esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbial Life on the Surface of Microlgistics in Natural Waters
## 1 Introduction
It has been demonstrated that there are adverse impacts of microplastics on aquatic and soil living organisms due to impaired reproduction, malnutrition, internal abrasions, and blockages [20; 21; 22], and also adverse human health impacts have been identified [23; 24; 25]. Considering their high surface area and hydrophobicity of their surfaces, microplastics can act as sorbents for other environmental pollutants, persistent organic pollutants [26], hydrocarbons [27], pharmaceuticals [28] and other pollutants [29]. Desorption of pollutants, if the particles enter the living body, is one of the microplastic toxicity mechanisms [22]. Sorption of pollutants onto microplastic particles influences polymer, additives to it, particle size and surface area, as well as ageing and supporting development of functional groups [30]. Another major group of factors affecting microplastics--pollutant interaction are the properties of corresponding environments and, in this respect, of key importance are the presence of natural organic matter (NOM) and their refractory part--humic substances. As the concentrations of NOM in aquatic environments are by order higher than those of microplastics, it can be expected that they play a major role at the interaction with pollutants (Figure 1) as well as support the development of microbial coverage on the surface of particles of microplastics. Recently, the attention of researchers has been directed towards the study of nanoplastics which are produced during degradation of microplastic [31]. Microbial life on the surface of microplastic particles is one of the key topics to understanding their role in the environment. Microplastics serve as habitats for microorganisms including pathogenic ones, places for biofilms formation, and means for microbes' transportation in new areas. Microorganisms developed on microplastics can cause an impact on the environment and human health. Microorganisms attached to the surface of microplastic particles could interact with them, be used as substrates for growth, change properties and biodegrade. However, as it was indicated in work [32], while the problems of plastics and microplastic pollution are widely discussed, the impact of plastic on microbial life remains poorly understood. The aim of the article is to review the impacts of microorganisms on the surface of microplastic particles to understand the role of these interactions in the environment and the impact on the fate of microplastics.
## 7 Results and Discussion
### Laser Scanning
#### 7.1.2 The Village Mansouria  
Dozens of other objects of various sizes were also documented. Some of the objects were the size of an entire roofed village--the ZEB-REVO mobile laser scanner, with its advantageous high speed, was used here. Smaller sections were documented by the BLK360 scanner (better resolution and photographic texture), and close-range photogrammetry was used for rooms, artifacts, archaeological finds, or small objects. With such extensive documentation and low expedition costs, however, it is not possible to use only one technology.  
For a small building, all three technologies can be used in general, but for larger, irregular buildings with many narrow-roofed corridors, it is quite clear that ZEB-REVO or similar equipment based on SLAM technology is unmatched in terms of economy and speed. It is certain that in enclosed dark spaces, which were documented during the expedition in a short time, it was not possible to define any control points. This logically led to the deformation of the models (Figures 24-26).  
For the Mansouria village case, the ZEB-REVO was used. Two measurements of the roofed village were performed. It was a large residential complex, documented inside and out. We also encountered deformation of the models during data processing.  
Both point clouds were then merged into one, but there was a significant discrepancy of up to 1 m. It was due to the accumulation of errors in IMU. To improve the result, both models had to be connected by a transformation based on tie points. The result is enough for this project; it is a settlement of unfired clay blocks.  
In ArcGIS, the models were therefore transformed by affine transformation to 30 tie points with a resulting error of up to 15 cm, which is already acceptable. The deformation of both models caused the non-unification of data in identical areas. They were intentionally measured during the scanning, as an unknown deformation was expected. Data merging must be done using identical scanned parts in both models (Figure 26).  
Figure 24: After joining both models from ZEB-REVO (**a**), discrepancies of about 1.2 m occurred in the lower right part of the model (**b**).  
The ZEB-REVO is sufficiently accurate for common documentation, especially of historical, irregular, and difficult-to-define objects (such as unfired brick objects). However, in terms of comparing accuracy and processing, it is significantly more economical and faster [40].  
Figure 26: After transformation of both models from vectorized scans, an appropriate ground plan was made.  
Figure 25: Deformation of both models.
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 2 Principle of Photovoltaic Power Generation and Consumption Mode
### Photovoltaic Power Generation Systems and Their Consumption Methods
#### 2.2.2 Consumption Methods for Distributed PV Access to Distribution Grids  
At this stage, for distributed PV access to the distribution network, the main elimination methods are as follows:  
First, grid planning, optimization of access methods [8], and the capacity accessed in advance to do a good job of calculation can be based on the results of load forecasting or transformer and other capacity planning.  
The second is energy storage technology [9], by storing the power generated by distributed PV systems, which can be used at night or low load to store the power generated by PV power generation through batteries or other energy storage devices for emergency use. This approach can improve energy utilization, but the cost is high, and the life and maintenance of the energy storage equipment are issues to be considered.  
Third, smart grid technology [10]: smart grid technology can realize real-time monitoring and management of distributed PV systems, as well as dynamic adjustment of grid load.  
Fourth, microgrid technology [11]: microgrid technology can combine distributed PV systems with traditional power grids to form a small independent power system to meet the energy demand in the region. This approach can improve the self-sufficiency and reliability of distributed PV systems, and also reduce the dependence on the traditional power grid.  
Subsequent chapters of this paper will also focus on the above aspects to explore the consumption methods of distribution grids containing distributed PV.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions
### Results of Our Approach against the Baseline  
Moreover, as part of a closer look at our experiments, we analyzed the results of each road segment, and these are summarized in Table 6. In this table, the name of each road segment and its length in km, the MAE per 100 km between the baseline and our approach, and the percent improvement are shown. In this case, the baseline was the LSTM only, while our approach used the SA-LSTM with the Butterworth low-pass filter method. According to the table, the average MAE of our approach for all the road segments was 12.15, while the baseline was 27.12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Techniques** & **MAE (minutes) per 100 km** \\ \hline LSTM (only) & 27.12 \\ SA-LSTM (Self-Attention LSTM) & **26.89** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance report of comparison of the LSTM and SA-LSTM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Techniques** & **MAE (minutes) per 100 km** \\ \hline ARIMA & 31.25 \\ SVR & 29.96 \\ LSTM & **27.12** \\ ConvLSTM & 28.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance report of the time-series prediction techniques: Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Convolutional LSTM (ConvLSTM), evaluated by Mean Absolute Error (MAE).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Techniques** & **MAE (minutes) per 100 km** \\ \hline SA-LSTM + Kalman filter & 20.95 \\ SA-LSTM + Savitzky–Golay filter & 20.96 \\ SA-LSTM + Butterworth low-pass filter & **12.15** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance report of the comparison of SA-LSTM and SA-LSTM with a filter technique.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 3 Experiment and Analysis
### Static Test Results  
First, the antenna was mounted on the roof of an automobile. The vehicle is stationary for 40 s in an open-sky environment, IF data are collected.  
Figure 3: Experimental environments and setup.  
Figure 2: The relationship between position error and code phase error.  
The velocity error results are shown in Figure 5d-f and Table 2. In the east, north, and up direction, the proposed method presents an RMSE of 0.20 m/s, 0.73 m/s, and 0.47 m/s, respectively, compared with 0.87 m/s, 1.56 m/s, and 1.16 m/s of the traditional method. It is obviously superior in terms of stability, and has a more accurate error estimation accuracy.
## 2 Experimental Methods  
The milling operation was performed in VMC-1000 CNC vertical machining center (Hummer, Taichung, Taiwan) with a spindle power of 15 kW and a maximum speed of 8000 rpm. After the Kistler 9257B dynamometer (Kistler, Winterthur, Switzerland) detected cutting signals, the charge amplifier (Kistler type 5070A) (Kistler, Winterthur, Switzerland) amplified the signals and then, through a data accusation card (sampling rate = 5000 Hz), transmitted to a personal computer to compute the cutting forces. The obtained data were processed in software Kistler DynoWare (Kistler, Winterthur, Switzerland). After experimental work was completed, the surface roughness of the machined surface was measured using Surtonic 3+ surface roughness measuring instrument ([PERSON], Leicester, UK). The diameter of the device probe was 6 mm and the whole system was set at a sampling length of 0.8 mm. The tool wear was determined by using optical microscope. The surface roughness values (\(Ra\)) were taken at four positions. Mechanical and chemical properties of the Ti-6242S alloy are given in Tables 1 and 2, respectively.  
\begin{table}
\begin{tabular}{c c} \hline Tensile Strength, Ultimate (N/mm\({}^{2}\)) & 1110 \\ \hline Tensile Strength, Yield (N/mm\({}^{2}\)) & 1050 \\ \hline Elongation at Break (\%) & 13 \\ \hline Vickers Hardness (HV) & 333 \\ \hline Elasticity Modules (N/mm\({}^{2}\)) & 118,000 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of Ti-6242S.  
The milling experiments of Ti-6242S were carried out at different cutting parameters using WC end mills which have 10 mm diameter. The used cutting parameters are given in Table 3.
## 2 Materials and Methods  
### EEG Experiment  
Eyes-open resting-state data were collected in a cohort of 19 healthy young adults (age \(28\pm 6\) years, 5 males) and were previously used in other studies from our group [11; 12; 49]. Ethical approval was granted by the Ethics Committee of ETH Zurich; informed consent was obtained from all participants and the experiment was performed in accordance with the relevant guidelines and regulations. For each participant, we recorded hdEEG signals for 5 min at 1000 Hz sampling rate using a 256-channel HydroCel Geodesic Sensor Net by Electrical Geodesics (Eugene, OR, USA). Electromyograms and vertical and horizontal electrooculograms (EOG) were collected in addition to the hdEEG signals. Positions of the EEG sensors and of three landmarks (nasion, left and right preauricular points) were localized using a Geodesic Photogrammetry System (GPS) [42]. In a separate session, we acquired a T1-weighted whole-head anatomical image using a Philips Ingenia 3T MR scanner (Best, The Netherlands) with a turbo field echo sequence. Scanning parameters were: TR = 8.25 ms, TE = 3.8 ms, flip angle = 8\({}^{\circ}\), voxel size = 1 mm\({}^{3}\) isotropic.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 2 Background  
### IoT Architecture  
With the rapid development of the Internet, more and more household and industrial devices are connected to the Internet, which offers us diversified lives. IoT architecture is mainly developed in two directions: consumer-level and industry-level.  
On the consumer-level, we have several device types like industrial manufacturing, smart home, smart medical, and smart cars if they are divided by application scenarios. Among them, the development of smart home is relatively mature. The Internet giants--Samsung, Google, Apple, and XiaoMi have a large share of the market. In addition, the IoT platforms like SmartThings [20], Google Weave [21], Apple HomeKit [22], HomeAssistant [23], and XiaoMi IoT [24] are released. By investigating these platforms, we find that most IoT adopts the \"Device <<>Cloud<<>User\"'s architecture, as Figure 1 shows. The smart devices are generally deployed at homes. They communicate with cloud servers, and directly or indirectly access the network through WiFi [25], ZigBee [26], Blue-tooth [27], or other protocols. They upload the data that are collected by the sensor and receive the control command, which is issued to the actuator. The IoT architecture not only relies on the cloud from the vendor, but also on the cloud from a third party. It supports mutually and offers diverse services for various functions. Users can connect to the cloud to view the status attribute and download data by their mobile phone or PC. For some simple scenarios such as wearable devices, the \"Device <<> User\" architecture is more practical.  
On the industry-level, the IoT architecture continues the Information Technology (IT) approach to centrally manage to interact between users and devices by servers in Figure 2. The difference is that the apparatus must first communicate with the Programmable Logic Controller (PLC) through operational technology (OT). Thus, the devices in the industry are equivalent to PLC and \"sensors + actuators.\" Security research focuses on PLC. The \"Device <<> User\" architecture also exists in industrial scenarios. Administrators use configuration software to control devices. Although user-oriented industrial terminals such as smart meters have also tried the cloud model, there is no large-scale promotion due to security considerations.
# Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning
## 3 Methods
### Convolutional Neural Network
#### 3.2.2 CNN Regression Model  
The CNN was a type of feed-forward convolutional neural network consisting of an input layer, convolutional layers, activation layers, pooling layers, normalization layers, and a fully connected layer. The convolution layer was used to extract local features from the data and was usually followed by an activation layer. Commonly used nonlinear activation functions include Sigmod, Tanh, ReLU, and Leaky ReLU. The pooling layer was used to greatly reduce the number of parameters (dimensionality reduction) and alleviate the problem of overfitting. The fully connected layer performed a nonlinear combination of the extracted features to obtain the output, which was located in the last part of the hidden layers of the CNN. The pooling layer was mainly used to reduce the dimension of the input data. Since the data volume of a single slope was much smaller than that of an image, the pooling layer was not added to the CNN in this study to prevent the loss of effective information with increasing network depth.  
In this experiment, the shallow CNN model was built based on Matlab R2021a (Math Works, Inc., Natick, MA, USA). The network structure is shown in Figure 15, including an input layer and three convolutional layers with the Leaky ReLU activation function, followed by a fully connected layer and a regression layer. The parameters of the 1D CNN and 2D CNN models are shown in Table 5.  
Figure 14: CNN input data.  
Figure 15: CNN regression mode.  
The 3000 soil slope samples from Section 2.2 were divided into a training set (80%), a validation set (10%), and a test set (10%). The physical and mechanical parameters of each slope in the training set were used as input data, along with the coordinates of each soil layer, and the corresponding safety factor was used as output. The CNN-based soil slope stability prediction model was trained using this dataset. Finally, the accuracy of the CNN model was tested on the test set (10%), and an actual slope was input into the neural network to validate the universality of the CNN model.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## Proposed Method
### Statistical Features and Evaluation Model  
Considering the computational complexity and the accuracy performance, three Gaussian windows (with \(K=L=2,4,6\)) are employed in the proposed BIQA-SC. For a given image, three NFMs and two DFMs can be obtained. Statistics including GGD parameters, AGGD parameters, and PLCC values among neighboring pixels are extracted from the three NFMs. The Weibull parameters are computed for the two DFMs. To achieve a better performance, the above features are extracted from the original resolution and a reduced resolution (down-sampled by a factor of 2). The color features are only extracted from the original resolution.  
To comprehensively investigate the effectiveness of the proposed method, we train three support vector machine regressor (SVR) models by using different feature sets for estimating the perceptual quality score. We use only GGD and AGGD parameters extracted from the multiple NFMs to train the first SVR model, which is denoted by BIQA-SC-I. In the second model, which is denoted by BIQA-SC-II, GGD and AGGD parameters, PLCC values, and Weibull parameters are used to train the evaluation model. In the third model, which is denoted by BIQA-SC, all features extracted from the NFMs, the DFMs, and the HSV color space are used to train the evaluation model. The LIBSVM package [38] is utilized to implement the SVR models, and the radial basis function is employed as the regression kernel.  
Figure 6: 3D scatter plots between the color features extracted from HSV color space on LIVE [28] database: (**a**) features in the saturation channel; (**b**) features in the hue channel; and (**c**) features in the value channel.
# Approximation of Non-Linear Stress-Strain Curve for GFRP Tensile Specimens by Inverse Method
## 2 Experiment and FEA Setup  
### Experimental Setup (ASTM D638-14)  
In this experiment, SUPRAN(r) PP 1320 (wt 20% GF), 1330 (wt 30% GF), and 1340 (wt 40% GF) of SAMBARK LFT were used. Each material is based on polypropylene, and 6\(\sim\)25 mm-long glass fiber accounts for 20%, 30%, and 40% of the total weight of the material. Figure 1a shows the conceptual diagram of the injection process, and Figure 1b shows the test specimen produced by the injection process. The process conditions were set to the injection temperature of 220 \({}^{\circ}\)C, pressure of 50 MPa, mold temperature of 4 \({}^{\circ}\)C, and cooling time of 15 s, respectively. ASTM D638-14 [23] was applied for the tensile test method. The specimens' geometry was the dog-bone type, as shown in Figure 1c, and they were fabricated by injection molding, as shown in Figure 1b. To observe the various tendencies of the inverse analysis for GFRP, specimens were fabricated using the properties of GFRP wt 20%, 30%, and 40% GF. Three tensile specimens were fabricated for each GFRP property.  
Tensile tests on the specimens were conducted using the MTS 810 material test system. Figure 2 shows the schematic of the tensile test setup. MTS 634 12F was used as the strain gauge equipment for measuring gauge length (G), as shown in Figure 1c. Tensile tests were conducted considering a cross-head speed of 5 mm/min, in accordance with ASTM D638-14. The variations of the load and gauge length were obtained as data and reflected to the engineering stress and strain.  
Figure 3 shows the properties obtained through tensile tests. For each property, three tests were conducted, and the data corresponding to the median values were selected as representative values. Force (F\({}_{\mathrm{a}}\)) derived from the tensile tester and displacement (\(\Delta\)G) derived from the strain gauge were used to express the engineering strain-stress data as \(\sigma_{\mathrm{e}}\) and \(\varepsilon_{\mathrm{e}}\) as shown below, along with the true stress presented by ASTM D638-14:  
\[\sigma_{e}=F_{a}\times\left(W\times T\right)\ \left[MPa\right] \tag{1}\]  
\[\varepsilon_{e}=\Delta G/G\ \left[mm/mm\right] \tag{2}\]  
\[\sigma_{t}=\sigma_{e}(1+\varepsilon_{e})\ \left[mm/mm\right] \tag{3}\]  
Figure 1: \(Cont.\)  
Table 1 lists the Young's modulus (E), Poisson's ratio (v), yield stress (\(\sigma_{y}\)), and tensile strength (\(\sigma_{UTS}\)) derived through tensile tests.  
Figure 1: ASTM D638-14 specimen for tensile test: (**a**) schematic diagram of injection molding; (**b**) test specimen of glass fiber-reinforced polymer (GFRP); (**c**) specification of specimen for ASTM D638-14.  
Figure 2: Schematic of tensile test.