esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper is focused on the development of a Cellular Automata algorithm with the refined mesh adaptation technique and the implementation of this algorithm in topology optimization problems. Traditionally, a Cellular Automaton is created based on regular discretization of the design domain into a lattice of cells, the states of which are updated by applying simple local rules. It is expected that during the topology optimization process the local rules responsible for the evaluation of cell states can drive the solution to solid/void resulting structures. In the proposed approach, the finite elements are equivalent to cells of an automaton and the states of cells are represented by design variables. While optimizing engineering structural elements, the important issue is to obtain well-defined solutions: in particular, topologies with smooth boundaries. The quality of the structural topology boundaries depends on the resolution level of mesh discretization: the greater the number of elements in the mesh, the better the representation of the optimized structure. However, the use of fine meshes implies a high computational cost. We propose, therefore, an adaptive way to refine the mesh. This allowed us to reduce the number of design variables without losing the accuracy of results and without an excessive increase in the number of elements caused by use of a fine mesh for a whole structure. In particular, it is not necessary to cover void regions with a very fine mesh. The implementation of a fine grid is expected mainly in the so-called grey regions where it has to be decided whether a cell becomes solid or void. The benefit of the proposed approach, besides the possibility of obtaining high-resolution, sharply resolved fine optimal topologies with a relatively low computational cost, is also that the checkerboard effect, mesh dependency, and the so-called grey areas can be eliminated without using any additional filtering. Moreover, the algorithm presented is versatile, which allows its easy combination with any structural analysis solver built on the finite element method.  
topology optimization; cellular automata; adaptive mesh refinement +
Footnote †: journal: Applied Soft. 2020, 10, 3691; doi:10.3390/app10113691
## 4 Classification Results and Discussion
### Comparison of Convergence Speeds of Different Models  
In neural network training, the loss rate represents the disparity between the model's predicted values and the actual values on the training data, serving as a crucial metric for assessing model performance. The change in loss rate reflects the convergence speed and training effectiveness of the model. ViT and Swin T neural networks share similar structures and both compute loss rates using cross-entropy, making them suitable for comparison. The variation of loss rates for these two models under different data preprocessing methods and dataset sizes across training epochs is illustrated in Figures 14 and 15.  
Figure 14: The trend of model loss rate changes with different volumes of unprocessed data.  
Figure 13: The trend of model accuracy changes with different volumes of preprocessed data.  
Figure 15: The trend of model loss rate changes with different volumes of preprocessed data.  
For the Swin T network, the loss rate generally decreases with increasing training epochs, with lower and more stable loss rates observed under larger dataset sizes. This suggests that increasing the dataset size can accelerate the convergence speed and improve the training effectiveness of the Swin T network. Moreover, training with data preprocessed using the improved CELMD method results in smaller loss rates and faster convergence speed compared to direct training. In comparison to the ViT neural network, regardless of the dataset size or the use of improved CELMD preprocessing, the Swin T network consistently exhibits lower loss rates and faster convergence speed.
## 4 Discussion
Previous studies using InceptionV3 and U-Net models only considered the ST RoI in CBCT and X-ray images, respectively, and excluded the complex and crucial bridging structures, focusing only on regular circular, flat, and oval classes. Unlike these models, the proposed SellaMorph-Net model requires no significant image post-treatment, substantially reducing performance time. Additionally, the AGM and SE blocks are used in the network to enhance the input images' feature quality and the segmentation map to direct the network. The zero-shoot classification approach and the colour-coded layer added to the model effectively reduce outliers and misclassification. Further, to evaluate the performance of the proposed hybrid model, we applied testing on a public dataset of the IEEE ISBI 2015 Challenge [53] for cephalometric landmark detections. The dataset consists of 400 cephalometric radiograph images, of which 150 are for training, 150 for validation, and 100 for testing. Table 7 provides the comparative results of the proposed method's performance for our and publicly available datasets. Although the proposed model has demonstrated outstanding performance in various settings, we have identified some limitations that must be considered. Firstly, the proposed system only achieves segmentation with colour-coded class differentiation. Secondly, this approach is computationally expensive due to the use of advanced feature enhancement techniques and the encoder brought in. To provide a more comprehensive CAD pipeline for lat ceph radiographs, we plan to incorporate additional downstream tasks such as ST classification and identification of dentofacial anomalies in future research. Additionally, we aim to increase the model's speed by using pruning techniques to reduce the number of model parameters. Table 8 presents an overview of studies that have reported Sella Turcica segmentation. Upon examining the literature, it becomes apparent that the ST is usually segmented as round, oval, or flat. However, the proposed model can accurately segment the Sella Turcica not only from regular ST but also from complex altered ST, including circular, flat, oval, and bridging classes.
# Pointing Accuracy of an Operational Polarimetric Weather Radar
## 2 Antenna Pointing
We mention this here because we will use the relative difference of computed pointing bias from those two options in order to interpret the pointing bias estimates in relation to the drive train system. Those differences have to be known in case the angle acquisition has to be re-configured as a consequence of an absolute encoder failure. Note that the motor angles are from relative encoders, which means that a procedure based on homing sensors is necessary to re-home the antenna after the system was without power. Ideally the navigation performance of a radar should be weakly dependent on azimuth and elevation position. To what degree this is the case will be assessed in this work. Figure 1: Schematic sketch of the antenna-pedestal assembly on a radar tower. This cartoon provides a simplified view on different reference planes which need to be leveled so that a 360\({}^{\circ}\) rotation of the antenna is in an exact horizontal plane, independent from azimuth position. Figure 3: Principle sketch of the two angle reading sources within the DWD radar system, namely the absolute encoder and the relative encoder of the motor. These are shown together with the respective optional communication paths which are needed to tag a received ray with a time stamp and the corresponding elevation and azimuth angle. Figure 2: Schematic picture of the antenna drive system which illustrates two angle sources within the DWSR5001/SDP/CE. There are the motor encoder angles of the motor which steers the bull gear. Position commands are sent to the motor encoder. The actual angles that are used to tag each pulse are from the absolute encoder.
## 2 Explanation Methods
### Anchor  
The Anchor method was developed by [PERSON] et al. [14], and they compared it to their previous work on LIME. The Anchor method is also called scoped rules method because it is based on IF-THEN rules, and these rules can be used more than one time since they are scoped. Anchors use the concept of coverage to specify which other, presumably unobserved, situations they apply to. Anchors minimize the number of model calls and combine reinforcement learning techniques [23] with a graph search algorithm [24] or recover from local optimum.  
If we suppose that \(D\) is the perturbation generated using the LIME method near the interested instance, the Anchor method constrains the perturbation space \(D\) with Anchor \(A\) that contains set of predicates (\(D(.|A)\)). For the input instance \(x\), \(A(x)\) returns \(1\) if all its rules are true. Therefore, \(A\) is an Anchor if \(A(x)=1\), and \(A\) represents a sufficient condition for the prediction \(f(x)\), with high probability. This means that for a perturbed instance \(x^{\prime}\) from \(D\left(\frac{x^{\prime}}{A}\right)\), \(f(x)=f(x^{\prime})\). Formally \(A\) is an Anchor if  
\[\mathbb{E}_{D(x^{\prime}|A)}\Big{[}1_{f(x)=f(x^{\prime})}\Big{]}\geq\tau,\ A(x)=1, \tag{3}\]  
where \(\tau\) is the desired level of precision. As the precision of the Anchor refers to the proportion of true predictions by the Anchor rules, we can use (3) to express the precision of \(A\) that achieves \(\text{prec}(A)\geq\ \tau\) as:  
\[\text{prec}(A)=\mathbb{E}_{D(x^{\prime}|A)}\Big{[}1_{f(x)=f(x^{\prime})} \Big{]}, \tag{4}\]  
For an arbitrary \(D\) and black-box model \(f\), directly computing this precision is impossible. Instead, a probabilistic definition is introduced where Anchors accept the precision constraint with a high probability:  
\[P(\text{prec}(A)\geq\tau)\geq 1-\delta \tag{5}\]  
The Anchor coverage is defined as the proportion of the input instances that are covered by the Anchor. Formally, the coverage of an Anchor can be expressed as the probability that it applies to instances from \(D\), i.e., \(\text{cov}(A)=\mathbb{E}_{D(x^{\prime})}[A(x^{\prime})]\). Thus, the searching process for an Anchor \(A\) is defined as combinatorial optimization problem:  
\[\underset{A\text{ s.t. }P(\text{prec}(A)\geq\tau)\geq 1-\delta}{\text{max}} \text{cov}(A) \tag{6}\]  
The Anchor method utilizes a multi-armed bandit formulation algorithm [25]. This algorithm can randomly build the Anchors with the highest coverage and a particular threshold of precision.
# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 4 Building Blocks of Photonic Neural Networks
demonstrated a photonic digital-to-analog converter fabricated on a silicon-on-insulator platform incorporating microring resonator-based modulators capable of 4 GSample/s conversion rate in a 2-bit resolution operation [127]. In this current scenario of growth and strengthening of photonic technologies, photonic neural network architectures with low heat dissipation, sub-nanosecond latencies, and high parallelism powered by photonic integrated circuits are now a feasible reality [128; 129].
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 3 Calibration of SMAP-R Signals
### SMAP Antenna Pattern Impact  
For the SMAP antenna pattern we have assumed a linear decay of the antenna gain from the maximum at 40\({}^{\circ}\) incidence angle to the extremes at 37.3\({}^{\circ}\) and 42.7\({}^{\circ}\) incidence angle from 36 dB (\(G_{\text{max}\_dB}\)) to 33 dB in the extremes. This follows Equation (3):  
\[G_{rx}(\theta_{i})=G_{\text{max}\_dB}-\frac{3 dB}{2.7^{\circ}}\ast abs(40^{ \circ}-\theta_{i})\ [\text{dB}] \tag{3}\]  
Note that \(G_{rx}\) is a function of \(\theta_{i}\). Corrections needed for coherently dominated scattering surfaces (\(\sim\)1 km \(\times\) 1 km) are less significant than corrections needed for incoherently dominated scattering surfaces. Far from the \(-\)3 dB beamwidth, the SMAP antenna gain decreases rapidly and signals coming further away from the \(-\)3 dB beamwidth are expected to be greatly attenuated. As published in [42], the antenna pattern is very symmetric in azimuth within the 3 dB antenna beamwidth, consequently we have disregarded azimuthal variations.
# Recombination of Poly(Acrylic Acid) Radicals in Acidic Aqueous Solutions: A Pulse Radiolysis Study
## 2 Materials and Methods
### PAA Solution Characterization
#### 2.3.2 Gel Permeation Chromatography (GPC/SEC)  
The weight-average molecular weight (\(M_{w}\)), number-average molecular weight (\(M_{n}\)), and Z-average radius of gyration (\(R_{g}\)) of linear non-irradiated PAA and OAA were determined by GPC/SEC triple detection system: multiangle laser light scattering detector (Brookhaven Instruments Corporation, Holtsville, NY, USA) and combo refractive index-viscometry detector (Testa Analytical Solutions, Berlin, Germany). Suprema Lux analytical Linear XL column (suitable for neutral and anionic polymers) with Suprema Lux analytical SDV precolumn were used in all experiments (PSS Polymer Standards Service GmbH, Mainz, Germany).  
GPC measurements were performed in a buffer, passed through a paper filter, of 0.1 mol dm\({}^{-3}\) Na\({}_{2}\)HPO\({}_{4}\), pH 9.4, that ensured a compact conformation of polyelectrolyte macromolecules and reduced interactions between the sample and column [66]. Flow rate and temperature were maintained at 1 cm\({}^{3}\) min\({}^{-1}\) and 30 \(\pm\) 0.1 \({}^{\circ}\)C, respectively. PAA solutions were filtered through a 0.45 \(\upmu\)m-pore-size filter (Minisart NML, Sartorius, Gottingen, Germany) before measurement.
## 2 Related Work  
FL is widely preferred over conventional centralized machine learning schemes, as it can guarantee privacy [16]. In FL, training is performed on local devices using their local datasets and aggregating them to the cloud server to produce a new global model. The method is iteratively repeated until the accuracy reaches the desired level [23]. Despite this safe training, FL suffers from considerable propagation delays, particularly in large-scale networks. The devices are distributed over large distances, resulting in communication bottlenecks [24]. A plethora of studies has been conducted to reduce the communication costs. A lossy compression technique to minimize the cloud-to-device communication overhead has been proposed [25]. The authors proposed communication- mitigated FL, where they selected only relevant updates to be forwarded for aggregation and achieved high performance [26]. Extensive communication often results in higher computational costs. To mitigate this challenge, the authors proposed a hybrid approach that simultaneously manages the communication cost through compression and reduces the computation cost through differential privacy [27]. Several studies have been conducted to enable distributed FL frameworks, which focus on the fixed architecture of the distributed FL [28; 29]. In large-scale networks, many mobile devices participate in the training; these devices differ from each other in terms of datasets, computation capacity, and battery level, and can easily be managed through proper hierarchical task division [30]. In Table 1, we summarize the existing studies and their limitations.  
Considering these limitations, we propose the SHFL framework for smart healthcare systems. The proposed SHFL framework is leveraged with a hierarchical architecture that distributes the network load evenly among all connected devices, which results in the efficient management of computational resources. To secure sensitive client information, we introduce K-anonymity in FL, which helps minimize security threats from adversaries in the network. Finally, the hierarchical architecture facilitates efficient communication between the clients and the cloud server. The experimental results prove that the proposed SHFL achieves higher accuracy than the existing solutions.
## 4 Experimental Results and Discussion
### Experimental Results Analysis
results show in Table 7. After parameter adjustment, the XGBoost classification algorithm has a 99.87% classification accuracy on the fusion feature set, an increase of 0.08%, and a logarithmic loss of 0.007, a decrease of 0.002. It further illustrates that the tree-boosting-based malware classification system can effectively distinguish the malware families. The categorization model's log-normalized confusion matrix is illustrated in this publication to assess the viability of the suggested system. As can be seen from Figure 5, the true positive rate of the system for each type of malware family is above 97%, especially for the R and K3 families, which can achieve 100%. Among them, the G family has a true positive rate of 99.21%, which may have a similar malicious pattern to the O family. Overall, the malware classification system proposed in this paper can effectively classify malware and its variants.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology  
To improve the connection stability and to reduce the packet loss during the handover from one RSU to another, this research introduces the mRSU; in addition, to evaluate the results, the urban mobility traffic scenario is considered. For the simulation and results, the NS-3 simulator is selected, in which the scenario is designed, and then the results of the simulation are compared to other conditions in which the RSU is static. As in [46], the standard energy that is assumed for the storage, carrying, and transmission of the message of 500 KB is 1.7 J, but this is only the assumed value. Our results indicate the actual value at the node that is required to store, carry, and transmit the message. We then note the value that a mobile node consumes, while performing normal operations; after this, the sender sends a message and we note the change in energy consumption at the receiver node. The value that is finally found is 4.3136 J for a message of 0.16 KB.  
To measure the energy and power consumption when sending or receiving a message, in this research, a DC power supply is used, which will change in current and voltage when sending or receiving a message. Thus, by obtaining the values of the current and voltage, the power can be determined; then, by obtaining the value of the power, the energy consumption is calculated. At the start, the DC power supply is used during the movement of the vehicle. Here, to determine the values for current and voltage, a DC power supply (Power Net 1502 DD) of 15 V and 2 Amperes (A) is used.  
Initially, the voltage encoder wheel is set to 4.4 V because the nodes mostly bear a voltage of around 4.4 V and, after increasing this, there will be the possibility of IC damage; thus, we set the voltage at 4.4 V and the current to a maximum of 2 Ampere to calculate the power consumption of the mRSU before sending and receiving the message as shown in Figure 3. Next, we connect the positive terminal of the power supply with the positive terminal of the node and the negative one with the negative terminal. Next, before sending or receiving any message, we first observe on the screen that the mRSU is consuming a current of 480 mA at a voltage of 4.017 V; there is a small variation from the set value, so the power is calculated by using the product of the voltage and current, which is 1.92816 Watts before sending any message. Then, by maintaining the same motion at the speed of 15 km/h, we send a message of 300 KB to another node while keeping the DC power supply connected to our mRSU, and we note the readings. There is a change in value and the current value reaches 362 mA, while the voltage reaches 4.002 V, thus, the power value at this point is 1.449086 Watts as shown in Figure 4. Therefore, taking the difference between the two values of power, we determine the amount of power consumed when sending the message. We calculate this energy value using the change in the values of current and voltage, and we then calculate the power; from the power, the energy consumption can be calculated.
## 5 Data Processing and Results
### Data Pre-Processing
The histograms (with 100 bins) of MSE for LRPF and GBR are shown in Figure 3. As can be seen, although the shapes of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**X Predictor** & **Missing Data Status** & **Y Predictor** & **Missing Data Status** \\ \hline \(X_{11}\) & No missing data & \(Y_{11}\) & No missing data \\ \(X_{12}\) & No missing data & \(Y_{12}\) & 2023 data missing \\ \(X_{13}\) & No missing data & \(Y_{13}\) & 2023 data missing \\ \(X_{14}\) & No missing data & \(Y_{14}\) & 2023 data missing \\  & & & 2002, 2003, 2004, 2005, 2006 \\ \(X_{15}\) & No missing data & \(Y_{15}\) & missing partial data; 2023 data \\  & & & missing \\ X\({}_{16}\) & No missing data & \(Y_{16}\) & 2023 data missing \\  & 2002, 2003, 2004, 2008 missing & \(Y_{17}\) & 2023 data missing \\ \(X_{17}\) & partial data & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Missing data status in the reference dataset \(R\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**X Predictor** & **Missing Data Status** & **Y Predictor** & **Missing Data Status** \\ \hline \(X_{11}\) & No missing data & \(Y_{11}\) & No missing data \\ \(X_{12}\) & 2002, 2003 data missing & \(Y_{12}\) & 2023 data missing \\ \(X_{13}\) & 2006 partial data missing & \(Y_{13}\) & 2023 data missing \\  & & & 2002, 2003, 2004, 2005, 2006 \\ \(X_{14}\) & 2002, 2003, 2004 data missing & \(Y_{14}\) & partial data missing; 2023 data \\  & 2002, 2003, 2004, 2020, 2021 histograms are quite similar, with the great majority of MSE close to the smallest bins, the MSE for GBR is \(10^{4}\) smaller than that for LRPF. Hence, it is obvious that we should use GBR to generate missing data. Nevertheless, the presence of a few occurrences at very large MSE bins indicate that some of the generated data could be of low quality. How to further enhance the quality of the generated data is left for future research because the fraction of potentially bad data is very small (which would have a negligible negative impact on the results). For completeness, we also show the MSE distribution of regression for generating the missing data for the reference dataset in Figure 4. The primary purpose is still to demonstrate which regression model should be used to generate the missing data. Similar to the case for generating missing data in the dataset for our current study, GBR again outperforms LRPF with much smaller MSE for the reference dataset.
###### Abstract
In addition, To further improve the precision of detection, we propose a filtering strategy based on the empirical planting rules of tree crowns to remove false positives in the detection results of CAS-DA. Accordingly, our contributions are as follows. (1) A cascade of region-proposal networks for tree-crown detection is proposed. It takes features from different convolutional layers stage by stage, filters easy samples, and mines hard samples to alleviate the data imbalance and enhance the classification capacity of RPNs. (2) We integrate the proposed cascade network and Strong Weak Faster R-CNN into CAS-DA and construct the loss function of multiple stages so that the CAS-DA can be trained in an end-to-end manner. (3) A practical filtering strategy based on planting rules is designed to further eliminate the wrongly detected trees effectively. Extensive cross-regional experiments are conducted on three datasets collected by satellites and UAVs, including adaptation between satellite images and drone images and adaptation between different satellite images. The experimental results show that our method achieves an average F1-score of 68.95% and 88.83% in the two series of experiments, outperforming the other existing DA approaches by an obvious margin of 11.88%-40% and 0.50%-5.02%, respectively. This proves the effectiveness of our proposed method in cross-domain tree-crown detection and counting from multisource remote sensing images.
# Climate-Quality Calibration for Low Earth-Orbit Microwave Radiometry +
## 3 Co-Located Radio-Occultation Reference
Using the RO reference in this way avoids the accumulation of error variances in a chain of radiometer calibration transfers by single-differencing, which would introduce an uncertainty in the inferred multi-decadal trend.