esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Closest Resemblance Algorithm
### Phase 1: Learning Phase
#### 3.1.1 Reference Intervals Based on Mean and Standard Deviation  
To classify unlabeled data, the _CR_ classifier determines the prototype of the classes by determining the reference interval of each feature based on the mean and standard deviation from the training set. The mean (\(\mu\)) represents the \"center\" of the data distribution for a specific feature within a class. The standard deviation (\(\sigma\)) captures the spread of the data around the mean. They provide basic summaries of the data's central tendency (\(\mu\)) and spread (standard deviation \(\sigma\)) without assuming a specific underlying distribution. This technique accounts for the spread of feature values within each class, creating intervals that extend \(t>0\) standard deviations from the mean. It adapts to the variability within each class.  
For each class \(C^{h}\) and for each feature \(f_{j}\), the mean \(\mu\) and the standard deviation \(\sigma\) are calculated from the training data \(X\), as presented in Equation (2).  
\[I_{j}(f_{j},C^{h})=[\mu(f_{j},C^{h})-t*\sigma(f_{j},C^{h}),\mu(f_{j},C^{h})+t* \sigma(f_{j},C^{h})] \tag{2}\]  
By defining the interval as in Equation (2), we capture a range that encompasses a certain portion, determined by \(t\), of the data points \(X=\{x_{1},x_{2},\ldots,x_{L}\}\) in the class \(C^{h}\) for the feature \(f_{j}\). Varying \(t\) allows for control over the strictness of the interval. A higher \(t\) captures a wider range, a lower \(t\) captures a tighter range. This approach utilizes the mean and standard deviation to build the prototype of classes from the training set by leveraging [PERSON]'s theorem; we establish boundaries around the mean based on a chosen number \(t\) of standard deviations. Data points falling within these boundaries are considered typical, while those exceeding the boundaries are classified as outliers of the corresponding class. This  
Figure 2: Reference intervals based on percentiles and mean and standard deviation.  
method is particularly advantageous when the underlying data distribution is unknown, as [PERSON]'s theorem makes minimal assumptions about the data.  
**[PERSON]'s theorem:** For any data distribution, at least a proportion equal to \((1-(\frac{t}{2})^{2})\) of the data points will fall within \(t\) standard deviations of the mean, where \(t\) is any positive constant.  
[PERSON]'s theorem provides a lower bound to the proportion of measurements that are within a certain number of standard deviations from the mean.
## 5 Optimization of Group-Hole Grouting Parameters
### Reduce the Spacing of the Grouting Holes  
The group-hole grouting model involves arranging 12 grouting holes equidistantly along the contour line of the working face. Among these holes, three near the lower part are considered first sequence holes, five on the upper part are second sequence holes, and each of the left and right gangs has two holes as third sequence holes. The optimization model for the grouting interval is depicted in Figure 16, and the simulation calculation results are presented in Figure 17.  
By comparing Figures 11 and 17, it can be observed that the final shape of the grouting curtain body resembles that of a round table. Additionally, the surface of the grouting curtain body is smoother, and its overall integrity is significantly improved compared to the prior form. Once the first hole grouting sequence is completed, the slurry can fully diffuse near the bottom plate due to the narrow spacing between the grouting holes. It can also diffuse into the two gangs at the grouting surface. This results in the formation of a complete and thick grouted stone body in the bottom plate as the slurry comes into  
Figure 15: The grout diffusion pattern changes after increasing the grouting rate. (**a**) First sequence hole grouting; (**b**) Second sequence hole grouting; (**c**) Third sequence hole grouting.  
contact with each other at the bottom of the grouting holes. Once the second hole grouting sequence is completed, the slurry will be evenly distributed throughout all parts, resulting in the formation of a dense interior with little porosity, creating an optimal stone structure. Nevertheless, there was a lack of contact between the slurry at the bottom of the hole grouting sequence and both the bottom plate and the top plate. Therefore, it is necessary to adhere to the third hole grouting sequence in order to reintroduce this reinforcement by injecting it again. After the third hole operations were finished, a roughly spherical mass of slurry stones formed at the working face. The slurry expands laterally, with the horizontal gap between the two clusters steadily increasing as the distance between the section and the working face increases. The ultimate shape of the slurry clusters shows a broader width on both sides. The hollow region has significantly decreased in size after optimization, while the curtain body can still maintain its complete and thick spherical shape at the bottom of the grouting holes. The improvement of the final curtain body's quality can be achieved by effectively reducing the gap between injection holes.
# An Experimental Study of the Performance of a Crossed Rib Diffuser in Room Acoustic Control
## 3 The Experiment Using a Small Meeting Room
### Comparisons of Room Acoustic Parameters  
#### 3.2.1 Reverberation Time \(T_{20}\)  
Figure 10 presents a comparison of spatially averaged \(T_{20}\)s measured in each case. By installation of acoustic diffusers, cases 1-6 show shorter \(T_{20}\) than case 0 at all frequency bands. In particular, cases 3 and 6 using highly absorptive CRD-A reduced \(T_{20}\) greatly. Comparing cases 3 and 6, the reverberation time in case 6 decreased as the frequency increased; and a dip is apparent at 1 kHz in case 3. This dip occurred because the diffusivity of sound field at high frequencies lowered in case 3, where the highly absorptive surface was concentrated on one surface of the room. On the other hand, case 2 showed a \(T_{20}\) shorter than in case 1 at frequencies higher than 1 kHz. Case 5 showed a \(T_{20}\) shorter than in case 4 at frequencies higher than 500 Hz. Additionally, the \(T_{20}\) of case 2 was shorter than that of case 4 above 1 kHz. These results clarified that CRD with smaller installation area can reduce the reverberation time in a wide bandwidth more than R-1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Case** & **Location** & **Material** \\ \hline
0 & – & – \\
1 & Surface 1 & R-1 \\
2 & Surface 1 & CRD \\
3 & Surface 1 & CRD-A \\
4 & Surface 1 + Surface 2 & R-1 \\
5 & Surface 1 + Surface 2 & CRD \\
6 & Surface 1 + Surface 2 & CRD-A \\ \hline \end{tabular}
\end{table}
Table 1: Room conditions for impulse response measurements.  
Figure 9: Status of measurements for case 2 (**Upper**) and case 5 (**Lower**).
# A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond
## 2 Materials and Methods
### Proposed Mechanism
#### 2.2.2 The NUIR Sub-Process  
The NUIR sub-process (Algorithm 2) runs in parallel with the main mechanism without halting it. Here, at each agent request submission, the \(NewUserRequestInterrupt\) interrupt is generated that triggers this routine to split the request into multiple meal requests and add them to the list of unaddressed agent requests (lines 4 to 7). Additionally, when any involved agent rejects or does not accept a match in \(T_{w}\) minutes of match generation by, the interrupt \(MappingRejectionInterrupt\) is generated that triggers this routine to add all of the involved agent requests of this rejected match to the aforementioned list (lines 8 to 10) for being considered in the next iteration of the mechanism. ```
1 global\(\mathbb{C}\)// Agent requests available to parallel processes
2 for\(i\gets 1\)to\(n\)do// Handle all requests
3 ifInterruptthen// Check for interrupts from the app
4 ifInterrupt\(=\)NewUserRequestInterruptthen// New request
5\(\mathbb{A}\gets Interrupt.GetRequest\)// Get interrupt request details
6 if\(\mathbb{A}.RequestType=\mathbb{D}.UserType\cup\mathbb{A}.Amount\geq 2\times T_{m}\)then
7\(\mathbb{C}.AddRequest(\mathbb{A}.SplitIntoMeals(T_{m}))\)// Accept request in meals
8 ifInterrupt\(=\)MappingRejectionInterruptthen// Mapping rejected
9 foreach\(\mathbb{A}\in Interrupt.GetRequest\)do// Each interrupt request
10\(\mathbb{C}.AddRequest(\mathbb{A})\)// Add request to list
11
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22.2.3 The CTFU Sub-Process

The CTFU sub-process (Algorithm 3) classifies the agent requests into either volunteers, or perishable/non-perishable categories for donors/receivers (lines 2 to 10) to facilitate perishable food matching before the non-perishable ones. #### 2.2.4 The CA-DTB Sub-Process

The algorithm for the CA-DTB sub-process is as follows. The CA-DTB sub-process (Algorithm 4) gathers current agent requests (lines 2 and 3), and assigns volunteers, if available, to each donor \(\mathbb{D}_{i}\) (line 5), maximizing the surplus transportation distance (lines 10 and 20). Next, agent preferences are updated by extraction and augmentation as shown in Table 3 (line 23 to 25). After this, each receiver \(\mathbb{R}_{i}\) is assigned to the eligible donor who has the highest preference for \(\mathbb{R}_{i}\) (lines 26 to 31), and the match is stored in \(\mathbb{M}\). Receivers accept donations such that:

\[\sum_{j=1}^{m}\mathbb{D}_{j}.Amount-\epsilon=\mathbb{R}_{i}.Amount;\epsilon \to 0. \tag{1}\]

This step is repeated until one side of the market is cleared. Ties are broken first with food donation start/requirement end timings (duplicates allowed), and next with request arrival timings (total order maintaining sequences without duplicates). All matched requests have to be accepted by the involved agents in a time window of \(T_{w}\) minutes, beyond which the match is automatically rejected and the requests involved are automatically lined up for the next round of matching.
## 3 Monte Carlo Simulation  
Monte Carlo simulations (MCSs) can be used to evaluate measurement uncertainty and method robustness in several applications [28; 29; 30; 31; 32; 33]; the approach with MCS is also suitable for the uncertainty estimation in DOP measurements as image analysis-based methods [31; 32; 33] may not be expressed in an analytical form that is suitable for error analysis by the propagation error law.  
As in [22], a first series of MCSs (\(10^{4}\) cycles) were carried out to evaluate the standard deviation \(\sigma\) (SD) of the SNR threshold (\(th_{SNR}\)), through 2.5 and 97.5 percentiles. The following uniform distributions were assigned to the variables influencing \(th_{SNR}\): ROI width \(l\) and ROI shift \(s\) (\(s\) is evaluated with respect to the ROI initial position), both expressed as number of pixels (Table 3a,b). The aforementioned distributions were used, in an analogous way, in order to retrieve the GSMF coefficients contained in the logistic function \(b(z)\). Afterwards, a second series of MCSs (\(10^{5}\) cycles) was carried out for the AdSTM to evaluate DOP distribution histogram for each probe, according to the corresponding \(th_{SNR}\) distribution reported in Table 3a,b, together with the previously-assigned distributions \(l\) (mean value: 30 px, SD: 1 px) and \(s\), (mean value: 0 px, SD: 2 px). A similar procedure was applied for the TTM where, in addition to \(l\) and \(s\) distributions, the previously-retrieved GSMF coefficients distributions for DOP assessment were used. The lower number of iterations in the first series was due to the high computational cost of the non-linear curve fitting.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Settings**} \\ \cline{2-3}  & **Set 1** & \multicolumn{1}{c}{**Set 2**} \\ \hline Nominal frequency (MHz) & L: 5-10 & C: 2-4 & V: 2-3 \\ \hline Dynamic range (dB) & Maximum & \multicolumn{1}{c}{Medium} \\ \hline Field of view (mm) & L: 70 (74\({}^{1}\)) & C: 180 (184\({}^{1}\)) & V: 180 (191\({}^{1}\)) \\ \hline Focus depth (mm) & L: 20–40 & C: 80–120 & V: 80–100 \\ \hline Overall gain (dB) & \multicolumn{1}{c}{Medium} \\ \hline STC (dB) & Slide switches aligned in central position\({}^{2}\) \\ \hline Transmitted power & Maximum & \multicolumn{1}{c}{Medium/Maximum} \\ \hline Zoom & \multicolumn{1}{c}{No} \\ \hline Post-processing & \multicolumn{1}{c}{Linear} \\ \hline Image format & DICOM \\ \hline \hline \end{tabular}  
\(\mathrm{L}=\mathrm{linear}\) array probe; \(\mathrm{C}=\mathrm{convex}\) array probe, \(\mathrm{V}=\mathrm{vector}\)/phased array probe. \({}^{1}\) The field of view (FOV) values are referred to the D diagnostic system only. \({}^{2}\) For C probe in set 1 configuration for the D diagnostic system only, STC slide switches have been aligned in right position corresponding to the highest gain level. The US systems in the tests are the following: HS 60 (Samsung), Affiniti 70 (Philips), Acuson S2000 (Siemens), and MyLab X8 (Esoote).  
\end{table}
Table 2: Ultrasound system settings.
# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 1 Introduction
We also develop several traffic analytical query processing algorithms that utilize the timeline modeling and TQ-index. Third, we suggest an intuitive timeline visualization method that displays congestion events of road traffic ordered by time along with the affected congestion lists and other useful analytical information, as depicted in Figure 1. Finally, we present the performance evaluation of the QET system using real traffic data such as a Busan ITS dataset [17] and a Seattle freeway traffic dataset [18]. Our QET system can handle different traffic data formats by converting raw traffic data and road network information into the timeline data model. The effectiveness of the TQ-index is measured by evaluating the query processing time and memory consumption. This paper is an extended version of our previous publications [19; 20; 21]. The new contributions in this paper are as follows: First, we provide a complete formal timeline model for traffic congestion. Second, we present comprehensive algorithms for constructing the TQ-index and processing analytical traffic queries. In addition, we discuss the proofs of theorems to validate the correctness of the algorithms. Finally, we performed experiments to demonstrate the efficiency of the QET system. We conducted an extensive performance evaluation with real traffic data sets from Busan, Korea, and Seattle, WA, USA. The remainder of this paper is organized as follows. Section 2 provides an overview of related work. We explain our background and motivation in Section 3. Then, we describe our proposed system and timeline modeling in Section 4. In Section 5, we present the detailed steps for constructing the TQ-index and explain how to utilize the TQ-index for processing analytical traffic queries. Section 6 discusses the demonstration and performance evaluation of our proposed system. We conclude our work in Section 7.
## 4 Evaluation
### Concurrency
#### 4.1.4 Performance  
The performance metrics in this experiment are the number of frames/buffers processed by each block in the pipeline, so the higher the number in _Pulled_ (number of frames successfully acquired by a _Camera_ block), _Detected_ (number of frames processed by an _Object_ or _Motion_ detection block), and _Saved_ (number of images written by the respective _ImageWriter_ blocks) columns in Table 2, the better the performance of the pipeline. Conversely, the target values for the _Lost_ (number of frames missed by _Camera_ blocks due to system overload) and _Queued_ (number of frames processed by _Object/Motion_ detection blocks but still pending to be saved by the _ImageWriter_) columns are 0 for optimal performance. It should also be noted that the values in different columns are not directly comparable in isolation. The pipeline configuration that processed less of the captured frames but saves more of the processed results can be argued to perform better than the one processing everything but saving little of the results. We provide a complex analysis of the measured metrics in the remaining sections.  
It is apparent from Table 2 that all stereo multiprocessing configurations are not capable of processing all camera frames in the throttled scenario, with a clear trend of higher performance for higher throughput serialisation. Nevertheless, the hybrid approach does succeed in this task for single camera video. The threading only configuration shows the highest performance as it does not need to incur any additional performance overhead due to serialisation, with the maximum CPU resources spent processing and saving the images (this does not mean that such a configuration is optimal in a general case). Image writing is largely limited by the maximum disk write speed and the remaining CPU resources available after object/motion detection. Some configurations (e.g., REIP Multiprocessing) are less efficient in data management and result in less of the results being saved (compared to REIP Hybrid) despite high processing rates.  
It should be noted that it is only in this specific data acquisition and processing pipeline (Figure 4a), where both cameras are operating at the same frame rate (14.4 fps) and object/motion detection blocks are highly optimised (object detection is performed on GPU), that we do not observe data loss for camera blocks due to the Python GIL in the threading only configuration. The data loss would be inevitable when using data sources with significantly different sampling rates. For instance, the microphone block requires being serviced more often than the camera block and will experience overrun errors with data loss due to the Python GIL if there is another block (in the same task/process) performing heavy computation that does not release the GIL in a timely manner. Thismakes the Task feature of the REIP SDK essential for easy decoupling of data source blocks from data processing blocks by placing them into a dedicated task/process with an independent Python interpreter.
## 2 Materials and Methods
### Design of Blade
#### 2.3.1 Design of Tooth Profile of Blade  
The design of the tooth profile of the blade has a direct impact on the film removal effect, which is based on the structural parameters and arrangement of the arc-shaped teeth. As shown in Figure 3, the design of the tooth profile of the blade includes the determination of three parameters \(a\), \(b\) and \(c\). The tooth profile of the blade is designed as an isosceles trapezoid. The arc-shaped teeth are installed in a staggered arrangement, and the distance between the adjacent arc-shaped teeth is 60 mm. For \(c\), \(c\) is the distance between adjacent teeth. According to the working principle of film removal, the arc-shaped tooth needs to pass through the adjacent teeth of the blade. It is necessary to ensure the distance between the adjacent arc-shaped teeth such as the distance between adjacent teeth. Therefore, \(c\) should be 60 mm. For \(b\), \(b\) is the tooth height of the blade. In the process of film removal, the tooth height of the blade should ensure that the blade can reach the area where the residual film is located. As in the pretest of the film picking device, the research group found that the residual film was mainly concentrated approximately 80 mm from the arc-shaped tooth tip in the vertical direction, and the width of the arc-shaped tooth at this position was 25 mm. Therefore, \(b\geq 80\) mm and when \(b\) is 120 mm, the requirement of film removal can be met. For \(a\), \(a\) is the maximum distance between the teeth of adjacent blades. When the value of \(a\) is too large, the arc-shaped tooth passes through the blade and cannot play the role of film removal, so \(a\leq 25\) mm. If the value of \(a\) is too small, the blade deforms greatly, and the force on the residual film on the arc-shaped tooth is large during the operation, which is conducive to film removal, but easily causes residual film entrainment on the film removal blades. Therefore, to sum up, \(a=20\) mm is suitable for the design.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### Benchmarking and Comparison against State-of-the-Art
#### 5.2.2 Heart Rate Variability Estimation  
The task of assessing HRV is much more noise-sensitive than estimating heart rate. In Table 3, the results of heart rate variability estimation are listed for all datasets. Since HRV is a relatively long-term measure, these HRV metrics are computed over complete video lengths. Our unsupervised method sets the first HRV evaluation benchmarks on most of the datasets, and outperforms all previous methods on PURE and UBFC-RPPG datasets, including deep learning based fully supervised ones (PulseGAN [35]).  
Based on the HRV literature [10] and considering that the average human heart rate variability is in the range of 19-75 ms RMSSD, error rates close to or less than \(\sim\)30 ms RMSSD can be considered acceptably accurate for distinguishing between broad HRV level groups. Our method shows good performance on datasets that are known to have low video compression and relatively less movement (PURE, UBFC-RPPG, VicarPPG, StableSet). Reasonable performance is also obtained on some datasets containing movement (MoLi-PPG and EatingSet); while good results are obtained on VicarPPG 2 in spite of subject movement. However, accuracy is very poor on the remaining datasets that either contain high compression noise (MAHNOB-HCI, COHFACE), or exhibit large/fast movements (VIPL-HR, ECG-Fitness).
## 3 Principles of Operation
### Fuel Cells
#### 3.3.2 Activation Loss  
The major polarization is the activation loss, which is pronounced in the low and medium current density regions. In these regions, electronic barriers must be overcome before the advent of current and ionic flow. The activation loss is directly proportional to the current density, which can be expressed as follows:  
\[\eta^{act}=\frac{R\cdot T}{\alpha\cdot n\cdot F}\cdot\ln\left(\frac{i}{i0}\right) \tag{21}\]  
where \(R\) is the universal gas constant, \(T\) is the temperature, \(\alpha\) is the charge transfer coefficient, \(n\) is the number of electrons involved, \(F\) is the Faraday constant, \(i\) is the current density and \(i^{0}\) is the exchange current density. The activation loss is due to the sluggish electrochemical reactions at the electrode surface, at which the fuel is oxidized and the oxidant is reduced. The activation barrier must be overcome by the reaction species in successful fuel cell reactions.