esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
###### Abstract  
Generation of digital phase-only Fresnel holograms is an important research area in digital holography, as it leads to a substantial simplification of a holographic display system. However, the quality of the reconstructed image of a hologram without the magnitude component is heavily degraded. The problem can be reduced by down-sampling the intensity of an image prior to generating the hologram. The method, referred to as \"sampled phase-only hologram\" (SPOH) generation, results in reconstructed images that are masked with the pattern of the down-sampling lattice. This paper reports a novel, low complexity method to alleviate this problem through the concept of comb filtering. Results reveal prominent enhancement on the reconstructed image of a SPOH.  
phase-only hologram; sampled phase-only hologram; grid-cross down-sampling; comb filtering; time-division comb filtering Article
# Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data
## 3 Results and Discussion
### Performance of Predication-Based Metrics  
Aiming to assess the practicality of using the three proposed models to characterize cardiac function, we computed all metrics described in Section 2.4 and compared results based on predicted and ground-truth boundaries. The results of paired \(t\)-tests on all 80 metrics, with [PERSON]'s multiple comparisons corrections, are shown in Supplemental Table S1, stratified by model. Following trends observed in Section 3.1, the  
Figure 3: Example predictions on a series of anterior free-wall test set data, based on the implemented 6:1:1 Monte Carlo cross-validation. Raw predictions for 17 separate mice are shown overlaid onto the ground-truth data for unassisted (model 2) predictions at both the (**A**) endocardial and (**D**) epicardial borders, as well as for the (**B**,**E**) assisted (model 3) predictions. Squared error plots at each temporal sample for the (**C**) endocardial and (**F**) epicardial positions demonstrate the potential lower errors resulting from the assisted approach.  
number of metrics that were flagged as significantly different (i.e., adjusted \(p<0.001\)) from the ground-truth values were relatively low amongst models 1 (8/80), 2 (7/80), and 3 (8/80). Interestingly, seven metrics showed significant differences regardless of model, suggesting further refinement of the modelling approach would be needed to trust those values if based on machine learning predictions alone. As shown in Table S1, these metrics were all variants of early diastolic strain rate: (1) circumferential early diastolic strain rates at the mid-ventricle and apex; and (2) longitudinal strain at each of the six rotations around the \(z\)-axis, except at the posterior-septum. While it is not clear why early diastolic strain rate has trouble being properly inferred using our methods, it may be due to the incorporation of severely diseased mice with abnormal diastolic kinematic profiles adversely skewing the imposed temporal-smoothing.  
It should be noted that no global function metrics (i.e., end-diastolic or peak-systolic volumes, ejection fraction, or stroke volume) or peak-strain values showed any statistically significant differences between the gold-standard and prediction-based analyses. These results appear promising as the errors are only observed with strain-rate values, specifically early diastolic strain rate, suggesting that the machine learning model predictions of left-ventricle boundaries can be used reliably to assess both global cardiac function and peak circumferential and longitudinal strain. Nevertheless, it is important to note that accurate measurements of early diastolic strain rate are critical to the assessment of diastolic dysfunction [33]. Future work increasing model complexity or implementing novel strategies is thus critical to providing more reliable assessments of cardiac kinematics and function for researchers and clinicians.
## 2 Design of the Unit Cell and Simulation  
Two tuning forks with a dumbbell shape dual band (C- and Ku-) metamaterial absorber structure based on octagonal close ring resonator (OCRR) are shown in Figure 1. FR4 of thickness 1.6 mm is used as substrate material with dielectric constant of 4.6 and loss tangent of 0.025. The parameter of the unit cell is shown in Table 1. The dimension of the FR-4 substrate is 8 \(\times\) 8 \(\times\) 1.6 mm\({}^{3}\). The schematic diagram of the proposed PMA unit cell design comprises a dielectric substrate, which is crammed in with two metallic layers which are shown in Figure 1a. The perspective view of the unit cell is shown in Figure 1b. The entire upper metallic layers including OCRR are made of copper which has a conductivity of 5.8 \(\times\) 10\({}^{7}\) Sm\({}^{-1}\). The octagonal ring resonator is attached with two tuning forks and a dumbbell-shaped copper (pure) strip is placed between two tuning forks. The bottom layer is a thin copper sheet of dimension 8 \(\times\) 8 \(\times\) 0.035 mm\({}^{3}\), as shown in Figure 1c. The radius of the bigger outsider octagon and smaller inner octagon are 4 mm and 3.5 mm, respectively. Therefore, the width of the each OCRR is 0.50 mm. The gap (G) between two tuning forks is 0.40 mm. The length of the handle (a), breadth (c) and height (d) of the tuning forks are 1.8 mm, 4 mm and 1.3 mm respectively. The size of each head of the dumbbell is 1 \(\times\) 1.6 mm\({}^{2}\) and the dumbbell handle is 0.5 \(\times\) 1 mm\({}^{2}\).  
A FIT-based CST simulator is familiarized to simulate the projected design for comprehensive study of the characteristics structured in this article. The incidence wave is applied along the \(Z\)-axis while the unit cell boundary conditions are applied along \(X\)-axis and \(Y\)-axis. \(Z\)-axis is also used for open space condition.
# Dynamic Initial Weight Assignment for MaxSAT
## 2 Preliminaries  
We considered the SAT formula (\(\mathcal{F}\)) encoded in Conjunctive Normal Form (CNF), which consists of a conjunction (\(\bigwedge\)) of Clauses (Clause (Cl)), where each clause consists of disjunctions (\(\bigvee\)) of Literals (Literal (Lit)). A literal \((Lit_{i})\) is a propositional variable whose value is either true or false. A variable or its negation could occur in some clauses. For example, given two clauses, Clause 1 (\(Cl_{1}\)) and Clause 2 (\(Cl_{2}\)), variable \(X\) may occur in \(Cl_{1}\) as in \((x\lor y\lor z)\) and occur in \(Cl_{2}\) (\(x\lor u\lor\
eg z\)). We denote that Clauses 1 and 2 are neighbors as variable \(X\in(Cl_{1},Cl_{2})\) because they share the same signed literal \(x\) of variable \(X\). Given a third clause \(Cl_{3}\) (\(a\lor b\lor\
eg z\)), the clauses \(Cl_{1}\) and \(Cl_{3}\) are not neighbors as the variable \(Z\) occurs in both clauses with different literal signs. A solution to the MaxSAT formula (\(\mathcal{F}\)) is an assignment that satisfies a maximum number of clauses in \(\mathcal{F}\). A clause \(Cl_{i}\) is satisfied if \(\exists\)\(Lit_{\mathbb{C}}li=\) true \(\in\)\(Cl_{i}\). The cost function \(cost(\mathcal{F})\) denotes the number of satisfied clauses at any given stage during the search, and we denote \(bestCost(\mathcal{F})\) as the maximum number of satisfied clauses reached by a given assignment during the search (or the minimum number of unsatisfied clauses reached). We also denote the time at which \(bestCost(\mathcal{F})\) is reached by \(time(bestCost)\).  
Furthermore, as our study crucially depended on the general problems' structure, we denote the following:  
* \(TotalnumberofClauses(nCls)\): the total number of clauses within the formula \(\mathcal{F}\).
* \(AverageNeighborhoodSize(AvgNeighbS)\): the average of the clauses' neighborhood size. For each problem set, the sum of literal occurrences for each clause is divided over the total number of the clauses in \(\mathcal{F}\): \[AvgNeighbS=\frac{\sum\limits_{i=1}^{nCl}Lit\_Occurrences(Cl_{i})}{nCl}\] (1)
* \(SolutionCost(SolCost)(\mathcal{F})=\) the total number of unsatisfied clauses in the current assignment.
* \(MoveCost(MoveCost)=\) the difference of the number of clauses that will become unsatisfied and the number of clauses that will become satisfied by flipping a literal from true to false or vice versa. \[AverageClauseSize(AvgClSize)=\frac{\sum\limits_{i=1}^{nCl}number\_ Literals(Cl_{i})}{nCl}\] (2)
* \(Score(m)=\) the total number of false clauses in \(\mathcal{F}\) if a literal is flipped (changing its value from zero to one or vice versa), where (m) is short for search move, which denotes flipping a literal in the current assignment. \[Score(m)=Makes(m)-Breaks(m)\] (3)
## Abbreviations  
List of symbols and their meanings:  
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}}
**Symbol** & **Definition** & **Context** \\ \(i,j\) & Pixel location indices (rows and columns) & Gaussian blur, image processing \\ \(\sigma\) & Standard deviation, spread of Gaussian function & Gaussian blur, OpenPose \\ \(F\) & Feature map from baseline CNN & OpenPose module \\ \(p\) & Position vector in 2D space & OpenPose, Part Affinity Fields \\ \(p\) & Velocity of key points & OpenPose module \\ \(k\) & Key point & OpenPose module \\ \(S^{*}_{jk}\) & Confidence map value at position \(p\) for person \(k\) and joint \(j\) & OpenPose confidence maps \\ \(L^{*}_{c\dot{k}}\) & Part Affinity Field value at position \(p\) for limb \(c\) & Part Affinity Fields \\ \(d_{j1,d}\),\(d_{\dot{2}}\) & Candidate locations of body parts for connection & OpenPose Part Affinity Fields \\ \(E\) & Line integral of PAF along the line connecting two key points & OpenPose module, testing phase \\ \(A^{k}_{ij}\) & Activation map values at indices \((i,j)\) & GradCAM \\ \(v^{k}_{ij}\) & Weights associated with class \(c\) for feature map \(k\) & GradCAM \\ \(\gamma^{\dot{\tau}}\) & Output score for class \(c\) & GradCAM \\ \(Z\) & Normalization factor (size of feature map) & GradCAM, global average pooling \\ \(x(t)\) & Signal & STFT \\ \(\omega(\tau)\) & Window function & STFT \\ \(m,n\) & Kernel indices & Gaussian blur \\ \(K\) & Kernel size & Gaussian blur \\ \(f(i-m,j-n)\) & Input image being convolved & Gaussian blur \\ \(x_{j1,k},x_{j2,k}\) & Actual locations of anatomical features along a limb & OpenPose \\ \(\tau,\omega\) & Time and frequency variables & STFT \\ \end{tabular}
## 4 Results
### Spatial Representativeness Determined by Direct Comparison  
We refined the step size to find the highest correlation coefficient between TM and MODIS to determine the proper aggregation scale and use it as the representative spatial scale of MODIS data. The step size was set to 2 TM pixels. Figure 4 shows that the coefficient varied with aggregation scale. TM data were aggregated from 15 \(\times\) 15 to 61 \(\times\) 61 TM pixels. From Figure 4 we can see that when  
Figure 3: Histogram of \(R_{CV}\) at (**a**)–(**h**): scale 1–scale 8. The median is the median value of \(R_{CV}\) at each scale.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Land Cover Type** & **Suggested Spatial Scale** & **Suggested Spatial Representativeness in Meters** \\ \hline Evergreen Broadleaf forest & 2 & 630 \\ Deciduous Broadleaf forest & 2 & 630 \\ Mixed forest & 3 & 690 \\ Open shrulands & 2 & 630 \\ Woody savannas & 2 & 630 \\ Grasslands & 2 & 630 \\ Croplands & 3 & 690 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spatial representativeness of MODIS 500 m albedo product for different land cover types.  
23 \(\times\) 23 TM pixels were aggregated, the correlation coefficient was the highest and the root mean squared error was the lowest compared with MODIS data. This corresponds to the results in Table 3 in some degree.  
We then checked the comparative accuracy of different land cover types. Table 4 shows the accuracy of comparison in different land cover types at the suggested aggregation scale according to Table 3. For all the land cover types, the RMSE was less than 0.03 and bias less than 0.018. The \(R^{2}\) of evergreen broadleaf forests was the lowest, mainly because the land surface of this type was snow free, and all albedo values clustered together. For all other land cover types, the \(R^{2}\) value was higher than 0.86, and for croplands, it reaches 0.965.
## 2 Materials and Methods
### Angle Sensor  
In this study, we used an angle sensor to obtain references for the true angle and verify the accuracy of angle estimation. The angle sensor used was a WitMotion WT9011 DCL Bluetooth 5.0 Accelerometer Gyroscope Angle Sensor with an electronic compass, a magnetometer, and an inclinometer (accuracy: \(X\)- and \(Y\)-axes--0.2\({}^{\circ}\), \(Z\)-axis--1\({}^{\circ}\); output rate: 0.2 Hz-200 Hz; maximum distance: 90 m; dimensions: 23.5 mm \(\times\) 32.5 mm \(\times\) 11.6 mm; weight: 9 g; manufacturer: WitMotion). This model was selected due to its high accuracy, compact size, and affordability. These characteristics made it suitable for capturing precise thumb motion while maintaining its ease of use and cost-effectiveness. The angle sensors were calibrated prior to measurement according to the manufacturer's instructions. Following calibration, the accuracy of the sensors was reported to be 97% or higher for all measurements. The angle data were sampled at 100 Hz, and the signals were synchronized. The data were immediately transferred to a laptop (Surface Pro 8, Microsoft Corporation, Redmond, WA, USA) via a Bluetooth personal area network.  
To evaluate the reliability of the angle sensors, we created devices that replicate the movements of the right thumb. For radial abduction, a cylinder was tilted from a horizontal position to 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\). For pronation, horizontal planes were created on the side of the cylinder at \(-\)30\({}^{\circ}\), 0\({}^{\circ}\), 30\({}^{\circ}\), and 60\({}^{\circ}\) tilts from the top surface. A total of 16 such devices were fabricated using a 3D printer (Stereo Lithography Apparatus, DMM.make). The accuracy of the angles of these devices was measured within \(\pm\)0.5\({}^{\circ}\) using a square ruler. To reproduce palmar abduction, a protractor was used to mark the angles at 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\). The angle sensor was affixed to the horizontal plane of the device using tape, and the device was moved along the lines corresponding to each marked angle. At each angle, 112 values displayed by the application were recorded over 2 s. This procedure was repeated 10 times for each mark, and the mean absolute error (MAE) was calculated (Figure 2).  
In our previous research [19], true values were measured using a protractor, which posed challenges in terms of reproducibility and accuracy. In contrast, in this study, an angle sensor was used, enabling more precise three-dimensional measurements and reducing examiner variability. This is a key innovation of this study.
# Embracing the Future Internet of Things +
## 4 Realization of Hyper-Connected IoT realm
### Smart-City Magnifier: Smart Cities Enabled by Future Hyper-Connected IoT
Since the future smart cities take place in a digital real-time world, real-time information fusion through the hyper-connected IoT framework are required to provide contextualized information to the different levels for executing necessary actuations. A first task performs _Data Contextualization_ that associates virtual entities to the incoming observations. Contextualizing, in this scope, is the act of inferring the real-world things (e.g., a building, a street, a square, a suburb, a city etc.) to which each geotagged observation belongs, making usage of the knowledge storage offered by external knowledge bases such as OpenStreetMap [26]. One observation might be associated to one or more real-world things, for instance, an observation of outdoor temperature sensor attached to a building wall can be associated to such building, to the street where the building is located, to the neighbourhood and to the city. At the same time, one thing can have more than one observations associated, for example, if two temperature sensors are located on two buildings in the same street, such street has two sources of temperature observations. Since the data contextualization does not need raw data from other sources, it can be executed on the edge or on the cloud specific of the data source domain without accessing the federation. The _Data Aggregation_ groups the incoming observations by the inferred things. The aggregation makes usage, for instance, of statistics means. The _Indicators Computation_ is calculating smart city Key Performance Indicators (KPIs) [27] assessing the status of city and identifying critical situations. The latter two data analytics tasks necessitates cross-domain data and therefore they make data requests trough the federation. Relating the Smart-City Magnifier with the conceptualization of Figure 5, data contextualization and aggregation tasks are knowledge extraction whereas indicators computation task is situation classification. The output of the analytics is handled again by the federated IoT Brokers. The dashboard is then acquiring the information and visualizing it on a map presenting the alerts and warnings with the means of several visualization widget tools. The same visualization dashboard is used for visualizing  
Figure 6: Realization of the Smart-City Magnifier application leveraging _semantic mediation_, _semantic interoperability_, _resource orchestration_ and _federation of IoT platforms_. all the situations inferred from the IoT silos integrated in Europe, Korea and New Zealand, with a total of 13 city areas (Guildford in the United Kingdom, Santander and Murcia in Spain, Seoul in South Korea, Toulouse and Paris in France, Heraklion and Volos in Greece, Waterford in Ireland, Minervino Murge in Italy, Wellington and Christchurch in New Zealand). Since the analytics processing tasks are working on homogeneous semantically annotated data, the geographic scope of the dashboard map can simply be navigated to focus on a different city.
# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 2 Mathematical Model of Cooperative Search
### The Updating Mechanism of the Target Existing Probability Map  
The target existing probability map represents the possibility that the target exists in a specific grid, and it is the guidance of the searching behavior of the UAV agent. This probability map can be updated based on the feedback of the airborne sensors in the emergent searching process. When UAV arrives at the corresponding grid, the sensor on UAV obtains the target existing probability of the current grid and eight neighboring grids. However, the sensor can only sense whether there is a goal in the current grid. Then, the onboard computer calculates and updates the target existing probability of the current grid according to Equations (3)-(5). The updating mechanism is set as follows.  
* The grid \((m,n)\) is not searched by UAV agents: \[P_{mn}(t)=P_{mn}(t-1),\] (3)
* The grid \((m,n)\) is searched by UAV, and the target exists in this grid: \[P_{mn}(t)=\frac{P_{d}P_{mn}(t-1)}{(P_{d}-P_{f})P_{mn}(t-1)+P_{f}},\] (4)
* The grid \((m,n)\) is searched by UAV, and the target does not exist in this grid: \[P_{mn}(t)=\frac{(1-P_{d})P_{mn}(t-1)}{1-P_{f}},\] (5) where \(P_{d}\) and \(P_{f}\) represent the detection probability and the false alarm probability, respectively. According to the above equations, the existing probability distribution of the targets can be adopted to guide UAV agent to move the grid with the maximum probability in each step and find the target when the probability exceeds the threshold \(\theta\).
# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 2 Background
### Welm  
WELMs are an extension of the traditional ELM algorithm. WELMs incorporate a weighting mechanism that assigns different importance levels to each sample in the training set, allowing for more flexible and adaptive learning.  
In WELMs, the key difference lies in the inclusion of sample weights during the learning process. The sample weights represent the relative importance or significance of each training example. By assigning higher weights to certain samples, the algorithm can focus more on learning from those instances, giving them more influence on the model's decision making.  
In the following an overview of how WELMs work, building upon the steps described for ELMs:  
1. Input Layer: The input layer represents the features or attributes of the input data, similar to ELMs.
2. Hidden Layer: The hidden layer consists of randomly initialized neurons. Each neuron has its own set of weights connecting it to the input layer.
3. Activation Function: An activation function is applied to the weighted sum of inputs for each neuron in the hidden layer, introducing nonlinearity.
4. Output Layer: The output layer produces the final predictions or classifications using an appropriate activation function.
5. Random Weight Initialization: The weights connecting the input layer to the hidden layer are randomly generated, just like in ELMs.
6. Calculate Hidden Layer Outputs: The random weights and the input data are multiplied, and the activation function is applied to obtain the output of the hidden layer.
7. Weight Assignment: WELMs introduce the concept of sample weights. Each training example is assigned a weight that represents its importance. These weights can be determined based on various factors, such as data distribution, class imbalance, or domain knowledge.
8. Solve Weighted Output Weights: WELMs modify the process of solving the output weights by considering the sample weights. Instead of a simple linear regression, a weighted least squares approach is employed. The algorithm seeks to minimize the weighted difference between the actual outputs and the desired outputs, giving more weight to the samples with higher importance.
9. Prediction: Once the output weights are obtained, the WELM can make predictions on new data by propagating the input through the network and computing the output using the learned weights.  
By incorporating sample weights, WELMs offer greater adaptability and flexibility in learning from data. Samples that are deemed more important contribute more significantly to the model's training, enabling the algorithm to focus on specific instances or address class effectively imbalances.  
Weighted extreme learning machines can be particularly beneficial in scenarios where certain data points are more informative or have higher relevance than others. By assigning appropriate weights, the algorithm can effectively adapt to the varying importance levels of training examples and improve the overall performance.