esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Tests
### Effect of Bridge TemperatureStatistical Approach to Damage Detection  
In this section, the statistical approach for detecting a change in the bridge stiffness due to change in temperature is investigated. In this study, the internal bridge deck temperature, and its subsequent change over a year, is used as a proxy for the bridge condition; if the BWIM system responds to changes in temperature, it should also be sensitive to damage. Figure 16 shows the change in the internal temperature of the Sentvid bridge over one year. It can be seen in Figure 16 that while there is considerable variability, there is a clear difference between summer and winter temperatures. The internal temperatures are divided into 6 intervals based on equal numbers of trucks, i.e., 868 trucks per interval (i.e., 5208 in total). This approach resulted in the intervals of 6.5-22 \({}^{\circ}\)C, 22-25 \({}^{\circ}\)C, 25-30 \({}^{\circ}\)C, 30-33 \({}^{\circ}\)C, 33-35 \({}^{\circ}\)C and 35-44 \({}^{\circ}\)C.  
The BWIM analysis is carried out to compare the change in the statistical properties of the inferred steer-axle weights with the change in the mean internal bridge temperature. The influence line response from Figure 14 is used for this analysis which is calculated using a constant internal bridge temperature of 20 \({}^{\circ}\)C. The reason for using the steer-axle weight is the low variability of this parameter; the steer axle takes the weight of the engine and is therefore less variable than the weights of the other axles.  
Figure 14: Bridge mid-span acceleration responses to unit-weight axle from 10 truck responses and the mean influence line response at 20 \({}^{\circ}\)C temperature.  
Figure 15: Typical measured bridge acceleration response to a 5-axle truck and corresponding calculated response.
## 3 General Results and Discussion
### Mixed Perovskites
#### 3.3.5. Implication  
No previous literature has investigated or discussed perovskites mixtures. This paper exhibits a new method that demonstrates the possibility of retaining the stability and improving the efficiency of existing perovskites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compounds** & **Bandgaps (eV)** & **Corrected (eV)** & **Formation Energy (eV/atom)** \\ \hline Cs\({}_{2}\)Sn\({}_{2}\)Cl\({}_{3}\)I\({}_{3}\)Sr\({}_{2}\)TiRhO\({}_{6}\) & 1.2 & 1.68 & 68.304 \\ Cs\({}_{2}\)Sn\({}_{2}\)Cl\({}_{6}\)Sr\({}_{2}\)Ti\({}_{2}\)O\({}_{6}\) & 1.1 & 1.54 & 90.223 \\ Cs\({}_{4}\)Sn\({}_{4}\)Cl\({}_{6}\)I\({}_{6}\) & 1.9 & 2.66 & 0.023 \\ CsSnCl\({}_{3}\)Sr\({}_{3}\)Ti\({}_{2}\)RhO\({}_{9}\) & 1.05 & 1.47 & 34.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Electronic bandgap for 2 \(\times\) 2 \(\times\) 1 mixed perovskites as well as formation energy.  
This paper also demonstrates the utility of adjusting bandgaps as a way of making unfit materials suitable for use. Finally, this paper explored the properties of mixed perovskites in hopes of contributing to their use in future applications.
## 2 Dual-Channel Transformer Model  
The dual-channel transformer model framework is shown in Figure 2. Given the satellite image \(\mathrm{M}\in\mathbb{R}^{\mathrm{C}\times H\times W}\), the batch size is entered as \(B\). After the feature extraction network EfficientNet, two feature layers, \(P_{t}\) and \(P_{r}\), with different rate sizes are randomly selected and assigned to the two regression subnetworks.  
In order to convert activation maps into transformer-compatible inputs, we need to convert \(\mathrm{P}\in\mathbb{R}^{B\times\mathrm{C}\times H\times W}\) to \(\widetilde{\mathrm{P}}\in\mathbb{R}^{B\times X\times Y}\). A dimension editor is designed to use 1\(\times\)1 convolution to flatten the activation maps according to the processing rules, and \(\mathrm{P}\in\mathbb{R}^{B\times C\times H\times W}\) is processed into \(\widetilde{\mathrm{P}}\in\mathbb{R}^{B\times X_{t}\times Y_{t}}\) and \(\widetilde{\mathrm{P}}\in\mathbb{R}^{B\times X_{r}\times Y_{r}}\), respectively. The transformer comprises an encoder and a decoder, and its processing flow is  
\[Z^{l}=Decoder(Encoder(Z^{l-1})), \tag{1}\]  
where \(Z^{l}\) is obtained after multiple-transformer processing, and then \(Z^{l}\) is processed as one-dimensional sequence feature \(S\) through the flattening layer. We then input \(S\) to the fully connected layer to output the pose information. In the orientation regression network, the quaternion SoftMax-like activation function is used.
## 2 Datasets and Algorithms  
### Dataset Preparation  
Two key databases were used in this study to prepare the datasets for investigating heart rhythm:  
_MIT-BIH Arrhythmia Database:_ This collection focuses on electrocardiogram (ECG) recordings. It is known for having various arrhythmia types, making it ideal for developing and testing algorithms that detect and classify these irregular heartbeats [22].  
_BIDMC Congestive Heart Failure Database:_ This database from Harvard Medical School stores a wider range of medical data from patients with heart failure [23]. It includes ECGs alongside clinical notes, imaging scans, and other physiological signals. Figure 1 shows the structure of the databases, with corresponding subcategories, which were used to prepare the two datasets for training and evaluating the proposed model.  
Two different datasets from these databases were prepared using different numbers of classes. In one dataset, we only used the MIT-BIH database with five classes (the normal sinus rhythm (NSR or N) class, supraventricular ectopic beat (S), ventricular ectopic beat (V), fused beat (F), and unknown beats (Q)), as shown in Figure 1. Four of these classes, S, V, F, and Q, represent various types of arrhythmias, each with irregularities in the ECG recording. The fifth, NSR or N, represents the normal beat, the baseline against which the other classes were judged.  
The other dataset had three classes and was prepared by using normal and abnormal rhythms (normal sinus rhythm (NSR) and arrhythmia (ARR) classes) from the MIT-BIH database and the congestive heart failure (CHF) class from the BIDMC database.  
For the three-class dataset, based on studies from the literature [19], we started with 162 recordings from three groups: arrhythmia (ARR), normal sinus rhythm (NSR), and congestive heart failure (CHF). The breakdown was 96 recordings for ARR, 30 for CHF, and 36 for NSR. We randomly selected 30 recordings from each class, resulting in a total of 90 recordings. Each recording initially had a massive sample with 65,536 features. We segmented this sample into smaller, non-overlapping intervals of 500 features each. This segmentation resulted in 131 samples for each original recording. Consequently, each class (ARR, NSR, CHF) had 3930 individual samples. In total, we had 11,790 samples, each of length 500, extracted from the 90 recordings. For the five-class dataset, which was collected only from the MIT-BIH database, we arranged samples of length 187, the same as what is presented in the literature. In total, the 5-class dataset had 10,000 samples equally distributed among the 5 classes.  
Figures 2 and 3 show examples of the segmented samples from the three-class and five-class ECG datasets of length 500 and 187, respectively.  
Figure 1: The databases used in preparing the two datasets for the proposed model and their subcategories.
# Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling
## The Proposed Algorithm  
### The Main Idea  
The major steps of the proposed algorithm are:  
1. Initialize the variables of GA and SA; determine the initial temperature of annealing \(T_{0}\), temperature reduction parameter \(k\), size of population \(s\), crossover probability \(p_{cross}\) and mutation probability \(p_{mutation}\) by the result obtained from the experiment in Section 4.2;
2. Randomly generate initial population \(p_{0}\), and encode each chromosome; the size of \(p_{0}\) is 10;
3. Calculate the mean absolute error \(\epsilon\) of the sampling scheme corresponding to each chromosome according to the sampling rules, then the fitness of each chromosome is denoted as \(f\); let \(f=1-\epsilon\). Determine the fitness function of each chromosome in the population (\(f=1-\epsilon\)); see Section 3.4;
4. Roulette wheel method is used to select \(s\) chromosomes from \(p_{0}\) to constitute new population \(p\), the probability of choosing an individual depends directly on its fitness value \(f\);
5. Randomly choose two chromosomes from \(p\), and apply the crossover operator to them;
6. Apply the mutation operator described in Section 3.3 to the new population;
7. Let the current population be the new population;
8. If the mean absolute error \(\epsilon\) is less than 0.2%, then the convergence criterion is satisfied; stop. Otherwise, go to Step 4.
## 3 Methodologies
### CMGraph for Efficient Congestion Monitoring  
The information on crowd mobility acquired by fusing CCTV and floor plan information encompasses both spatial data of the physical space and temporal data describing the evolution of crowd movements over time. In order to analyze crowd flows in a physical space, however, there is a need for tools and techniques to visualize and interpret the spatiotemporal data. The spatiotemporal data must be adequately represented, as a simple representation in Euclidean space is insufficient for representing spatial connectivity. In this section, we propose a new spatiotemporal graph formulation, Crowd Mobility Graph (CMGraph), which can be used to represent the macroscopic crowd flow between egress regions. We also illustrate how this new representation can be leveraged to achieve efficient real-time and future time congestion monitoring.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 5 Simulation Results
### Orientation--3d Crelb  
We implemented four orientation estimators in our MATLAB simulation platform: Tri-Axial Attitude Determination (TRIAD) [56]; Davenport's Q-method [57; 58]; QUaternion ESTimator (QUEST) [59]; and the optimal linear attitude estimator (OLAE) [60]. We placed two ground nodes \(g_{1}\) and \(g_{2}\) at (0,0,1) and (0,1,0) meters and defined the normalized vector \(v_{0}\) between nodes \(G\) and \(H\). We then generated normalized antenna position vectors \(b_{1}\) and \(b_{2}\) by rotating \(v_{k}\) using a direction cosine matrix (DCM) with angles \(\alpha=\beta=\gamma=\frac{\pi}{6}\). We evaluated \(\kappa\) using Equation (53).  
In Figure 8, we plot the performance of these estimators versus the 3D orientation CRLB as a function of the positioning precision \(\sigma_{p}\). There is some slight variation between the estimators in different regimes, but they are all consistent with the proposed bound. In Figure 9, we substitute \(\kappa\) for \(\sigma_{p}\) following Equation (53) to instead plot the performance as a function of the concentration.  
Figure 7: The 3D position CRLB vs. ranging precision for several GDoP values. These curves can help inform system design decisions such as antenna placement or required ranging precision. In general, a GDoP of 1 is considered “ideal”, between 2 and 10 is considered “good”, and anything beyond 20 is considered “poor”.  
Figure 8: The 3D orientation CRLB vs. positioning precision for several attitude estimators.  
Figure 9: The 3D orientation CRLB vs. concentration for several attitude estimators.
# Unified Analytic Melt-Shear Model in the Limit of Quantum Melting
###### Abstract  
Quantum melting is the phenomenon of cold (zero-temperature) melting of a pressure-ionized substance which represents a lattice of bare ions immersed in the background of free electrons, i.e., the so-called one-component plasma (OCP). It occurs when the compression of the substance corresponds to the zero-point fluctuations of its ions being so large that the ionic ordered state can no longer exist. Quantum melting corresponds to the classical melting curve reaching a turnaround point beyond which it starts going down and eventually terminates, when zero temperature is reached, at some critical density. This phenomenon, as well as the opposite phenomenon of quantum crystallization, may occur in dense stellar objects such as white dwarfs, and may play an important role in their evolution that requires a reliable thermoelasticity model for proper physical description. Here we suggest a modification of our unified analytic melt-shear thermoelasticity model in the region of quantum melting, and derive the corresponding Gruneisen parameters. We demonstrate how the new functional form for the cold shear modulus can be combined with a known equation of state. One of the constituents of the new model is the melting curve of OCP crystal which we also present. The inclusion of quantum melting implies that the modified model becomes applicable in the entire density range of the existence of the solid state, up to the critical density of quantum melting above which the solid state does not exist. Our approach can be generalized to model melting curves and cold shear moduli of different solid phases of a multi-phase  
so-called Wigner electron lattice) and its subsequent transformation into a more disordered structure (an electron fluid), that is, quantum melting, was directly observed.  
In the three-dimensional case, the phenomenon of quantum melting is usually studied theoretically, and in this work we will carry out such theoretical study in the one-component plasma (OCP) model of a material, which consists of some type of charged particles (atomic nuclei, for example) immersed into a rigid neutralizing background of electrons which makes the system electrically neutral but does not screen Coulomb interactions between the charged particles.  
As discussed in more detail below, quantum melting, as well as the opposite phenomenon of quantum crystallization, may occur in dense stellar objects such as white dwarfs, and may play an important role in their evolution that requires a reliable thermoelasticity model for a proper physical description. Here we suggest a modification of our own unified analytic melt-shear thermoelasticity model in the region of quantum melting. We start with a brief review of this thermoelasticity model.
## 2 Materials and Methods
### Automatic Detection Algorithm  
The RQA-based method has its limitations. Indeed, these thresholds have been identified so far by visually inspecting where DET/LAM underwent a sudden change or  
Figure 2: Standard CPET equipment: (**a**) mask, volume sensor, and gas analyzer tubing; (**b**) blood pressure monitor; (**c**) smartwatch for HR data collection; (**d**) ergometer (cycle or treadmill); (**e**) gas analyzer; and (**f**) display.  
reached saturation. The need now is to determine these thresholds without the requirement of external user intervention but rather in an automated manner. To achieve this result, we developed a novel algorithm written in Python (version 5.3.3) that can automatically detect the most convex minima of a time series. This algorithm has been already presented in [6]. For more details, see Appendix D.  
Here we report the main steps:  
1. Data cleaning and preprocessing on HR and VO2 time series.
2. The HR time series is divided into partially overlapping epochs of width = 100 points, shifted by 1 point, and for every epoch, RQA is performed, and DET values are calculated.
3. The new DET time series is then analyzed to identify the most convex minima. After calculating the first and second derivatives, a point is considered a minimum if its second derivative is greater than or equal to the mean of the second derivative plus two times its standard deviation: \[f^{\prime\prime}(t)>mean\left(f^{\prime\prime}(t)\right)+2\times SD\left(f^{ \prime\prime}(t)\right)\] (1) The value \(mean(f^{\prime\prime}(t))+2\times SD(f^{\prime\prime}(t))\) is named cut-off or radius.  
This condition selects points where the curvature is significantly convex. Finally, the function returns a list of indices (minima) corresponding to these minima points.
4. The AerT is identified by locating the most convex minimum of DET using the formula:  
\[\text{DET}_{\text{min}}=\text{max}\left(\frac{d^{2}\text{DET}(t)}{dt^{2}}\right) \tag{2}\]  
This selection is among all relative minima of DET. The point at which AerT occurs is termed DETmin, identified at the time point tVT1.
## 5 Meteorological Prediction Test
### Fuzzy Rules Generated
#### 5.5.2 Rainfall Prediction  
The results obtained in the training and testing of the fuzzy neural network in predicting rainfall rates in the state capital of Minas Gerais are listed in Figures 17 and 18, respectively. As the rainfall level experienced extreme variations during the collected period, the model had some difficulties in identifying the actual values. However, it should be noted that, despite significant variations, the model was able to understand the trend of growth and decrease in rainfall rates. This feature is critical for models that work with time series. Thus, predictions can be estimated following the error raised by the experiments, where it should be highlighted that they are very close to other approaches traditionally used for this purpose.  
The fuzzy neural network model highlighted the behavior of the various dimensions present in the problem (Figure 21). Rainfall rates increase as wind speeds increase, and we are in the early months of the year. Similarly, it is possible to analyze that, for the city of Belo Horizonte, there are high rainfall rates when a low insolation level high follows the cloudiness level in the city. Another critical factor is that rainfall in the city is concentrated to a greater extent when the maximum temperature in the city is around 24 \({}^{\circ}\)C, and the wind direction is high. At this maximum temperature and with high atmospheric pressure indices, there are also rain spikes in the state capital analyzed. Finally, in the 2000s, when there were high average wind speeds, rainfall rates were high for the city.
## 4 Experiments
### Experimental Setup
#### 4.1.4 Implementation Details  
All methods are implemented in PyTorch 2.3.0. Set the client sample ratio (\(csr\)) in FL to 10%, which means randomly selecting 10% of clients to participate in FL in each communication round and selecting clients from the remaining 90% to simulate communication-constrained ones. We will provide experimental results for \(csr=20\%\) and 50% if necessary. Except for partial experiments in Section 4.2.2, we always make all the remaining 90% of clients participate in I2C training. To ensure a fair comparison, we set the size of user-item embeddings as 32, the batchsize as 256 and local epoch \(E\) as 1. The total number of communication rounds is set to 300 for the MovieLens-1M dataset and 250 for other datasets, as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Interactions** & **Users** & **Items** & **Sparsity** \\ \hline MovieLens-100K & 100,000 & 943 & 1682 & 93.70\% \\ MovieLens-1M & 1,000,209 & 6040 & 3706 & 95.53\% \\ Lastfm-2K & 185,650 & 1600 & 12,454 & 99.07\% \\ Foursquare & 101,483 & 7390 & 10,159 & 99.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset statistics.  
these values have been experimentally determined to ensure convergence for all training methods. The learning rate is optimized within the range of [0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.5], with the detailed configurations for each of the four datasets outlined in Table 11. Adjust other model specifications based on their respective original papers.
## 2 Method  
### Coherent Plane-Wave Compounding  
Different time delays are applied to excite the array elements to emit plane-waves from multiple angles in CPWC. The plane-wave imaging results at different angles can be obtained using DAS beamforming of the echo signals from the region of interest. Then, the CPWC image can be reconstructed by averaging those plane-wave imaging results.  
Assume that \(N\) plane-waves are used to compound an image, where each plane-wave has a steering angle of \(\theta_{n}(n=1,2,\ldots,N)\). In an imaging region with a lateral direction \(x\) and an depth direction \(z\), the \(n\)-th plane-wave imaging result at a point \(\mathbf{p}=(x_{p},z_{p})\) is \(s(x_{p},z_{p},\theta_{n})\). Then, the CPWC output at the point \(\mathbf{p}=(x_{p},z_{p})\) is expressed as [9]:  
\[S_{CPWC}(x_{p},z_{p})=\frac{1}{N}\sum_{n=1}^{N}s(x_{p},z_{p},\theta_{n}). \tag{1}\]
## 4 Training and Simulation
### Training Results
#### 4.2.2 Different Mini-Batch Sizes  
The choice of mini-batch size in the MADDPG algorithm plays a critical role in balancing exploration capabilities with training stability, influencing both the convergence speed and reward values of the algorithm. Figure 7b shows the convergence performance of the PER-MADDPG algorithm under various mini-batch sizes, specifically for configurations of 256, 512, and 1024. We observed smoother gradient updates and more robust convergence results with greater mini-batch sizes, particularly 512 and 1024. In contrast, a mini-batch size of 256 was associated with less accurate gradient estimation, resulting in diminished reward values and a slow convergence speed. Considering the balance between computational resource demands and algorithmic efficiency, we adopted a mini-batch size of 512 for subsequent analyses of the PER-MADDPG algorithm.
## 3 Results
### Surgical Approaches  
The sinus tarsi approach (STA) was compared to the extended lateral approach (ELA) in 13 papers. A total of 9 out of the 13 papers supported the superiority of minimally invasive STA versus ELA, while the other 4 papers declared no statistical difference between them [1; 2; 3; 4; 5; 6; 10; 16; 17; 18; 19; 20; 21].  
In general, all the papers underlined the superiority of STA to ELA due to anatomical reduction of the calcaneus, reduction of incision complications incidence, and shortened operative time and postoperative stay [3]. No differences were found in the clinical efficacy of STA versus ELA in treating displaced intraarticular calcaneal fractures (DIACF) [5]. Taken all together, STA can be considered more effective than ELA in the treatment of calcaneal fractures, with the advantages of less intra-operative bleeding, a higher (and better) rate of foot function, lower incidence of postoperative complications, and, thus, higher safety [4]. Moreover, statistically significant differences were found in wound complications, superficial infection, sural nerve injury, visual analog scale (VAS), the American Orthopaedic Foot & Ankle Society (AOFAS) score, operative time, time from trauma to operating room, calcaneal height, and postoperative Bohler's angle, where all favored the minimal incision approach [6].