esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
###### Abstract  
In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.  
Keywords: lossy compression; adaptive vector quantization; image sequences; data compression +
Footnote †: This paper is an extended version of our paper published in Data Compression Conference 2016, Communication Processing and Security 2016.
# A New Type Bionic Foldable Wing Design for High Maneuverable Unmanned Aerial Vehicle
## 4 Manufacture and Test Flight of Fold-Wing UAV
### Fabrication and Assembly  
In order to increase the bearing capacity, stiffeners were arranged in the main force part of the morphing mechanism. The middle fuselage and tail fuselage were also equipped with strengthening frames and reducing holes, respectively, to provide reliable mechanical properties while minimizing weight. After the assembly of the fuselage, the heat-shrinkable film skin was attached to the outside of the fuselage, which can enhance the overall strength of the fuselage while ensuring the aerodynamic performance of the fuselage. This UAV adopts a double vertical tail. Compared with the single vertical tail, a double vertical tail aircraft has a smaller height, which can reduce the rolling moment of the vertical tail during sideslip, and increase the stability and maneuverability of the aircraft. In addition, it can improve the heading stability of the aircraft under the condition of a high angle of attack.  
Wing skins need to be soft, load-carrying, and lightweight. We have considered polyester material, carbon fiber material, PVC material, etc. Finally, a 0.25 mm-thick PVC sheet was selected. Compared with a carbon fiber sheet, a PVC sheet can fit the fin better and has a lighter weight. Compared with polyester materials, PVC plates are not easy to fold and collapse. Moreover, it is difficult to deform under aerodynamic load. On the other hand, PVC material has a smooth appearance, small sliding friction, and will not lose aerodynamic performance.  
Ground experiments were carried out to verify the reliability of the movement form and structure of the morphing wing. During the extension and folding of the wings, the mechanism responded quickly and moved smoothly without generating noise. There was no interference between the ribs, and the bionic feathers folded without folding. After several tests, the folded structure and fuselage were not damaged, and the strength met the requirements. The full-spread state of the wing is shown in Figure 25a, and the folding state of the wing is shown in Figure 25b. The ground test performance during folding is shown in Table 3. The error between the test result and the design value was within the acceptable range.
# A Method for Singular Points Detection Based on Faster-RCNN
## 2 Proposed Method  
In this section, we present a fast and exact scheme for singular points detection based on Faster-RCNN by analyzing the differences between singular points and generic objects in details.  
Figure 1: Fingerprint singular points: (**a**) core points, (**b**) delta point.  
Figure 2 shows the overall flowchart of the proposed method: Generating proposals in pixel level (Step 1): a raw fingerprint image is input into the networks to extract features and the shared feature map is used to generate corresponding features of each grid cell to predict the probability whether there might exist a singular point or not and calculate coarse location information of the singular point through classification and regression tasks. Thus, proposals will be generated by ranking the probability and the coarse location information; Classifying the candidate patches generated by the proposals, refining the location and calculating the orientation information (Step 2): this is regarded as a singular point extractor, which is applied on all candidate patches to detect singular points. Sections 2.1 and 2.2 describe the two processes in detail.
# Contamination with Pharmaceuticals in Aquatic Environment: Focus on Analytical Methodologies
## 1 Introduction
Antiepileptic and psychiatric medications, such as pharmaceutical agents carbamazepine and fluoxetine, have been demonstrated to exert an influence on the behavior of aquatic species while also causing neurotoxicity [37]. The toxic effects of these pharmaceuticals are not limited to the effects of individual compounds [38]. The combined effects of pharmaceuticals with other environmental contaminants can be additive or synergistic, leading to an exacerbation of the harm they cause to aquatic life. Pharmaceuticals in the environment rarely exist in isolation. Instead, they form complex mixtures, which include active parent compounds, metabolites, and degradation products. These mixtures pose a greater risk than individual compounds, as they may interact with one another, leading to enhanced toxicity. Consequently, ecotoxicological studies are increasingly focusing on these combined effects, though assessing and managing such risks remains challenging [38]. The presence of pharmaceutical mixtures in aquatic environments gives rise to a number of significant regulatory and treatment challenges. The current wastewater treatment plants are not equipped to fully remove these complex compounds, which have the potential to exert long-lasting effects on ecosystems and human health [3; 6]. This manuscript aims to raise awareness about pharmaceuticals' widespread presence and risks in the environment, emphasizing the need for improved monitoring, detection, and extraction methods for these contaminants in water samples. One of the main focuses of this manuscript is to address the challenges and complexities involved in accurately identifying and quantifying pharmaceuticals in environmental waters. Therefore, this manuscript highlights the importance of developing advanced analytical techniques, including optimized extraction modes, preconcentration procedures, and more sensitive detection methods such as high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and solid-phase extraction (SPE). These techniques are essential for identifying pharmaceuticals at low concentrations and ensuring that their presence and risks are accurately assessed.
## 3 Engineering Overview and Model Establishment
### Calculation Model and Its Parameters of Dam Structure with Hydraulic Fracturing  
#### 3.4.1 Failure Criterion  
Crack propagation is determined based on the traction separation criterion [23] of the cohesive zone model in damage mechanics. When the crack begins to pass through the element at the crack tip after reaching the failure condition, a cohesive crack is formed. As the load increases, the stiffness of the crack tip element decreases continuously, with damage value gradually accumulating. After the damage value of the crack tip element reaches the critical value, the cohesive cracks begin to expand into the macroscopic cracks, as shown in Figure 7. The maximum principal stress criterion (Equation (11)) is selected to determine whether the damage of the element has begun, and it is considered that the compressive stress will not cause damage of the element. Weather the crack has been formed is determined with failure displacement, with the crack propagation direction orthogonal to the maximum principal stress direction. The tensile strength of the material is used as the maximum principal stress parameter of the model. The failure displacement can be calculated as the product of the plastic strain in the material stress-strain curve and the element length in the finite element. Taking the parameter mat-5 as an example, the tensile strength is 0.86 MPa, the plastic strain is 0.0026, and the element length is 1 m; as a result, the maximum principal stress is 0.86 MPa, and the failure displacement is 0.0026 m. Based on [PERSON]'s model, the hydraulic fracturing of the dam body is carried out by adding the traction separation criterion mentioned above.  
\[f=\left\{\frac{<\sigma_{\max}>}{\sigma_{\max}^{0}}\right\},<\sigma_{\max}>= \left\{\begin{array}{c}0,\sigma_{\max}<0\\ \sigma_{\max},\sigma_{\max}>0\end{array}\right\} \tag{11}\]
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Compression Ratio  
The first set of results we display in this section is compression ratios acquired by comparing compressed files' data sizes with uncompressed raw files' sizes. The results can be seen in Table 2 as sizes in Megabytes, and in Figure 5, grouped by individual benchmarks with each bar, but the black one, representing one compression method. Blue-shaded bars represent CBPC and CBPC2 compressions, while the grey-shaded bar represents the ZIP compression ratio. The black bar is fixed to 1 representing the raw and uncompressed image size. Both CBPC and CBPC2 compression methods significantly reduce uncompressed images, with compression levels ranging from 22% and 25% for the X-ray benchmark to 47% and 50% for CT benchmarks. When assessing compression ratios of CBPC and CBPC2 compression methods with respect to ZIP compression, results vary between more considerable reductions for Angio, X-ray, and MR-1 benchmarks and relatively minor reductions for CT and MR-2 benchmarks. When assessing the benefits of improvements of the CBPC2 compression method concerning CBPC, every benchmark shows an improvement of at least 2 percentage points, with MR-2 being an example of an improvement of 6 percentage points.
## 3 Electron Spin-Dependent Processes in Si Nanocrystals
### Si NPs with Electron Spin Centers as Potential Contrast Agents for \({}^{1}h\) Mri
The experiments were carried out with grafted cancer tumor (lung carcinoma 3 LL). In vivo MRI visualization of mice with tumor was carried out by using a protocol, which included the following steps: (i) obtaining whole body PD-weighted MRI before injection the NP suspension; (ii) obtaining whole body PD-weighted MRI 20 min after intratumorally injection of 0.5 mL suspension of Si NPs with concentration of 0.5 mg/mL. It was found that tumor area with injected NPs became darker and have more clear boundaries (see Figure 41). The MRI study of the mice for the time period of 24 h after injection of Si NPs showed a decrease of the MRI contrast. This fact can be explained by biodegradation of Si NPs similarly to that of pure Si NPs [101]. Moreover, aqueous suspensions of Si NPs were explored as efficient sensitizers of electromagnetic radiofrequency (RF) hyperthermia, which is a prospective approach for mild cancer therapy with non-magnetic Si NPs. In Reference [101] the stronger heating rate was reported for iron-doped Si NPs with hydrodynamic size-50 nm (see Table 2). The obtained results indicate possible ways for applications of Si-based NPs in the MRI diagnostics and RF therapy of cancer, that is, theranostics. Additional promises in biomedical applications of Si NPs can arise from multinuclear MRI diagnostics by using both the standard \({}^{1}\)H mode and hyperpolarized \({}^{29}\)Si NPs/MPs (see Section 2) as well as other NPs with conventional biomedical nuclei, for example, \({}^{13}\)C, \({}^{19}\)F and so forth (see, for example, Reference [104]). Besides the MRI diagnostic modality, it was recently demonstrated the possibility to use Si NPs with iron impurities to suppress the cancer tumor growth in vivo [103]. The intratumoral injection and intravenous administration of iron-doped Si NPs resulted in a decrease of the tumor volume by 32% and 23%, respectively. While the intratumoral injection induces the stronger antitumor effect, the observed decrease in tumor volume after intravenous administration confirms accumulation of Si NPs in the tumor due to as-called enhanced permeability and retention effect, which is well known for various NPs [105]. It was found that the antitumor effect of iron-doped Si NPs correlate with lifetime  
Figure 40: EPR spectra of Si NPs with different iron content increasing as 0.004, 0.02 and 0.2 at. % for curves 1, 2 and 3, correspondingly. (Reprinted from Reference [101] by permission from John Wiley and Sons © 2019). of laboratory animals [103]. Figure 42 shows temporal dependences of the number of mice with cancer tumor after intravenous and intratumoral administration of NPs. The average lifetime of laboratory animals of the experimental group was about 30% longer than for the control one. The observed antitumor effect of iron-doped Si NPs can be related to dissolution of Si NPs accumulated in tumor followed with release of iron ions, which induce reactive oxygen species with high toxicity for cancer cells [103].
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Stair Removal  
In the stair removal phase, sets of features with high disturbances in the pressure readings are rejected, as they mostly correspond either to vertical transitions (e.g., stairs or elevators) or to outliers (e.g., high wind velocities). Such features of high disturbance are identified using the moving window standard deviation.  
This approach is equivalent to  
\[\sigma=\sqrt{\kappa}\]  
where  
\[\kappa=\sigma^{2}=\frac{1}{N-1}\left(q-\frac{s^{2}}{N}\right)\]  
with  
\[q=\sum_{i=1}^{N}x_{i}^{2}\qquad\text{and}\qquad s=\sum_{i=1}^{N}x_{i}\]  
where \(x_{i}\) is the instance of the input signal and \(N\) is the number of elements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Proximity** & **Light** & **GPS** & **Magnetic** & **Indoor** & **Outdoor** & **Fusion Model** \\ \hline False & 0 & 0 & 0 & F & T & Voting \\ False & 0 & 0 & 1 & F & T & Voting \\ False & 0 & 1 & 0 & F & T & Voting \\ False & 0 & 1 & 1 & T & F & Voting \\ False & 1 & 0 & 0 & F & T & Voting \\ False & 1 & 0 & 1 & T & F & Voting \\ False & 1 & 1 & 0 & T & F & Voting \\ False & 1 & 1 & 1 & T & F & Voting \\ True & — & 0 & 0 & F & T & _and_ \\ True & — & 0 & 1 & F & T & _and_ \\ True & — & 1 & 0 & F & T & _and_ \\ True & — & 1 & 1 & T & F & _and_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fusion Rules.  
Figure 10: Magnetometer signal from walking into four consecutive buildings and corresponding smoothed moving STD of the disturbance, with kernel size of 200 samples, and the final binary classification.
## 4 Ground Evaluation Experiment  
### Experimental Setup  
Using the sensitive structure and torsion pendulum test facility designed in Section 3, the performance of the IS is experimentally verified and the system resolution is evaluated. The verification includes the control ability test and the system resolution estimate. The experiments are performed in a laboratory of CIOMP, Changchun City, Jilin, China. This laboratory has a Class 1000 cleanliness, a temperature control level of \(\pm\)0.1 \({}^{\circ}\)C, and a two-stage vibration isolation system based on an isolation foundation and an air floating platform. In addition, quiet nights are chosen for experiments to further minimize the influence of the environmental noise in the system measurement. The sensitive structure and the FEE are connected through a coaxial cable, the FEE structure and the torsion pendulum must be grounded as well. The system is shown in Figure 10.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
###### Abstract  
Relectance coil guns are electromagnetic launchers having a good ratio of energy transmitted to actuator volume, making them a good choice for propelling objects with a limited actuator space. In this paper, we focus on an application, which is launching real size soccer balls with a size constrained robot. As the size of the actuator cannot be increased, kicking strength can only be improved by enhancing electrical to mechanical energy conversion, compared to existing systems. For this, we propose to modify its inner structure, splitting the coil and the energy storage capacitor into several ones, and triggering the coils successively for propagating the magnetic force in order to improve efficiency. This article first presents a model of reluctance electromagnetic coil guns using a coupled electromagnetic, electrical and mechanical models. Four different coil gun structures are then simulated, concluding that splitting the kicking coil into two half size ones is the best trade-off for optimizing energy transfer, while maintaining an acceptable system complexity and controllability. This optimization results in robust enhancement and leads to an increase by 104% of the energy conversion compared to a reference launcher used. This result has been validated experimentally on our RoboCup robots. This paper also proves that splitting the coil into a higher number of coils is not an interesting trade-off. Beyond results on the chosen case study, this paper presents an optimization technique based on mixed mechanic, electric and electromagnetic modelling that can be applied to any reluctance coil gun.  
coil gun; reluctance; electromagnetic launcher; mechatronics; electronics; mechanics; simulation; RoboCup
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 2 Database Collection
### Statistical Details of the Dataset  
As stated earlier, the amount of the heating load was applied as the main target of the energy-efficient buildings, while the main influential parameters were roof area, wall area, relative compactness, surface area, overall height, glazing area, glazing area distribution, and orientation. The statistical explanation of energy-efficient residential buildings including conditional variables is tabulated in Table 1. In addition, Figure 2 shows the variables of relative compactness, wall area, surface area, overall height, roof area, glazing area, orientation (i.e., north, northeast, east, southeast, southwest, west, northwest), heating load, and glazing area distribution on the x-axis, against a heating load (Figure 3) on the y-axis.  
Figure 1: Graphical view of data preparation.  
Figure 2: Schematic view of some of the input data layers (X\({}_{1}\)–X\({}_{8}\) as shown in Table 1) in predicting heating load. (**a**) X\({}_{1}\) (Relative Compactness); (**b**) X\({}_{2}\) (Surface Area); (**c**) X\({}_{3}\) (Wall Area); (**d**) X\({}_{4}\) (Roof Area); (**e**) X\({}_{5}\) (Overall Height); (**f**) X\({}_{6}\) (Orientation); (**g**) X\({}_{7}\) (Glazing Area); (**h**) X\({}_{8}\) (Glazing Area Distribution).
## 7 Conclusions and Discussions
### Discussions  
To resolve the shortcomings of the classic APF method and the classic RRT algorithm, this paper proposes an improved path planning method that combines the APF method and the RRT algorithm for the path planning of the manipulator. First, the distance between the obstacle and the nearest node was evaluated. Through the distance value, the I-APF and I-RRT algorithms were used to explore the path. This simultaneously solved the shortcoming that the APF method cannot reach the target point when there are obstacles at the target point and made full use of the efficient obstacle avoidance ability of the RRT algorithm. The two algorithms alternated exploring the path until the target point was reached. The I-APF method introduced the heuristic method of breaking away from the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RRT** & **RRT\({}^{*}\)** & **P\_RRT\({}^{*}\)** & **APF-RRT** \\ \hline Search time & 19.1 & 9.3 & 6.2 & 3.0 \\ Search success rate & 90\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Search time and search success rate of different algorithms.  
Figure 14: The position change of each joint of the robotic arm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Joint 0** & **Joint 1** & **Joint 2** & **Joint 3** & **Joint 4** & **Joint 5** \\ \hline The starting position & 0.00112 & 0.00322 & \(-0.00114\) & \(5.793\times 10^{-5}\) & \(4.422\times 10^{-6}\) & \(5.521\times 10^{-6}\) \\ The ending position & \(-1.42552\) & \(-0.67803\) & \(-0.27888\) & \(4.10375\) & \(-0.70497\) & \(-3.14746\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initiation and termination of the robotic arm joint.  
local minimum based on the number of adjacent obstacles to solve the local minimum problem in the algorithm. The I-RRT algorithm included the triangular nearest neighbor node selection strategy, which effectively improved the obstacle avoidance ability and efficiency of the algorithm. In the same obstacle environment, compared with the classic RRT algorithm, the search time of the improved APF and RRT hybrid algorithm was reduced by 92.5%, the number of sampling nodes was reduced by 87.9%, and the path length was reduced by 23.4%. Compared with the RRT* algorithm, the search time of the APF and RRT hybrid algorithm was reduced by 72.2%, the number of sampling nodes was reduced by 47.4%, and the path length was reduced by 17.4%. Compared with the P_RRT* algorithm, the search time of the APF and RRT hybrid algorithm was reduced by 62.1%, the number of sampling nodes was reduced by 45.2%, and the path length was reduced by 13.0%. With an increase in the number of obstacles, the improved hybrid algorithm also showed the excellent effect of steady increases in the search time, number of sampling nodes, and path length.
## 3 System Model and Problem Formulation
### Energy Consumption Model  
In NR-V2X, when vehicle \(i\) reselects resources, the energy consumption for the previously reserved resources is given by the following:  
\[E_{i}^{t}=p_{i}^{t}l_{i}^{t}RC_{i}^{0}. \tag{13}\]  
\(p_{i}^{t}l_{i}\) represents the energy consumption generated by using reserved resources at a time, and \(l_{i}\) represents the time when vehicle \(i\) utilizes resources, which is the size of one time slot. As shown in Equation (2), when the transmission interval is smaller, \(RC_{i}^{0}\) is larger, and the energy consumption during this time period is greater, indicating a trade-off between energy consumption and AoI.  
The average energy consumption of all vehicles in the system is defined as  
\[\tilde{E}=\frac{1}{T}\frac{1}{N_{v}}\sum_{i\in\mathcal{T}}\sum_{i\in N_{v}}E_{ i}^{t}. \tag{14}\]
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
###### Abstract  
Aluminum foam materials have gained significant attention over the past decade, particularly in the automotive industry, due to their excellent stiffness-to-weight ratio and superior energy absorption capabilities. In this study, a multiscale numerical material model was developed to accurately and efficiently simulate the vibrational behavior of aluminum foams. The foam specimens were categorized into four density classes based on their measured mass and calculated volume. Two specimens were selected to conduct CT (computerized tomography) scans and quantify the volume of air in their density class. Based on the CT measurements, a representative volume element (RVE) was built using ANSYS Material Designer (MD). The newly obtained material was employed in conducting normal mode numerical simulations. The resonance frequencies and response amplitudes were compared with physical experiments and showed correlation within 3%. These findings underscore the efficacy of using CT scans in foam to develop material models and accurately predict structural behavior. By conducting comprehensive investigations and numerical simulations, we established a correlation between physical tests and simulation results, highlighting the reliability of the developed models.  
aluminum foams; computerized tomography scan; resonance tests; multiscale modeling; numerical simulations Article