esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Uncertainty Estimation for Embedding Vectors
#### 3.3.7 Loss  
As detailed in Equation (18), the loss function consists of the classification and data uncertainty loss \(\mathcal{L}_{\text{C\&DU}}\), Triplet Hard Loss \(\mathcal{L}_{\text{THH}}\)[67], and a weight decay term \(\lambda\cdot\mathcal{L}_{2}^{\text{reg}}\).  
\[\mathcal{L} =\mathcal{L}_{\text{C\&DU}}+\mathcal{L}_{\text{THH}}+\lambda \cdot\mathcal{L}_{2}^{\text{reg}} \tag{18}\] \[\mathcal{L}_{\text{C\&DU}} =\frac{1}{\sigma}\cdot\mathcal{L}_{\text{CE}}+\ln\sigma, \text{with}\;\sigma=\frac{1}{d}\Big{\|}\Sigma^{(D)}\Big{\|}_{1} \tag{19}\]  
The classification and data uncertainty loss \(\mathcal{L}_{\text{C\&DU}}\), as defined in Equation (19), is essentially a weighted cross entropy loss (\(\mathcal{L}_{\text{CE}}\)). The size of the logit vector \(y=g(\mu)\), \(y\in\mathbb{R}^{T}\), which is needed to compute the cross entropy loss \(\mathcal{L}_{\text{CE}}\), depends on the number \(T\) of individuals in the training dataset.  
The cross-entropy loss is multiplied by the inverse of the data uncertainty term (\(\frac{1}{\sigma}\cdot\mathcal{L}_{\text{CE}}\)). As very small uncertainty values would result in an increased loss, the model is forced to learn appropriate uncertainty values greater than zero. Furthermore, large uncertainty values are encouraged in cases where the classification is poor. As a consequence, a trivial solution to minimize this part of the loss would be reached by assigning very large uncertainty values regardless of the input. Thus, we employ the logarithmic addition of the uncertainty term \(\sigma\) as a penalizing term.  
The application of weight decay and batch normalization layers supports this limiting influence by ensuring that weights and activations remain relatively small, including the activations utilized as uncertainty values.  
In addition to these effects, which only regulate the magnitude of the uncertainty values, the information propagated back from the cross entropy and triplet loss terms ensure that the uncertainty values are relevant to the feature vector entries of the same position and work as correction terms utilized via element-wise division as intended. This is because the embedding vector that serves as a basis for these loss terms is itself a result of said element-wise division.  
The classification and data uncertainty loss \(\mathcal{L}_{\text{C\&DU}}\) represents the second way in which our approach utilizes the uncertainty values to improve the embedding. The third and final application of the estimated uncertainty values will be presented in Section 4.3.4.
## 3 Results  
In this section, we present results on pBRL performance, obtained with field data from commissioning measurements at the UAB Campus and the pathfinder campaign at the ORM, from 2021 to 2023, see Figure 20 for both locations. We discuss different sky conditions, from clean to hazy, including a night with the presence of volcanic ashes, which furthers scientific interest in our data. Based on the signal preparation presented in Sections 2.2 and 2.5, we have developed high-level analysis routines to calculate and visualize relevant atmospheric parameters.  
Unfortunately, during the ORM campaign, the pBRL operations were limited to astronomical twilight periods on full moon nights. This significantly reduced the amount of useful data to a few minutes per night, for a total of \(\sim\)10 h of observation in \(\sim\)30 nights. Furthermore, because the pBRL could not be included in ORM's Laser Traffic Control System (LTCS) [(69; 70)], we operated the pBRL at about 10% of its laser power. Finally, we also initially operated the elastic line PMTs intentionally with reduced high voltages (and therefore gain) to protect the LOTRs from too high input voltages given the large mirror area and a recent re-aluminization [(16)]. During this period, PC was therefore not possible for the  
Figure 19: **Left**: A comparison of ground-layer extinction profiles retrieved from the Raman line at 387 nm (using Equation (50) with \(k=1\)) (blue) and the Klett–Fernald inversion of the elastic lines (355 nm, violet and 532 nm, green). Backscatter profiles retrieved with Equation (51) are shown for \(\lambda_{e}=355\) nm (dashed, pink) and \(\lambda_{e}=532\) nm (dashed, olive green). All three lines are affected by LOTR ringing, with the 355 nm line most strongly affected. A filter window of 1 km has been applied to the Raman inversion, resulting in worse height resolution than the (unfiltered) inverted elastic lines. The dim dashed lines show a similar filter applied to the backscatter profiles. **Right**: the corresponding LIDAR Ratios.  
elastic channels. Globally, these limitations resulted in reduced performance, particularly in the far range, during the initial commissioning period of the pBRL at the ORM.
## 2 Radiative Transfer
### Irradiance Transmission  
The transmittance of the radiant flux originating from a Lambertian source through a plane parallel atmospheric layer can be computed by integrating over the hemisphere (see, e.g., the textbook of [PERSON] (2008)):  
\[T_{F}^{g}(\tau_{g})=\frac{1}{\pi}\ \int_{0}^{2\ \pi}\int_{0}^{\frac{\pi}{2}}e^{- \frac{\tau_{g}}{\cos\ \theta}}\ \cos\ \theta\ \sin\ \theta\ d\varphi. \tag{6}\]  
Integration over the azimuth angle \(\varphi\) and substituting \(\zeta=1/\cos\ \theta\) gives  
\[T_{F}^{g}(\tau_{g})=2\ \int_{1}^{\infty}\frac{e^{-\tau_{g}\ \zeta}}{\zeta^{3}}\ d\zeta, \tag{7}\]  
which is basically the definition of the third exponential integral \(E_{3}\)  
\[T_{F}^{g}(\tau_{g})=2\ E_{3}(\tau_{g}). \tag{8}\]  
Analogously, the flux transmitted through the atmosphere below the scattering layer (with gaseous optical thickness \(\tau_{\downarrow}\)) plus the scattering layer becomes  
\[T_{F}^{g*}(\tau_{\downarrow}+\tau_{e})=2\ E_{3}(\tau_{\downarrow}+\tau_{e}). \tag{9}\]  
So that the relative additional extinction due to the scattering layer becomes  
\[E_{F}(\tau_{e},\tau_{\downarrow})=1-\frac{E_{3}(\tau_{\downarrow}+\tau_{e})} {E_{3}(\tau_{\downarrow})}. \tag{10}\]  
This can be separated into a fraction of scattered and absorbed flux within the scattering layer:  
\[S_{F}(\tau_{s},\tau_{e},\tau_{\downarrow}) = \frac{\tau_{s}}{\tau_{e}}\ E_{F}(\tau_{e},\tau_{\downarrow}) \tag{11}\] \[A_{F}(\tau_{s},\tau_{e},\tau_{\downarrow}) = \frac{\tau_{a}}{\tau_{e}}\ E_{F}(\tau_{e},\tau_{\downarrow}) \tag{12}\]  
Note that Equation (7) could also be interpreted as theorem of equivalence in the form used by [PERSON] and [PERSON] (2008) but accounting only for photon path extensions and a PPDF specific for an isotropic scattering layer.
## 3 CUH Model: Framework and Theory
### Three-Dimensional Constitutive Model Framework
When \(\chi=0\), the function, \(f\), degenerates into an elliptical yield surface, as shown in Figure 1d. The parameter, \(p_{\rm s}\), represents the hardening parameter. The plastic compression coefficient is defined as \(c_{\rm P}=(\lambda-\kappa)/(1+e_{0})\), where \(\lambda\) and \(\kappa\) are the compression and rebound indices from isotropic compression tests, respectively. The variable \(p_{x0}\) denotes the horizontal coordinate of the initial yield surface intersection with the \(p\)-axis under the initial conditions. The term \(H\) represents the hardening parameter:  
\[\tilde{H}=\int\frac{\tilde{M}_{\rm Y}^{4}-\tilde{\eta}^{4}}{\tilde{M}_{\rm c} ^{4}-\tilde{\eta}^{4}}\mathrm{d}e_{\
u}^{\rm P} \tag{29}\]  
In this equation, \(\tilde{\eta}=\frac{\tilde{q}}{\hat{p}}\) represents the current stress ratio in the transformed stress space, \(\tilde{M}_{Y}\) is the potential failure stress ratio, and \(d\hat{e}_{\rm P}^{p}\) is the increment of plastic volumetric  
Figure 1: Transformed stress method: (**a**) SMP and von Mises Yield Criteria in True Stress Space; (**b**) Transformation Illustration on the \(\pi\)-Plane; (**c**) CSUH Model Yield Surface(\(\chi=0\)) in Principal Stress Space; (**d**) Yield Surface in the p–q Plane. strain. During the shearing process, the characteristic state stress ratio, \(\tilde{M_{c}}\), and the potential failure stress ratio, \(\tilde{M_{Y}}\), change with the state parameter, \(\xi\), which indicates the current degree of compaction, as shown in Equations (30) and (31). From these equations, it is evident that \(\tilde{H}\) is also state-dependent. \[\tilde{M_{c}}=M\cdot\exp(-m\cdot\xi) \tag{30}\]  
\[\tilde{M_{Y}}=6\left[\sqrt{\frac{12(3-M)}{M^{2}}\exp\left(-\frac{\xi}{\lambda- \kappa}\right)+1}+1\right]^{-1} \tag{31}\]  
In these equations, the dilatancy parameter, \(m\), scales the influence of \(\xi\) and thereby affects \(\tilde{M_{c}}\). The state parameter, \(\xi\), is defined as follows:  
\[\xi=Z-\lambda\ln\left(\frac{\beta+p_{\mathrm{s}}}{1+p_{\mathrm{s}}}\right)-( \lambda-\kappa)\ln\left(\frac{\left(\frac{M^{2}+\beta^{2}}{M^{2}-\chi\beta^{2} }\right)\bar{p}+p_{\mathrm{s}}}{\bar{p}+p_{\mathrm{s}}}\right)-e \tag{32}\]  
where \(p_{\mathrm{s}}\) is the hardening parameter:  
\[p_{\mathrm{s}}=\exp\left(\frac{N-Z}{\lambda}\right)-1 \tag{33}\]  
The Unified Hardening Model for Clays and Sands (CSUH) has eight fundamental parameters: \(M\), \(\
u\), \(\kappa\), \(\lambda\), \(N\), \(Z\), \(\chi\) and \(m\). For the same type of soil, regardless of different stress paths and initial states (initial confining pressure \(p0\) and initial void ratio \(\epsilon 0\)), the same set of parameters is used.
# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 2 The Proposed System Overview
### The Deep Learning Module  
#### 2.2.1 Feature Extraction/Classification  
In the literature, many techniques for feature extraction have been proposed. In recent years, deep convolutional neural networks (DCNNs) have attracted great attention due to their outstanding performance. In image classification issues, including image analysis as in [31, 32], CNNs have been proven to be successful. A convolutional neural network (CNN) is made up of a series of trainable stages stacked on top of one another, a supervised classifier, and feature maps [33].  
Transfer learning is used on our first model, which is based on the VGG16 architecture. The model was pre-trained on ImageNet, and the first four blocks of residual layers were kept frozen, except for the batch normalization (BN) layers, which required retraining to achieve better convergence. By applying transfer learning, a model can be trained using smaller sets of training data while still being capable of accurate predictions, mostly due to the learned parameters from the source model (in our case the pretrained ImageNet). An additional fully connected (FC) layer with a size of 1024 is added to the overall architecture, followed by a dropout regularization layer to ensure generalization performance. For the output layer, a final FC layer is added. Our model has three output classes: normal, benign, and malignant. This model, which we will refer to as VGG16-NBM, is used to categorize each image patch from the entire breast MRI as either normal, benign, or malignant.  
A similar VGG16 model was also constructed with four outputs corresponding to the four different categories of breast density: ACR-A, ACR-B, ACR-C, and ACR-D. Similar approaches have also been reported in the literature that perform very well [34]. We will refer to this model as VGG16-ACR, and its task is to recognize the density class for each image patch.  
For the training of the two VGG16-based models (VGG16-NBM and VGG16-ACR), the INbreast database was used. The input to the VGG16 models is patches the size of  
Figure 3: The architecture of the U-NET model with an input sample size of 572 \(\times\) 572 pixels as input. The training procedure was conducted using the INbreast database.  
256 \(\times\) 256 pixels. To augment our training set, we exploited the capability of GAN topologies to produce artificial samples of a specific domain as presented in the following section.
## 2 Methodology
### Distribution Probability Adjustment Confidence
#### 2.4.1 DPAC Function  
The DPAC function \(f(x)\) is equivalent to Formula (2). The \(a_{0}\), \(a_{1}\),... \(a_{n+1}\) of the function are the confidence adjustment coefficients; \(r_{0}\), \(r_{1}\),... \(r_{n+1}\) are the range of distribution quantities. As there is no target in the regional distribution, \(x\) = 0, the function output is \(a_{0}\). When \(x>0\) and \(x<r_{1}\), the function output is from a\({}_{1}\) to an. Set the domain of the definition (\(r_{0}\), \(r_{1}\),... \(r_{n+1}\)) of the DPAC function according to the distribution gradient and experience after obtaining the target distribution. If the highest point is H, (\(r_{0}\), \(r_{1}\),... \(r_{n+1}\)) can be set as (\(b_{0}\times H\), \(b_{1}\times H\),... \(b_{n+1}\times H\)), where \(b_{0}<b_{1}<...<b_{n+1}\), and \(b_{0}\), \(b_{1}\),... \(b_{n+1}\in\) [0,1). After the DPAC function definition domains (\(r_{0}\), \(r_{1}\),... \(r_{n+1}\)) are determined, the initial values of the adjustment coefficients (\(a_{0}\), \(a_{1}\),... \(a_{n+1}\)) are set according to the value range and experience of the domain of definition. In general, the regions with small \(r(i)\) or a large amount of interference require more strict review, and the adjustment coefficient should less than 1, and adjustment coefficients greater than 1 can be used in other regions. After repeated experiments and adjustments, the network can be optimized. Formula (2) is a structural example. The specific value of this function can be adjusted according to the actual situation to achieve optimal model output.  
\[f(x)=\left\{\begin{array}{lr}a_{0}&(x=0)\\ a_{1}&(0<x\leq r_{1})\\ \ldots&(r_{1}\ldots<x\leq\ldots r_{n})\\ a_{n+1}&(r_{n}<x)\end{array}\right. \tag{2}\]
# Which, When, and How: Hierarchical Clustering with Human-Machine Cooperation
## 6 Experiments
### Evaluation Results (Iris Flower Data Set)  
The evaluation results are shown in Tables 1 and 2. All the algorithms stop iterations when there are three clusters remaining (the true number of clusters). In HMCAHC, the human agrees with the machine on the 1 st and 2 nd questions, but disagrees with the machine on the 3 rd and 4 th. For the \"which\" problem, it can be seen that asking the most questionable pair of data points in the dirtiest cluster (HMCAHC) is better than a committee-based sampling strategy (HMCAHC-which). This is because HMCAHC uses the uncertainty sampling with density weights. While the gap between the mutual information of the HMCAHC and the TAHC is about 0.1, the HMCAHC-which has almost no performance improvement. For the \"when\" problem, although asking questions upon a significant certainty reduction (HMCAHC-when1) and upon a maximum entropy reduction (HMCAHC-when2) provides a good foundation for the early clustering stage, they fail to reserve enough questions for the hard decisions. The error propagation is disastrous, and thus shuts down the performance of the HMCAHC-when1 and the HMCAHC-when2 in the later clustering stage. For the \"how\" problem, although the global adjustment (HMCAHC-how2) brings a slightly better result, this performance improvement is at the cost of a much higher time consumption. On the other hand, although the local adjustment (HMCAHC-how1) has less time consumption, it has a much worse result. Our back-tracingscheme obtains a result that is close to the global adjustment, at the cost of an acceptable time consumption. Meanwhile, both PCMK-Means and Active PCK-Means perform more poorly than HMCAMC. This is because they use the questions in the beginning as the clustering constraints. They fail to reserve the question operations for difficult decisions. For further analysis, Figure 7a shows that the machine's certainty provides a good estimation of the true minimum purity during the clustering-building process. Figure 7b shows the relationship between the available number of question operations and the resulting mutual information of HMCAHC. It can be seen that a few questions can significantly improve the clustering results; however, too many question operations are unnecessary due to the marginal improvement. Compared to [PERSON] approach, the proposed algorithm also has a performance improvement of 0.02 in terms of mutual information. Moreover, our approach requires less human operations than [PERSON] approach. While our approach only asks the human to compare a few pairs of data points, [PERSON] approach requires the human to compare a data point with a set of data points (our experiments set the set size to be 10).
# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
## 4 Discussion  
According to Table 1, the system's operation is stable, and the heart rate measurement results showed accuracies between 98.87% (30 BPM) and 99.95% (120 BPM). For instance, at 30 BPM, 66 measurements were 30 BPM, and 34 were 29 BPM, resulting in a mean of 29.66 and an accuracy of 98.87%. These results were satisfactory compared to those obtained by [26], which reached accuracies between 98.18% and 100% using the same algorithm  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Heart Rate** & **Performance in One** & **Mean (bpm)** & **Std. Deviation** & **Accuracy** \\ \hline
30 & 30 bpm (66 samples) & 29.66 & 0.48 & 98.88\% \\  & 60 bpm (87 samples) & 59.87 & 0.34 & 99.78\% \\  & 120 bpm (94 samples) & 119.94 & 0.24 & 99.95\% \\  & 180 bpm (87 samples) & 179.27 & 0.68 & 99.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heart rate estimation performance of the system.  
Figure 18: Acquisition system with 30 beats per minute.  
in a similar code for Matlab and with data from the MIT-BIH database and also reached accuracies between 42.47% and 97.02% using a code based on the Wavelet Transform [39]. In Table 2, a comparison of the presented method with some methods in the literature is illustrated.  
A 100% accuracy was obtained in [41] for the correlation of heart rate (mean: \(75.6\pm 16.4\) beats/min) between a prototype and a lead I ECG from a standard 12-lead device used in a clinical setting. In [42], the measured heart rates obtained through a chest strap and a vest were compared with a gold standard reference (3-lead clinical Holter ECG), yielding accuracies of 99.21% and 96.28%, respectively, and mean absolute percentage errors (MAPEs) of 0.76% and 3.32%. An accuracy of 98.12% was obtained in measuring fetal heartbeats using a Powerlab DAC [40].
# Dim Staring Debris Targets Detection Method with Dense Long Trailing Star
## 2 Semi-Occluded Object Detection Algorithm Based on FCN
### Simulation Scenario and Parameter Description  
The simulation parameters are shown in Table 1. The stellar trailing imaging length is 26 pixels, and the width is 3-4 pixels. The imaging size of the debris target is 3 \(\times\) 3 pixels; the inter-frame imaging interval is 32 pixels; the SNR of both star and target is five; the position difference of debris between frames is 0.5 pixels. The average distance between stars is 20 pixels.  
Only weak targets are considered, and the generation of bright and large debris targets and stars is not considered, nor are the effects of bright and large targets on the imaging of weak targets considered.  
A total of 100 groups of continuous image data were generated, with 15 consecutive frames in each group. Considering the effectiveness of simulation and testing, only gaze imaging is considered for the debris point target, and the debris target with other motion conditions is not considered in this paper.  
Figure 1 shows the superposition data of three frames of images. Red, green, and blue, respectively, represent the trailing stars of the three frames before and after, and the debris point target is in the center of the image. Figure 2 shows the gray levels of the local target and the star. It can be seen that the amplitude of the star trailing is discontinuous and fluctuates greatly under the influence of noise, and the middle of the trailing is prone to \"fracture\". Therefore, the process should not only eliminate the background through  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Symbol** & **Parameter** & **Value** \\ \hline
1 & \(L_{f}\) & Start trailing length & 26 pixels \\ \hline
2 & \(W_{f}\) & Start trailing width & 4 pixels \\ \hline
3 & \(L_{d}\) & Debris size & 3 \(\times\) 3 pixels \\ \hline
4 & \(F_{i}\) & Inter-frame spacing & 50 pixels \\ \hline
5 & \(SNR\) & Signal-to-noise ratio & 5 \\ \hline
6 & \(J\) & Debris inter-frame jitter & 0.5 pixel \\ \hline
7 & \(P_{i}\) & Mean stellar spacing & 20 pixels \\ \hline
8 & \(\theta\) & Trailing angle & 70 degrees \\ \hline \end{tabular}
\end{table}
Table 1: Description of simulation parameters.  
the motion difference between the frames, but also identify the whole trailing information according to the distribution features of the image.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 3 Faults in WSNs
### Gain Fault  
As the name suggests, the rate of change of data is more than the expected reading in the gain fault [1]. This happens because of the multiplicative nature of \(\beta\) with data point \(x\). This fault is represented in Equation (2).  
\[x^{\prime}=\beta x+\eta \tag{2}\]  
where \(\beta\) represents the constant, which is multiplied with the normal reading.
## 3 Results
### Modeling
#### 3.3.2 Predictive Models  
A 10-fold cross-validation was performed over the training set to compare the predictive methods RT, KNN, MLP, support vector regressor SVR, RF, GBoost, and XGBoost in predicting compressive strength. An analysis of variance (ANOVA) was conducted to assess whether there are significant differences in the performance of the machine learning methods; however, there was not enough evidence to reject the null hypothesis that there are no significant differences between the methods. The same results were found using the Tukey method, where no significant differences were found between each pair of methods. Consequently, the methods were compared using boxplots (Figure 4), where a smaller spread suggests higher consistency in the model's performance. It can be seen that RT and KNN have the highest errors. SVR has a very high error in one of the cross-validation iterations, which significantly increases its average MAPE. XGBoost has a median biased towards higher values, which could indicate the presence of outliers or a skewed distribution. Finally, the three methods with the best performance were selected: MLP, RF, and GBoost.  
In the three predictive models, the differences between the errors in the training and test sets were found to be relatively small, suggesting that the model generalizes well to new data and does not suffer from overfitting. Overfitting occurs when a model fits too closely to the training data and does not generalize well to new data, which would be reflected in a significant discrepancy between performance in the training and test sets.