esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression
## 3 Application in Mineral Exploration
The intersection of the two lines was selected as the final threshold. A total of 10 medium and high magnetization anomalies were obtained by 3D inversion, and were labeled M1-M10. The spatial location map is shown in Figure 11a. Figure 11b shows the inversion results at different depths. The distribution of anomalies corresponds to the surface magnetic anomalies, and their buried depth is between 100 m and 500 m. Figure 11c-f show the location characteristics of ore bodies at different sections. Combining existing geological data and inversion results, the M1 anomaly was located in the Xiang Shan (XS) pyrite area, which is a mined-out area above 100 m underground. The inversion results show that there are magnetite ore bodies under the mined-out area, inclined to the southeast with a deepest depth of 500 m. The M2 anomalous body is located in the south of the Nan Shan (NS) iron mining pit. A large number of diorite porphyrite, magnetite orebody, and pyrite ore body are exposed on the surface. It is speculated that the magnetite (mineralized) body exists underground at a buried depth of 200-400 m. Diorite porphyry and magnetite ore bodies are  
Figure 11: Inversion results: (**a**) spatial distribution of the 3D inversion results; (**b**) the inversion results at different depths; (**c–f**) show the distribution characteristics of ore bodies in the profile x = 900 m, x = 1200 m, x = 1800 m, and x = 2200 m, respectively. Figure 10: The scattered point location map of data extracted by the compression algorithm for the Ma’anshan region. The black cross represents the position of the sampling data. exposed on the surface where the M3 anomaly is located. Ao Shan (AS) iron ore is located near the southeast side. It is speculated that the anomaly is a magnetized ore body. M4 anomaly body is located on the north side of the AS iron ore pit. The exposed stratum is the Lower Cretaceous, and the residual magnetization is large. It is presumed to be a magnetized mineralized body. The buried depth is about 100 m, the thickness is about 400 m, and the width is more than 300 m. The M5 anomalous body is located in the Luo Bo Shan (LBS) mining area. The magnetite at the top of the location has been mined. There is magnetite in the deep rock body at a depth of more than 300 m, and the thickness is less than 100 m. The M8 anomaly shows obvious high magnetization, and it is speculated that there are magnetite ore bodies in the lower part. The anomalous bodies (M6, M7, M9, M10) in the northeast and southeast of the survey area have low magnetization and are presumed to be diorite porphyrite. Therefore, there are five magnetite bodies, M1, M2, M3, M4, and M8 in this area, which are large in scale and they have potential for development.
###### Abstract  
This paper aims to address the sustainability of power resources and environmental conditions for telecommunication base stations (BSs) at off-grid sites. Accordingly, this study examined the feasibility of using a hybrid solar photovoltaic (SPV)/wind turbine generator (WTG) system to feed the remote Long Term Evolution-macro base stations at off-grid sites of South Korea the energy necessary to minimise both the operational expenditure and greenhouse gas emissions. Three key aspects have been discussed: (i) optimal system architecture; (ii) energy yield analysis; and (iii) economic analysis. In addition, this study compares the feasibility of using a hybrid SPV/WTG system vs. a diesel generator. The simulation results show that by applying the proposed SPV/WTG system scheme to the cellular system, the total operational expenditure can be up to 48.52% more efficient and sustainability can be ensured with better planning by providing cleaner energy.  
Keywords:hybrid energy system; remote sites; cellular networks; operational expenditure; South Korea +
Footnote †: journal:  
_Article_
## 3 Proposed System Model
### Application Interface Design  
The application uses pre-trained machine learning models to analyze incoming sensor data. Each environmental factor (sound, light, temperature, humidity, eCO\({}_{2}\), CO\({}_{2}\), TVOCs, and crowd) has a dedicated model. Additionally, a \"general assessment\" model integrates data from all sensors to provide an overall evaluation.  
The application uses pre-trained machine learning models (pkl files) for each environmental sensor and a general assessment. Different algorithms were tested during training, and the best-performing model (based on accuracy, precision, recall, and F1 score) was selected for each sensor and the general assessment.  
This systematic approach aims to provide the highest prediction accuracy and reliability for each sensor and the general assessment, offering users reliable data to make more informed and effective decisions. The user interface of the application, as presented for user service, is shown below.  
Figure 3 presents an interface that visualizes environmental quality by analyzing various parameters, including sound, light, temperature, ventilation, and crowd density. Color-coded graphs indicate the positive and negative impacts of each parameter, providing a quick overview of the overall environmental condition. This clear data visualization enables users to understand the environment and take appropriate actions for improvement.  
This interface provides a comprehensive environmental quality assessment by integrating the effects of various parameters (sound, light, temperature, ventilation, and  
Figure 3: Environmental-parameter-based general environmental quality assessment interface.  
crowd). Measurement results are clearly presented, with green indicating positive effects and red indicating negative effects, allowing for easy interpretation. Users can quickly identify which factors are negatively impacting the overall environmental quality score. This design prioritizes the clear and understandable presentation of environmental data.  
The interface presents a detailed environmental evaluation, visualized through graphs and other elements. This includes an overall score, a breakdown of how each environmental parameter (sound, light, temperature, ventilation, crowd) contributes to that score, and the visualization of their positive/negative impacts. This allows users to understand the overall environment and identify specific areas for improvement.  
The interface evaluation results, including visualizations, will be detailed. This discussion will cover the overall score, score decline distribution, and environmental parameter impacts.
## 2 LiCSAR and LiCSBAS
### Prepare Stack of Unwrapped Data
#### 2.3.3 Step 0-3: Tropospheric Noise Correction Using GACOS (Optional)  
Interferograms are contaminated by phase noise from path delays due to spatially- and temporally-varying tropospheric water vapor, temperature, and pressure, which can reach 10 cm or more [39; 40]. In standard time series analysis algorithms, spatiotemporal filtering (i.e., high-pass in time and low-pass in space) is used to separate spatially correlated noise from deformation signals under the assumption that the noise component is uncorrelated in time [3; 5; 7]. This option is also available in LiCSBAS (Step 1-6 explained in Section 2.4.6), but the assumption that noise is uncorrelated in time is not always correct. If the tropospheric delay is correlated in time and/or the deformation is uncorrelated in time, the deformation time series may be poorly recovered [41; 42; 43]. Furthermore, temporal filtering does not improve the accuracy of the average velocities retrieved from the time series. Mitigation of the tropospheric noise before the spatiotemporal filtering using independent data improves the accuracy of the derived displacement time series and the average velocities.  
GACOS provides tropospheric delay maps derived from the high-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) data [44] for correction of the tropospheric noise present in interferograms [38]. The GACOS corrections are globally available in near real-time, and users can request the delay maps for specific areas and times through a web application system [45] and download these corrections once they have been produced.  
Step 0-3 applies the GACOS corrections to the stack of unwrapped interferograms. Currently, users need to separately prepare the GACOS data for the processing area and acquisition dates beforehand. However, a one-stop service for LiCSAR is being planned in which the GACOS data for all Sentinel-1 acquisitions will be provided along with the interferograms via the COMET-LiCS web portal.  
This step is optional but recommended to improve the accuracy of the time series (see Section 3.4). However, since the GACOS corrections may have a negative impact in some cases [46; 47], users should check if reasonable noise mitigation has been achieved after applying the corrections. LiCSBAS calculates the STD of unwrapped phases before and after the GACOS corrections and their reduction rates for each interferogram, as well as creates quick-look images of unwrapped interferograms before and after the correction and the correction itself to aid in checking the GACOS effects (Figure S1). In case the GACOS corrections have a negative impact, other methods (e.g., linear, power-law [40], or spatially varying scaling [48] methods for the topography-correlated component) could be useful and may be integrated in the future.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 2 Data
### Acquisition of Hyperspectral Images  
After the sample processing was completed, the hyperspectral device model HyperSIS-VNIR-PFH by the ZOLIX brand was used to acquire hyperspectral data from the samples. The data acquisition device is shown in Figure 2. The device is capable of capturing spectral range from 865.11 (nm) to 1711.71 (nm), with a spectral image taken every 3.32 nm, resulting in a total of 256 spectral reflectance images across different wavelengths. Each imaging session involved four apple samples.  
The imaging device performed reflectance calibration on the hyperspectral images using a white calibration chart and a black calibration chart. The white calibration chart was made of polytetrafluoroethylene (PTFE) material, which has a reflectance close to 1. The black calibration chart was achieved through blocking the imaging lens to obtain an image with a reflectance close to 0. The reflectance calibration equation is as follows:  
\[P^{\prime}{}_{ij}=\frac{P_{ij}-D_{ij}}{W_{ij}-D_{ij}} \tag{1}\]  
where \(P_{ij}\) represents the value of the pre-calibrated image at the \(i\)-th row and \(j\)-th column in the specified band. \(P^{\prime}{}_{ij}\) represents the value of the post-calibrated image at the \(i\)-th row and  
Figure 2: Hyperspectral imaging equipment. 1. CCD camera; 2. CCD array detector; 3. lens; 4. light source; 5. motorized stage; 6. dark box; 7. motor control box; 8. motor; 9. computer.  
\(j\)-th column in the specified band. \(D_{ij}\) and \(W_{ij}\), respectively, represent the corresponding values of the black calibration image and white calibration image at the \(i\)-th row and \(j\)-th column. The files generated after imaging by this device include the hyperspectral source file (raw format) and its corresponding header file (hdr format).  
After obtaining hyperspectral images, to acquire spectral information from the surface of an apple, the first step involved selecting a region of interest (ROI) to capture the image of the apple portion, as shown in Figure 3. Once the ROI was determined, the average spectrum of the ROI was calculated to represent the spectral information of that area. Subsequently, the obtained spectral vectors underwent data smoothing to eliminate noise, ultimately obtaining usable spectral information.
# A Review of Validation Methods for the Intracranial Response of FEHM to Blunt Impacts
## 1 Introduction
This review hopes to compliment such works by providing similar material on the validation stages, completing the model development process. The process of validating FEHM consists of replicating impacts created in a laboratory environment on human head samples, either as live subjects or cadaver specimens, in the numerical environment. Responses recorded in the empirical method are then compared to equivalents in the simulation environment. The intracranial response of FEHM has been validated against intracranial pressure (ICP), relative brain displacement and brain strain behaviours, with the vast majority of existing FEHM using a small group of empirical studies; [PERSON] et al. [34] and [PERSON] et al. [35; 36], although the exact cases taken from each, their method of application to simulations and response evaluation varies significantly. These and other experimental sources previously applied during FEHM validation are summarised with particular focus on the data provided allowing impacts to be replicated within simulation environments and the impact response data provided. Potential alternative sources offering similar validation potential through altered methodology are also introduced. Latter sections consider the implementation of these empirical data. Section 3.1 identifies the specific cases against which existing FEHM are validated while Sections 3.2 and 3.3 discuss the methods used to generate impact scenarios during simulations and how outputs are compared and assessed to the empirical data. Finally, works considering the validation process and its implementation are highlighted in Section 4.
# Impact of Weather Factors on Unmanned Aerial Vehicles' Wireless Communications
## 2 Methodology
### Mathematical Models
To ensure the accuracy of the computational model, we applied necessary corrections to the values of the atmospheric parameters for each segment of the height [6].
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### The State of the Nodes  
According to the state of nodes, we classify UWSNs into three taxonomies, namely, stationary, mobile and hybrid UWSNs. The feature of the stationary network is the node fixed at a particular location. Hence, it can be used to monitor a given region. In the mobile network, the mobility of the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Ref.** & **Range Measurement** & **Coverage** & **Localization Time** & **Accuracy** & **Computational Complexity** & **Energy Consumption** \\ \hline
[39] & Range-free and asynchronous & Large & Short & High & Low & Low \\
[40] & Range-free and asynchronous & Large & Short & High & Low & Low \\
[41] & Range-free and asynchronous & Limited & Average & Low & Low & High \\
[42] & Range-based and synchronous & Large & Medium & Average & Medium & Medium \\
[43] & Range-based and synchronous & Large & Short & Average & Medium & Low \\
[44] & Range-based and synchronous & Large & Medium & Low & Medium & Medium \\
[45] & Range-based and asynchronous & Large & Medium & Average & High & Medium \\
[46] & Range-based and asynchronous & Large & Medium & Medium & Medium & Medium \\
[47] & Range-based and asynchronous & Large & Medium & Medium & Low & Low \\
[48] & Range-based and asynchronous & Large & Short & Depend & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of localization algorithms for using range measurements.  
Figure 10: A network model for UWSNs that implements 3 DUT [48].  
node can be free or controlled. In [11], propelled equipment such as AUVs and Unmanned Underwater Vehicles (UUVs) can control the mobility of the nodes. As for the hybrid networks, the stationary and mobile nodes coexist. Then, we continue to classify stationary, mobile, and hybrid networks into two subcategories as active and silent messaging properties. The silent messaging property means that only the anchors can send the localization messages, but the underwater nodes can just receive but not send, while the dynamic messaging property is both the anchors and underwater nodes that can send a message to realize localization.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
###### Abstract  
This study presents a detailed characterization of iterative steganography, a unique class of information-hiding techniques, and proposes a formal mathematical model for their description. A novel quantitative measure, the Incremental Information Function (IIF), is introduced to evaluate the process of information gain in iterative steganographic methods. The IIF offers a comprehensive framework for analyzing the step-by-step process of embedding information into a cover medium, focusing on the cumulative effects of each iteration in the encoding and decoding cycles. The practical application and efficacy of the proposed method are demonstrated using detailed case studies in video steganography. These examples highlight the utility of the IIF in delineating the properties and characteristics of iterative steganographic techniques. The findings reveal that the IIF effectively captures the incremental nature of information embedding and serves as a valuable tool for assessing the robustness and capacity of steganographic systems. This research provides significant insights into the field of information hiding, particularly in the development and evaluation of advanced steganographic methods. The IIF emerges as an innovative and practical analytical tool for researchers, offering a quantitative approach to understanding and optimizing iterative steganographic techniques.  
Keywords: steganography; information quantification; iterative steganography; Incremental Information Function (IIF); steganographic modeling; information hiding +
Footnote †: journal: _Entropy_
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**MB**} & \multicolumn{2}{c}{**GS**} \\ \hline
**Species** & **Mean (\%)** & **std. dev. (\%)** & **Mean (\%)** & **std. dev. (\%)** & \(p\) **Values** \\ \hline _Phascalorcbacterium_ sp. YIT 12067 & 1.176 & 1.325 & 1.365 & 1.042 & 0.692 \\ _Acidaminococcus fermentants_ & 0.448 & 0.833 & 0.572 & 0.529 & 0.654 \\ _Aerococcus vitridants_ & 0.118 & 0.174 & 0.612 & 0.333 & 0.000 \\ _Bacteroides plelebus_ & 2.619 & 2.635 & 0.370 & 0.456 & 0.007 \\ _Bacteroides fragilis_ & 1.689 & 2.246 & 0.628 & 0.938 & 0.126 \\ _Bacteroides stercoris_ & 0.984 & 0.883 & 0.741 & 1.515 & 0.643 \\ _Bacteroides coprocola_ & 0.963 & 0.790 & 0.390 & 0.460 & 0.033 \\ _Bacteroides vulgatus_ & 0.907 & 2.099 & 0.055 & 0.121 & 0.152 \\ _Bacteroides uniformis_ & 0.785 & 0.844 & 0.184 & 0.204 & 0.021 \\ _Bacteroides oortus_ & 0.506 & 0.464 & 0.360 & 0.565 & 0.497 \\ _Clostitution biferentants_ & 5.080 & 3.341 & 3.625 & 1.436 & 0.158 \\ _Clostritium sordelli_ & 3.167 & 3.022 & 3.073 & 1.121 & 0.916 \\ _Clostritium barlettii_ & 1.769 & 1.213 & 0.904 & 0.366 & 0.022 \\ _Clostritium scindens_ & 1.142 & 1.020 & 0.230 & 0.120 & 0.005 \\ _Clostritium hiranonis_ & 0.979 & 1.494 & 0.329 & 0.980 & 0.203 \\ _Clostritium perfrigens_ & 0.226 & 0.370 & 1.738 & 1.664 & 0.012 \\ _Clostritium aminobutyricrum_ & 0.044 & 0.120 & 0.813 & 0.551 & 0.001 \\ _Collinsella intestinalis_ & 1.840 & 1.534 & 1.121 & 0.482 & 0.116 \\ _Slackia heliotriinducns_ & 0.890 & 0.734 & 0.684 & 0.363 & 0.370 \\ _Desulfonauticus autotrophicus_ & 0.101 & 0.147 & 0.923 & 0.495 & 0.000 \\ _Clostritium ramosum_ & 1.684 & 2.043 & 0.499 & 0.608 & 0.055 \\ _Catenibacterium mitsuokai_ & 1.503 & 2.894 & 0.680 & 0.972 & 0.333 \\ _Eubacterium biforme_ & 1.060 & 1.492 & 0.879 & 0.730 & 0.695 \\ _Lactobacillus vitulinus_ & 0.823 & 1.583 & 0.348 & 0.688 & 0.326 \\ _Clostritium spiroforme_ & 0.790 & 0.914 & 0.390 & 0.239 & 0.135 \\ _Eubacterium cylindroules_ & 0.650 & 0.643 & 0.563 & 0.351 & 0.670 \\ _Streptococcus pleomorphous_ & 0.620 & 0.956 & 0.481 & 0.417 & 0.630 \\ _Eubacterium fissicata_ & 1.079 & 1.098 & 0.397 & 0.176 & 0.037 \\ _Fushobacterium nucleatum_ & 6.030 & 3.870 & 4.852 & 2.987 & 0.398 \\ _Fushobacterium moriferrum_ & 2.156 & 1.684 & 1.167 & 0.892 & 0.072 \\ _Fushobacterium varium_ & 2.109 & 1.455 & 1.945 & 1.408 & 0.779 \\ _Fushobacterium ulcerans_ & 2.002 & 1.457 & 1.547 & 0.961 & 0.359 \\ _Fushobacterium emitum_ & 1.698 & 1.015 & 0.702 & 0.539 & 0.005 \\ _Fushobacterium perfetets_ & 1.628 & 1.375 & 0.602 & 0.592 & 0.021 \\ _Fushobacterium periodoticum_ & 0.794 & 0.851 & 0.418 & 0.612 & 0.211 \\ _Hespellia porcina_ & 0.682 & 0.472 & 0.451 & 0.191 & 0.112 \\ _Robinsoniella poriensis_ & 0.519 & 0.513 & 0.442 & 0.501 & 0.707 \\ \hline \end{tabular}
\end{table} 
Table 6: Mean relative frequency of abundance (%) and corresponding standard deviation (SD) (%) of the species identified in the caeca of mixed breeds (MB) and German Shepherd (GS) dogs. The species indicated are those showing a mean relative frequency of abundance \(\geq\)0.5% in at least one group. \(p\) values in bold are those who are significantly different.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Framework Validation and Performance Analysis
These validation results demonstrate the CMS framework's effectiveness in bridging satellite and network operations while providing measurable performance improvements over traditional approaches, confirming its suitability for practical NTN deployment scenarios.