esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 4 Experiments
### Experimental Design and Setup
**Feature Selection** We carried out feature selection with decision trees for every feature vector. Afterwards, we removed irrelevant features (_importance_ = 0, labeled as \"negligible\" in Table 6). Additionally, we analyzed the most relevant features for each case. The scheme used for feature selection is a linear decision tree implemented in Python with the scikit-learn library tools (scikit-learn v0.19.1, [20], [[http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html](http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html)]([http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html](http://scikit-learn.org/.../sklearn.tree.DecisionTreeClassifier.html))). Decision tree maximum depth and minimum leaf size were adjusted by means of a randomized parameter search in order to optimize the performance and avoid overfitting ([[http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)]([http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html))). 6. **PCA-Based Transformation and Reduction** To facilitate algorithm operations and work with a further reduced feature set, we applied PCA (Principal Component Analysis) as a last step of the preprocessing. PCA is a way to minimize feature dependencies, simplify learning models, and optimize classification performance [21]. First, training means were removed from training and test data to eliminate the intercept. This step is required when applying PCA to guarantee accurate approximations and reduce the mean square error [22]. Second, training and test data were projected based on training data PCA coefficients. Last, feature selection was applied again to remove final irrelevant features (if any) from the new problem space and reduce the dimensions of the resulting vector from PCA. This second feature selection step was also driven by the GT-labels (_external_ criteria) to avoid losing information about classes that show low representativity in the training data. 7. **Cross-Validated Supervised Classification** Applied algorithms were all based on models and operated on training and test datasets following a 5-fold cross-validated evaluation. All algorithms were also implemented in Python with the scikit-learn library (scikit-learn v0.19.1, [20]). Since some parameters showed a considerable sensitivity and broad ranges, the parameterization of learners was adjusted by means of evolutionary search ([[https://github.com/rsteca/sklearn-deap](https://github.com/rsteca/sklearn-deap)]([https://github.com/rsteca/sklearn-deap](https://github.com/rsteca/sklearn-deap))).
# The Application of Microplasma in the Terahertz Field: A Review
## 3 Challenges
### Microplasma Photonic Crystals  
Previous experimental works have demonstrated that some specially designed microplasma photonic crystals have THz stopbands at 0.1-0.2 THz, which could be used to control the THz wave propagation by changing plasma parameters. Simulation results have also predicted that THz radiation with frequency up to several THz can be tuned by microplasma photonic crystals as long as the plasma density increases to \(10^{15}\) cm\({}^{-3}\) or higher. Further experimental verification of this prediction is needed. In addition, the effects of the plasma uniformity, the transient discharge process, and the 3D complex structure on the THz bands of the microplasma photonic crystals are required to be investigated.  
Figure 9: Schematic diagram of the coherent detection of broadband THz waves with ambient air [62].
## 2 Materials and Methods
### Analytical Methods
#### 2.3.2 Shale Oil  
Shale oil was characterized by multiple analytical methods. Ultimate analysis was carried out with a vario MICRO cube elemental analyzer (Elementer, Langenselbold, Germany). The combustion furnace's temperature was 960 \({}^{\circ}\)C, and the pressure of high-purity oxygen was 0.20 MPa. Gas chromatography-mass spectrometry (GC-MS) analysis was performed by an Agilent 7890B-5975C instrument to determine the composition of shale oil. Utilizing the Agilent MSD Chem Station (version F. 01. 01) software workstation, data were acquired and analyzed. The inlet was set to split mode, and the temperature was maintained at 300 \({}^{\circ}\)C. The carrier gas was helium, with a flow rate of 2 mL/min and a division ratio of 50:1. The oven was preheated to 60 \({}^{\circ}\)C for 3 min and then heated at a rate of 5 \({}^{\circ}\)C/min until a final temperature of 280 \({}^{\circ}\)C was reached and maintained for 15 min. The oven was equipped with an HP-5 MS column (0.32 mm inner radius; 0.25 \(\upmu\)m film thickness). The MS detector employed an electron ionization detector with settings of 70 eV and 230 \({}^{\circ}\)C. The simulated distillation experiment of shale oil was conducted in accordance with GBT 18611-2001 using an Agilent 7890A gas chromatograph, which had an auto-sampler and an automatic injector. According to SY/T 5119-2016, the quantitative separation of shale oil group components (SARA analysis) was implemented by using an MK-6s rod thin-layer flame ionization analyzer (IATROSCAN, Tokyo, Japan).  
Prior to the GC-MS and simulated distillation experiments, 0.04 g of shale oil was dissolved in 2 g of CS\({}_{2}\) and stirred to dissolve it completely. Moreover, the ultimate, GC-MS, simulated distillation and SARA analyses were performed several times after the shale oil obtained under the same conditions was fully mixed to ensure stability and reproducibility. The average of two groups' SARA, simulated distillation, and ultimate analysis results (error less than 5%) served as the basis for analysis and discussion.
## 2 Simulation and Experiments Methods
### Fabrication of the CMA Mould  
CMA moulds were fabricated by the combined rough-fine grinding, as illustrated in Figure 3. The operations are selected for rough grinding with a metal-bonded grinding wheel (600\(\#\)), and the resin-bonded grinding wheel (3000\(\#\)) was selected for fine-grinding CMA moulds. The micro-textured grinding wheels were employed in grinding RB-SiC moulds. Compared with the existing ultrathin arc-shaped diamond wheel grinding process, the proposed technology is more efficient in the fabrication cycle, benefiting from the new tool-clamping method. It can complete the rough-fine processing of CMA moulds in one cycle without multiple dressings and reduce the machine and auxiliary time. Detailed texturing information can be found in Section 3. During the machining process, the metal-bonded and resin-bonded diamond wheels are fixed on the grinder spindle, and the gasket is placed between the grinding wheels to prevent interference, shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Lens and Imaging System** & **Parameters** \\ \hline Shape of lens & Sinusoidal \\ Amplitude/mm & 0.25 \\ Period/mm & 0.8 \\ Material & K-PG325 \\ Thickness of lens/mm & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of CMAs and imaging system.  
Figure 2: Simulated excimer laser beam profile: (**a**) Pseudocolor representation of the strength distribution on the detector; (**b**) the x-direction standardized strength distribution chart; and (**c**) the y-direction standardized strength distribution chart.
# A Psychoacoustic Investigation on the Effect of External Shading Devices on Building Facades
## 3 Results and Discussion
### Analysis of Variance: Significance and Fixed Factors Effects
#### 3.2.4 Analysis of Variance and Effect of Size of the SPL Comparisons  
The ANOVA highlighted statistically significant variations in sound pressure levels due to the separate actions of the sound source position and the louvres' tilt angle, but similarly to what was observed for the loudness levels, their interaction is not statistically significant.  
The \(\eta_{\mathrm{p}}\)\({}^{2}\) analysis shows that the SPL variations over the facade, in both cases with standard and sound absorbing louvres, are mainly influenced by the sound source position. This is an important finding that means that the noise protection given by the shading device increases faster with the building height than with the louvres' tilt angle. This aspect is ascribed to the fact that at higher floors the louvres appear closer to each other and denser, with respect to the noise source placed at the bottom.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### Global  
[PERSON] and [PERSON] [118] analyzed 13 years (1995-2007) of data from 155 GNSS stations globally and distributed and studied the seasonal variation of IWV. Globally, seasonal maxima were found in summer and minima in winter. Annual cycle amplitude was 10-20 \(\pm\) 0.5 mm in mid-latitudes and 5 \(\pm\) 0.5 mm in high latitudes and equatorial areas. Semiannual variation amplitudes were less important, at about 0.5 \(\pm\) 0.2 mm. The diurnal cycle was also studied and it was found to have an amplitude of 0.2-1.2 \(\pm\) 0.1 mm, and the maximum and minimum occurred around noon and midnight, respectively. The semidirurnal cycle was reported to have a weaker amplitude below 0.3 mm. [PERSON] et al. [109] agreed with this, finding that annual variability was the more important component in IWV variation. This last work used 192 IGS stations distributed globally to interpolate and analyze IWV at a global scale during the period 2004-2017. Despite the different periods with very few years in common, both works show similar results.  
Regarding trends, [PERSON] and [PERSON] [119] presented a global analysis of the trends of IWV using different products, including reanalysis ERA-Interim and NCEP, SSM/I microwave satellite, radiosondes and GNSS. Particularly, 100 GNSS stations were used in the study, with a time-span of 15 years. The other products had longer time-series, reaching 36 years of reanalysis and radiosondes. Therefore, the trends were calculated at different timescales to allow comparisons. The 15 year period 2000-2014 was concluded to have stronger upward trends than longer periods. The trends were not homogeneous, being negative in Asia, the United States or North Africa, while large positive trends were found in the North and West Pacific, Europe, the eastern Indian Ocean, and southern Asia. The polar region is the one with a larger increase in IWV. Another interesting issue discussed in this work is the relationship between surface temperature and IWV. According to Clausius-Clapeyron equation, the increase rate of water vapor should be 6-13%K\({}^{-1}\). However, some regions depart greatly from this range, reaching negative values in this rate. Such large departures were associated to regional moisture divergence/convergence. At a global scale, the Clausius-Clapeyron estimate remains valid. This issue was also studied by [PERSON] et al. [120], who found that using night surface temperature had better correlation with IWV, and therefore it was suggested to use it for studies on water vapor feedback.
## 3 Proposed Method
### Framework and Overview  
Given a query vehicle image, the target of vehicle re-ID is to obtain a set of images from the gallery with the same ID as that of the candidate image. At present, we believe that vehicles with the same ID have more similar image feature embeddings. Therefore, these feature embeddings must be extracted and the similarity score between the embeddings of this candidate image and those of other images in the gallery must be calculated. The training set is then defined as \(\{x_{i},y_{i}\}_{i=1}^{N}\), where \(y_{i}\) represents the identification label of image \(x_{i}\) and \(N\) represents the number of training images. The similarity between query image \(q\) and gallery image \(g\) is defined as \(D(\phi(q;\mathbf{\theta}),\phi(g;\mathbf{\theta}))\), where \(\phi(\cdot;\mathbf{\theta})\) is the feature extractor and \(D(\cdot)\) is a metric function. To obtain a better feature extractor, the parameter \(\theta\) must be learned through gradient descent:  
\[\mathbf{\theta}=\underset{\mathbf{\theta}}{\arg\min}L\left(\phi(\mathbf{x};\mathbf{\theta})^{ \top}\mathbf{w},\mathbf{y}\right)\,, \tag{1}\]  
where \(L\) is the loss function, and \(\mathbf{w}\) is the weight vector.  
Figure 3 shows the framework of the proposed LFASM model for vehicle re-ID. It comprises two branches: one in charge of the ID classification, and the other used for Siamese local feature matching to better supervise our attention module. Each branch comprises two modules including a local net to output an attention descriptor, \(\mathbf{m}\in\mathbb{R}^{C\times H\times W}\). The score from the array \(\mathbf{m}\) represents the amount ofattention required in this area. An attention-based feature extractor is used to extract the deep features of input images.
## 2 Material and Methods
At that moment, the scent diffuser was turned on and sent out the odor of oranges. In both scenes (the meadow with flowers of chamomile and the garden with the box of oranges), the subjects could turn their gaze 360 degrees, and in the scene with the meadow with flowers of chamomile, they could bend down to see the flowers better. In the scene with the meadow with flowers, when they leaned over, the scent diffuser turned on and sent out the odor of chamomile, while in the scene with the garden with a table with a box with oranges, when the subject picked up an orange and brought it to his/her nose, the scent diffuser sent the odor of oranges. The subjects were instructed to remember an episode from their life, with the memory being triggered by the combination of the VR environment and the odor. When their memory was activated, they had to relive it and, as they relived it, they described it in detail. They had to say where they were in that episode, how old they were, what season it was, who they were with, how the people were dressed, what they were doing, and what emotions they felt when recalling the episode. They were also asked how pleasant or unpleasant the memory was, how vivid, and how personally relevant. \begin{table}
\begin{tabular}{c c} \hline \hline
**Nr. Crt.** & **Question** \\ \hline
1. & Before participating in the experiment, how did you feel emotionally? Describe your emotional state in a few words. \\  & Throughout the experiment, what kind of emotions did you predominantly \\
2. & experience, and at what point during the experiment? Happiness, sadness, fear, anger, surprise, and disgust. Any others? More specifically, which ones? \\  & Did you notice any change in mood or emotional state after completing the VR experience and exposure to olfactory stimuli? (Was the overall emotional state improved after the experiment? Do you believe it reduced stress?) If yes, what did this consist of? \\
4. & Did olfactory stimuli contribute to the recall of autobiographical memories? \\  & Describe in a few words the contribution you believe olfactory stimuli had. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluative analysis of emotional and olfactory stimuli responses during the experiment.
###### Abstract  
In the current study, novel films with chitosan/nano/SiO\({}_{2}\)/nisin films and their antimicrobial application on Cantaloupe fruit shelf-life have been studied. Novel films were prepared by the addition of 1% chitosan, 1% nano silicon dioxide, and 1% nisin and freeze-dried for the performance study. Physicochemical properties such as tensile strength, optical, and thermal properties with the performance characteristics of the novel films were measured. Coated and uncoated cantaloupes with various coating solutions were stored and chilled at 4 \({}^{\circ}\)C in a relative humidity of 70% for up to nine days. The microbial population measurements have been detected every three days. Results show that the fourier transform infrared intensity (FTIR) of nano/SiO\({}_{2}\) and with the addition of nisin (nano/SiO\({}_{2}\)/n) were higher than chitosan (CH) film except in the wavenumber (3150-3750 cm\({}^{-1}\)) films peaks. Novel nanofilms enhanced tensile strength as well as optical and thermal properties. XRD analysis reported two distinct peak values of 32.08 and 45.99 to correspond to nano/SiO\({}_{2}\)/n film orientation (7095) and (3316), respectively. Zeta potential values and turbidity were increased, while nano/SiO\({}_{2}\) films decreased the hydrophobicity of the film surface by 80.07\({}^{\circ}\). The coating treatments with nano/SiO\({}_{2}\) and nano/SiO\({}_{2}\)/n both reduced the yeast and mold counts 2.49 and 1.92 log CFU/g, respectively, on day nine. In summary, chitosan/nano/SiO\({}_{2}\)/n novel film improved the functional properties of coating films, and those bio-nancomposites are effective in food packaging.  
nano/SiO\({}_{2}\) films; performance study; antimicrobial application; cantaloupe; shelf-life +
Footnote †: journal: Applied Sciences
# A Partitioned IRS-Aided Transmit SM Scheme for Wireless Communication
## 6 Conclusions  
In this paper, a PIRS-TSM scheme is proposed and efficiently improves the reliability and SE of the wireless system. The utilization of IRS partitioning improves the BER performance for antenna-indexed data bit transmission. In addition, both antenna index and modulated symbol transmission improve the SE of the system and achieve configurability between the number of transmit antennas and symbol modulation order, resulting in better performance than PIRS-TSSK or PIRS-TGSSK. Simulations also indicate that under the same SE, the BER performance is better and the computational complexity is lower when fewer transmit antennas and higher symbol modulation orders are set in PIRS-TSM.  
**Author Contributions:** Conceptualization, L.X. and Y.P.; methodology, L.X.; validation, L.X.; formal analysis, L.X.; investigation, L.X.; writing--original draft preparation, L.X. and Y.P.; writing--review and editing, M.Y., H.W., R.Y., F.A.-H., M.M.M. and Y.P.; visualization, L.X.; supervision, Y.P.; project administration, Y.P.; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Performance comparison among PIRS-TSM, PIRS-TSSK, PIRS-TGSSK, and PIRS-FGSSK when SE is 4 bit/s/Hz.  
**Funding:** This work was supported by the Science and Technology Development Fund, Macau, SAR (0085/2024/RIA2), Dongguan Polytechnic National Double High Program Electronic Information Engineering Technology Fund (ZXF004), and the Scientific Research Projects of Department of Education of GuangDong Province (No. 2023 KCXTD082). Also, this research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2025-71).  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2025-71).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Discrete-Time Adaptive Delay Compensator Based on Kalman Filter
### Discrete Model of Control Plant  
Different from the widely investigated adaptive delay compensation methods that employ the inverse of the continuous model of the control plant [19; 28; 37], this proposed KF-ADC method employs the discrete-time model of the control plant because of its execution convenience on digital signal processing boards. Whether using the Auto-Regressive with Exogenous discrete-time model [38] or transforming the model from the Laplace domain to the time domain, one can always obtain a difference equation description of the control plant by  
\[d_{k}^{c}=\sum_{i=1}^{p}d_{k-i}^{c}x_{i}^{c}+\sum_{j=1}^{q}d_{k-j+1}^{\text{m} }x_{j}^{\text{m}}, \tag{3}\]  
where \(d^{c}\) and \(d^{\text{m}}\) represent the displacement command and measurement of the control plant, respectively, \(x\) is the system model parameter, \(p\) and \(q\) are two adjustable parameters determined by the orders of the control plant, and the subscript \(k\) denotes the \(k\)-th time step. Equation (3) can be further reorganized into a more compact form, namely,  
\[d_{k}^{c}=\varphi_{k}^{\text{T}}\mathbf{x},\] (4a) where \[\varphi_{k}^{\text{T}}=\left[d_{k-1}^{c},d_{k-2}^{c},\cdots,d_{k-p}^{c},d_{k} ^{\text{m}},d_{k-1}^{\text{m}},\cdots,d_{k-q}^{\text{m}}\right], \tag{4b}\] \[\mathbf{x}=\left[x_{1}^{c},x_{2}^{c},\cdots,x_{p}^{c},x_{1}^{\text{m}},x_{2} ^{\text{m}},\cdots,x_{q}^{\text{m}}\right]^{\text{T}}. \tag{4c}\]  
Please note that before establishing this discrete-time model, the orders of the control plant must be determined beforehand.  
Figure 3: Block diagram of RTHS using the KF-ADC strategy.
## 3 Results
### Cell Culture Optimization into the EUs
#### 3.1.3 Cell Resilience to Temperature  
For the execution of the MyoGravity project, from handover to NASA integrators to the installation into KUBIK, the EUs would be assembled with control electronics integrated inside the KIC-SL containers (Kayser Italia Containers-Single Level) and then, for the upload on board the launcher, placed inside the BIOKIT transportation container, which is a soft pouch containing phase change materials (PCM bricks) preheated at 27 \({}^{\circ}\)C, and is used to control temperature. Once aboard the ISS, the KIC-SL containers would be inserted inside the KUBIK incubator developed by European Space Agency (ESA) and operative in the ISS Columbus module, set at 37 \({}^{\circ}\)C. Five days was the maximum time expected to reach the ISS, during which significant variations of temperature were possible. In fact, during the flight phases the temperature control was based on a passive system rather than an active incubator. Using data from previous expeditions, an expected temperature (T) profile in the worst-case scenario was defined in which 27.6 \({}^{\circ}\)C was the lowest T value (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**HEPES (mM)** & **20** & **25** & **40** \\ \hline Cell survival (\%) & 98.3 \(\pm\) 3.2 & 95.1 \(\pm\) 3.5 & 70.7 \(\pm\) 2.6 \\ \hline Cell proliferation (\%) & 97.5 \(\pm\) 3.9 & 83.4 \(\pm\) 4.2 & 25.2 \(\pm\) 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cell survival and proliferation rates in dependence on HEPES concentration. Percentage cell survival and proliferation of huMPCs cultured in EUs in the presence of the indicated HEPES concentrations with respect to huMPCs cultured in parallel in plates in standard medium conditions.  
Figure 2: The T (\({}^{\circ}\)C) profile over time (h) in the worst-case scenario during the transport of EUs inside the KIC-SL containers and inside the TBC transportation container until the integration into the KUBIK on board the ISS.  
Thus, we performed tests to evaluate the survival and proliferation rates of huMPCs at the T characterizing the shipment from handover to the integration into the KUBIK on board the ISS. Based on the worst-case scenario T profile (Figure 2), we tested the cell resilience at 25, 27 and 30 \({}^{\circ}\)C. The results obtained showed that the lowest temperature required to ensure the normal survival of huMPCs for 5 days was 27 \({}^{\circ}\)C, as evaluated by vital cell count (Table 2).