esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 5 Grasping Stage
### Problem Statement  
Since the initial position of the object is practically known, the first thing that the gripper must do is to find and center the object. Three cases could be present: (i) the initial position of the object is in the center between the fingers; the (ii) the initial position of the object is near to the left finger; (iii) the initial position of the object is near to the right finger (Figure 4).  
Then, before the gripper fingers can apply force to the object, it must be centered. If not, the differences in torques while exerting force with unbalanced fingers position can: damage the object or the motors, or make the object slide. Since, at the beginning, none of the force sensors are in contact, it was decided to divide the control system into two different objectives. The former, solving angular positions of the fingers about the object (angular position), and the latter, taking into account the force sensors when they are active.  
Figure 3: Communication structure of the gripper in the ROS. Nodes at the left are connected directly to hardware, whereas the node in the right (the controller) only communicates with hardware through messages sent to the public memory of ROS (at the center of the figure).  
To deal with these two objectives, it was decided to design a PID position controller and a PID force controller, commuting between them the issues regarding position errors. Once we designed and applied those controllers, and considering the global task of firm and safely handling, the performances of these controllers were not very satisfying. Thus, to compare results, but above all, to increase performance, it was decided to design two fuzzy controllers (one Mamdani type and another type-II). Their advantage over PID controllers is that it is possible to have a different output for each fuzzy-controller, and to avoid sensors' noise, which affects system performance.  
**Control objectives.** Considering that the robotic arm has its gripper near to the object's position, and taking into account the structure of the gripper, the objective of the gripper control system is to achieve firm and delicate object manipulation with the measurement of the angular position and force exerted by each finger. Then, (see Figure 4), it was necessary to design two-feedback laws, position control and force control, such that:  
\[\lim_{t\rightarrow\infty}e_{p}(t)\leq h=\lim_{t\rightarrow\infty}(Ref_{pos}- Pos_{\omega(t)})\leq h,h=\pm 2 Ref_{pos} \tag{1}\]  
\[\lim_{t\rightarrow\infty}e_{f}(t)=0=\lim_{t\rightarrow\infty}(Ref_{force}-F(t))=0 \tag{2}\]
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments  
### Datasets  
#### 4.1.1 MPDD  
MPDD [2] is a challenging dataset that focuses on detecting defects in the manufacturing process of painted metal parts. It reflects the real-world situations encountered by human workers on production lines. The dataset includes six categories of metal parts. The images were captured under various spatial orientations, positions, and distance conditions with different light intensities and non-uniform backgrounds. The training set consisted of 888 normal samples, whereas the test set consisted of 176 normal and 282 abnormal samples.
# Performance Analysis of Hybrid Kinematic Mechanism for Fusion Reactor Maintenance
## 3 HKM Trajectory Planning  
### Cartesian Space Trajectory Planning  
As shown in Figures 4 and 5, to simplify the calculation, the trajectory of the HKM lifting BBs is represented by straight lines. There are five BBs, containing three outboard BBs and two inboard BBs. Here, the left outboard BB (marked in blue in Figure 4a) and left inboard BB (marked in blue in Figure 5a) are taken as examples to illustrate the whole process. Firstly, the center point between the three lifting twist locks of the HKM end-effector was set as the coordinate origin. Secondly the key dimensions and relative coordinate positions on the model were measured and calculated. Finally, the installation and disassembly of the BBs can be accomplished by successive inverse solutions of the HKM. See the online Supplementary Materials for a Video S1 of HKM lifting BBs.  
The motion trajectory is shown in Figures 4b and 5b: the red trajectory shows the forward process of the HKM to engage and connect to the BBs, and the blue trajectory shows the return process of the HKM to remove the BBs.
## 3 Results and Discussion
### Effect of Additive on Biomethane Potential  
The batch AD of the silages were conducted at a mesophilic temperature of 37 \({}^{\circ}\)C (\(\pm 1\)) for 28 days. The highest daily methane production occurred on the first day and then fell continuously in all silages except in the CTL silage, wherein the daily methane production increased until the second day (Figure 1), an indication that the additives increased the digestibility of the substrate as also reported during the AD of straw [48] and other residues [12]. Probably due to the depletion of nutrients, the methane production rate declined after 15 days and the experiment was stopped after 28 days, in accordance with the VDI 4630 [49] methodology.  
The additive used in our study had a positive significant effect (\(p<0.05\)) on the BMP of the silages in direct proportion to the substrate to additive ratio (Figure 2). However, only the BMP of A50:1 silage was significantly higher (\(p<0.05\)) than the BMP of the CTL silage. The A50:1 silage had the highest BMP of 516 mL/gVS, followed by the B150:1 silage (441 mL/gVS) and C500:1 silage (432 mL/gVS), representing increases of 45.35%, 24.23%, and 21.69%, respectively, compared to the BMP of CTL silage. The higher BMP of the treated silages compared to the CTL could be attributed to the higher TFP content of those silages [7].  
Figure 1: Daily methane production rate of the silages. Error bars indicate standard deviation.  
The BMP of CTL silage compares with the 361 mL/gVS reported by [PERSON] et al. [22]. Table 3 shows a summary of the percentage increase in BMP of some silages as a result of the addition of a biological additive during ensiling. It can be seen that the additive in our study has the potential to significantly enhance biogas production.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 4 Communication, Navigation, and Observation
### Satellite Communication  
While multi-hop mesh networks can dynamically extend network coverage through the movement of nodes, there is a limit to their communication range. Currently, the _Mars Express_ orbiter of ESA and the _Mars Reconnaissance_ orbiter of NASA, among others, provide connectivity to the Mars rovers on a larger scale [8]. Moreover, the orbiters help explore future landing sites. However, a full constellation of Mars orbiters is required to provide full coverage to all future ground- and air-based vehicles across the surface of Mars [47]. In this regard, SpaceX has recently pitched 'Marslink', a version of Starlink for the Red Planet [48]. For example, when a rover is exploring a new area and loses the local mesh communication signal, it can automatically fall back to a direct-to-satellite link to keep communicating observations. The latter can subsequently be forwarded through inter-satellite links (ISL).  
In addition to the need for a larger antenna, the drawback associated with a direct-to-satellite communication link lies in its elevated energy consumption. A similar energy-intensive characteristic is observed with respect to the costly satellite link established for communication back to Earth. In order to counter this, low-power edge artificial intelligence (AI) algorithms can reduce the need for bandwidth-intensive and expensive downlinks through automated decision-making onboard satellites.
## 3 Results
### Numerical Model Assessment
#### 3.2.2 Mesh Sensitivity Study  
Thermal results do not change on varying cell dimensions (data not reported), on the contrary, the calculated displacements strongly depend on mesh size as shown in Figure 5 for point B, taken as reference. The mesh sensitivity study was performed, changing the base cell size in the range 2.5-40.0 mm with a refinement ratio equal to 2.  
Figure 4: Thermal conductivity vs. temperature; comparison between ISO curves and experimental data.  
Decreasing the cell dimensions, the calculated displacements asymptotically reached the experimental value (19 mm). The asymptotic solution can be evaluated by the Grid Convergence Index (GCI) [27], expressed in the following equation:  
\[\mathrm{GCI}\;=\;1.25\;\Big{\{}\left(\left|\mathrm{F}_{\mathrm{i+1}}-\mathrm{F} _{\mathrm{i}}\right|\big{/}\mathrm{F}_{\mathrm{i}}\right)\left[1/(\mathrm{r} ^{\mathrm{P}}-1)\right]\Big{\}}, \tag{9}\]  
where r is the refinement ratio, F\({}_{\mathrm{i}}\) are the model values with i that decreases with the grid refinement and p is the solution convergence order expressed in the following equation:  
\[\mathrm{p}\;=\;\ln[(\mathrm{F}_{\mathrm{i+2}}\;-\;\mathrm{F}_{\mathrm{i+1}}) /(\mathrm{F}_{\mathrm{i+1}}-\mathrm{F}_{\mathrm{i}})]/\ln(\mathrm{r}), \tag{10}\]  
Comparing two successive values of \(\mathrm{GCI}\) using the following equation, the asymptotic solution can be estimated; when the value of the parameter A\({}_{\mathrm{r}}\) is near to 1, the desired condition is satisfied.  
\[\mathrm{A}_{\mathrm{r}}=\;\mathrm{r}^{\mathrm{P}}\;(\mathrm{GCI}_{\mathrm{i} }/\mathrm{GCI}_{\mathrm{i+1}}), \tag{11}\]  
In this case, considering the grids having dimensions 5 mm, 10 mm and 20 mm, a value of A\({}_{\mathrm{r}}\) equal to 1.005 was obtained, with \(\mathrm{GCI}_{\mathrm{1}}\) and \(\mathrm{GCI}_{\mathrm{2}}\) equal to 0.009 and 0.003, respectively.  
Accordingly, a mesh size of 10 mm was used in all the computations as it is in the asymptotic range and it represents the best compromise between computational burden and the accuracy of the results.
## 2 Data and Methods
### Data  
#### 2.2.1 NDVI Data  
The NDVI, also known as the normalized difference vegetation index, can differentiate vegetation from water and soil, providing an objective reflection of changes in vegetation coverage. It is the optimal indicator of vegetation growth status and coverage. In this study, 500 m spatial resolution MODIS data, MOD13A1 (v061), with a temporal resolution of 16 days and a time span of 22 years (2001-2022), were acquired from the NASA Earthdata Cloud ([[https://appeears.earthdatacloud.nasa.gov/](https://appeears.earthdatacloud.nasa.gov/)]([https://appeears.earthdatacloud.nasa.gov/](https://appeears.earthdatacloud.nasa.gov/)), accessed on 12 December 2023) website. This dataset comprised two major vegetation layers, the NDVI and EVI, and the NDVI layer was chosen for download. The dataset offered temporal continuity and extensive coverage, making it highly suitable for this research. Then, by setting the NDVI annual average threshold to 0.1, the pixels with an NDVI annual average below 0.1 were excluded. This approach effectively removes areas of low or sparse vegetation, bare soil, and water bodies, thus ensuring the accuracy of the results [38; 39; 40]. This threshold was selected to enhance the reliability of the analysis by ensuring that only the areas with healthy vegetation cover were considered in order to obtain more accurate research outcomes.
## 2 Materials and Methods  
### Satellite Data  
NDVI and GPP are commonly used to evaluate vegetation greenness and productivity. In this study, we used monthly NDVI data from the period of 1982-2018, which were obtained from the Advanced Very High Resolution Radiometer (AVHRR) satellite program under the Global Inventory Modeling and Mapping Studies (GIMMS) project. The NDVI data, which have a spatial resolution of 0.083\({}^{\circ}\), was sourced from the [[https://cmrearnddata.nasa.gov/search/concepts/C2759076389-ORNL_CLOUD.html](https://cmrearnddata.nasa.gov/search/concepts/C2759076389-ORNL_CLOUD.html)]([https://cmrearnddata.nasa.gov/search/concepts/C2759076389-ORNL_CLOUD.html](https://cmrearnddata.nasa.gov/search/concepts/C2759076389-ORNL_CLOUD.html)) (accessed on 18 May 2023). For GPP, we employed the NIRv_GPP product, which has an initially spatial resolution of 0.05\({}^{\circ}\). The data can be obtained from [[https://data.tpdc.ac.cn/zh-hans/data/ddoff4/df4_df4_df4_d5f4f2d-ac96-55827](https://data.tpdc.ac.cn/zh-hans/data/ddoff4/df4_df4_df4_d5f4f2d-ac96-55827) ab93 cc5]([https://data.tpdc.ac.cn/zh-hans/data/ddoff4/df4_df4_df4_d5f4f2d-ac96-55827](https://data.tpdc.ac.cn/zh-hans/data/ddoff4/df4_df4_df4_d5f4f2d-ac96-55827) ab93 cc5) (accessed on 20 May 2023). To match the NDVI data used in our study, we aggregated the GPP data to a spatial resolution of 0.083\({}^{\circ}\). Here, we focused on the growing season, defined consistently as from May to October in the study region [40]. The growing season NDVI and GPP values were calculated by averaging monthly NDVI and GPP values from May to October per year. In this study, pixels with a mean NDVI value below 0.1 during the growing season were excluded from the analysis of the relationship between NDVI and GPP.
# Subwavelength Chiral Spiral Acoustic Metamaterials for a Robust Topological Acoustic Insulator
## 2 Structure Design  
The left-handed Archimedean spiral structure is shown in Figure 1a. The spiral equation is \(r(s)=R-(R-r)s\), \(\phi(s)=2\pi ms\), where \(R\) is the outer radius of the Archimedean spiral, \(r\) is the inner radius of the spiral, \(n\) is the number of turns of the spiral, and \(s\in[0;1]\), and \(w\) is the width of the spiral. The whole spiral structure is considered as the scatterer distributed in the honeycomb unit cell filled with air, shown in Figure 1b. The geometric parameters of the spiral are: the inner radius of the spiral \(r=0.3\) cm, the outer radius \(R=1.2\) cm, the number of turns of the spiral \(n=2\), the width of the spiral \(w=0.1\) cm, and the lattice constant \(a=4\) cm. The material parameters are as follows: steel spiral structure (mass density \(\rho_{1}=7700\) kg/m\({}^{3}\), sound velocity \(c_{1}=5400\) m/s), and the gray air area (mass density \(\rho_{0}=1.29\) kg/m\({}^{3}\), sound velocity \(c_{0}=340\) m/s).
# Amplification Ratio of a Recycled Plastics-Compliant Mechanism Flexure Hinge
## 3 Results and Discussion
### Comparison between PP, ABS, and HDPE Flexure Hinges  
In this section, the displacements of the HDPE are compared with the previous results of the ABS and PP flexure hinges. The input and output equations at different recycle ratios are presented in Table 5. Figure 6 shows a more visual representation of these results.  
Figure 6: Comparison between the displacement ratios between the PP, ABS, and HDPE flexure hinges at different recycle ratios.  
Figure 6 shows the comparison of the amplification ratio among the PP, ABS, and HDPE flexure hinges at different recycle ratios. The pure PP, ABS, and HDPE flexure hinges achieve the highest amplification ratios of 5.728, 8.249, and 5.668. The average amplification ratio values of all recycle ratios are 5.35, 7.60, and 5.02 corresponding to the PP, ABS, and HDPE flexure hinges. Generally, the ABS flexure hinge has the highest amplification ratios, followed by the PP flexure hinge. HDPE flexure hinge has the lowest highest amplification ratios among these plastic types. The reason is for this is the order's gradual reduction in the tensile strength and elastic modulus of the PP, ABS, and HDPE plastics. Moreover, the highest amplification ratio is 8.249, gained by the ABS flexure hinge in the packing pressure case. In reverse, the HDPE flexure hinge in the cooling time case has the lowest amplification ratio of 4.436.  
Table 6 presents the amplification ratios of some common flexure hinge materials to compare the amplification ratio of these plastic hinges with other materials. The aluminum alloys and spring steel could produce a flexure hinge with a greater amplification ratio compared to the PP, ABS, and HDPE flexure hinges due to the stronger mechanical properties [31; 32]. The amplification ratios of the PP, ABS, and HDPE flexure hinges are compatible with smart memory alloys and titanium alloys [33; 34].  
In general, the HDPE flexure hinge has the lowest amplification ratio compared to the ABS and PP flexure hinges. It is interesting to note that these plastic hinges have amplification ratios that could be like those of titanium alloy hinges but lower than those of the spring steel and aluminum alloy hinges. More research could explore the effects of the hinge shape on the performance of the injected plastic flexure hinge.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods
### Data  
#### 2.2.1 The Sentinel-1 SAR Data  
The rainy and cloudy weather in Poyang Lake results in limited high-quality cloud-free optical images, such as from the non-commercial Landsat or Sentinel-2 platforms, to provide enough data for dynamic monitoring of changes in water surface areas of Poyang Lake [37; 40]. Thus, to extract water body data for Poyang Lake and monitor the dynamics in the study area over the past 7 years, 455 scenes of the level-1 Ground Range Detected (GRD) Sentinel-1 C-band (5.405 GHz) SAR data from the Interferometric Wide swath (IW) and VV/VH polarization mode were collected from October 2014, to March 2021 from Copernicus Open Access Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)), accessed on 18 April 2021). The blue polygons in Figure 1b show the swaths for these 455 scenes of SAR data. After Sentinel-1 slice assembly, 455 scenes of Sentinel-1 SAR data formed 324 effective observations due to the slice assembly of the two consecutive'slices'. In these 324 SAR observations, there were 187 observations from Sentinel-1A and 137 from Sentinel-1B, with nine observations operating in descending mode and 315 observations in ascending mode (Figure 3).  
The temporal distribution of the acquisition dates and the numbers for these 324 SAR observations are shown in Table A1 and Figure 4. From Table A1, we can see that, as the launch of Sentinel-1B on 25 April 2016, the observation times in one year increased and could reach up to 60 times (in the years 2017, 2018, 2019, and 2020). However, due to the commissioning phase of Sentinel-1A, there are not available data from January 2015 to March 2015. From Figure 4, we can also see that (1) the minimum number of observation times for all 75 months is 1 (April, May, and November 2015; June 2016), (2) the maximum number of observation times is six (January 2017, May 2017, July 2018, August 2019, and August 2020), and (3) there are five observations in most months.  
Figure 3: The number and percentage of the observations for different satellites and overpasses.
## 2 Research Status and Development Trends for Computer Aided Detection and Diagnosis of MRI
### Integration of Radionics with DCE-MRI Datasets
Computer-Aided MRI Diagnosis of Breast Cancer Using Convolutional Neural Networks and Adversarial Learning  
With the development of medical technology, more and more imaging modalities are being used in hospitals. MRI machines can generate a large number of pathological images even from a single patient, overloading doctors that perform image-based diagnosis. This can also lead to cases of misdiagnosis or missed diagnosis; in addition, diagnosis is based on a doctor's judgement, which is subjective, so the effectiveness of diagnosis varies. Training a doctor is time consuming and requires significant capital investment, and it is not uncommon in many hospitals to find it difficult to meet the current clinical demand [21]. Over the past few years, digital image processing by using convolutional neural networks (CNN-based deep learning), has become a well established modality. This is combined with deep learning methods to successfully perform segmentation of brain MRI images and classify lesions based on histopathological images, etc. Deep learning methods have been used in computer-aided design (CAD), such as IBM's diagnostic robot \"[PERSON]\" [22; 23]. [PERSON]'s working principle is to use deep CNNs to learn a large amount of image features through pattern matching, then establish a disease diagnosis model, and finally provide, in an automated fashion, the patient's diagnosis results or treatment suggestions to the doctor. [PERSON]'s example shows that the application of deep learning methods in CAD is a general trend. There are several levels of involvement of an expert in the diagnostic process. It is possible to perform fully automated recognition (e.g., based on the overall three-dimensional shape of a tumour or lesion or the statistical significance of the increased vascularisation), semi-automatic recognition (e.g., based on threshold segmentation, area growth or fuzzy C clustering, etc.) and interactive recognition (after an expert manually selects the outline of the lesion and then cuts the edge). Compared with the semi-automatic recognition of the lesion area, automatic recognition has the advantages of improved speed and wide applicability at low cost. However, the accuracy of fully automatic tumour identification needs to be further improved [24; 25]. There is tendency to use self-supervised and semi-supervised deep learning methods to detect and classify tumours using breast DCE-MRI. Adversarial learning reduces the reliance on manual annotation data, enabling accurate and rapid diagnosis of breast cancer. This learning modality constitutes a new method for the processing of high-dimensional medical images and establishes a complementary diagnostic approach for breast cancer.