esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Voltage-Harmonics Mitigation
#### 3.6.2 Measurement and Quantification  
Assume that the DSO sends an enable signal to DRES, so that the latter starts acting also as an active filter. The filtering action will stop either when a disable signal is received by the DSO or the total harmonic distortion (THD) of the voltage at the DRES terminals falls below a predefined value also determined by the DSO. The contribution of a DRES to harmonic mitigation can be quantified as described in (22) considering the fact that a DRES -even when not acting as active filter- injects harmonic currents, which should be below the limits specified by standards [10]. These currents can be considered as \"background\" harmonics for which the DRES should be remunerated or generally rewarded.  
\[I_{h}^{2}(i)=\frac{1}{T}\int\limits_{0}^{T}i_{h,i}^{2}(t)dt \tag{22}\]  
\[D_{h,\text{day}}=\sum_{k=1}^{1440}\left[I_{h}(k)-I_{1}(k)\cdot\lambda_{h} \right]\,,\,I_{h}(k)>I_{1}(k)\cdot\lambda_{h} \tag{23}\]\[D_{\text{day}}=\sqrt{\sum_{h=1}^{25}D_{h,\text{day}}^{2}} \tag{24}\]  
The instantaneous harmonic current \(i_{h,i}\) is used in (22) to find its squared RMS value over a period T = 1 min, within the \(i_{\text{th}}\) minute. The actual contribution, \(D_{h,\text{day}}\), of a DRES over a day (one day has 1440 min) for the mitigation of the \(h^{\text{th}}\) harmonic is given in (23) where, \(I_{1}(k)\) is the 1-min rms value of the fundamental current injected by the converter during the \(k^{\text{th}}\) minute and \(\lambda_{h}\) is the coefficient of the individual harmonic as defined in [10]. For example, \(\lambda_{h}=0.04\) for \(h<11\), \(\lambda_{h}=0.02\) for \(11\leq h<17\), etc. Finally, the total daily contribution of a DRES in harmonic mitigation is calculated by \(D_{\text{day}}\), which is the suggested metric for the remuneration of a DRES. Considering that the switching frequencies of the DRES are in the range of 10-20 kHz (the converters in wind turbines of the order of MW may have switching frequencies that are even lower than 10 kHz), it is reasonable to focus on mitigation of harmonics up to the 25 th order. The instantaneous values of currents in (22) have to be measured at the DRES converter terminals. The selection of \(T=1\) min instead of \(T=10\) min (as specified in EN 50160) is justified by the fact that the quantification will be used for remuneration of the service. However, this averaging period is a parameter that can be changed. It affects only the memory capacity of the converter. The procedure for measuring the harmonic voltages and currents should be according the Class A mentioned in the relevant standards [86; 112].
# Failure Mechanism of Back-Break in Bench Blasting of Thin Terrane
## 1 Introduction
The failure characteristics of layered rock mass, however, are reflected not only in the material properties of the rock mass but also in the structural characteristics of the rock mass [16]. [PERSON] [17] found the structural failure forms of bedding slope rock mass such as shear sliding, arch or arch-bending structural failure forms. [PERSON] [18] observed the bending failure characteristics of the overlying layered roof rock layer in the goaf and established a structural mechanical model of a composite rock beam to explain the failure mechanism of the layered rock mass. [PERSON] et al. [19] also established a structural stress model of the blasted area in a bedding step rock mass by describing the important role of detonation gas and analyzed the bending failure mechanism of the bedding step rock mass by using the simple supported beam theory and deduced the relationship between the shape of the explosive pile and the parameters of drilling and blasting. The analysis of the failure mechanism of a layered rock mass from the perspective of structural failure is of great significance in fully understanding blasting failure. Many scholars have studied back-break of bench blasting. [PERSON] discussed the influence factors of back-break and applied a statistical model to predict the possibility of back-break [20]. [PERSON] also used a neural network method to quantitatively analyze the influence degree of various factors on the formation of back-break [21]. A few scholars have also adopted numerical methods to analyze the formation mechanism of back-break [22]. However, there is little research on the influence of structural characteristics on back-break. Therefore, this paper analyzes the formation mechanism of back-break based on the structural dynamic characteristics of a layered rock mass. In the above-mentioned structural stress analyses of layered rock mass, however, the failure process of the rock mass is regarded as a static failure while in bench blasting the action time of the explosion load is on the order of 10 ms, which is an obvious dynamic failure process [23]. The structural static analysis method cannot fully reflect the failure characteristics of the layered rock mass under blasting. In our analysis,the structural dynamics method is used to analyze the dynamic stress and deformation characteristics of the rock mass structure under a blasting load. Therefore, according to the structural characteristics of a layered rock mass, a dynamic model suitable for the structural failure analysis of a bench layered rock mass is established and the failure range of the remaining rock and the shape characteristics of a new free surface are studied. At the same time, the numerical simulation results and field test results from the Changjiu (Shenshan) limestone mine, which has the largest production scale of aggregate in the world, verify the feasibility of the model and the correctness of the theoretical analysis.
## 2 Method
### Decode Architecture
The dot product between Pred predicted segmentation map and GT segmentation map: \[|X\cap Y|= \begin{bmatrix}0.12&0.08&0.02&0.03\\ 0.02&0.11&0.05&0.04\\ 0.78&0.89&0.84&0.88\\ 0.91&0.79&0.93&0.83\end{bmatrix} \odot \begin{bmatrix}0&0&0&0\\ 0&0&0&0\\ 1&1&1&1\\ 1&1&1&1\end{bmatrix} \] (6)
2. The result of element-wise multiplication is the sum of the sum of elements: \[|X\cap Y|= \begin{bmatrix}0&0&0&0\\ 0&0&0&0\\ 0.78&0.89&0.84&0.88\\ 0.91&0.79&0.93&0.83\end{bmatrix} \to 6.85\] (7)  
For a binary classification problem, where the ground truth segmentation map (GT) contains only values of 0 and 1, all pixels in the predicted segmentation map (Pred) that are not activated in the ground truth segmentation map can effectively be set to zero. For activated pixels, the focus is primarily on penalizing low-confidence predictions, with higher values receiving better Dice coefficients. Due to the blurred nature of edge features in infrared images, an edge weighting function is added on top of this to enhance the importance of edge weights. Due to the blurred nature of edge features in infrared images, an edge weighting function is introduced to enhance the importance of edge pixels. The edge datum E, obtained by combining the edge features processed through the Sobel operator and filtering out high-frequency data interference from water bodies and background regions in the ground truth (GT) target area, represents the intensity of edge pixels. These edge features capture the most critical information regarding the boundaries of objects, which are vital for accurate segmentation. By focusing on the transitions between different regions, edges provide key structural cues that help the model distinguish between the object and background. The edge datum E is then normalized to the range [0, 1], ensuring that it fits within a standard scale for further processing. The incorporation of edge information enhances segmentation by emphasizing finer details, especially at the boundaries of the target objects. This is particularly useful in scenarios where the contrast between the object and the background is low, or the edges are blurred. Edge detection helps the model focus on the critical boundaries that define the object, leading to more precise segmentation, particularly for small or intricate structures. To improve the accuracy of edge segmentation, the weighted Dice coefficient is used, which is calculated using the following formula:  
\[Dice_{Edge}=\frac{2\sum_{i,j}E(i,j)X(i,j)Y(i,j)}{\sum_{i,j}E(i,j)X(i,j)+\sum_{i,j}E(i,j)Y(i,j)+\varepsilon} \tag{8}\]  
Among them, represents the value of edge data \(E\
## 5 Methodology
### LSTM Encoder-GRU Decoder Architecture with Teacher Forcing
#### 5.1.3 Justification for Using GRU  
1. **Sequential Modeling:** Because each point in a trajectory depends on the ones before it, trajectory data are inherently sequential. GRUs excel at sequential modeling as they maintain a hidden state that evolves, allowing them to capture dependencies in sequential data.
2. **Memory Cells with Gates:** GRUs are equipped with memory cells and gating mechanisms, enabling them to reset and update their internal state dynamically. The reset gate allows the model to choose which historical data to disregard, while the update gate determines which fresh data to incorporate. This flexibility mitigates the vanishing gradient issue and facilitates the capture of long-term interdependencies.
3. **Efficient Training:** GRUs are designed for computational efficiency, making them suitable for handling lengthy sequences often encountered in trajectory data. The efficiency of GRUs may contribute to quicker convergence during training.
4. **Parameter Efficiency:** Compared to LSTM networks, GRUs have fewer parameters, enhancing their parameter efficiency. This characteristic is particularly beneficial when working with trajectory data that may have limited samples. The reduced number of parameters helps prevent overfitting and may lead to better generalization for new trajectories.  
Figure 3: Encoder-Decoder Architecture with Teacher Forcing: The red-labeled points represent the true outputs used during training
## 2 Baselines in the Literature
### Baseline Methodologies
#### 2.2.4 Historical  
The historical (rolling) baseline methodology uses historical measurements over a longer period. This method is extensively described in the literature. Implementations can be based on the average value of the measurements of x out of y days [11], or using an exponential moving average [15].  
For this baselining methodology, proneness to manipulation not only refers to gaming, but also to the discussed user dilemma: the baseline is determined using historical values, so by providing flexibility today, the future baseline of the flexibility source is influenced (typically at the expense of the user's business case). Gaming is harder with the historical baseline, as this implies aggregators would need to structurally adjust their behaviour on days no flexibility is activated.  
The baselining methodology is easy to understand, but slightly more complicated to implement. ICT infrastructure is necessary to determine the baselines. This ICT infrastructure needs to be interfaced with (amongst others) measurements and the measurement data should be correct and available at all times. In terms of transparency it is paramount that market parties have an agreement on the measurement data used, as this is necessary for reproducible and verifiable results. For traditional sources (e.g., large, predictable industrial loads) this method scores well in terms of accuracy. The accuracy however quickly drops when flexibility assets are volatile and have different profiles every day. This baselining methodology is commonly referred to in the literature, and can be found in for example references [11; 15; 16; 17; 19].
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 4 Experiment
### Evaluation Criteria  
The correct matching rate (CMR) was selected as the evaluation criterion; CMR \(=\) CM/M; where M is the number of total matched point pairs, and CM is the number of correctly matched results. A matching result is correct if the overlapping area ratio (OAR) between the matching and true positions reaches 90%. The OAR is calculated according to the following equation:  
\[OAR=\frac{f(TW-\triangle x)f(TW-\triangle y)}{TW^{2}} \tag{9}\]  
where \(TW\) is the template length; \(\Delta x\) and \(\Delta y\) denote the errors between the matching and true positions, respectively; and \(f(\cdot)\) is a truncate function, which is defined as follows:  
\[f(x)=\begin{cases}x,&x>0\\ 0,&x\leq 0\end{cases} \tag{10}\]
# Hyperspectral Python: HypPy
## 2 Design and Implementation
### Image Formats
#### 2.2.3 Spectral Library Formats  
HypPy supports two basic types of spectral libraries: ENVI spectral libraries and ASCII or text-based spectral libraries. In ENVI spectral libraries, a major drawback is that the list of wavelengths has to be the same for all the spectra in the spectral library. Therefore, the number of bands and the central wavelengths of all the spectra must be the same for all included spectra. An advantage of this format is that it is a binary format and can be addressed like an image, which offers the possibility to process spectral libraries in the same manner as spectral images. This can be used to test processing chains on spectral libraries before running them on images. A process for detecting certain minerals can be tested on the USGS spectral library [12], for instance, before applying it to an image. Problems with false positives of certain minerals can be caught in the early stages of developing a processing chain.  
On the other hand, the ASCII spectral libraries offer more flexibility. Text files can be imported into other programs, and the spectral ranges of the individual spectra do not need to be all the same. A complication is that spectra must be resampled before use. HypPy resamples spectra on-the-fly, using the interpld() function from the scipy.interpolate module to unite both sets of wavelengths. An intermediate step of resampling spectra is not needed.
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 3 Properties and Geomechanics of Bedded Evaporites
### Physical Properties
According to [105], this is because oil companies are generally less interested in collecting data on evaporites, which primarily function as caprocks in petroleum systems. Despite this data shortage, ref. [86] utilized seismic and well data to interpret evaporite minerals such as halite, anhydrite, and carmallite. In terms of well logs, ref. [158] included various log responses for halite and two major potash minerals: carmallite and sylvite. For trona beds, both [159] and [160] discussed the use of well logs--including gamma-ray, neutron, sonic, and density logs--to determine the physical properties of trona beds. Log responses for trona beds are detailed in [161]. Based on these log responses, it is possible to estimate the physical properties of halite, potash, and trona beds. Most core analyses have been conducted to examine rock salt's mechanical properties, which will be discussed in the following section. It is also important to note that other physical factors such as the solubility of evaporites and interlayers affect geomechanical properties. For this purpose, the effect of insolubles is further described in later sections.
## 2 Data and Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Hg (nT)**} & \multicolumn{1}{c}{**D\({}_{\textbf{B}}\) (min)**} & \multicolumn{1}{c}{**Z\({}_{\textbf{B}}\) (nT)**} & \multicolumn{1}{c}{**\(\delta\) H**} & \multicolumn{1}{c}{**\(\delta\) D**} & \multicolumn{1}{c}{\(\delta\) Z**} \\ \hline
07.02.2020 & 39,534.5 & \(-\)36.68 & 18,166.4 & & & \\ \hline
18.02.2020 & 39,534.5 & \(-\)36.68 & 18,166.6 & \(-\)0.01 & 0 & 0.26 \\ \hline
28.02.2020 & 39,534.3 & \(-\)36.60 & 18,166.9 & \(-\)0.21 & 0.08 & 0.22 \\ \hline
04.03.2020 & 39,535.0 & \(-\)36.69 & 18,166.8 & 0.72 & \(-\)0.09 & \(-\)0.05 \\ \hline
12.03.2020 & 39,534.8 & \(-\)36.66 & 18,166.8 & \(-\)0.19 & 0.03 & \(-\)0.04 \\ \hline
18.03.2020 & 39,534.5 & \(-\)36.67 & 18,166.9 & \(-\)0.33 & \(-\)0.01 & 0.09 \\ \hline \multicolumn{5}{c}{Average of variable amplitudes} & \multicolumn{1}{c}{**0.29**} & \multicolumn{1}{c}{**0.05**} & \multicolumn{1}{c}{**0.13**} \\ \hline \multicolumn{5}{c}{Standard deviation of variable amplitudes} & \multicolumn{1}{c}{**0.42**} & \multicolumn{1}{c}{**0.06**} & \multicolumn{1}{c}{**0.14**} \\ \hline
27.03.2020 & 39,535.5 & \(-\)36.64 & 18,166.7 & & \\ \hline
02.04.2020 & 39,535.2 & \(-\)36.62 & 18,166.4 & \(-\)0.27 & 0.02 & \(-\)0.29 \\ \hline \end{tabular}
\end{table} 
Table 2: Values of baselines, average and standard deviation before and during lockdown. Figure 4: HDZ baselines observed (circles) and adopted (line) along with their scatter value before (7 February 2020 to 18 March 2020, red color line) and during (27 March 2020 to 4 May 2020, green color line) the lockdown period, with annotation of dates of observations and K-index value during the time the absolute observations were conducted. Pink vertical lines indicate the dates of diurnal variations, as shown in Figure 1.
## 3 Experimental Validation and Analysis
### Training of LSTM Model and Prediction Results
#### 3.4.2 Analysis of Predicted Results  
To further evaluate the advantages of the CEEMDAN-SC-LSTM model in deformation prediction, this study selected the CEEMDAN-LSTM and a single LSTM prediction model as comparative experiments. To visually compare the predictive performance of different models, a comparison chart of the prediction results, as shown in Figure 13, was plotted. Additionally, to further illustrate the prediction accuracy of the three models, their prediction errors were visualized in Figure 14.  
Figures 13 and 14 depict the prediction and error curves for GNSS displacement and acceleration, respectively, obtained by the CEEMDAN-SC-LSTM, CEEMDAN-LSTM, and LSTM models. It is evident from the figures that compared to CEEMDAN-LSTM and LSTM, the CEEMDAN-SC-LSTM prediction model exhibits closer alignment with the real values and smaller prediction errors for both GNSS displacement and acceleration. These findings indicate that the proposed model can more accurately capture the dynamic variations in the data, thereby generating more precise predictions.  
Furthermore, by comparing the prediction error metrics of the three different models, we can further validate the high-precision predictive capability of the proposed model. As shown in Table 1, for both GNSS displacement and acceleration predictions, the CEEMDAN-SC-LSTM model achieves the minimum values in terms of RMSE, MAE, and MAPE.  
Figure 14: Prediction Errors: (**a**) Displacement prediction error; (**b**) Acceleration prediction error.  
Figure 13: Prediction Results: (**a**) Displacement prediction results; (**b**) Acceleration prediction results.  
Specifically, in GNSS displacement prediction, the CEEMDAN-SC-LSTM model's RMSE value is 0.0107 m, significantly lower than that of the CEEMDAN-LSTM model (0.0301 m) and the LSTM model (0.0278 m). Similarly, in acceleration prediction, the RMSE value of the proposed model is also substantially lower than the other two models. Additionally, the proposed model demonstrates significant advantages in terms of MAE and MAPE metrics. These results demonstrate that the proposed model exhibits higher predictive accuracy and stronger generalization ability in predicting GNSS displacement and acceleration.
# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 2 Data and Methodology  
### XCO\({}_{2}\) Retrievals and Mapping XCO\({}_{2}\) Dataset  
XCO\({}_{2}\) retrieval data products from January 2010 to December 2015 are collected from GOSAT Atmospheric CO\({}_{2}\) Observations from Space (ACOS v7.3). This dataset was produced by the Orbiting Carbon Observatory (OCO) team of the US National Aeronautics and Space Administration (NASA) using a full physics algorithm to retrieve XCO\({}_{2}\) from GOSAT's Spectrometer (TANSO-FTS) calibratedspectra measurements (Level 1B) [19]. In order to ensure high reliability of the data, only those data over land with high gain are used after screening and correction of systematic bias as described in the ACOS Level 2 Standard Product and Lite Data Product Data User's Guide, v7.3 [20]. ACOS XCO\({}_{2}\) retrievals have a standard deviation of error of 1.48 ppm when comparing with ground-based measures of Total Carbon Column Observing Network (TCCON) [21]. Figure 1 shows the total counts of XCO\({}_{2}\) data points during 6 years from January 2010 to December 2015, and their temporal variation in the study area. An annual increase of XCO\({}_{2}\) and the seasonal variation can be clearly seen.  
However, XCO\({}_{2}\) data are irregularly distributed and have many gaps in space and time as shown in Figure 1a because of the limitation of GOSAT observation mode, cloudy and data screening. To investigate the space-time changes of XCO\({}_{2}\), we generate a mapping XCO\({}_{2}\) dataset in which those gaps are filled using the kriging interpolation method based on the spatio-temporal geo-statistics model [22; 23; 24]. The mapping XCO\({}_{2}\) dataset is generated mainly in Chinese mainland area from 18\({}^{\circ}\) N to 57\({}^{\circ}\) N and from 65\({}^{\circ}\) E to 148\({}^{\circ}\) E with 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) grid cells and 3-day interval in time from 1 January 2010 to 31 December 2015. In order to match with collected ODIAC emission dataset in 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), we resampled the spatial resolution of data we used in this paper to 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\). This mapping dataset is hereafter referred to as Mapping-XCO\({}_{2}\).
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Aloha-Based Protocols
#### 5.1.2 Diversity Aloha/Slotted Aloha (DA/DSA)  
This protocol is considered for systems that have large transmission delays (e.g., satellites in GEO orbits) and for which confirmations of packet reception are impractical [50]. In the diversity Aloha/slotted Aloha (DA/DSA) protocol each terminal sends two or more copies of a packet at different randomly selected times, without waiting for the reception of an ACK. The idea is to increase the probability of packet reception and to avoid retransmissions; however, the consequence is an overloaded channel. An application of this protocol in satellite environments is observed in the IP-over-Satellite system for sending short packets and registration [51; 52].  
The maximum performance of this protocol is reported to be lower than for slotted Aloha. In DSA, a maximum normalized throughput of \(S\simeq 0.3\) is achieved for a \(C\simeq 0.6\). However, when \(C<0.5\), the performance of DSA is slightly better. In the case of a \(PLR=10^{-2}\), the protocol supports a normalized load of \(C\simeq 0.05\) (compared to a \(C\simeq 0.01\) in S-Aloha). DSA is similar in behavior to S-Aloha in terms of implementation complexity and energy efficiency.  
DA/DSA is suitable for links with large delays and offered loads less than \(C=0.5\). Nonetheless, in IoT scenarios the channel load will tend to increase progressively with time, consequently DA/DSA may not meet the scalability requirement, a crucial aspect in an IoT system like the one described in the study scenario.