esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of Source Codes
### Design of Rate-Compatible Source Codes for JSCCM systems
A low-rate source code can be constructed from the proposed rate-\(3/4\) protograph found in (17) by code lengthening. Specifically, a base matrix with rate-\(R_{s2}\) is constructed by lengthening the base matrix of a rate-\(R_{s1}\) (\(R_{s1}>R_{s2}\)) source code (a base code) in the form of  
\[\textbf{B}_{s}^{R_{s2}}=\left[ \begin{array}{cc}\textbf{B}_{s}^{R_{s1}}& \textbf{B}_{e}\end{array} \right], \tag{18}\]  
where \(\textbf{B}_{s}^{R_{s1}}\) is the base matrix of the rate-\(R_{s1}\) source code, and \(\textbf{B}_{e}\) is an extension matrix. Similarly, row weight distribution candidates for different source coding rates are sought via Algorithm 1 with \(m=4\), \(\Delta=0.6\) dB, and \(\delta=0.02\), which are presented in Table 1. With the same setting of \(m\), the target system rates \(R\) are dominated by the source coding rates according to (2). Then, we impose the row weight constraints on the elements of the extension matrix and search for the base matrix with the lowest source decodingthreshold for each source coding rate. Finally, we obtain a rate-compatible family of source codes with rates from \(1/3\) to \(3/4\). \[\mathbf{B}_{s}^{3/5}=\left[ \begin{array}{c|c}\mathbf{B}_{s}^{3/4}&2\\ &2\\ 0\end{array} \right]. \tag{19}\]  
\[\mathbf{B}_{s}^{1/2}=\left[ \begin{array}{c|c}\mathbf{B}_{s}^{3/5}&2\\ &2\\ &2\end{array} \right]. \tag{20}\]  
\[\mathbf{B}_{s}^{1/3}=\left[ \begin{array}{c|c}\mathbf{B}_{s}^{1/2}&3&0&1\\ &3&0&1\\ &3&2&1\end{array} \right]. \tag{21}\]  
Table 2 shows the source decoding thresholds of the rate-compatible codes. It is found that the proposed codes exhibit threshold gaps between \(0.0512\) and \(0.0702\) to the corresponding source coding rates. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Source Coding Rate**} & \multicolumn{2}{c}{**Target System Rate**} & \multirow{2}{*}{**Size of \(\mathbf{B}_{s}\)**} & \multirow{2}{*}{\(\mathbf{W_{\Delta}}\)} \\  & & & & \\ \cline{1-1} \(R_{s}=3/4\) & \(R=4\) & \(3\times 4\) & \((3,3,3),(3,3,5),(3,5,3),(5,3,3)\) \\ \(R_{s}=3/5\) & \(R=5\) & \(3\times 5\) & \((5,3,5),(3,3,3),(5,5,5)\) \\ \(R_{s}=1/2\) & \(R=6\) & \(3\times 6\) & \((3,5,5),(5,5,7),(7,5,5),(5,7,5)\) \\ \(R_{s}=1/3\) & \(R=9\) & \(3\times 9\) & \((7,7,7),(7,7,9),(9,7,7),(7,9,7)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Row weight distribution candidates for different source coding rates (\(\Delta=0.6\) dB). Figure 3: BER performance of JSCCM systems for different source codes (\(R_{s}=3/4\)) with different source probabilities.
## 5 Proposed Method
### Binary Forecast Imbalanced Problem  
Overloads in SAG mills are critical yet infrequent events, representing less than 2% of labeled data and posing a binary forecasting problem with significant class imbalance. Developing a model that accurately distinguishes between normal and overload states is challenging due to potential bias toward the majority class. To address this, we frame the problem as a multivariate binary forecasting task [13; 34], leveraging the complex interactions among multiple operational variables. Our CNN model aims to unravel these interactions and understand the underlying patterns leading to overloads. The data's unbalanced and multivariate nature necessitates a specialized approach. We design a classification system capable of capturing subtle nuances indicative of impending overloads while effectively managing the inherent data imbalance. In the context of overload forecasting within semi-autogenous (SAG) mills, we define the binary condition of overload at any given discrete time \(t\) as an element of the vector \(\mathbf{y}\), where \(\mathbf{y}=[y_{1},\ldots,y_{T^{\prime}}]^{T}\), and \(T^{\prime}\) encompasses all discrete and equidistant time instances in the set \(\{1,\ldots,T^{\prime}\}\). Here, \(y_{t}=1\) signifies an overload at time \(t\), and \(y_{t}=0\) otherwise. Correspondingly, we denote the matrix \(\mathbf{X}\) as a \(T^{\prime}\times N\) matrix, where \(\mathbf{X}=[\mathbf{x}_{1}|\ldots|\mathbf{x}_{T^{\prime}}]^{T}\). Each vector \(\mathbf{x}_{t}\) within this matrix is composed of \(N\) selected features that characterize the operational state at time \(t\), with \(x_{t}^{(j)}\) representing the value of the \(j\)th feature at that time. We cast our approach within a multivariate time-series framework, perceiving it as a stochastic process, to forecast an impending overload \(y_{t+k}\) at a future time step \(t+k\) based on current and past observations. Recognizing the complexity of the process and the unknown nature of the underlying distribution, our model endeavors to learn from historical data. The task is thus to predict the probability of an overload \(k\) steps ahead, using a window of \(w\) past observations. This predictive task can be mathematically modeled as estimating the conditional probability \(p(y_{t+k}|x_{t},\ldots,x_{t-w+1})\).
## 4 System Overview  
The system implemented utilizes a parallel-series (PS) topology, powered by a current source. In this configuration, the correlation between the output power and the load is predetermined. Unlike most wireless power transfer (WPT) systems that are driven by inverters requiring at least four switches, our approach addresses the issue of significant switch losses in high-frequency applications. As an alternative to conventional inverters, a Class-E power amplifier (PA) was employed. This choice is particularly advantageous for low-power and very-high-frequency WPT applications. The Class-E PA demonstrates its efficacy even in dynamic charging scenarios, such as when there is misalignment between the transmitter and receiver coils [24; 25].  
This alternative exhibits notable characteristics, including a simplistic design, and conditions of zero voltage switching, as well as zero voltage derivative switching [25; 26; 27; 28]. The Class-E power amplifier (PA) is capable of achieving efficiencies of approximately 90%, especially when operating at frequencies within the MHz range. However, it is important to note that the Class-E PA demonstrates sensitivity to variations in load [25].  
In Figure 16, the Class-E power amplifier (PA) circuit is depicted on the left side, while the wireless power transfer (WPT) circuit is presented on the right side. The operating range of the Class-E PA was selected to be between 12 V and 14 V, thereby aligning with the predetermined voltage and power requirements at the load. The parameters of the circuit are concisely summarized in Table 5.  
Figure 16: Circuit schematic.
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results  
In this section, we present our experimental results and analysis. First, we introduce the applied evaluation metrics in Section 3.1. Second, the implementation details and the experimental setup are given in Section 3.2. Subsequently, a detailed parameter study is presented in Section 3.3, in which we extensively reason the design choices of our proposed method. In Section 3.4, we explore the performance of our proposed method on different distortion types and distortion intensity levels. Subsequently, we examine the relationship between the performance and the amount of training data in Section 3.5. In Section 3.6, a comparison to other state-of-the-art method is carried out using  
Figure 4: Activation map visualization of a reference-distorted image pair. (**a**) Visualization of the first 16 activation maps of AlexNet’s [4]_conv1_ layer using the reference image in Figure 2(a). (**b**) Visualization of the first 16 activation maps of AlexNet’s [4]_conv1_ layer using the distorted image in Figure 2(b).  
Figure 3: A reference-distorted image pair from KADID-10k [44]. (**a**) Reference image. (**b**) Distorted image.  
Figure 2: Compilation of layer-wise feature vectors. The activation maps of a distorted-reference image pair are compared to each other in a given convolutional layer with the help of traditional image similarity metrics (SM).  
six benchmark IQA databases, such as KADID-10k [44], TID2013 [53], TID2008 [46], VCL-FER [47], CSIQ [49], and MDID [48]. The results of the cross database are presented in Section 3.7. Table 1 illustrates some facts about the publicly available IQA databases used in this paper. It allows comparisons between the number of reference and test images, image resolutions, the number of distortion levels, and the number of distortion types.
# Investigation of the Pressure Coefficients Correlation Field for Low-Rise Building Roofs
## 4 Conclusions  
Based on the experimental wind-tunnel studies carried out in the CRIACIV boundary layer wind tunnel (Italy) [28; 29; 30; 31; 32; 33; 34; 35; 36; 37], this paper has examined the results for four rectangular models of low-rise buildings with HPR. In particular, it has discussed the dependence of the pressure coefficients correlation value on the height and curvature of the scale models and the mean direction of the wind. This research demonstrates:* the correlation of wind-induced pressure fields on hyperbolic paraboloid roofs plays a relevant role for design purposes; for complex geometries, the variations of correlation functions between central and perimeter parts of the roof are very relevant;
* the obtained experimental results highlight the dominant influence of the angle of wind incidence when defining and investigating wind loads, in addition to the importance of the building's plan dimensions and height; in particular: 1. for a wind angle of 0\({}^{\circ}\), the correlation between the pressure at a given point on the roof and the surrounding points was mainly influenced by the curvature of the roof, with the exception of areas near the leading edge, where the correlation was also significantly influenced by the height of the model;
2. for a wind angle of 90\({}^{\circ}\), the correlation was influenced by both the height and the curvature for almost the entire roof, except the areas near the leading edge, where the height of the model's windward surface played a predominant role compared with that of curvature;
3. for a wind angle f 45\({}^{\circ}\), the correlation was influenced by the way in which the flow detached from two edges of different lengths and with opposite curvatures, much more than by the curvature or height of the model;  
The correlation between wind-induced loads at points of the roof far from each other can be of considerable importance in the characterization of the average pressures or suctions to be considered in the design and verification of the main load-bearing elements of this kind of structure. However, these aspects of wind-induced loads have seldom been considered in the scientific literature. It is therefore worth extending this research in future to explore the role of the correlation in different configurations of low-rise building roofs. Such research can also be very useful for investigating wind modes and their effects on structures [43; 44].  
Conceptualization, F.R. and V.S.; methodology, F.R. and V.S.; software, F.R. and V.S.; validation, F.R. and V.S.; investigation, F.R.; data curation, F.R.; writing--original draft preparation, F.R., V.S., and M.F.S.; writing--review and editing, F.R., V.S., and M.F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
1. The deep neural network model achieved the complex non-linear mapping from the core NMR T\({}_{2}\) spectrum to the movable fluid percentage, and the prediction effect of DNN model was compared with that of BPNN, KNN and SVR model. The experimental results demonstrated that for the 10 core data of Changqing Oilfield, the \(R_{2}\) correlation coefficient between the predicted value of the DNN model and the real movable fluid percentage of core is as high as 0.9632. The prediction RMSE of the DNN model is reduced to 2.447, and a good prediction effect is achieved.  
2. Compared with the method of predicting unconventional reservoir saturation based on logging data, the method proposed in this article to predict the percentage of movable fluid in unconventional reservoirs based on laboratory NMR data has achieved better prediction results and faster prediction speed. Therefore, this method can provide certain guidance for the intelligent development of laboratory reservoir parameter measurement.  
3. The study found that the prediction accuracy of DNN model gradually decreased when the number of hidden layers of the deep neural network model was greater thanfive. The reason for the above phenomenon may be that there are fewer training data and the model's overfitting in the later stage of training. In the future research work, the above two aspects will be optimized: (1) increasing the core NMR data of the training set; (2) taking a variety of methods to solve the overfitting in the later stage of model training.  
Conceptualization and methodology, Z.Y., J.W. and Y.L.; investigation, J.W.; software, J.W.; formal analysis, J.W., X.Z., Z.N.; data curation, J.W., Y.L., Z.N.; writing--original draft preparation, J.W.; funding acquisition, Y.L., Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PetroChina Company Limited, grant numbers 2019D-500809.  
Not applicable.  
Data Availability StatementData are available on request the corresponding author ([EMAIL_ADDRESS]).  
Figure 5: Prediction results of different models.
#### 3.1.3 Variation of Steam to Carbon Ratio \(n_{\rm SC}\)  
Figure 4 shows the influence of the steam to carbon ratio \(n_{\rm SC}\) on the permanent gases. The \(n_{\rm SC}\) had a strong influence on the permanent gas composition, since H\({}_{2}\) and CO\({}_{2}\) strongly increased and CO decreased with \(n_{\rm SC}\). This was due to the water gas shift reaction (CO + H\({}_{2}\)O \(\rightarrow\) H\({}_{2}\) + CO\({}_{2}\)) that was driven to the right side by adding steam. Therefore also the dry gas yield rose, since water was converted to H\({}_{2}\). The CH\({}_{4}\) and C\({}_{x}\)H\({}_{y}\) concentrations were mostly unaffected. Naturally, as more steam was introduced, the H\({}_{2}\)O concentration increased with \(n_{\rm SC}\). Parameter variation in steam-oxygen gasification of wood in a comparable facility was conducted by [22] and in air-steam gasification of corn straw by [51], where similar trends for H\({}_{2}\), CO, CO\({}_{2}\) and H\({}_{2}\)O were found, but other than in this work, a slight decrease of CH\({}_{4}\) and C\({}_{x}\)H\({}_{y}\) was reported with increasing \(n_{\rm SC}\) in the reference. High CO and lower H\({}_{2}\) and CO\({}_{2}\) concentrations at low \(n_{\rm SC}\) as in this work were also reported by [21]. For steam-air gasification of refuse-derived fuel in a rotary kila also a rise in gas yield with \(n_{\rm SC}\) was reported [53].  
Figure 4: Permanent gas concentrations for different \(n_{\rm SC}\) in dry N\({}_{2}\)-free basis, for H\({}_{2}\)O wet N\({}_{2}\)-free basis and dry N\({}_{2}\)-free gas yield per dry ash free fuel.  
Figure 3: Permanent gas concentrations in dry N\({}_{2}\)-free basis, for H\({}_{2}\)O wet N\({}_{2}\)-free basis and dry N\({}_{2}\)-free gas yield per dry ash free fuel for different temperatures \(\vartheta\) at constant \(n_{\rm O2}\).  
These results indicate, that through a higher \(n_{\text{SC}}\) a conversion of CO to H\({}_{2}\) and CO\({}_{2}\) can be achieved to improve the H\({}_{2}\)-yield or to adjust the H\({}_{2}\)/CO-ratio. However, higher \(n_{\text{SC}}\) requires more energy-intensive steam production, so that in practice it always needs to be considered if a higher \(n_{\text{SC}}\) or a downstream catalytic WGS unit, which are currently developed for uncleaned syngas [54], is preferred.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 8 Verification and Validation of the Simulation Model
### Validation
#### 8.2.1 Comparison with Other Models  
In this method, the output of the simulation model (tool) was compared with a simulation-based tool developed earlier by the same department [14]. Although both models used the same input data and time periods over the same geographical area (Bay of Bothnia), the numbers of ports and vessels considered were different. In addition, some of the underlying modelling assumptions regarding convoys, channel formation, and icebreaker cooperation were also different. Hence, instead of a quantitative comparison, a qualitative comparison of the two models was performed. The direction of variation (increase/decrease) in system outputs with changes in input parameters was compared. Table 4 shows the results from this comparison study. The parameter considered was the vessel speed threshold, which is the speed limit below which the vessel will be eligible for icebreaker assistance. The threshold was gradually increased, from 3 to 6 kn in the current model and 2-6 kn in [14].  
Figure 7: (**a**) Visualization of ice fields and dirways. (**b**) Vessel names and speeds.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 3 Strategies for Scanning the Impact Area
### Scanning Strategy Using Multi-UAV System without Area Decomposition  
In the strategy proposed in this work, the UAVs should fly information in a side-by-side approach, while scanning the area as shown in Figure 3. In this scenario, the UAVs maintain the same distance between each other. The images are acquired periodically as a mosaic, which has the size of thecoverage area of the camera embedded on the aircrafts times the number of UAVs in the network. Thus, it is possible to estimate the area shared between neighbors due to the overlapping, which is necessary to ensure that the impact area is properly scanned. In this scanning strategy, the distance between the two UAVs is determined to ensure the correct image superposition, which depends on the camera aperture and flight altitude. This kind of formation eliminated redundant links and, typically, the distance between two neighboring UAVs is shorter than the maximum range for the communication system embedded on the aircrafts. Therefore, this method will tend to provide more reliable communication.  
Applying the algorithm for spiral scanning as described by [PERSON] [44], it is possible to keep the UAVs in the formation and as close to each other as possible. An alternative to the strategy lies in the back-and-forth methodology, where the inversion occurs in both directions, resulting in almost the same trajectories for all nodes. The spiral and back-and-forth methods are shown in Figure 4a,b respectively.  
Fully scanning an area while maintaining all UAVs in a fixed formation is useful for situations where the processing speed and time for mission completion are the main factors to be considered. In this case, at least one UAV capable of maintaining a reliable communication link with the base  
Figure 4: Area scanning strategies for the formation of unmanned aerial vehicles (UAVs). (a) Spiral pattern; (b) Back and forth pattern.  
Figure 3: Flight formation for spiral scanning.  
station throughout the whole scanning area is a mandatory requirement. Moreover, the communication link must ensure that there is enough bandwidth to intermediate the communication between other nodes with the base station. In this manner, the network can be established using the node with the widest range as a hub for the neighbors that are unable to communicate directly with the base station. In the case of a node failure, the neighboring nodes must reorganize themselves to maintain the formation. If a single node has the requirement to work as a hub, its failure will compromise the whole system as a consequence. Thus, it is recommended to use more than one hub in the network to provide redundancy and mitigate failure.
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 4 Experiments
### Evaluation Metrics  
We employed two metrics: 3D error and 2D error. 3D error measures the 3D Euclidean distance between point cloud transformed by ground truth 6 DoF pose and by predicted pose. 2D error measures the 2D image pixel distance between the pixel in which the 3D point is projected onto the image using ground-truth pose and the predicted pose.  
For the 3D error, we employed the 3D average distance metric for the symmetric object proposed by [PERSON] et al. [34]:  
\[E_{3D}=\frac{1}{|v|}\sum_{\mathbf{x}_{1}\in v}\min_{\mathbf{x}_{2}\in v}|| \mathrm{Tr}_{\Phi}(\mathbf{x}_{1})-\mathrm{Tr}_{\Phi}(\mathbf{x}_{2})||_{2}, \tag{10}\]  
where \(x\) is a point sampled from object point cloud \(v\), \(\hat{\Phi}\) is the ground truth pose, and \(\Phi\) is the predicted pose.  
For the 2D error, we employed 2D projection metric (2D error) which is suited for applications such as a augmented reality [35]. An error is calculated as follows:  
\[E_{2D}=\frac{1}{|v|}\sum_{\mathbf{x}_{1}\in v}\min_{\mathbf{x}_{2}\in v}|| \Gamma(\mathrm{Tr}_{\Phi}(\mathbf{x}_{1}))-\Gamma(\mathrm{Tr}_{\Phi}(\mathbf{ x}_{2}))||_{2}, \tag{11}\]  
where \(\Gamma\) is a camera intrinsic parameters which project the 3D points to the 2D image plane. Both metrics are that lower is better.  
We sample \(M\) points from the superquadric surface to generate \(x\). By sampling a point \(\mathbf{x}\) from a unit superquadric surface, according to Equation (2), the points that have a high curvature are emphasized. For an unbiased sample distribution, we need to apply equidistant rendering using spherical angles, as introduced by [PERSON] et al. [36],  
\[\mathbf{x}(\eta,\omega)=\left[\begin{array}{c}\rho cos(\eta)cos(\omega)\\ \rho cos(\eta)sin(\omega)\\ \rho sin(\eta)\end{array}\right], \tag{12}\]  
where,  
\[\rho=\left((|cos(\omega)cos(\eta)|^{\frac{2}{2}}+|sin(\omega)cos(\eta)|^{ \frac{2}{2}}\right)^{\frac{\epsilon_{2}}{\epsilon_{1}}}+|sin(\eta)|^{\frac{2} {\epsilon_{1}}})^{-\frac{\epsilon_{1}}{2}}. \tag{13}\]  
The generated ground-truth models are visualized in Figure 4 right. Note that this model is only used to annotate the ground-truth pose and calculate the evaluation metrics.
## 2 Materials and Methods
### Soil, Amendments, Liquid Fertilizer Characteristics  
For soil chemical analysis, soil samples were air dried and passed through a 2 mm sieve after hairy vetch cultivation. The pH and electrical conductivity (EC) of the soils were measured in a 1:5 suspension of solid:water using a combined pH-EC meter (Thermo Orion 920A, Thermo Fisher Scientific, Waltham, MA, USA). The total concentration of As was determined by digesting soil sample with aqua regia, a mixture of HCl and HNO\({}_{3}\), in accordance with ISO 11466 [14]. The bioavailability of As was evaluated by extraction with a 0.5 M CaCl\({}_{2}\) solution according to the procedure of [PERSON] and Millan [15]. The As concentration in solution was determined using an inductively coupled plasma optical emission spectrometer (IPC-OES, Agilent, Santa Clara, CA, USA). Dissolved organic carbon (DOC) was analyzed using an automatic total organic carbon analyzer (Shimadzu, TOC-VCPH, Tokyo, Japan) after shaking 10 g of soil and 100 mL of deionized water for 6 h [16]. To confirm the changes in inorganic nitrogen content by liquid fertilizer and plant cultivation, ammonium and nitrate ion were determined separately using Kjeldahl distillation (Distillation Unit B-324, BUCHI, Flawil, Switzerland) method after extraction with 2 M KCl solution [17]. The pH, EC and total As concentration of the amendments were measured in the same methods as the soil analysis method. The pH and EC of liquid fertilizer were also determined using the same pH-EC meter. Total nitrogen (TN) and total ammoniacal nitrogen (TAN) contents of liquid fertilizer were determined using a Kjeldahl digester (KjelDigester K-446, BUCHI, Flawil, Switzerland), distillation and titration method.