esa-sceva/satcom-chunk-collection huggingface dataset:
# Agricultural Residue Management for Sustainable Power Generation: The Poland Case Study
## 4 Results
### Carbon Dioxide Emission  
This study is focused on carbon dioxide emissions. This emission is a result of power and heat generation based on hydrocarbon fuels burning. Crop residues (straw and sugar beet leaves) and manure are examined as feedstock for energy generation and substitution of fossil fuels. A decrease in carbon dioxide emissions is calculated by the Equation (6).  
During 2011-2018, the shares of hard coal and lignite ranged from 88 to 94% in power generation. In the above period Poland consumed 796-908 PJ of hard coal and 466-539 PJ of lignite [98; 99]. Their lower heating values ranged: from 21.072 to 21.673 MJ kg\({}^{-1}\) for hard coal, and from 8.022 to 8.365 MJ kg\({}^{-1}\) for lignite. A lower heating value of biomass is less compared to coal; therefore, biomass needs to be burned in more amount than coal to produce the same quantity of energy.  
Our calculations indicate that one ton of straw (used for power or heat production) reduces carbon dioxide emission by 1417.8 kg for hard coal and 1578.15 kg for lignite. The theoretical potential of carbon dioxide emission reduced by straw utilization is around 13.91 Mt for hard coal and 15.72 Mt for lignite (Table 3).  
Biogas can be used for both power generation and cogeneration. Cogeneration reduces the consumption of electricity and heat produced by coal based power and heat plants. It results in improving a carbon dioxide emission indicator and more effective than sole biogas based power generation (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Hard Coal** & **Lignite** \\ \hline
**Initial data** & & & \\ \hline Carbon dioxide emission factor & kg GJ\({}^{-1}\) & 94.52 & 105.21 \\ Carbon dioxide emission factor associated with straw formation & kgCO\({}_{2}\):GJ\({}^{-1}\) & 12.1 & \\ Total annual consumption for power and heat production & PJ & 867.00 & 466.00 \\ \hline
**Straw-based power generation** & & & \\ \hline Volume of fossil fuel substituted by one ton of straw & t & 0.71 & 1.84 \\ Carbon dioxide emission reduced, kg per ton of straw & kg t\({}^{-1}\) & 1417.80 & 1578.15 \\ Theoretical energy potential of straw & PJ & 168.78 & 168.78 \\ Theoretical share of fossil fuel substituted & \% & 19.47 & 36.22 \\ Theoretical potential of carbon dioxide emission reduced & Mt year\({}^{-1}\) & 13.91 & 15.72 \\ \hline
**Biogas-based power generation** & & & \\ \hline Volume of fossil fuel substituted by one 1000 cubic meters of biogas & t & 1.14 & 2.94 \\ Carbon dioxide emission reduced, kg per m\({}^{3}\) of biogas & kg t\({}^{-1}\) & 2268.48 & 2525.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A decrease in carbon dioxide emission.  
Figure 8: Structure of energy potential, %.
## 5 Simulation Results and Discussion  
We processed the datasets on an _Amazon elastic compute-2 (EC-2)_ instance and the PFL collaborators were constructed using _TensorFlow_ and the _TensorFlow Federated_ frameworks. For PFL model training, we split the _V2X-Sim_ dataset into 60% training and 40% testing data, and for UAV trajectory prediction, we split the _LTE-1_/Q dataset into 60% training and 40% testing data. We randomly train the agents for approximately 25-30 sets of training instances. Each training set consists of 50 instances, comprising model hyper-parameter transmissions from the vehicles to the UAV. The packet size was varied between 1 byte and 10 megabytes and the number of packets was varied from 100 to 4000. The inter-arrival time interval for packets at the UAV was varied between 100 ms and 1000 ms. In each instance, the capacity of the queue was set as up to 10,000 packets in each bin. Table 2 lists the main parameters used in the simulations.
# A Spatial 4-DOF Laser Collimation Measurement System
## 2 Optical Principle
### Ranging Optical Path  
As shown in Figure 2, the laser diode emits laser light, which becomes collimated after collimation by the collimator. A beam is reflected by the beam-splitter (BS1), which hits the plane mirror (M), is reflected by the plane mirror (M), and passes through the half-wave plate HWP, the polarization beam-splitter (PBS1), PBS2, the quarter-wave plate (QWP), the beam-splitter (BS3), the semi-transparent and semi-reflective mirror HW, and the corner cone prism (RR1). After being reflected by RR1, it hits the beam splitter (BS3), is reflected by BS3, and then hits the image plane of CMOS3. When the corner cone prism RR1 moves, the light spot on the image plane of CMOS3 moves; this light path was used to measure the displacement in the x/y direction.  
Figure 1: Optical schematic diagram of the system.  
As shown in Figure 3, another beam is passed through the beam-splitter (BS1 and BS2), hits the corner cone prism RR2, and is reflected by the corner cone prism (RR2), before returning, hitting the beam-splitter (BS2), being reflected by BS2, and hitting the CMOS1 image plane. When angular cone prism RR2 moves, the light spot on the image plane of CMOS1 moves, and this light path is used to measure the displacement in the x/y direction (Figure 4).  
When the angle cone prism moves up and down or left and right, it will cause a change in the incident point, which will lead to a corresponding relative displacement between the outgoing beam, that is, a change in the position in which the detector receives the light. According to the formula, when the angular cone prism is moved x distance, 2x displacement will occur between the outgoing light and the original outgoing light. The four-quadrant detector will convert the change in spot position into a change in the current through photoelectric conversion, and the displacement value can be obtained after signal processing and calibration.  
\[\Delta_{X}=\frac{\Delta_{\text{CMOS}}}{2} \tag{1}\]  
\[\Delta_{Y}=\frac{\Delta_{\text{CMOS}}}{2} \tag{2}\]  
Figure 4: Principal diagram of ranging light path.  
Figure 3: X/Y direction ranging optical path.  
Figure 2: X/Y direction straightness ranging optical path.  
In sum, as long as the deviation of the laser beam in the x/y direction on the four-quadrant detector CMOS \(\Delta_{CMOS}\) is obtained, a change in the straightness of \(\Delta_{X}\) and \(\Delta_{Y}\) in the x/y direction can be obtained.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 3 Proposed Solution
### 5G Core Network (5 GCN)
#### 3.5.2 CUPS--Architecture for Control and User Plane Separation  
The 5G/GC(Core) SA network was conceived based on the SBA and the PR of Figure 11 for the communication between the nodes with the network functions (NFs) in which the 3 GPP defined an application programming interface (API) that works over the Hypertext Transfer Protocol (HTTP) and follows the Representational State Transfer (REST) model [38]. With the SBA NFs for the access and mobility management function (AMF) and the user plane function (UPF), we will have the division of the 5G NR protocol stack through the user plane (UP) and the control plane (CP) forming the 5G NR/NGC SA Protocol Structure Stack's separation of control plane and users (CUPS) architecture via F1-C, F1-U and E1, with the gNB-DU, splitting the gNB-CU in gNB-CU-CP and gNB-CU-UP [52,54]. Figures 11 and 12 depict the UP architecture that provides user data, while the CP provides connection configuration for mobility and security between the UE and the 5G(gNB) base station [23]. The 5G NR transports information over multiple physical channels. Both gNB-CU-CP and gNB-CU-DP are NF functions for virtualized networks running on the CU server in 5G vRAN with multiple instances and CUPS concept for expansion on demand, in which [52,54]:  
* A gNB can consist of one gNB-CU-CP for several gNB-CU-UPs and one gNB-DU;
* At the same time, one gNB can consist of one gNB-CU-CP for several gNB-CU-UPs and several gNB-DUs;
* gNB-CU-CP is connected to gNB-DU by the F1-C interface;
* gNB-CU-UP is connected to gNB-DU by the F1-U interface;
* gNB-CU-CP is connected to gNB-CU-UP by the E1 interface;
* One gNB-DU is connected to only one gNB-CU-CP;
* One gNB-CU-UP is connected to only one gNB-CU-CP. The gNB-CU-Control Plane (gNB-CU-CP) consists of:  
* A logical node hosting the RRC;
* The CP part of the PDCP protocol of the gNB-CU for an gNB;
* The gNB-CU-CP terminates the E1 interface connected with the gNB-CU-UP and the F1-C interface connected with the gNB-DU [53]. The gNB-CU-User Plane (gNB-CU-UP) consists of:  
* A logical node hosting the UP part of the PDCP protocol of the gNB-CU for an gNB, and the UP part of the PDCP protocol and the SDAP protocol of the gNB-CU;
* The gNB-CU-UP terminates the E1 interface connected with the gNB-CU-CP and the F1-U interface connected with the gNB-DU [53]. 5.3 User Plane and Control Plane for UE/Device and gNB Protocol Stack with Network Functions AMF and UPF  
The 5G NR/NGC SA protocol stack for the Access Stratum (AS) is divided into CP and UP with the interfaces shown as UE, gNB, AMF and UPF in Figure 12, based on Figures 2 and 3.
# Organic Crystals for THz Photonics
## 3 THz-Wave Generation and Detection via NLO Processes  
In the early years of organic NLO crystals development, the most promising application considered was second-harmonic generation from the infrared radiation emitted e.g., from widely available infrared diode lasers to the visible radiation, motivated in particular by the very large NLO coefficients of organic materials, one order of magnitude larger compared to inorganic alternatives. Due to the huge progress in visible lasers development and the problems with photostability for second-harmonic generation, since the generated second-harmonic wavelengths are typically close to the absorption edge of these crystals, the interest has been moved to high-speed electro-optic applications [6; 25; 26] and in particular to THz photonics applications discussed in this review, for which photostability is usually high enough even for commercial applications. In this section, we summarize the theoretical basis of THz-wave generation relevant for organic crystals, the difference-frequency generation and the optical rectification and then present some examples of organic THz sources in Section 4.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 2 Equation of Motion of the Proposed USV
### Kinematics of USV  
The USV is modeled as a 3 DOF system (x, y, and r axes (x, y, and r axes) [20]. The USV movements in heave (w), roll (p), and pitch (q) directions are presumed to be relatively small; therefore they can be ignored. The USV kinematics is described in two terms of reference coordinates, namely the USV frame of reference (u, v, w) and the Earth frame of reference (x, y, z). The free body diagram of this model is shown in Figure 1.  
Figure 1: Free Body Diagram (a = nose to nose distance, b = USV length, c = thruster to center of gravity horizontal distance, d = thruster to nose tip distance, xg = centre of gravity distance to USV frame of reference).  
The standard notation used to define 3 DOF models in this study, based on the SNAME notation 1950 [21], is shown in Table 1.  
To transform the equation from the USV frame of reference to the Earth frame of reference, the kinematics equation is revealed in Equation (1).  
\[\dot{\eta}=\mathrm{J}(\eta)\mathrm{V} \tag{1}\]  
where \(\eta=\left[\mathrm{x}\ \mathrm{y}\ \mathrm{\dot{\psi}}\right]^{\mathrm{T}}\) is the position (x, y) and yaw (\(\mathrm{\dot{\psi}}\)) of USV in the Earth frame of reference while \(\dot{\eta}=\left[\dot{\mathrm{x}}\ \dot{\mathrm{y}}\ \dot{\mathrm{\psi}}\right]^{\mathrm{T}}\) describe linear velocity in x, y-direction, and angular velocity in yaw (\(\mathrm{\dot{\psi}}\)) direction. \(\mathrm{J}(\eta)\) is defined as the transformation matrix as shown in Equation (2).  
\[\mathrm{J}(\eta)=\left[\begin{array}{ccc}\cos\mathrm{\psi}&-\sin\mathrm{ \psi}&0\\ \sin\mathrm{\psi}&\cos\mathrm{\psi}&0\\ 0&0&1\end{array}\right] \tag{2}\]  
\(\mathrm{V}=\left[\mathrm{u}\ \mathrm{v}\ \mathrm{r}\right]^{\mathrm{T}}\) is interpreted as linear velocity (u, v direction) and angular velocity (r direction) in the USV frame of reference or body coordinate system. Hence, The USV kinematics equation is given by Equations (3)-(7)  
\[\dot{\eta}=\left[\begin{array}{ccc}\cos\mathrm{\psi}&-\sin\mathrm{\psi}&0\\ \sin\mathrm{\psi}&\cos\mathrm{\psi}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}\mathrm{u}\\ \mathrm{v}\\ \mathrm{r}\end{array}\right] \tag{3}\]  
\[\left[\begin{array}{c}\dot{\mathrm{x}}\\ \dot{\mathrm{y}}\\ \dot{\mathrm{\psi}}\end{array}\right]=\left[\begin{array}{ccc}\cos\mathrm{ \psi}&-\sin\mathrm{\psi}&0\\ \sin\mathrm{\psi}&\cos\mathrm{\psi}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}\mathrm{u}\\ \mathrm{v}\\ \mathrm{r}\end{array}\right] \tag{4}\]  
\[\dot{\mathrm{x}}=\mathrm{ucos\psi}-\mathrm{vsin\psi} \tag{5}\]  
\[\dot{\mathrm{y}}=\mathrm{usin\psi}+\mathrm{vcos\psi} \tag{6}\]  
\[\dot{\mathrm{\psi}}=\mathrm{r} \tag{7}\]
# _remote sensing_
## 3 Results
### Crop Diseases Results
#### 3.2.2 Wheat Yellow Rust Mapping
These results suggest that remote sensing monitoring of wheat yellow rust at leaf, canopy and regional scales is feasible. When developing the monitoring model, information on the spatial and temporal variation of spectral features is the basis for small-scale disease monitoring. As the monitoring scale rises, crop growth and environmental information needs to be added into the spectral feature set to achieve more accurate disease monitoring. The results show that there is consistency in the sensitive spectral bands of diseases at the leaf scale and canopy scale, and the regional scale disease monitoring model developed  
Figure 11: Monitoring and mapping of yellow rust and ground truth maps in Ningqiang County, Shaanxi Province (China). The window shows an enlarged view of the stripe rust distribution in the sub-area. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**State**} & \multicolumn{8}{c}{**Classification Accuracy/\%**} \\ \cline{3-10}  & & **7 DAI** & **14 DAI** & **21 DAI** & **28 DAI** & **31 DAI** & **34 DAI** & **41 DAI** \\ \hline KPCA- & Healthy & 88.7 & 92.4 & 97.5 & 99.2 & 98.8 & 96.7 & 98.9 \\ SVM & Infected & 84.2 & 90.1 & 95.3 & 97.9 & 100 & 100 & 98.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Monitoring accuracy of KPCA-SVM based on UAV hyperspectral imagery, at different days after infection (DAI). based on the sensitive spectral bands also achieved accurate monitoring. The disease monitoring methods and results presented in this section can provide a reference for subsequent research of remote sensing monitoring of crop pests and diseases.
## 2 System Model
### Terrestrial Access Points (APs) and Sensor Nodes (SNs)  
The environment topology influences the wireless channel since it can impose restrictions on the locations where the APs may be installed and the locations where SNs may exist. APs and SNs are modeled following the traditional models of BSs and UTs in modern cellular systems. Leveraging the work in [44], APs and SNs are randomly distributed in outdoor areas according to a homogeneous Poisson point process (PPP). This allows studying the heterogeneity relation with AP or SN density. While modeling AP locations as PPP can result in some APs being very close to each other, which may not reflect real AP locations, this model can provide similar performance results when compared with real cases [45; 46]. To obtain a realistic distribution, an AP that is less than 200 m apart from an adjacent AP is eliminated. Frequency reuse-3 is considered in the system. AP sites are modeled such that each consists of three sectors that are 120\({}^{\circ}\) apart, and an antenna is assigned to each sector. Because indoor users and sensors can offload their data traffic through in-building solutions and Wi-Fi, they can be neglected for the purpose of this study. The height of each AP and each SN is chosen to be 10 m and 1.5 m, respectively, according to [34]. The MATLAB Phased Array System Toolbox 2023 is used to implement the antenna element pattern defined in [47]. To increase the directional gain and SINR, the AP antenna element is used to form an 8-by-8 rectangular antenna array. On the other hand, a 2-by-2 rectangular antenna array is considered for each SN.
# A Global Review of PWR Nuclear Power Plants
## 2 International Development of the PWR Design
### American PWR Evolution
#### 2.2.2 Babcock & Wilcox PWR Design  
Throughout the 1970s and 1980s, 10 Babcock & Wilcox (B&W) PWR-designed nuclear power plants began commercial operation: _Three Mile Island-1 and 2, Arkansas Nuclear One-1, David Besse-1, Oconee 1-2 and 3, Indian Point-1, Rancho Seco-1_, and _Cristal River-3_. In the B&W PWR design the primary circuit is based on 2-loops each formed by (i) a pipe from the vessel to the top of the steam generator, called the hot branch, and (ii) two lines that came from the lower part of the SG and connect to the vessel, called the cold branch of the primary circuit, in each of which a reactor coolant pump is installed (Figure 8).  
Figure 7: Westinghouse PWR designs: (**a**) 1-loop; (**b**) 2-loop; (**c**) 3-loop; and (**d**) 4-loop.  
One of the most important differences between Babcock & Wilcox PWR and other PWR designs is the steam generator. The B&W PWR uses a type of steam generator called a _once-through steam generator_ (OTSG), in which the heat transfer between the coolant--which enters through the top and descends inside a bundle of tubes--and the water from the supply, occurs in countercurrent, thereby generating steam at constant pressure in any power range. This difference supposes an operational advantage for other designs, since in a traditional steam-generator, i.e., U-tube steam generator (UTSG), the vapor pressure depends on the power [27; 28].
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
## 2 Materials and Methods  
### Methodology  
The framework of the proposed method is shown in Figure 2.  
First, a UGV equipped with LiDAR is utilized by inspectors to construct scene maps, and numerous images are captured with a depth camera. These images, rich with details on the spatial structure of the scene and the extent of wall spall, are utilized to perform dense reconstruction based on orbslam.  
Second, the large-scale point cloud semantic segmentation framework RandLA-Net is adopted to develop the wall spall point cloud semantic segmentation model RandLA-Spall. The point cloud of the self-constructed dataset is annotated to train and test RandLA-Spall.  
Finally, segmented defects are clustered, and an algorithm is used to find the Minimum Bounding Box (MBB) for each cluster, calculating spall dimensions including length and width. Based on these dimensions, spall severity is assessed, offering a basis for building maintenance.
# Current Trends in Orthognathic Surgery in Poland--A Retrospective Analysis of 124 Cases
## 1 Introduction
### Contemporary Aspects of Orthognathic Surgery  
Nowadays, orthognathic surgery is actually an independent surgical specialty closely related to orthodontics [13]. Corrective osteectomies of the mandible in various modifications, Le Fort I maxillary osteotomies, jaw segmentation or genioplasty are routine surgeries aimed at correcting skeletal deformities of the face [14].  
Recent years have seen the development of diagnostics and 3D planning in orthognathic surgeries, as well as attempts to use digital technologies supporting surgical treatment [15]. The improvement of surgical techniques and access to new technologies significantly reduce the risk of complications, improve safety of surgery and its availability to patients [16; 17; 18].
## 3 Results and Discussion
### The Comparison of Lp_HOGS with Lp_LOGS
#### 3.1.3 The Motion Blur  
Finally, 30%, 40%, and 50% salt-and-pepper noise were added to the test images with a \(7\times 7\) motion blur. The experimental results are listed in Table 3.  
The three performance indicators obtained by the Lp_HOGS model were higher than those obtained by the Lp_LOGS model. The Lp_HOGS model achieved average PSNR values that were 1.387, 1.774, and 2.372 dB higher than those of the Lp_LOGS model when the salt-and-pepper noise was 30%, 40%, and 50%, respectively. The difference in the PSNR values showed that Lp_HOGS had more obvious advantages over Lp_LOGS in the motion blur than in the Gaussian and box blurs. The images of a truck with different degrees of degradation are shown in Figure 6. The deblurring effects of Lp_LOGS and Lp_HOGS are compared in Figure 7.  
A comparison of Figure 6a,c with Figure 7b,c revealed that Lp_LOGS and Lp_HOGS achieved satisfactory deblurring effects in terms of motion blur. Furthermore, the performance edge of the proposed model was similar to that under the Gaussian and the box blurs. The deblurring effects are depicted in Figure 7b,c,e,f. Figure 5: A comparison of deblurring effects of the two models on a station image. (**a**) The original station image, (**b**) the image deblurred using Lp_LOGS, and (**c**) the image deblurred using Lp_HOGS. (**d–f**) The Local enlarged images from the red boxes in (**a–c**), respectively. Figure 6: The truck image with different degrees of degradation. (**a**) The original image, (**b**) the \(7\times 7\) motion blur, and (**c**) the \(7\times 7\) motion blur + 50% salt-and-pepper noise.