Trimean}=(Q_{1}+2Q_{2}+Q_{3})/4\), where \(Q_{1}(\mathit{Best}\ 25\%)\), \(Q_{2}\), and \(Q_{3}(\mathit{Worst}\ 25\%)\) are the first, second, and third quantiles of recovery or reproduction AE, respectively. In addition, the maximums of both AEs are used as extra metrics to assess the robustness of the illuminant models along with the metric of \(Q_{3}(\mathit{Worst}\ 25\%)\).
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 3 Validation of the Procedure  
An experimental application was performed using optical angled PC polish fiber endface connectors, which are shown in Figure 3. Fiber optics are connected to obtain a signal in the telecommunication industry, and their alignment must be in a range designed to avoid failure due to high insertion loss, even when submitted to environmental or mechanical stress during their function in service of field applications. For this purpose, the thermal and humidity tests were performed using an environmental chamber. In contrast, the vibration tests were carried out using an electromechanical PM250 HP shaker and power amplifier with an MB dynamic controller. The fixture on the vibration shaker allowed the fiber adapters used for connecting the fiber connectors to be mounted on the three mutually perpendicular axes. Both the chamber and the shaker provided the load and stress of the profiles performed on the fiber optic connections and its signal under test. The main idea was to determine the percentage of life consumed by the service life testing in terms of time to failure, as defined in Figure 2.
## 2 Materials and Methods  
### External Conditions During the Monitoring  
To compare the radiative conditions that occurred from July to September 2023 in Lublin against the 1991-2023 multi-year period, data from the product \"SID--Surface Incoming Direct Radiation, version 004\" [44] were used. This product was derived from the observations made by the first and second generations of geostationary Meteosat satellites, which monitored the atmosphere over Europe, Africa, the Atlantic Ocean, and parts of South America. The dataset was obtained from the SARAH-3 (Surface Solar Radiation Data Set-Heliosat) database, which was provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) under the Satellite Application Facility on Climate Monitoring (CM SAF). The SARAH-3 dataset had a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), offering daily mean values of surface incoming direct solar radiation. In turn, the data for all three months regarding the maximum air temperature (T\({}_{\rm max}\)) for the years 1991-2023 were obtained from the Lublin-Radawiec meteorological station (\(\varphi\) = 51\({}^{\circ}\)13\({}^{\prime}\)00\({}^{\prime\prime}\) N, \(\lambda\) = 22\({}^{\circ}\)24\({}^{\prime}\)23\({}^{\prime\prime}\) E, h = 238 m a.s.l.), located near the southwestern periphery of the city and belonging to the Institute of Meteorology and Water Management-National Research Institute. The statistical significance of the trends was assessed using the Mann-Kendall test at significance levels of \(p\) < 0.05 [45], and the magnitude of changes over time (slope) was calculated using [PERSON]'s method [45].  
The diurnal course of the solar and thermal conditions in Lublin from July to September 2023 was also analysed. Actinometric measurements were carried out on an observation terrace located on the roof of the Institute of Pedagogy at UMCS, situated in the north western part of Lublin. The measurements included two components of solar radiation, namely the integral of total radiation (TSR), measured by a CMP6 pyranometer from Kipp & Zonen, and the integral of diffuse radiation (DIF), also measured by a CMP6 pyranometer equipped with a shading ring (Shadow Ring CM121B, Kipp & Zonen). The surface incoming direct radiation (SID) was calculated as the difference between TSR and DIF. Data on the air temperature (T\({}_{\rm avg}\), T\({}_{\rm max}\), and T\({}_{\rm min}\)) were collected from two meteorological stations located at Liteswi Square (city centre) and the Botanical Garden (an open area in the northwestern part of Lublin), both belonging to the Department of Hydrology and Climatology at Maria Curie-Sklodowska University (UMCS) in Lublin. At both stations, measurements were taken at 10 min intervals using a Vaisala HMP 155 sensor placed in a radiation shield at a height of 200 cm above ground level.  
Multi-Year and Daily Variability of Radiative and Thermal Conditions in July, August, and September in Lublin