esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression of Next-Generation Sequencing Data and of DNA Digital Files +
## 2 Ngs
### Compression of Next-Generation Sequencing Data  
Large-scale sequencing of DNA samples has brought new interest and it has produced new research in genomic data. The huge increase in the amount of genomic data that is communicated over the internet today has made the study of data compression algorithms specifically targeted to this new type of data necessary.  
The NGS workflow we have seen in Section 2.1 implies a pipeline: first of all, sequencing produces a FASTQ file with the readings of the genome. This file is then the input of an alignment program that produces a SAM (or a BAM) file that is subsequently analyzed by a variant caller that stores all the variants in a VCF file.  
Potentially, this VCF file has now all the information that could make a reconstruction of the genome possible.  
Once all these pipeline steps are completed, we might theoretically store only the VCF file that contains all the information that the actual state-of-the-art knowledge on genetic data about the sequenced genome considers relevant and not the previous and much heavier FASTQ and SAM files.  
Generally, this is not a good idea. For example, the alignment and the variant calling tools could be amended over time and the knowledge of what is relevant in genetic data might change after new researches. Re-analyzing the raw data (FASTQ) in the future might permit the finding of new variants.  
For the implementation of specific genomic compression tools, two approaches are commonly used: (i) compression of raw NGS data (i.e., FASTQ and SAM files) and (ii) compression of assembled genomes.  
The compression of the raw sequencing data, i.e., the content of the FASTQ and SAM files, is mainly devoted to the compression of identifiers, readings, and quality scores. It has been proved that compressing the quality scores is a more difficult task than compressing the readings, due to the higher entropy rate and due to the use of a much larger alphabet. If compressed in a lossless way, the quality score size can be up to 70% smaller than in the original file [10].
# A Hierarchical Coordinative Control Strategy for Solid State Transformer Based DC Microgrids
## 5 Conclusions  
In this paper, the topology of the SST is improved, and a hierarchical coordinative control strategy for an SST integrated DC microgrid system is proposed to solve the dynamic voltage quality problem of SST DC ports caused by the random and intermittent characteristics of distributed generators. Through mathematical analysis and simulation, conclusions can be drawn as follows:  
1. At present, the main factors affecting the practical application of SSTs are high cost and low energy transmission efficiency. The adopted input-stage MMC instead of cascaded H-bridge can significantly reduce the required number of power electronic switches and high-frequency transformers in the SST, thus reducing the system cost. As for the isolation stage that accounts primarily for SST power losses, the efficiency of the CLLC resonant converter adopted in this paper can reach above 98% at rated power, which is greatly improved, compared with the DAB converter, whose efficiency is only about 90%.
2. Under the independent control, the power response speed of the SST is slow, and the output fluctuation of the microgrid cannot be compensated quickly and timely, bringing serious dynamic voltage stability problems for the DC link voltages. The hierarchical coordinative control strategy proposed in this paper can not only improve the external power characteristics of the DC microgrid, but also realize a coordinated action between the input stage and isolation stage of the SST, which can greatly suppress the voltage fluctuation of the HVDC and LVDC bus.
3. The coordinative control strategy is not only applicable to the microgrid output fluctuation situation, but also effective to other transient conditions, such as the removal of the microgrid under fault, and the island switching process.  
**Author Contributions**: conceptualization: [PERSON] and [PERSON]; methodology: [PERSON] and [PERSON]; software: [PERSON], [PERSON], and C.Y.; validation: [PERSON]; formal analysis: [PERSON]; writing--original draft preparation: [PERSON], [PERSON], and [PERSON]; writing--review and editing: [PERSON], [PERSON], and [PERSON]; visualization: [PERSON] and [PERSON]; supervision: [PERSON]; project administration: [PERSON]; funding acquisition: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Research and Development Program of Shaanxi Province, grant number 2019 ZDLGY18-06.  
**Conflicts of Interest**: The authors declare that there are no conflicts of interest.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 3 Results
### Cost-Benefit Ratio (CBR)  
The benefit-cost ratio (BCR) evaluates a project's feasibility by comparing its benefits and costs. Simply put, CBR is all about considering whether a particular project is worth your time and money by looking at its cost and the benefits you will receive from such a project. A CBR greater than one indicates that the benefits exceed the costs, suggesting the project is worthwhile. A CBR equal to one means that the benefits and costs are equal. Conversely, a CBR less than one suggests that the project's costs outweigh its benefits.  
\[CBR=\frac{Total\ Benefit\ over\ 25\ years}{Total\ cost=(capital\ investment+Operating\ cost\ over\ 25\ years)}\]  
\[CBR=1.96\]  
The project's CBR exceeds one, suggesting that investing in the hydrogen project in Nigeria is highly economical. Not only will it generate revenue for the government, but it will also provide an alternative to gas flaring by directing unused gas toward hydrogen production.
## 5 Experiment
### Experiment Setting
#### 5.1.2 Experimental Environment  
We used the same hyperparameters as those provided by the authors in [14; 15] to ensure fair comparisons. The model is trained for 100 epochs using mini-batches of size 32. The initial learning rate is set to 0.01 and reduced by a factor of 0.1 at the 50 th and 75 th epochs. We used the Adam optimizer [48] for training. Each convolution layer has 32 channels. We set \(p\) and \(q\) to 3 for inputs of the model, which means that the model takes the daily data from past 3 h and daily data from past 3 days to forecast traffic [14]. We downscaled the key, query, and value tensors in non-local attention by a factor of 4 using the softmax function and 2 using the Gaussian kernel to reduce memory usage. We used 3 models for performance comparison, STDenseNet as baseline model, and HSTNet and Multi-view Spatial-Temporal Graph Network (MVSTGN). And we set the values of the cross-domain dataset to 0 to ensure fair comparison. We incorporated attention modules in the attention layer described in Section 4.3. Experiments were conducted using an RTX 3090 GPU, and the code was implemented in PyTorch 2.0.1 [49].  
For evaluation, we used Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE) as the performance metrics. RMSE is a metric that represents the square root of the average squared difference between the actual values and predicted values. Meanwhile, MAE represents the average absolute value of the difference between them. And SMAPE represents the relative error between actual value and predicted value, ranging from 0% to 200%. A lower value indicates a better prediction performance. When large errors occur, RMSE metric increases significantly. Therefore, we used RMSE as the evaluation metric due to its sensitivity to large errors, which can occur in unexpected situations such as traffic surges. And we evaluated with MAE and SMAPE to see how well the model captures absolute and relative errors. The metrics can be expressed as  
\[RMSE=\sqrt{\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}\Bigl{(}y_{(i,j)}- \hat{y}_{(i,j)}\Bigr{)}^{2}}, \tag{10}\]  
\[MAE=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}\Bigl{|}y_{(i,j)}-\hat{y}_{ (i,j)}\Bigr{|}, \tag{11}\]  
\[SMAPE=\frac{100\%}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}\frac{|y_{(i,j)}- \hat{y}_{(i,j)}|}{(|y_{(i,j)}|+|\hat{y}_{(i,j)}|)/2}, \tag{12}\]  
where \(y_{(i,j)}\) denotes the pixel of ground truth located at \((i,j)\), and \(\hat{y}_{(i,j)}\) denotes the pixel of model's predicted value located at \((i,j)\). Both \(y\) and \(\hat{y}\) have a width \(W\) and height \(H\). Each experiment was conducted 100 times with different random seeds, and the final results are presented as the average metrics across all runs. For the Trentino dataset, the experiments were conducted 10 times.
## Results and Discussions
### Characteristics of Extracted Product  
#### 3.7.1 XRD Diffraction Analysis  
The XRD patterns of the product (RFTW) obtained under the optimal process conditions and fresh FTWR were showed in Figure 10. The characteristic peaks of the XRD pattern of Fresh FTWR are not obvious, and the intensity was weak, which may be on account of the low crystallinity of fresh FTWR. Obvious characteristic peaks could be seen in the XRD pattern of RFTW after extraction. The XRD pattern of the RFTW showed that the diffraction peaks at 21.6\({}^{\circ}\), 23.9\({}^{\circ}\), 30.0\({}^{\circ}\), and 36.1\({}^{\circ}\) correspond to (110), (200), (210), and (020) crystal planes, respectively. The untreated FTWR has no obvious peaks. The PDF card of (CH\({}_{2}\))\({}_{\rm x}\) has the highest matching degree. Apparently, RFTW contains amounts of olefins. However, taking into account the complex composition of the system, one could assume that this is a mixture of crystalline and amorphous phases.
# Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton
## 2 Materials
### Problem Analysis and Requirements
#### 2.2.1 Robotic Manipulator  
The robot serves as the motion generator responsible for replicating human-like behavior, necessitating the consideration of various aspects. Primarily, the robot must generate trajectories aligning with the desired use cases, imposing requirements on positioning, velocity, and acceleration. Whether the robot is directly connected to the exoskeleton or indirectly via other objects, its workspace must enable trajectory following while avoiding singularities and collisions.  
The dynamics of the manipulator introduce additional requirements. Firstly, robot motors must generate the desired accelerations while compensating for forces from the exoskeleton, as well as counteracting gravity and inertia forces from potential tools. Secondly, for contact tasks, the robot must create desired contact wrenches while interacting with and moving the exoskeleton. From a control perspective, dynamics are intriguing. Research indicates that humans can adjust their endpoint stiffness under externally applied forces [37; 38] and even learn models of unstable dynamics [39; 40]. To realistically capture human dynamics, a torque-controllable robot is a natural choice.
###### Abstract  
The goal of the study presented in this paper is to evaluate the performance of a proposed transmission scheme in multiuser multiple-input multiple-output (MIMO) configurations, via code reuse. Hence, non-orthogonal multiple access (NOMA) is performed. To this end, a correlation matrix of the received data is constructed at the transmitter, with feedback as only the primary eigenvector of the equivalent channel matrix, which is derived after principal component analysis (PCA) at the receiver. Afterwards, users experiencing improved channel quality (i.e., diagonal terms of the correlation matrix) along with reduced multiple access interference (i.e., the inner product of transmission vectors) are the potential candidates for their assigned code to be reused. As the results indicate, considering various MIMO configurations, the proposed approach can achieve almost 33% code assignment gain (CAG), when successive interference cancellation (SIC) is employed in mobile receivers. However, even in the absence of SIC, CAG is still maintained with a tolerable average bit error rate (BER) degradation.  
NOMA; PCA; MIMO systems +
Footnote †: journal: Accepted: 13 August 2020; Published: 18 August 2020
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 2 Suspension Systems
### Types of the Suspension Systems
This design addresses the structural complexities, energy consumption, costs, and reliability issues associated with active suspensions, while achieving damping effects comparable to active suspensions. Consequently, it facilitates the expansion of controllable suspension systems from exclusive use in high-end vehicle models to broader applications across a wider range of vehicles. Common types of semi-active suspensions currently include magnetorheological (MR) suspensions, electromagnetic valve suspensions, and air suspensions. In summary, semi-active suspension systems exhibit superior damping effectiveness compared to passive suspensions, featuring faster response speed and enhanced adaptability to varying road conditions. When appropriate control strategies are implemented, semi-active suspensions achieve damping performance comparable to active suspensions, alongside benefits such as simpler structure and reduced energy consumption. Considering cost constraints and development expenses, semi-active suspensions combine the reliability of passive suspensions with well-balanced handling stability and ride comfort, thus presenting promising prospects. Researchers have extensively researched semi-active suspensions, thus establishing them as the most widely deployed controllable suspension system in today's market. As shown in Table 1, the performance advantages, disadvantages, and application ranges of the three types of suspensions mentioned above are listed. The table provides a clear illustration of the distinct strengths and weaknesses of passive and active suspensions. In contrast, semi-active suspensions integrate the structural simplicity of passive suspensions with the variable damping capability of active suspensions.
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 3 Antenna Radiation Properties
### CP Performance  
The axial ratio (AR) is defined as the ratio between the major to the minor axes of the polarization ellipse in the range of \(1\leq\text{AR}\leq\infty\). CP performance is achieved along the _xz_- or _yz_-planes in unidirectional crossed dipole antennas [35; 36; 37; 38]. As shown in Figure 10, where the spherical contour of the AR is plotted at 2.0, 2.6, 3.2, and 3.6 GHz within the operating frequency, the polarization is linear with symmetry in the _xy_-plane and shows CP nearly at the _xz_-plane at 2.0 and 2.6 GHz, and the spatial coverage where AR is less than 3 dB is wider by 40% and 44.8% at the higher frequencies of 3.2 and 3.6 GHz, respectively. Note that, at even higher frequencies of 3.8 and 4.0 GHz, where the gain deviations are slightly higher than 10 dB at 11.7 and 12.8 dB, the 3 dB coverage is 43.3% and 40.7%, respectively. The AR value of the region with less than 3 dB is highlighted with the dashed line in the figures.  
When compared with the quasi-isotropic antenna in [2], which is composed of electric and magnetic dipoles, and where an AR of less than 3 dB is observed only at a few spots, a wider spatial CP coverage is achieved at the proposed antenna. The CP is created when the linear polarized signals in the same strength are in a 90\({}^{\circ}\) phase difference. The desired phase difference is obtained from the vacant ring, and not only the two linear signal paths with an original crossed dipole, as shown in Figure 4a, but also several linear signal paths can be added from the forked-shape arms of the proposed design, as shown in Figure 4c. This also explains the wider AR spatial coverages at 3.2 GHz and 3.6 GHz in Figure 10, where more signal paths can be formed on the electrically larger dimension at higher frequencies. The CP performance might be further improved by adjusting the size, number,  
Figure 10: Simulation results of CP performance: (**a**) 2.0 GHz, (**b**) 2.6 GHz, (**c**) 3.2 GHz, and (**d**) 3.6 GHz.  
and angle of the fork-shaped arms. The proposed antenna shows a better CP performance compared with the other quasi-isotropic antenna designs, such as the electric and magnetic dipole combinations [2] and the folded electric dipoles [26]. In [29], the quasi-isotropic antenna for AoP applications provides radiation with a spatial CP coverage performance of 85%, which is wider than that of the proposed antenna. However, such a feature occurs only at a single resonant frequency.
## 4 Simulation Analysis
### Simulation and Result Analysis  
#### 4.2.1 Standard INS+LEO2-Satellite Integrated Navigation Algorithm  
The research object of the experiment is the aerial vehicle, and the navigation satellite system is the SpaceX satellite system in the LEO constellations. The research scenario is through two satellites + INS in SpaceX combined with the integrated navigation filtering method, completing a three-dimensional position calculation of the carrier. The simulation result from the standard INS+LEO2-satellite integrated navigation and positioning is shown in Figure 3 (in the figure, EPE (East position error, EPE), EVE (East velocity error, EVE) and AEE (Attitude error of east, AEE), respectively represent the position error, velocity error and attitude error in the east direction, while the other parameters can be deduced by analogy).  
From Figure 3 we can see that the standard INS+LEO2-satellite integrated navigation positioning can overcome the INS position divergence and velocity errors, and the final navigation trajectory coincides with the flight trajectory of the carrier. Although the velocity and attitude errors fluctuate, the actual navigation and positioning meet basic location requirements.  
Figure 3: Standard INS+LEO2-satellite integrated navigation positioning results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Orbital Parameter Type** & **Value** & **Error Parameter Type** & **Value** \\ \hline Signal spatial error (m) & 0.01 & \begin{tabular}{c} Range rate tracking error \\ (m/s) \\ \end{tabular} & 0.002 \\ Signal ionospheric delay (m) & 0.02 & \
\begin{tabular}{c} Distance noise (m) \\ \end{tabular} & 0.5 \\ Signal tropospheric delay (m) & 0.002 &
\begin{tabular}{c} Range rate noise (m/s) \\ \end{tabular} & 0.05 \\ Code tracking error (m) & 0.1 & MSR & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environmental parameters.
## 5 Performance Analysis
### Validations of Unknown Detection Probability
\begin{table}
\begin{tabular}{c c c c} \hline
**Target Index** & **Initialization Targets States** & **Birth Time (s)** & **Death Time (s)** \\ \hline Target 1 & \([-310,-280,5.23,4.12,0]\) & 1 & 100 \\ Target 2 & \([-300,480,4.12,-3.23,-2.78]\) & 20 & 100 \\ Target 3 & \([-410,470,3.45,-4.32,0.41]\) & 58 & 100 \\ Target 4 & \([0,0,27.44,-4.54,-0.56]\) & 15 & 78 \\ Target 5 & \([-310,-280,3.65,3.56,0.61]\) & 37 & 90 \\ Target 6 & \([-390,110,5.32,-2.35,-2.21]\) & 1 & 50 \\ Target 7 & \([20,350,4.23,-4.12,3.02]\) & 45 & 80 \\ Target 8 & \([0,-490,3.23,5.23,-0.34]\) & 67 & 92 \\ Target 9 & \([-388,108,3.54,3.37,0.43]\) & 1 & 100 \\ Target 10 & \([-388,108,1.32,0.32,0.75]\) & 1 & 30 \\ Target 11 & \([-19,22,2.12,-4.72,-4.27]\) & 67 & 100 \\ Target 12 & \([202,0,-0.37,-1.53,-0.23]\) & 45 & 90 \\ \hline \end{tabular}
\end{table} 
Table 1: The initialization of true targets, along with their birth time and death time. The GOSPA distance [36], including the location error (LE) for detected targets, the missed targets error (ME), and false targets error (FE), is shown in Figure 7 for unknown detection probability. It can be noted that the GOSPA, LE, ME, and FE for known detection probabilities (\(p_{D}=0.7\), \(p_{D}=0.9\), or \(p_{D}=0.96\)) are not presented in this section. There were abrupt changes for LE, ME, and FE, as shown in Figure 7, at the change junctions of the detection probability, e.g., at 17-18 s, 37-38 s, 48-49 s, 71-72 s, and 82-83 s. The GCI-IGGM-PMBM filter presented relative superiority over other filters except for the BGGIW-PMBM filter at lower detection probability. Energy consumption is an important metric of multitarget tracking schemes for energy and computation-limited sensor networks, especially for our aquaculture sensor networks. Energy consumption comparisons for these tracking schemes are demonstrated in Figure 8 with unknown detection probability. The energy cost was similar at the stable stage for  
Figure 6: Performance comparisons of GCI-IGGM–PMBM with other multitarget tracking schemes with unknown detection probability: (**a**) detection probability estimation; (**b**) OSPA estimation; (**c**) cardinality estimation. Figure 7: Performance comparisons of GCI-IGGM–PMBM with other multitarget tracking schemes with unknown detection probability: (**a**) GOSPA; (**b**) LE; (**c**) ME; (**d**) FE. each stable detection probability, whereas it changed sharply at the change junctions of detection probability. The anchor rate plays an important role in energy consumption, as more anchors bring about more energy efficiency, because more information is processed more quickly by more anchors, and the initialized positioning stage originating from more anchors is more accurate.
## 3 Experiments and Results Analysis
### Experimental Setup  
The proposed network was run on the following hardware: CPU Intel 13700 KF; GPU NVIDIA Geforce RTX 4070 ti Super 16 GB; Memory DDR5 5600 MHz 32 GB\(\times\)4; SSD PCIe Gen 4.0 2 TB. The network was deployed and executed on the PyTorch 1.10.0 framework. For optimization, the network used L1 loss and the Adam [27] algorithm, with the first-order and second-order moment exponential decay rates set to \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\), and the numerical stability constant set to \(\varepsilon=10^{-8}\). A cosine annealing learning rate schedule was used to accelerate convergence, with the momentum set to 0.9. The initial maximum learning rate was set to \(10^{-3}\), the minimum learning rate to \(10^{-7}\), and the cosine cycle to \(2.5\times 10^{5}\) iterations. The number of Feature Extraction Parts was 3, with the number of PCCSTBs in each FE part being 4, 2, and 1, respectively. The feature channels, attention heads, and MLP expansion factor were set to 60, 6, and 2, respectively.