esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurements  
The proposed modulator based on hybrid structure is fabricated with the following process. Firstly, the Al layer with the thickness of 100 nm is deposited onto the PET substrate with the thickness of 30 \(\upmu\)m. With photo lithography and wet etching, the square slit ring arrayed structure is fabricated, and its microscopic image is shown in Figure 4a. The photography indicates that the square slits are distributed uniformly. Secondly, the PMMA solution is spin coated onto the metamaterial with the thickness of 50 nm. After drying, the PMMA acts as the dielectric layer to separate the graphene and the metallic square slit ring structure. Then the upper Ag electrode is deposited onto the PMMA and followed by transferring the graphene film grown by chemical vapor deposition (CVD) with wet method [25]. The graphene on the PMMA is partly touching the Ag electrode to connect the applied modulation voltage.  
Figure 3: Relationship between resonant frequency and modulation depth with: (**a**) inner side length; (**b**) slit gap width of the slit ring; (**c**) thickness of dielectric layer.  
After fabrication, the device is measured with a THz time-domain spectroscopy (THz-TDS) system (Advanced Photonix, Inc., Michigan, USA). With this equipment, the transmission spectrum at different modulation voltage is obtained and shown in Figure 4b. The black square dot curve indicates that the resonant is achieved at the frequency of 0.6 THz when the modulation voltage is \(-10\) V, which is the same as the theoretical result when the Fermi level of the graphene is 0.1 eV. By changing the modulation voltage to \(-15\) V, the circle dot curve indicates that the resonant frequency is tuned to 0.775 THz and the transmission peak is reduced as the theory predicts. The resonant frequency is close to the theoretical result of \(\mathit{Ef}=0.3\) eV. The blue star curve standing for the experimental modulation depth is calculated with Formula (1). It indicates that the maximum modulation depth is 65.1% at the frequency of 0.575 THz, which is close to the theoretical result in Figure 1b. It demonstrates that by separating the graphene layer and the metallic metamaterial with the dielectric layer of PMMA, the resonance of the metamaterial is tuned with the active material. And benefiting from the enhanced interaction between the graphene and the THz wave in the localized field of the metamaterial, the modulation of the transmitted THz wave is enhanced.  
At last, the characteristic of the THz modulation device enhanced with the metallic square slit array is compared with other graphene based THz modulation works. The result is shown in Table 1. It indicates that the amplitude THz modulation depth of the device without the metallic structure enhancement is less than 25%. Comparing with the device with metallic structure directly touching the graphene layer, the modulation depth of the works is close to each other, but the Fermi level tuning requires a wider range for the previous reported work.
## 3 Spatial-Spectral-Emissivity Land-Cover Classification Based on Conditional Random Fields (SSECRF)
### Methodology of SSECRF
#### 3.2.1 Feature Extraction from the Thermal Infrared Hyperspectral Imagery  
1. Emissivity retrieval from the thermal infrared hyperspectral imagery  
The emissivity retrieved from the thermal infrared hyperspectral imagery is often seen as an indicator of material composition, i.e., the intrinsic property description of natural materials [20]. The emissivity information can help us to classify and identify minerals more accurately. In this paper, the FLAASH-IR [19] algorithm is used to transfer the raw radiance value of thermal infrared hyperspectral imagery to emissivity. The algorithm suppresses the influence of the atmosphere in the radiance spectra, and it can obtain a competitive emissivity result from the thermal infrared hyperspectral imagery.  
2. Feature Dimensionality Reduction for the Emissivity Imagery  
PCA is widely used for feature dimensionality reduction in remote sensing imagery preprocessing, and it can be described as a multidimensional orthogonal linear transformation method based on statistical features. However, the PCA transform is sensitive to image noise [36], i.e., a principal component with a large amount of information may have a lower signal-to-noise ratio. Therefore, it is not the best method to transfer the emissivity imagery from a high-dimensional feature space to a lower one. The minimum noise fraction (MNF) transform, as proposed by [PERSON] et al. [37], consists of principal component analysis with two stacking treatments. The noise in the transformation process has unit variance, and the band is not relevant, so the MNF transform is superior to the PCA transform [38].
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AN\({}^{2}\) & Turbine loading parameter \\ ATR & Average Temperature Response \\ BE & Block Energy \\ BPR & Bypass Ratio \\ C\({}_{L}\) & High Lift Coefficient \\ CAPEX & Capital Expenditures \\ CFRP & Carbon Fibre Reinforced Polymers \\ Cic & Contrail Induced Cloudiness \\ CPACS & Common Parametric Aircraft Configuration Schema \\ DAC & Direct Air Capturing \\ DOC & Direct Operating Costs \\ EI & Emission Index \\ EIS & Entry Into Service \\ FAR & Fuel-to-Air Ratio \\ FL & Flight Level \\ FLOX & Flameless Oxidation \\ GTlab & Gas Turbine Laboratory \\ HEFA & Hydroprocessed Esters and Fatty Acids \\ ISA & International Standard Atmosphere \\ ICAO & International Civil Aviation Organization \\ LBO & Lean Blow Off \\ LH2 & Liquid Hydrogen \\ LHV & Lower Heating Value \\ LoD & Lift-over-Drag ratio \\ LTO & Landing and Take-Off cycle \\ \(\dot{m}_{Air}\) & Air mass flow \\ MEM & Manufacturing Empty Mass \\ MLM & Maximum Landing Mass \\ MTOM & Maximum Take-Off Mass \\ nvPM & non volatile particles \\ OEM & Operating Empty Mass \\ OPR & Overall Pressure Ratio \\ \(p_{3}\) & Pressure at combustion chamber entry \\ PAX & Passengers \\ PFR & Plug Flow Reactor \\ PSR & Perfectly Stirred Reactor \\ PtL & Power to Liquid \\ \(R_{FL}\) & Weighting factor \\ \(R\) & Universal gas constant \(R=8.3143\) J/(Mol K) \\ RC & Recurring Costs \\ RCE & Remote Component Environment \\ RF & Radiative Forcing \\ S\({}_{\rm D}\) & Air Split \\ SAC & Schmidt-Appleman Criterion \\ SAF & Sustainable Aviation Fuel \\ SL & Sea Level \\ \(T_{3}\) & Temperature at combustion chamber entry \\ \(T_{\Phi}\) & Equilibrium temperature \\ \(T_{\Phi=1}\) & Stoichiometry temperature \\ \(T_{FL}\) & Flame temperature \\ \(T_{PZ}\) & Primary zone temperature \\ TET & Turbine Entry Temperature \\ TLARs & Top Level Aircraft Requirements \\ \end{tabular}
## 3 Results and Discussions
### Vacuum Impregnation of Soluble Phenols from Hibiscus sabdariffa Aqueous Extract into Apple Slices  
In total, fifteen experiments were conducted using a 3\({}^{3}\) Box-Behnken design to examine the effect of independent variables on the VI of soluble phenols from the HS extract in fresh apple slices. The experiments were randomized to reduce systematic errors. The results are listed in Table 1, which includes the experimental conditions for VI, experimental and predicted values, relative error, and impregnation degree.  
Statistical differences (\(p<0.05\)) were observed between the untreated (C+) and vacuum-impregnated apple slices (T1-T15). The results showed that the impregnation degree (ID) of TSP ranged from 9.31-16.17%, which is comparable to that of vacuum-impregnated cranberries with ascorbic acid (ID = 11%) [17]. [PERSON] et al. mentioned that the amount of impregnated solution depends on several factors, including the viscosity of the solution and porosity of the food matrix [32; 33]. The highest TSP impregnation content  
Figure 1: Visual appearance of fresh apple slices before (C+) and after vacuum impregnation (T1–T15) of aqueous extract from _Hibiscus sabdariffa_ calves.  
(142.73 mg/GAE/100 g) was achieved at \(-\)0.4 bar of vacuum pressure, 6 min of impregnation time, and 3 min of restoration time, while the lowest TSP impregnation was observed under \(-\)0.2 bar of vacuum pressure, 2 min impregnation time, and 3 min of restoration time (63.63 mg GAE/100 g), similar to those observed at \(-\)0.2 bar of vacuum pressure, 6 min of impregnation time, and 1 min of restoration time (68.19 mg GAE/100 g) or at \(-\)0.4 bar of vacuum pressure, 2 min of impregnation time, and 2 min of restoration time. [PERSON] et al. [4] informed an increase in TSP from 402 to 670 mg/GAE 100 g after 360 min of VI; however, these effects were temperature dependent (45-65 \({}^{\circ}\)C). It has been reported that hibiscus extract can improve the phenolic content up to 4.8 times of honeybee melon fruit after VI effects influenced by the geometry and thickness of fruit and the concentration of aqueous extracts [30]. Similarly, [PERSON] et al. [14] informed and increase in phenolic compounds in chokeberry fruits after VI (1542.89-2528.58 mg GAE/100 g); however, these results were influenced by the vacuum pressure used (4-8 kPa). In most cases, VI is used to improve the technological functions of fruits through a mass transfer process that facilitates or accelerates the incorporation of desirable bioactive molecules from natural extracts into food products via hydrodynamic forces [32].
## 4 Discussion
### Spectral Index Evaluation  
From our results, it is clear that vegetation variability drives the grazing patterns of sheep and that fine-scale mapping of vegetation will aid farm planning and managementactivities, particularly in the Upper Karoo where vegetation is sparse. While widely used, NDVI is not the only remotely sensed spectral index used to assess vegetation conditions. For instance, SAVI has been used with great success in arid regions with low vegetation cover [2; 95; 98]. However, vegetation indices are affected by a range of biophysical factors [95] and it is often difficult to select the most appropriate index for livestock management.  
In this study, eight spectral indices were compared to determine which index would best describe the grazing preference of sheep in the Upper Karoo. Three of the eight indices (i.e., GCI, MSAVI2 and NDVI) showed a strong relationship with the hotspot analysis Gi* Z-scores. Based on our data, NDVI was found to be the most suitable index for describing the grazing preferences of sheep. The strongest regression models were achieved with MSAVI2 and NDVI. It is interesting to note that these two indices are the only two that only utilize the NIR- and red bands. This agrees with previous work that the Red and NIR2 bands were among the three most important Sentinel-2 MSI bands to estimate fractional vegetation coverage (FVC) [99]. FVC is an important parameter to characterize land surface vegetation [100] and previous work has also noted that simple Vis, such as MSAVI 2 and NDVI that combines only the visible red- and NIR bands have enhanced sensitivity to detect green vegetation in sparsely vegetated areas [96; 99].
## 3 Kinematic Analysis  
### Inverse Position  
The position vector of the moving platform center \(O_{1}\) in the static coordinate system \(\mathcal{O}\)-\(XYZ\) is \(\mathbf{O}\mathbf{O}_{1}=[x,y,z]^{\mathrm{T}}\), and \(l_{i}\) (\(i=1\), 2, 3, 4) is the length vector of each branched-chaindriving pair. The attitude transformation matrix \(\mathbf{T}\) of the moving coordinate system relative to the static coordinate system is  
\[\mathbf{T}=\begin{bmatrix}\cos\beta&\sin\beta\sin\alpha&\sin\beta\cos\alpha\\ 0&\cos\alpha&-\sin\alpha\\ -\sin\beta&\cos\beta\sin\alpha&\cos\beta\cos\alpha\end{bmatrix} \tag{19}\]  
where \(\alpha\) and \(\beta\) are the rotation angles of the moving platform around the \(X\) and \(Y\) axes, respectively. When branched chain 1 is in the RPS mode and the vA pair is in the revolute-pair mode, the degree of freedom of the mechanism is 2T1R, and thus, \(\beta=0\).  
\({}^{\text{O}}\mathbf{A}_{i}\) (\(i=1,\,2,\,3,\,4\)) is the position vector of \(A_{i}\) (\(i=1,\,2,\,3,\,4\)) in the static coordinate system \(\text{O-XYZ}\), and \({}^{\text{O}_{1}}\mathbf{C}_{i}\) (\(i=1,\,2,\,3,\,4\)) is the position vector of \(C_{i}\) (\(i=1,\,2,\,3,\,4\)) in the moving coordinate system \(\text{O}_{1}\)-\(X_{1}Y_{1}Z_{1}\). Their expressions are as follows:  
\[\begin{array}{l}{}^{\text{O}}\mathbf{A}_{i}=\begin{bmatrix}0&0&0\end{bmatrix}^{ \text{T}}&{}^{\text{O}}\mathbf{A}_{2}=\begin{bmatrix}a&b&0\end{bmatrix}^{\text{T} }\\ {}^{\text{O}}\mathbf{A}_{3}=\begin{bmatrix}-a&b&0\end{bmatrix}^{\text{T}}&{}^{ \text{O}}\mathbf{A}_{4}\begin{bmatrix}{}{}^{\text{O}}\mathbf{C}_{4}-b&0\end{bmatrix}^{ \text{T}}\\ {}^{\text{O}}\mathbf{C}_{1}=\begin{bmatrix}0&0&0\end{bmatrix}^{\text{T}}&{}^{ \text{O}}\mathbf{C}_{2}=\begin{bmatrix}c&d&0\end{bmatrix}^{\text{T}}\\ {}^{\text{O}}\mathbf{C}_{3}=\begin{bmatrix}-c&d&0\end{bmatrix}^{\text{T}}&{}^{ \text{O}}\mathbf{C}_{4}=\begin{bmatrix}0&-d&0\end{bmatrix}^{\text{T}}\end{array} \tag{20}\]  
\({}^{\text{O}_{1}}\mathbf{C}_{i}\) (\(i=1,\,2,\,3,\,4\)) is transformed into the static coordinate system \(\text{O-XYZ}\), and its expression is as follows:  
\[{}^{\text{O}}\mathbf{C}_{i}=\mathbf{T}^{\text{O}_{1}}\mathbf{C}_{i}+\mathbf{O}\mathbf{O}_{1}\quad( i=1,\,2,\,3,\,4) \tag{21}\]  
The length vector of the driving pair in branched chain \(i\) (\(i=1,\,2,\,3\)) can be expressed as  
\[\mathbf{I}_{i}={}^{\text{O}}\mathbf{C}_{i}-{}^{\text{O}}\mathbf{A}_{i}\qquad(i=1,\,2,\,3) \tag{22}\]  
When the mechanism is in the 1T2R motion mode, the length vector of the driving pair in branched chain 4 can be expressed as  
\[\mathbf{I}_{4}={}^{\text{O}}\mathbf{C}_{4}-{}^{\text{O}}\mathbf{A}_{4} \tag{23}\]  
When the mechanism is in the 2T1R motion mode, the length vector of the driving pair in branched chain 4 can be expressed as  
\[\mathbf{I}_{4}=X_{\text{O}\mathbf{C}_{4}} \tag{24}\]  
The inverse kinematics solution of the reconfigurable parallel mechanism can be obtained by calculating the module length of the length vector \(\mathbf{I}_{i}\) of the driving pair in each branched chain.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
###### Abstract
Thus, it is imperative to comprehensively examine DQ issues and propose an integrated solution that addresses various data quality challenges. To address this issue, this paper conducts a quantitative analysis of literature published in recent years [6], to study DQ and governance problems. The data sources include five public databases: China National Knowledge Internet (CNKI), Web of Science (WOS), IEEE Xplore (IEEE), Association for Computing Machinery (ACM), and Springer-Verlag (Springer). Through a comprehensive and systematic exploration, we first classify these articles into six categories based on the research problems they addressed. Secondly, we systematically investigate data quality issues on a real-world big data platform within the context of a public bus transportation system, and present the corresponding solutions. Finally, two typical DQ problems are used as case studies to introduce DQ improvement methods and to evaluate their effects. All methods are implemented in Spark, a large-scale data processing engine, to handle datasets comprising hundreds of gigabytes. In summary, the contributions of this paper can be outlined as follows:  
1. We conducted a comprehensive quantitative analysis of DQ-related literature published in recent years, comprising a total of 26,160 publications sourced from five renowned databases. Subsequently, we categorized the emerging DQ issues from this literature into six distinct categories: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. 2. We systematically examined DQ problems in the bus transportation system based on the aforementioned categories. Subsequently, we provided the corresponding data cleaning methods for each category. Finally, we presented two representative examples: noise filtering and filling missing values, to illustrate the practical DQ improvement process. The first example is designed for raw GPS trajectories, while the second is intended for bus arrival and departure information. 3. We validated these two approaches utilizing a real-world big data platform. Experimental results demonstrate that: (1) The GPS noise filtering solution we proposedsurpassed the baseline and achieved an accuracy of 97%; (2) The multi-source data fusion method can achieve a 100% missing repair rate (MRR) for bus arrival and departure, and the average relative error (ARE) of bus arrival and departure times at stations was less than 1%; the correlation coefficient (R) was also close to 1. 4. We designed and implemented a pipeline-based solution on the Spark platform that automatically conducts preprocessing and data quality cleaning algorithms. Participants can customize their own algorithms based on this solution to enhance data quality for large-scale datasets.
# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 2 Related Work
### Image-Based Space On-Orbit Service Technology
It implements feature extraction through convolutional neural networks and uses datasets for supervised training, allowing the algorithm to predict the position of spacecraft quickly. In 2019, [PERSON] et al. [36] used a convolutional neural network combined with an advanced object detection algorithm to detect the 2D boundary box of the spacecraft in the image. [PERSON] et al. [4] adopted the ResNet [17] architecture with pre-trained weights as the network backbone. They removed unnecessary pooling layers to preserve spatial feature resolution to compress CNN features. In 2021, [PERSON] et al. [7] built the first dataset for space object detection and segmentation, which annotated different components of the spacecraft. After training the dataset using deep learning methods, they could implement the components recognition. [PERSON] et al. conducted experiments with multiple state-of-the-art detection and segmentation networks. They benchmarked the dataset, but they lacked improvements to the network structure and included no analysis of the experimental results. Our study improves the network structure and optimizes the effect of our model through structures such as channel attention mechanism and dilated convolution, and we have further analyzed and discussed the spacecraft segmentation issue.
## 1 Introduction
For YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7.85}\), the presence of only [FeO\({}_{6}\)] octahedral units in the FeO\({}_{1+\delta}\) layers has been described [20]. It has also been reported that in the YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7.56}\) term, the oxygen non-stoichiometry accommodates the creation of vacancies in the FeO\({}_{1+\delta}\) planes in such a way that Fe adopts tetrahedral [FeO\({}_{4}\)] and octahedral [FeO\({}_{6}\)] coordination [21]. On the other hand, the low oxygen term YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7/08}\) consists of [FeO\({}_{4}\)] tetrahedra. It is worth mentioning that the YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7+\delta}\) system has been demonstrated to exhibit partial occupancy of Cu sites with Fe [18; 22]. Indeed, the synthesized compounds YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7/86}\)[20] and YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7.56}\)[21] present anti-site disorder, with a Cu/Fe mixing close to 15%. The focus of this work is to study the MIEC properties of YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7+\delta}\) oxides, which were reported to present electrocatalytic activity. Specifically, this work aims to investigate oxygen diffusion in these materials and gain insights into how vacancies/oxygen content influence the diffusion mechanism. To achieve this goal, crystallographic models  
Figure 1: (**a**) Crystal structure of the ideal stoichiometric oxide YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{8}\) (S.G. P4/_mmm_, ICSD file 11514). The unit cell has dimensions of a\({}_{\text{p}}\)\(\times\) a\({}_{\text{p}}\)\(\times\) 3 a\({}_{\text{p}}\), where a\({}_{\text{p}}\) refers to the cubic perovskite lattice parameter. (**b**) Crystal structure of the ideal stoichiometric oxide YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7}\) (S.G. _Ima2_, Initial cell parameters and atomic positions were taken from ICSD record of YSr\({}_{2}\)Cu\({}_{2}\)GaO\({}_{7.46}\), ICSD file 71263). Color code: Fe in brown, Cu in blue, Sr in green, Y in grey, and O in red. for the idealized YSr\({}_{2}\)Cu\({}_{2}\)FeO\({}_{7+\delta}\) terms with \(\delta\) = 0.5, 0.25, and 0.75 are proposed, and the ionic mobility is inferred from _Ab initio_ molecular dynamics (AIMD). The ultimate goal of this work in the long term is to favor advancements in catalytic materials that will help overcome limitations in the development of efficient hydrogen production and utilization technologies.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 3 Proposed Lightweight Model for Intrusion Detection
### Data Modeling  
The data modeling process begins by dividing the data frame into segments. In the suggested model, the classification model is trained on 80% of the data frame and evaluated model on the remaining 20%. To accomplish this, the model imports the \"train_test_split\" function from \"sklearn.model_selection\" with a 20% testing rate. After splitting the data into training and testing samples, they are provided to the classifier for training and evaluation. A SMOTE technique is implemented to oversample the attack class.  
SMOTE is a significant technique for detecting DDoS attacks due to its ability to address class imbalance. Solving this imbalance problem on these 2 datasets can avoid overfitting the model. It generates synthetic samples of the minority class (DDoS attacks), effectively balancing the dataset and improving training and classification performance.  
SMOTE mitigates bias towards the majority class, enabling accurate detection of DDoS attack patterns. By oversampling the minority class, it enhances the classifier's ability to identify and distinguish DDoS attacks from normal traffic, reducing the risk of false negatives. It improves the classifier's generalization ability by providing diverse samples, enabling effective handling of unseen attack patterns. It is compatible with various machine learning algorithms and reduces the need for collecting real-world attack instances. Its effectiveness in improving DDoS attack detection has been demonstrated in numerous studies; it enhances network security and mitigates potential damages caused by such attacks. Furthermore, a 5-fold split is employed only for the multiple-class classification to enhance accuracy during the assessment of the model.