esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Mutual Coherence  
In Figure 2, we show the mutual coherence of the Kronecker-product-based frames with \(N=64\) and \(256\), respectively. The optimized Kronecker-product-based frames have the number of phases \(q=4,8\), and \(16\), respectively. For \(q=4\), the mutual coherence is unstable about the frame length \(m\). However, for \(q=16\), the optimized Kronecker-product-based frames show similar mutual coherence with the optimized harmonic frame. This result demonstrates that the optimized Kronecker-product-based frames of a small number of phases \(q\ll N\) can perform similarly to the optimized harmonic frames of the number of phases \(N\). This means that the optimized Kronecker-product-based frames are more suitable for practical applications, thanks to the small number of phases. In particular, we found ETFs with the number of phases \(q=4\) and \(q=8\) in dimension \(N=64\) and frame length \(m=28\) by the algorithm of Section 4. As far as we know, these have never been reported. As witnessed by Figure 3 in [41], we also observed a small increase in mutual coherence at some \(m\) from the optimized Hadamard-based frames, despite the increase in the frame vector length, which is due to the restricted, binary entries of the Hadamard matrix.  
In Figure 3, we designed the Kronecker-product-based frames with various dimensions \(N\) for fixed frame lengths \(m=32\) and \(64\), respectively, where \(p=4,8\) and \(16\). For the optimized Hadamard-based frames, we examined the mutual coherence for \(N=128,160\), and \(192\), where the Hadamard matrices exist. As \(q=N/p\), the number of phases of the Kronecker-product-based frames are \(p\) times lower than that of the optimized harmonic frames. We observe that the mutual coherence of the optimized Kronecker-product-based frames is similar to those of the optimized harmonic frames in various \(N\), even if they have significantly lower number of phases. The mutual coherence of optimized Hadamard-based frames is slightly higher than those of the other optimized frames.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Evaluation Metrics
#### 3.4.3 Mean Absolute Error (MAE)  
MAE captures the average magnitude of absolute errors:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-\hat{y}_{i}| \tag{11}\]  
Unlike RMSE, MAE treats all errors equally and is less influenced by outliers.  
All metrics are computed on the test set and used to compare the proposed model against baseline methods. Higher \(R^{2}\) and lower \(\text{RMSE}/\text{MAE}\) values indicate better predictive performance.
## 3 Results
### Control 90 Days  
In this group, we observed partial repair of the NCA and the NBA. Compared to the Control 30 days group, more areas isolated of immature bone-tissue formation were observed close to the fibrous connective tissue in the area of the surgical defect. In the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{3}{c}{**30 Days**} & \multicolumn{3}{c}{**90 Days**} \\ \cline{2-6}  & & **Mean (\%)** & **SD** & **Mean (\%)** & **SD** \\ \hline \multirow{2}{*}{Control} & NBA & 2.46 \({}^{\text{A}}\) & \(\pm\)0.43 & 15.10 \({}^{\text{A}}\) & \(\pm\)1.83 \\  & NCA & 0.09 \({}^{\text{a}}\) & \(\pm\)0.09 & 22.01 \({}^{\text{a}}\) & \(\pm\)2.75 \\ \hline \multirow{2}{*}{RTR\({}^{\text{\textregistered}}\)} & NBA & 31.31 \({}^{\text{B}}\) & \(\pm\)3.88 & 36.80 \({}^{\text{B}}\) & \(\pm\)5.87 \\  & NCA & 34.95 \({}^{\text{b}}\) & \(\pm\)5.01 & 42.84 \({}^{\text{b}}\) & \(\pm\)5.50 \\ \hline \hline \end{tabular} Different superscript letters represent statistically significant differences \(p<0.05\). Comparisons: capital letters (Control vs. RTR in NBA); lowercase (Control vs. RTR in NCA).  
\end{table}
Table 1: Mean percentage and standard deviation of total newly neoformed bone area (NBA) and newly neoformed cortical areas (NCA) in the control and RTR groups at 30 and 90 days.  
Figure 3: (**A**) Control 30 days: minimum bone repair; (**B**) RTR\({}^{\text{\textregistered}}\) 30 days: cortical neoformation was complete with inflammatory infiltrate; (**C**) Control 90 days: partial repair of the cortical bone; (**D**) RTR\({}^{\text{\textregistered}}\) 90 days: bone cortical repair with a thinner neoformed tissue.  
cortical area, the repair was more extensive, with an area 22.01% greater compared to that in the 30 days group (Figure 3C) (Table 1).
## 3 Materials and Methods  
This article proposes a general, fully automated method for classifying satellite images using R libraries by machine-based k-means clustering, which requires no prior information  
Figure 1: Study area with the location of the three target cities in Congo River Basin, DCR. Cartography: Generic Mapping Tools (GMT). Data source: GEBCO/SRTM. Map source: authors.  
and no learning on land cover classes. On one hand, the nature of land cover types is captured by considering the spectral attributes of the pixels in the respective Landsat bands. By carefully analysing these attributes, we reveal and demonstrate that the irregularity of the spectrum in the wavelength corresponding to the landscape properties is highly related to the reflectance of the surface, which results in the brightness of pixels in the image. Thus, we applied the computational model of R to effectively capture and visualise this irregularity and transfer it from the frequency optical domain to the geo-information domain.
## 5 Experimental Results
### Competing Methods  
Using the Poseidon3D dataset introduced in Section 2, we compare the performance of our projection-based method with two representatives of point-based approaches (PointNet [8], PointNet++ [9]), one representative of edge-based approaches (SparseMeshCNN [17]) and one representative of the traditional segmentation algorithm based on the Graph Cut method. Their brief summarization follows.  
* Localized PointNet [8]: We implemented the architecture by following the original paper. The input is \(N\times 3\) or \(N\times 6\), where each row denotes the 3D coordinates of a mesh cell, possibly extended by its normal vector. To make the comparison more fair, we cut a submesh of the original dental scan for each tooth, following the localized processing. We sample \(N=8192\) points from the mesh cut by farthest point sampling (FPS). We train the network with a batch size of 8 and point Dice loss.
* Localized PointNet++ [9]: This method intuitively extends PointNet by hierarchical modeling of spatial relations between neighboring points. Our architecture follows the segmentation architecture presented in the original paper. We apply the same point sampling strategy as in the case of PointNet. Similarly to PointNet, each tooth is analyzed locally, and the task is formulated as binary segmentation. Sampling and training configuration also remain the same.
* Localized SparseMeshCNN [17]: This edge-based method directly taps into the geometric nature of meshes and redefines convolution over mesh edges. To build hierarchical networks, mesh pooling via edge collapse is used. SparseMeshCNN extends the original MeshCNN by using sparse tensors. This network is again used to predict binary masks on mesh cuts in a detect-and-segment manner. As in the original work, the input feature for every edge is a 5d vector: the dihedral angle, two inner angles, and two edge-length ratios for each face. Each mesh cut is decimated to contain approximately 8000 edges.
* Graph Cut: This is a conventional segmentation method based on classic 2D image Graph Cut [25]. In 3D shape analysis, the graph is constructed from the mesh structure, with edge weights reflecting local curvature information. The Graph Cut algorithm is then applied to find the optimal cut, separating teeth from gum while following regions of lower curvature [26].
## 2 Materials and Methods
### Input Data for Simulations
#### 2.3.1 Electrical Performance and Direct Current (DC) Losses  
For the bifacial performance tools that include an electrical model, we used the following data and assumptions for DC performance, AC performance, and DC losses. The current-voltage (I-V) characteristics of the PERC panels were measured before field deployment in an QuickSun Xe-arc flasher (Endeas, Espoo, Finland) using the single lamp front and rear side measurement method specified in IEC TS 60904-1-2 [37]. The modules are temperature stabilized in the flash chamber within \(\pm\)1 \({}^{\circ}\)C of 25 \({}^{\circ}\)C, which ensured that the PV cells are close to the ambient room temperature. Measurements of the ambient (i.e., room) temperature are then used as a surrogate for cell temperature (i.e., cell temperature is not directly measured). The calibration module is a cell-technology-matched PERC module with traceability to Fraunhofer ISE's CalLab.  
The average front-side \(P_{MP}\) measurements of the samples were roughly 8 W low (\(-\)2.7%) to the manufacturer's rating. The literature review presented in [38] shows that such divergence from the manufacturer's nameplate rating is common among external testing laboratories. I-V curves at multiple irradiances (200-1000 W\(\cdot\)m\({}^{-2}\)) were also acquired in the DTU flasher system. We used the multi-irradiance I-V data, and a method described by [39] implemented in pvlib python [40] to extract the 1-diode model parameters that are used in PlantPredict, PVsyst, and SolarFarmer. The 1-diode model parameters used in SAM and MoBiDiG were extracted using a method described by [41] and incorporated into SAM's user interface [42] (see Table A3). The inverter performance was taken from  
Figure 1: Aerial view of the test facility. There are eight horizontal single-axis trackers (HSATs) and eight fixed-tilt rows. The bifacial (bi-fi) and monofacial (mo-fi) systems investigated in this paper are highlighted in white and orange, respectively.  
the manufacturer's datasheet. After 14 months of field exposure, we repeated the indoor flash I-V tests on a sample of monofacial modules (\(n=25\)). The results from the tests showed \(P_{MP}\) losses of 1% or less due to first-year degradation. These losses are most likely due to light-induced degradation (LID) since the electroluminescence images showed no signs of cell cracking or potential-induced degradation (PID). In each simulation, the total DC losses applied amount to 2.3% (see Table A5). This figure includes losses from LID, wiring, soiling, and module mismatch.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Requests-Shaping
#### 4.1.1 Requests Shaping Algorithm  
The requests-shaping algorithm behaves similarly to a token-bucket algorithm wherein a token represents a packet. Hence, the bucket is filled with a number of tokens equal to the size of the congestion window. Then, when a packet arrives, it is enqueued in the buffer and, if there is at least one token in the bucket, the Proxy forwards the packet to the Client and it removes one token from the bucket. When the Proxy receives a response to a previous transmitted request, it adds one token to the bucket, checks if a packet is queued for transmission, and possibly transmits it also removing one token from the bucket.  
In an extreme case the maximum size of the congestion window is set to one, i.e., there is only one outstanding request. However, in some cases, it can be more efficient to exploit the network resources by sending concurrent requests, i.e., having a maximum congestion window size greater than one. Indeed, the size of the congestion window, i.e., the number of concurrent requests, depends on the number of requests that can be sent through the network without congestion it. This in turn, depends on several factors, including the following: the network/communication technology that is used; the network topology, i.e., the average number of hops to reach the destination; the memory and processing capabilities of the devices, i.e., the size of the buffer used to store incoming packets, and the time needed to process and forward a packet.
## 2 Materials and Methods
### Experimental Equipment and Experimental Scheme
#### 2.2.3 Tracer-Labeled Liquid Water Migration and Visualization Verification  
In the traditional one-dimensional moisture migration test, only moisture migration is monitored through sensor readings. Owing to the height difference in the arrangement of the sensor devices, it is difficult to determine the height of moisture migration on the basis of sensor readings when moisture migrates to the middle position between two sensors. Therefore, this method lacks visibility. Fluorescein (chemical formula: C\({}_{20}\)H\({}_{12}\)O\({}_{5}\)) is commonly used as a tracer for sewage discharge detection. It is soluble in water and appears yellow--green under purple light irradiation [39], allowing for real-time tracking of the migration and redistribution spatiotemporal characteristics of liquid water by fluorescein doping. Therefore, the use of fluorescein as a tracer to label liquid water is the key to visualizing the migration of liquid water, which can more intuitively reflect the characteristics of water vapor migration in coarse-grained saline soil subgrades.  
On the basis of the research results of [PERSON] et al. [40] and [PERSON] et al. [41], a fluorescence solution evaporation condensation test was conducted, as shown in Figure 4a. The soil sample was mixed with fluorescein in an evaporating dish and covered with a glass plate for heating. Research has revealed that small water droplets that condense on glass cover plates do not exhibit color under purple light irradiation, indicating that fluorescein only migrates with liquid water and not with water vapor. Therefore, under purple light irradiation, the addition of fluorescein to replenished external water can be used to track the change process of the migration height of liquid water. The actual effect is shown in Figure 4b.  
Figure 4: Tracer-labeled liquid water migration and visualization validation.
## 9 Results and Model Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **NB** & **AdaBoost** & **LR** & **RF** & **SVM** \\ \hline Mean accuracy & 0.9210 & 0.9964 & 0.9579 & 0.9999 & 0.9524 \\ Mean precision & 0.1030 & 0.7991 & 0.2187 & 0.9953 & 0.1984 \\ Mean recall & 0.7272 & 0.9962 & 0.977 & 1 & 0.9791 \\ Mean F1 & 0.1805 & 0.8785 & 0.3573 & 0.9976 & 0.3299 \\ \hline Std. accuracy & \(1.1980\times 10^{-3}\) & \(3.2419\times 10^{-3}\) & \(1.6584\times 10^{-3}\) & \(4.0069\times 10^{-3}\) & \(1.9381\times 10^{-3}\) \\ Std. precision & \(3.8186\times 10^{-3}\) & 0.1779 & \(6.2834\times 10^{-3}\) & \(3.3197\times 10^{-3}\) & \(5.5056\times 10^{-3}\) \\ Std. recall & \(3.6408\times 10^{-2}\) & \(3.9638\times 10^{-3}\) & \(1.0289\times 10^{-3}\) & \(0\) & \(1.0395\times 10^{-2}\) \\ Std. F1 & \(6.9215\times 10^{-3}\) & 0.10756 & \(8.2403\times 10^{-3}\) & \(1.6677\times 10^{-3}\) & \(7.1099\times 10^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 10: Best scoring model by iteration. Figure 8: Metrics by iteration: (**a**) accuracy, (**b**) precision, (**c**) recall, and (**d**) F1. 0.34. On the other hand, RF and AdaBoost presented kappa statistics of 0.86 and 0.99, respectively, guaranteeing an excellent classification performance.
## 2 The Proposed Method
### Data Preprocessing and Implementation Details  
#### 2.5.1 Data Preprocessing  
The experimental dataset in this study comprised 860 high-resolution railway track images collected using a high-resolution camera installed on a railway inspection vehicle. The original images had a resolution of 4400 \(\times\) 2000 pixels, and each image contained four distinct railway fasteners. Since individual fasteners occupied relatively small regionsin raw images, direct full-image input would introduce background noise interference and risk GPU memory overload due to the high resolution. To address these challenges, this research first annotated fastener regions using labeling (Figure 6) and implemented object localization and cropping through the YOLOv5 model to extract independent fastener targets. To preserve geometric integrity during resizing, an adaptive padding strategy was implemented: the cropped rectangular fastener images underwent zero-padding along their shorter edges to maintain the original aspect ratio, followed by bilinear interpolation-based resizing to 128 \(\times\) 128 pixels, thereby eliminating scale variance interference in feature extraction. Following this, data augmentation operations were implemented through geometric transformations (horizontal flipping) and photometric adjustments (\(\pm\)20% brightness variation) to expand the training dataset and enhance model robustness against spatial transformations and illumination changes. Ultimately, this pipeline established a custom railway fastener dataset, delivering high-quality input data for the training and validation of subsequent unsupervised anomaly detection models.
# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 4 Evaluation
For instance, the creation of artificial data based on available instances of these states can increase their representation and aid the model in understanding these states better. Additionally, Synthetic Minority Over-sampling Technique (SMOTE) [30] could be utilized to handle data imbalance. It works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space, and drawing a new sample at a point along that line. Alternation of the loss function can serve as a promising strategy to overcome this challenge. By customizing the loss function to penalize the misclassification of less common states more severely, the model can be influenced to concentrate more on these underrepresented states during the learning process. Utilizing Transfer Learning, a method that applies insights from pre-trained models can effectively mitigate data imbalance. By leveraging a model previously trained on a more extensive and diverse dataset, transfer learning can boost the model's performance on the less represented fault states. Active learning is a further approach that can assist in countering data imbalance. In active learning, the model participates in the data collection procedure, allowing it to request more samples of the underrepresented states if needed. This process can contribute to a more balanced dataset and, consequently, the enhanced performance of the model. Finally, ensemble methods, which amalgamate the predictions of several models, can aid in improving the predictive performance on infrequent fault states. Ensemble methods  
Figure 4: Performance comparison of image classification methods. Figure 5: Training time comparison. can offset the limitations of individual models, resulting in a more sturdy and balanced predictive capability. In practical deployment, a feedback loop can be established for continuous model updating with fresh data, enabling real-time optimization and adaptation to changing network conditions. This process, coupled with an integrated approach to data collection [31], diagnosis, and action, can effectively facilitate network self-healing. Considering the diversity and complexity of network issues, continuous training with new problem instances is necessary to comprehensively cover all potential faults and their variations, which might require regular model updates using newly collected data, the frequency of which depends on the network size, its rate of change, and available computational resources.
## 2 System Overview and Vehicle Model  
### System Overview  
Figure 1 depicts a control system that integrates a vehicle model, a motor model, a battery model, and an adaptive compound controller. The controller improves lateral stability and energy efficiency by dynamically modifying the external yaw moment. The modified yaw moment is determined through a hierarchical structure consisting of a reference model, an upper-layer controller, an adaptive-layer controller, and a lower-layer controller. The upper-layer controller uses MPC to generate the external yaw moment based on the reference model. The adaptive-layer controller evaluates vehicle stability using phase plane diagrams and modifies the external yaw moment accordingly. Finally, the lower-layer controller optimizes tire workload and minimizes total power loss across the four motors using nonlinear torque distribution methods.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 3 ANN-Based Design Methodology for Low-Power Interconnect
### Proposed Design Methodology
#### 3.3.1 The Surrogate Model Based on ANN  
The interconnect design contains two essential kinds of parameters related to the structure and the materials. The structure parameters are comprised of the physical dimensions determine the design geometry and are limited only by the area and fabrication technology. Conversely, design materials presenting the properties of dielectric and metal are typically constant during the design process. Hence, the structure dimensions such as width, space are the input of the ANN model. Additionally, the frequency is one of the model inputs due to the frequency-dependent feature. Moreover, the channel properties in the frequency domain including S-parameter and \(Z_{0}\) are the model output. The output data included S-parameter and \(Z_{0}\) are produced by simulating structures with full-wave simulations HFSS and Q2D, respectively [21]. In this approach, a surrogate model based ANN is employed to speed up the frequency domain design procedure. In the training phase, the ANN model maps the output and input through the given training data. These data are randomized before training and is divided into training data and testing data with a ratio of 80% and 20%, respectively. While the training data directly provide information to the model, the testing data is employed to track the prediction accuracy during the training process. Furthermore, the validation data are utilized to ensure the model accuracy after it is established. Finally, an ANN model that quickly provides accurate frequency-dependent characteristics of any design structure is created.  
The performance metric is defined to evaluate the ANN prediction performance. In this design methodology, the goal is to predict the interconnect properties included the S-parameter and \(Z_{0}\) of a specific frequency range. Thus, we use residual (R) and root mean square error (RMSE) as the fundamental metrics to evaluate prediction performance.  
Figure 5: Proposed Design Flow.  
The residual from a fitted model is defined as the differences between the predicted data and real data at each predictor value. On the other hand, root mean square error (RMSE) that measures the average of the square of the errors is also a performance metric that we considered. In addition, error in terms of percentage is considered for the reference purpose only because it relies on the real data that can be very small. In this case, the slight difference in prediction can cause a high error in terms of percentage. The residual, RMSE, and error are described in Equations (3)-(5), respectively.  
\[R=Y-Y^{\prime} \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(Y-Y^{\prime})^{2}} \tag{4}\]  
\[Error=\frac{1}{N}\sum_{i=1}^{N}\frac{|Y-Y^{\prime}|}{|Y|}100 \tag{5}\]  
where:  
\(Y\): the actual value  
\(Y^{\prime}\): the prediction value  
\(N\): the number of frequency points