esa-sceva/satcom-chunk-collection huggingface dataset:
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 2 Why Space Debris Remediation? The Facts
### The Future of the Outer Space Environment
#### 2.3.2 The Need to Act  
As any significant accident in outer space leads to irreparable damage in orbital stability, it is not enough to mitigate the production of new space debris. In particular, the fact that in higher altitudes objects may remain over hundreds or even thousands of years, means that a potentially catastrophic effect for functional objects remains. Mitigation can indeed contribute to stabilizing the outer space environment, but further measures are necessary. For example, in LEO mitigation measures can only slow down the pace of growth but are not enough to stop it. Therefore, further measures aiming at reducing the existing space debris population through remediation are needed if the most used orbits are to remain usable. For example, a long-term scenario with five ADR missions per year clearly shows that remediation for large objects would lower the number of collisions in densely populated orbital regions from 10 to 5 and is, thus, advantageous [23].  
While it has been estimated that the (isolated) application of SDR measures will not lead to a rapid change in the negative trends, there could be an apparent benefit to operational space objects in the long-term if ADR is performed in conjunction with space debris mitigation [44].
# A Decoding Algorithm for Convolutional Codes
## 2 Preliminaries and Notations
### Linear Codes and Error-Correcting Pairs
In particular, a code is generalized Reed-Solomon if and only if its dual is generalized Reed-Solomon. Let \(\mathcal{C}\) be a \(\mathbb{F}_{q}\)-linear code of length \(n\). Following [6] (SS 2), a pair \((\mathcal{A},\mathcal{B})\) of \(\mathbb{F}_{q}\)-linear codes of length \(n\) is called an _\(e\)-error-correcting pair_ for the \((n,k)\)-linear code \(\mathcal{C}\) if it verifies the following conditions:  
\(\mathbf{1}\). \(\mathcal{A}*\mathcal{B}\subseteq\mathcal{C}^{\perp}\). \(\mathbf{2}\). \(k(\mathcal{A})>e\). \(\mathbf{3}\). \(d(\mathcal{A})+d(\mathcal{C})>n\). \(\mathbf{4}\). \(d(\mathcal{B}^{\perp})>e\). We say that \(\mathcal{C}\) has an error-correcting pair if there is an \(e\)-error-correcting pair over a finite extension of \(\mathbb{F}_{q}\) for \(e=\left\lfloor\frac{d(\mathcal{C})-1}{2}\right\rfloor\). In the case of generalized Reed-Solomon codes, an explicit description of such a pair is known (see [9], Ex. 4.2.). Indeed, if \(\mathcal{C}=\mathrm{GRS}_{k}(\mathbf{a},\mathbf{b})\) and \(\mathcal{C}^{\perp}=\mathrm{GRS}_{n-k}(\mathbf{a},\mathbf{c})\), then:  
\[\mathcal{A}:=\mathrm{GRS}_{e+1}(\mathbf{a},\mathbf{c})\qquad\mathcal{B}:= \mathrm{GRS}_{e}(\mathbf{a},\mathbf{1}), \tag{3}\]  
where \(\mathbf{1}:=(1,\ldots,1)\in\mathbb{F}_{q}^{n}\), form an \(e\)-error-correcting pair for \(\mathcal{C}\) with \(e=\left\lfloor\frac{n-k}{2}\right\rfloor\). Observe that the pair \((\mathcal{A},\mathcal{B})\) of (3) is defined on base field \(\mathbb{F}_{q}\). In addition, for a received word \(r\in\mathbb{F}_{q}^{n}\), the _error locating map_ of \(r\) with respect to the \((n,k)\)-linear code \(\mathcal{C}\), denoted by \(E_{r}\), is defined by:  
\[E_{r}:\mathcal{A} \longrightarrow\mathrm{Hom}_{\mathbb{F}_{q}}(\mathcal{B},\mathbb{ F}_{q})\] \[u\longmapsto v=uG,\]0  
We finish this section by introducing some notations used in the explanation of the algorithm. We denote by \((a)_{0}:=\{i\,|\,a_{i}=0\}\), the zero set of an element \(a\in\mathbb{F}_{q}^{n}\). For \(I=\{i_{1},\ldots,i_{e}\}\) a set of indices with \(1\leq i_{1}<\ldots<i_{e}\leq n\), we denote:  
\[u\longmapsto v=uG,\]1  
the inclusion map that sends \(a=(a_{1},\ldots,a_{e})\in\mathbb{F}_{q}^{e}\) to the vector whose \(i_{j}\)th entry is \(a_{j}\) for \(j=1,\ldots,e\) and zeros everywhere else. Then, we have that the _restricted syndrome map_ of a linear code \(\mathcal{C}\) is:  
\[u\longmapsto v=uG,\]2  
given by the composition \(\sigma_{I}=\sigma\circ i_{I}\) (see (2)).
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 3 FL-MD3 QN-Based Intelligent Access
### System Model  
The multi-site, multi-modal, multi-user IoT intelligent access framework is shown in Figure 2. First, different sites upload locally collected 26-dimensional IoT data to the BS. The BS processes these data using the SW-Based DPP algorithm to generate feature data. The feature data are then uploaded to edge servers. Edge servers train the MD3 QN algorithm to generate base model parameters. The Federated Averaging (FAVG) module aggregates base model parameters from all sites to obtain global model parameters. These parameters are transmitted back via the BS to IoT users across sites for updates. The process repeats until the aggregated model converges to target parameters. Finally, a global strategy balancing efficiency and security is output and distributed to all users. This ensures data privacy while optimizing BS resource allocation.
###### Abstract  
Photogrammetry captures and stitches multiple images together to generate a digital model of the human body, called an avatar, making it potentially useful in healthcare. Its validity for anthropometry remains to be established. We evaluated the reliability and agreement of measurements derived from a three-dimensional digital avatar generated by photogrammetry compared to manual collection. Fifty-three volunteers (34.02 \(\pm\) 11.94 years of age, 64% female, 22.5 kg\(\cdot\)m\({}^{-2}\) body mass index) were recruited, and twenty-two body regions (neck, armpits, biceps, elbows, wrists, chest, breast, waist, belly, hip, thighs, knees, calves, ankles) were taken by an individual rater with a tape measure. Digital measurements were generated from photogrammetry. Participants' intraclass correlation coefficients indicated strong consistency, with agreement of over 90% for limb regions such as biceps, elbows, wrists, thighs, knees, calves, and ankles, while chest and armpits showed lowest agreement (<60%). Random errors were low in limb regions, while trunk measurements showed highest errors (up to >1 cm) and variation. Bland-Altman analysis revealed wider limits of agreements and higher biases for chest (\(-\)2.44 cm), waist and belly (around \(-\)1.2 cm), and armpits (around \(-\)1.1 cm) compared to limbs. Our findings suggest that photogrammetry-based digital avatars can be a promising tool for anthropometric assessment, particularly for limbs, but may require refinement in trunk-related regions.  
b _Applied sciences Article_
## 1 Introduction
** To address these concerns, we propose an innovative algorithm for electrical impedance tomography. Our algorithm leverages a graph convolutional neural network (GCNN) model. Specifically, we convert the finite-element model (FEM) grid data related to the positive problem into a network graph. Subsequently, we employ the ILM method to update the parameters in the non-linear inverse problem associated with the EIT imaging process. Overall, we make the following three main contributions:  
* Solving the problem of inadequate previous knowledge in EIT experiments is commonly dependent on simulation data, which are obtained from the ACT3 and the KIT4 system. With these systems, we carry out finite element calculations to generate prior data for solving the EIT forward problem. Acquiring a training set by training on public datasets will enhance the understanding and adaptability of the training model to various EIT problems. * We presents an enhanced LM graph neural network algorithm for EIT imaging. The proposed algorithm utilizes the ILM algorithm to update the parameters of the ill-conditioned non-linear inverse problem in the EIT process. The presented algorithm effectively addresses the limitations of the inverse problem, successfully suppressing or removing artifacts, ultimately enhancing its overall effectiveness. * The proposed algorithm's accuracy is assessed through experiments, and the feasibility of the algorithm is validated using the ACT3 and KIT4 datasets. Experimental findings indicate that the physical models of ACT3 and KIT4 display superior performance. Organization.The organization of this paper is as follows. Section 2 will present the mathematical model of EIT. Section 3 will provide a comprehensive overview of the GCNN and the enhanced Levenberg-Marquardt algorithm used in EIT. In Section 4, the training data will be discussed and the experiment will be thoroughly analyzed. Ultimately, Section 5 will derive conclusive remarks.
## 1 Introduction
Lift improvement is obtained by implementing the MT downwards (on the pressure side) and lift reduction is obtained by deploying the MT upwards (on the suction side). The implementation of this device near the airfoil (TE) provokes changes in the flow, causing modifications in the circulation of the flow on the airfoil. The effective camber of the airfoil is modified, promoting changes in the lift and drag forces. Placing a MT at the pressure surface the lift is increased, however if it is placed on the suction, the opposite effect is reached. The main advantages of MTs are: small size, low cost of manufacturing, low power requirements for its activation, and simplicity of the device design. Multiple studies into this topic were made by [PERSON] et al. [20] and [PERSON] et al. [21], including wind-tunnel experiments in order to determine their optimal height and location. The aim of the current study is to find the optimal MT position to increase DU91W(2)250 airfoil aerodynamic performance and to investigate its influence on the average power output of a 5 MW  
Figure 1: Microtabs (MT) concept and enlargement view of the streamlines around the MT mounted on the pressure side of an airfoil [15]. Wind turbine. Firstly, a parametric study of a MT mounted on the pressure surface of the airfoil DU91W(2)250 has been carried out and best case has been selected. This airfoil has been selected because it is used on the 5 MW reference wind turbine of the NREL, as described in [PERSON] et al. [22]. Second, Blade Element Momentum (BEM) based computations have been performed to investigate the effect of this MTs on the wind turbine power output. The results on the rotor thrust and blade root bending moment are also presented.
# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
## 3 Experiments
### Quantitative Experiment
Tables 1 and 2 showcase the outcomes of employing two distinct anomaly detection methodologies: the original Martingale and the \(Z\)-score enhanced Martingale, on multiple datasets. These methodologies were evaluated based on three key performance metrics: accuracy, precision, and R-rank. The R-rank, also known as the Relative Rank, is a metric used to evaluate the performance of a classification model. It represents the relative position of the true positive rate (sensitivity) against the false positive rate (\(1-\) specificity) on a receiver operating characteristic (ROC) curve. To calculate R-rank, we take the area under the ROC curve and divide by the total area of the ROC chart. Regarding the original Martingale results (Table 1), diverse performance levels are evident across different datasets. For instance, dataset Chfdb_chf01_275_f1 attained an accuracy of 91% and a precision of 79%, signifying a relatively high accuracy in identifying anomalies, albeit with some false positives. However, the R-rank for this dataset is notably low at 26%, indicating that the detected anomalies may not consistently rank highly in importance or severity. Conversely, dataset Chfdb_chf13_45590_f2 exhibited higher precision (75%) and R-rank (28%) with a slightly improved accuracy of 94%, indicating better performance in correctly identifying anomalies with higher importance. Transitioning to the results of the \(Z\)-score enhanced Martingale (Table 2), a similar trend in performance metrics is observed with some variations. For instance, in dataset Chfdb_chf01_275_f1, the \(Z\)-score enhanced Martingale achieved the same accuracy (91%) as the original Martingale but with a slightly lower precision (75%). However, the R-rank notably increased to 62%, indicating that the detected anomalies are more likely to be of higher importance or severity compared to the original Martingale results. Similarly, in dataset  
Figure 2: A sample of an ECG plot produced from the BIDMC Congestive Heart Failure Database data used in this project. Chfdb_chf13_45590_f2, the \(Z\)-score enhanced Martingale demonstrated perfect precision (100%) and a significantly higher R-rank (55%) compared to the original Martingale results, suggesting a more effective prioritization of detected anomalies. Overall, these results underscore the potential advantages of employing the \(Z\)-score enhanced Martingale approach, particularly in enhancing the ranking of detected anomalies based on their importance or severity. Nonetheless, further analysis and validation are essential to comprehensively understand the implications of these performance disparities and to evaluate the robustness of the \(Z\)-score enhanced Martingale approach across various datasets and contexts.
## Appendix A SCOPE Model Parameters  
We used the global MODIS leaf chlorophyll content (LCC) product [54] as the Cab parameter. It provided LCC data from 2000 to 2020 at 8-day intervals with a spatial resolution of 500 m. We resampled the data to 0.001\({}^{\circ}\) using bilinear interpolation and then calculated the average within a 500 m buffer around the site. To match the input data of the SCOPE model in time, the LCC data were linearly interpolated to daily values, with the same data used for each half hour of the day. As the data are available only until 2020, we estimated the Cab for 2021 using the chlorophyll sensitive index (CSI) [39], which is considered to have a good linear relationship with chlorophyll and performed well within a 1 km range of the LCC at two sites in the study area (Figure A1). LAI data were obtained from 250 m of GLASS LAI data [43], with the average of a 5 \(\times\) 5 pixel grid centered around the site used as the site's LAI value. LAI interpolation followed the same procedure as the LCC. The various parameters required by the SCOPE 2.1 model in this study are summarized in Table A1.  
Figure 13: Seasonal changes in average eSIF (**a**) and TROPOSIF (**b**) in the study area.
## 4 Proposed Work
### Optimal Threshold
```
1:ThresholdMin\(\leftarrow\) Based on Equation (9)
2:ThresholdMax\(\leftarrow\) Based on Equation (10)
3:InputTasks\(\leftarrow\) Set of initially admitted tasks \(Tn\)
4:Hyper-period\(\leftarrow\) LCM(P(11), P(2), P(3).. P(tn))
5:Overhead\(\leftarrow\) 0
6:ThresholdCoefficient\(\leftarrow\) 0
7:CurrentThreshold\(\leftarrow\) ThresholdMin
8:loop:
9:whileTruedo
10:Hyper-period\(\leftarrow\) 0
11:periods\(\leftarrow\) []
12:ThresholdCoefficient\(\leftarrow\) ThresholdCoefficient\(+\) 1
13:Overhead\(\leftarrow\) Overhead\(+\) 1
14:forallTasksdo
15:period(i)\(\leftarrow\) random(i)
16:periods[]\(\leftarrow\) period(i)
17:Hyper-period\(\leftarrow\) LCM(periods[])
18:ifHyper-period\(>\) CurrentThresholdthen
19:[CuttentThreshold, ThresholdCoefficient\(\leftarrow\) FunctionCall: AdjustThreshold(CurrentThreshold, ThresholdCoefficient)]
20:Continue
21:else
22:Hyper-period Tuning Done
23:break
24:AlgorithmOverhead\(\leftarrow\) overhead
25:OptimalThreshold\(\leftarrow\) CurrentThreshold
```  
**Algorithm 1** Adaptive Period Tuning AlgorithmAlgorithm 1 describes the overall approach to tune the hyper-period within the optimal threshold. The algorithm receives parameters like minimum threshold, maximum threshold, and set of input tasks. The overhead of the algorithm is initialised to 0. The hyper-period is computed and compared against the optimal threshold value. If it is less than the optimal threshold, the algorithm passes the optimal task set to the scheduler. In contrast, if it is higher than the optimal threshold, the overhead is incremented, and the process repeats until an optimal task set is achieved. The threshold is adjusted if the input tasks are coming with a high value of periods and there is more possibility of getting a higher value of threshold each time. The threshold adjustment has been performed in Algorithm 2. The algorithm is executed once after 100 times of failed attempts. The threshold is updated based on the current threshold and maximum threshold. The updated threshold is returned to Algorithm 1. Algorithm 1 now has more chance of accepting the tasks because it has adjusted the threshold based on the mode of input tasks. This adjustment goes on until it reaches the maximum threshold of the system. At this point, no more adjustment is performed, and the system is said to be saturated.
###### Abstract  
For many years, the navigation team at the French Space Agency (CNES) has been developing its Precise Point Positioning project. The goal was initially to promote a technique called undifferenced ambiguity resolution. One of the main characteristics of this technique is the capability for a user receiver to perform centimeter-level accuracy in real time. To do so, a demonstrator has been built. Its architecture is composed of three main elements: a correction processing software called the server part, a means to transmit the corrections using standardized messages, and a user software capable of handling the corrections to compute an accurate positioning at the user level. In this paper, we present the recent advances in the CNES precise point positioning demonstrator. They are composed of some evolution of the network of stations and server software, the implementation of the new state space representation standard, a new method for instantaneous ambiguity resolution using uncombined four-frequency signals, its implementation in real-time at the server and the user level, and the use of high-rate Doppler measurements to improve the accuracy of the solution in harsh urban environments. On top of that, the computation of high-accuracy post-processed phase biases with the majority of current GNSS signals supported, compatible with the uncombined method and a new online positioning service to demonstrate the capacity of the user software, is demonstrated.  
precise point positioning; real-time; ambiguity resolution; multi frequency GNSS; online service; post-processed biases; Doppler shift measurements +
Footnote †: journal: remote sensing  
Article  
The CNES solutions for Improving the Positioning Accuracy with Post-Processed Phase Biases, a Snapshot Mode, and High-Frequency Doppler Measurements Embedded in Recent Advances of the PPP-WIZARD Demonstrator. _Remote Sens._**2023**, _15_, 4231. [[https://doi.org/10.3390/rs15174231](https://doi.org/10.3390/rs15174231)]([https://doi.org/10.3390/rs15174231](https://doi.org/10.3390/rs15174231))
## 3 Proposed Method
### Dynamic Scheduling  
The dynamic scheduling mechanism is the core innovation of the TEDDY framework, as shown in Figure 2b. Traditional camera networks often operate with all cameras active, resulting in unnecessary energy consumption and redundant processing. In contrast, the TEDDY framework optimizes resource utilization by activating only the edge devices that are relevant to the target's current and predicted movement. The activation or deactivation of edge devices is determined by the presence of a target (see Section 3.2.1) and dynamic selection by the base station (see Section 3.2.2).
## 3 Results and Discussion
### Molar Ratio  
To determine the molar ratio of the metal ion to the ligand molecule. The metal ion standard solution \(1\times 10^{-4}\) M was pipetted into seven volumetric flasks (0, 1, 2,..., 6 mL), and a solution of \(1\times 10^{-4}\) M ligand (6, 5, 4,..., 0 mL) was added, respectively. All the measurements were carried by a spectroscopic method in a 200-500 nm range. The absorbance measurements were performed by keeping the metal ion concentration constant at 0.72 \(\times\) 10\({}^{-3}\) M, while the ligand varied, as shown in Table S3. The absorbance of the sample solution was measured against blank at the maximum wavelength, and the values were then plotted against the ratio of [M])/([M] + [L]) [27]. From the results, we found the inflection was at 0.33 for Co(II) Cu(II) and Zn(II), as shown in Figure 1, while the inflection at 0.5 indicated to 1:1 for Mn(II) and Ni(II) complexes and 1:2 for the other metals.  
Figure 1: Curves for the molar ratio (1:2) and (1:1).
## 3 Results
### Liquid Products-Bio-Oils  
According to the previous results concerning the yield of solid, liquid and gaseous products of the pyrolysis process as a function of temperature, as well as the elemental analysis of biochars, it was decided to analyze the properties of bio- oils obtained at 450 \({}^{\circ}\)C. Table 4, which compares the properties of the current samples with those of heavy fuel oil and typical flash pyrolysis oil [31], shows that the densities of FOR and PCO were practically the same as that of heavy oil, while the density of RDF was similar to that of flash pyrolysis oil. The pH values of the bio-oils studied, ranging between 2.1 and 3.1, were close to the values of flash pyrolysis oil and within the range of literature data for other biomass materials [8; 17; 41; 42]. The dynamic viscosities of FOR and PCO oils, which were measured at 1.5 cP and 7 cP, respectively, were lower than the values reported from flash pyrolysis of similar fuels at 500 \({}^{\circ}\)C, i.e., 26 cP and 10 cP, respectively. Meanwhile, the viscosity value of RDF oil was within the range of typical flash pyrolysis oil. This property of bio-oils is very important for the design and operation of fuel injection systems, as well as for the quality of combustion of the oil [43]. A lower viscosity enhances the fluidity of the oil and its combustion properties.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **PCO** & **FOR** & **RDF** & **Heavy Fuel Oil [31]** & **Flash Pyrolysis Oil [31]** \\ \hline Density (g/cm\({}^{3}\)) & 0.94 & 0.93 & 1.30 & 0.94 & 1.23 \\ Dynamic viscosity & 7.0 & 1.5 & 110.6 & 180.0 & 55–180 \\ (cP) & 3.0 & 2.1 & 3.1 & - & 2.4 \\ C (\%) & 59.0 & 52.4 & 43.5 & 85.2 & 44–46 \\ H (\%) & 8.9 & 7.9 & 5.0 & 11.1 & 6.6–7.1 \\ N (\%) & 1.2 & 0.1 & 3.9 & 0.3 & - \\ S (\%) & - & - & - & 2.3 & - \\ O (\%) & 30.9 & 39.6 & 47.6 & 1.0 & 47–49 \\ HHV (MJ/kg) & 27.1 & 21.9 & 13.4 & 40.0 & 15.0–17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and chemical properties of bio-oils.  
Figure 3: Concentration of inorganic species in biochars.  
From the ultimate analysis of the bio- oils studied (Table 4), it can be seen that PCO and FOR bio-oils, with higher concentrations of carbon and hydrogen and lower concentrations of oxygen than the flash pyrolysis oil and, therefore, a higher calorific value, can be considered superior, while the quality of RDF bio-oil was somewhat lower.  
Furthermore, although the high oxygen content of these oils is beneficial for catalytic steam reforming processes for hydrogen production [31], a mild catalytic deoxygenation process is required for them in order to substitute fuel oil in static applications such as boilers, engines and turbines. However, high-severity catalytic processes, which minimize the concentration of oxygen, are recommended for these oils to be utilized as transportation fuels [31; 41].