esa-sceva/satcom-chunk-collection huggingface dataset:
# Factors Affecting Implementation of Radiological Protection Aspects of Imaging in Radiotherapy
## 3 Results
### Imaging during the Treatment Cycle
#### 3.2.3 The Balance between Image Quality and Imaging Dose  
Less effort has been devoted to optimization of radiological protection for imaging in radiotherapy than in diagnostic radiology, as staff are accustomed to dealing with the much higher doses from therapeutic radiation. However, since exposures are repeated and normal tissues surrounding the tumour target are irradiated, imaging protocols should be optimized with radiological protection considerations, especially since the cumulative dose from daily kV CBCT over a whole treatment course can reach the level of a radiotherapy treatment fraction dose [13; 19; 22]. The level of image quality required for delineation of organs and target alignment in radiotherapy involves a trade-off with radiation exposure. Ideally the dose level for imaging should be the minimum necessary for providing the level of detail needed to verify alignments for accurate treatment delivery. This should not in general be as detailed as that required for diagnosis of disease. But, before optimization of radiological protection can begin, measures of the image quality linked to clinical requirements and a knowledge of the dose levels delivered during imaging are required. The survey showed that the majority of centres in most countries carried out objective evaluations of image quality, but only about half made dose measurements in Australasia, the USA, Colombia, Algeria and Egypt (Figure 4) as part of their quality control (QC) program. The proportion of centres carrying out QC on a monthly basis for imaging equipment declined with HDI (R = 0.784, \(p\) = 0.012).  
The survey further revealed that although patient doses from kV CBCT and kV planar imaging were recorded by 50% of the radiotherapy centres that took part in the survey in  
Figure 4: Proportion of radiotherapy centres in each country carrying out QC tests on image quality and dose, and those recording patient dose quantities [14].  
Cyprus, Germany, and Saudi Arabia, there was little recording of dose information at other centres. The recording of patient doses from imaging requires equipment, funding, and experience, but these were not necessarily the barriers, since countries with higher HDIs, such as Australia/New Zealand and the USA also did not record this information.
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 2 Materials and Methods  
### MIMO System Model  
In this study, we investigate a MIMO system utilizing spatial multiplexing, wherein the receiver antennas concurrently receive symbols transmitted from the transmitter. The system comprises \(N_{r}\) receiving antennas and \(N_{t}\) transmitting antennas as shown in Figure 1. The received symbols, denoted as \(y_{N_{r}}\), at the receiver side can be expressed as follows:  
\[\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{N_{r}}\end{bmatrix}=\begin{bmatrix}h_{1,1}&\dots&h_{1,N_{t}}\\ \vdots&\dots&\vdots\\ h_{N_{r},1}&\dots&h_{N_{r},N_{r}}\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ \vdots\\ x_{N_{t}}\end{bmatrix}+\begin{bmatrix}n_{1}\\ \vdots\\ n_{N_{r}}\end{bmatrix} \tag{1}\]  
In aforementioned Equation (1), \(h_{N_{r},N_{t}}\) denotes the entries of the channel matrix corresponding to the communication link between the \(N_{t}\)-th transmitter antenna and the \(N_{r}\)-th receiver antenna. The term \(n_{N_{r}}\) signifies the additive white Gaussian noise (AWGN) present at the \(N_{r}\)-th receiver antenna, characterized by zero mean and variance \(\sigma^{2}\). \(x_{N_{r}}\), \(y_{N_{r}}\), \(n_{N_{r}}\) represent complex-valued numbers, and \(h_{N_{r},N_{t}}\) signifies a complex-valued channel which is assumed to exhibit flat Rayleigh fading, with the channel entries, \(h_{N_{r},N_{t}}\), being independently and identically distributed (i.i.d.) with zero mean and unit variance.  
Figure 1: Simplified block diagram of a MIMO system.  
The matrix \(H\) and the vectors \(y\), \(x\), \(n\) have complex values, due to the necessity of using real numbers in the deep learning structure, and the MIMO channel model is expressed as follows for the simulation environment within the scope of the study:  
\[\left\{\begin{array}{c}y_{r}=H_{r}x_{r}+n_{r}\\ y_{r}=\begin{bmatrix}Re(y)\\ Im(y)\end{bmatrix}\ \in\ R^{2N_{r}x1}\\ x_{r}=\begin{bmatrix}Re(x)\\ Im(x)\end{bmatrix}\ \in\ R^{2N_{t}x1}\\ H_{r}=\begin{bmatrix}Re(H)\ -Im(H)\\ Im(H)\end{bmatrix}\ \in\ R^{2N_{r}x2N_{t}}\end{array}\right. \tag{2}\]  
The MIMO system's simplified block diagram is shown in Figure 1. Multiple antennas are used in this system, both at the transmitter and receiver ends.  
The numerous signal routes between the antennas are depicted in Figure 1 by connecting each transmitting antenna to each receiving antenna. Through the use of spatial diversity and the ability to transmit many data streams at once, this arrangement improves the capacity and dependability of the system. The intricate interaction and signal propagation in a MIMO system are highlighted by the dotted lines.
## 2 Numerical and Experimental Methods
### Wind Tunnel Experiments  
To check the fidelity of the numerical simulations, the time-averaged aerodynamic forces acting on the airfoils are measured in the subsonic wind tunnel at Tamkang University. The test section of the wind tunnel has a length of 7.4 m and a cross-section of 1.5 m in width and 1.8 m in height. The free stream turbulence is controlled below 0.6% in the test section. An example of the setup is shown in Figure 4. The two-dimensional airfoil body has a chord length of 0.06 m and a height of 0.6 m. The upstream speed used in the tests is 15 m/s, leading to \(Re_{C}=60\),000. The shape of the body is made by 3D printing. To have sufficient stiffness for the model, a circular steel rod is embedded in the airfoil. Two wooden end plates are used to maintain the flow conditions. Beneath the airfoil, the aerodynamic forces are measured using a JR3 force balance. Preliminary validation tests showed that the errors of the JR3 force measurements are within 10%. A small gap of about 0.003 m exists between the top plate and airfoil to ensure that the aerodynamic force is fully transferred to the bottom support while the effects of the tip vortex are sufficiently suppressed. The gap distance only satisfies the 5% of the model height (0.6 m) proposed by [30]. Therefore, no correction is applied to the force measurement results. A Pitot tube placed aside is used to measure the free stream reference dynamic pressure. The measurements of both forces and wind speeds are sampled at a rate of 1000 Hz for 120 s.
## 6 Experiments
### Results of Self-Calibration  
The self-calibrating method described in Section 4.2 was evaluated on the basis of comparing results obtained from images for which the method was applied with results obtained from images which were not processed by the method. Thus, two collections of image sets were prepared. The collection \(A\) contained images calibrated only with the pairwise method described in Section 4.1. The collection \(B\) were images acquired from the collection \(A\) after self-calibration. Collections \(A\) and \(B\) were prepared from the same images which were used for making test data in the testbed. All six sets of images were included in both of these collections.  
Collections \(A\) and \(B\) had a function of input data used for acquiring disparity maps. First, considered pairs of images included in collection were processed by stereo matching algorithms. Then, results were merged using the EEMM method. Therefore, disparity maps were results of processing images from all five cameras included in the array. Algorithms considered in this experiments were ELAS, StereoSGBM and GC Expansion. Figure 7 shows results with regard to the collection used.  
The first raw of charts in Figure 7 presents results for collection \(A\) (marked with \(Col\)\(A\)) and \(B\) (marked with (\(Col\)\(B\)). The lower raw visualize decreases in values of metrics obtained for collection \(B\) with regard to their values for collection \(A\). Results of COV and BMO quality metrics are similar as they differ between collections less than 2.5%. However, the value of BMP is on average 34.2% lower than in case of using collection A. It is a significant improvement which shows that self-calibration method described in Section 4.2 is a valid technique of calibrating EBCA.  
The required applied shifts were different for different data sets. On average the shift \(m_{2}\) for the up camera was equal to 0.83, shift \(m_{3}\) corresponding to the right camera was equal to 0.33 and \(m_{4}\) for the down camera was 3.33. There were also differences in values of \(m\) for different data sets. However, the greatest deviation from the average values was lower than 5 points. For example, \(a_{2}\) for set \(RC_{1}\) was equal to \(-4\), but for set \(RC_{2}\) it was equal to 5. These values are high enough to influence the quality of resulting disparity maps, because they indicate that for the same points disparities calculated by different camera pairs differ between each other to this extent.  
Differences in shifts for different data sets were foremost cause by different characteristics of these sets. As presented in Table 1 there are different ranges of disparities occurring in sets. Distances from which images were taken for different sets also varied. Moreover, sizes of sets were different. It is also presented in Table 1. Raw images \(I_{R}\) were made with the resolution of 1280 x 720 however depending on the set parts of different sizes were cut out from raw images. Moreover, there are also variations caused by contents of images causing that SIFT matches different points depending on the analyzed set.
# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 4 Findings and Discussions
A series of algorithms to process a massive volume of data, then utilizing MILP processes, requires significant handling time. Therefore, the prior processes are set to be employed offline and stored as an individual profile of a node in the area. The classification technique from supervised learning is used to learn offline data, reducing the handling time by providing instantaneous traffic labelling from steering functions. Compared with the previous study done in [5], the processes defined in Figure 2 produce a base value set to represent the time taken to collect data, transform, cluster and perform the resource assignment (Steps 2 to 5), as shown in Table 2, which is the most time needed to execute the overall processes contributed by the clustering activities. Figure 4 illustrates the share of time required to cluster data with parameters specified in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset, \(i\)**} & \multicolumn{2}{c}{**Set 1.1**} & \multicolumn{2}{c}{**Set 5.1**} & \multicolumn{2}{c}{**Set 10.1**} \\ \hline
**No** & **Split Ratio, \(j\)** & **Model** & **Cluster** & **Transport** & **Cluster** & **Transport** & **Cluster** & **Transport** \\ \hline \multicolumn{8}{c}{Downlink} \\ \hline
1 & 1\% & \(M_{i1}\) & 0.00\% & 33.56\% & 8.19\% & 35.45\% & 0.00\% & 25.48\% \\ \hline
2 & 10\% & \(M_{i2}\) & 0.00\% & 22.12\% & 0.21\% & 1.72\% & 0.00\% & 3.33\% \\ \hline
3 & 50\% & \(M_{i3}\) & 0.00\% & 9.18\% & 0.08\% & 0.17\% & 0.00\% & 0.02\% \\ \hline
4 & 100\% & \(M_{i4}\) & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\ \hline \multicolumn{8}{c}{Uplink} \\ \hline
5 & 1\% & \(M_{i1}\) & 30.58\% & 50.53\% & 1.39\% & 73.62\% & 44.90\% & 73.44\% \\ \hline
6 & 10\% & \(M_{i2}\) & 45.94\% & 41.78\% & 0.00\% & 2.13\% & 0.00\% & 0.50\% \\ \hline
7 & 50\% & \(M_{i3}\) & 0.00\% & 4.63\% & 0.00\% & 0.00\% & 0.00\% & 0.21\% \\ \hline
8 & 100\% & \(M_{i4}\) & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\ \hline \hline \end{tabular} \% value: represents the number of errors (unmatched classification output against the actual values).  
\end{table} 
Table 5: The performance of the model created based on the volume of UEs in the dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Set 1.1: Model, \(M_{14}\)**} & \multicolumn{3}{c}{**Set 5.1: Model, \(M_{24}\)**} & \multicolumn{3}{c}{**Set 10.1: Model, \(M_{34}\)**} \\ \hline
**No** & **Dataset, \(i\)** & **Cluster** & **Transport** & **Cluster** & **Transport** & **Cluster** & **Transport** \\ \hline \multicolumn{6}{c}{Downlink} \\ \hline  
\hline \end{tabular} \% value: represents the number of errors (unmatched classification output against the actual values).  
\end{table} 
Table 6: The performance of the classification model on the datasets, i.  
Figure 4: The distribution of time taken to execute both methods.
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results of First-Principles Studies of Localization
#### 7.5.2 Application to (Ga,Mn)N  
Another class of functional materials in which disorder plays an important role are diluted magnetic semiconductors (DMS). Magnetic impurities give rise to magnetic order in these systems via the creation of a magnetic impurity band.  
To study localization of the impurity band is not only important for the transport properties but is also essential to understand the magnetic exchange mechanism these materials. When the carriers in the impurity band are localized, itinerant mechanisms of magnetism, such as double exchange, are ruled out, in favor of other mechanisms such as superexchange [218].  
Among the DMS materials, (Ga,Mn)N is of particular interest since Dietl [219] predicted its Curie temperature to be above room temperature. However, until now, this prediction remains far from being fulfilled as various experiments lead to controversial conclusions concerning the ferromagnetism [220; 221; 222; 223; 224].  
To enhance the understanding of magnetism in (Ga,Mn)N we have studied localization in this material from first principles. Figure 46 shows the calculated ADOS and TDOS of the minority band for various Mn concentrations. We can see that for Mn impurity concentrations less than 10% (the compositional limit of (Ga,Mn)N), the chemical potential always sits above the mobility edge, indicating that it is insulating due to localization. Moreover, when the Mn concentration is below 3%, the TDOS of the impurity band vanishes completely, leading to the complete localization of the impurity band supporting the dominance of the ferromagnetic superexchange mechanism over the double exchange mechanism for the low concentration.  
Figure 45: The average and typical density of states of KFe\({}_{2}\)Se\({}_{2}\) with 12.5% Fe vacancy concentration calculated by multiband DCA and TMDCA with cluster size \(N_{c}=1\) and \(N_{c}=16\), compared with the average density of states of the clean (no vacancy) KFe\({}_{2}\)Se\({}_{2}\). Reprint from [12].
# Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
## 3 Simulation Analysis and Results
### Model Training
#### 3.1.3 Long Short-Term Memory (LSTM)  
This paper presents a deep learning model for time series prediction using LSTM networks. The model has four layers, including three LSTM layers and one dense layer. The first LSTM layer has 200 units, the second has 100 units, and the third has 64. All three LSTM layers use the ReLU activation function. This model can also learn hierarchical representations of input data with multiple time steps and features (Figure 8a). The image depicts a recurrent neural network (RNN) architecture for processing 3D input data that employs Gated Recurrent Units (GRUs). The architecture is made up of five layers: input, GRU, dropout, batch normalization, and dense. The GRU layers handle sequential data well, extracting higher-level features while reducing overfitting and enhancing training stability. The final layer is a fully linked layer with three units representing output classes from a classification operation.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 2 Preliminaries
### Huffman Coding  
Huffman coding is a lossless encoding method in statistics. Its principle is based on the frequency of data (pixels in an image), using fewer bits to encode data with a higher frequency, which can reduce the overall coding length to achieve the purpose of data compression. Huffman coding produces the lowest number of redundant codes, has low computational complexity, and is easy to implement. Huffman coding has been effectively applied to text, image, video compression, and so on. For a more detailed description of Huffman coding, refer to [25].
# The Sun and Space Weather
## 2 Basic Properties of CMEs
### Physical Properties  
CMEs are magnetized plasmas formed out of closed magnetic field regions. The CME core is the eruptive prominence and hence has the lowest temperature (\(\sim\)10\({}^{4}\) K, [23; 24; 25]). The flux rope forms out of the sheared loops in the corona, so the temperature should be coronal (\(\sim\)2 MK). The flux rope is a low-beta entity (the magnetic pressure is much larger than the thermal pressure), and the magnetic field strength is well above that in the ambient medium. The shock sheath consists of the ambient plasma compressed by the shock, so the temperature, density, and magnetic field strength are all higher than those in the ambient medium. Interplanetary CMEs (ICMEs) do confirm the basic spatial structure with an often well-defined shock, sheath, and driving flux rope. The intervals of high-density prominence material are the coolest within MCs and show low Fe and O charge states [26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36]. Recent statistics indicates that about 36% of MCs possess prominence material, indicated by the unusual O5+ and/or Fe6+ abundances [36]. In most cases, the prominence material is located at the back end of MCs, consistent with the spatial ordering observed near the Sun. However, there are reports on filament material located in the front of MCs [37; 34; 38]. In most of the 1-au flux ropes, heavy ions are in high-charge states, indicating hot plasma entering from the flare site into the flux rope and the charge states are frozen soon after the entrance [39]. Interestingly, both magnetic cloud (MC) and non-cloud ICMEs show charge-state enhancement, indicating that both types of ICMEs have flux rope structures paired with post-eruption arcades formed in the reconnection process [40]. Furthermore, [PERSON] et al. [41] have shown that a flux rope can be fit to most of the non-cloud ICMEs with slight changes in the ICME boundaries.
# Evaluation of Workability and Mechanical Properties of Bottom Ash Aggregate Concrete
## 3 Experimental Details
### Casting, Curing, and Testing  
The initial slump and air contents of the freshly mixed concrete were tested according to ASTM C143 [26] and ASTM C231 [27], respectively. The compressive and splitting tensile strengths and elasticity modulus of concrete were measured using \(100\times 200\) mm cylinder specimens. A compressor meter equipped with two 5-mm linear variable differential transformers was mounted on the cylinder specimens to record the concrete strain according to the applied loads. In accordance with ASTM C469 [28], the modulus of elasticity of concrete was calculated as the slope of the secant line joining zero stress and 40% peak stress. The modulus of rupture of the concrete was measured using prismatic beams with dimensions of \(100\times 100\times 400\) mm. In addition, the shear friction strength of the concrete specimens was recorded by push-off tests under a concentric load acting as pure shear in the shear plane of the test zone. The push-off specimens had width, height, depth, and critical shear plane area of 300 mm, 800 mm, 120 mm, and 200 \(\times\) 120 mm, respectively. The compressive strength of the concrete was recorded at ages of 1, 3, 7, 28, 56 and 91 days, whereas the other mechanical properties were measured at 91 days. To verify the segregation of the concrete, digital image analysis was conducted. The 100 \(\times\) 200 mm cylinder was cut longitudinally, and images of the cutting plane were captured using a high-resolution digital camera. The grayscale contrast between BA particles, granite particles, and paste was profiled via image analysis of all quarter zones of the cutting plane. The area of aggregate particles in each quarter zone was recorded from the image analysis to calculate the proportion of each component.
## 3 Fundamentals of GLI-Split
### Security Concerns and Countermeasures
#### 3.8.3 Flow Interception Through Spoofed Mobility Updates  
When two upgraded GLI-nodes in different GLI-domains communicate with each other, a malicious GLI-gateway of another domain can deviate the flows to intercept them. This is illustrated in Figure 7. The attacking GLI-gateway sends a mobility update message to both GLI-nodes, saying that the locator of the other node has changed to the locator of the malicious GLI-gateway. Thus, the attacker attracts the traffic from both nodes and can forward it to the other node. The GLI-gateway can thereby intercept the traffic, even though it is not on the path between the two communicating nodes.  
This problem can be avoided if mobility update messages are signed by the sender and validated by the receiver. The use of a nonce has been proposed as a solution for that problem in ILNP [22].
## Appendix A Multiple Linear Regression Analysis of IEQ Parameter Correlations
### Regression Model Structure  
The regression models were structured to analyze the relationship between pairs of IEQ parameters while controlling for the following covariates:  
1. Building type (categorical variable with four levels): * Primary/elementary education (includes kindergarten, nursery, K-12 primary). * Secondary education (includes high schools, junior high schools, middle schools). * Higher education (universities). * Other educational facilities.
2. Study period (categorical variable with two levels): * Recent studies (2023-2024). * Earlier studies (2020-2022).
3. Parameter measurement context (derived from study methodologies): The general form of each regression model was \[Y=\beta_{0}+\beta_{1}X+\beta_{2}BT_{1}+\beta_{3}BT_{2}+\beta_{4}BT_{3}+\beta_{5 }SP+\varepsilon\] (A1) where * \(Y\) is the dependent IEQ parameter. * \(X\) is the independent IEQ parameter. * \(BT_{1},BT_{2}\), and \(BT_{3}\) are dummy variables for building types. * \(SP\) is the study period indicator. * \(\beta_{0},\beta_{1},...,\beta_{5}\) are regression coefficients. * \(\varepsilon\) is the error term.