esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Deep-Learning Approaches  
Two experiments were conducted to create and compare both models, one based on the transfer-learning approach and the other using a pre-trained network. This method allows a complex CNN architecture to be used, thereby, saving training time and obtaining better performance as will be seen in the results section. The trained networks chosen for the deep-learning experiments were GoogLeNet and Resnet50. These networks were selected initially due to the expectations existing in this field in the literature [30; 31].  
Once we obtained a high effectiveness of those networks for microstructure images, we did not consider it necessary to introduce other possibilities. Both networks were trained on the ImageNet database [32], which has a wide range of images, 1000 object categories, such as keyboard, mouse, pencil and many animals. These categories are out of the microstructure image domain involved herein; however, as it is shown in the results section and some recent work [33], many of the pre-training parameters can be transferred to improve the new classification models.  
The input data consisted of a 19,200 images dataset, i.e., 6400, for each category, of which, 70% were used for training, 20% for validation and 10% for testing. The input images were at a 224 \(\times\) 224 px resolution, cropped to this size from the original pictures. The first experiment used GoogLeNet, a pre-trained convolutional neural-network model that was 22 layers deep. GoogLeNet is based on a codenamed \"Inception\" architecture [30]. ResNet50 was the pre-trained network for the second experiment to create the model. ResNet50 is a well-known net, structured with 50 layers and also trained in the same 1000 categories mentioned above [31].  
The steps to reuse both pre-trained networks are as follows:  
* Loading the pre-trained network where early layers learned low-level features (edges, blobs and colors) and the last layers learned specific features (1 million images and 1000 classes).
* Replacing final layers (loss3-classifier for GoogLeNet and fc1000 for ResNet50) with new layers to learn features specific to the dataset.
* Training the network with three classes (6400 images for each class, annealing, quenching and quenching plus tempering samples). The hyperparameters used for the experiments were: optimizer SGDM, minibatch size 64, max. epochs 3 and learning rate \(10^{-3}\).  
* Predicting and assessing the network accuracy.
# Paraphrase Identification with Lexical, Syntactic and Sentential Encodings
## 3 LSSE Learning Model
### Lexical and Sentential Contexts Learning with BERT  
The contextualized word representation that we use is BERT which is a Bidirectional Transformer model [11]. A transformer encoder computes the representation of each token through an attention mechanism concerning the surrounding tokens.  
BERT architecture consists of two steps: pre-training and fine-tuning. The pre-training BERT model is trained on unlabeled data over different pre-training tasks. It can be easily fine-tuned for NLP tasks by just adding a fully-connected layer. It is pre-trained by using a combination of masked token prediction and next sentence prediction tasks. The input of the BERT is two sentences that are concatenated by a special token \([\text{SEP}]\). It consists of tokens that are segmented by BERT tokenizer using WordPiece embeddings vocabulary [38]. The representation of each token is the sum of the corresponding token, segment, and position embeddings. The first token of every input is the special token of \([\text{CLS}]\), and the final hidden state corresponding to this \([\text{CLS}]\) token is regarded as an aggregated representation of the input sentence pair. We used this aggregated representation as our sentential encoding of two sentences as well as each token embeddings.  
Figure 2: LSSE learning model: \(S_{1}\) consisting a sequence of \(w_{1},w_{2},\cdots,w_{m}\) and \(S_{2}\) which consists of \(w_{1},w_{2},\cdots,w_{n}\) are a pair of the input sentences. \(\mathbf{x}_{i}\) refers to a word encoding and \(\mathbf{p}_{i}\) indicates its position encoding. \(\mathbf{a}_{i}\in\mathbb{R}^{d}\) refers to the encoded node feature, i.e., it is obtained by summing up word and position encodings. \(\mathbf{h}_{i}^{(k)}\in\mathbb{R}^{d}\) is the hidden state of node \(v_{i}\) (\(w_{i}\)) in the \(k+1\)-th layer.
## 1 Introduction
### Contributions  
The main contributions of this work compared with the previous work are as follows:  
\(\bullet\) A V-MIMO model is proposed by networking constellation of interconnected HAPs with the concept of MAV. We explain the concept in the users' perspective, and conclude that the improvement in signal-to-noise ratio (SNR) of MAV compared with a single HAP in view is the number of HAP in view.  
\(\bullet\) The diversity performance is investigated in a shadowed Rician fading, which consists of probability density function (PDF) and cumulative distribution function (CDF) of the received SNR, average symbol error rate (ASER) with BPSK and QPSK.  
\(\bullet\) The system capacity is analyzed for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factor for different network configurations are investigated.
## 3 Results
### Storage Experiment 1--Microbiological and Metabolic Analysis
#### 3.1.3 Headspace Analysis in Closed Storage Tubes  
The O\({}_{2}\) level was followed up in the headspace of closed storage recipients. O\({}_{2}\) was rapidly consumed, particularly during 20 \({}^{\circ}\)C storage (Figure 3). At day 2, the headspace of the 20 \({}^{\circ}\)C stored algae contained only \(1.1\pm 0.1\) mol% O\({}_{2}\) while that of 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C stored algae contained \(4.4\pm 0.2\) and \(4.1\pm 0.1\) mol% O\({}_{2}\), respectively. A gradual O\({}_{2}\) decrease was observed over the following days for all test conditions. It should be taken into account that during GC injection, small levels of O\({}_{2}\) coming from ambient air may interfere with these results as traces of O\({}_{2}\) (\(\sim\)0.1-0.2 mol%) were also observed after injection of pure H\({}_{2}\) standards (result not shown).  
Figure 2: Yeast and mold counts—storage experiment 1. (**a**) Yeast and mold count after 0, 7 and 14 days of storage in algae samples without added acetic acid. (**b**) Effect of storing algae in open or closed recipients without acetic acid addition. (**c**) Effect of acetic acid addition for algae stored at 8 \({}^{\circ}\)C in open tubes. Lowercase letters (a,b) denote significant differences. Values within one figure that are not labelled by the same letter are significantly different. The t\({}_{0}\) value was not included in the statistical analysis of the data shown in Figure 1a. Error bars represent standard deviations.  
GC-MS analysis of the headspace of algae in closed recipients was performed after 14 days of storage (Supplementary Materials Figure S1). A strong H\({}_{2}\)S signal was observed for the 20 \({}^{\circ}\)C stored algae but no H\({}_{2}\)S was seen in the headspace of algae stored at 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C. Moreover, a small methanethiol signal was seen for 20 \({}^{\circ}\)C stored algae but not for 4 \({}^{\circ}\)C and 8 \({}^{\circ}\)C stored algae (Supplementary Materials Figure S1).
## 2 Related Work
FS methods can improve accuracy, efficiency, effectiveness, and even interpretability to the learning process. For this reason, a large number of automatic FS methods have been developed in the past. In FS, a subset of features is selected from the original set, based on the evaluation of the actual intrinsic information of each feature, namely, the redundancy and the relevance [31]. During this process, features are classified into the following four groups according to their eventual effective information: (1) noisy and irrelevant; (2) redundant and weakly relevant; (3) weakly relevant and non-redundant; (4) strongly relevant. Popular approaches to carry this out are filter methods, wrapper methods, and embedded methods. Filter methods analyze the usefulness of each single feature through the use of relevance techniques, mainly from hypothesis tests or estimates of mutual information [35]. Wrapper methods solve ML problems to assess the relevance of each feature in the input space [36]. Finally, embedded methods, such as recursive feature elimination (RFE) [37], aim to increase their efficiency by combining the FS procedure with training a subsequent learning machine. Many of these embedded methods impose a regularization on the solution. A special mention is required for a recently proposed novel feature selection method, called IVI [1; 13]. This technique is capable of isolating informative, redundant, and noisy features automatically. One of its main characteristics is being able to transform the distribution of the input variable space into a coefficient feature space by using existing linear classifiers or efficient weight generators. At this point, it is necessary to mention that a large number of feature selection methods have been published in the literature, with uneven results in their application in different disciplines. It is not the object of this article to carry out a detailed analysis of each and every one of these techniques, but for the reader's convenience and with the intention of offering a summary of the different typologies of published methods, hereafter in Table 1 a schematic summary is presented for the different types of techniques as published in various reviews [32; 38], including a new category for the informative variable identifier (IVI) that we included in this paper [13].
# Factors Affecting the Efficiency of Hydrophobic Coatings--Experience from Application on Sandstone
## 2 Materials and Methods  
### Materials  
The Horice sandstone, quarried in the Czech Republic, was chosen due to its high porosity (resulting in both fast water infiltration and high hydraulic conductivity), as well as due to its mechanical stability in water. This sandstone was widely used for constructions in the Czechoslovak Republic in the 20 th century, as well as for reconstruction works after the Second World War. It was the most widespread decorative stone in Slovakia [21], used on public representative buildings, bank buildings, hospitals, statues, and monuments (Figure 1). Many of these buildings are already in need of restoration. The Horice sandstone is a fine-grained sandstone, variable in color, mostly light beige to yellowish brown, with irregular rusty streaks of limonite pigment. Quartz is the dominant mineral (>90%); other minerals such as glauconite, feldspars, mica, or heavy minerals are minor. The sandstone contains intergranular cement, which is a mixture of kaolinite, illite, and fine quartz grains [22]. The basic physical properties of sandstone are as follows: bulk density \(\rho_{d}=1930\) kg\(\cdot\)m\({}^{-3}\), total porosity \(n=26\%\), open porosity \(n_{o}=18.3\%\), water absorption \(N_{NAI}=9.5\)-\(12.2\%\), and uniaxial compressive strength \(UCS=29.9\) MPa [20]. The rock material for the research was taken directly from the Podhorni Ujezd quarry in the Podkrkonosiarea, which is currently the largest sandstone quarry in the Czech Republic. Test cubes with an edge length of 50 mm were sawn from the sandstone blocks taken from the quarry.  
The efficiency of two chemical products, Antipluviol S and Funcosil SNL, was evaluated. Both may be classified as silicon-based impregnation coatings and are currently available on the market. The product Antipluviol S from the producer MAPEI S.p.A. (Milan, Italy) is a colorless hydrophobic transparent liquid, silane and siloxane based. Funcosil SNL, produced by Remmers Gruppe AG (Loningen, Germany), is a low-molecular siloxane that reacts in the building material with the atmospheric moisture to water-repellent polysiloxane. According to the producer's recommendation, both coatings are also suitable for use on natural stone. They are instantly ready for the application, with no need to dilute or to modify them. Some of their technical specifications are given in Table 1.
## 2 Materials and Methods
### Preprocessing Steps
#### 2.3.1 Brain Extraction Methods  
The protocol for manual segmentation for BE, showing excellent (greater than 0.98) intra- and inter-rater reliability, and for the selection of automated methods was previously described and justified [10]. Five BE methods were selected for this study: manual segmentation, two conventional methods (Brain Extraction Tool (BET2) [51] and Simple Watershed Scaling (SWS) [52]), and two deep learning methods (HD Brain Extraction Tool (HD-BET) [53] and SynthStrip [54]).
# Three Dimensional CS-FEM Phase-Field Modeling Technique for Brittle Fracture in Elastic Solids
## 3 Numerical Modeling  
We present several numerical examples in this section to validate the accuracy of the 3D-CS-FEM-phase field method for linear elastic brittle fracture and also to discuss some shortcomings of the implementation in ABAQUS. First, three benchmark examples were tested and subsequently we simulated comparatively complex models. All models were meshed using eight-node hexahedral elements and eight smoothing domains were used for CS-FEM formulations. Since the phase-field represents a diffused fracture representation, no special crack-tip element was necessary for capturing the crack path. The mesh size was significantly reduced to successfully resolve the length scale parameter near the expected crack propagation zone. As discussed by [PERSON] et al. [34], the approximate element size should be less than half of the corresponding length scale parameter. Contour plots representing the phase-field variable which signifies the crack path are presented for all examples.
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 3 Results
### Variable Importance of the Random Forests  
Figure 5 shows the top 10 mean absolute SHAP values for data collected under halogen and LED light sources (HAL+LED), with RGB values, illuminance, and color temperature as the model input (HAL+LED_RGB+ICT+ILL+ Figure 5a); for data taken under LED light sources (LED) with RGB values and illuminance as the input values (LED_RGB+ILL Figure 5b); and for data taken under HSL values and illuminance (LED_HSL+ILL; Figure 5c). The figure compares the differences in light source information and color information among the three models from the top five CORs of the Random Forests in Figure 2, excluding the two models using illuminance-limited data and the model using GB values. In the HAL+LED_RGB+ICT+ILL model, the mean value of G contributed the most (Figure 5a). The LED_RGB+ILL model indicated that the median value of B had the highest contribution to the SPAD value prediction, followed by the mean value of B (Figure 5b). The LED_HSL+ILL model exhibited the median value of H had the highest contribution to the model (Figure 5c).  
Figure 4: Results of SPAD value prediction with Random Forests using limited illuminance data (training data: \(<\)300 lux; test data: \(>\)300 lux): (**a**) LED_RES_RGB+ILL, (**b**) LED_RES_HSL+ILL, and (**c**) LED_RES_GB+ILL.  
Figure 5: The top 10 mean absolute SHAP values of the best model in the SPAD value prediction with the Random Forests using halogen and/or LED light source data: (**a**) HAL+LED_RGB+ICT+ILL, (**b**) LED_RGB+ILL, and (**c**) LED_HSL+ILL.  
For the HAL+LED_RGB+LCT+ILL, LED_RGB+ILL, and LED_HSL-ILL models, the PD and ICE for the top three variables of the average absolute SHAP values for each model are shown in Figure 6. PD and ICE--out of importance, not shown in Figure 6--are shown in Figure 6. SPAD values tended to be higher when the median value of B was low (Figure 6d). SPAD values were higher when G was low (Figure 6a,b), and SPAD values were higher when R was high (Figure 6a,b). For illuminance above 4000 lx, the SPAD values tended to increase slightly, when the illuminance was high (Figure 6c). In addition, the SPAD value tended to be high when the H value was low (Figure 6g). This change in the H value indicates that the SPAD value decreases as the leaf color changes from green to blue in the hue circle. The S and L values did not change significantly with respect to the SPAD values (Figure 6h,i).
# On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation
## 3 Cross-Checking in the HY-2 Satellite Constellation
Figure 4: _Cont._  
Although products of all CMRs in the HY-2 satellite constellation meet specifications, the BT differences between different CMRs still leads to differences in AWV or WPD in close spatio-temporal ranges. In statistics, normalization can unify singular sample data to the same dimension, which may improve the accuracy of the retrieval model's output results [29]. Therefore, the next study will normalize the BT data dimension for the CMRs of the HY-2 satellite constellation, to reduce the difference of the observed BT for different CMRs.
## 2 SATis5 Use Cases  
The SATis5 project focuses on the 5G use cases of eMBB, and mMTC [21] and in particular on the backhauling use cases analyzed as a 3 GPP SA1 Study Item and specified in [22]. Satellite use cases for eMBB have been shown in [23]. SATis5 has selected satellite use cases for eMBB and mMTC to be further investigated and tested through over-the-air demonstrations based on the SATis5 testbed [21]. The SATis5 use cases for eMBB and mMTC correspond to the four satellite use case categories shown in Figure 3. It should be mentioned that the eMBB and mMTC use cases are associated to several 5G market verticals, such as media and entertainment, transportation, logistics and automotive, manufacturing, health, utilities, agriculture industry, and public safety.  
Satellite are very efficient in multicasting or broadcasting large eMBB data over a wide area directly to the users. 5G Satellite networks help to relieve the eMBB traffic of terrestrial 5G networks during rush hours by multicasting or broadcasting non time-sensitive eMBB data in idle times. An eMBB use case for _Trunking and Head-End Feed_ would be the distribution of rich to very rich TV content due to new media encoding format such as 3D or Ultra High Definition video content.  
Figure 2: Key satellite features for 5G ecosystem.  
Furthermore, disasters could lead to the unavailability of the eMBB terrestrial service in certain areas. The deployment of terrestrial 5G services may not be provided for _Backhauling and Tower Feed_ due to economic profitability reasons in this areas. In addition, some users would like to use eMBB services such as broadband access or governmental public safety applications.  
eMBB use cases for _Comms on the Move_ and _Hybrid Multiplay_ are related to grant users permanent access to 5G services while switching without service interruption between satellite and terrestrial networks. Moreover, this kind of use cases including moving land mobile or maritime and airborne platforms. In the eMBB satellite use case category, users may experience conditions where 5G services can only offered by satellite networks during the movement of the user [22].  
Typical mMTC use cases link small sensor devices to track containers including _Comms on the Move_ applications with drone swarms, as shown in [24]. _Backhauling and Tower Feed_ use cases for mMTC include low bit-rate broadcast services such as emergency messages for sensor devices.
## 3 Methods
### Calibration Set and Validation Set Selection Methods  
The MVARC-R-KS method [3] aimed at adaptively selecting the calibration set through multivariate analysis. The procedure of selecting the calibration set using the MVARC-R-KS method comprised two phases [3]. Phase 1 initially mined the clustering distribution zones using the MVARC method, which integrated the a priori algorithm, a density-based clustering algorithm, and the Delaunay triangulation [3]. The association between the SOM content and VNIR spectra may vary according to location and surrounding environment, contributing to the generally strong spatial variability of soil samples. Hence, to open out the spatial heterogeneity of soil samples, environmental variables and SOM content were set as input data for the MVARC method. The spatial heterogeneity of soil samples was checked by Moran's I; results are given in Table 1. Samples in the same cluster mined by the MVARC method were relatively similar with respect to the impact of the surrounding environment. In addition, the influence of environmental variables on SOM in clusters presented significant differences. Phase 2 utilized the Rank-KS algorithm [22] to select the calibration set from the clustering zones. The eventually selected calibration set was an internal component concentration and spectrum representations, as well as an external environment representation. In addition, the size of the calibration set was set as approximately 70% of the soil samples, and the remaining 30% of the samples were used as the validation set, referring to previous studies [23; 24].  
Apart from the MVARC-R-KS method, three commonly used calibration set and validation set selection methods---C method, KS method, and Rank-KS method---were utilized for comparison to verify the representation of the selected calibration set.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Conventional Approach
#### 2.4.2 Open-Loop and Closed-Loop Hybrid Tracking  
The concept of hybrid open-loop and closed-loop tracking was first introduced by [PERSON], who explored open-loop phase tracking supported by closed-loop pseudocode tracking, as shown in Figure 11. He chose a KF with a likelihood-maxima fusion operator based on the von Mises distribution as the estimator [47]. [PERSON] extended the estimation dimension of the estimator and optimized open-loop tracking by introducing a detector in the closed-loop pseudocode tracking loop to identify and correct pseudo-code phase errors [48].  
[PERSON] addressed the limitations of conventional closed-loop tracking, which lacks degrees of freedom, by proposing a quasi-open-loop tracking structure that combines both open-loop and closed-loop methods. This structure simplifies filter design by updating parameters at three different rates, thus increasing the degrees of freedom within the system [49].  
[PERSON] proposed an intermittent tracking method for open-loop GNSS signals using Kalman filtering, thoroughly analyzing the relationship between tracking accuracy and duty cycle [48].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique** & **Advantages** & **Challenges** & **Representative References** \\ \hline \multirow{3}{*}{FFT Discriminator} & Generates 3D signal image for robust satellite signal detection. & High computational complexity; limited real-time applicability. & [PERSON] (2005) [42], [PERSON] (2006) [43], [PERSON] (2016) [11] \\ \hline \multirow{3}{*}{Enhanced Kay’s Method} & Improved estimation accuracy in low SNR scenarios. & Increased algorithm complexity; sensitive to model mismatch. & [PERSON] (2012) [44] \\ \hline \multirow{3}{*}{Four-Dimensional UKF Estimator} & Captures high-order Doppler effects; robust against dynamics. & Increased state dimension leads to higher computational load. & [PERSON] (2010) [138], [PERSON] (2020) [45] \\ \hline \multirow{3}{*}{Two-Stage Frequency Estimation} & Combines coarse and fine estimation for improved frequency accuracy. & Requires careful transition design between coarse and fine stages. & [PERSON] (2022) [46] \\ \hline \end{tabular}
\end{table}
Table 8: Summary of discriminator optimization techniques for open-loop tracking.  
In summary, the development of hybrid open-loop and closed-loop tracking techniques, which combine the advantages of both approaches, has evolved from initial concept proposals to the realization of various technologies. The introduction and refinement of these techniques not only enhance the robustness of GNSS receivers in complex environments but also provide new solutions for high-precision positioning.