esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Benchmarking
### Benchmarking against All Available Algorithms  
Figure 3 shows the running time (in seconds) of four algorithms for computing the bottleneck distance between two persistence diagrams: the Dionysus implementation (in R), the current state-of-the-art Hera (implemented in C++ and wrapped in Python), a new implementation Persim (Python) [20], and Lumawig, implemented both in R and Python. The benchmarking is done by first simulating 100 0-dimensional persistence diagrams with 50 points. Each dimension zero persistence diagram is simulated using a set of positive numbers as death times uniformly chosen from a range twice as wide as the number of points. We pair each diagram with another simulated diagram not necessarily with the same number of points (as much as 80% more or less points), then compute the bottleneck distance (up to 10 decimal places) between the pair using the bottleneck implementations above. The running time of each algorithm is recorded, and the distribution summary of 100 run times for each algorithm is plotted out as a boxplot. For Hera, we follow the experimental setup from [13] and set \(\delta=0.01\). We repeat this process while increasing the number of points in the base persistence diagram by 50 until we reach 500 points for each base persistence diagram.  
We also compare the computed bottleneck distance against the output of Dionysus. Relative differences of the outputs of three other implementations from Dionysus are computed for all 100 pairs of persistence diagrams. From these bottleneck computations, descriptive summaries are obtained and plotted as boxplots in Figure 4. Is it worth noting that Hera consistently overestimates the zero dimensional bottleneck distance relative to Dionysus as seen in Figure 4a. Another important observation is that the output of LumawigPy recovers that of Persim at a much less computational running time. Finally, we highlight that LumawigR recovers the exact output values of the original implementation in Dionysus.  
Figure 3: Boxplots of running times (seconds in log scale) from different algorithms.  
We remark here that while this stage of benchmarking is indeed confined within extremely small data sets, this situation in fact represents what is currently accessible to most researchers, and highlights the clear computational obstruction in the use of persistence diagrams and bottleneck distance in applications with currently available algorithms.
# Effects of Glazing with Preservatives on the Quality Changes of Squid during Frozen Storage
## 3 Results and Discussion
### Protein Degradation and Oxidationanalysis  
#### 3.5.1 Changes in Free Amino Acids (FAAs)  
As is known, squid is rich in proteins and the formation of FAs caused by the degradation of proteins [39]. The FAs of squids in the first, third and sixth month during frozen storage are shown in Table 1. It is worth noting that the most abundant amino acids in squid was proline, followed by alanine and arginine, which had a similar finding in squid by [PERSON] et al. [39]. As storage time went on, the total contents of FAs increased gradually for all samples. This increase was mainly related to the rapid degradation of protein [26]. However, the total contents of FAs of SG-1 DSE, SG-3 DSE and SG-5 DSE groups were 841.17, 780.16 and 899.63 mg/100 g, which lower than that of CK, WG and that of WG-1 DSE groups (2485.43, 1072.28 and 982.23 mg/100 g) after six months of frozen storage. Besides, SG-3 DSE group showed lowest the total contents of FAs than those of SG-1 DSE and SG-5 DSE groups during frozen storage. As an off-taste amino acid, histidine had similar change properties. The content of histidine increased from 7.03 mg/100 g in first to 96.92 mg/100 g in the sixth month in the CK samples, while in the glazing groups, the WG, WG-1 DSE, SG-1 DSE, SG-3 DSE and SG-5 DSE were only 69.88, 48.48, 40.29, 33.52 and 34.01 mg/100 g after six months of storage. These findings suggested that the squid, glazed with sodium polyacrylate and D-sodium erythorbate, could significantly (\(p<0.05\)) inhibit the degradation of proteins, especially in the SG-3 DSE group. D-sodium erythorbate, as a preservative with strong antioxidant activity, might develop its antioxidant effects in fish preservation and thus decrease the degradation of proteins. Similarly, [PERSON] et al. [40] introduced the idea that the combined glazing significantly stabilized the proteins by decreasing in the oxidation of protein in frozen shrimp, which was mainly governed by the antioxidant effect of sodium erythorbate. With the addition of sodium polyacrylate, the glazing effectively maintained the quality of squid during frozen storage, which was due to the formed glazing barrier prevented oxidation reactions from oxygen permeation.  
Figure 4: MDA values of CK, WG, WG-1 DSE, SG-1 DSE, SG-3 DSE and SG-5 DSE groups during frozen storage. Different lower case in different groups from same day indicates significant differences (\(p<0.05\)). Different larger case in different groups from different day indicates significant differences (\(p<0.05\)).
## 4 Discussion
However, because HAPI codes are sparse and they only have 128 non-zero bits, the multiplication stages of the cross-correlation of a single transmit-receive combination need only be performed 128 times for each lag. For a 4.5 cm imaging depth, we require 2400 reconstructed depth samples. At 40 megasamples/second sampling rates and with 256 imaging lines this would require 2.4 GFLOPS to maintain real-time reconstruction rates. This can be performed in real-time using modern computing architectures, some of which are capable of hundreds of TFLOPS. Few current diagnostic systems may support the transmission of such long codes. However, their generation using custom pulser systems would not be difficult using FPGAs or other programmable devices. Their analysis and computation (beamforming, etc.) would not be difficult to implement in real-time using modern GPUs.
###### Abstract  
Three-dimensional reconstruction using monocular camera images is a well-established research topic. While multi-image approaches like Structure from Motion produce sparse point clouds, single-image depth estimation via machine learning promises denser results. However, many models estimate relative depth, and even those providing metric depth often struggle with unseen data due to unfamiliar camera parameters or domain-specific challenges. Accurate metric 3D reconstruction is critical for railway applications, such as ensuring structural gauge clearance from vegetation to meet legal requirements. We propose a novel method to scale 3D point clouds using the track gauge, which typically only varies in very limited values between large areas or countries worldwide (e.g., 1.435 m in Europe). Our approach leverages state-of-the-art image segmentation to detect rails and measure the track gauge from a train driver's perspective. Additionally, we extend our method to estimate a reasonable railway-specific extrinsic camera calibration. Evaluations show that our method reduces the average Chamfer distance to LiDAR point clouds from 1.94 m (benchmark UniDepth) to 0.41 m for image-wise calibration and 0.71 m for average calibration.  
Article  
1 Chair of Electronic Measurement and Diagnostic Technology, Institute of Energy and Automation Technology, Faculty IV-Electrical Engineering and Computer Science, Technische Universitat Berlin, Einstein-Unter 17, 10587 Berlin, Germany 1  
###### Abstract  
Three-dimensional reconstruction using monocular camera images is a well-established research topic. While multi-image approaches like Structure from Motion produce sparse point clouds, single-image depth estimation via machine learning promises denser results. However, many models estimate relative depth, and even those providing metric depth often struggle with unseen data due to unfamiliar camera parameters or domain-specific challenges. Accurate metric 3D reconstruction is critical for railway applications, such as ensuring structural gauge clearance from vegetation to meet legal requirements. We propose a novel method to scale 3D point clouds using the track gauge, which typically only varies in very limited values between large areas or countries worldwide (e.g., 1.435 m in Europe). Our approach leverages state-of-the-art image segmentation to detect rails and measure the track gauge from a train driver's perspective. Additionally, we extend our method to estimate a reasonable railway-specific extrinsic camera calibration. Evaluations show that our method reduces the average Chamfer distance to LiDAR point clouds from 1.94 m (benchmark UniDepth) to 0.41 m for image-wise calibration and 0.71 m for average calibration.  
Keywords:railway; predictive maintenance; driver assistance; monocular metric 3D reconstruction; metric depth estimation; scaling; extrinsic calibration; depth; disparity; image segmentation; LiDAR +
Footnote †: journal: Medical Image and Vision
## 2 Proposed Superpixel-Based Model
### Image Sparsification  
As mentioned in Section 1, commonly used superpixel methods, such as the simple linear iterative clustering (SLIC)-based superpixel generating algorithm [22], have the advantage of adhering accurately to the boundaries of objects or local structures for images with low-level noise. However, the performance of SLIC segmentation will be highly degraded for ultrasound images which contain high-level noise. In order to always obtain robust SLIC segmentation for ultrasound images, we first adopt the median filter to pre-process the input ultrasound images. The purpose of this pre-processing is to reduce the influence of high-frequency noises (not speckles) and improve the robustness of SLIC segmentation. Therefore, we set the median filter to a small size; namely \(3\times 3\).  
After the SLIC superpixel segmentation, we keep only the central points of all the superpixels and the label of each pixel and, for each superpixel, we reassign the value of its center as the median value of all the pixels within the superpixel. Now, we obtain a de-noised sparse distribution to sparsely represent the input image. We will use this sparse distribution, later, to reconstruct the low-frequency component of the given ultrasound image.
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Cell-ID Method  
In this subsection, we describe the Cell-ID method and propose its convergence method with fingerprinting. The conceptual diagram of the positioning system using the Cell-ID technique is shown in Figure 4.  
As shown in Figure 4, when there are two LED APs and one UE, the UE receives signals from both APs. In most cases, the signal received by the UE can be classified as either a strong signal or a weak signal. At this time, Cell-ID-based positioning determines the position of the UE based on the location of the LED AP that offers a stronger signal.  
Figure 4: Conceptual diagram of Cell-ID method.  
Hence, the Cell-ID method is one of the simplest positioning schemes. In the figure, # represents ID of AP.  
However, this method only determines the approximate location of the UE, making it challenging to achieve high precision. Therefore, it can be considered to apply it in combination with other positioning methods. In this study, the initial state of the DDQN agent is determined using the positioning results of the Cell-ID technique. The double deep Q-Network is explained in detail in the following subsections.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 4 Limitations on the Control Gain
### Stroke Saturation  
A significant limiting factor on the maximum feasible gain concerns the actuator saturation. An inertial actuator can saturate in two ways, depending on the frequency of the vibration mode. If the mode frequency is lower than the saturation break frequency of the actuator (\(\omega_{sat,break}=\sqrt{\frac{F_{s,max}}{m_{a}\,d}}\), where \(F_{a,max}\) is the maximum electromagnetic force that the actuator can exert, \(m_{a}\) is the proof-mass and \(d\) is the maximum stroke), then stroke saturation will occur, otherwise current saturation arises, as explained in [21]. The two phenomena are different, thus they require a specific saturation definition.  
Starting from the stroke saturation and assuming the \(i\)-th mode of the primary structure to be excited, the stroke saturation required to control the \(i\)-th vibration mode can be defined as:  
\[S_{i,d}=\frac{|x_{F_{A}}+x_{y_{i}}|}{d} \tag{22}\]  
where \(x_{F_{A}}\) is the vibrating mass displacement due to electromagnetic force, \(x_{y_{i}}\) is the one due to the velocity of the structure and \(d\) is the maximum stroke allowed. Then, Equation (22) can be expressed in terms of current and gain as follows:  
\[S_{i,d} =\frac{1}{d}\left|\frac{B_{a}l_{a}}{-\omega_{i}^{2}m_{a}+j\omega_ {i}c_{a}+k_{a}}\,I_{i}+\frac{j\omega_{i}c_{a}+k_{a}}{j\omega_{i}\,(-\omega_{i }^{2}m_{a}+j\omega_{i}c_{a}+k_{a})}\dot{y}_{i}\right|\] \[=\frac{1}{d}\left|\frac{g_{i}}{j\omega_{i}(-\omega_{i}^{2}m_{a}+ j\omega_{i}c_{a}+k_{a})}+\frac{j\omega_{i}c_{a}+k_{a}}{-\omega_{i}^{2}\,(- \omega_{i}^{2}m_{a}+j\omega_{i}c_{a}+k_{a})}\right|\dot{y}_{i}| \tag{23}\]  
The only term of the band-pass filter still included into the equation is \(g_{i}\), since the compensator transfer function [10] evaluated on the resonance is equal to 1. However, the filtering effect of the compensator introduce the assumption that the acceleration measured by the actuator is just related to the \(i\)-th mode. For this reason, the notation of the acceleration \(|\ddot{y}|\) becomes \(|\ddot{y}_{i}|\). By splitting again the imaginary and the real part of the expression into the absolute value operator:  
\[S_{i,d}=\left|\frac{j(E\,B+G\,A)+E\,A-G\,B}{A^{2}+B^{2}}\right|\dot{y}_{i}| \tag{24}\]  
where:\[A = m_{a}\omega_{i}^{5}-k\omega_{i}^{3}\] \[B = c_{a}\omega_{i}^{4}\] \[D = c_{a}\omega_{i}^{2}\] \[E = k_{a}\omega_{i}\] \[G = g_{i}\omega_{i}^{2}+D \tag{25}\]  
Since the aim is to calculate the maximum gain, we impose \(S_{i,d}=1\) and then we can solve Equation (24) with respect to G (which depends on \(g_{i}\)):  
\[g_{i,stroke}=\frac{\sqrt{\frac{d^{2}\left[\left(m_{a}\omega_{i}^{5}-k_{a} \omega_{i}^{3}\right)^{2}+\left(c_{a}\omega_{i}^{4}\right)^{2}\right]}{max(|g_{ i}(\omega_{i})|)^{2}}-k_{a}^{2}\omega_{i}^{2}}-c_{a}\omega_{i}^{2}}\] \[\omega_{i}^{2} \tag{26}\]
## 3 System and Problem Definition for Scheduling Problem
### Proposed Approach Based on Reinforcement Learning
#### 3.4.2 Upper Confidence Bound Algorithm  
The premise of the UCB algorithm is to model the uncertainty of information gathered through experimentation, allowing for exploitation to occur naturally as uncertainty is reduced. The UCB algorithm records the average rewards received for each action \(\hat{r_{i}}\) alongside the number of times that action was chosen \(C_{i}\). It uses this count of previous rewards to calculate the uncertainty of the recorded average. This uncertainty and the average are combined to give a largest possible estimate for the actual mean of the reward distribution.  
\[\hat{\mu}_{i}=\hat{r}_{i}+\sqrt{\frac{2\log(\frac{1}{\delta})}{C_{i}}} \tag{1}\]  
where \(\delta\) is a confidence value which is usually chosen or search for via parameter optimization. If the value is very small then the result is optimistic of a higher possible mean, while a high value implies less optimism. Possible values for \(\delta\) are explored in experimentation. The algorithm simply chooses the action with the maximum \(\hat{\mu}_{i}\) as it is predicted to be the action with the best average rewards. As actions are repeated, the uncertainty represented by the second part of Equation (1) decreases due to the increased \(C_{i}\). However, the equation relies on \(C_{i}\
eq 0\). Due to this requirement, the algorithm must first attempt each action at least once before estimating the means. Usually, implementations of UCB will spend the first \(k\) rounds, where \(k=|A|\), selecting each action in turn. This gives an initial estimate for the average reward, though with a high uncertainty.  
The final selection rule for UCB is as follows.  
\[a_{t}=\begin{cases}\operatorname*{arg\ max}_{i}\hat{\mu}_{i}\,,&\text{if }t>k \,;\\ t\,,&\text{otherwise}\,.\end{cases} \tag{2}\]  
However, UCB makes various assumptions which do not fit our adapted model, such as that the rewards are in the interval \([0,1]\). The next section describes these assumptions and the required adaptations to be compatible with our HPC system.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 3 Methods
### EEG Features Extraction  
#### 3.2.1 Time Domain  
ERPs are widely used to reveal the brain's response patterns to specific stimuli, with the P300 component being a sensitive indicator of attention allocation due to its amplitude and latency [28]. When participants focus on or anticipate a stimulus, the P300 waveform exhibits significant amplitude changes. To quantify this attention-related brain activity, we selected the peak amplitude of the P300 component as a key EEG feature. For all trials in the focused and distracted groups, a time window of [\(-\)200, 800] ms around stimulus presentation was chosen, and epoch data were averaged across participants to enhance the signal-to-noise ratio (SNR) and clarify ERP components. By averaging ERP waveforms across the two groups, we identified the peak latency of the P300 waveform and calculated the average amplitude within 20 ms before and after this peak, representing the peak amplitude of the P300 waveform for both groups.
## 3 Results
### Results of Gap Acceptance
#### 3.1.2 Gap Acceptance Under Partial eHMI Penetration Condition
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Predictors** & **Estimate** & **Sid. Error** & **z Value** & **Pr (\(\mathbf{\text{<}|\mathbf{\text{z}}|}\))** \\ \hline (Intercept) & \(-0.914\) & \(0.480\) & \(-1.904\) & \(0.057\) \\ eHMILowLow & \(-0.030\) & \(0.271\) & \(-0.111\) & \(0.912\) \\ eHMIMedium & \(-0.737\) & \(0.267\) & \(-2.756\) & \(0.006\) \\ eHMIHighHigh & \(-0.061\) & \(0.269\) & \(-0.227\) & \(0.821\) \\ eHMILowmediummedium & \(0.038\) & \(0.267\) & \(0.141\) & \(0.888\) \\ eHMIMediumhighhigh & \(-0.354\) & \(0.266\) & \(-1.333\) & \(0.183\) \\ Speed36 km/h & \(1.871\) & \(0.167\) & \(11.231\) & \(<\)\(0.001\) \\ eGapSize & \(4.392\) & \(0.404\) & \(10.861\) & \(<\)\(0.001\) \\ eHMILowLow: GapSize & \(-0.663\) & \(0.470\) & \(-1.411\) & \(0.158\) \\ eHMIMediumLow: GapSize & \(-1.320\) & \(0.437\) & \(-3.019\) & \(0.003\) \\ eHMIHigh: GapSize & \(-0.790\) & \(0.462\) & \(-1.709\) & \(0.087\) \\ eHMILowmediummedium & \(-0.911\) & \(0.456\) & \(-2.001\) & \(0.045\) \\ eHMIMediumhigh: GapSize & \(-1.129\) & \(0.445\) & \(-2.538\) & \(0.011\) \\ Speed36 km/h: GapSize & \(0.202\) & \(0.242\) & \(0.835\) & \(0.404\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GLMM results for gap acceptance of AVs with eHMI under partial eHMI penetration conditions. Figure 6: The predicted outcome of gap acceptance in probability and logit space for vehicles with eHMI under partial eHMI penetration condition. Figure 7: The predicted outcome of gap acceptance in probability and logit space for vehicles without eHMI under partial eHMI penetration condition.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material Constant**} & \multicolumn{6}{c}{**Natural Frequency Reduction**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline \(E_{1}\) & \(-10.29\%\) & \(-6.35\%\) & \(-1.64\%\) & \(-2.79\%\) & \(-9.18\%\) \\ \hline \(E_{2}\) & \(-0.05\%\) & \(-0.14\%\) & \(-5.49\%\) & \(-1.49\%\) & \(-6.98\%\) \\ \hline \(G_{12}\) & \(-0.30\%\) & \(-3.81\%\) & \(-3.09\%\) & \(-1.28\%\) & \(-1.77\%\) \\ \hline \(G_{23}\) & \(0.00\%\) & \(-0.08\%\) & \(-0.12\%\) & \(-0.27\%\) & \(0.00\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Percentage changes in natural frequency induced by reductions in the material constants of a graphite/epoxy plate (\(a/h=20\)). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Current Material Constant** & \(E_{1}\) & \(G_{12}\) & \(E_{2}\) \\ \hline Actual & 89.6 & 3.58 & 8.8 \\ \hline Identified & 89.52 & 3.57 & 8.81 \\ \hline \({}^{\star}\) error (\%) & 0.09 & 0.53 & 0.07 \\ \hline \({}^{\star}\) error \% = (\(\mid\) Actual \(-\) Identified \(\mid\)/ Actual) \(\times\) 100\%. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Current material constants of graphite/epoxy [0\({}^{\circ}\)]\({}_{8}\) cantilever plate (\(a/h=120\)). the actual ones. It is noted that the present material constant identification procedure can produce acceptable results for the relatively thick plate, with a maximum error of less than 4.5%. The above examples have shown that the information of frequency sensitivity analysis can greatly reduce the number of material constants required to be used in the minimization process so that only a few iterations, usually fewer than 10, are needed to identify any material constant. The attainment of good estimates of material constants in an efficient and effective way is the merit of the proposed material constant identification procedure. It is also worth pointing out that the proposed procedure can be applied to identify the material constants of laminated composite plates with other boundary conditions.
## 3 System Model and Framework
### Wireless Links Constraints  
In the framework of the Internet of Things, the topmost layer which is composed of base stations provides the connectivity of the all the BS using wired links. Hence, the bandwidth of the BS layer is greater than that of all the nodes in the lower layers. The relay nodes transfer the data to the CHs so constraints on the wireless links of RN are given by Equation (10):  
\[r_{uv}\cdot S_{uv}\leq S_{uv}\ \ \forall u,v\in RN\bigcup NN \tag{10}\]  
The constraints on the wireless links of CCO, CH and BS are shown in Equation (10); they have communication with lower and upper layers so both the tasks are considered in this constraint:  
\[r_{xy}\cdot S_{xy}+r_{yx}\cdot S_{yx}\leq S_{max} \tag{11}\]  
where \(\ \forall x\in RN\,\ \forall y\in CH\ \ \forall x\in CH\,\ \forall y\in CCO\ \ \forall x\in CCO\,\ \forall y\in BS\.\)  
Nodes nearest to the BS deplete their energy faster than other nodes as they transmit the data to BS on the behalf of other nodes. Thiscan partition the network. To avoid this problem, we propose a hierarchical IoT framework so that the load should be balanced on the nodes (RN, CH and CCO). Each RN transmits the data of the local nodes which have a minimum distance from them, CHs and CCOshave the load of the nodes of one cluster only. Thus load balancing on their wireless links support an energy efficient IoT.