esa-sceva/satcom-chunk-collection huggingface dataset:
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 5 Experiments and Discussion
### Thin-Film Thickness Uncertainty Verification  
#### 5.1.1 Standard Film Samples Measurement  
A set of Polyvinyl chloride (PVC) standard film samples with a thickness range of 2-50 (\(\pm\)0.3 + 0.5% H) \(\upmu\)m are fixed by a custom film clamping device as shown in Figure 9. First, we measure the standard film of 18 \(\upmu\)m thickness at a random point for 20 times to verify the single point repeatability of the system. After EMD filtering, CIFFT and LSP algorithms are adopted to calculate the thickness, respectively. As shown in Figure 10a, the measurement result indicates that the thickness deviation of LSP fluctuates in the range of \(\pm\)0.1 \(\upmu\)m. The mean deviation of LSP is \(-\)0.038 \(\upmu\)m, while the mean deviation of CIFFT is \(-\)0.147 \(\upmu\)m. Comparing to CIFFT, LSP has a lower uncertainty and a smaller deviation for film thickness, which proves that the system has good repeatability in the single-point thickness measurement. Secondly, to further verify the stability of the area measurement of the experimental system, we measure 16 signals in different areas in a circular hole with a diameter of 7 mm in the standard film sample group. The measurement results of the \(4\times 4\) grid uniformly distributed sampling points are shown in Figure 10b. The center point of the error bar and the longitudinal length of the shaded area represent the mean and standard deviation of the measurement error related to the standard value, respectively. The maximum absolute error of LSP is 0.9202 \(\upmu\)m, and the repeatability is no more than 2.5%. The experiment result proves that the proposed PPS algorithm is stable and reliable and meets industrial requirement repeatability. The average calculation time is 0.01 s for each measurement. Due to the anisotropic tensile forces in the clamping process of the standard film, the wrinkles and uneven thickness distribution of film are inevitable, which causes the measurement results of several film thicknesses in this experiment to be too large. Overall, the measurement uncertainty of the PPS algorithm proposed in this paper is stable at 0.1 \(\upmu\)m.
## 3 Evaluation of PM\({}_{10}\) Generated in Cutting Operation through Chamber Experiment (Level 2)
### Experimental Results and Analysis
#### 3.2.2 Deposited PM  
The characteristics of deposited PM generated from the asphalt concrete specimen were analyzed by measuring silt loading. The results are shown in Table 7. First, as a result of testing for each cutting depth in the dry state, the quantity of deposited PM increased as a function of the cutting depth. The highest quantity of PM was generated from the asphalt concrete specimen-cutting activity when the cutting blade contacted the specimen at the time at which the cutting activity started. At this time, the scattered PM generated on the surface yielded the highest peak in the previous section. Subsequently, the deposited PM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test ID** & **PM of Internal Filter (g)** & **PM of External Filter (g)** & **Total PM (g)** \\ \hline
1 (dry) & 5.31 & 17.53 & 22.84 \\
2 (dry) & 1.38 & 15.27 & 16.65 \\
3 (dry) & 1.34 & 14.88 & 16.22 \\
4 (wet) & 0.23 & 0.88 & 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weight of PM captured by the filter.  
Figure 8: Particle number distribution as a function of diameter at different cutting depths.  
increased as cutting progressed. This suggests that the deposited PM on the surface of the asphalt concrete specimen increased as a function of the cutting depth.  
In the case of Test ID 4 in which wet cutting was performed, the PMC of scattered PM\({}_{10}\) decreased by more than 90%, as shown in the analysis in the previous section, but the deposited PM tended to increase by approximately \(\geq\)37% as a result of the sL analysis. As a result of the sL measurement, the deposited PM generated from the cutting activity tended to increase as a function of the cutting depth, and increased further in the case of wet cutting.  
The ratio of deposited and scattered PM was verified by the total PM collected from the filter in Table 6 and the total PM collected from the chamber and specimen in Table 7. In Test IDs 1-4, the ratios of scattered PM to deposited PM were 10.25%, 4.90%, 3.54%, and 0.20%, respectively. Thus, the proportion of scattered PM tended to decrease as the cutting depth increased.  
The particle size of the deposited PM was analyzed using a particle size analyzer (HELOS 514 SC, RODOS T4.1) which can measure in the range of 0.5-875 \(\upmu\)m. The particle size analysis results for each sample are shown in Figure 9. In the case of test IDs 1 and 2, the ratios of particle sizes below PM\({}_{10}\) were 24.08% and 26.04%, respectively. Moreover, it was found that most of the particles had sizes in the range of 10-100 \(\upmu\)m. Meanwhile, the ratios of particle size below PM\({}_{10}\) of Test IDs 3 and 4 were 36.55% and 37.74%, respectively. Unlike test IDs 1 and 2, most of the particles had sizes which were in the range of 10-20 \(\upmu\)m. Similar to the previous OPC analysis results, as the cutting depth increased, PM\({}_{10}\) trapped inside the asphalt specimen increased and it can be seen that such PM\({}_{10}\) is included in the deposited PM.
## 4 Methodology and Toolchain
### Network Simulation and Testbed Evaluation  
The next step in the envisioned process pipeline is the design and development of a network simulation environment. As the name suggests, the main purpose of this simulation is to serve as a virtual representation of the network infrastructure and topology available in the production environment of a real-world urban IoT platform. In this way, the research efforts and the corresponding solutions proposed in the scope of this work can be evaluated with regards to their performance and their ability to provide the desired security guarantees. More specifically, the network simulation was developed as a testbed for the anomaly detection solutions examined throughout the KIVEP project, and it is structured as depicted in Figure 5.  
The IoT devices in the diagram in Figure 5 serve as a representation of IoT parking lot sensors deployed on the premises of an industrial partner providing smart city solutions. The next important component is the so-called \"middlebox\" which is envisioned as a virtual environment that performs the function of intrusion detection (i.e., the protocol anomaly detection). The final location of the \"middlebox\" is not strictly established and might change depending on the needs of the project and the potential deployment challenges encountered along the way. After performing the protocol anomaly detection, the aim of the middlebox is to forward the analyzed traffic to back-end servers, where the data will be further processed and made available to the intended audience through the IoT platform and the sensor providers.
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Ship Arrivals and Departures
#### 4.4.9 Consequences  
The advantages of this pattern include:  
* Availability: port facilities work 24 h a day. By previously scheduling with the harbor master, vessels can arrive at a port at any time. Ports are always open and available to vessels when needed;  
Figure 12: Sequence diagram for UC ship arrival.  
* Scalability: ports and harbor masters provision berths for vessels to dock depending on traffic and the number of vessels entering the port;
* Berth scheduling: proper berth assignment and scheduling ensure that the vessel characteristics are compatible with the characteristics of the berth to which the vessel is scheduled. It improves the level of port services and reduces the port's cost of production;
* Safety and port Integrity: arrival and departure procedures ensure that the essential safety steps are followed both for the people on board as well as those at the seaport. Most of the collisions and consequent grounding of ships are avoided by utilizing experienced marine pilots and tugboat pilots to aid in the mooring of a vessel. The pattern has some liabilities:
* Having experienced marine pilots and tugboats reserved and waiting to help the ships dock increases the cost of running the port.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods
### Sentinel 2 Data  
The Sen2 Cor method [21] was used to mask for clouds and cloud shadows the Sentinel 2 Level-1C data from the Copernicus Open Access Hub, and then the iCOR atmospheric correction scheme [22] was used to atmospherically correct the data. An extra geometric adjustment based on manually determined ground control points was performed on Sentinel 2 scenes that were badly coregistered (i.e., had a multitemporal coregistration error of >0.5 pixels). At a spatial resolution of 10-m, we utilized the NDVI value derived from the red (B4) and near-infrared (B8) bands [23]. The NDVI measure was chosen as a typical optical descriptor because of its past effectiveness in crop categorization studies [20]. While NDVI was reported to saturate during the most productive parts of the growing season [24], utilizing this index in time series rather than single-date images was proven to overcome this problem. The categorization algorithm used these NDVI values as input. However, crop categorization over a wide area encompassing many image tiles is complicated by frequent and uniform cloud cover. An improved version of a pixel-wise weighted least-squares smoothing of the NDVI data over time [25] was used to eliminate cloud blockage. Between 1 March 2020, and 31 August 2020, smoothed NDVI images were created at 10-day intervals.  
Figure 3: Sentinel 1 VH backscatter mosaics from 12 days in RGB composite. Dates are 1–13 March 2020 (red), 17–29 June 2020 (green), and 16–28 August 2020 (blue).
# Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations
## 1 Introduction
### Contributions  
While there have been numerous studies of blockchain technology in smart grids and P2P energy trading, the authors contend that the state-of-the-art of blockchain-enabled applications in the IoV has received little attention. The approach proposed in [17] does not address the entire scope and applicability of blockchain opportunities in the IoV. Instead,the security aspects of the IoV systems and the issues of integrating blockchain within the IoV were given emphasis. Other studies focus on the specific function of blockchain, such as the creation of decentralized and data-intensive smart grids [14; 18], and the integration of blockchain with IoT [19; 20], while some of them are focused on the energy sector, providing a holistic view of the blockchain capabilities in this sector [21; 22; 23]. Conclusively, there is an absence of a concrete and comprehensive evaluation of existing blockchain related state-of-the-art analysis, relevant to the current opportunities and limitations in the IoV domain. The latter was the major driver for undertaking this research. Our study adds to a comprehensive understanding of blockchain capabilities and gives a picture of existing blockchain-enabled applications across the IoV domain and, specifically, the IoV-assisted smart grids. Even though smart grid is a well-known term, which refers to an electricity network/grid enabling a two-way flow of electricity and data, an IoV-assisted smart grid may be a little ambiguous. In the context of this research, an IoV-assisted smart grid refers to a smart grid which is supported by the IoV key functionalities (i.e., as those presented at the beginning of this paper), which could assist it in order to achieve flexible resource demand response (DR).  
Based on a systematic literature review methodology, the authors provide the following key contributions:  
1. A detailed taxonomy of the range of blockchain application areas in the field of IoV.
2. A discussion of the observed IoV challenges of different segments and scenarios of the smart grid domain with the goal of understanding why blockchain should be used and how it may contribute to solving such challenges.
3. A research agenda around the current limitations of blockchain technology in the field of IoV and future research directions are presented.
## 4 Results and Discussions
### Results of Clustering Algorithms
#### 4.1.3 Agglomerative Clustering  
After the Mean shift, Agglomerative Clustering was tested, using the scikit learn Agglomerative Clustering library. The algorithm generated a dendrogram in order to better visualize the clusters and to find the optimal number of clusters. Looking at the dendrogram (Figure 5), we can see that the highest vertical distance that doesn't intersect with any clusters is the middle blue one. Given that 2 vertical lines cross the threshold, we can say that the optimal number of clusters is 2. So, seeing the dendrogram, we know that at the end we will have two groups of students.  
Figure 5: Dendogram created from Agglomerative Clustering.  
The algorithm continues the execution with the creation of an instance of Agglomerative Clustering using the Euclidean distance as the measure of distance between points and also ward linkage in order to calculate the proximity of clusters.  
The results of Agglomerative Clustering are 2 clusters:  
* Cluster 0: (1, 6, 7, 8, 9, 17, 22, 23, 24, 25, 29, 30, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 48, 49, 50, 52, 53)
* Cluster 1: (0, 2, 3, 4, 5, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 26, 27, 28, 31, 32, 39, 40, 45, 46, 47, 51, 54)
###### Abstract  
This study investigated the influence of different biodiesel blends produced from residual sunflower oil and palm oil from agroiondustry liquid waste on the characteristics of the combustion process, performance, and emissions in a single-cylinder diesel engine. For the analysis of the combustion process, a diagnostic model was developed based on the cylinder pressure signal, which allows the calculation of the heat release rate, the accumulated heat rate, and the temperature in the combustion chamber. This is to assess the influence of these parameters on engine emissions. The experiments on the diesel engine were carried out using five types of fuel: conventional diesel, two biodiesel blends of residual palm oil (PB5 and PB10), and two biodiesel blends formed with palm oil and sunflower oil residues (PB5 SB5 and PB10 SB5). The engine was running in four different modes, which covered its entire operating area. Experimental results show that the in-cylinder pressure curves decrease as the percentage of biodiesel in the fuel increases. Similarly, the results showed a decrease in the heat release rate for biodiesel blends. The diagrams of the accumulated heat release curves were larger for fuels with higher biodiesel content. This effect is reflected in the thermal efficiency of biodiesel blends since the maximum thermal efficiencies were 29.4%, 30%, 30.6%, 31.2%, and 31.8% for PB10 SB5, PB5 SB5, PB10, PB5, and diesel, respectively. The emission analysis showed that the blends of biodiesel PB5 SB5 and PB10 SB allowed a greater reduction in the emissions of CO, CO\({}_{2}\), HC, and opacity of smoke in all the modes of operation tested, in comparison with the blends of biodiesel PB5 and PB10. However, NOx emissions increased. In general, biodiesel with the percentage of residual sunflower oil does not cause a significant change in the combustion process and engine performance, when compared to biodiesel that includes only residual palm oil.  
**Keywords:** biodiesel; combustion process; emissions; diesel engine; thermodynamic model
# Optical and Transport Properties of Ni-MoS\({}_{2}\)
###### Abstract  
In this paper, MoS\({}_{2}\) and Ni-MoS\({}_{2}\) crystal layers were fabricated by the chemical vapor transport method with iodine as the transport agent. Two direct band edge transitions of excitons at 1.9 and 2.1 eV were observed successfully for both MoS\({}_{2}\) and Ni-MoS\({}_{2}\) samples using temperature-dependent optical reflectance (R) measurement. Hall effect measurements were carried out to analyze the transport behavior of carriers in MoS\({}_{2}\) and Ni-MoS\({}_{2}\), which indicate that the Ni-MoS\({}_{2}\) sample is \(n\)-type and has a higher resistance and lower mobility than the MoS\({}_{2}\) sample has. A photoconductivity spectrum was performed which shows an additional Ni doping level existing at 1.2 eV and a higher photocurrent generating only for Ni-MoS\({}_{2}\). The differences between MoS\({}_{2}\) and Ni-MoS\({}_{2}\) could be attributed to the effect of Ni atoms causing small lattice imperfections to form trap states around 1.2 eV. The temperature-dependent conductivity shows the presence of two shallow levels with activation energies (84 and 6.7 meV in MoS\({}_{2}\); 57 and 6.5 meV in Ni-MoS\({}_{2}\)). Therefore, the Ni doping level leads to high resistance, low mobility and small activation energies. A series of experimental results could provide useful guidance for the fabrication of optoelectronic devices using MoS\({}_{2}\) structures.  
reflectance; Hall effect; photoconductivity; Ni dopants; MoS\({}_{2}\) Article
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
###### Abstract  
Human pose estimation identifies and classifies key joints of the human body in images or videos. Existing pose estimation methods can precisely capture human movements in real time but require significant computational time and resources, which restricts their usage in specific conditions. Thus, we propose a lightweight pose estimation model--SMS-Net--based on the sequentially stacked structure of the hourglass network. The proposed model uses various lightweight techniques to enable high-speed pose estimation while requiring minimal storage space and computation. Specifically, a shuffle-gated block was introduced to reduce the computational load and number of parameters during the feature extraction process of the encoder composing each hourglass network. A multi-dilation block was used in the decoder to secure the receptive fields of various scales without increasing the computational load. The performance of the proposed model was assessed using the MPII and Common Objects in Context (COCO) datasets used for pose estimation and certain performance metrics and compared with state-of-the-art lightweight pose estimation models. Furthermore, an ablation study was performed to assess the impact of each module on network performance and efficiency. The results demonstrate that the proposed model achieved an improved balance between computational efficiency and performance compared to existing models in human pose estimation. Overall, the study findings can provide a basis for applications in computer vision technology.  
computer vision; deep learning; feature extraction; human pose estimation; internet of things; lightweight models +
Footnote †: journal: Applied sciences
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology
### Design of the Conv-for-Sum Module
#### 3.3.2 Channels of Convolutional Neural Networks  
Due to the richness of the CSI feature matrix information, in order to minimize the loss of feature information and to reduce the total number of parameters of the model, we set 256 channels in the convolutional layer. Through experimental comparison, we found that the convergence speed and positioning accuracy of 256 channels are better than fully connected 512- and 128-channel layers in stages.  
Moreover, since the extracted feature information is rich, the dimension of the flattened feature is larger. To minimize the risk of overfitting, we use a fully connected three-segment layer.  
In summary, we sequentially compute the output of the Conv-for-Sum module using the following equation.  
\[Output=BatchNorm(ReLU(Conv_{2}(Conv_{1}(X)))) \tag{8}\]  
where \(X\) is the output of the Dual-Encoder module and \(Conv_{1}\) is the convolutional layer of \(3\times 3\) convolution kernel, \(Conv_{2}\) is the convolutional layer of the \(1\times 1\) convolution kernel, \(ReLU\) is the activation function, \(BatchNorm\) is the normalization layer, and \(Output\) is the output of the Conv-for-Sum module.
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 2 Theory and Methodology
### Causes of Noise in RTM  
Traditional RTM imaging methods generally consist of three steps. First, an appropriate forward-modeling method is chosen to propagate the seismic wavelet forward and obtain the complete source wavefield. Second, the seismic records obtained from the receivers are used as boundary and final conditions. Then, back-propagation is performed to reconstruct the complete receiver wavefield. Finally, based on certain imaging conditions, the imaging results for a single source-receiver pair are obtained. The final imaging result is achieved by stacking the imaging results of all source-receiver pairs.  
In RTM, in addition to reflections from the interfaces, certain types of noise are generated, primarily low-frequency artifacts and arc noise. This is caused by the incorrect cross-correlation of the source and receiver wavefields in regions beyond the reflection interfaces. In surface seismic imaging, this noise can be attenuated by stacking, resulting in high-quality imaging results. However, for VSP imaging, the unique characteristics of the observation system result in the receivers being distributed only within the preferred region of the observation well. Consequently, single imaging results corresponding to different sources exhibit a strong correlation. This indicates that noise near the observation well cannot be effectively attenuated during stacking. In addition, because this noise is located around the observation well and has the same frequency as the correct reflection events, it severely affects the quality of the imaging results.  
To address this issue, this study proposes a common-receiver algorithm. By altering the excitation location of the wavefield, the generated noise is shifted to the sides of the effective imaging area, thereby reducing its impact on the imaging quality within the illumination range in VSP RTM imaging. This improvement enhances the overall quality of the imaging results.
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 3 Results and Discussions  
### Optimization of Separation  
Hydrocarbons, alcohols, aldehydes, ketones, furans, esters, sulfur-containing compounds, and other heterolytic compounds such as pyridines and pyrazines compose the main volatile compounds in different foods [23]. Complex compositions of volatiles would bring big challenges in their separation. Here, GC columns with stationary phases of nonpolar (5%-phenyl)-methylpolysiloxane and polar polyethylene glycol were compared. Apart from the boiling temperature, the polarity was another influencing factor in the separation mechanism for polar GC columns. This will lead to the decreased coelution and better separation of volatile compounds when the polar stationary phase of polyethylene glycol was used. In actuality, using mutton as the research model, the number of identified volatile compounds was increased by more than 50% when polar stationary phase polyethylene glycol was used, indicating that the polar stationary phase would be more suitable for volatiles' profiling.