esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Cultural and Social Considerations  
Our research revealed important insights about the intersection of technology and human behavior in facility usage. The model's ability to capture and respond to gender-specific usage patterns represents a significant advancement in addressing equity in facility design. The data showed distinct patterns in usage timing and duration between gender groups, information that traditional planning methods often overlook.  
The cultural sensitivity of the model also emerged as a crucial factor. In the Singapore context, where our study was conducted, the system successfully adapted to local customs and preferences that differ from Western norms assumed in many international building codes. This cultural adaptability suggests potential applications in diverse global contexts, though careful consideration of local factors would be necessary for successful implementation.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 1 Introduction
This payload would be comprised of state-of-the-art sensors with pronounced spectroscopic performances (resolution of 5-10% at 662 keV) and timing (a few \(\mu\)s time resolution), that would allow detection of TGFs and GRBs and possibly function as early warning system for multi-messenger science, if operated in constellation [16]. The targeted capital investment required for the realization of the 1U gamma-ray detector payload presented in this paper is in the order of 30k USD. The technical characteristics of the satellite bus, along with operations and data streaming will also be discussed. This paper is organized as follows. Section 2 reports the main CubeSat subsystems along with a general overview of typical mission constraints. In Section 3, the layout of a gamma-ray detector fitting in 1U of CubeSats orbiting at LEO altitudes is presented. A discussion about suitable operating scenarios for a CubeSat sensitive to gamma-rays is reported in Section 4. In Section 5, the detector simulation and the performance assessment for tagging distinctive gamma-ray events is discussed. Finally, mechanical and thermal analysis of the payload are presented in Section 6 to demonstrate the validity of the payload design to withstand launch and space environments.
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### Measurements  
A promising technique is based on the modal analysis of rock bolts. The principle of this method considers the main frequencies of resonance induced by mechanical excitation on the protruding part of the rock bolt. The vibration is recorded by an accelerometer located close to the source (Figure 7). The collected signal is then processed to estimate the spectral response, and the main peaks of the amplitude spectrum are then detected. The interpretation assumes that the spectral response is significantly affected by the quality of the grouting.  
The result of the experimental activity requires accurate data processing to detect the main resonance frequencies. The observed resonant frequencies are then compared with the results of the model simulation. Consequently, our research involved the development of a reliable method to model the theoretical response of rock bolts to simulate the dynamic response obtained during the experimental activity.
# Mini-Review: Modeling and Performance Analysis of Nanocarbon Interconnects
## 2 Graphene Nanoribbon (GNR) Interconnects  
Physically, graphene is a 2D monolayer of carbon atoms packed into a honeycomb lattice, and the quasi-1D graphene nanoribbon (GNR), as shown in Figure 1a, can be utilized as on-chip interconnects [23]. Depending on the edge shape, a GNR can be zigzag, armchair, or chiral (other shapes). The zigzag GNR is always metallic, whereas the armchair GNR is metallic or semiconducting, depending on the number of carbon atoms across its width. Different from the GNR, a CNT's chirality is defined by its circumferential edge shape. A single-walled carbon nanotube (SWCNT) can be visualized as a seamlessly rolled-up GNR, on the basis of which a novel fabrication method has been developed to unzip the CNT to form a GNR [24]. A multi-walled carbon nanotube (MWCNT) is a parallel assembly of coaxial SWCNTs, and the neighboring shells in an MWCNT are separated by the van der Waals gap.
# GNSS Profile from the Greenland Korth Expeditions in the Context of Satellite Data
## 6 Updated Estimate of Mass Change in the Greenland Ice Sheet from Satellite Gravimetry
Towards the end of the GRACE mission in 2017, as a consequence of a lower number of monthly fields used in the fit, the estimated uncertainty band of GRACE started to widen to some extent. This is the case also for GRACE-FO, for which so  
Figure 10: Greenland ice sheet mass balance. Datasets computed at yearly intervals: long-term reconciled estimate by the IMBIE team (in blue), satellite gravimetry mass changes derived from GRACE and GRACE-FO (in red) and from Swarm (in green). Respective uncertainty estimates are shown as transparent bands (2-sigma). far only a limited number of monthly solutions is available (6/2018-9/2020). In the last period covering 2018-2019, the estimated trend from GRACE-FO observations seems to indicate an increased mass loss again. This conclusion is supported by the Swarm trend estimates (in green). Finally, based on the obtained yearly trend values, in Table 2 we present average mass trends over the previously described periods, where the Greenland mass balance displayed some overall characteristic features (Figure 9a-c).
# Research and Progress on Truing and Sharpening Process of Diamond Abrasive Grinding Tools
## 3 Dressing Method of Diamond Abrasive Grinding Tools
### Truing Technology of Diamond Abrasive Grinding Tools
#### 3.1.1 Diamond Pen Truing Method  
The diamond pen truing method is a traditional mechanical truing method; a schematic diagram is shown in Figure 2. The diamond abrasive grinding wheel rotates clockwise. The diamond pen is installed on the fixed frame and forms a 90\({}^{\circ}\) angle with the diamond abrasive grinding wheel. The tip of the diamond pen aims at the high point of the abrasive grains on the diamond abrasive grinding wheel. The abrasive grains are slightly broken. The abrasive grains fall off due to the removal of the bond material.  
[PERSON] et al. [5] set the diamond pen to grind each high point on the surface of the diamond abrasive grinding wheel to meet the grinding requirements of the diamond abrasive grinding wheel. When using the diamond pen truing method to dress the diamond abrasive grinding wheel, the grinding wheel speed should be maintained at a low speed, otherwise, it is difficult to achieve the expected dressing effect.  
Due to its simple structure and low investment cost, the diamond pen truing method is widely used in the field of grinding, but the wear of the diamond pen is quite serious, and the shape accuracy and [PERSON] accuracy are not high, which affects the surface integrity of diamond abrasive grinding tools. The grinding surface roughness (expressed as _Ra_) gradually decreases with increase in dressing times (Figure 3). When the dressing times reaches about 500 times, _Ra_ tends to be stable [6], and the dressing efficiency is approximately 30\(\sim\)40%. Therefore, scholars have sought to improve and develop the traditional diamond pen truing method which is organically integrated with other dressing methods. For example, based on the principle of vibration cutting, [PERSON] et al. [7,8] employed an ultrasonic vibration dressing method together with a diamond pen. This technology could eliminate the shortcomings of the traditional turning dressing of diamond pens and obtain good morphology, to achieve the purpose of improving the shape accuracy and grinding performance of the grinding wheel, and greatly improve efficiency.  
Figure 2: Schematic diagram of diamond pen dressing diamond abrasive grinding wheel.
## 6 Conclusions  
This study used the Inception model for landslide susceptibility assessment and combined the daily rainfall-disaster factor to complete the landslide hazard assessment for the \"25 April 2010 heavy rainfall in Zayu, Tibet.\" The BPNN, ResNet, CNN, and VGG-16 models were introduced for comparative analysis, evaluating the fitting effects of these models for landslide susceptibility analysis. An accuracy evaluation index system was established to assess the prediction results of each model. Finally, SHAP was used to perform an interpretability analysis of the Inception model. The following conclusions were drawn:  
* Regarding landslide susceptibility assessment, the Inception model had the highest accuracy and the best fitting effect, followed by the CNN and BPNN models, with ResNet and VGG-16 showing an average performance.
* The results of the landslide hazard assessment for the \"25 April 2010 heavy rainfall in Zayu, Tibet\" showed that the highest risk areas were mainly distributed in the southern part of the study area. The overall landslide hazard in the area started to increase on 22 April and peaked on 24 April. According to relevant news reports, most landslide events in Zayu County occurred between 24 and 25 April, consistent with the evaluation results.
* From the model interpretation results, in the Inception model, the most important influencing factor was TWI, followed by NDWI, road line density, and distance to fault, which made lesser contributions to the model output.
* Using advanced deep learning models to predict the potential risks of landslides in specific areas accurately is a highly feasible approach that combines scientific value with practicality. This method fully utilizes deep learning technology's powerful data processing capabilities and advantages. This technology is expected to play a vital role in landslide prevention and control, as well as in improving disaster management capabilities. However, there are still many uncertainties in predicting the occurrence of landslide disasters. Therefore, it is necessary to combine various methods, such as remote sensing interpretation, to improve the overall accuracy of prediction.  
Conceptualization, L.S.; methodology, L.S.; software, Y.G.; validation, L.S.; formal analysis, L.S. and L.X.; investigation, L.S. and Y.G.; resources, D.M.; data curation, Y.G.; writing--original draft preparation, L.S. and Y.G.; writing--review and editing, L.S. and L.X.; visualization, L.S. and Y.G.; supervision, L.X. and D.M. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Methodology  
In this study, we produced five input maps that were resampled to 0.48 m GSD from high-resolution RGB imagery and lidar DSM and DTM data. The three maps from the RGB image include: reflectance values from each tree, reflectance values from each colony, and the variance of reflectance values (i.e., a texture map of reflectance). The two maps from lidar data reflect the tree height of each individual species, and the variance of the height values. We classify forest vertical structure by applying an Artificial Neural Network (ANN) to the five maps. An ANN is a large network of extremely simple computational units that are massively interconnected with nodes and processed in parallel. In this study, a multi-layer artificial neural network was used. It contains three layers: an input layer, a hidden layer, and an output layer, and each layer is composed of nodes. Each node has a weight, which is adjusted from randomly generated initial values through the iterative experiment to obtain the most reasonable output [25; 26]. Figure 4 shows the detailed workflow used to classify the forest vertical structure from RGB orthophoto and lidar data using a machine learning approach. The image processing steps such as median, NL-means filtering, texture calculation was implemented by using C language, the MATLAB software was used for the ANN processing, and ER-mapper was used to display input and output maps.  
Figure 3: Example images from the study area showing the different forest vertical structures. (**A**) A single-layered forest that is a broad-leaved forest. The chestnut tree (Castanea crenata var. dulcis) is dominant; (**B**) a double-layered forest, which is a coniferous forest in which the nut pine (Pinus koraiensis) is dominant; and (**C**) a triple-layered forest that is a broad-leaved forest. The sawtooth oak (Quercus acutissima) is dominant.
# Biochemical Characterization of Some Plum Cultivars Available on the Romanian Market
###### Abstract  
Four plum cultivars commonly grown in Romania, 'Angeleno', 'Anna Spath', 'Silvia' and 'Stanley', were analyzed with regard to several parameters, including water content, juice pH, phenolic compounds (total and some major classes), carotenoids, ascorbic acid and soluble sugars. Among the four studied cultivars, 'Silvia' ranked the highest in terms of total phenolic content (82,345 mg/kg DW), flavonoids (43,171 mg/kg) and anthocyanins (1991 mg/kg). Hydroxybenzoic acids were found to be an important phenolic component; up to 33,971 mg/kg was found in 'Angeleno'. The hydroxcinnamic acid content was highest in 'Stanley', and stilbenes were most prevalent in 'Anna Spath'. Tannins were found in significant amounts only in fruit peel and were only present in some of the cultivars. Carotenoids were mostly present in the epicarp, with the maximum amount identified in 'Anna Spath' (313 mg/kg). The main compounds were \(\beta\)-carotene (of which the highest amount was identified in 'Silvia'), lutein (with the highest amount found in 'Silvia') and lycopene (with the highest amount detected in 'Stanley'). 'Silvia' also contained the highest amounts of ascorbic acid (7895 mg/kg in pulp and 12,229 mg/kg in peel). The highest soluble sugar content was found in the pulp of 'Stanley' (142,120 mg/kg), while 'Anna Spath' contained the lowest amount of soluble sugar. 'Stanley' also had less acidic pulp juice. Among the four cultivars, 'Silvia' ranked the highest in most classes of bioactive compounds, including total phenolic compounds, flavonoids, anthocyanins, tannins (peel only), \(\beta\)-carotene, lutein and ascorbic acid.  
plum; fruit; phenolic compounds; carotenoids; ascorbic acid; soluble sugars +
Footnote †: journal: Applied sciences
# An Optimization Framework for Wind Farm Design in Complex Terrain
## 1 Introduction
They also developed a new greedy algorithm for this problem in a later study [13]. [PERSON] and [PERSON] [14] considered the layout optimization problem for a wind farm on a 2D Gaussian hill, using computational fluid dynamics (CFD) simulations for the background flow field and an adapted Jensen wake model for the wake effect. They used the random search algorithm [15; 16] to optimize the layout for maximizing the total power. [PERSON] et al. [17] proposed an algorithm that couples CFD with mixed-integer programming (MIP) to optimize the layouts in complex terrain. In this study, the wind farm domain was discretized into cells to use the MIP method. All the studies mentioned above aimed to maximize either the total power [12; 13; 14] or the total kinetic energy [17] of wind farm, with constraints on wind farm boundary and minimal distance requirements. In this paper, we present an optimization framework for wind farm design in complex terrain, which was developed in the Sino-Danish research cooperation project, FarmOpt [18]. This framework combines the state-of-the-art flow field solver, a fast engineering wake model, and an advanced optimization algorithm to solve the design optimization problem of wind farms in complex terrain. Various realistic objectives, constraints, and requirements that one might encounter in real life wind farm developments can be considered in this framework. We will first introduce the modelling methodology in Section 2. Then, the optimization framework is presented in Section 3. Results of a real wind farm case study will be described and discussed in Section 4. Section 5 compares the performance of the Random Search (RS) algorithm with two popular algorithms, i.e., Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Finally, conclusions are given in Section 6.
# Recoil Control of Deepwater-Drilling Riser with Optimal Guaranteed Cost \(H_{\infty}\) Control
## 4 Simulation Results
### Analyses of the Fluid Discharge Model  
Now, we compare the whole fluid column model from the length, velocity, and total friction. The two cases of constant and time-varying friction coefficients are considered, respectively.  
Case I: The friction coefficients, \(f_{w}\) and \(f_{m}\) of the seawater and drilling mud, are constant. In (2), set \(f_{w}=0.002\) and \(f_{m}=0.004\); the values are taken from [23].  
Case II: The fluid friction coefficients \(f_{w}\) and \(f_{m}\) are time-varying, and satisfy the Haaland formula as [21]:  
\[\frac{1}{\sqrt{f_{w}}}=-1.8\text{log}\left[\frac{6.9}{R_{ew}}+\left(\frac{ \varepsilon}{3.7D_{s}}\right)^{1.11}\right] \tag{40}\]  
\[\frac{1}{\sqrt{f_{m}}}=-1.8\text{log}\left[\frac{6.9}{R_{em}}+\left(\frac{ \varepsilon}{3.7D_{s}}\right)^{1.11}\right] \tag{41}\]  
where \(\varepsilon\) is the roughness parameter of the riser; \(R_{ew}\) and \(R_{em}\) are the Reynolds numbers of the seawater and drilling mud, respectively.  
In the above two cases, the friction force of seawater and mud discharge is computed, and the corresponding discharging time, velocity, and the total friction force of the fluid given by Table 2. It can be observed that the discharge velocity of the fluid for the time-varying friction coefficients case is slower than that for the constant case. Consequently, the overall discharge time of the fluid becomes large.  
In next subsection, based on the obtained data regarding the different friction force, we investigate the anti-recoil effectiveness of the proposed optimal guaranteed cost \(H_{\infty}\) control scheme.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Discharging Time (s)** & **Velocity (m/s\({}^{2}\))** & **Friction Force (10\({}^{5}\) N)** \\ \hline Case I & 51.13 & 24.3094 & 5.6475 \\ Case II & 114.29 & 11.9815 & 8.0178 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discharging time, velocity, and friction force of the fluid discharge under two different cases.  
Figure 2: Response of heave motion of platform.
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications  
The heart of any IoT platform is the smart devices that build up the sensing layer. Data acquisition is performed using several sophisticated smart IoT devices that are deployed in different locations to collect various types of data over a long period depending on the targeted application. The collected data are usually large and contain some redundant information. Therefore, the data are first transmitted to a local processing unit with adequate storage and computing capacities to perform different pre-processing techniques on the samples to extract different information and features. The processing unit also plays the role of a gateway to route only the useful information to the cloud rather than transmitting the whole collected data, this approach can significantly reduce the network bandwidth.  
CS presents a very promising paradigm to be explored in IoT applications. A CS-based IoT framework can be implemented on three levels. First, at the sensing level, where data acquisition and transmission process presents a challenging task in term of power consumption, thus minimizing the latter is the critical issue to tackle. Therefore, CS can be deployed effectively as a compression technique to develop an energy-efficient scheme for data acquisition and transmission. Second, CS can be deployed on the embedded systems implemented on the local processing unit to realize \"CS-based Edge computing platform\" where the compressed data transmitted from the sensors can be aggregated, stored and reconstructed to extract salient information and important features to be sent to the cloud which hosts the application layer. The latter represents the third level in which CS can be explored along with other data analytic algorithms to execute actions and provide solutions based on the specific tasks that the IoT is supposed to deliver. Figure 2 illustrates the possible scenarios where CS can be deployed over IoT platform.
## 2 Materials and Methods
### Satellite Data and Image Processing  
Satellite images with 10 m resolution are preferably suitable for data analysis and modeling agricultural studies such as decision-making, practicing sustainable agriculture, and execution of site-specific management practices in fields [49]. Freely available data from Sentinel-2 fulfill the requirements, as it has both the 20 m and 10 m resolution spectral bands. Therefore, level 2A cloud-free Sentinel-2 (sensors A and B) satellite images were downloaded from the datahub of the European Space Agency ([[https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)]([https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)); accessed on 9 February 2024). The downloaded S2 images were pre-processed and analyzed for surface reflectance using the SNAP software program (Ver. 3.4.1). Each band of processed images was resampled to 10 m pixel resolution. The individual bands (2, 3, 4, and 8A) and selected vegetation indices, VI (Table 2), were computed for the carrot crop monitoring and yield prediction employing the machine-learning (ML) algorithm such as random forest. As the study intended to predict carrot production, vegetation indices related to chlorophyll and vegetation analysis were incorporated for the modeling.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index** & **Formula** & **Purpose** & **Reference** \\ \hline Normalized Difference & NDVI = (Band 8 – Band 4)/(Band 8 + Band 4) & Chlorophyll, vegetation health & [50] \\ Vegetation Index & & & \\ \hline Renormalized Difference & & & \\ Vegetation Index & & & \\ \hline Green Normalized Difference & & & \\ Vegetation Index & & & \\ \hline Structure Intensive Pigment & & & \\ Vegetation Index & & & \\ \hline Green Leaf Index & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of vegetation indices used for the study.
## 2 System Model  
### Multi-Layer Satellite System  
As depicted in Figure 1, a co-frequency three-tier satellite system is considered wherein the GEO satellite and its UT are communicated with fixed links, NGEO UTs can flexible access the two-tier NGEO satellite networks controlled by a system control center (SCC) via Feeder link. Specifically, the SCC oversees and manages the system status, handling access control and allocating communication resource. Typically, GEO Satellite S1 operates as a non-cooperative satellite with communication priority, whereas S2, S3, and S4 operate in different non-stationary orbits within the NGEO system. Notably, S3 and S4 share the same orbit, while S2 is positioned in a higher orbit than S3. The satellite position at GEO is fixed, with the GEO UT positioned at the system's origin.  
We consider single-beam antenna satellites, with the beam center of the NGEO satellite aligned with the core of Earth, disregarding the effects of beamforming. In the Ku/Ka band, the high gain directional antenna is widely used for the NGEO UT/satellite to compensate for large-scale fading. In-line interference, however, is the side effect when directed to the GEO system. We assume that NGEO UT has multiple beams for tracking the targeted satellites (i.e., S2-S4) and can accordingly align its boresight to the satellite for optimal transmission.