esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Decision of the Construction Dimension of Spatial Model  
The spatial position of monitoring instruments is usually expressed using dam wheelbase, pile number and elevation, which are essential parameters for constructing the VIP-cokriging spatial model. Depending on the spatial arrangement characteristics of similar measurement points, it is generally considered to construct one-dimensional, two-dimensional, and three-dimensional models. The one-dimensional models are usually constructed along a line of measurement, such as the horizontal displacement one-dimensional model of gravity dam crest and the uplift pressure one-dimensional model of sluice foundation, etc. The two-dimensional models are often constructed in conjunction with typical sections of dam safety monitoring, such as the internal horizontal displacement two-dimensional model of earth-rock dam and the temperature two-dimensional model of gravity dam section. The three-dimensional models mainly aim at the effect variables of the whole dam or the entire dam area, such as the appearance deformation three-dimensional model of the earth-rock dam, the three-dimensional model for seepage around the dam, and so on.  
Figure 1: VIP-cokriging spatial model construction flow chart.  
Due to the complexity and difficulty of implementing kriging interpolation in three-dimensional space, and the covariance is only a function of the distance between points. Cokriging is only applicable to two-dimensional plane coordinate systems. Therefore, when constructing the three-dimensional model, it is necessary to transform the three-dimensional spatial coordinates of the buried position of the monitoring equipment into two-dimensional plane coordinates, that is, the measuring points are projected onto the model building plane, and keep the distance between the measuring points after two-dimensional transformation basically consistent with the distance under the original coordinates. In practical engineering, different two-dimensionalization methods of three-dimensional coordinates should be selected according to the arrangement of instruments to ensure the application effect of the model after two-dimensional coordinates.
## 3 Methodology of the Proposed Concept
### Data Acquisition  
The dataset was acquired in our testbed, the duration of data gathering was from July 2019 to the present. Thermal images were taken every three hours from 6:00 AM to 6:00 PM. The experimental set-up is shown in Figure 4. All acquired thermal solar images were segmented into solar cell sizes, as shown in Figure 5, to provide more detailed information by region or cell area rather than the entire solar panel. This way, the feature extraction approach will provide a more significant set of solar panel features and characteristics. Table 1 shows the technical specifications of the thermal camera used in this experiment. The technical specifications of the solar panel are shown in Table 2. The proposed block of the algorithm is shown in Figure 6.  
Figure 3: The CDF key description.  
Figure 2: (**a**) Thermal solar cell image; (**b**) corresponding histogram; (**c**) corresponding CDF.  
Figure 4: Experimental set-up.  
Figure 5: Segmented solar cell sizes to provide a more detailed feature extraction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Brand/Company Name** & **FLIR E8** \\ \hline Field of view (FOV) & 45\({}^{\circ}\times 34^{\circ}\) \\ Object temperature range & \(-\)20 to 250 \({}^{\circ}\)C \\ Image frequency & 9 Hz \\ Thermal sensitivity & \textless{}0.06 \({}^{\circ}\)C \\ Accuracy & \(\pm\) 2 \({}^{\circ}\)C \\ Thermal palettes & Iron, Rainbow, Greyscale \\ File format & Radiometric JPG \\ On-board digital camera & 640 \(\times\) 480 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal camera technical specifications.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Feature Extraction from Quad-Polarimetric SAR Data
#### 2.2.7 Conformity Coefficient  
The conformity coefficient \(\mu\) was firstly used in compact polarimetric SAR applications for soil moisture estimations ([PERSON] et al., 2008). In a fully polarimetric scheme, it can be approximated as [6]:  
\[\mu\cong\frac{2(\text{Re}(S_{HH}S_{VV}^{*})-|S_{HV}|^{2})}{|S_{HH}|^{2}+2|S_{ HV}|^{2}+|S_{VV}|^{2}} \tag{15}\]  
The conformity coefficient \(\mu\) evaluates whether surface scattering is the dominant among all the scattering mechanisms. For a slick-free sea surface, Bragg scattering results in a very small cross-pol power and high _HH-VV_ correlations and \(\text{Re}(S_{HH}S_{VV}^{*})>|S_{HV}|^{2}\); hence, \(\mu\) is positive. However, for hydrocarbon-covered areas, as non-Bragg scattering exists, _HH-VV_ correlation is lower, and cross-pol component largely increases, which is very likely to have \(\text{Re}(S_{HH}S_{VV}^{*})<|S_{HV}|^{2}\); hence, \(\mu\) is negative. For weak-damping cases, such as biogenic slicks, since Bragg scattering is still dominant, \(\text{Re}(S_{HH}S_{VV}^{*})>\left|S_{HV}\right|^{2}\) is still valid and results in positive \(\mu\). Under this rationale, conformity coefficients can be used to effectively distinguish hydrocarbon slicks from biogenic slicks.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 4 Development of Terminal Control Software
### Communication Module  
The communication module adopts a layered design, enabling plug-and-play management to enhance system robustness, scalability, and maintainability. The overall structure design is shown in Figure 11. All modules were developed in C/C++ for easy portability. The transport interface layer is responsible for providing upper layers with unified connection, transmission, reception, and sending interfaces, with specific details implemented by the underlying modules. The link layer is responsible for the reliable transmission of data packets, providing reliable data streams for the upper layers, including packet splitting, assembly, numbering, and encapsulation. The ZigBee protocol layer is responsible for data packet communication between ZigBee modules, without guaranteeing transmission  
Figure 10: Structural design of the control software.  
reliability. The underlying communication protocols for Bluetooth, WiFi, 4G, and Ethernet are implemented by drivers, providing unified Socket interface calls.
###### Abstract
Subsequent maturation of the bone in direct contact with the implant surface results in secondary or biological stability [20; 21], which depends mainly on the implant's macro design and physical properties [22]. Therefore, implant design is fundamental to achieving adequate primary and secondary stability, which is essential for successful osseointegration. However, several design factors can affect this primary implant stability, such as implant diameter and length, thread design, and implant shape [23; 24]. Against this background, this clinical study aimed to compare the primary and secondary stability, determined based on the insertion torque and the ISQ at the moment of implant placement and after three months, of two different implant macro geometries (cylindrical and conical) as a function of patient-related factors (gender, age, and bone quality determined objectively in the CBCT), implant-related factors (width, length, dental arch, and implant location in the arch), and intraoperative factors (bone quality determined by the clinician's tactile sensation during implant placement). In addition, we also aimed to determine the correlation (overall and within each macro geometry) between the patient-related, implant-related, and intraoperative variables. The null hypothesis was that no variable could predict implant stability.
## 1 Introduction  
Eutectic Sn-Pb is the most crucial solder alloy with excellent electrical conductivity, good wettability, acceptable plasticity and low melting point properties [1]. In previous decades, it has been used as a joining material for the packaging and interconnection of electronic components and devices, and as solders for temperature-sensitive components, optoelectronics modules, printed circuit, plumbing and assembly of sheet metal parts [2; 3; 4]. It has also played an important role in a number of other industrial applications, such as water piping, beverage cars, and automobile bodies [5; 6; 7]. [PERSON] et al. proposed a new technology for quenching Sn-Pb droplets, and a higher cooling rate was obtained using this method [8]. [PERSON] et al. predicted the nucleation kinetics of Sn-Pb droplets through combining the nucleation temperature and droplet motion [9]. The solidification behavior of Sn-Pb droplets was investigated based on the microstructure and solidification path [10]. Melt atomization is a dominant technique and commercially used to yield metal and alloy powders, including electronic grade solder powders [11; 12; 13]. Before preparing a solder paste by mixing solder powders with flux, storage or transport is generally necessary. During this period, microstructural coarsening of the solder powder is observed. However, few studies have investigated the microstructural evolution of rapidly solidified eutectic Sn-Pb alloy powders.  
Reducing the free energy is the driving force behind coarsening for obtaining a more stable microstructure [14; 15]. [PERSON] et al. reported the size effect of Sn-Pb microstructures on microdevices, and they indicated that stable microstructures are crucial in several applications [16]. The effect of the two primary phases on Sn-Pb microstructures was studied using thermal analysis method [17].  
[PERSON] et al. investigated the effect of processing conditions on the particle formation of Sn-37 wt% Pb, and adequate clearance is essential based on the results [18].  
Because a solder paste is affected by the quality of the powder, studying the microstructural evolution of rapidly solidified solder powders at room or elevated temperature is essential. However, little information presently exists on the morphology evolution of eutectic Sn-Pb alloy powders. Therefore, in this study, the microstructures and stability of eutectic Sn-Pb alloy powders generated through atomization were investigated. The microstructural evolution of eutectic Sn-Pb alloy powders at ambient and elevated temperature was studied. Moreover, the Pb-rich coarsening rate kinetics and diffusion mechanism in Sn-Pb alloy powders produced through atomization were discussed.
## 4 Experimental Results and Discussion
### Network Architecture  
In our experiments, we conducted experiments with the standard U-Net. The U-Net model includes four blocks of the encoder path, one bottleneck layer, four blocks of the decoder path, and a classification layer at the end of the network. Each block had a sequence of two \(3\times 3\) convolutional operations with a batch-norm layer and ReLU activation following. At the end of the network, a \(1\times 1\) convolution operation with sigmoid activation is applied in order to generate the final segmentation map. The filter numbers of the encoder and decoder layer are 32, 64, 128, 256, and that for the bottleneck layer, is 512.  
Figure 1 describes how to integrate our proposed scAG to the baseline U-Net. Similar to the works in [32; 33], we utilize deep supervision [34] that allows for more direct back propagation to the hidden layers for faster convergence and better accuracy. Using deep supervision forces the attention map at the intermediate features to be eagerly discriminative at each specific level, thus improving the performance of the model.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 5 Validation and Discussion
### Results
#### 5.2.2 Scenario (b): \(\Delta\tau\) Smaller Than 1 chip  
In this scenario, we consider a relative delay of a quarter of a chip, which is about 75 m in path difference. In this case, there is a strong interference between the two signals (see Figure 2 (middle)). The results are shown in Figure 4 where again top plots are for the GPS L1 C/A signal, bottom plots for the chirp, left plots for the time-delay estimation and right plots for the Doppler frequency estimation. In this case we observe that even with such a strong interference, the time-delay estimation with a sub-optimal CRE is possible. We can notice that having a close secondary signal has an impact on the  
Figure 2: Normalized Cross-Correlation Function (CCF) for a GPS L1 C/A signal (**left**) and a chirp signal (**right**) for scenario (a) (top, \(\Delta\tau=2\) chips), scenario (b) (middle, \(\Delta\tau=1/4\) chips) and scenario (c) (bottom, \(\Delta\tau=1/8\) chips). In these figures, the LOS and NLOS signals are in phase (\(\Delta\phi=0^{\circ}\)) and the amplitude of the secondary signal is half the amplitude of the main one (\(\rho_{1}=\rho_{0}/2\)).  
estimation threshold, which is now SNR_OUT = 22 dB for the chirp signal, and SNR_OUT = 27 dB for the GPS signal. As expected the impact is larger for the GPS signal because of its correlation function, as it is shown in Figure 2 (middle-left). This impact can also be acknowledged in the Doppler frequency estimation, where for the GPS signal threshold region appears.  
Figure 4: Scenario (b): Estimation of the delay (**left**) and the Doppler frequency (**right**) for \(\Delta\tau=1/4\) chips with CRE. (**top**) is for a GPS L1 C/A signal, (**bottom**) is for an LFM chirp signal.  
Figure 3: Scenario (a): Estimation of the delay (**left**) and the Doppler frequency (**right**) for \(\Delta\tau=2\) chips with CRE. (**top**) is for a GPS L1 C/A signal, (**bottom**) is for an LFM chirp signal.
# A Survey of the Applications of Text Mining for the Food Domain
## 5 Text Mining in Food Quality Control
### Extraction of Recall Information  
Food recalls directly impact consumer well-being and safety. The timely extraction of recall information is crucial for food enterprises to proactively initiate recalls and prevent hazards from escalating. Researchers are increasingly exploring text mining to analyze unstructured data sources, such as recall notices and media reports, aiming to enhance recall responses and strengthen food quality and safety. For example, [PERSON] et al. [24] utilized text matching techniques to correlate food reviews on Amazon.com with FDA recalls between 2012 and 2014. This innovative approach leveraged consumer reviews to identify unsafe food products promptly, mitigating their impact on public health and the economy. It also addressed challenges related to urbanization, underreporting of illnesses, and tracing the connection between tainted food and subsequent illnesses.  
Contemporary research focuses on analyzing unstructured data with a limited text size and complex semantic expressions. Researchers work on improving information-extraction techniques and integrating this information with structured databases for precise recall delineation. [PERSON] et al. [80] proposed a deep learning and machine learning approach, using time-series prediction and historical recall announcements to foresee future recalls by type. This proactive approach enables timely recalls, enhancing food safety across the supply chain. The use of data-augmentation methodologies further expands the depth and breadth of data sources. Deep learning for predicting food recalls enhances overall safety and control of the food system, contributing to the development of an intelligent and collaborative food regulatory framework [54].  
Mining and analyzing recall notices are essential tools for food companies to conduct swift and effective recalls. Researchers using techniques such as text categorization and named entity recognition were able to extract key information from a large number of recall notices, helping food companies accurately identify the affected products, the cause of the recall, and the affected regions so that they can take targeted action.
# Voltage Problems on Farms with Agricultural Biogas Plants--A Case Study
## 3 Results and Discussion
### Analysis of the Feeding System of the Biogas Plant under Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Before the Changes**} & \multicolumn{3}{c}{**After Changes**} \\
**Parameter** & **P\({}_{\text{HL1}}\)** & **P\({}_{\text{HL2}}\)** & **P\({}_{\text{HL3}}\)** & **P\({}_{\text{HL1}}\)** & **P\({}_{\text{HL2}}\)** & **P\({}_{\text{HL3}}\)** \\ \hline Average value & 2.688 & 2.762 & 2.887 & 0.993 & 1.141 & 1.074 \\ Minimum value & 1.540 & 1.620 & 1.690 & 0.720 & 0.830 & 0.790 \\ Maximum value & 3.840 & 3.960 & 4.160 & 2.180 & 1.830 & 1.860 \\ Quantile & 3.500 & 3.580 & 3.767 & 1.384 & 1.691 & 1.440 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of statistical analysis of the coefficients of higher harmonic content of voltage—THD\({}_{\text{U}}\). On the other hand, agricultural biogas plants, including micro gas plants, are generally considered stable energy sources with minimal voltage issues. However, the authors' research indicates that these plants can also face difficulties maintaining voltage within regulatory limits. The analysis presented in this article applies to other facilities connected to the low-voltage network. To determine the maximum connectable power of a source, it is essential to understand the impedance of the supply system and the voltage level maintained at the transformer station and to apply well-known power and voltage loss relationships. The technical solutions proposed in this paper aim to mitigate voltage level issues in micro gas plants. For biogas plants fed by longer lines, more significant, cross-sectional area cables are recommended to reduce voltage drops and power losses.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Supply-and-Demand Balancing
#### 2.1.2 Forecasting  
Given VRE uncertainty, forecasting must improve for the optimal use of balancing resources. VRE forecasting errors increase reserve procurement and render the commitment of generators and storage units sub-optimal. Forecasting of wind and solar PV energy must improve along with forecasting of the weather, such as wind speed, solar irradiance, and ambient temperature [25]; weather forecast uncertainties seriously affect when and how wind and PV generators cannot only supply energy but also provide essential services for dynamic ramping, a fast-operating reserve, and the mitigation of line congestion [26, 27, 28], for example. Moreover, forecasting must integrate weather-dependent supply (i.e., VRE sources) with weather-driven demand (e.g., electrified transportation and space heating and cooling) [29]. In other words, forecasting must consider all uncertainties in power generation and transmission and weather-driven demand together to capture the correlations thereof. This requires a shift from deterministic forecasting to probabilistic, stochastic forecasting. It is increasingly important to forecast not only power supply, delivery, and demand, but also their uncertainties [29]. For example, what will be the moment of inertia at time \(h\)? By how much will the frequency regulation reserve capacity and dynamic line rating fall at time \(h\)? How much of the total load demand will be flexible at time \(h\)? Such forecasting outputs can be incorporated into algorithms and tools used for optimal scheduling, outage assessment, and real-time control.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 3 Description of the Proposed System Architecture  
This section provides a description of the deep learning-based two-dimensional on-off keying multiple-input multiple-output (2D OOK-MIMO) OCC. The architecture of a 2D OOK-MIMO transceiver that incorporates a deep learning system is illustrated in Figure 1. The transmitter encodes the data information, and the fundamental principle of the OCC system is to calibrate the optical signal intensity for dependable data transmission and reception [21]. The enhancement of the security and precision of the communication system can be achieved through the utilization of a promising modulation scheme. The primary form of amplitude-shift keying modulation that serves as the foundation is the OOK scheme. This scheme utilizes two distinct data statuses, namely, \"ON\" and \"OFF\", which are symbolized by the binary digits \"1\" and \"0\", respectively.  
Compared to the OOK described in [21; 28; 29], we detected LEDs using YOLOv8 algorithms. By positioning each LED in an LED array according to the spatial frame format of our proposed scheme, which is based on image processing techniques, the receiver side can readily detect and resolve data code.
## 5 Impact of Observation Geometry and Multi-Angle Retrieval
### Influence of Observation Geometry  
Figure 3 shows the DFS of each retrieved aerosol parameter for different observation geometries in both nadir viewing mode and target mode. It is clear that the DFS of AOD and PSD variance for all aerosol types are close to 1.0, while the DFS of the real and imaginary parts of the refractive indices and PSD mode radius are less and dependent on aerosol type. For example, among four aerosol types in the retrieval at nadir viewing with small SZA (20\({}^{\circ}\)), dust has the largest total information with 4.81 DFS, while the total DFS for soot is the smallest, only 3.05 (Figure 3). The difference of DFS between different aerosol types is determined by their scattering properties (Figure 2) and indicates the sensitivity of CAPI measurement to aerosol properties.  
The ground pixels in the wide FOV of CAPI will be observed at different VZA (from 0\({}^{\circ}\)-16\({}^{\circ}\)) or different SZA. The viewing geometry can lead to some changes in aerosol information due to different aerosol scattering phase functions when scattering angle varies. Thus, we also compare the impact of different viewing geometries on the information obtained regarding aerosol parameters in Figure 3. For example, for dust at 20\({}^{\circ}\) SZA, DFS at the forward 16\({}^{\circ}\) VZA (blue bar) is lower than that at nadir, which is consistent with the lower scattering phase function at VZA of 16\({}^{\circ}\) (Figure 2). However, the DFS of soot for the four viewing scenarios in the nadir observation in our simulation are similar, due to the rather uniform distribution of its scattering phase function (Figure 2). Similarly, the comparison of the DFS at the same VZA, but different SZA (green and yellow bars in Figure 3) also proves the consistent relationship between phase function and aerosol DFS.  
In addition to considering the vegetation fraction of surface albedo in the state vector, we also compare the DFS over two surface albedos, the bright and dark surface defined in Section 4, in Figure 3. Consistent with the sensitivity to AOD shown in Figure 1, the DFS for soot over the dark surface at 20\({}^{\circ}\) SZA is smaller than that over the bright surface due to less sensitivity (less absolute value of Jacobians  
Figure 2: Scattering phase functions of four aerosol types used in this study. Black, red, green and yellow thick solid lines represent the scattering phase function of dust, soot, sea salt and sulfate. Scattering angles corresponding to different solar zenith angles and viewing zenith angles are indicated by cyan and blue thin solid or dashed lines, respectively. VZA, viewing zenith angle.  
at 0.5 \(\times\)\(\alpha(\lambda)\), such as the red markers in Figure 1). Similarly, the larger DFS for dust and sea salt at 20\({}^{\circ}\) SZA over the dark surface correspond to larger Jacobians (black and green markers in Figure 1). Therefore, although the wavelength dependence of surface albedo remains unchanged, the surface albedo value could still have an impact on aerosol information content.