esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pose Optimization Based on Point-Line Features
### Line Feature Reprojection Error  
The representation of spatial line segments can adopt various parameterization methods, such as Plucker coordinates or parameterization through two endpoints. Nevertheless, owing to intrinsic errors in inline feature extraction, the endpoints of corresponding line features frequently fail to align precisely. Consequently, the computation of the line feature reprojection error diverges from conventional point feature methodologies. The geometric distance between the projected points and the corresponding line segment endpoints cannot accurately assess the reprojection error for line segment endpoints. Indeed, for a given spatial line segment, points along it retain collinearity from various perspectives, adhering to the collinearity constraint requirement. Despite potential misalignment of the ends of the matched line features, their projected points from multiple perspectives must reside on the same line to maintain geometric consistency.  
Figure 2 illustrates the spatial line segment \(P_{W}Q_{W}\), which is projected onto the picture plane, with the homogeneous coordinates of the ends represented as \(P^{\prime}=[u_{1}\),\(\varphi_{1}\),\(1]^{T}\) and \(Q^{\prime}=[u_{2}\),\(\varphi_{2}\),\(1]^{T}\), respectively. In the picture plane, the homogeneous coordinates of the two ends of the corresponding two-dimensional matched line feature of the spatial line segment \(P_{W}Q_{W}\) are \(P\) and \(Q\), where \(P\), \(Q\in R\). In the two-dimensional image plane, assuming the line coefficients of this line equation are \(l_{0}=[a\), \(b\), \(c]^{T}\), we define the normalized line coefficients as follows:  
\[l=\frac{l_{0}}{\sqrt{a^{2}+b^{2}}} \tag{6}\]The overall reprojection error can be represented by computing the error of the endpoints of the line segments based on the line feature reprojection error. Assuming that the two endpoints of the line segment in the image plane are \(P^{\prime}\) and \(Q^{\prime}\), then the total reprojection error of the line feature can be expressed as the sum of the squared endpoint errors. The precise formula is as follows:  
\[E_{\text{line}}(P^{\prime},Q^{\prime},I)=E_{\text{pl}}^{2}(P^{\prime},I)+E_{ \text{pl}}^{2}(Q^{\prime},I) \tag{7}\]  
\(E_{pl}\) (\(P^{\prime}\), \(I\)) denotes the distance error from the point to the line, defined as follows:  
\[E_{\text{pl}}(P^{\prime},I)=I^{T}P^{\prime} \tag{8}\]  
This error computation approach accurately represents the reprojection consistency of line features from various perspectives. Integrating depth information from an RGB-D camera enhances the optimization of reprojection error, resulting in more precise projections of line features in multi-view settings. The depth data from the RGB-D camera assign an accurate depth value to each pixel, enabling the reprojection error calculation to incorporate the geometric relationships in the 2D image and the depth information from the 3D space, thus improving the precision of line feature matching.
## 3 Point Cloud Reconstruction
### Conversions of Coordinates
#### 3.2.2 The Scanning Coordinate  
Laser streaks on the target object were reflected using a scanner mirror and incident to the sensor shown in Figure 5, so the distance and angle inversed using the calibration matrixes represent the image point \(P_{i}\) instead of the target \(P_{t}\).  
Ideally, there would be a simple relationship between \(P_{i}\) and \(P_{t}\). However, the scanning motor and image sensor are not coaxial, which leads to rotation between the two axes systems. In addition, the normal oscillating mirror and the spin axis are not at a 45\({}^{\circ}\) angle, leading to a more complex reflection between image and object systems.  
We define the Euler angles between the sensor and mirror as \(r,s\), and \(t\) and the normal space angles as \(\Phi\) and \(\Omega\). The parameter \(\Phi\) is the angle between the normal mirror and the motor's rotational axis, and \(\Omega\) is the placement error in the direction of motor rotation.  
A high-precision circular grating ruler was fixed on the motor, from which we could read the mirror rotation angle. Not surprisingly, there was also an error between the grating ruler and scanning system. Thus, we should not only define the raster reading \(\varphi\) but also another angle \(\Psi\) to modify it.  
Now, the matrix \(R(r,s,t)\) can be used to describe the first rotation transformation \(R_{L}^{S}\), and \(R(0,0,\Psi+\varphi)\) can indicate the second. Then, the norm vector can be deduced as  
\[\mathbf{u}=R(0,0,\Psi+\varphi)\times\begin{bmatrix}\cos\Phi\cos\Omega\\ \cos\Phi\sin\Omega\\ \sin\Phi\end{bmatrix}. \tag{9}\]  
Figure 5: Specular reflection diagram.  
This step converts the coordinate from the sensor to the scanning system and contains two rotations and one reflection transformation.
## 2 Materials and Methods
### Micro-CT Analyses  
In micro-CT devices, in contrast to clinical CTs, typically the X-ray source and detector are stationary and the sample is rotated. A multitude of angular projection images over a range typically of 180 to 360\({}^{\circ}\) is acquired, allowing for a 3D reconstruction of the specimen. Micro-CT use micro- or nanofocus X-ray tubes and high-resolution detectors that allow spatial resolutions in the range of a few micrometers, however come at a cost of long scanning times, typically of several hours.  
Images were acquired at the Museum fur Naturkunde (Berlin, Germany) by using the micro-CT \"Nanotom S\" (GE Phoenix) equipped with a Tungsten target, able to achieve a resolution of 1 \(\upmu\)m. The average energy was 20 keV, the acceleration voltage 50 kV, and the current 200/250 \(\upmu\)A; every specimen was rotated of 360\({}^{\circ}\) at steps of 0.25\({}^{\circ}\) for a total of 1441 projections (2304 \(\times\) 2304 pixels). Additionally, 3D data sets with a voxel size between 1 \(\upmu\)m and 0.3 \(\upmu\)m were reconstructed depending on the size of the specimen. In this case, the whole specimens were acquired in a single scan, and the data did not need to be reduced before further processing. See Table 2 for an overview of the scan settings.
## 2 Materials and Methods
### Geometrical Core Concepts
As it results from Figure 6, the lower the value of (s) independent variable is, the higher the values of the corresponding \(y_{00}^{\prime}(t,s)\) signals are (the maximum steady-state value of the output signal is \(y_{\alpha}^{\prime}=1\) [mm] obtained for \(s=s_{0}=0\) mm and the minimum steady-state value of the output signal is \(\rm{yy}_{\beta}=0.5\) mm obtained for \(s=s_{\rm f}^{\prime}=24\) mm);
6. Even if the applied force (\(u_{0}(t)\)) accurately decreases to the residual value (\(\widetilde{u}_{\rm f}\)) in 1.2 weeks, its effect lasts approximately seven weeks; practically, relative to the settling time of the responses presented in Figure 6, the input signal (\(u_{0}(t)\)) can be assimilated with an impulse type signal; the graphical representation from Figure 3, being a qualitative one, does not respect the scale between the input and the output signal (for a better highlighting of the signals variation form). The (T\({}_{1}\)) and (T\({}_{2}\)) time constants were determined for the settling time of the curves from Figure 6 (experimentally determined) to be respected. Moreover, in the case of the (T\({}_{1}\)) and (T\({}_{2}\)) time constants identification procedure, some intermediary values of \(y_{00}^{\prime}(t,s_{0})\) signal were used, obtained through the expert method (these values were provided by the orthodontist, based on his experimental experience). As an important remark, we must mention that the movement evolution \(y_{00}^{\prime}(t,s_{0})\) during the entire orthodontic activation cycle can be monitored by the orthodontist in order to verify the scaling correctness of the (K\({}_{\rm y}\)) coefficient (at the \(K_{\rm y}=0.1\) mm/grf value). Since we have used the \(y_{\alpha}^{\prime}=1\) mm determined as an average of experimental data, the perfect match of the evolution \(y_{00}^{\prime}(t,s_{0})\) from Figure 6 with an experimental curve can be  
Figure 6: The evolution of the \(y_{00}^{\prime}(t,s)\) output signal, in relation to both time (t) and (s). only a coincidence, but the relative match (with a relative small error) with all experimental curves, from which we have extracted the information used for the (K\({}_{\rm y}\)) coefficient scaling, is viable. Another important remark is the fact that, experimentally, we have obtained some intermediary values of the mentioned curves, from which we have approximated their most probable form. In the case of considering a larger group of patients, the mathematical model of the biomedical process presented in Equation (5) remains valid (being a general model), but the structure parameters (K\({}_{\rm y}\)), (T\({}_{1}\)), and (T\({}_{2}\)) depend on the tissue resistance of each patient. Possibilities to measure or to estimate the tissue resistance for each patient will be presented in future research papers written by this paper's authors.
## 1 Introduction
This study aims to separately calculate the ultimate service value and intermediate service value of the Liaohe estuarine wetland due to their different content, which can avoid the repeat calculation of wetland service value, paying particular attention to the impact of changes in service provision, and improving the assessment theory of wetland value. In addition, the scientific and practical effectiveness of the assessment results using the method of pure economic value evaluation is questionable due to publicity, externality, and the difficulty in monetizing wetland-ecosystem service value. For the current popular market-price method, it is difficult to reflect consumer surplus, which can lead to imprecise results of unit value and ultimate-value evaluation. Energy theory, proposed by [PERSON] [18] in the 1990s, can effectively solve the above problems, because the energy flow and value flow are comprehensively considered in this theory. This theory has been very popular in ecosystem evaluation since its beginning. [PERSON] et al. accounted for the natural capital and ecosystem of the national forest system of the United States based on energy theory [19]. [PERSON] et al. calculated the ecosystem service value of the subtropics forest in southeastern China [20]. Therefore, energy theory was adopted to evaluate the service value of the Liaohe estuarine wetland. The Liaohe estuarine wetland is located downstream of Liaohe River, one of China's seven major rivers. It is on the path of eight migratory routes for migratory birds around the world, and can covertly provide the largest habitat for endangered species. This wetland is also the southernmost global area for leopard seal and red-crowned-crane reproduction. Therefore, it becomes an important transit point and destination for East Asia-Australia migratory birds. In this wetland, reed wetland is irrigated and harvested by mainly artificial covers, and _Suada heteroptera_ with regional characteristic form a vermelha landscape, creating one of the most famous and representative regions of biodiversity conservation in the world. In this study, the energy method was adopted to evaluate the intermediate and ultimate ecosystem service values of the Liaohe estuarine wetland, drawing more attention to the service process of the wetland system and providing the theoretical basis for the protection and rational utilization of Liaohe estuarine wetland resources.
## 3 Results
### Extracted Themes
#### 3.4.6 The Role of Networking and Communication in eHealth for Transplantation  
The effectiveness of eHealth interventions in transplantation depends on networking infrastructure and secure digital communication systems. Cloud computing allows for seamless integration of patient records across transplant centers, while IoMT-enabled devices enhance continuous patient monitoring through secure data exchange [10; 25]. Moreover, blockchain-based data security has emerged as a crucial aspect of ensuring privacy in digital health applications for transplant recipients [15]. The introduction of 5G-enabled telehealth has transformed post-transplant care by enabling real-time, low-latency remote monitoring, allowing healthcare professionals to track patients' vital signs and intervene quickly in case of complications or rejection [21]. The speed and reliability of 5G, combined with edge computing, enhance data processing and emergency response times, ensuring timely care. Networked eHealth solutions further improve coordination by enabling transplant teams to access electronic health records (EHRs) across different locations, ensuring specialists have up-to-date information for informed decision-making [24]. As digital health adoption grows, securing patient data is essential. Encrypted cloud-based transplant databases provide a secure platform for storing and sharing medical records and protecting against cyber threats while ensuring compliance with privacy regulations. By integrating 5G, eHealth solutions, and secure cloud computing, digital health technologies enhance post-transplant care, improving efficiency, security, and patient outcomes [9].
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA-LSTM Predictive Analysis at Short-Medium and Long Scales  
The experiments used the CUDA version of the Pytorch framework to build a multi-input single-output two-layer LSTM model for training and validation, with the following model parameters: the batch size was set to 64, the epoch was set to 20, the training hidden layer size hidden size was set to 128, the forgetting rate drop out was set to 0.2, and patience was set 5; the model was configured to terminate if the loss rate of the validation set did not significantly improve after five consecutive training sessions.  
In this article, the previous moment parameters such as AQI, temperature, humidity, barometric pressure, and wind speed were employed as inputs, and AQI values were produced as outputs for the next moment. The model's accuracy was determined by comparing the output AQI values to the actual AQI values in the dataset.
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 6 Ovarian Pathologies  
Ovarian lesions are a frequent cause of gynaecological pathologies with both benign and malignant conditions frequently encountered in clinical practice. Ovarian cancer is in the seventh place for cancer incidence in females and it is associated with high mortality [143]. Epithelial tumours are the most common cause of ovarian cancer, and they include a wide spectrum of lesions with different histopathological features, risk factors, treatment options, and prognosis [143]. Particularly, the most common ovarian lesions are serous and mucinous tumours. In this complex clinical context, radiomics can serve as a relevant tool to improve the diagnosis, management, and prediction of prognosis in patients with ovarian pathologies [144]. Several studies explored the performance of radiomics with a plethora of different aims and outcomes in ovarian pathologies. Relevant multicentric studies are summarized in Table 3.  
Ultrasound, CT, and MRI are the most used imaging techniques in patients with ovarian pathologies. Ultrasound is the first imaging modality for the assessment of ovarian pathologies but the differential diagnosis between different entities may be challenging based only on the qualitative assessment. Initial single-centre studies applied the radiomics analysis on ultrasound images to predict the histopathological types and grades of epithelial ovarian cancers [145; 146] and prognosis [147] with reported good performances but validation can be problematic as the ultrasound images' acquisitions depend on operator experience. Considering the main aim of this review and the application of CT and MRI in clinical practice, further discussion will be focused on the application of cross-section imaging in different scenarios.
# Electrochemical Removal of Chromium (VI) from Wastewater
## 3 Results and Discussions
### Kinetic Model  
The experimental data was analyzed using Equation (3), with the results displayed in Figure 5a-f and Table 1. The results shown in Figure 5a-c indicate that the reduction efficiency was well-fitted,  
Figure 4: Effect of current intensity on reduction efficiency of chromium (VI).  
Figure 3: Effect of reaction temperature on the reduction efficiency of chromium (VI).  
as the coefficients (\(R^{2}\)) were all close to 1, which indicates that the kinetic model followed a pseudo-first-order model equation. The results shown in Figure 5a indicate that the reduction rate constant (K\({}_{\text{obs}}\)) increased when the concentration of H\({}_{2}\)SO\({}_{4}\) ([H\({}_{2}\)SO\({}_{4}\)]) was increased. Figure 5d displays the relationship between K\({}_{\text{obs}}\) and [H\({}_{2}\)SO\({}_{4}\)]. The results show that K\({}_{\text{obs}}\) changed linearly with [H\({}_{2}\)SO\({}_{4}\)]--the relationship between them could be expressed as Equation (11). The effect of current density ([j]) was a little complicated, with the K\({}_{\text{obs}}\) shown to be multipower with [j] following Equation (12). The relationship between K\({}_{\text{obs}}\) and the reaction temperature (T) could be expressed with the Arrhenius equation (Equation (13)) and the specific apparent activation energy could be calculated. The results shown in Figure 5f show that the simulated Arrhenius equation and the \(Ea\) was calculated as 4.74 KJ/mol, so the Arrhenius equation could be changed to make Equation (14):  
\[\text{K}_{\text{obs}}=8.50\text{E}^{-5}[\text{H}_{2}\text{SO}_{4}] \tag{11}\]  
\[\text{K}_{\text{obs}}=3\text{E}^{-8}[\text{j}]^{4} \tag{12}\]  
\[\text{lnK}_{\text{obs}}=\text{lnA}-\text{Ea}/\text{RT} \tag{13}\]  
\[\text{K}_{\text{obs}}\text{=A}\text{e}^{-4170/\text{RT}}, \tag{14}\]  
where Ea is the apparent activation energy, A is the pre-exponential factor, R is the molar gas constant, and K is the reduction rate constant at different reaction temperatures.  
In summary, the chromium (VI) could be efficiently reduced by electricity, and the reduction process was significantly affected by the concentration of H\({}_{2}\)SO\({}_{4}\), current density, and reaction temperature. The reduction rate constant could be express as Equation (15).  
\[\text{K}_{\text{obs}}=\frac{\text{dc}}{\text{dt}}=\frac{\text{d}[\text{Cr}]} {\text{dt}}\text{=k}[\text{H}_{2}\text{SO}_{4}]^{4}\text{.}[\text{j}]^{4} \text{.}\text{e}^{-4170/\text{RT}}. \tag{15}\]  
Figure 5: Pseudo-first-order models for the reduction of chromium (VI). (**a**): Effect of H\({}_{2}\)SO\({}_{4}\) concentration; (**b**): Effect of current density; (**c**): Effect of reaction temperature; (**d**): Plot of K\({}_{\text{obs}}\) as a function of H\({}_{2}\)SO\({}_{4}\) concentration; (**e**): Plot of K\({}_{\text{obs}}\) as a function of current density; (**f**): Plot of K\({}_{\text{obs}}\) as a function of reaction temperature concentration.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 6 Conclusions  
In this work, we have investigated the 3 GPP C-V2X Mode 3 communications, employing the bandwidth partitioning in order to reduce the number of packet collisions in broadcast transmission. In order to reduce the need for signalling between the vehicles and RSUs, we have considered the use of Re-selection Counter to perform the resource allocation in infrastructure nodes. With the investigated scheme, the RSU can send the resource allocation grant without the need to receive a request from a vehicle, thus resulting in the decrease in signalling. Figure 10: The average frequency reuse distance of CAM broadcasting messages by vehicles for different vehicle density factors for both bandwidth partitioning techniques: Full Frequency Reuse (FFR) and Partial Frequency Reuse (PFR). Figure 9: The Minimum Frequency Reuse Distance of CAM broadcasting messages in different vehicle density factors for both Bandwidth partitioning techniques: Full Frequency Reuse (FFR) and Partial Frequency Reuse (PFR). Two novel applications of bandwidth partitioning techniques have been suggested, namely, Full Frequency Reuse, and Partial Frequency Reuse with the aim to reduce the interference from simultaneously broadcasting vehicles. If the vehicles' broadcast areas overlap, the use of bandwidth partitioning reduces the likelihood of using the same resources by these vehicles, which should avoid packet collisions experienced by some receivers. Moreover, a Broadcast Collision Zone has been formulated, which can arise when the frequency reuse distance is lower than the minimum required distance. Both techniques have been evaluated in system-level simulations for different densities of vehicles in terms of the Packet Reception Ratio, Minimum Frequency Reuse Distance, and Average Frequency Reuse Distance. The simulation results show promising performance for PFR, compared with FFR. It has turned out that the re-partitioning of the frequency band into two sub-bands for central and edge users performs better than a fixed split of the available band into two non-overlapping sub-bands for neighbouring RSUs. It is worth mentioning that both proposed band partitioning methods perform better than when no band partitioning is applied. Individual contributions of the authors to the paper are the following: conceptualization, S.S.; methodology, S.S. and K.W. ; software, S.S.; validation, S.S., P.S. and K.W. ; formal analysis, S.S., K.W. and P.S. ; investigation, S.S.; writing--original draft preparation, S.S. and K.W. ; writing--review and editing, S.S., K.W. and P.S. ; visualization, S.S.; supervision, K.W. ; project administration, K.W. ; funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript. This work was funded by the Poznan University of Technology, grant number 0312/SBAD/8153. The authors declare no conflict of interest. Notation used in mathematical derivations:
# On the Spectral Efficiency for Distributed Massive MIMO Systems
## 4 Correlation and Los on Emulated D-MIMO Systems above 6 Ghz  
### Emulation Scenario  
In this section, an analysis on the TX correlation and the effect of the line-of-sight (LoS) in D-MIMO systems is performed. For that purpose, several measurements of the propagation channel are simulated in NYUSIM [26]. NYUSIM is a mmWave simulator that has a wide variety of configuration parameters for the simulated environment. In particular, it allows to simulate scenarios for a wide frequency range and several propagation models. In this study, simulations from 10 GHz to 78 GHz are carried out for two environment models: urban macrocell (UMa) and urban microcell (UMi). Several channel matrices \(H\) were simulated for a wide range of configuration parameters. Table 2 shows the choice of simulation parameters in detail. Note that each simulation was iterated ten times in order to avoid outliers in the channel matrices \(H\). The chosen distances, antenna spacing, and frequencies ensure the UE far-field condition for all simulations shown in this section.