esa-sceva/satcom-chunk-collection huggingface dataset:
# Position Detection of Doors and Windows Based on DSPP-YOLO
## 3 Dspp-Yolo
### Pseudocode  
In Figure 2, the flow of DSPP-YOLO algorithm is introduced. This section presents the pseudocode of feature extraction in Algorithm 1 and training model in Algorithm 2. ```
1. For \(X=1\); \(X<=(total\ number\ of\ training\ images); \(X++\)
2. Read photos of doors and Windows
3. Divide the picture into \(n\times n\) areas for use
4. Search for areas with possible target centers
5. Generate bounding boxes in possible regions
6. Predict target width and height
7. According to anchor boxes size and object size adjust bounding boxes size
8. Predict the target category
9. Calculate the confidence score of bounding boxes
10. Output the center coordinates, width and height, and object category of the bounding box with the highest confidence
11. end for \begin{table}
\begin{tabular}{c c c c c} \hline
**Feature Map** & \(13\times 13\) & \(26\times 26\) & \(52\times 52\) & \(104\times 104\) \\ \hline Receptive field & Big & Medium & Small & Smaller \\ \hline \multirow{3}{*}{Anchor box} & \((199\times 279)\) & \((61\times 167)\) & \((44\times 90)\) & \((24\times 32)\) \\  & \((145\times 195)\) & \((83\times 102)\) & \((16\times 105)\) & \((18\times 16)\) \\  & \((97\times 218)\) & \((36\times 102)\) & \((26\times 55)\) & \((6\times 24)\) \\ \hline \end{tabular}
\end{table} 
Table 1: Anchor box size after clustering based on K-Means++. ## 4 Experimental Analysis

### Experimental Data Set and Environment

Data set: the experimental data used in this experiment is divided into two parts, one part is the public images collected on the internet, the other part is the multi-angle, multi-state images collected by different devices based on the experimental scene, which are taken in the classrooms and laboratory. There are 1105 samples. The data set of this experiment covers rich picture information of different shapes, different shooting angles, different sizes and different scenes of the target, which can increase the generalization ability of the network. However, some pictures are not clear enough. As shown in Table 2 are the hardware configuration. The total samples are proportionally divided into training set and test set, and 25% of the training set is divided into cross-validation set, which is used to verify the periodic learning results in the training process. Figure 5 shows part of the dataset from different sources. (a) It's some pictures we found from the Internet. (b) It's some pictures we had taken with a mobile phone from different angles. (c) It's some pictures we had taken with a car from different angles. All images are not preprocessed and in rgb format.
## 3 Methods
### Analysis of fMRI Synchronization-Based Connectivity  
#### 3.2.1 Identification and Analysis of Functional Modules  
Louvain algorithm [45] has been used to find communities of ROIs in the three functional networks obtaining three partitions for each fMRI session. This method aims at maximizing a modularity metric that evaluates the quality of a partition by comparing the density of connections within a community to that expected in a random network. This algorithm optimizes the modularity function defined as:  
\[Q=\frac{1}{2m}\sum_{i,j}\bigg{(}A_{i,j}-\gamma\frac{k_{i}k_{j}}{2m}\bigg{)} \delta(c_{i},c_{j}), \tag{6}\]  
where \(A_{i,j}\) is the edge weight between \(i\) and \(j\), \(k_{i}\) is the sum of the weights of the links attached to node \(i\), \(c_{i}\) is the community assigned to the node \(i\), \(m\) is the sum of all of the links of the networks and \(\delta(\alpha,\beta)=1\) if \(\alpha=\beta\) and \(\delta(\alpha,\beta)=0\) if \(\alpha\
eq\beta\).  
Figure 1: Example of synchronization quantification: for two fully synchronized sine waves (top panel **A**), two sine waves synchronized for half of the observational period (middle panel **A**) and two sine waves synchronized in intermittent way (bottom panel **A**), the CRPs are computed by using the Takens’ theorem (panel **B**) and the LOS is extracted from each plot (panel **C**) in order to assess the SYNC index.  
An overview of the analysis is shown in Figure 2. To select the best resolution value, the algorithm was applied for \(\gamma=(0,0.1,...,1.7)\). We performed 1000 iterations of the Louvain algorithm for each value of \(\gamma\). Finally, we selected the \(\gamma\) value corresponding to the maximum \(Q\) value averaged over the 1000 partitions. This procedure returns a robust resolution index value, but also a set of different partitions corresponding to the optimal \(\gamma\) value. To identify the most representative community partition, we applied a consensus algorithm. This approach removes the statistical fluctuations of the modularity algorithm and provides a stable output, as well as identifying only one partition for each network [46].  
We compared the community partitions resulting from the three networks within each session by using the normalized mutual information (NMI) [47]. For two networks with partitions, respectively, \(A\) and \(B\), it is defined as:  
\[NMI(A,B)=2\frac{I(A,B)}{[H(A)+H(B)]}, \tag{7}\]  
where \(I(A,B)\) is the mutual information between the two partitions and \(H(A)\) and \(H(B)\) are the entropy of \(A\) and \(B\). This metric ranges between zero (if and are completely independent) and one (if and are identical).
# Developing a Specific MRI Technology to Identify Complications Caused by Breast Implants
## 1 Introduction  
Advancements in aesthetic medicine have allowed breast augmentation to become a popular plastic surgery worldwide. Two common techniques are fine-needle injection and silicone implants. In the former, injections of various biological materials or gel fillings are placed into the pectoralis major muscle beneath the breasts using a fine needle. Complications such as nodule or blood clot formation, evidenced by lumps, inflammation or infection, pain, or secretions, can result [1, 2]. On the other hand, silicone implants can leak or deform. Both types present a risk of rupture, resulting in complications over time. In 2003, [PERSON] studied the rate of rupture among silicone implants, finding that the risk increases with implant age and that it is at least 15% 3 to 10 years after augmentation [3]. In 2017, [PERSON] analyzed the rupture rates of three types of commercial silicone implants and found that the risks started to increase 6 to 8 years after augmentation [4]. In 2018, [PERSON] reported a case of pericarditis originating from breast implant rupture. An implant rupture can be symptomless, but it can result in isolated silicone granulomas or serious systemic diseases [5, 6]. Although new-generation silicone implants can last longer than legacy implants, the risk of rupture in the long term remains high [7], and once rupture results in complications, the conventional practice is to perform reconstructive surgery, removing the implant or resecting the lesion.  
For many years, breast implant leaks have been diagnosed using medical imaging such as mammography, computed tomography, ultrasound, or magnetic resonance imaging (MRI), each providing its own sensitivity and specificity [8; 9; 10; 11]. Of these, MRI is recommended for evaluating implants, given its ability to show the distribution of whatever substance was injected for breast augmentation and to detect certain complications. It can locate the leak, and it provides high diagnostic accuracy [12; 13; 14; 15; 16]. However, when the inner layer of the breast implant ruptures, leaving the outer shell intact, MRI can lead to erroneous diagnoses [17]. Many studies of diagnoses based on only MRI and ultrasound recruited symptomatic participants, and because the sensitivity of MRI might not be as great with asymptomatic cases or with screenings, the accuracy of MRI-based diagnoses might be overestimated [18]. Breast cancers and complications originating from implants can be identified using gadolinium-enhanced MRI [19]; however, fibromas from silicone injections are likely to be confused with breast cancers.  
Long-term complications from silicone injections can result in siliconoma or calcifications, either of which can mimic breast cancer or even normal mammary gland tissue in a mammogram [20]. Therefore, a silicone-specific MRI technique is needed for patients with breast augmentations. With this study, we aimed to develop an MRI technique that can produce water-specific and silicone-specific images, allowing differentiation between breast augmentation and lesions.
## 5 Three-Dimensional Motion Planning Algorithm
### Results
#### 5.2.4 Comparison to Inverse Kinematics (IK) Solver  
We tested Model II against the Newton-Raphson (NR) solver from the Robotics-Toolbox [23] on 5000 trials, moving from random initial configurations \(\mathbf{q}_{init}\) to desired MA positions \(\mathbf{x}_{des}\). These random configurations and positions were uniformly sampled from the 50,000 samples that were used to test the NN.  
For each IK query, \(m\) solutions were generated using the NR. These \(m\) solutions were grouped into five sets, each solving for an arm with the first five \(k\) joints (\(k=1,\ldots 5\)) and an additional prismatic joint simulating the MA at the final link, as illustrated in Figure 14. The solution with the minimum MA actuation time, \(\tau\), according to Equation (3), was selected from the \(m\) solutions. In addition to the positional error over the training and test data, Table 4 presents the mean runtime of the computer to generate the data presented in Table 3.  
Figure 14: Illustration of the configurations for different arm setups (\(k=1,\ldots 5\)). Each configuration shows the prismatic joint in red, simulating the MA at the last link for solving IK queries. These configurations were used to select the solution with the minimum actuation time from the NR solver’s generated solutions.  
Figure 13: Starting with the non-zero initial configuration, the model with the time constraint only made a significant change in the first joint (**B**), whereas the model without time constraints made changes in all the joints (**A**). As a result, there was a substantial time-save of approximately \(5\) s.  
The NR-based IK was evaluated with \(m=1000\) and \(m=10000\). The results for the numerical IK showed that the positional errors were higher than for the NN, but that the action time was not adequately minimized. In addition, the runtime for an IK query was at least two orders of magnitude longer than for the NN, leading to an excessively long planning time, which is not suitable for real-time applications.
## 3 Results and Discussion
Overall, across both datasets, the G-NMS method consistently showed high performance in AMI and ACC, affirming its effectiveness in clustering with few exceptions in Dataset 2 Difficult 1 (D1) and slightly lagging in Difficult 2 (D2) spike clustering. Moreover, K-means also demonstrated strong and consistent results across all metrics. FCM's performance, particularly in AMI and ACC, was noteworthy, especially in Dataset 2. The other methods exhibited varying levels of effectiveness, with some, like ISO-SPLIT, showing significant differences between the two datasets. Table 2 serves as a comprehensive guide to the parameter configurations for the G-NMS algorithm, tailored for different experimental scenarios within the datasets. It specifically outlines the settings for two key parameters: the intersection over union (IoU, \(\alpha\)) threshold and the bandwidth (h) of the Gaussian kernel. The IoU threshold is a crucial factor in the nonmaximum suppression (NMS) algorithm, effectively utilized for suppressing Gershgorin circles (GCs). Simultaneously, the bandwidth of the Gaussian kernel plays a  
Figure 7: Average accuracy of clustering algorithms across experimental groupings. The line graph and inset table detail the performance of seven clustering techniques (K-means, DBSCAN, HDBSCAN, FCM, agglomerative, ISO-SPLIT, and G-NMS) across experimental groupings (S1, E1, D1, D2). The table provides numerical values for average accuracy and standard deviation, reflecting the consistency and variability of each method within the respective groups. pivotal role in accurately estimating the probability density function (PDF), integral to the algorithm's performance. For instance, in the Dataset 1 experiments \"SNR0.5_Spk-1,2\", \"SNR1.25_Spk-2,3\", and \"SNR2.0_Spk-3,1\", the IoU value is consistently set at 0.1, while the bandwidth varies from 0.4 to 0.1 as the noise (SNR) in the spikes increases, yet the ACC performance remains almost consistent. In contrast, for Dataset 2, both the IoU values and bandwidth are adjusted to achieve higher ACC performance. Notably, in the \"D1_SNR0.2_Spk-3,1\" experiment with the noisiest spikes, a lower bandwidth of 0.06 and an IoU of 0.4 were selected, but the ACC for G-NMS was still low, at 68.639%. In comparison, the \"D2_SNR0.05_Spk-1,2\" and \"D2_SNR0.2_Spk-1,2\" experiments, despite having different noise levels, maintained the same IoU at 0.4 and bandwidth at 0.1, resulting in higher ACCs of 94.855% and 95.907%, respectively. These results underscore the importance of optimal parameter selection based on the noise level in the input spikes. A higher noise level suggests the need for a lower bandwidth to capture finer changes in the distribution, while the IoU parameter should ideally range between 0.4 to 0.1 to ensure minimal yet effective suppression of Gershgorin circles (GCs). Selecting an IoU below 0.1 might result in too few GCs for effective kernel density estimation (KDE). The code and other supporting recourse for proposed method are available inside Supplementary Materials.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 3 Results  
The testing of the quality of digital terrain models was based on the utilisation of two methods. The first method was involved analysing the accuracy of derived slopes from the models. The second method was a comparison of the results of a CCM raster analysis for three selected vehicles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Mean Slope**} & \multicolumn{2}{c}{**Mean Altitude**} \\  & & **(Above Sea Level)** & **Coordinates (WGS84)** \\ \hline Dobruska & 6\({}^{\circ}\) & 420 m & 50.31\({}^{\circ}\) N, 16.22\({}^{\circ}\) E \\ Horni Cerekev & 5\({}^{\circ}\) & 645 m & 49.32\({}^{\circ}\) N, 15.25\({}^{\circ}\) E \\ Znojmo & 3\({}^{\circ}\) & 211 m & 48.83\({}^{\circ}\) N, 16.16\({}^{\circ}\) E \\ Kdyné & 9\({}^{\circ}\) & 576 m & 49.39\({}^{\circ}\) N, 13.11\({}^{\circ}\) E \\ Dolni Morava & 15\({}^{\circ}\) & 670 m & 50.11\({}^{\circ}\) N, 16.86\({}^{\circ}\) E \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the 5 selected locations (3 km \(\times\) 3 km) for testing the elevation models.
## 3 Numerical Experiments and Analysis
### CC-Based EA  
In this subsection we investigate the effect of CC-SHADE's main parameters on its performance. The population size was set to 25, 50, 100, 150, and 200. The number of subcomponents was set to 1, 2, 5, 10, 20, 50, 100, 200, 500, and 1000. Figure 1 presents heatmaps for each benchmark problem and each combination of the parameters. The \(x\)-axis denotes the number of subcomponents, the \(y\)-axis denotes the population size. The total number of combinations is equal to 50 for each benchmark problem. The performance of each parameters' combination is presented as a rank. The biggest number denotes the best average fitness value obtained in 25 independent runs. If two or more combinations of parameters have the same averaged fitness value, then their ranks are averaged. The ranks are colored in heatmaps from white (light) for the worst combination to dark blue (dark) for the best combination. The rank distributions are different in heatmaps for different optimization problems. Figure 2 shows the ranks sum for the algorithm's parameters for all benchmark problems. The \(x\)-axis denotes the number of subcomponents, the \(y\)-axis denotes the number of individuals using the results from Figure 1. The highest sum of ranks is the best achieved result. Dark color denotes the best average combination of parameters. The best average combination of parameters for CC-SHADE are 50 subcomponents and 25 individuals.  
Table 1 shows the best combination(s) of parameters for each benchmark problem. The first column denotes the problem number, the second column denotes the best combination using the following notation, \"CC \(\times\) pop_size\", where CC is the best subcomponent size, and pop_size is the best population size. The last column denotes the class of a benchmark problem. As we can see from the results in Table 1, we cannot define the best combination of parameter combination for all problems. Additionally, we cannot find any pattern of the best parameters for each class of LSGO problems.  
Figure 1: Ranking CC-SHADE algorithms with the static population size and the number of sub-components. Benchmark problems are: (**a**) \(F_{1}\); (**b**) \(F_{2}\); (**c**) \(F_{3}\); (**d**) \(F_{4}\); (**e**) \(F_{5}\); (**f**) \(F_{6}\); (**g**) \(F_{7}\); (**h**) \(F_{8}\), (**i**) \(F_{9}\); (**j**) \(F_{10}\); (**k**) \(F_{11}\); (**l**) \(F_{12}\); (**m**) \(F_{13}\); (**n**) \(F_{14}\); (o) \(F_{15}\).
# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **MPJPE(\(\downarrow\))** & **PA-MPJPE(\(\downarrow\))** \\ \hline \hline [PERSON] et al. [4] & - & 157.0 \\ SMPLify [9] & 199.2 & 106.1 \\ TP-NET [51] & 163.7 & 92.3 \\ HMR [5] & 130.0 & 76.7 \\ Temporal-HMR [47] & 127.1 & 80.1 \\ [PERSON] et al. [64] (_semi-sup_) & 125.8 & 78.2 \\ _Ours (semi-sup)_ & **106.0** & **66.0** \\ \hline \hline [PERSON] et al. [64] (_weakly-sup_) & 153.4 & 89.8 \\ _Ours (weakly-sup)_ & **107.4** & **69.1** \\ \hline \hline [PERSON] et al. [64] (_self-sup_) & 187.1 & 102.7 \\ _Ours (self-sup)_ & **124.7** & **88.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation with wild 3 DPW dataset in a _fully-unseen_ setting. Unlike Temporal-HMR, we do not use any temporal supervision. Methods in the first seven rows use equivalent 2D and 3D pose supervision.
## 5 Parametric Study of the PMA Unit Cell  
### Design Optimization  
The suggested PMA development was settled on the basis of the trial-and-error technique to acquire the maximum resonance frequencies with excellent absorptivity. The design of the PMA structure was started by taking an octagonal close ring shape resonator that exhibits single resonance frequency (14.6 GHz) with 57.7% absorption, as indicated in Figure 9a. In the next step, two opposite tuning forks were added to the octagonal ring shape resonator as shown in Figure 9b. According to Figure 9b, this design shows two resonance frequencies (6.69 GHz and 15 GHz) with 97.7% and 99.7% absorption. The scheme was finalized by inserting a dumbbell shape resonator between the two tuning forks shown in Figure 9c. The final complete unit cell represented two main resonance frequencies (6.45 GHz and 14.89 GHz) with excellent absorption (99.15% and 99.76%). The absorption graph of all design is shown in Figure 9d.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author Name** & **Design Structure** & **Size (mm\({}^{2}\))** & **Proposed Band** & **Absorption (\%)** & **Year of** \\  & & & & & **Published** \\ \hline Proposed PMA & Tuning fork shape & \(8\times 8\) & C-Ku & 99.76 & – \\ [PERSON] et al [37] & L-shape & \(8\times 8\) & X-band & 90 & 2021 \\ [PERSON] et al [22] & S-shape & \(20\times 20\) & S-, X-, Ku-band & 55 & 2017 \\ [PERSON] et al [36] & L-shape & \(9\times 9\) & X-, Ku-band & 95 & 2017 \\ [PERSON] et al [35] & Square shape & \(10\times 10\) & C-, X-, Ku-band & 93 & 2017 \\ [PERSON] et al [34] & O-shape & \(12\times 12\) & X-band & 98.90 & 2016 \\ [PERSON] et al [19] & Modified square & \(8\times 8\) & X-band & 95.81 & 2013 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different published PMA with proposed PMA.  
Figure 9: Design optimization (**a**) Design 1 (**b**) Design 2 (**c**) Design 3 (**d**) Absorption graph scenario for different design.
## 5 Identification of Damage on an Experimental Beam
### Results  
The measured natural frequencies are used to identify the damage properties. First, the 2D beam model based on the FE mesh (100 elements) depicted in Figure 1 is correlated to the experiment. The bending stiffness updating technique is employed in a 2D damaged model to obtain the updated model. It should be pointed out that the mass of accelerometers is not taken into account in the FE model as localized mass; however, the model is updated based on the natural frequencies of the experimental beam with accelerometers.  
In this experimental case, the damage is first localized and quantified; After, based on the severity, its geometry is recovered. The FSC is computed as a function of position and severity using **Step 2** and plotted as a color map on Figure 17a. As previously mentioned, the colorbar indicates FSC value and the white circle gives position and severity, according to the minimization. In this case, the damage severity is 34.93% at 0.72 m beam position, where the FSC minimum value is 0.039. Note that the estimation of localization (0.72 m) is very close to the real position (0.71 m). Again, it takes 5-10 s to solve the minimization problem.  
The obtained severity (34.93%) is searched in the damage library in order to extract width and depth using **Step 3**. Again, several sizes of damage correspond to a single severity, as shown in Figure 17b. For the sake of the demonstration, we here assume the width to be the actual width (1.3 mm). It is found that the depth corresponding to 1.3 mm width is 1.62 mm (see Figure 17b). This estimation is close to the real value of depth (1.55 mm). It indicates that the physical parameters are correctly identified through the damage library. Hence, the proposed strategy constitutes an efficient identification tool for the estimation of geometry damage properties in an experimental context.  
Figure 16: Experimentally tested cantilever beam where A shows experimental set up and input/output measurement points with accelerometers; B displays acquisition card and software; C shows details on the rectangular geometry.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 2 3 GPP Release 15: Birth of 5G
It incorporates mechanisms for adaptive rate control and explicit congestion notifications, which optimize network performance under varying traffic conditions. Additionally, NG-RAN implements a robust security framework to protect user data and signaling communications [33]. The security mechanisms include device and user authentication, encryption, and integrity protection, enabling secure operations across multiple network domains. NG-RAN's advanced features further improve its performance. Carrier aggregation (CA) allows the combination of multiple carriers to achieve higher data throughput [20]. Beamforming and MIMO technologies improve signal quality and spectral efficiency, enabling better coverage and capacity. Network slicing provides logical partitioning of the network, allowing operators to tailor services for specific use cases such as IoT, critical communications, or high-speed broadband [20]. In summary, Release 15 of 3 GPP marked the \"birth of 5G\", setting the stage for its evolution. It introduced a flexible design capable of addressing the diverse and demanding requirements of modern mobile communications. This release established the foundation for the subsequent enhancements and expansions that drive the 5G-and-beyond ecosystem.