esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 3 Results and Discussions  
### Chemicals of Coatings  
In order to identify the chemicals of prepared coatings, infrared spectral analysis was performed and the results are shown in Figure 2. It was seen that all the coatings exhibited similar infrared spectrums. The main absorption peaks located at 3450 cm\({}^{-1}\) and 1109 cm\({}^{-1}\) belonged to the vibrations of Si-OH and Si-O-Si correspondingly, indicating most siloxanes had been hydrolyzed and polycondensation. The peak at 2969 cm\({}^{-1}\), 1260 cm\({}^{-1}\) and 849 cm\({}^{-1}\) belonged to the stretching vibrations of Si-O-C\({}_{2}\)H\({}_{5}\)[16], suggesting insufficient hydrolysis of siloxane monomer. Specifically, the peak at 2969 cm\({}^{-1}\) was related to the C-H stretching vibration of residual CH\({}_{3}\) from non-hydrolyzed Si-O-C\({}_{2}\)H\({}_{5}\) groups, and the small peaks near and below 2969 cm\({}^{-1}\) were assigned to asymmetric and symmetric C-H vibrations of CH\({}_{2}\). Moreover, the peaks between 1500-1260 cm\({}^{-1}\) were assigned to various C-H vibrations, and around 849 cm\({}^{-1}\) with a band of 900-750 cm\({}^{-1}\) were assigned to CH\({}_{2}\)[17] Due to the addition of KH570, the vibration absorption peaks of olefin stretching (1702 cm\({}^{-1}\)) and C=O ester contraction (1723 cm\({}^{-1}\)) were also observed [18]. The peaks located in 400-500 cm\({}^{-1}\) belonged to the absorption peaks of Si-O-Si [19].  
In an acid environment, the alkoxysilane monomers would undergo hydrolysis and condensation reactions according to Figure 3. Organic groups (methyl, phenyl, etc.) directly linked to silicon atoms do not participate in the hydrolysis and condensation reactions, while the main one involved in the reaction process is oxyethyl and oxymethyl. When using acid as a catalyst, H\({}^{+}\) attacks -OCH\({}_{2}\)CH\({}_{3}\) and/or -OCH\({}_{3}\) groups and results in the protonation and the shift of extranuclear electron cloud to it, weakening the Si-O bond until it breaks and forms Si-OH. So far, one-step hydrolysis has been completed. After hydrolysis, the generated Si-OH will further participate in the condensation reaction with Si-OH, Si-OCH\({}_{2}\)CH\({}_{3}\) and Si-OCH\({}_{3}\) under the catalyst action of acid, forming a three-dimensional network structure of Si-O-Si, water and alcohol substance. It is worth noting that although a single monomer is hydrolyzed first and then condensed, hydrolysis and condensation occur simultaneously in the whole system, so incomplete hydrolysis may occur.  
Figure 2: Infrared spectra of nano SiO\({}_{2}\) particles modified coatings with different weight ratio.
## 3 Categorization of Visual Inspection (Tasks)
### Overview on How to Solve Automated Visual Inspection with Deep-Learning Models
#### 3.3.4 Visual Inspection via Multi-Class Localization  
The majority of the literature reviewed used multi-class localization for VI. In 83 publications, it is shown how to approach different use cases, like crack or damage detection, with multi-class localization. Table 5 provides a detailed overview. As for the two classification approaches, damage detection is the most investigated VI use case, with 58  publications. Therein, YOLO and Faster RCNNs are the two most used models, with over ten publications. They are followed by CNNs and Mask RCNN models, which are utilized more than five times. FCN, SSD, and UNet can also be used as approaches to multi-class damage detection. [PERSON] et al. [164] conducted a comparative study of several CNN models highly utilized in AVI and three vision transformer models, namely, detection transformer (DETR), deformable detection transformer (DDETR), and Retina-Swin, on three different damage detection use cases on freight cars. Multi-class localization was used in 15 publications for crack detection. In five publications, the authors performed crack detection with a YOLO model. Crack detection can also be performed with AlexNet, DeepLab, FCN, Mask RCNN, and UNet, which was shown in different publications. In three different publications, the authors show how to conduct quality inspection with YOLO. YOLO can be used in a tobacco use case, as well (other), as shown by [PERSON] et al. [165].  
\begin{table}
\begin{tabular}{l l l l} \hline
**VI Use Case** & **Model** & **Count** & **References** \\ \hline \multirow{7}{*}{Crack Detection} & CNN & 8 & [47; 59; 62; 126; 127; 128; 129; 130] \\  & CNN LSTM & 1 & [122] \\  & Attention CNN & 1 & [120] \\  & Custom encoder–decoder CNN & 1 & [121] \\  & DeepLab & 3 & [131; 132; 133] \\  & Ensemble & 3 & [134; 135; 136] \\  & Fully convolutional network (FCN) & 2 & [137; 138] \\  & Faster RCNN & 1 & [139] \\  & UNet & 6 & [140; 141; 142; 143; 144; 145] \\ \hline \multirow{7}{*}{D Damage Detection} & DenseNet & 1 & [146] \\  & Faster RCNN & 1 & [147] \\  & GAN & 1 & [148] \\  & Mask RCNN & 2 & [149; 150] \\  & ResNet & 1 & [48] \\  & SSD & 1 & [151] \\  & Swin & 1 & [123] \\  & Transformer & 1 & [152] \\  & UNet & 3 & [153; 154; 155] \\  & VAE & 1 & [49] \\  & ViT & 1 & [156] \\  & YOLO & 2 & [157; 158] \\ \hline \multirow{2}{*}{Completeness Check} & Mask RCNN & 1 & [159] \\  & YOLO & 1 & [160] \\ \hline Quality Inspection & YOLO & 1 & [161] \\ \hline \multirow{2}{*}{Other} & Faster RCNN & 1 & [125] \\  & UNet & 2 & [44; 162] \\ \multirow{2}{*}{Other} & YOLO & 2 & [124; 163] \\ \hline \end{tabular}
\end{table}
Table 4: Overview of VI use cases and models that solve these use cases via localization.
## 3 Data Implementation  
### Random Forest  
Random Forest (RF) is a supervised learning algorithm made up of a collection of tree-structured classifiers, defined as a decision tree, applied throughout a given dataset on multiple sub-samples ([PERSON] et al., 2017). The decision tree is built up from several nodes, connected by branches, descending from the root node, placed at the top by convention, to the leaf nodes ([PERSON] et al., 2017). Features are tested at the decision nodes, leading onto a branch. Those branches can lead to another decision node or conclude in a leaf node. The algorithm represents supervised learning, which requires a training data set provided with values of the target variable.  
Originally, the specific decision trees to be used are the Classification Furthermore, Regression Trees (CART) algorithm, where each decision node produces two branches, so the tree is binary. Its growth happens through an \"exhaustive search of all available variables and all possible splitting values\", selecting the optimal measurement vectors that reduce the highest impurity ([PERSON] et al., 2017; [PERSON] et al., 2017). The process to generate a decision tree starts with splitting the root node into binary pieces. The splitting procedure is based on the following evaluation of candidate splits \(s\) at node \(t\):  
\[\Delta i(s,t)=i(t)-p_{L}i(t_{L})-p_{R}i(t_{R}) \tag{1}\]  
where \(\Delta i(s,t)\) is a measure of impurity reduction from split \(s,i(t)\) represents the impurity before splitting, and \(i(t_{L})\) and \(i(t_{R})\) show the impurity of the left child node \(T_{L}\) and of the right child node \(T_{R}\) after halving node \(t\) by split \(s\). In order to measure these impurities, there are several approximations ([PERSON] et al., 2017), but the criterion for split is by default the Gini impurity ([PERSON], 2017), which measures how often a randomly chosen element from a set would be incorrectly labeled from a random distribution of labels in the subset.  
A random forest, of which an example is shown in Figure 2, can be defined as a \"combination of tree predictors such that each tree depends on the values of a random vector sampled independently, with the same distribution for all trees in the forest\", and each tree votes for the most popular class at a given input ([PERSON] et al., 2017). The procedure is as follows: a random vector \(\theta_{k}\) is generated, independent of the past random vectors \(\theta_{1},\ldots,\theta_{k-1}\) but with the same distribution; and a tree is grown using the training set and \(\theta_{k}\), resulting in a classifier. The number of trees that can be added to a random forest can increase without limit, and should not cause overfitting problems on the model [25]. The training algorithm for the random forest is bagging, also called bootstrap aggregation, which consists of creating, or replacing, subsets of training data through random samples, represented in Figure 2 as \"Out of bag\" (OOB) throughout the original data, and fitting new trees onto those samples [27].
## 3 Results  
### Toxicity of the PAM and its Possible Degradation under Anaerobic Conditions  
#### 3.1.1 Toxicity of the PAM  
Toxicity of the polyacrylamide flocculant for the microorganisms of the methanogenic community was determined using the mixture of simple substrates: acetate, propionate, and butyrate (series T). The tested PAM concentrations cover the range from the working concentrations used in wastewater treatment (5 mg/g TS) to extremely high ones (200-400 mg/g TS), which may potentially have a significant inhibitory effect on methanogenesis. Experimental results are presented in Figure 1a and Table 1.  
Figure 1: Dynamics of methane accumulation (ml/g volatile solids (VS) initial) in experiments on the determination of polyacrylamide (PAM) toxicity (**a**) and PAM biodegradability (**b**). Specific methane yield was calculated considering the organic matter of the PAM. Active methanogenesis in all samples continued for 22 days. According to the data presented in Figure 1a, the addition of the flocculant even at its minimal concentration (5 mg/g TS) caused a decrease in the initial rate of methanogenesis. However, no inhibitory effect on anaerobic digestion was observed after 11 days of the experiment at flocculant concentrations of up to 40 mg/g TS. This finding was confirmed by the values of total methane yield which were similar to those of the control samples. PAM concentrations of up to 40 mg/g TS did not affect the final methane yield. In the T-400 series (400 mg PAM/g TS), the initial methanogenesis rate decreased significantly (by 76.5 \(\pm\) 2.4%) compared to the control samples without PAM. However, intensification of methanogenesis was observed after 15 days of incubation. The methane yield at the end of the experiment was 57.6 \(\pm\) 1.9% of the yield in the control samples. Importantly, the highest tested PAM concentration (400 mg/g TS), despite its pronounced inhibitory action, did not cause termination of methanogenesis when VFA were used as the substrate. The VFA consumption rate and amount of consumed VFA varied depending on PAM concentration in the samples (Table 1). During the first 6 days of the experiment, -74% of the added substrates (considering the VFA formed during the decomposition of VS of inoculum) were consumed in the control samples. In the samples T-5, T-40, and T-80, the amounts of consumed substrates were 64.6, 43.1, and 38.3%, respectively. PAM was found to have an inhibitory effect on propionate degradation. After 42 days propionate was completely consumed only in the samples with PAM concentrations below 40 mg/g TS. Propionate consumption in the samples T-80 and T-200 was 58.6 and 51.7%, respectively. Propionate concentration in the sample T-400 did not change. Acetate concen
## 4 The Proposed Reliable Transport Scheme  
### An Overview of the Reliable Transport Process  
File transmission was carried out in cycles; the transmission process of each cycle is shown in Figure 1. At the beginning of cycle \(r\), the source node determined the number of packets to be injected into the network \(M_{r}\); the diffusion timer \(T_{s,r}\), and the waiting timer \(T_{w,r}\), were based on the index value of the latest feedback \(ACK_{r-1}\) received in the previous cycle (\(r\)\(-\)1). After these packets were sent out, their relay nodes were allowed to replicate and forward the packets over the network for the duration of timer \(T_{s,r}\). Then the source node waited for \(T_{w,r}\) until it received an ACK from the destination. Thus, the duration of a cycle was \(\frac{M_{r}}{\lambda}+T_{s,r}+T_{w,r}\). At the end of the cycle, the entire network was cleaned up and all packets in the relays were cleared (except ACKs) to avoid network congestion and the selfish behavior of relays caused by overuse of cache. The next cycle would continue unless the ACK received by the source node indicated that the destination node had collected \(K\)-independent coded packets and reconstructed the source file.  
It is important to note that ACKs were not cleared by the node at the end of the cycle. During the whole period of file delivery, the destination node would feedback an ACK to the network every time it received a new independently coded packet, and route it in epidemic fashion. The ACK took the number of packets still needed currently for file decoding by the destination node as the index value  
Figure 1: File transmission cycles.  
\(q\). When a relay node carrying ACK \(q^{\prime}\) encountered a new ACK \(q\), if the index value \(q^{\prime}\) was greater than \(q\), it would override ACK \(q^{\prime}\) with ACK \(q\) to ensure that the latest feedback information could be transmitted in the network. Once the source node received ACK 0, the file delivery was completed and the transfer process was terminated. Otherwise, the next cycle began.
## 5 Experiments and Analysis
### Feature Engineering for Regression
#### 5.1.2 Boruta Feature Selection  
In order to rank the most significant features in predicting \"weighted_score\", we use the Boruta feature selection method for the same regression issue. According to the data, the characteristics with the most importance are code_module, studied_credits, total_click, late_rate, and fail_rate, in that order. The fact that \"code_module\" and \"total_click\" are also shown to be top indicators in the Lasso regression study bolsters their significance in predicting \"weighted_score\". It is possible that \"studied_credits\", \"late_rate\", and \"fail_rate\" are also significant in predicting \"weighted_score\", since they are among the top five rated qualities. Even when the number of characteristics is substantial, the outcomes of Boruta feature selection show its efficacy in selecting the most significant ones.  
Regarding feature engineering for regression issues, the usage of Lasso regression and Boruta feature selection has been shown to be beneficial. These results have real-world implications for boosting the precision of regression models in fields. Our research indicates that by using these two methods, a deeper knowledge of the data and the most crucial elements for prediction may be attained in the following sub-sections of the analysis part.
## 3 Results and Discussion
### Measurement of Magnetic Hysteresis Curve of Actuator Core
Due to the method used, the magnitude of the magnetic field sensed by the magnetometer probe was given as a superposition of the stray field of the inhomogeneously magnetized core and the excitation magnetic field generated by the actuator winding. Therefore, the output of the performed measurement corresponds to a functional dependency of a measured stray magnetic flux density on the excitation current, see Figure 6.  
Although in Figure 6 it is possible to see a small correlation between the shape of both characteristics, due to the form of the measured data interpretation (dependence of the superimposed magnetic flux density on the excitation current) conditioned by the used measurement method, information about the magnetic properties of the core is, in the case of the red magnetization curve, absent. Ultimately, based on the used measurement method, it is not possible to objectively evaluate the response of the investigated material to the external excitation field; only the magnitude of the generated magnetic dipole moment, given as a sum of a winding dipole moment and a dipole moment of the magnetized ferromagnetic core, can be quantified. On the contrary, the magnetic hysteresis curve of the ferromagnetic core acquired by the presented measuring workstation, using the method based on measuring the stray magnetic field of the open-ended samples, describes the variation of the core magnetic polarization as a function of the excitation magnetic field. Therefore, since a change in the state of the core magnetization is for a complete magnetization reversal cycle described by the reconstructed hysteresis curve, it is possible to determine its magnetic properties via typical state describing magnetic quantities, such as remanent magnetic polarization, saturation polarization, coercivity, effective permeability (if the value of the demagnetization factor is known), etcetera, even at the time of its design phase. It should be noted that when comparing the magnetic hysteresis curve between the open sample and the toroidal core, only the saturation would acquire the same value among these magnetization quantities. Therefore, for the verification, we present the obtained value of saturation polarization, which, as can be seen from Figure 6, reaches a value of about 0.6 T, corresponding to the saturation stated by the manufacturer for the same amorphous alloy designed in the shape of the toroidal core [49].
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
## 3 Results
### Laser Spot Size  
The laser used in this experiment could collect energy with a minimum diameter of 20 \(\upmu\)m whilst passing through the beam expander and F-theta lens. It was also possible to enlarge the spot size by adjusting the expander ratio and focus position. In general, when a laser is used for ablation, the smaller the spot size, the better the precision. On the other hand, in the case of melting or sintering (such as welding), a spot size that is too small makes it difficult to make an interconnection between materials. In our study, the polymer binder ablation and the metal powder sintering had to be generated at the same time, so it was essential to select an appropriate spot size. Therefore, the results were observed when changing the spot size to 20, 50, and 100 \(\upmu\)m whilst the rest of the conditions were fixed.  
Figure 2: Rheological properties of composite resin according to the volume ratio of aluminum powder.  
When the spot size was as small as 20 \(\upmu\)m, which was similar to the size of the aluminum powder used, high energy was momentarily concentrated on a narrow area. Therefore, irregular pattern damage, as shown in Figure 4, was observed due to contractions based on ablation and thermal shock. As a result, the resistance was also measured as an irregular value of over 0.65 \(\Omega\cdot\)cm\({}^{-1}\). On the other hand, when the spot size was 50 \(\upmu\)m, which was larger than the aluminum powder used, the polymer was removed and the interconnection by sintering between the aluminum powders was properly formed. The resistance also showed a relatively stable value of 0.33 \(\Omega\cdot\)cm\({}^{-1}\). However, when the spot size was increased to 100 \(\upmu\)m, the energy density was excessively lowered and the polymer binder itself was not sufficiently ablated, so an interconnection between the aluminum powders could not be formed. As a result, a conductive pattern was not formed, making it impossible to measure the resistance value. Based on this, in this study, a 50 \(\upmu\)m spot size was selected as an appropriate condition for the binder ablation and metal powder sintering.
## 2 Study Area, Data and Methods
### Data
#### 2.2.2 Meteorological Data  
Hourly air temperature and relative humidity (RH) at 23 automatic stations were obtained from the China Meteorological Administration for the period of 2014-2018. The hourly vapor pressure deficit (VPD) was estimated with relative humidity and air temperature data following the World Meteorological Organization Commission for Instruments and Methods of Observation Guide conversion equation [37]. The hourly surface downward solar radiation (DSR) ERA5 reanalysis data were provided by the European Center for Medium-Range Weather Forecasts at a 0.25\({}^{\circ}\) spatial resolution.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 3 Stratifying Privacy-Preserving Solutions
### Privacy-Preserving Data Sharing  
Privacy-preserving data-sharing techniques enable collaboration and analysis while protecting patient privacy. These techniques focus on methods that facilitate secure data sharing among multiple entities.  
1. Blockchain and Distributed Ledger Technologies: A blockchain provides a platform for managing and storing encrypted patient data in a decentralized and inviolable manner, ensuring data integrity, controlled access, and auditability [14].
2. Secure Multi-Party Computation (SMC): SMC allows for computations on distributed data while protecting individual privacy. It leverages cryptographic techniques to perform calculations across multiple parties without revealing sensitive information.
3. Federated Learning (FL): FL facilitates collaborative machine learning (ML) without the need to share raw data between entities. Instead, ML models are trained locally on individual datasets, and only the model updates are shared. This approach preservesconfidentiality and enables collective intelligence while maintaining individual data sanctuaries, making it an effective privacy-preserving mechanism in healthcare [14].
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Antenna Prototype Model  
The top and bottom views of the fabricated miniaturized Koch fractal antenna are represented in Figure 13a,b. The FSS is fabricated on a 35.94 mm \(\times\) 33.96 mm FR-4 substrate, as shown in Figure 13c. The antenna is placed above a 7.0 mm air gap by using foam pieces as separators, as shown in Figure 13d.
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 3 Methodology
### Planar Primitive Detection  
In this section, we detect the planar primitive to extract the planar features for further processing. The detection of planar primitives can be summarized as fitting the mathematical model to the data. The solutions to this problem can be classified into three types: A RANSAC-based algorithm, a region-growing-based algorithm, and a parameter space-based algorithm.  
We first use the region-growing algorithm to gain the initial planar primitives. To refine these initial labels of planar primitives, a multi-label graph cuts optimization method is utilized. Through this label optimization step, most of the erroneously labeled primitives can be revised. Compared with other detection methods, this approach can segment the point clouds in a suitable scale.
## 4 Results
### Baselining Methods
#### 4.4.1 Method I  
Method I is the most simple way of implementing a baseline. The last measured value before flexibility activation is used as the baseline for the duration of the flexibility activation. In particular with curtailment option 1 (Figure 13), this causes significant levels of inaccuracy: when curtailing the entire peak production, method I per definition underestimates the baseline. Therefore, this baselining method is less suitable for situations in which peak PV production is continuously curtailed. In case the DSO requests flexibility from the market (curtailment option 2), this baseline's accuracy will largely depend on the moment the DSO starts with flexibility activation, and its duration. It can be observed in Figures 14 and 15 that when curtailment occurs on the peak of the day (e.g., 4 July), the baseline is an overestimate of the reference profile. Vice-versa, when curtailment starts before the peak, like in curtailment option 1 the baseline is expected to be underestimated. For flexibility activation over multiple timesteps, method I is also inaccurate. Due to the method's simplicity, it might however perform satisfactorily for flexibility activation during a single (short) timestep, as the error will be limited.