esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### CIT Methods
After adding the regularization constraints, the electron density to be solved can be expressed as follows:  
\[x=\left(A^{T}A+\lambda R\right)^{-1}A^{T}y \tag{4}\]  
The physical background constraints required for ionospheric tomography in this paper are provided by the IRI2020 model, and the IRI2020 model selection parameters are shown in Table 2. For the global ionospheric tomography inversion, considering the data coverage and the efficiency of tomography inversion, the longitude range is set to \(-180^{\circ}\)-\(180^{\circ}\), the latitude range to \(-87.5^{\circ}\)-\(87.5^{\circ}\), the altitude range to \(100\)-\(1000\) km, and the spatial resolution to \(5^{\circ}\times 2.5^{\circ}\times 50\) km (longitude \(\times\) latitude \(\times\) altitude), with a total of 92,016 voxels for one tomography inversion. In order to quantitatively describe the CIT results of the different plans, we calculated the bias, root mean square (\(RMS\)), and percentage of RMS gain (_Percentage_) as shown in Equations (5)-(7):  
\[bias=Ne_{CIT}-Ne_{NeQuick2} \tag{5}\]  
\[RMS=\sqrt{\frac{1}{n}\overline{\sum_{i=1}^{n}\left|Ne_{CIT}(i)-Ne_{NeQuick2}(i) \right|^{2}}} \tag{6}\]  
\[Percentage=\frac{RMS_{plan}-RMS_{GNSS}}{RMS_{GNSS}}\times 100\% \tag{7}\]  
where \(Ne_{CIT}\) denotes the IED obtained by CIT for different plans, \(Ne_{NeQuick2}\) denotes the IED output from the NeQuick2 model, \(i\) denotes the number of the statistic. \(RMS_{plan}\) and \(RMS_{GNSS}\) denote the RMS calculated by IED obtained from CIT for different plans and GNSS, respectively. _Percentage_ denotes the RMS gain of the different plans over GNSS with the addition of LEO satellite observations.
# Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
## 4 Experiment
### Experiment Result  
To verify how much normal products can be overseen with only one pre-trained model, we apply hierarchical clustering to the data from three products, i.e., _(STE, STI, GTC)_, having normal and abnormal sounds. Figure 11 summarizes the hierarchical clustering results on each latent vector obtained from SSS-AE. We denote the distance value merged with each of the abnormal products with _Error 1_ as a magenta color dotted line and _Error 2_ as a red dotted line. First, in the results of the hierarchical clustering, we found that the datanumbers from normal products grouped into the same cluster under _Error 1_ for product data _{STE, STI, GTC}_ were 1, 6, and 2. In the case of _Error 2_, 23, 26, and 30 can be overseen in the same cluster. _Error 2_ has a relatively higher noise compared to _Error 1_, and more products can be grouped and overseen in one cluster, but _Error 1_, with relatively weak noise, is overseen, similar to a normal product. It is up to the user to decide whether to group and manage more products under strong noise, such as _Error 2_, or group them under weak noise, such as _Error 1_, and manage them in the same cluster. We set _Error 1_ as a threshold and verified our methodology with newly collected SMD data. Figure 10 is the result of hierarchically grouping the products that can be grouped by the _Error 1_ threshold of the trained product data _{STI}_ and the products that can be grouped by the _Error 1_ threshold among the newly collected data. The black label refers to the data collected previously, and the blue label refers to the newly collected data. The magenta label refers to abnormal data at the level of _Error 1_, and the red label refers to abnormal data at the level of _Error 2_. The left and center dendrograms are hierarchically clustered results with newly collected product data _{GTD, NAC}_, respectively, which involves the process of finding product data within the _Error 1_ threshold among the newly collected data. The dendrogram at the right is a hierarchical clustering result that includes product data _{GTD, NAC, GTD-2}_, which shows that the proposed method works by hierarchically clustering products within the _Error 1_ threshold among the newly collected data and their abnormal data. Normal data are grouped together with normal data, the existing _Error 1_ level of abnormal data is then clustered hierarchically, and the newly collected _Error 2_ level of abnormal product data _{GTD-2}_ is clustered with abnormal product data _{STI-2}_. This shows that product data that can be grouped through hierarchical clustering were found, and anomaly detection was possible through actual abnormal data.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 1 Introduction
[27] proposed a vertical variable stiffness isolation bearing incorporating articulated springs and four-bar linkage mechanisms. Meanwhile, [PERSON] et al. [28; 29] designed a vertical isolation variable stiffness (VIVS) device consisting of multiple hydraulic cylinders, which can provide variable vertical stiffness at different loading stages to suppress large displacements under intense seismic motions. Regrettably, the high cost of this device hinders its widespread application. Based on the aforementioned research background, this paper proposes an improved Vertically Variable Stiffness Disc Spring Isolator (VVSDSI) and conducts systematic investigations into its design theory and mechanical behavior, as illustrated in Figure 1. First, the structural configuration and operational characteristics of the VVSDSI isolator are elucidated, accompanied by the establishment of its theoretical hysteretic model. Subsequently, experimental investigations are performed to examine the load-displacement curve characteristics of the isolator under ultimate loading conditions. Furthermore, parametric analyses are conducted to evaluate the influences of the preloading displacement and loading amplitude on the hysteretic curves, equivalent stiffness, and equivalent damping ratio of the isolator. Finally, a numerical model of the VVSDSI isolator is developed usingABAQUS v.2023 finite element software, with its validity rigorously validated against experimental results.
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 5 Discussions and Prospection of Seed Tape Sowing
### Fundamental Issues in Seed Tape Sowing That Still Need Further Discussion
#### 5.1.2 The Interaction of Soil-Machinery  
The stability of soil cover depth is one of the key factors affecting the seed emergence rate. The quality of soil preparation before sowing would directly affect the soil covering effect of seed tape. The results showed that the soil thickness and stability of seed tape were more ideal when soil preparation was higher. On the contrary, the stability of soil depth is poor, and even the seed tape is exposed to the surface. These problems greatly limited the development of the technological advantages of seed tape.  
In order to improve the adaptability of the rope-seeder to soil conditions and improve the quality of soil covering the seed tape, we can start from the following aspects.  
1. Strengthening basic theoretical research on soil-mechanical interaction.
2. Optimize the structure of the rope seeding machine, such as ditching, guide rope and other links.
3. Before laying the seed tape, install the earth breaking device and the rope seeding machine for the composite operation.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 2 Faults in DC Microgrids
### High-impedance Faults  
High-impedance faults are difficult to detect because of their low current, which is similar in magnitude to that of the normal operating current. An additional problem is the arcing phenomenon caused by random reignition of the fault current. The randomness of the response makes the modeling of HIFs difficult, which is a prerequisite for the development of detection methods, especially those based on signal processing and intelligent classification. However, the HIF pattern can be well approximated with a model that includes a fault buildup, a shoulder, and a nonlinearity stage [40]. Such a model was proposed in [41]:  
\[i_{j+1}=i_{j}-\frac{R\cdot i_{j}+k/i_{j}^{1.2}+35-V_{DC}\sin(\omega t)}{R-1.2k/i_ {j}^{2.2}}, \tag{1}\]  
\[2n\pi+\pi/3<\omega t<2n\pi+2\pi/3,n\in\mathbb{N}_{0}, \tag{2}\]where \(k\) is the arc constant, \(i\) the HIF fault current, \(V_{DC}\) the nominal voltage, and \(R\) the equivalent fault resistance. The resulting HIF current is shown in Figure 1.  
This model was used in [42] to develop an HIF detection method for DC aircraft systems based on the second derivative of the voltage and a comb filter. It is also used for HIF detection in DC distribution networks, where a mathematical-morphology-based fault detection method is proposed [43].  
To implement HIF in Matlab/Simulink, the electrical model from [44] is used. The model consists of impedance that is used to simulate the buildup stage and diodes with sources connected in series to create repetitive nonlinear behaviour.
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 3 Results of the Review
### Transition from 5G to 6G  
A comparison of 5G and 6G networks in terms of services, key technologies, and communication techniques is given in Table 3.  
The sixth-generation spectrum covers several bands from 410 MHz to 71 GHz and THz frequencies [3]. Sixth-generation technologies will operate on the same frequencies as 4G long-term evolution (LTE) (410 MHz to 6 GHz) and 5G new radio (410 MHz to 7.125 GHz and 24.25 to 71 GHz), plus additional frequencies (7.125 to 24.25 GHz) and sub-THz frequencies. The key enabling technologies for 6G are:  
* THz communications;
* Holographic beamforming;
* Cell-free MIMO,
* AI/machine learning (ML);
* Blockchain;  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Fourth-Generation** & **Fifth-Generation** & **Sixth-Generation** \\  & **(4G)** & **(5G)** & **(6G)** \\ \hline Mobility [km/h] & 350 & 500 & \textgreater{}1000 \\ \hline Latency [ms] & \textless{}100 & \textless{}10 & \textless{}0.1 \\ \hline Connectivity density [devices/km\({}^{2}\)] & \(10^{5}\) & \(10^{6}\) & \(10^{7}\) \\ \hline Area traffic capacity [Gbps/m\({}^{2}\)] & 0.001 & 0.01 & 1 \\ \hline Peak data rate [Tbps] & 0.002 & 0.02 & \textgreater{}1 \\ \hline User experiences data rate [Gbps] & 0.01 & 0.1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of capabilities of 4G, 5G, and 6G systems [3].  
* Quantum communications;
* Enhanced edge computing;
* Intelligent reflecting surface;
* New multiple access techniques;
* Zero energy interface [3].  
Despite advanced research on 6G wireless network technology, it is expected that it will be implemented around 2030 and will allow the expansion of mobile communication to all fields of use, including the IIoT for the needs of Industry 4.0 and 5.0 (with people and the environment at the focus of attention). The development and standardization of 6G specifications is planned for 2025-2029, with the first laboratory tests and 6G pilots scheduled to begin in 2028, preparing 6G for commercial launch in 2030. It appears that the closeness of this date to deadlines for targets under the Green Deal is intentional. Additionally, these changes are leading to reconsideration of traditional security and privacy methods (innovative authentication, encryption, access control, communication, and malicious activity detection), as well as the use of AI/ML [4].
# Service for Deploying Digital Twins of QKD Networks
## 4 Implementation
### QKD Network Client  
In order to write their applications and deliver them to the QKD network digital twin, users must use a specific API, in the first version of the service, the one defined by ETSI GS QKD 004 standard [27].  
To facilitate the creation of applications and to make it as user friendly as possible to conduct experiments on our QKD network environment, a QKD Network client package has been developed. In this package, the three executing methods that the implemented API supports have been defined; therefore, to write a Python application that makes use of the QKD network digital twin, the user may install and import this package, thus being able to easily call the three execution methods discussed in the previous section: _open_connect_, _get_key_, and _close_.
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 4 Results and Discussion
### Sensitivity Analysis of the Template Size in Color-Based Segmentation  
The selection of the template is related to the shape, size, and separation between olive trees. The trees can be approximated as a circular shape with a variable diameter between 3 and 6 m. The separation between the trees is not constant in all the fields, with values between 7 and 13 m, and different distance separations can also be found in the same image. Moreover, due to the terrain slope, the flight altitude (AGL) is not constant, which makes the tree size and separation in pixels be non-constant. These variations in the scale due to different distance to the ground are resolved in the first step of the segmentation by changing the size of all the images according to their flight altitude, but we can still find areas with trees of different sizes and separations. In the results presented so far, the distance transform over a circle of 30 pixels (about 3.7 m) in diameter centered on a square of size 80 \(\times\) 80 (about 10 \(\times\) 10 m) has been used as a template. Table 3 shows the results for less accurate template sizes for image DJI_0232.  
As can be observed, decreasing the size of the template causes the appearance of false positives, and the precision drops to 81.4% in the worst case. On the contrary, increasing the size of the template increases the number of false negatives, with a recall of 89.2% in the worst case. The size of the template is related to the separation between trees, while the diameter is related to the size of the tree. If the diameter of the circle is too large, small trees are not detected. Furthermore, two nearby olive trees can be detected as one, causing many-to-one correspondences. Except for the worst case (d = 20 pixels, s = 40 pixels), the \(F_{1}\) score remains over 94% (96% excluding errors in the borderline).  
Figure 10: Color-based segmentation results (white circles) vs. ground-truth reference (yellow crosses) for image DJI_0232 (worst case). (**a**) Original image. (**b**) Detected and annotated trees.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 4 Power System Design Methodology
### Mass and Weight Calculations  
The following formulae give the mass of the battery package, based on the required energy (Equation (14)) and maximum power (Equation (15)); where DoD stands for depth of discharge and EOLC stands for end-of-life capacity, both having a value between 0 and 1:  
\[m_{bat}=\frac{E_{\text{req}}}{E_{\text{sp}}}\frac{1}{\text{DoD}\times\text{EOLC}} \tag{14}\]  
\[m_{bat}=\frac{P_{\text{max}}}{P_{\text{den}}}\frac{1}{\text{DoD}\times\text{ EOLC}} \tag{15}\]  
The mass of the final battery was the larger of the two values calculated with Equations (14) and (15). The volume of the battery can then be determined with Equation (16):  
\[v_{bat}=m_{bat}\times\frac{E_{\text{sp}}}{E_{\text{vol}}} \tag{16}\]  
where \(E_{\text{sp}}\) is the specific energy in \(\text{Wh}/\text{kg}\) and \(E_{\text{vol}}\) is the volumetric energy density in \(\text{Wh}/\text{m}^{3}\).  
After determining the mass and volume of the battery pack, the next step is to obtain the masses for the other parts of the powertrain. The two parts that are considered in the preliminary design are the motor controllers and the electric motors. This is done by multiplying the maximum power by the combined specific power of the motor controllers and the electric motors. This combined specific power is calculated with Equation (17):  
\[P_{\text{sp, tot}}=\left(\frac{1}{P_{\text{sp, mot}}}+\frac{1}{P_{\text{sp, mc}}}\right)^{-1} \tag{17}\]
###### Abstract  
Coffee is a beverage that is consumed due to its flavor and fragrance. In this investigation, we demonstrated the relations between different dry fermentation processes of coffee (aerobic, anaerobic, and atmosphere modified with CO\({}_{2}\)) and fermentation times (0, 24, 48, 72, and 96 h), with pH, acidity, and seven volatile marker compounds of coffee. Volatile compounds were extracted by solid phase microextraction (SPME) and an analysis was performed by gas chromatography-mass spectrometry (GC-MS). A significant effect (\(p<0.05\)) between the fermentation time and a decrease in pH was demonstrated, as well as between the fermentation time and increasing acidity (\(p<0.05\)). Acetic acid was positively correlated with the fermentation time, unlike 2-methylpyrazine, 2-furanmethanol, 2,6-dimethylpyrazine, and 5-methylfurral, which were negatively correlated with the fermentation time. The aerobic and anaerobic fermentation treatments obtained high affinity with the seven volatile marker compounds analyzed due to the optimal environment for the development of the microorganisms that acted in this process. In contrast, in the fermentation process in an atmosphere modified with CO\({}_{2}\), a negative affinity with the seven volatile compounds was evidenced, because this gas inactivated the development of microorganisms and inhibited their activity in the fermentation process.  
c 20222270040  
characteristic of this beverage, whose concentration can drastically change depending on postharvest methods [6]. Fermentation time increases acids' volatile compounds [7] but, in general, decreases the concentration of volatile compounds [8], because during a long fermentation time, volatile compound synthesis decreases with the restriction of enzymatic activity, reducing aroma precursors [9], such as pyrazines [10] and furfurfurans [11].  
Volatile compounds are determined by gas chromatography-mass spectrometry (GC-MS), which is a precise analysis of the type and concentration of volatile compounds after an adequate extraction method [8]. For the extraction of these compounds, several techniques have been applied in the food industry for quality control. Nevertheless, solid phase microextraction (SPME) is a simple extraction method, solventless, robust, and replicable, because the fiber accumulates analytes with different types of sorbents, improving coffee volatile compound extraction [12].  
Determining the relationship between fermentation and volatile compounds is complicated [3]. The objective of this investigation was to determine the relationship between the fermentation time and the volatile compound concentration of roasted coffee. Moreover, during the fermentation process, three environments were evaluated: aerobic, anaerobic, and an atmosphere modified with carbon dioxide (CO\({}_{2}\)).
## 2 Study Area and Data
### Datasets  
#### 2.2.1 Remote Sensing Data  
Fifteen-day composite multi-sensor NDVI time series data were acquired from the Vegetation Index and Phenology Laboratory at the University of Arizona for the period 1989 to 2014 at 5.6 km spatial resolution. This product is a sensor-independent and continuous NDVI record derived from AVHRR (1981-1999), SPOT (1998-2002), and MODIS (2000-2014) [23; 24]. This 26-year NDVI time series consists of 15-day composites, resulting in 624 temporal images. Detailed information on the datasets is available on the Vegetation Index and Phenology Laboratory website: [[https://vip.arizona.edu/VIP_MEASURES_Project.php](https://vip.arizona.edu/VIP_MEASURES_Project.php)]([https://vip.arizona.edu/VIP_MEASURES_Project.php](https://vip.arizona.edu/VIP_MEASURES_Project.php)).  
Climate data, precipitation and temperature, used in the study were generated from Oregon State University's Parameter-elevation Regressions on Independent Slopes Model (PRISM) [25] dataset at 4 km spatial and monthly temporal resolution. Average seasonal monthly precipitation and temperature data were generated for the period 1989-2014 and were resampled to the NDVI spatial resolution of 5.6 km. As the land cover of the region is fairly stable due to the little natural and anthropogenic changes, the 2005 North American land Cover product at 250 m spatial resolution was used to extract the main land cover types. The land cover database was created by the North American Land Change Monitoring System [26]. According to this dataset, the land of the Hopi tribe and Navajo Nation is mainly characterized by shrubland, grassland, and needleleaf forest (Figure 4b). The pixel size of the land cover types was resampled to 5.6 km, using the dominant class approach, to match the NDVI pixel size. Topographic data used in the study are based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). Elevations range between 821 m and 3771 m (Figure 4a) corresponding to ecological zones with sparse to high vegetation cover (Figure 4b). The pixel size of the DEM data was also resampled to 5.6 km to match the NDVI time series.
# Application of Iron-Modified Activated Carbon for Phosphate Removal in Aqueous Systems
## 3 Results and Discussion
### Effect of SMAC on the Sediment Layer
The findings indicate that SMAC is effective not only in reducing phosphate at the soil layers, but also in maintaining low phosphate levels in the water body itself. Therefore, SMAC can be considered a promising material for applications in dynamic environments such as river beds and coastal areas influenced by waves and tides. Furthermore, if SMAC maintains phosphate concentrations within aquatic systems at stable levels, then it can contribute to the suppression of algal blooms and the improvement of water quality. Additionally, it may play a significant role in protecting aquatic ecosystems.