esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigated efficient extraction methods for cannabinoids and terpenes from the above-ground parts of Futura 75, focusing on two techniques: pressurized extraction and magnetic stirrer-assisted extraction. The effects of solvent type, temperature, time, and pressure were evaluated using five organic solvents and two binary solvent systems. Cannabinoid profiles of obtained extracts were analyzed using gas chromatography coupled with mass spectrometry (GC-MS), while terpene profiles were characterized through solid-phase microextraction (SPME) combined with GC-MS. Next, two selected extracts with the highest content of cannabinoid and terpene fractions (Futu1 and Futu2) were tested for antiproliferative activity toward cancer cell lines (MV4-11, AGS, HT-29, MDA-MB-468, MCF-7) and their cytotoxicity was evaluated on non-tumorigenic MCF-10A cells. Extract Futu1 contained 51.57% cannabinoids, 9.8% monoterpenes, and 90.2% sesquiterpenes in the terpene fraction. Futu2 exhibited a higher proportion of monoterpenes in the terpene fraction (19.6% monoterpenes and 80.4% sesquiterpenes) and consisted of 49.49% cannabinoids. Both extracts exhibited higher selectivity for cancer cells over non-tumorigenic cells, with Futu2 demonstrating stronger antiproliferative properties. Interestingly, lower concentrations of extracts and tested free, single cannabinoids stimulated the growth of leukemia (MV4-11) and breast cancer (MDA-MB-468) cell lines while their higher concentrations suppressed proliferation.  
cannabinoids; terpenes; solvent extraction; food safety; anticancer activity 1
## 2 Methodology
### Mathematical Modeling
#### 2.2.4 Economic and Financial Calculations  
Net Present Value (NPV) of the project is calculated based on Equation (23), where \(i\) is the discount rate and \(F_{n}\) is the net cash flow in year n. Net cash flow is the difference between net savings and net expenditure over the year. Net savings include the savings from energy and carbon emissions savings, while the net expenditure includes the capital cost, principal and interest payment, and Operation and Maintenance cost.  
\[NPV=\ \sum_{n=\ 0}^{N}\frac{F_{n}}{\left(1+i\right)^{n}} \tag{23}\]  
Annual loan payment for the project is calculated using the following Formula (24), where \(i\) and \(n\) are interest rate and the loan payback period in years, respectively.  
\[\text{Annual loan payment amount}=\text{Total loan amount}\times\left[\frac{i \times(1+i)^{n}}{\left(1+i\right)^{n}-1}\right] \tag{24}\]  
Levelized cost of energy (LCOE) is that cost that, if assigned to every unit energy produced by the system over the analysis period, will equal the total life-cycle cost of the project discounted back to the base year. LCOE is calculated using an Equation (25). The numerator in Equation (25) represents all costs of the project over its life while the denominator is the total energy generated from the project over its life, both converted to a present value. Here, \(C_{n}\) is the total cost and \(Q_{n}\) is the energy produced in year \(n\).  
\[\mathit{LCOE}=\ \frac{\sum_{n=\ 0}^{N}\frac{C_{n}}{\left(1+i\right)^{n}}}{ \sum_{n=\ 1}^{N}\frac{Q_{n}}{\left(1+i\right)^{n}}} \tag{25}\]Table 1 summarizes the different economic parameters used in the analysis. The solar installation and system commissioning time is assumed to take a year. For both systems, half of the total capital cost comes from the company's equity whereas another half from a loan. The borrowed loan is paid back in 10 equal installments in 10 years from the time after the system is commissioned. This loan repayment schedule is also known as 'even total payment' method. The installation cost of PV and battery systems are obtained from the National Renewable Energy Laboratory (NREL) report as of 2018 pricing structure [37]. Additionally, the cost of the panel, inverter, and battery is verified with other manufacturing companies [46; 49; 50; 51]. The cost of a refrigeration plant and its accessories are obtained from a plant manufacturing company. Fixed cost, variable cost, direct cost, and indirect cost associated with the project execution are also accounted for in the analysis. The proposed system assumes that the solar system and battery system are co-located. The cost of the solar system is expressed in USD/kW while the cost of the storage system in USD/kWh and the cost of refrigeration plant in USD/tonne of ice generation.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
###### Abstract  
The relative pose estimation of the space target is indispensable for on-orbit autonomous service missions. Line segment detection is an important step in pose estimation. The traditional line segment detectors show impressive performance under sufficient illumination, while it is easy to fail under complex illumination conditions where the illumination is too bright or too dark. We propose a robust line segment detector for space applications considering the complex illumination in space environments. An improved two-dimensional histogram construction strategy is used to optimize the Otsu method to improve the accuracy of anchor map extraction. To further improve line segment detection's effect, we introduce an aggregation method that uses the angle difference between segments, the distance between endpoints, and the overlap degree of segments to filter the aggregation candidate segments and connect disjoint line segments that probably came from the same segment. We demonstrate the performance of the proposed line segment detector using a variety of images collected on a semiphysical simulation platform. The results show that our method has better performance than traditional line segment detectors including LSD, Linelet, etc., in terms of line detection precision.  
complex illumination; space target; line segment detection; Otsu; aggregation Article
## 4 Evaluation and Results
### Results
#### 4.2.4 Resource Block at RAN  
As mentioned earlier, the RBs used by all the participating UEs and the QoE achieved by all those users are highly interlinked with each other. To maintain a threshold of QoE, an eNB may need to assign a greater number of RBs to a specific user with a bad CQI. In the case of unicast, each user is tuned to a dedicated physical channel. Therefore, each UE needs a specific amount of RBs on its channel. However, in the case of multicast transmission, multiple users are tuned on a shared multicast channel, making it possible for the RBs assigned to the channel to be used by all the UEs simultaneously. Figure 6 shows that, when the unicast mode is used, then the total number of RBs used by all the users is 25,730. However, the number of RBs reduces to 4360 in VNF based multicast mode. Therefore, the burden on the RAN link can also be reduced significantly in VNF based multicast mode. The results also show that predictive multicast can further increase the efficiency of VNF-AS design.  
Figure 5: QoE score (multicast vs. unicast).  
Figure 6: Resource blocks used (multicast vs. unicast).
# Charging Platform of Chess-Pad Configuration for Unmanned Aerial Vehicle (UAV)
###### Abstract  
The authors of this study designed and optimized a charging landing pad system that mitigates the landing accuracy issues of unmanned aerial vehicles (UAVs). The study looks at the charging process, energy conversion during periodic landing on a unique platform, and an onboard and on-ground scheme design procedure. The circuit is fixed on the UAV platform and comprises six integrated bridge rectifier diodes to alter the four connection pin terminals' charge polarity. The inclusion of a current indicator shows the flow of charge during successful docking. The charging platform consists of square conductive copper plates of specific dimensions that provide positive and negative polarity in a chess form to ensure the contact of various polarities. This design considers two power supply options: a solar panel and a standard mains supply. The contact point coordinate probability when landing is the crucial aspect of this design. A first version of the proposed system was implemented to measure its effectiveness for commercial drones. This system provides an automated recharge station with reliable performance. Numerical experiments showed that the system's energy conversion remains efficient regardless of drone orientation over the platform or the environment's nature.  
UAV charging system; charging platform; charging efficiency Article
## 2 Derivation of the Azimuth and the Elevation Error under Frequency Mismatch
### Second Condition: \(\cos(\psi^{(0)}+\Delta\psi)=0\)  
Substituting \(\cos(\psi^{(0)}+\Delta\psi)=0\) in the constraint that the partial derivative of cost function of an interferometer algorithm with respect to \(\psi\) at \(\theta=\theta^{(0)}+\Delta\theta,\psi=\psi^{(0)}+\Delta\psi\) should be zero, we get the following equation in \(\Delta\theta\):  
\[\cos(\psi^{(0)})cos(\Delta\theta)=0. \tag{41}\]  
It turns out that no \(\Delta\theta\) satisfies (41) since \(|\Delta\theta|\) is much less than \(\frac{\pi}{2}\).
# Blind Image Deblurring Based on Local Edges Selection
## 3 Results and Discussion
### Image Deblurring Results
Some methods cannot obtain clear images successfully, the image is still suffered from serious blur, such as [1; 41]; In some deblurring images, such as the images estimated by [7; 19; 20; 22], ringing artifacts affect the deblurred results. It can be seen more clearly from the magnified views that the ringing artifacts are better suppressed by the proposed method, and the estimated image is clearer than others. Then, quantitative comparisons are carried out based on the 48 images in [PERSON]'s dataset. Figure 10 shows the comparisons of the average SSIM and PSNR values. The proposed method outperforms others in quality indices--the SSIM and PSNR are consistently higher than others. Figure 8: Quantitative results on the dataset of [PERSON] et al. Figure 9: Image deblurring results on the dataset of [PERSON] et al. In addition, we test the performance based on the dataset of real captured standing trees, the ground truth images are shown in Figure 11. The images of living trees contain rich texture information, which are good typical images to test the results of deblurring. We obtain 32 blurred images based on the blurring kernels in [PERSON]'s dataset and the four ground truths of images. The comparison methods include those in [7; 9; 19; 20; 22; 27; 30; 31; 36]. Figure 12 shows one of the results in the 32 blurring images. From the visualization results, the proposed method can reserve more texture information of an image compared with other methods--the image details can be seen clearly from the magnified views in Figure 12,m. Figure 13 shows the average SSIM values and the success rate. The proposed method still has advantages over others--the SSIM are higher than others, and the success rate is the highest among the methods consistently. Figure 12: Image deblurring results of standing trees. The experimental results also show that the proposed method can deblur natural blurred images well. In Figure 14, we compare the deblurring results for a natural blurred image with other methods. The clear image, their magnified views and the estimated blurring kernels are also shown together in the figure. Compared with other methods, the clear image obtained by the proposed method has fewer ringing artifacts, more image details and better color fidelity. Figure 14: _Cont._  
Figure 13: Image deblurring performance of living standing trees. Figure 15 shows other blind deblurring results. The images are chosen from [PERSON]'s dataset [20], [PERSON]'s dataset [7], and the dataset of ours. Results show that the proposed method can deblur natural images well--the clear images have good color fidelity and clarity. The proposed method is effective in image blind deblurring. Figure 7.0 The deblurring results comparison of natural blurred image. Figure 7.1 _Cont._
## 1 Introduction
### Query-by-Example Spoken Term Detection Overview  
Query-by-example spoken term detection also aims to search within audio archives, but it is based on an acoustic (spoken) input. In QBe STD, we consider the scenario in which the user finds a segment of speech that contains terms of interest within a speech data repository, and the user's purpose is to find similar speech segments within that repository. The speech segment found is the query and the system outputs other similar segments from the repository, which we will henceforth refer to as utterances. Alternatively, the query can be uttered by the user. This is a highly valuable task for blind people or devices that do not have a text-based input, and, consequently, the query must be given in another format, such as speech.  
Query-by-example spoken term detection has been traditionally addressed using three different approaches: methods based on the word/subword transcription of the query, methods based on template matching of features and hybrid approaches. In the last couple of years, however, there have been new proposals based on deep learning.
# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
## 5 Conclusions and Discussion  
This study proposed the vegetation removal method on a 3D point cloud using the photogrammetry and the 2D segmentation by U-Net. The study demonstrated that the proposed method effectively removed seasonal change due to vegetation on the cut-slope in Doam Dam in South Korea. This showed a feasibility of the 3D point clouds to monitor long-term changes in cut-slopes. The findings in the study are summarised as following.  
* The colour index was found to be successful for vegetation segmentation when the contrast between vegetation and the background was high, and the colour of vegetation was relatively simple. However, it was not successful otherwise.
* Deep learning-based 2D image segmentation using U-Net was robust to deal with such complex situations, showing the similar performance with the manually trimmed image segmentation.  
Figure 19: Multiscale Model to Model Cloud Comparisondistances between winter and summer: (**a**) with vegetation, and (**b**) without vegetation.  
Figure 20: Zoomed view of Multiscale Model to Model Cloud Comparison distances between winter and summer: (**a**) with vegetation and (**b**) without vegetation.  
* The effort to build the 2D segmentation deep learning model was significantly reduced by using U-Net with the dataset prepared by both the colour index method and manual trimming.
* Two base models, VGG16 and MobileNetV2, were used to build the U-Nets. The VGG16 U-Net showed the better performance, but the MobileNetV2 U-Net took much less time for training with a smaller model size. The segmentation accuracy of VGG16 and MobileNetV2 U-Net were approximately 95% and 90%, respectively.
* The proposed vegetation removal method on the 3D point cloud reconstruction was found to remove vegetation successfully on the two 3D point clouds constructed with images taken at two different times.
* The M3C2 distance between two vegetation-removed point clouds of the cut-slope successfully removed the negative effect due to seasonal vegetation on the cut-slope.  
Conceptualisation, K.-Y.K.; software, Y.W.; investigation and writing, Y.W. and K.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport of the Korean government (project number: 19 SCIP-C151408-01).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experiments
### Experimental Results
#### 4.2.3 RQ3: Impact of the Early-Stop Strategy on FedRecI2C  
In FedRecI2C, the early-stop strategy refers to removing the I2C training for communication-constrained clients from the system after a suitable number of communication rounds, whereas the federated training for communication-capable clients persists.  
According to Figure 7, early-stop slightly affects the final performance of FRSs with I2 CG and the direction of this impact depends on the FRS and the dataset. Specifically, regardless of whether it is PFedMF-I2 CG or FedMF-I2 CG, early-stop moderately inhibits the performance decline on the MovieLens-100K dataset, with earlier cessation yielding a more pronounced effect. Premature early-stop may compromise the performance of PFedMF-I2 CG (on Lastfm-2K) and FedMF-I2 CG (Foursquare), which otherwise benefit from I2 CG. Furthermore, choosing the right timing for early-stop and halting I2 CG promptly can effectively minimize the extra computational burden imposed by I2 CG. For example, consider the case in Figure 7e. Here, we focus on FedMF-I2 CG on the MovieLens-100K dataset. When we remove I2 CG after the 50 th communication round (es = 50), two main results occur. Firstly, the final model performance is superior compared to the scenario without early-stop. Secondly, there is a conservation of resources. Specifically, communication resources for downloading the global item models from the server are saved. Also, the computation resources required for local updates during the subsequent 200 communication rounds for communication-constrained clients are conserved.  
As discussed in Section 4.2.2, PFedMF-I2 CL integrates both federated training and pure local training, ultimately achieving a dynamic equilibrium between the two. If pure  
Figure 7: Impact of early-stop on recommendation performance of PFedMF/FedMF with I2 CG. (**a–d**) PFedMF-I2 CG: (**a**) MovieLens-100K. (**b**) MovieLens-1M. (**c**) Lastfm-2K. (**d**) Foursquare. (**e–h**) FedMF-I2 CG: (**e**) MovieLens-100K. (**f**) MovieLens-1M. (**g**) Lastfm-2K. (**h**) Foursquare.  
local training is halted before the convergence of the system, the model's performance will degrade to that of FL (\(csr=10\%\)), as shown in Figure 8a-c. However, as Figure 8d shows, the removal of I2 CL at the communication round of 10 does not diminish the performance of PFedMF-I2 CL, which demonstrates the uniqueness of the Foursquare dataset. This observation may be attributed to the unique distribution characteristics of the Foursquare dataset, which motivates us to investigate the impact of early-stop on the performance of I2 CL across more datasets in our future work.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design
### Problem Reformulation  
According to the problem description in the previous section, the problem is a combinatorial optimization problem. It is very difficult to solve the original problem directly. To simplify solving the problem, we solve this optimization problem by a sampling of latitudes from 0 to 90 degrees discretely at equal intervals, and the phase difference between adjacent satellites is an integer multiple of the sampling interval. We denote the sampling interval as \(\Delta\psi\) and \(\delta=L\Delta\psi\), where \(L\) is an integer. The discrete optimization problem can be described as the following optimization problem.  
\[\mathbf{P}2:\underset{\{\chi[p],K[p]\}}{\text{maximize}} \frac{1}{\left\lfloor\frac{90}{\Delta\Psi}\right\rfloor}\sum_{p=1}^{ \left\lfloor\frac{80}{\Delta\Psi}\right\rfloor}\sum_{i=K[p]}^{N}c_{i}[p]\] (19) subject to: \[0\leq\chi[p]\leq\chi_{max}, \tag{19a}\] \[\chi[p]\geq\chi[p+1],\] (19b) \[0\leq K[p]\leq N,\] (19c) \[K[p]\geq K[p+1],\] (19d) \[K[p]\leq\in Z^{+},\] (19e) \[\psi_{down}[p]-\psi_{up}[p+L]\geq\epsilon,\] (19f) \[(p+L)\Delta\psi\leq 90,\] (19g) \[\mu_{min}^{i}[p]\geq\mu_{th},\forall i\in\{1,2,\cdot,N\}, \tag{19h}\]  
By discretizing the latitude, we transform the optimization problem \(\mathbf{P}1\) into the optimization problem \(\mathbf{P}2\) in which \([p]\) means \((p\Delta\Psi)\). Since \(\chi(\psi)\) and \(K(\psi)\) increase with decreasing \(\psi\), the constraints (19a) and (19c) need to be satisfied.
## 3 The Proposed Evaluation Method
### Preprocessing Evaluation Data
#### 3.3.1 Quantitative Indicators  
Quantitative indicators are numerically measurable evaluation criteria.  
For \"very small\" indicators (denoted as \(w_{ij}\)), lower values indicate better performance. Given \(n\) evaluation schemes, \(w_{ij}\prime\) can be normalized using Equation (27).  
\[w_{ij}\prime=\frac{\max\limits_{i}\{w_{ij}\}-w_{ij}}{\max\limits_{i}\{w_{ij}\}- \min\limits_{i}\{w_{ij}\}} \tag{27}\]  
where \(i\) is the number of evaluation schemes, \(i=[1,2,\ldots,n]\); \(j\) is the number of evaluation indicators in the scheme; \(\max\limits_{i}\{w_{ij}\}\) and \(\min\limits_{i}\{w_{ij}\}\) are the maximum and minimum values in \(w_{ij}\), respectively.  
For \"very large\" indicators (denoted as \(w_{ij}\)), higher values indicate better performance. Given \(n\) evaluation schemes, \(w_{ij}\prime\) can be normalized using Equation (28).  
\[w_{ij}\prime=\frac{w_{ij}-\min\limits_{i}\{w_{ij}\}}{\max\limits_{i}\{w_{ij}\} -\min\limits_{i}\{w_{ij}\}} \tag{28}\]  
where \(i\) is the number of evaluation schemes, \(i=[1,2,\ldots,n]\); \(j\) is the number of evaluation indicators in the scheme; \(\max\limits_{i}\{w_{ij}\}\) and \(\min\limits_{i}\{w_{ij}\}\) are the maximum and minimum values in \(w_{ij}\), respectively.  
For interval indicator \(w_{ij}\), performance improves as values approach ideal value \(a_{j}\). Given \(n\) evaluation schemes, the normalized value \(w_{ij}\prime\) is computed via Equation (29).  
\[w_{ij}\prime=\frac{\max\{\left|w_{ij}-a_{j}\right|\}-\left|w_{ij}-a_{j}\right| }{\max\{\left|w_{ij}-a_{j}\right|\}-\min\{\left|w_{ij}-a_{j}\right|\}} \tag{29}\]  
where \(i\) is the number of evaluation schemes, \(i=[1,2,\ldots,n]\); \(j\) is the number of evaluation indicators in the scheme; \(\max\{\left|w_{ij}-a_{j}\right|\}\) and \(\min\{\left|w_{ij}-a_{j}\right|\}\) are the maximum and minimum values in the absolute values of the difference between \(w_{ij}\) and \(a_{j}\), respectively.
## 3 Proposed Work
### Model Development and Training
#### 3.3.4 Long Short-Term Memory (LSTM)
Training LSTM models involves multiple epochs, where an epoch represents one complete pass through the entire training dataset. During each epoch, the model updates its weights based on prediction error, gradually improving its ability to forecast Remaining Useful Life (RUL). In this study, the model was trained across several epochs to refine its internal parameters and capture the temporal patterns within the battery degradation data. As illustrated in Figure 13, the training and validation loss curves exhibit stable convergence with minimal divergence, indicating strong generalization and a well-balanced learning process. To enhance robustness and mitigate overfitting, Dropout regularization was applied after each LSTM layer, with a dropout rate of 0.3. Additionally, L2 regularization (\(\lambda\) = 0.01) was applied to each LSTM layer to penalize overly complex weights and encourage generalizable patterns. The network ends with a Dense output layer containing a single unit responsible for generating the final RUL prediction. The model was optimized using the Adam optimizer with a learning rate of 0.0001, employing Mean Squared Error (MSE) as the loss function and Mean Absolute Error (MAE) as the performance metric. The final model consisted of 131,489 trainable parameters, resulting in a smooth, consistent learning trajectory and a close alignment between training and validation losses--further validating the effectiveness of the model design and hyperparameter configuration, as shown in Figure 14.