esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, this numerical study demonstrates the innovative integration of computer vision with topology optimization for additive manufacturing, specifically targeting the minimization of eigenfrequency in a design domain. Using custom-developed photogrammetry software, dynamic, high-resolution images were captured, forming the basis for precise 3D models. These models were converted into STL files through an automated, high-precision process, and subsequently meshed with an adaptive algorithm within the COMSOL Multiphysics environment, controlled via a custom MATLAB API. The study investigated two cases of a pillar with a central mass on top and an eccentric mass, where the center of gravity is positioned on the left side of the top surface.  
The results from the first case highlight the significant advancements realized through topology optimization. Specifically, there was a 57% increase in performance, demonstrated by a substantial elevation in eigenfrequency, coupled with a 50% reduction in structural weight. Importantly, these enhancements were achieved while preserving the desired vibration characteristics, thereby affirming the effectiveness of the applied modifications. These findings provide a robust foundation for future innovations in extraterrestrial construction and resource-efficient structural design. Similarly, the second case exhibited comparable improvements, with a notable increase in eigenfrequency, further validating the high-performance outcomes of the optimization process. This dual success underscores  
Figure 6: Topology optimization designs of eccentric mass shown from multiple perspectives: (**a**) isometric projection of the design, (**b**) middle-section view, (**c**) front view, (**d**) rear view, (**e**) top view, and (**f**) side view.  
Figure 7: Eigen frequency history of the pillar design domain with eccentric mass.  
the potential for implementing such optimization strategies in demanding environments where material efficiency and structural integrity are paramount. Moreover, the eccentric mass case results showed a significant 64% increase in eigenfrequency and a 50% weight reduction without compromising vibration characteristics, demonstrating that the optimized design maintains structural integrity while enhancing dynamic performance, highlighting the efficacy of the applied modifications.  
Conceptualization, M.A.A.; methodology, M.A.A. and M.S.; numerical simulations, M.A.A., M.S. and M.N.; writing--original draft preparation, M.A.A., M.S. and M.N.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 3 Results  
### CNN Architectures  
A model's performance depends on the architecture and the size of the data. In this sense, there are different CNN architectures that have been proposed: AlexNet [169], VGG-16 [170], ResNet [171], Inception (GoogleNet) [172], and DenseNet [173]. These networks have shown promising performance in recent works for image detection and classification. Table 5 shows a brief description of these networks.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Model** & **Description** & **Training Method** & **Application** \\ \hline \multirow{8}{*}{[PERSON] et al. [169]} & \multirow{8}{*}{[PERSON] et al. [169]} & \multirow{8}{*}{AlexNet} & A deep CNN evaluated using the Imagenet [65] LSVRC-2010 dataset [173], with top-1 and top-5 & \multirow{8}{*}{Dropout model} & \multirow{8}{*}{Classification} \\  & & & error rates of 37.5\% and 17.0\%, respectively. This achieved a top-5 test error rate of 15.3\% compared to 26.2\% (ImageNet Large-Scale Visual (ILSVRC) 2012). & & \\ \hline \multirow{8}{*}{[PERSON] et al. [174]} & \multirow{8}{*}{DL-CNN} & CAD system for masses in DBT volume, which is trained using transfer learning. The best AUC obtained was 9.933 and the improvement was statistically significant (\(p<0.05\)). & \multirow{8}{*}{CNN architecture} & Detection \\  & & & significant was 9.933 and the improvement was statistically significant (\(p<0.05\)). & & \\ \hline \multirow{8}{*}{[PERSON] et al. [170]} & \multirow{8}{*}{VGG-VD} & The very deep (VD)-CNN models (VGG-VD16 and VGG-VD19 [158]) were evaluated in ILSVRC 2014 (ImageNet). & Deep ConvNet architecture & \multirow{8}{*}{Classification} \\ \hline [PERSON] et al. [171] & ResNet & An ensemble of these residual nets achieved a 3.57\% error on the ImageNet (ILSVRC 2015) test set. & ResNet with a depth of up to 152 layers 8\% deeper & \multirow{8}{*}{Classification} \\ \hline [PERSON] et al. [172] & DenseNet & DenseNet was proposed to reduce the vanishing gradient problem, to reduce the number of parameters, and to strengthen the feature propagation. & & \\ \hline [PERSON] et al. [27] & Inception v5 & A deep CNN was evaluated in ILSVRC 2014. & Deep-CNN & Classification and detection \\ \hline \multirow{8}{*}{[PERSON] et al. [175]} & \multirow{8}{*}{VGGNet} & BreakHist dataset with 58 malignant and 24 benign cases was evaluated with a deep CNN. The best accuracy percentage was reached with 100x (89.06\%). & \multirow{8}{*}{MIL architecture} & \multirow{8}{*}{Histopathology} \\ \cline{1-1} \cline{5-
# Optimal Shape Design of Direct-Drive Permanent Magnet Generator for 1 kW-Class Wind Turbines
## 4 Optimal Shape Design
### Optimal Shape Design Using Desirability Function  
Through the statistical analysis using the response surface methodology, the optimal design parameters were identified by utilizing the desirability function [34]. The response variables, namely the weight and active power, were transformed into individual desirability function [34]. The response variables were calculated using the following procedure:  
\[\begin{split}\text{Power}=(-907.24)+(3.89*\text{D}_{\text{i}})+( 14.26*1)+(10.99*\text{b}_{1})+(15.50*\text{b}_{2})+(14.62*\text{h}_{2})+(-6.24* \text{h}_{1})\\ +\left(-0.025*\text{N}_{\text{ph}}\right)+(\text{D}_{\text{i}}-68.85 )*(1-35.08)*0.21+(\text{D}_{\text{i}}-68 ity values \(\mathrm{d_{j}}\) for each of the seven design parameters. These values were integrated into the global desirability function \(\mathrm{D_{s}}\), which is presented by the following equation:  
\[\mathrm{D_{s}}=\left(\mathrm{d_{1}^{n_{\mathrm{i}}}\times d_{2}^{n_{\mathrm{2}}} \times\cdots\times d_{j}^{n_{\mathrm{i}}}}\right)^{\left(1/\sum\mathrm{n_{i}} \right)} \tag{14}\]  
where \(\mathrm{n_{i}}\) is the weight-function criteria, set by the designer to determine the relative importance of response variables such as the weight and active power for the generator.  
In this study, the individual desirability and weight-function criteria for the weight of the generator were set to 0.8 and 1.0, respectively. For the active power, on the other hand, the individual desirability and weight-function criteria were set to 0.95 and 3.0, respectively, given that it is considered as a constraint condition and hence, more crucial than the weight of the generator.  
The impact of seven parameters on response variables can be investigated by using prediction profilers. As shown in Figure 12, the maximized global desirability \(\mathrm{D_{s}}\) reached up to a value of 0.83 and the prediction profilers, related to the desirability function, identified slot length 1 and slot width \(\mathrm{b_{2}}\) as the primary factors to optimize the global desirability. However, the desirability function exhibited a decreasing trend when the slot length 1 exceeded 39.5 mm. This was attributed to the constant desirability for the active power, while the desirability for the weight decreased. The reduction of the inner diameter \(\mathrm{D_{i}}\) and slot height \(\mathrm{h_{2}}\) was necessary to optimize the global desirability, despite their high sensitivity to both the active power and weight. It was found that these two parameters had lower effects on the response variables than the slot length 1. The other parameters were adjusted to maximize the global desirability.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 3 SBAUPT-Based Pedestrian Dead Reckoning (S-PDR)
### Step Detection  
To enhance the performance of a PDR system, a robust step detection technique is needed. The step detection technique should be able to detect steps at varying motion speeds and under different smartphone use cases. Furthermore, it should have the ability to verify steps, whether making sure that a detected step is an actual step and not a similar pattern mistaken for a step--false positive--or a step went undetected--false negative. The developed algorithm [97] proposed an adaptive cut-off frequency filtering of the sensor measurements to adaptively remove motion noise, then uses peak and valley pair detection with temporal and spatial filtering for step detection, fusion of alternative sensors for enhanced step detection, and finally a step verification module. The following subsections briefly describe the novelties.
## 4 Evaluation Methods
### Meteor  
As mentioned in Section 1, the precision-oriented nature of BLEU is the source of a few weaknesses, and so the Metric for Evaluation of Translation with Explicit Ordering (METEOR), a recall-oriented metric, is used to tackle these shortcomings [30]. METEOR calculates the harmonic mean, as opposed to the geometric mean, by combining precision and recall with a greater bias towards recall [30]. The computation of the final METEOR score requires multiple stages; the first stage is exact matching where sentences in the translated output and reference text that are completely alike are aligned [30]. The next stage called stem matching refers to the process of aligning words that have the same morphological stem [30]. Finally, in the synonym-matching stage, words that are synonyms of each other (according to WordNet, a lexical database of the English language [35]) are aligned [30]. At each stage, only words that are not aligned are allowed to be matched in the succeeding stage. Furthermore, a fragmentation penalty (FP) is applied to account for the differences in word order [30]. The METEOR score is then calculated by taking the product of the harmonic mean and \((1-FP)\), which outputs a score in the range of 0-1.  
This paper uses the BLEU score metric to compare the performance of various MT models since BLEU is widely used in the literature and this aids in the comparison of results. Table 1 below provides a summary of the advantages and disadvantages of the metrics covered above.
# An Application of Simulated Annealing in Compensation of Nonlinearity of Scanners
## 1 Introduction  
Two orthogonal scanners are generally required in the implementation of any optical system based on flying spot for transversal scanning [1; 2; 3]. Galvo scanners are made of high-speed, current sensitive, limited rotation electrical motors which spin mounted mirrors [2; 4; 5]. The amount of rotation is determined by the current or voltage applied [6; 7]. A galvo-scanner is composed of a small coil of wire wound on an armature that is suspended in the gap of a moving permanent magnetic field by means of pivots and bearings [2; 8]. When the coil is flown by a current, it develops a magnetic field that acts against the static field in the gap, causing the coil to rotate [7]. Such scanners can be operated in open or closed loops [4]. To operate in a closed loop, the galvo scanner is equipped with a position sensing device and a position correction device. Feedback signals are sent from a driver to stabilise and linearize the angular deviation. A position detection device can be constructed using a vane that occludes the light from a light emitting diode (LED) falling onto a photodetector [9]. The algorithm implemented in the driver circuit compares the actual position of the mirror with the expected position for the given applied voltage [10; 11]. The driver generates an error correction signal which is combined with the input signal. Despite this correction, at high rates, the angular deviation becomes nonlinear [12]. Galvo-scanners are mainly driven by triangular or saw-tooth signals. The frequency range of the signal is limited by the mechanical design of the galvo-scanner [13]. One common problem with galvo-scanners, when driven at high rates, is that they are not able to follow the shape of the input signal perfectly. Therefore, they do not perform as expected (see Figure 1), and create artefacts in the acquired signal [14; 15; 16; 17; 18]. Several procedures are known to correct for such nonlinearities, the most popular one being to alter the image depending on the position signal. This however requires a specialized frame grabber, complex software, and expensive galvo-scanners, equipped with positionsensors. Alternatively, if the shape of the voltage signal sent to the galvo scanner can be amended to slightly deviate from that of a ramp, it becomes possible to reduce the artefacts [19].  
In this study, an optimization algorithm, based on a simulated annealing (SA) algorithm [20; 21; 22; 23; 24; 25; 26], is developed to synthesize the optimal shape of the signal applied to the fast galvo-scanner in the scanning head. The reconstructed signal is composed of many small linear signals with different slopes. The algorithm finds the best set of slopes. However, while the resultant shape of the input voltage to the galvo-scanner is not saw-tooth or triangular anymore, a significant reduction in the nonlinearity of the scanner is obtained.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 2 Preliminaries
### Computing Alternative Graphs in Static Road Networks
#### 2.2.4 The Penalty Method  
The Penalty method [14] provides alternative routes by iteratively running shortest-path queries and adjusting the weight of the arcs along the resulting path. Initially, a shortest-path query is performed. The resulting shortest path \(\pi_{o,d}\) is chosen as the first route, and it is then penalized by increasing the weight of all its constituent arcs.  
Consequently, the method enters a loop in which, in each repetition, a new shortest-path query is executed in the graph with the modified weights. The resulting shortest path \(\pi^{\prime}_{o,d}\) is again penalized, and, if it is substantially different from the previously selected \(od\)-routes and also not too long in the original graph, then it is appended to the solution set; otherwise, it is simply ignored. This repetitive process ends when a sufficient number of alternative routes, with the desired characteristics, are discovered, or the weight adjustments of \(od\)-paths bring no better results anymore. For a suitable penalty scheme, the resulting set of alternative \(od\)-routes can be of high quality.
## 7 Results and Discussion
### ItemTest vs. Sing-Ant  
The Impiniy xArray reader already comes with an inbuilt location sensing algorithm that is accessible through ItemTest, their default application for connecting the reader. This, however, cannot be compared to the multiple advantages that Sing-Ant possesses. Firstly, there is environmental bias in the ItemTest location method due to a lack of reference tags, so when deploying, one has to factor in the environment to operate. Sing-Ant overcomes this by using reference tags, since they carry the same environmental bias as the target tags. Secondly, the stability of target location is greatly improved. Because ItemTest recalculates the position after every beam change, its position is always shifting, even if the target tag has not moved, and there are always shifting changes. Due to the nature of recalculating the position of each tag entry, it is scanned every time. Because of the margin of error when reporting analog values, we notice that every update event log ends up shifting the position of the tag, even when stationary, whereas, with Sing-Ant, unless the displacement is large enough to move the target tag near another landmark, the location values remain constant. Third, there are redundant entries when using ItemTest. Because it recalculates the position after every successful tag report event, a single beam can choose over five entries from the same tag, and the result is needless overcalculation, which reduces the robustness. Meanwhile, with Sing-Ant, only after a cluster, core or sector has been finished is the location calculated. Thus, when multiple entries are noticed, only one is used for calculation. This helps to stabilize the final results as unnecessary entries are not considered. Some of the key differences are shown in Table 6.
# Automatic Initialization Methods for Photonic Components on a Silicon-Based Optical Switch
## 3 Optimization Method
### 2D Nelder-Mead for Maximization  
Our optimization problem had 2 design variables, as only 2 heaters could be operated independently. Therefore, the simplex is a triangle, having \(n=2+1=3\) vertices. Let \(\mathbf{x}=(x_{i},\)\(x_{j})\) be one point of the search domain (_i,j_ specify the 2 dimensions of the problem), and _f(x)_ be the target function to maximize. At the beginning, the NM algorithm samples three points: \(x_{k}\), with \(k=1,\,2,\,3\) (the vertices of a triangle, where each vertex has two coordinates, i.e., the two biases to apply to the heaters of a switching node or of an interleaver). Then, f is evaluated at each point, returning \(z_{k}=\)_f(x\({}_{k}\))_. The obtained values are then reordered so that \(z_{1}\geq z_{2}\geq z_{3}\). Consequently, the sampled points (the vertices of the starting triangle in the search domain) are referred to as:  
\[\begin{array}{l}\mathbf{a}\Big{|}f(\mathbf{a})=z_{1};\\ \mathbf{b}\Big{|}f(\mathbf{b})=z_{2};\\ \mathbf{c}\Big{|}f(\mathbf{c})=z_{3};\end{array} \tag{1}\]where \(\mathbf{a}=(a_{i},a_{j})\) is the vertex with the best (higher) value of the target function, \(\mathbf{b}=(b_{i},b_{j})\) is the vertex with neither the best nor the worst value of the target function, and \(\mathbf{c}=(c_{i},c_{j})\) is the vertex with the lower value of the target function. Then the NM algorithm sets:  
\[\begin{array}{ll}\mathbf{m}=\frac{\mathbf{a}+\mathbf{b}}{2};&\text{ midpoint}\\ \mathbf{r}=2\mathbf{m}-\mathbf{c};&\text{reflectationpoint}\\ \mathbf{e}=2\mathbf{r}-\mathbf{m};&\text{expansionpoint}\\ \mathbf{t_{1}}=\frac{\mathbf{c}+\mathbf{m}}{2}\text{ and }\mathbf{t_{2}}=\frac{\mathbf{r}+\mathbf{m}}{2};& \text{contractionpoints}\\ \mathbf{s}=\frac{\mathbf{a}+\mathbf{c}}{2};&\text{shrinkagepoint}\end{array} \tag{2}\]  
Then, the NM goes through the steps described in Table 1 to find a new triangle to analyze in the next iteration.  
In our optimization problem, the design variables have upper and lower constraints (i.e., from 0 to the maximum bias applicable to the heaters). Thus, we need to set a constraint box on the variables, via projection [25]. Projection of variables can be achieved by forcing:  
\[x_{i,j}=\left\{\begin{array}{ll}x_{i,j}\text{\emph{upper bond}}&\text{if }x_{i,j}>x_{i,j}\text{\emph{upper bond}}\\ x_{i,j}\text{\emph{lower bond}}&\text{if }x_{i,j}<x_{i,j}\text{\emph{lower bond}} \end{array}\right.; \tag{3}\]  
where \(i,j\) represent the 2 dimensions of the search domain. When a new point is sampled by an NM iteration and one or both of its coordinates fall out of the bonds, the new point is pushed back into the constraint box. This operation guarantees the optimal solution to lie in a feasible point, in terms of heating.
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 7 Final Discussions  
\(\Gamma_{\text{LHCP,CyGNSS}}\) and the sensitivity of \(\Gamma_{\text{LHCP,CyGNSS}}\) to SMC appeared to have a dependence with \(\theta_{\text{e}}\) through h since the effective surface roughness was an important parameter that determined the amount of reflected power. The use of the polarimetric ratio (ratio of the reflected signals' power at two different polarizations) could provide a significant correction of h in SMC estimation, and potentially this technique could also be used in future missions, especially if daily/hourly SMC monitoring is required. On the other hand, further research is required in single-polarization missions. Multi-temporal techniques are based on change detection, under the assumption that surface roughness does not change over consecutive overpasses and thus the variability on the radar coefficient is due to SMC. However, this is partially realistic because [55]: (a) isolating SMC changes from other factors such as VWC would be difficult, and (b) the actual surface roughness has a dependence with SMC. Some different retrieval algorithms of SMC and surface roughness h are based on multi-angular information. This approach could be useful in the case of CyGNSS eight- microsatellite constellation mission. Results from this study show that GNSS-R had an optimum sensitivity to SMC at different \(\theta_{\text{e}}\), depending on the roughness levels. As such, this relationship could be used to select the most appropriate angular range in SMC determination. On the other hand, this property can be exploited to mitigate roughness effects at an improved temporal resolution as compared to multi-temporal techniques. Additionally, the experimental results show that the coherently scattered electromagnetic field was higher than  
Figure 8: Close-up study over Argentinian Pampas (**a–c**), India (**d–f**), and Sudan (**g–i**). CyGNSS reflectivity \(\Gamma_{\text{LHCP,CyGNSS}}\) (**a**,**d**,**g**), soil moisture content (SMC) (**b**,**e**,**h**), and surface roughness parameter h (**c**,**f**,**i**) are displayed here.  
theoretical predictions. This is an important finding, which paves the way for SMC retrieval from nanosatellites because the coherent component was roughly independent of the platform's height. Thus, relatively small antennas can be sufficient to obtain accurate measurements.
## 4 Results and Discussion
### Accuracy, Precision, and Detection Limits  
The analytical results obtained for the determination of B, P, and Mo content in three different biosludge samples and a certified sewage sludge sample, run in duplicate (i.e., each element was determined six times), are presented in Tables 5 and 6. Overall, the accuracy obtained for the certified reference sample using method USEPA 3051A was 90%-94%, with a precision of <2% Relative Standard Deviation. More consistent results were obtained for the elements present at higher concentrations. The obtained precisions were less than 3% RSD for the three real analytical biosludge samples. The error deviation determined for both MP-AES and ICP-OES techniques was <6.5%. The LODs obtained for B, P, and Mo using all the three methods are presented in Table 7. Clearly, MP-AES and ICP-OES are much better than FAAS. Moreover, the LODs for P and Mo in case of MP-AES are smaller than corresponding values for ICP-OES.