esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of Object Detectors
### Evaluation of Detector Trained by the Dataset for Mobile Batteries  
To meet the primary aim of this project, the dataset focusing only on mobile battery detection was conclusively updated to check the feasibility of the YOLOv4 architecture in detecting specific used small-sized electronics. The first dataset had 1690 images, divided into 60% for training, 10% for validation, and 30% for performance tests after training. Three hyperparameters were set in the training process: a learning rate of 0.001, an epoch of 70, and a batch size of four. Among them, the batch size was set to four because of the GPU's (NVIDIA RTX A4500 Laptop GPU, Nvidia, Santa Clara, CA, USA) memory limitations. The Adam optimizer [60] was used to adapt the learning rates, with an initial learning rate of 0.001, a decay rate of the gradient moving average of 0.9, and a decay rate of the squared gradient moving average of 0.999.  
To check the detector's feasibility, a precision-recall curve and AP for mobile batteries were used to check its performance. As shown in Figure 9, the closer the curve is to the upper right corner, the higher the detector's performance. The AP for mobile batteries reached 0.996.
## 2 Materials and Methods
### Critical Infrastructure Analysis
#### 2.2.1 Interconnected Infrastructure  
Network science methods based on graph theory have been applied to analyze critical infrastructures that include the interconnected elements or operations, such as power grids [20], transportation networks [21], water systems [22], and optical backbone communication networks [23]. Interconnected infrastructures are modeled with a graph \(G=(V,E)\), where \(V\) is the set of vertices or network nodes (e.g., power plants and substations, pumps and pipe junctions, and optical switches), and \(E\) is the set of edges or links or connections (e.g., power lines, water pipes, and optical fibers) joining the nodes. Given a graph model of an interconnected infrastructure, one can use network science methods in part to determine the relative importance of nodes in the infrastructure based on graph metrics. This paper adopted centrality metrics similar to [16], as explained in the following.
## 3 Results  
### Obtaining and Determination of Polyphenols from Neem  
The overall extraction yield of molecules from neem leaves and seeds using a hydroethanolic solvent was significantly higher with PLE compared to MAE. Additionally, neem leaves were found to contain more extractable global molecules than neem seeds when using the hydroethanolic solvent, leading to the highest overall extraction yield being obtained from neem leaves via PLE. However, extracts obtained through MAE were richer in TPC and TPC compared to those from PLE (Table 2). As shown in Table 2, neem leaf extracts obtained through both PLE and MAE demonstrated a significantly higher polyphenol content compared to neem seed extracts.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**OEV (\%)**} & \multicolumn{2}{c}{**TPC (mg AGE/100 g)**} & \multicolumn{2}{c}{**TFC (mg QE/100 g)**} \\ \cline{2-7}  & **PLE** & **MAE** & **PLE** & **MAE** & **PLE** & **MAE** \\ \hline Leaves & 20 Aa & 6 Bb & 2893 Ee & 3136 Ee & 542 Hh & 797 li \\ Seeds & 5 Bb & 4 Dd & 417 Fe & 641 Gg & 365 Jl & 348 Kk \\ \hline \hline \end{tabular}  
Means followed by the equal capital letter on the same lines indicate that there is no significant difference (\(p\geq 0.05\)) between the extraction methods for OEV, TPC, and TPC in the same sample (Leaves or seeds). Means followed by the equal lowercase on the same column indicate that there is no significant difference (\(p\geq 0.05\)) between neem leaves and seeds for OEV, TPC, and TPC in the same extraction method (PLE or MAE).  
\end{table}
Table 2: Overall extraction yield (OEV), TPC, and TFC of neem extract obtained by PLE and MAE using a hydroethanolic (ethanol/water, 80:20 \(v/v\)) solvent.
###### Abstract  
The three-body tethered satellite system is a new potential technology for the purposes of orbital transportation. In contrast to conventional orbit transfer methods, this system is expected to transport space supplies to a predetermined orbital altitude without consuming fuel; however, the unwanted libration resulting from the Coriolis force acting on moving substellites may induce tumbling within the system. In order to analyze the strongly coupled characteristics of the libration motion and the variable-length tethers, a six-DOF dynamic model of the system based on [PERSON]'s law is established. By utilizing the dynamic equations and stability criterion for linear systems, three equilibrium configurations of the satellite system with two constant-length tethers are given. The coupling characteristics of the libration angles are analyzed based on mechanical features and simulations. The results demonstrate that part of the libration energy can transfer from out-of-plane motion to in-plane motion during out-of-plane libration, but not vice versa or when in-plane libration occurs alone. Furthermore, the dynamic characteristic of this system with a predesigned deployment strategy is surveyed. This investigation reveals that such a strategy can rapidly suppress the out-of-plane libration motion while maintaining purely sinusoidal oscillations in the in-plane motion.  
three-body tethered satellite system; dynamics modeling and simulation; nonlinear dynamic analysis; deployment strategy +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University,  
X’an710072, China, [EMAIL_ADDRESS]
## 3 The Experiment  
This section describes the validation experiments of the multisensor distance measuring device. An experiment at the Structural Laboratory Lluis Agullo of Universitat Poliecnica de Catalunya, Barcelona Tech (Spain) assessed the impact of sensor combination and provided a fair comparison between the introduced ToF and ultrasonic sensors. The jack seen in Figure 7 was used to measure the distance of an object from the distance measuring device. The distance between the machine's upper and lower jaw can be altered. This jack was selected for this laboratory experiment for three key reasons, as described below. (1) Dimension of target object: The area of its movable part (the upper jaw) is slightly above 0.5 square meters. This made the distance between the two jaws measurable by the HC-SR04 sensors [26]. (2) Movability: Since the upper jaw of this jack moves, different distances can be measured by the tailored distance measuring device. The distances were calculated by a steel measurement pattern. (3) Ambient light: A projector light was placed on the side of this jack to produce an extremely bright situation for simulating day and night in the laboratory. To show the capability of the proposed device under a noisy environment commonly found in the industry, the experiments were carried out during the active hours of the laboratory. In addition, the projector shown in Figure 7 was used for additional environmental noises.  
Ten static tests were carried out on this jack. In every test, the upper jaw of the jack was fixed with a measured distance from the lower jaw by a steel measurement pattern. Every test had a duration of 15 min. Additionally, the distance of the lower jaw from the tip of the sensors was measured for data postprocessing. The lower jaw of the jack was located precisely 60 mm lower than the tip of all sensors mounted on the device.  
Each of these ten experiments was performed once with the projector off and once with the projector on. This ambient light simulates the presence and the absence of the sun. In outdoor measurements, there are moments when the sun may be shining directly on the distance-measuring sensors. This work examines the sunlight effect through this laboratory experiment, which this paper refers to as experiments with excessive ambient light. The experiments without ambient light were acquired while the projector in Figure 6 was off. However, the laboratory has a permanent lighting system that could not be turned off during working hours. Table 2 shows a summary of the experimental tests.  
All the tests listed in Table 2 were conducted once with the projector on and once with the projector off. The steel measurement pattern was used to secure the measurements. The numbers refer to the tip of the measuring device's sensors to the jack's upper jaw.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Number** & **Steel Measurement Pattern (mm)** \\ \hline
1 & 290 \\
2 & 340 \\
3 & 440 \\
4 & 540 \\
5 & 640 \\
6 & 740 \\
7 & 834 \\
8 & 940 \\
9 & 1040 \\
10 & 1140 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the performed tests.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Hashing Techniques in Genome Alignment
#### 2.4.1 Tools Utilizing Hashing Techniques  
Several alignment tools employ hashing techniques to enhance their performance and accuracy, each optimized for specific types of analyses:  
* SOAP: SOAP (short oligonucleotide alignment program) uses a straightforward hashing strategy to align short reads against a reference genome [51]. It is particularly efficient in handling throughput sequencing data, making it a popular choice for SNP discovery and genotyping analyses (Figure 10). The complexity of SOAP depends on the size and complexity of the input sequences. It typically has a worst-case time complexity of \(O(n^{2})\), where \(n\) is the length of the input sequences;  
* Stampy: While primarily relying on the Burrows-Wheeler transform for alignment, Stampy incorporates hashing to improve alignment accuracy in regions with high sequence variability [52]. This makes it exceptionally useful for aligning reads from organisms with significant genetic diversity or in studies focusing on evolutionary differences;
* BLAST: Theasic local alignment search tool (BLAST) is a widely-used algorithm for comparing an inquiry sequence against a database or reference sequence [34]. BLAST employs hashing to rapidly find regions of local similarity, facilitating a broad range of analyses from gene identification to comparative genomics;
* GMAP: Optimized for spliced alignments, such as those needed in RNA-seq data analysis, GMAP uses hashing to index the genome [53], allowing efficient identification of splice junctions and exons (Figure 11). Its capability to handle long reads makes it suitable for transcriptome studies where reads span multiple exons. GMAP's complexity is \(O(n)\), where \(n\) is the length of the reference sequence.  
Figure 10: SOAP algorithm pseudo-code.
# Functional Evaluation of a Novel Multi-Axial Alveolar Distractor--Preliminary In Vivo Animal Study
## 3 Results  
The digital radiography results showed that the osteotomy areas at the mandible angulus were healed and the bone growth was completed 4 weeks after surgery for all rabbits using multi-axial alveolar bone distractors (Figure 7). The TC group in Figure 7 shows the X-ray image tracking observation under the condition that this group did not use any multi-axial alveolar bone distractor.  
Figure 7: The digital radiography results show that the osteotomy areas at the mandible angulus healed and the bone growth was completed 4 weeks after surgery for all rabbits using multi-axial alveolar bone distractors.  
The rabbit cadaver observation results show that the average and standard bone growth length deviation in the corresponding bone positions before and after transport bone plate movement in Groups TMB and TM were 17.68 \(\pm\) 1.51 mm and 14.79 \(\pm\) 0.97 mm, respectively. The corresponding buccal side distraction thicknesses for Groups TMB and TM after distraction, were 5.12 \(\pm\) 0.52 mm and 3.32 \(\pm\) 0.37 mm, respectively. The mean bone growth length value and buccal side distraction thicknesses for Group TMB were significantly greater (\(p<\) 0.05) than that of Group TM (Figures 4 and 5).  
The fracture test results showed that the bone specimen tensile strengths after distraction for Groups TMB and TM were 172.13 \(\pm\) 19.04 N (\(n\) = 3) and 119.27 \(\pm\) 17.52 N (\(n\) = 3), respectively. The results showed that the bone strength of Group TMB was higher than that of Group TM. However, the intact bone tensile strength was 304.24 \(\pm\) 73.90 N (\(n\) = 8) and presented significant differences between Groups TMB and TM. The distraction bone tensile strength to normal bone percentages were 57% (172 N/304 N) and 39% (119 N/304 N) for Groups TMB and TM, respectively.
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 4 Experiments and Analysis  
### Evaluation Metric  
We employed four quantitative metrics to assess the performance, which include model parameter count, inference time (IT) on SITS, overall accuracy (OA), and the mean Intersection over Union (mIoU). The number of parameters is a measure of the model's size, with smaller models being more resource-efficient. Inference time solely considers the time required for the model to process the data, and shorter inference times indicate faster prediction speeds for individual time series. \(OA\) evaluates the global accuracy of the extraction results, while \(mIoU\) quantifies the overlap between the predicted results and the ground truth labels. The expressions are as follows:  
\[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[mIoU=\frac{1}{N}\sum_{i=1}^{N}(\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}}) \tag{6}\]  
\begin{table}
\begin{tabular}{l} \hline \hline Training STENN for SITS Semantic Segmentation \\ \hline Input: SITS data \(X\); Ground truth labels \(Y\) \\ Output: Semantic Segmentation \(R\) \\
1. Load the training dataset \(X\), configure model parameters, and initialize the weights of the STENN model. \\
2. While step \(\leq\) Epoch: \\
3. Iterate through all the data in \(X\), and utilize the STENN model to generate the segmentation result \(R\). \\
4. Calculate the loss between \(R\) and \(Y\) using the specified loss function and update the entire model’s parameters based on the computed loss. \\
5. End the training loop once the defined number of training steps (epochs) has been reached. \\
6. Validate and test the trained STENN model, and save all the results. \\ \hline \hline \end{tabular}
\end{table}
Table 1: STENN training process.  
In which \(TP\) (true positives) represents the correct identification of positive instances, \(TN\) (true negatives) signifies the correct identification of negative instances, \(FP\) (false positives) denotes the erroneous identification of negative instances as positive, and \(FN\) (false negatives) indicates the erroneous identification of positive instances as negative. \(N\) is the number of categories, \(TP_{i}\) represents the true positives for the i category, \(FP_{i}\) signifies the false positives for the \(i\) category, and \(FN_{i}\) denotes the false negatives for the \(i\) category.  
All experiments were conducted on a desktop system with an Intel(R) Xeon(R) Gold 6130 CPU and NVIDIA Tesla V100-32G GPU.
## 4 Experiments
### Comparison Results with Unsupervised Deep Learning Methods
#### 4.3.2 Quantitative Examinations  
We mainly choose fichet inception distance (FID) [40], LPIPS [41], structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) as the evaluation indicators in this paper to measure the quality of the generated color images. It is worth noting that FID and LPIPS show high correspondence with human visual perception. Lower FID and LPIPS mean generated images are more realistic. On the contrary, larger values of SSIM and PSNR indicate better image quality.  
Table 1 shows the evaluation metrics for the different comparison experiments. It can be observed that our method significantly outperforms the other baselines in the FID, LPIPS, SSIM, and PSNR metrics. The specific indicators strongly demonstrate the effectiveness of our proposed method.
## 2 Methods  
### Sampling Site  
HONO, PM2.5, and additional gasses were sampled from the second floor of the College of Engineering Building No. 3 at Mokpo National University, Korea. A duct extending from the second-floor laboratory to the rooftop facilitated the transport of air, driven by a blower, to the measurement devices; these devices accessed the air via an inlet located within the duct (Figure 1). The sampling was conducted from 11 December to 25 December 2023. In this investigation, PM2.5 was analyzed in 1 h intervals, while HONO and HNO3 were analyzed every 40 min and other gasses were measured every minute. Meteorological data were acquired from a meteorological station located approximately 10 km away from the sampling site through the Open MET Data Portal ([[https://data.kma.go.kr/](https://data.kma.go.kr/)]([https://data.kma.go.kr/](https://data.kma.go.kr/))).  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Technique** & **Reference** \\ \hline \multirow{6}{*}{Spectroscopic analytical methods} & TLAS & [14] \\  & MAX-DOAS [1] & [15] \\  & IBBCEAS [2] & [21] \\  & CRDS [3] & [22] \\  & LIF [4] & [23] \\  & TD-CL [5] & [24] \\  & CIMS [6] & [25] \\ \hline \multirow{6}{*}{Wet chemical analytical methods} & sampler & detector \\ \cline{2-3}  & mist-chamber & ion chromatograph & [26] \\ \cline{1-1}  & stripping coil & long path absorption photometer & [10; 12; 27] \\ \cline{1-1}  & stripping coil & ion chromatograph & [26] \\ \cline{1-1}  & MARGA & ion chromatograph & [28] \\ \cline{1-1}  & annular denuder system & ion chromatograph & [29] \\ \hline \end{tabular} \({}^{1}\) Multi-axis differential optical absorption spectroscopy; [2] incoherent broadband cavity-enhanced absorption spectroscopy; [3] cavity ring-down spectroscopy; [4] laser-induced fluorescence; [5] thermal dissociation and chemiluminescence; [6] chemical ionization mass spectrometry.  
\end{table}
Table 1: Summary of analytical HONO techniques classified by spectroscopic methods and wet chemical methods.
## 3 Results and Discussion
### Optimization by Changing the Cell's Layers' Thickness
When modeling with Atacama Desert irradiance spectrum and light striking from the back part, the highest short-circuit current density is obtained for \(150\times 10^{-4}\) cm, so this is the chosen value for Atacama spectrum. The fill factor and the energy conversion efficiency decrease as thickness increases. When the light strikes from the back part, \(\eta\) decreases faster. Figure 12 in Appendix A shows the modeling of the \(a-Si:H(i)\) intrinsic layer, which is located between the \(a-Si:H(n^{+})\) and the \(c-Si\) layers. Bifaciality comes closer to 1 for a lower thickness of this layer. The open-circuit voltage is constant for \((1-5)\) nm thickness when modeling is performed considering that the light strikes the front and the back parts. The short-circuit current density is constant if the light strikes from the front, but if the light strikes from the back side, \(J_{sc}\) tends to decrease as thickness increases. In addition, the fill factor and the energy conversion efficiency tend to decrease as the thickness increases when light impinges from the back side. Here, 5 nm thickness was chosen for the intrinsic layer of the back part for the two spectra. The \(a-Si:H(i)\) intrinsic layer, located between \(a-Si:H(p^{+})\) and \(c-Si\), is also modelled and shown in Figure 13 (see Appendix A). Here, \(J_{sc}\) tends to decrease slightly in the \((1-5)\) nm thickness range. The open-circuit voltage decreases in the studied range with a larger slope when the light strikes from the back part. The fill factor remains constant when the light strikes from the back and the front sides. Thus, the thickness of the intrinsic layer located between \(p\)-\(type\) and \(\mathrm{n}\)-\(type\) layers is fixed at 4 nm.
## 4 DT Use Cases and Application in Healthcare
### Medical Training  
Virtual reality (VR) has long been utilized in medical training, enhancing traditional educational methods. This technology has supported doctors in treating patients and surgeons in performing robotic surgeries. With the integration of artificial intelligence (AI) and digital twin (DT) technology, augmented and virtual reality can now better address the training and educational needs of healthcare professionals. For instance, several companies simulate medical anatomy and surgical procedures, fostering interactive learning while reducing the reliance on cadavers [67]. Additionally, DT solutions allow medical schools to teach surgical techniques within simulated environments [39].
## 5 Experiments and Analysis  
### Experiment Settings  
The experiments were conducted on a computational platform running Python 3.75 equipped with a 3.80 GHz CPU and 32 GB of RAM. The UAV simulation environment was configured within a three-dimensional space of \(200\times 200\times 100\) m. The total service period spanned 320 s, discretized into 40 time slots, each lasting 1 s. The UAVs were constrained to a maximum flight speed of 20 m/s. The transmission bandwidth was set to 1 MHz, with a channel gain of \(-50\) dB at the reference distance of 1 m. The uplink transmission energy of the mobile devices was fixed at 0.1 W, while the non-line-of-sight (NLoS) penetration loss was set to 20 dB. The noise energy at the receiver was \(-100\) dBm. Each UAV was equipped with a battery capacity of 500 kJ and a maximum CPU frequency of 1.2 GHz. The mobile devices operated at a CPU frequency of 0.6 GHz, with a computational requirement of 1000 CPU cycles per bit of data processed. The weight factors for delay and energy consumption were assigned values of 0.9 and 0.1, respectively. For the MATD3-TORA algorithm, the scaling factors for the state variables of individual UAVs were carefully determined. Specifically, the scaling factor for battery energy was set to the maximum battery capacity of the UAV. The scaling factors for UAV and mobile device positions were derived from the spatial limits of the ground environment, namely, its length and width. Additionally, the scaling factors for the total task volume and the task volumes of individual mobile devices were aligned with their respective upper bounds. The above parameters were rigorously selected based on well-established methodologies from the literature [42; 43; 44; 45]. All environmental simulation parameters and their corresponding mathematical notations have been systematically defined to ensure reproducibility and consistency with prior research.