esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Behavioral Analysis  
#### 3.3.1 Transition Fines Content  
Many researchers have reported that the intergranular void ratio has a strong correlation with various aspects of silty sand behavior [25; 26; 27; 28; 29]. The concept of the intergranular void ratio is referred to as the void ratio where the part of volume of voids is occupied by the fine and this can be determined by Equation (1) [30]:  
\[e_{s}=\frac{e+\frac{C\times FC}{G_{f}\times 10}}{\frac{G}{G_{s}}\times\left(1- \frac{FC}{100}\right)} \tag{1}\]  
Figure 6: Variation of void ratio with vertical effective stress for each fines content.  
Figure 7: Variation of void ratio with fines content and vertical effective stress.  
in which \(G\) is the specific gravity of the soil itself or the mixture, \(G_{s}\) is the specific gravity of the host material, \(G_{f}\) is the specific gravity of fines, \(e\) is the void ratio, and \(e_{s}\) is the intergranular void ratio.  
[PERSON] and [PERSON] [31] suggested that transition fines content (TFC) could be an indicator that can classify a mixture's behavior into granular-dominant or clayey-dominant. The TFC can be signified when the value of the intergranular void ratio (\(e_{s}\)) is equal to the maximum void ratio of the host material (\(e_{max}\)). In this study, the CEN standard sand had an \(e_{max}\) = 0.7, obtained from the relative density test [32] and the values of \(e_{s}\) were computed by using Equation (1) with \(G_{s}\) = 2.607 and \(G_{f}\) = 2.8. The variation of intergranular void ratio with fines content in each vertical effective stress is indicated in Figure 8.  
As a general observation, the \(e_{s}\) was found to increase with an increase of fines content. The TFCs can be estimated by the intersection of the red dashed line (\(e_{max}\)). Therefore, the possible range of TFC was 21 to 26% for six different vertical effective stresses (24.52-784.53 kPa), respectively. The TFCs are shown in Table 2 and increase according to the vertical effective stress. This is because the higher effective stress led to more contact among granular materials, thus, higher TFCs were produced [8].  
\begin{table}
\begin{tabular}{c c} \hline
**Effective Stress (kPa)** & **TFC (\%)** \\ \hline
24.52 & 21 \\ \hline
49.03 & 22 \\ \hline
98.07 & 23 \\ \hline
196.13 & 24 \\ \hline
392.27 & 25 \\ \hline
784.53 & 26 \\ \hline \end{tabular}
\end{table}
Table 2: Transition fines content under different vertical effective stress.  
Figure 8: Variations of intergranular void ratio according to fines content.
## 5 Proposed System  
Our proposed QoSDSBS architecture model consists of MANET service composition, which uses a broker to integrate services. The broker will use the QoS as a metric to acquire distinct services from different nodes. For the routing services in the proposed architecture, the authors have considered a dynamic broker selection based on the QoS metrics. The QoS metrics include bandwidth, delay, throughput, load balancing, and node mobility. The entire node architecture is grouped into clusters based on its coverage area. Each cluster consists of one or two brokers that are dynamically selected. In the literature, the authors have considered one broker to avoid link failure. However, the centralized broker might be prone to aa single point of failure. In this work, we introduced the multiple broker concept using a dynamic broker-selection approach, which might eradicate the above-mentioned issue. This dynamic approach selects a broker based on the QoS metrics that provides reliability and scalability even if there is an increase in the number of nodes in the future. As depicted in Figure 4, the architecture consists of many mobile nodes and the cluster with a broker encircled is formed based on the coverage area.  
Figure 3: Layered architecture of QoSDSBS.  
The communication between the inter-cluster and intra-cluster takes place with the help of traditional routing protocol, namely the AODV. If a requester node in Cluster 1 requires a service from Cluster 4, then it broadcasts a request to the rest of the other nodes present in cluster1 to act as a broker. Then, the requester node will select the nearest broker and further process the reply message to the responding node as the broker. Next, the dynamic broker will provide the details of the available service providers in Cluster 4. Each broker will have the address of the service provider through which a requester node can get the service. The brokers are responsible to find the shortest path between cluster1 and Cluster 4 through the intermediate Cluster 3, using the AODV protocol. The dynamic broker is automatically selected in each cluster based on the QoS metrics.  
Our proposed architecture consists of four different modules. We will discuss each module precisely.
## 2 Materials and Methods
### Different Grounding Dimensions for the Antenna  
The grounding of the optimum antenna so far was also modified to 4 different lengths to determine its influence. The 5 different structures are presented in Figure 12 along with the grounding for the optimum structure considered so far.  
The modification of the grounding influences the bandwidth of the antenna, even increasing it when the grounding has the same dimensions as the dielectric, but the frequency of interest is not in the resulting bandwidth; thus, for this antenna, it is not what we want (Figure 13). In addition, if for the other considered groundings, the gain remains approximately the same, for the full grounding of the dielectric, the value decreases considerably to 0.6 (Table 5). The bandwidths are presented in Table 6, and it can be said that the structure with 8 mm grounding, the one from the previous study, is the best.  
Figure 12: Structures with different grounding dimensions: (**a**) 2 mm; (**b**) 4 mm; (**c**) 6 mm; (**d**) 10 mm; (**e**) total grounding.  
\begin{table}
\begin{tabular}{c c c c} \hline
**5 Directors** & **5 Directors + 6 mm** & **3 Directors** & **3 Directors + 6 mm** \\ \hline
16.51\% & 23.31\% & 16.22\% & 22.64\% \\ \hline \end{tabular}
\end{table}
Table 4: The bandwidth for the studied structures considering the increase in directors’ length.  
Figure 13: S parameters for the structures with different grounding.
# Balanced Loss Function for Accurate Surface Defect Segmentation
## 2 Method
### Loss Imbalance
#### 2.2.1 Label Imbalance  
Label imbalance is extensively studied in different learning tasks. In segmentation, it is caused by the disproportionate label distribution of pixels. For example, when positive examples largely outnumber the negative examples, without adjustment, positive examples will contribute most of the loss value. The prediction error on negative examples is scarcely penalized by the loss. The segmentation accuracy on negative examples is expected to be poor.  
For surface defect segmentation, this problem is challenging. Table 2 lists the label distribution (inter-defect-type and background-foreground) of various surface defect datasets. Pixel ratio is the average ratio of pixels of a defect type (showing pixel-level imbalance), and type ratio is the ratio of images of a defect type in the dataset (showing image-level imbalance). For KolektorSDD [19], KolektorSDD2 [17], and the magnetic tile dataset [4], the pixel-level imbalance is apparent. Some categories have no more than 1% pixels in an image. Although the Steverstal dataset [18] seems to be pixel balanced, image-level imbalance is still significant. KolektorSDD/KolektorSDD2 only have no more than 13% defective  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Meaning** \\ \hline \(Y\in\mathbb{R}^{+W\times H\times C}\) & Probabilistic prediction of the segmentation model. Section 2.1. \\ \(A\in\mathbb{N}^{W\times H}\) & Ground truth annotation. Section 2.1. \\ \(\hat{Y}\in\mathbb{R}^{W\times H\times C}\) & One-hot encoding of \(A\). Section 2.1. \\ \(N_{k}\) & The occurrences of label \(k\in\{1,...,C\}\) in \(A\). Section 2.3.1. \\ \(M_{class}\) & Dynamical class weighting mask. Equation (2). \\ \(M_{confusion}\) & Label confusion suppression mask. Equation (10). \\ \(TruncCE(\epsilon)\) & Truncated cross-entropy loss on prediction error \(\epsilon\). Equation (5). \\ \hline \end{tabular}
\end{table}
Table 1: Mathematical notations used in this paper.  
images, while Several and the magnetic tile dataset both have a class with under 10% images. In reality, surface defects usually occur very rarely, and different defect types have varying probabilities of occurrence, which is the root cause of the label imbalance problem.  
From the perspective of loss function, a simple approach to solve this problem is empirically assigning higher weight to less-represented classes, e.g., weighted cross-entropy loss [8]. However, the disadvantage is that it is tricky to manually set an appropriate weight, and it involves a lot of trial-and-error. Another popular approach is to use a region-based loss function, such as Dice loss [11] and IOU loss [10]. Region-based loss functions calculate the loss value via regional statistics on \(Y\) and \(\hat{Y}\). However, in this paper, we aim to handle other point-wise loss imbalance problems simultaneously, which is incompatible with region-based loss functions. To unify other parts of our method with this class loss balancing function, we propose a pixel-level dynamical weighting scheme in Section 2.3.1.
## 3 Results
### Iitate Village  
Sediment and water sampling were performed in the Iitate Village in 2012. The results are shown in Figure 5a,b, respectively. The concentration in sediments and unfiltered river water were 2-9 kBq\(\cdot\)kg\({}^{-1}\) and 100-180 mBq\(\cdot\)L\({}^{-1}\), respectively. In the case of the Minami-Soma City, a trend was present, with the concentration was generally high at the upstream area and gradually decreased downstream. However, such a trend was not observed in the sediment and river water in the Iitate Village.  
Figure 5: (a) \({}^{137}\)Cs concentration in sediments in the Iitate Village. (b) \({}^{137}\)Cs concentration in unfiltered river water in the Iitate Village. Sampling was performed in September 2012.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**River** & **Sampling Site** & **\({}^{137}\)Cs Concentration Ratio Filtered/Unfiltered** \\ \hline Odaka & D2 & 0.57 \(\pm\) 0.24 \\ Ohta & O2 & 0.80 \(\pm\) 0.12 \\ Niida & N5 & 0.57 \(\pm\) 0.10 \\ Mano & M5 & 0.40 \(\pm\) 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ratios of \({}^{137}\)Cs concentration in the filtered to the unfiltered component.
## 2 Methods
### Evaluation Techniques  
Two different measures are used to evaluate results:  
* Word Error Rate (WER). This is the most common metric used to evaluate the accuracy of speech recognition systems. In the article, we will use it to evaluate the accuracy of each of the systems trained with the different data augmentation strategies. The evaluation will be performed on the development and evaluation sets, distorted with different effects, and also on the real data. All WERs in this article have been computed using the compute_wer tool included as part of the KALDI toolkit [24]. To analyze the statistical significance of WER results, the bootstrapping method described in [35] has been used. In particular, the implementation of these methods in the compute_wer_bootci tool included in KALDI have been used to compute 95% confidence intervals of WER estimates, and to estimate the probability of improvement (_POI_) of one method over another as evaluated for the same test, allowing us to determine if the difference between two results is statistically significant at the 99% level.
* Mean Opinion Score (MOS). This metric is common in the evaluation of speech quality in fields such as speech enhancement and speech coding. In the article, we will use it to evaluate the quality of the data, both original and distorted and both simulated and real. Given that estimating the subjective mean opinion score is costly, we will use the objective estimation of MOS defined by an ITU-T P.563 single ended method for objective speech quality assessment in narrowband telephony applications [36]. In particular, we have used the reference software published along with the ITU-T P.563 standard to estimate the MOS value for each audio file. The software takes as input the audio file and produces the estimated MOS for the file. The MOS scale ranges from 1 to 5, 1 being the worst speech quality and 5 the best.
## 3 Related Work
### Gateway Nodes  
Similarly to the sink node, GW nodes periodically flood _PHE_ messages that will be used by U-sensors. When a GW receives data packets destined to the sink from the U-sensors, it stores them in a queue until it hears a beacon signal from an AUV. Upon reception of a beacon signal from an AUV, the GW and the AUV negotiate on the communication channel and send the stored data to the AUV using the selected channel.  
If a GW node receives _PHE_ messages generated by other GWs or the sink node, it simply discards the messages.  
The GWs can be pre-determined or dynamically selected by AUVs. In case the positions of GWs are pre-determined, more optimal trajectories of AUVs can be applied and dedicated and more powerful U-sensor nodes can be used as GWs.
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 4 Discussion
### Designing Application-Specific SiPMs
This would be eye-safe and could be delivered using a 850 nm transmitter that is designed to deliver 10 Gbps or higher [37]. Two or more of these devices could be used separately or in parallel to cover the 2 m by 2 m area covered by a single transmitter in a typical office environment [12]. The result would be an irradiance that could support 10 Gbps. In the absence of an ISI power penalty from either the transmitter or the receiver, 10 Gbps requires an average count rate of approximately 26 Gcps, which is a tiny fraction of the potential maximum count rate of 7509 Gcps. This maximum count rate suggests that the stacked receiver would have the capacity to tolerate a count rate penalty of 144; however, to avoid the negative impact from the non-linearity it may be more efficient to limit the penalty to half of this value. This would suggest that the receiver could support data rates of 30 Gbps. This is slightly higher than the data rate that is supported by receivers designed for fiber optical communications [37]; however, the existing receivers need 0.3 mW of received optical power to support a data rate of 25.78 Gbps and a BER of 5 \(\times\) 10\({}^{-5}\). Under ideal conditions this BER requires an average of approximately five photons per bit; however, the extinction ratio of the transmitter in this system is two, and Figure 3 shows that this means that the average number of photons per bit must be approximately 60. Taking these factors into account, without any ISI penalty the stacked receiver would require only 0.6 uW at the same data rate, wavelength, BER, and extinction ratio. Even with a significant ISI penalty the stacked receiver would therefore be expected to require only a small fraction of the optical power of the existing receiver to support 25.78 Gbps. Furthermore, the stacked receiver's dead time in Table 4, 8 ns, is the dead time used to reduce the afterpulsing probability for applications where afterpulses may be a significant \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **J30020** & **Stacked** \\ \hline Area (mm\({}^{2}\)) & 9 & 9 \\ Pitch (\(\upmu\)m) & 20 & 12.24 \\ Number of ucells & 14,410 & 60,073 \\ Recovery time/dead time (ns) & 15 & 8 \\ Maximum count rate (Gcps) & 436.7 & 7509 \\ PDE at 405 nm & 0.38 & 0.4 \\ Fill factor & 0.62 & 1 \\ Fast output pulse width (ns) & 1.4 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between a J30020 and a potential stacked SPAD receiver. problem. In contrast, previous Monte Carlo simulations have replicated experimental results despite the fact that afterpulsing is not included in these simulations. This suggests that it may be possible to reduce the dead time of these systems significantly. The result could be a receiver that can support data rates approaching 1 Tbps.
# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 2 Background
### Model Scaling for Deep Neural Networks  
Model scaling is the method where the capacity of the DNN is enlarged by expanding the underlying architectural structure to meet the varying requirements of different workloads [20; 25].  
Structure-wise, the baseline of a neural network can be expanded as follows:  
* In terms of _depth_ by adding more layers to capture a richer set of features and generalize when introduced to new tasks;
* In terms of _width_ by embedding more neurons per layer to capture more fine-grained features and intricate relationships within the data;
* In terms of _data clarity_ (i.e., resolution for images) to potentially capture more fine-grained patterns in the given input.  
An example of a neural network model structure that can be scaled depth-wise to introduce new variants are the popular Residual Networks (ResNets) [11]. The naming convention of a ResNet (i.e., ResNet-50 or ResNet-152) includes a number denoting the number of layers in the network.  
While scaling towards one dimension can increase model accuracy, as a network deepens (or widens), training speed is impacted due to the vanishing gradient problem where convergence is slowed down as the network parameters become very small while propagated back through the layers [26]. To compensate, _compound scaling_ is considered the norm today, where multiple dimensions are scaled together. An example of a family of neural networks adopting compound scaling is EfficientNet models for object detection, where depth, width, and input resolution are uniformly scaled based on a compound  
Figure 1: High-level overview of a deep neural network.  
coefficient. By doing so, EfficientNet models can achieve accuracy similar to that of other DNNs (i.e., ResNets) while utilizing fewer computational resources during model training and extracting meaningful cost savings [13].
# Impact of LGE-MRI in Arrhythmia Ablation
## 1 Introduction  
Magnetic resonance imaging (MRI) has become a cornerstone of the diagnostic and prognostic evaluation of patients with cardiac arrhythmias. It is widely used for qualitative and quantitative evaluation of cardiac conditions and support diagnosis, monitoring disease progression and treatment planning [1]. Nowadays, late gadolinium enhancement (LGE) MRI is being used to detect and quantify cardiac fibrosis in both ventricular and atrial arrhythmias [2; 3; 4; 5; 6].  
Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, being observed in up to 2% of the general population and in over 5-10% of the elderly population (>70 years of age). In this sense, due to the ageing of society and demographic changes, the overall prevalence is expected to increase even further [7]. Atrial fibrillation is associated with a fivefold risk of stroke and a threefold incidence of congestive heart failure and doubles the risk of mortality and dementia [8]. Isolation of the pulmonary veins (PV) by catheter ablation has emerged as a first-line therapy for patients with symptomatic AF not responding to pharmaceutical treatment [9]. PVI has achieved high success in paroxysmal atrial fibrillation. Nevertheless, in persistent AF, there is still a high rate of recurrence. A very recent study (ERASE AF) demonstrated that ablation of extra PV areas with low voltage detected with mapping catheters is helpful in these patients. In this sense, MRI could play a role in detecting these areas with LGE.  
Ventricular tachycardia (VT) is the most frequent etiology of sudden cardiovascular death (SCD) [10]. In patients with structural heart disease, VT is frequent and catheter ablation has become a standard treatment [11; 12], being the main mechanism responsible for a re-entrant circuit [13; 14; 15]. In this sense, there is an area of slow conduction of intermediate tissue (so called border zone (BZ)) inside the core scar connecting regions of healthy tissue. These areas of slow conduction are referred to as conducting channels (CCs) which can be rigorously defined during ablation procedures using electroanatomical maps (EAMs) [16; 17; 15; 18]. On the other hand, MRI allows the depiction of the BZ, healthy tissue andcore scar, identifying also CCs. A great concordance between EAMs and images obtained by MRI has been reported. Moreover, in a recent study [19] MRI has been carried out post mortem in cases of sudden cardiac death to research cardiac morphological alterations. Despite advancements in ablation technology and better understanding of arrhythmic substrates, VT recurrence rates are still high [20; 21; 22; 23]. For this reason, there is a clinical need to improve the characterization of the VT substrate and the efficacy of VT ablation. In this context, LGE-MRI may play an important role.  
The objective of this review is to analyse the application of LGE-MRI to improve ablation strategies for the two main cardiac arrhythmias: AF and VT.
## 2 Materials and Methods
### Pre-Processing (Corrections and Calibrations)  
All the images used in this study have been atmospherically and radiometrically corrected. Atmospheric correction has been done using \"Dark Object Subtraction\" (DOS1) model-based atmospheric correction algorithm [53; 54] and the radiometric calibration has been done using the \"conversion to TOA reflectance algorithm\" [55], through which digital numbers (DNs-radiance) values have been converted in the \"top of the atmosphere reflectance\". These pre-processing methods are considered by many scientists being the most efficient and recommended for urban studies [56; 57; 58].  
Given a remote sensing optical dataset, the first step is to convert the capture radiance, the raw digital number to the \"top of atmosphere\" values [59]. This step is necessary because it removes the cosine effect of different solar zenith angles due to the time difference between data acquisitions, it compensates for different values of the atmospheric solar irradiance arising from spectral band differences and corrects for the variation in the Earth-Sun distance between different data acquisition dates [59].  
Due to the fact that images are level 1 data products, geometric corrections have been already made by the image provider (The United States Geological Survey, European Space Agency--USGS) and, because the data meet the condition according to which the root-mean-square error (RMSE) value is less than half of the spatial resolution, no additional corrections are required.