esa-sceva/satcom-chunk-collection huggingface dataset:
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 2 System Model
### Log Likelihood Ratio Calculation  
Various channel coding techniques are commonly employed in wireless communication systems to improve their error performance, since channel reliability can be used to improve system stability. Conventional MIMO system signal detection generally uses a hard decision method to directly execute symbol decisions on the estimated value of the user-transmitted signal vector, i.e., \(\hat{s}\) in Equation (4). In order to output the soft detection information to the back end of the detector, after the MMSE detector estimates \(\hat{s}\), the LLR soft information used for channel decoding could be calculated with the following method. First, we needed to restore the estimated \(\hat{s}\) and the calculated \(W^{-1}\) to the equivalent complex field to get \(\hat{s}_{c}\) and \(W_{c}^{-1}\). Let \(U=W_{c}^{-1}G_{c}=W_{c}^{-1}H_{c}^{H}H_{c}\) denote the equalized channel matrix. The equalized signals obtained by the MMSE filter matrix could be obtained from Equations (2) and (4) as follows:  
\[\begin{array}{c}\hat{s}_{c}=W_{c}^{-1}G_{c}s_{c}+W_{c}^{-1}H_{c}^{H}n_{c}\\ =Us_{c}+W_{c}^{-1}H_{c}^{H}n_{c}\end{array} \tag{6}\]  
Then, the estimated value of the symbol transmitted by the \(i\)th user is \(\hat{s}_{c,i}=\mu_{i}s_{c,i}+e_{i}\), where \(\mu_{i}=[U]_{ii}=U_{ii}\) represented the effective channel gain after equalization, and \(e_{i}\) represented the noise plus interference (NPI) term contained in the \(\hat{s}_{c,j}\). The noise variance was \(\gamma_{i}^{2}=\sum_{j\
eq i}^{K}\left|U_{ji}\right|^{2}+E_{ii}\sigma^{2}\), where \(U_{ji}\) and \(E_{ii}\) represented the \((j,i)\)th element of the matrix \(U\) and the \(i\)th diagonal of the matrix \(E\), respectively, where \(E=W_{c}^{-1}H_{c}^{H}\big{(}W_{c}^{-1}H_{c}^{H}\big{)}^{H}=W_{c}^{-1}G_{c}W_{ c}^{-1}\). Using the max-log approximation representation given in [11], the LLR \(L_{i,b}\) corresponding to the \(b\)th bit transmitted by the \(i\)th user was expressed as:  
\[L_{i,b}=\mathrm{Y}_{i}\Bigg{(}\begin{array}{c}min\\ a\in O_{b}^{0}\end{array}\Bigg{|}\frac{\hat{s}_{c,i}}{\mu_{i}}a\Bigg{|}^{2}- \begin{array}{c}min\\ a^{\prime}\in O_{b}^{1}\end{array}\Bigg{|}\frac{\hat{s}_{c,i}}{\mu_{i}}a \Bigg{|}^{2}\Bigg{)} \tag{7}\]  
where \(\mathrm{Y}_{i}=\mu_{i}^{2}/\
u_{i}^{2}\) represented the signal-to-interference plus noise ratio (SINR), and \(O_{b}^{0}\) and \(O_{b}^{1}\) represented the modulation symbol set with the \(b\)th bit being 0 and 1, respectively.
# Natural Materials as Carriers of Microbial Consortium for Bioaugmentation of Anaerobic Digesters
###### Abstract  
The production of biogas is achieved during anaerobic digestion (AD) using organic matter as a substrate. In Mediterranean countries, a promising substrate is lignocellulosic biomass of perennial grass _Miscanthus x gigentes_, due to its potentially high biogas yields, which could be comparable to maize silage. During AD, bacteria convert biomass into more minor compounds, which are further converted to methane by methanogenic archaea. The selection of appropriate microbes for the degradation of the substrate is crucial, and the enhancement of this step lies in the immobilization of microbes on biocarriers. Described here, a microbial consortium, de novo isolated and conditioned to degrade the _Miscanthus_ biomass, was immobilized onto several natural biocarriers: natural zoelitized tuff, ZoeSand(r) (Velebit Agro, Zagreb, Croatia), perlite, and corroch. There was no statistically significant difference in the number of immobilized bacteria across the different materials. Therefore, all proved to be suitable for the immobilization of the consortium. In the consortium, five bacterial species with different shares in the consortium were identified: _Enterobacter cloacae_, _Klebsiella pneumoniae_, _Enterobacter asburiae_, _Laclexia alexarboxylation_, and _Exigubucterium indicum_. After immobilization on each carrier, the share of each species changed when compared to starting conditions, and the most dominant species was _E. cloacae_ (71-90%), while the share for other species ranged from 2 to 23%. The share of _E. indicum_ was 14% at the start. However, it diminished to less than 1% because it was overgrown during the competition with other bacterial species, not due to an inability to immobilize.  
biocarriers; biogas; zeolite; perlite; corroch; bacteria Article
## 3 Methodology
### Calibration Methods
#### 3.2.1 Platt Scaling  
Platt Scaling, also known as logistic calibration, corrects model miscalibration by applying a logistic transformation to raw prediction scores \(\hat{p}_{i}=M(\mathbf{x}_{i})\)[18]. This method assumes a sigmoidal relationship between uncalibrated outputs and true probabilities in the log-odds space. The calibration function is formally defined as:  
\[\phi_{\text{Platt}}(\hat{p}_{i})=\sigma(a\cdot\text{logit}(\hat{p}_{i})+b), \tag{3}\]where \(\sigma(z)=\frac{1}{1+e^{-z}}\) denotes the logistic sigmoid, \(\text{logit}(p)=\ln\left(\frac{p}{1-p}\right)\) converts probabilities to log-odds, and parameters \(a,b\in\mathbb{R}\) are optimized on the calibration set \(\mathcal{D}_{\text{cal}}\). These parameters minimize the negative log-likelihood objective:  
\[(a^{*},b^{*})=\underset{a,b}{\arg\min}\sum_{(\mathbf{x}_{i},y_{j})\in \mathcal{D}_{\text{cal}}}[y_{j}\ln\phi_{\text{Platt}}(\hat{p}_{j})+(1-y_{j}) \ln\bigl{(}1-\phi_{\text{Platt}}(\hat{p}_{j})\bigr{)}]. \tag{4}\]  
The coefficients \(a\) and \(b\) adjust the slope and intercept of the sigmoid curve, respectively, counteracting systematic biases in the base model's predictions. For instance, if \(M\) exhibits overconfidence (e.g., assigning \(\hat{p}_{i}=0.9\) to cases where only 70% are positive), Platt Scaling compensates by learning \(a<1\), effectively flattening the sigmoid to produce conservative probability estimates.
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 4 Discussion  
### The Relationship between SOC Storage and Climate  
Conversion of primary forests to farmlands typically leads to rapid depletion of soil organic carbon. The extent of SOC loss varies depending on factors such as soil sampling depth and climate zone, with tropical areas experiencing less loss than temperate areas. However, the mechanisms underlying SOC stability under different climatic conditions remain unclear and are thought to involve complex interactions among carbon sources, soil structure, environmental factors (e.g., temperature, precipitation, topography), and microbial activity [18]. Higher temperatures and humidity usually accelerate the decomposition of organic matter and the activity of microorganisms, which may lead to a decrease in SOC. However, in certain circumstances, extreme drought or cold conditions can also limit microbial activity and the rate of organic matter degradation, thereby promoting the accumulation of SOC. Climate is one of the key factors influencing the storage capacity of soil organic carbon, primarily achieved through modulation of the soil microbial community. Climate conditions also directly impact the growth status of vegetation, thus affecting the amount of external organic carbon entering the soil. Additionally, temperature and  
Figure 4: (**a**) Changes of absolute SOC storage in soil profiles at different sampling depths. (**b**) Percentage of SOC storage changes in soil profiles at different sampling depths.  
humidity conditions influence the rate of decomposition of organic matter and the migration process of organic compounds within the soil [19]. Gaining a deeper understanding of the interactions and mechanisms between climate and SOC is of great significance for understanding soil carbon cycling and addressing climate change. Further research can reveal patterns of SOC dynamics under different climate conditions, providing scientific foundations for the development of effective soil management and conservation strategies.
# Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
## 3 Methodology  
### Photogrammetry  
Photogrammetry is a technology that simultaneously recovers the position and orientation of the camera and the 3D shape of an object from multi-view images of the object taken from various angles using SfM [27]. This technology makes it possible to efficiently photograph and reconstruct the 3D shape of blast muckpile. In recent years, the availability of affordable and high-resolution digital cameras, along with the increased computational power of personal computers, has significantly improved the potential of photogrammetry for various applications [28]. The process of 3D photogrammetry consists of several stages, as shown in Figure 3, where the 3D shape is constructed by transforming the 2D image coordinates \((x,y)\) of several photographs into 3D coordinates \((X,Y,Z)\). First, the images are preprocessed, which involves the removal of lens distortion and the normalization of image brightness and contrast [29]. Next, a feature matching process is performed, in which distinctive points in the images are identified and matched across multiple images. The matched features are then used to estimate the camera's pose and calibrate the camera [30]. Drawing from recent research on the effectiveness of ANN in predicting blast impacts, it's crucial to adapt their methodologies to address current gaps in our study [31].  
Once the camera calibration is complete, a sparse 3D reconstruction is generated using SfM. This initial reconstruction is often too sparse for many applications, so a denser reconstruction is obtained using PMVS [19; 32]. Finally, a mesh model is created from the dense point cloud, and a texture is applied to the mesh to generate a realistic 3D model [33]. In this study, we used Agisoft Metashape, a 3D modeling software, which can extract feature points from multiple 2D photographs of a surveyed object using SfM and reconstruct a dense 3D point cloud from a sparse point cloud using PMVS [34]. It is also possible to reconstruct textures and place scales. The software offers various tools for processing and analyzing the reconstructed 3D model, such as tools for measuring distances, areas, and volumes, as well as for exporting the model to various formats [34]. While recognizing that the foundational system model has been discussed in prior works, this study introduces a novel integration of cloud data capabilities with deep learning algorithms for muckpile fragmentation estimation. Our approach aims to harness the expansive potential of cloud data to further enhance the efficiency and accuracy of fragmentation analysis.  
Figure 3: Example of Photogrammetric System.
## 2 Mathematical Modeling for UCAV Path Planning
### Evaluation Function  
Evaluation of path planning for UCAV mainly consists of the threat cost \(J_{t}\) and the fuel cost \(J_{t}\), the formula for calculation is as follows:  
\[J_{t}= \int_{0}^{L}w_{t}dl \tag{2}\] \[J_{f}= \int_{0}^{L}w_{f}dl \tag{3}\]  
where \(w_{t}\) is the threat cost of each point on the flight path, \(w_{t}\) is the fuel cost for each point on the route, and \(L\) is the total length of the route.  
In order to improve the computational efficiency, a more accurate approximation strategy can be used. In this work, the threat cost of the route between two discrete points was calculated. It is approximately equal to the sum of the threat cost of five points, as shown in Figure 2[18, 19].  
Figure 2: Computation for Threat Cost.  
If the \(i\) th edge is within the effect range, the calculation formula of the threat cost is as follows:  
\[w_{{}_{t,t_{\rm s}}}=\frac{L_{i}}{5}\cdot\sum_{k=1}^{N_{\rm t}}t_{k}\cdot(\frac{ 1}{d_{0.1,i,k}^{4}}+\frac{1}{d_{0.3,i,k}^{4}}+\frac{1}{d_{0.5,i,k}^{4}}+\frac{1 }{d_{0.7,i,k}^{4}}+\frac{1}{d_{0.9,i,k}^{4}}) \tag{4}\]  
where, \(N_{\rm t}\) is the number of threatening areas, \(L_{i}\) is the \(i\) th sub-path length, \(d_{0.1,i,k}\) is the distance from the 1/10 point on the \(i\) th edge to the \(k\) th threat, and \(t_{\rm k}\) is the threat level of \(k\) th threat.  
Assuming the speed of a UCAV is a constant, the fuel cost \(J_{\rm f}\) can be equivalent to the total length \(L\) of the flight path.  
Therefore, the total cost comes from a weighted sum of the threat and fuel cost. It can be defined as Equation (5).  
\[J=\lambda J_{{}_{t}}+(1-\lambda)J_{{}_{f}} \tag{5}\]  
where \(\lambda\) is a variable between 0 and 1, which is the balance between safety and fuel performance. If flight security is highly important to this task, we will select a larger \(\lambda\), while if the speed is vital to the flight task, we will choose a smaller \(\lambda\). In this paper, \(\lambda\) is equal to 0.5.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 4 Experiments  
According to the previous sections, the proposed method can directly output the estimated coordinate of the point-of-regard (PoR). Therefore, we directly compared the calculated the spatial coordinate of the PoR with the actual position of the object being watched in space. Although the common gaze estimation methods can provide 2D or 3D gaze point information, the error in the direction of the line-of-sight is a more direct evaluation criterion. In this paper, we directly compared the estimated PoR with the spatial coordinates of the object being watched, which is also a direct comparison method. The actual scene during experiment and experimental results are shown in Figure 10.  
Figure 10: The actual scene during experiment. The left picture shows a person is watching the six visible corners of a box. And the right graph shows the 3D point-of-regard estimation result of this observation experiment.  
Figure 9: The average distance error of calibration results in different numbers of calibration points.  
The gaze tracking device proposed in this paper is equipped with a pair of scene cameras. We accurately calculated the spatial coordinates of the object being watched by using these two cameras, and this measurement was independent of the PoR estimation. In the experiments, all coordinate values are in the device coordinate system, and each fixation time is set to 1 s.
## 2 Numerical Model and Methods  
### Physical Model and Computational Domain  
The schematic of RDC is shown in Figure 1. The RDC used in this paper is a coaxial ring type combustor with an inner diameter and outer diameter of 640 mm and 700 mm, respectively. The RDC consists of the inner wall, outer wall, air inlet and CH4 inlet of the combustor. Figure 1a describes three-dimensional model of RDC, the two-dimensional cross section along axis is shown in Figure 1b. It is to be found in Figure 1 that air is injected by a Laval nozzle shaped slot, and CH\({}_{4}\) is injected through the holes evenly spaced on a circle of inner wall. Among them, there are 60 spray holes evenly distributed on the inner wall of the combustor. This paper mainly investigates the effect of fuel and air injection on mixing effect. Injector spacing in not in the scope of study. This article studies the effects of injection strategies on blending effects and does not consider the effects of injector interactions on fuel and air blending effects. Therefore, there is no need to perform three-dimensional numerical simulation on RDC. At the same time, [PERSON] et al. [22] proposed that simplifying holes of fuel inlet into slot is accessible for cold mixing process study of RDC. Synthesizing the symmetry properties of combustor, Figure 1b was selected as the computational domain and \(x\) axis was set as symmetric axis. The geometry size of computational domain is listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \(I_{1}\)/mm & \(I_{2}\)/mm & \(I_{3}\)/mm & \(D_{1}\)/mm \\ \hline
15.9 & 1000.0 & 5.0 & 1.4 \\ \hline \(D_{2}\)/mm & \(D_{3}\)/mm & \(R_{\text{in}}\)/mm & \(R_{\text{out}}\)/mm \\ \hline
1.2 & 1.0 & 640.0 & 700.0 \\ \hline \end{tabular}
\end{table}
Table 1: Structure parameters of computational domain.  
This article summarizes four common inlet structures based on [27]. To obtain a stable global equivalent ratio, a mass flow inlet is selected. The mass flow setting is obtained based on the total intake air pressure under experimental conditions (0.5 Mpa). Four inlet structures were studied to investigate the effect of different inlet structures on mixing characteristic in combustor as shown in Figure 2. Air is injected axially in Structures 1 and 2, as for Structures 3 and 4, air is injected radially. This study aims to discuss the mixing process and flow field within combustor, besides the inlet structure size is micro compared with the combustor. Therefore, the flow field characteristic of CH\({}_{4}\) and air within inlet is neglected and the attention is focused on the combustor region.  
Figure 1: Schematic of RDC.  
Figure 2: Schematic of different inlet structures.
## 4 The Role of UAV-Base Station in Wireless Communications
### Capacity/Throughput Enhancement  
The exponential growth in the telecommunication and information technology landscape over the last decade has involved a tremendous increase in the amount of user demands and requests for more resources in terms of data traffic. To cope with the situation, macro BSs and small BSs are used to provide coverage to users. However, due to the continuous increase and fluctuation in data traffic at various locations in the network, the dense deployment of only terrestrial small BS networks is no longer sufficient to address this capacity demands. As a result, UAVs have been identified as a potential solution to provide more reliable and effective coverage to users due to their flexibility, adaptability, and quick configuration [98; 99].  
The authors in [100] considered the optimal deployment of UAVs in heterogeneous networks in order to enhance the capacity of the network. To determine the optimal geographic location to deploy the UAVs, a utility function was developed to model the traffic intensity in various parts of the network, after which a heuristic algorithm was proposed to assign the UAVs to their optimal positions. Compared with existing ground-based wireless networks, the suggested model was proven to offer superior capacity, consistency, and extended connectivity. The work in [101] considered the deployment of multiple-UAVs to offload traffic from a single terrestrial BS that is heavily loaded in order to maximize the throughput of ground users at the cell edge.
## 4 Results and Analysis  
### Results of D-InSAR Interferometry  
The coherence is an indicator of InSAR data quality and significantly influences the accuracy of the phase in interferograms [48]. As shown in Figure 3, the mean coherence is lower (0.39) for the data pairs with long time intervals and relatively large perpendicular baselines (20 January 2007 and 10 December 2008, B\({}_{\text{P}}\) = 643 m). Meanwhile, the mean coherence is higher (0.65) for the data pairs with short time intervals and relatively small perpendicular baselines (10 December 2008 and 25 January 2009, B\({}_{\text{P}}\) = 472 m). Good quality interferograms were generated from these two data pairs (Figure 4); the interferogram with a relatively small perpendicular baseline shows good coherence (Figure 3b), but no significant land deformation was found (Figure 4b). It is due to the short interval of time, which was 46 days. Therefore, we used the differential interferogram generated from the large perpendicular baseline pair, which showed clear deformation at two sites in the study area. These areas are indicated by blue dashed circles in Figure 4a. Although the small time baseline image pair was not used for final displacement map generation, it served as a good example of showing the difference of different perpendicular baseline image pairs, and, provided useful clues for further data selection and analyzes.  
In general, the sequence of color fringes in the interferogram can be used to determine whether uplift or subsidence has taken place. We chose two close-up views of typical fringe maps (Figure 4c,d) from these two deformation sites for further analysis. The ALOS-PALSAR data were in the ascending orbit in which observations were made from the west. The sequence of color fringes in these two figure shows a decrease in range (yellow-purple-blue), which denotes that the ground objective moves closer to the center of the interferometer. The \(\mathrm{P}\)-\(\mathrm{P}  
to the satellite. In this study, we did not consider the horizontal movement, and thus, it corresponded to an uplift. During the process of extracting the ground displacement, we unwrapped the interferogram in order to solve the 2\(\pi\) ambiguity, and corrected the satellite orbit inaccuracy and phase offset using the collected external ground control points (GCPs). The final geocoded ground displacement map in the LOS direction is shown in Figure 5. The figure shows that two sites in the north-eastern part of the city exhibit clear indications of land uplift, and the maximum LOS displacement was approximately 13 cm in the study period.
## 2 Electrical Distribution System  
### System Architecture  
The EDS in Seoul, Korea [2] has tens of thousands of pole transformers ranging widely over hundreds of square kilometers. A monitoring center in a residential division of the city is a data collection point which gathers the power quality information from scattered pole transformers deployed over the city.  
The distribution network for the EDS consists of three subsystems as shown in Figure 2: A collection subsystem, a relay subsystem, and a monitoring subsystem. The collection subsystem is composed of several distribution substations (hereafter, the term'substation' is exchangeable with 'distribution substation'). Each substation is connected to several feeders. Each feeder collects the power quality data from hundreds of pole transformers and delivers them to the substation. The relay subsystem is responsible for delivering the data gathered by the substations to the monitoring subsystem. The monitoring center in the monitoring subsystem processes the power quality data to recognize the current status of situations and takes appropriate actions based on the assessed situation [15,16].
## 3 Proposed LI2A Scheme
### Online Registration  
The registration phase executed between the User \(C_{i}\), the IIoT device \(IIoT_{j}\), and the TA:  
1. TA generates the master private key \(K_{m}\).
2. TA selects a unique identity for \(C_{i}\) and \(IIoT_{j}\), denoted as \(ID_{i}\) and \(ID_{j}\), respectively.
3. TA generates a prime number \(p\).
4. TA generates a random number \(r_{0}\leftarrow\{0,1\}^{q}\).
5. Once the TA generates all these parameters, it sends \(\{K_{m},ID_{i},ID_{j},r_{0},P,\tau\}\) to both \(C_{i}\) and \(IIoT_{j}\), where \(\tau\) is the current timestamp.  
Figure 2 demonstrates the online registration phase.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 6 Discussion and Conclusions  
Through the combination of two analysis techniques, this work derives a pointing offset correction for an historical dataset collected by NXPol-1. The study can be generalised to provide recommendations for both the correction of historical data and for future monitoring of radar systems, with a particular focus on deployments/radars at high latitudes.