esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Panel E \"Resurrezione\"  
As far as the SG_33 micro-fragment is concerned, the brown area analyzed using \(\mu\)-Raman (Figure 16) allowed for highlighting all the characteristic peaks of hematite, as a red pigment, mixed with carbon black, as well as calcite from the base.  
The FT-IR spectrum (Figure 17) clearly revealed the presence of the typical vibrational bands of calcite.  
Figure 16: (**a**) Micro-photo of the analyzed brown area, (**b**) \(\mu\)-Raman spectrum collected on the brown area of SG_33 sample.  
Figure 15: FT-IR spectrum of SG_28B sample.  
Again, the broad band between \(\sim\)890 cm\({}^{-1}\) and \(\sim\)1230 cm\({}^{-1}\) associated with the presence of silicates appears evident. Within it, the peaks at \(\sim\)910 cm\({}^{-1}\), \(\sim\)1032 cm\({}^{-1}\), and \(\sim\)1096 cm\({}^{-1}\), which can be associated to silicates, probably present in red ober [29], can be distinguished. Finally, the band at \(\sim\)1633 cm\({}^{-1}\) and the broad band centered at \(\sim\)3300 cm\({}^{-1}\), attributed to the OH groups, are indicative of the presence of clayey minerals. No organic binders are observed.  
The elemental composition of the sample SG_45B was investigated using ICP-MS. The concentrations of the main detected elements are reported in Table 4.  
The Pb contained in the sample constitutes a very small mass fraction (0.045%), so the analyzed material cannot be correctly identified using the \"lead white\" pigment, commonly used in these studies, which contains about 80% of lead. Even if the results allow for a partial identification of the sample, the most abundant compounds seem to be calcium based, such as gypsum or calcite.  
The doubtful origin of the lead poses a basic uncertainty to study the provenance of the raw material used in the pictorial layer. Moreover, the availability of a single sample introduces uncertainty about the overall representativeness of the entire painting.  
The lead isotope composition measured in the sample is quite rare due to the relatively high value of Pb207/206 and Pb208/206 ratio. Table 5 compares the isotopic ratios measured for SG_45B and those for some mining sites in the South of France that have the closest lead isotope signature related to the sample.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample ID** & **Element** & **Concentration [mg\(\cdot\)kg\({}^{-1}\)]** & **Concentration [\%]** \\ \hline \multirow{6}{*}{SG\_45B} & Na & 265 & 0.03 \\  & Mg & 2100 & 0.2 \\ \cline{1-1}  & Ca & 62,000 & 6.2 \\ \cline{1-1}  & Fe & 1000 & 0.1 \\ \cline{1-1}  & Cu & 74 & 0.007 \\ \cline{1-1}  & Zn & 520 & 0.05 \\ \cline{1-1}  & Ba & 1000 & 0.1 \\ \cline{1-1}  & Pb & 450 & 0.045 \\ \hline \end{tabular}
\end{table}
Table 4: Elemental composition of the sample measured using ICP-MS. The uncertainties are 20% of the concentration values.  
Figure 17: FT-IR spectrum of SG_33 sample.  
In particular, the Ferols mine, located in the region of present-day New Aquitaine, shows reasonable compatibility considering the limits discussed above.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 3 Model Construction
### mRMR-AFWA-LSSVM Model  
We applied mRMR to select the most important feature impact factor, and then put the selected feature impact factor data content into the AFWA optimized LSSVM model for prediction. Among them, AFWA improved the model accuracy by optimizing the important parameters of support vector machine--penalty parameter and kernel function width parameter.  
The main contents of this process include:  
* Analyzing the possible influencing factors according to the system structure of the multi-energy scenario, collecting the corresponding data content, and conducting preliminary processing of the data. The operation here is mainly data normalization, and the dimensionless data retains the internal meaning while facilitating the calculation of the model.
* The mRMR feature factor selection process, which selects the key influencing factors at the top of the score, and they have the maximum load forecasting correlation and minimum information redundancy.
* Optimizing its width coefficient and penalty parameters by embedding AFWA into the LSSVM model.
* The training and forecasting process of LSSVM, through which the quarterly forecasting quantity of total load demand and multi-energy coupling energy supply can be obtained respectively, and the predicted value of load needed to be provided with the grid can be obtained by making the difference between them.  
Finally, the energy demand forecast under the multi-energy coupling model was completed, and the forecast results could be analyzed. The model structure of mRMR-AFWA-LSSVM model for load demand forecasting is shown in Figure 6.  
Figure 6: Load demand forecasting model under multi-energy coupling (least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm—mRMR-AFWA-LSSVM). mRMR: minimal redundancy maximal relevance; LSSVM: minimal redundancy maximal relevance.
# Galileo Single Point Positioning Assessment Including FOC Satellites in Eccentric Orbits
## 3 Results
The time series of the east, north, and vertical error are reported respectively in the first, second, and third row of the figure. The fourth row shows the orbital errors of the E14 and E18 satellites, while the fifth row shows the time series of HDOP (blue), VDOP (red), and the number of visible satellites (yellow). Each column shows the data related to the same time interval. With regard to the first time interval (first column), the time series of the coordinate errors showed two peaks. The peak on the left was clearly due to the Milena ephemeris error, while the second was due to poor satellite geometry and the low number of visible satellites. Figure 17: Orbital error of the Doresa (E18) satellite plotted versus time. The ticks on the x-axis are spaced 86,400 s (equal to one day); in this way, each tick corresponds to a day. Figure 18: Analysis of the causes of the largest position errors. The time series of errors on the east, north, and vertical coordinates are represented in the first, second, and third row, respectively. The fourth row shows the orbital errors of the E14 (first and third columns) and E18 (second column) satellites. Finally, the fifth row shows the time series of HDOP (blue), VDOP (red), and the number of visible satellites (yellow). Each column shows the data related to the same time interval. Furthermore, the coordinate error of the second time series showed two peaks. This time, the first peak was due to the Doresa ephemeris error, while the second to the combination of this effect with the poor geometry. Finally, the third time interval showed only one peak exclusively due to Milena ephemeris error, the DOP values being very good in those time instants.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 2 Materials and Methods
### Methods
#### 2.3.4 Observed Grassland Degradation Points Collection  
Datasets of degraded grassland points were collected from scientific articles from 2000 to 2020. Peer-reviewed journal articles in Chinese journals were collected from the Chinese Journals Database (CNKI), and international journals were collected from the ISI Web of Science and Google Scholar. The following keywords were used: \"Qinghai-Tibet Plateau\" or \"Tibetan Plateau\" and \"grass\" or \"grassland\". To obtain datasets of grassland degradation points, we selected articles according to three criteria: (1) the degraded grassland was observed in the field; (2) the observed grassland degradation points were from 2000 to 2020; (3) the latitude and longitude of degraded grassland points were reported. The final database included 279 degraded grassland points from 86 published papers. Then, the observed grassland degradation points were used as verification points to verify the accuracy of the six vegetation indexes in reflecting grassland degradation. The higher the consistency between the remote sensing vegetation index and the observation point, the more accurately the remote sensing vegetation index can reflect the grassland degradation.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.7 Active-Bridge Active-Clamp Converter  
Another topology of the DC-DC converter for aerospace applications with galvanic isolation is the Active-Bridge Active-Clamp (ABAC) topology [98] as shown in Figure 16a. Its operating behavior is similar to that of the DAB converter. However, it provides a current-fed LV stage. Ref. [67] compared various parameters of the ABAC converter with those of the DAB converter. A constant current on the DC side, due to interleaved inductors with reduced filter requirements on the LV side, makes the ABAC converter superior to the DAB converter. Nevertheless, a capacitor filter is required to control the ripple resulting from asymmetries in the topology and from harmonics in the load current. A comparison of the weight and volume of the ABAC and DAB converters was provided in [67], showing that in DAB converters, the weight is mainly contributed by capacitors, whereas in ABAC converters, a major part of their weight and volume is contributed by inductors. Nonetheless, the inductance values associated with ABAC converters are relatively low, which reduces the converter's weight and volume. This tendency becomes more evident at higher powers, making these converters a strong candidate for high-power applications. Thus, the ABAC converter is a suitable replacement for the DAB converter, without loss of efficiency and with improved power density.
# Control Design and Validation for Floating Piston Electro-Pneumatic Gearbox Actuator
## 4 Controller Testing and Tuning on Test Bench
### LQR Controller  
Implementing a linear quadratic controller in an embedded environment for the given system is a complex task, as the K feedback matrix is a function of A and B state matrices, which are functions of the system state and the disturbances. There were two possible solutions considered: the first one is to calculate the feedback matrix online; however, this could not be achieved in real-time with the given ECU. The other solution is to calculate the possible values of the feedback matrix K through the calculation of A and B offline, for the finitely many control problems. Therefore, K can be stored in a look-up table.  
As taking into account all corresponding variables and storing the values of the whole K matrix would obviously exceed the capacity of the ECU, thus a parameter sensitivity analysis has been performed. As a result, three of the six variables have been neglected, only the temperatures of the flowing air and the main piston position have been taken into account, which also corresponds to the Q and R matrix weights. Due to the parameter sensitivity analysis, it is possible to store K matrix as function of these three variables, therefore there is no need to calculate A and B state matrices online. Besides, the rows of the feedback matrix would be multiplied by the error of the given state, and in this application, there is only a reference position provided to the controller, thus the error of the other states will be zero, which means that these values can be neglected. In this regard, the controller is used as an output feedback controller. Even in case of proper reference signals (which would be complicated to define), the value of the products of the given weights and the neglected errors would be smaller by at least two orders of magnitude, than the products of the position error and its weights, due to the applied Q and R weight matrices.  
Figure 10 shows Neutral to High gear changes performed by the LQR controller on the same set-up, and under similar conditions, the figure can be interpreted as Figure 9. The strategy of the LQR controller is similar to the PID controller, but it applies slightly longer solenoid valve activations, which helps with the quantitative requirements. Based on the controller testing, it was also seen that it is much less sensitive to the discretization than the PID controller.
## 3 Methods
### Multi-Layer Receptive Field Fusion Module  
In the off-road environments, the segmentation of the drivable area is significant. Also, the identification of obstacles is a major task, such as stones, plants, etc. Therefore, we designed a multi-layer receptive field fusion module (MRFFM) to fuse and stack the feature vectors under different receptive fields, so that the network can more effectively perceive different scales of objects in off-road environments. The structure of the MRFFM module is shown in Figure 3.  
Figure 2: The gated feature fusion module adaptively fuses the features of PointTensor and Cylinder.  
In the MRFFM module, the original PointTensor is first subjected to three sets of convolutions of different kernel sizes to obtain a feature tensor, which represents the feature extraction results with large, medium, and small receptive fields. We use this to denote the fusion result of small-scale features and medium-scale features, which can be expressed as:  
\[z=P(x_{1}\oplus x_{2})\otimes x_{1}+(1-P(x_{1}\oplus x_{2}))\otimes x_{2} \tag{3}\]  
Similarly, the final fusion output of the entire module can be expressed as:  
\[Output=P(x_{3}\oplus z)\otimes z+(1-P(x_{3}\oplus z))\otimes x_{3} \tag{4}\]  
In the above formula, \(P(\cdot)\) represents the output of the PointTensor attention module (PAM); \(\oplus\) represents the feature fusion, where element-wise summation is used; and \(\otimes\) represents element-wise multiplication. The detailed flow of the PAM is shown in Figure 4. Since the end of the module is the output of the Sigmoid function, the result is between 0-1, so the dotted line in Figure 3 that represents \(1-P(x_{1}\oplus x_{2})\) and the solid line that represents \(P(x_{1}\oplus x_{2})\) are in the range of 0-1. Therefore, through MRFFM, we can achieve weighted superposition of feature tensor with different scales.  
Inspired by attentional feature fusion (AFF) [27], we extend the multi-scale channel attention module to point cloud sequences and design the PointTensor attention module (PAM), which is shown in Figure 3. The input of this module is PointTensor. The global channel context, represented as \(G(X)\), can be obtained through global average pooling, and the other branch is the local channel context, which can be represented as \(L(X)\). The Conv module in Figure 4 adopts the convolution proposed by [PERSON] [28], which can quickly achieve local channel context aggregation of PointTensor. The final output of the module is calculated by the Sigmoid function and can be expressed as:  
\[Output=Sigmoid(G(X)\oplus L(X))\]  
Figure 4: PointTensor attention module, which helps the multi-layer receptive field module achieve better feature extraction of PointTensor.  
Figure 3: The multi-layer receptive field fusion module can extract features of objects of different scales, so that various obstacles in off-road scenes can be effectively identified.  
where \(\oplus\) means broadcasting addition. As a result, a 0-1 output can be obtained through the PAM module, and the feature tensor with different scales can be weighted and super-imposed.
## 3 Results
### Stability of the Candidate Reference Genes  
To further evaluate the expression stability of the eight candidate reference genes under two experimental conditions (temperature and developmental stage), the \(\Delta\)Ct method, BestKeeper, NormFinder, and geNorm were employed. RefFinder was used to calculate an overall stability ranking. For different temperature treatments, the analysis of the result of the \(\Delta\)Ct method and NormFinder clearly showed that _UBCE_, _PMP-2_ and _TUB_ were the most stable reference genes (Table 2). Based on BestKeeper, _HIS_ and _UBQ_ were the most stable reference genes, whereas, that _Actin_ and _18S rRNA_ were relatively unstable (Table 2). Based on the results from geNorm, all eight candidate genes were suitable for reference genes (MV \(<\) 1.5), _Actin_ and _EF1\(\gamma\)_ being the most stable genes (Figure 3a). Based on the results from all four statistical algorithms, _EF1\(\gamma\)_ and _UBCE_ were the most unstable reference genes. The stability ranking of genes from highest to lowest stability by RefFinder was _UBCE_ > _EF1\(\gamma\)_ > _TUB_ > _PMP-2_ > _Actin_ > _HIS_ > _UBQ_ > _18S rRNA_ (Figure 4a). Pairwise variation values of the expression stability of the candidate reference genes calculated by geNorm was V-value \(<\) 0.15 at V2/3, indicating that only two references genes were required to normalize the target gene data (Figure 5). Thus, the best combination of reference genes for temperature treatments of _B. xylophilus_ was _UBCE_ and _EF1\(\gamma\)_.  
Figure 2: Expression profiles of the eight candidate reference genes in different experimental conditions. (**a**) Different temperature treatment; (**b**) Different developmental stages. The expression levels of the candidate reference genes are shown as Ct values. The box represents the 75 th to 25 th percentiles and the line in the box represents the median. The whiskers on each box represent the minimum and maximum Ct values. For the different developmental stages, the \(\Delta\)Ct method and NormFinder showed that _EFI\(\gamma\)_ and _UBCE_ were the most stable genes, whereas _UBQ_ and _18S rRNA_ exhibited the greatest variation (Table 2). Based on BestKeeper, _PMP-2_ and _Actin_ were the most stable reference genes, whereas, that _UBQ_ and _18S rRNA_ were relatively unstable (Table 2). Additionally, GeNorm calculated the lowest M value for the _Actin/TUB_ pair, suggesting that they are the most stable reference genes (Figure 3b). RefFinder ranked the genes from most to least stable as follows: _EFI\(\gamma\)_ = _Actin_? _TUB_? _UBCE_? _PMP-2_? _HIS_? _UBQ_? _18S rRNA_ (Figure 4b). The geNorm pairwise variation analysis also revealed that the first V-value \(<\) 0.15 emerged at V2/3 (Figure 5). Thus, the best combination of reference genes for developmental stages samples of _B. xylophilus_ was _EFI\(\gamma\)_ and _Actin_.
# Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure
###### Abstract  
This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.  
high-throughput computing; cloud computing; satellite image processing; openEO
## 2 Electromagnetic Interference Characteristics of Converter Station to the Surrounding Environment
### Analysis of Electromagnetic Interference of Other Equipment  
In addition to the interference caused by the converter valve in the converter station, the corona and discharge phenomena of various high-voltage equipment will also cause electromagnetic interference. The frequency range of these electromagnetic interferences is between 100 kHz and 10 MHz. However, as long as the operating voltage is less than the corona initiation voltage, the equipment will not be corona. After the shielding ring is installed, the corona initiation voltage can basically be guaranteed to be above the normal operating voltage. Therefore, electromagnetic interference caused by corona can be avoided under normal circumstances.  
In addition, operational operations can also cause electromagnetic interference to the converter station, such as outage, standby, lockout, unlocking, and open circuit experiments. Although these operations will cause large electromagnetic interference, for the application of UAV inspection, it can be avoided by the reasonable arrangement of converter station actions and UAV inspection time [5].  
In summary, considering the application of drones, the electromagnetic interference of the converter station is mainly caused by the normal switching action of the converter valve. Other minor interferences, such as corona, converter station operation, etc., can be  
Figure 2: Schematic diagram of valve hall [15].  
avoided. Therefore, these effects can be ignored when designing, and the electromagnetic interference characteristics of the converter valve can be mainly considered [5].