esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Equipment and Methods of Capturing and Preparing Data  
### Motion Capture System  
We chose the method of studying the dynamics of changes in the breathing pattern based on the motion capture system with subsequent signal processing as a part of the classification task [19; 20; 21].  
The choice of motion capture system is important because one of our chief goals was wide accessibility of the products based on our results. There are various types and classes of motion capture systems, which differ in price, environment configuration, and complexity of use [22]. Based on a comparative analysis, the VIVE Tracker 3.0 hybrid inertial-optical motion capture system was chosen due to its price, accuracy, and low space requirements, which makes it possible to use it in small apartments. The configuration consists of the following elements:  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Study** & **Pros** & **Cons** \\ \hline [PERSON] et al. [10] & + High accuracy (\(>\)80\% AUC) + The convenience of data collection—Web and Android applications for collecting data using a microphone. & - Analysis of general respiratory metrics, whose values can be collected using already established devices with higher accuracy—spironetry, etc. - The need to assemble the installation around the patient. \\ \hline [PERSON] et al. [18] & + Higher accuracy compared to state-of-the-art models (approximately 95\% accuracy). + The system consists of only one speaker. & - Analysis of general respiratory metrics, whose values can be collected using already established devices with higher accuracy—spironetry, etc. - The need to assemble the installation around the patient. \\ \hline [PERSON] et al. [16] & + Higher accuracy compared to state-of-the-art models (approximately 95\% accuracy). + The system consists of only one speaker. & - It determines only one type of breathing—diaphragatic. \\ \hline [PERSON] et al. [17] & + Cheapness of operation—only an Android-based phone is required. + An average error in determining the type of breathing is 8\%. & - Determines only one type of breathing. - A small sample size—17 students aged 23–24 years. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of breath analysis methods.  
* Three VIVE Tracker markers (3.0) (the minimum number required to track the movements of the chest and abdomen);
* Three waist elastic belts with fasteners for markers;
* Two SteamVR Base Stations 2.0.  
The system gives each marker six output values. Three of the six indicators reflect their positioning in three-dimensional space \((x,y,z)\) relative to the internal local space; the remaining three are the rotation angles relative to the same axes. Access to these markers is implemented through the API for Unity and Unreal Engine.
# A Reinforcement Learning Approach to Speech Coding
## Appendix A DPCM and Tree Coding
### DPCM  
The most common example of predictive coding for speech is classical differential pulse code modulation as shown in Figure 11.  
Knowing that speech samples are highly correlated during Voiced speech segments, it is intuitive to use recent past speech samples to predict the next sample, thus reducing the dynamic range of the signal to be quantized and coded; however, the input speech samples are not available at the Decoder so past reconstructed samples must be used to form the predictor, as shown in Figure 11[32; 37]. The predicted value is subtracted from the input sample and the prediction error is passed through the scalar quantizer. Binary codes are assigned to the several quantizer levels and these codewords are sent to the Decoder. A characteristic of DPCM is that the Encoder includes a \"copy\" of the Decoder, so the Encoder and Decoder operate in concert unless there are bit errors during transmission over a channel. The predictor and quantizer are usually adaptive in some fashion, and there are extensive treatments in the literature of many possible designs [32; 37; 38], including using least squares lattice algorithms for the short term predictor [27].
## 2 Sensing and Actuation Principle
### Electrode Configuration  
The sensitive structure is the key hardware in an IS system, which consists of an EH and a TM. The EH is a closed cavity, the TM is placed in the center of the EH to form pairs of differential capacitors. In order to simultaneously measure and control the TM with multiple degrees of freedom, the differential capacitor pairs must have a reasonable electrode division and configuration. In a traditional division method [37], sets of differential capacitors are arranged along the three orthogonal axes, and each axis controls one translational freedom degree and one rotational freedom degree, as shown in Figure 3a. Therefore, six degrees of freedom control is achieved. Referring to the accelerometer of the GOCE satellite [38], the electrodes around the TM under study in this paper are separated, as shown in Figure 3b. Unlike the traditional division, there are three, one, and two electrode pairs along the axes-X, Y, and Z, controlling the corresponding number of degrees of freedom respectively. Since the X direction of the TM is the suspension direction of the ground high-voltage levitation experiment, the instability of the high-voltage signal will lead to a relatively low measurement resolution, so X is defined as the non-sensitive axis, while the other two axes are defined as the sensitive axis.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 4 Experimental Results
### Results  
According to the actual conditions of the experimental environment, the improved monocular imaging method was used to accurately perceive the tower's profile after HSV processing. The Axial eccentric distance of the tower was obtained after 11 times of image capture and real-time analysis and calculation, as shown in Table 1.  
Figure 11: Monocular visual image preprocessing: (**a**) Grayscale of image; (**b**) Contour extraction and denoising.  
The results from Table 1 indicate that the distance perception model combined with monocular imaging technology exhibits some errors in the distance calculation of fixed objects. The error range is approximately \(\pm\)0.2 m within the perception range of around 15 m. Through the improved monocular visual monitoring method presented in this paper, the perception accuracy of the tower tilt is improved by 7.5%, and the eccentric axial distance, a critical indicator of the stability and attitude prediction of the tower, is accurate to \(\pm\)2 mm\({}^{\prime}\).
## 4 Experiments
### Validation on Actual Collected Data  
Finally, we tested and validated the proposed method using actual campus-collected data, as shown in Figure 11, where each set of images is, from left to right, the corresponding RGB image collected by the ZED camera, the original point cloud, and the result after motion segmentation. The red box is the corresponding motion object on the RGB image, on the original point cloud, and on the motion segmentation result, and the blue box is the corresponding stationary object. It can be seen that in the campus road scene, our method can accurately predict the moving objects, and at the same time, the stationary objects in the scene are filtered out, which is convenient for subsequent motion vector estimation, and this result proves the feasibility of our method in motion segmentation.  
Figure 10: Comparison of the source frame with the reference frame under the predicted motion vector.  
Figure 9: The predication of the motion vector.  
Overall, the method proposed in this paper is able to quickly extract a motion object and estimate its motion vectors. Compared with typical motion segmentation methods, the proposed method can maintain high efficiency while keeping enough accuracy to provide accurate results for subsequent motion vector estimation. The estimation of motion object vectors through two stages has a faster speed than the direct estimation of the scene flow and also guarantees sufficient accuracy.
## 3 Methodology and Experiments  
In this study, an EEG cap of 32 standard Ag/AgCl electrodes from BrainProduct (Munich, Germany) was utilized to record EEG data at 5 kHz sampling rate, and the electrodes were positioned according to the extended international 10-20 system with FCz as a reference electrode. Additionally, an electrode was attached beneath the left eye of the subject for electrooculography (EOG) recording. A BrainAmp MR-plus EEG Amplifier (BrainProducts, Munich, Germany) was placed behind a Philips Achieva 3T MR scanner (Best, The Netherlands) at the Sir Peter Mansfield Magnetic Imaging Centre (SPMIC), University of Nottingham, for acquiring the EEG data while the MR scanner was running.  
Figure 1: Mold for making the spherical phantom.  
Figure 3: Spherical agar phantom (**A**), and human head-shaped agar phantom (**B**).  
Figure 2: Mold for making the head-shaped phantom.  
and the data were finally recorded into a personal computer by Vision Recorder (Version 1.10) software (Figure 4).  
The EEG and MR scanner clocks were synchronized throughout the experiments to guarantee a reliable sampling of the waveforms [7; 21]. An 8-channel radio frequency (RF) head coil was used to acquire the fMRI data. As shown in Figure 5A, the amplifier was placed outside the MR bore to isolate it from the vibrations of the MR scanner. Similarly, the cables were run along the cantilever beam and then were coupled to the EEG cap (Figure 5B). Suspending the cable above the mounting of the scanner bed also minimized the scanner's vibrations [8].
## 3 Phytocannabinoids
### Phytocannabinoids--THC  
THC is a psychoactive cannabinoid found in _Camubis sativa_ subsp. _india_ leaves and flowering plants, and its concentration in these tissues is 0.1-1.6% [35]. (\(-\))-trans-\(\Delta\)9-THC and (\(-\))-trans-\(\Delta\)8-THC are the most well-known and studied phytocannabinoids. The unsaturated bond in the cyclohexene ring is located between C-9 and C-10 in the more common dibenzopyran ring numbering system. In (\(-\))-trans-\(\Delta\)8-THC, the double bond is located between C8 and C9. THC is not soluble in water, and as a lipophilic substance it is distributed to the adipose tissue, liver, lung, spleen, and nervous system. THC exerts its effect by binding to CB1 receptors localized mainly in the nervous system and CB2 receptors found on immune cells [36]. These receptors are coupled with Gi proteins, lowering intracellular cAMP concentration via inhibition of adenylate cyclase. THC is metabolized into 11-hydroxy-\(\Delta\)9-tetrahydroocannabinol (11-OH-THC) and then into its primary inactive metabolite, 11-nor-9-carboxy-\(\Delta\)9-tetrahydroocannabinol (THC-COOH), which is glucuronidated. Then, 65% of THC is excreted with the feces and 20% with urine as hydroxylated and carboxylated metabolites [37]. THC's clinical properties include anti-emetic effects, appetite stimulation, anticonvulsant effects, and analgesia [35]. It is worth noting that this is the only cannabinoid exhibiting psychoactive properties.
## 3 Results and Discussion  
### Crystallinity  
The crystallinity of the prepared Zn-Fe LDH was analyzed using XRD, as shown in Figure 2. It is evident that Zn-Fe LDH exhibited 20 peaks appearing at 31.91\({}^{\circ}\), 34.51\({}^{\circ}\), 36.41\({}^{\circ}\), 47.77\({}^{\circ}\), 56.74\({}^{\circ}\), 62.9\({}^{\circ}\), 67.97\({}^{\circ}\), and 69.17\({}^{\circ}\), corresponding to the basal reflections (inter-planner spaces) of (003), (006), (009), (012), (015), (118), (110), and (113), respectively. The largest peak corresponding to (009) demonstrated that the prepared compound was a Zn-Fe LDH with high crystallinity, as previously reported [20,21].  
The peaks appearing at the lower angles (20) of 31.91\({}^{\circ}\), 34.51\({}^{\circ}\), and 36.41\({}^{\circ}\) were ascribed to maximum inter-planner spacing, representing the respective planes of (003), (006), and (009) [21]. The intensity of the peaks also varied due to the position of the atoms attached inside the LDH unit cell, with body-centered or face-centered arrangement [22]. In the XRD pattern, there was one large peak at 36.41\({}^{\circ}\), which was due to constructive interference  
Figure 1: Schematic view of the photocatalytic reactor setup and experimentation of the photocatalytic conversion of rice husk extract.  
Figure 2: X-Ray diffraction (XRD) pattern of Zn–Fe LDH.  
indicating that the atoms attached within the crystallite were in a body-centered position. Similarly, there were two mediums and five small peaks at 31.91\({}^{\circ}\), 34.51\({}^{\circ}\), 47.77\({}^{\circ}\), 56.74\({}^{\circ}\), 62.9\({}^{\circ}\), 67.97\({}^{\circ}\), and 69.17\({}^{\circ}\) because of destructive interference due to the body-centered arrangement of atoms [23]. For each peak, the size of the crystallite varied corresponding to different widths of the peaks, because the synthesized Zn-Fe LDH was not completely homogenized. A big crystallite showed all peaks diffracted at a single angle, but a small crystallite exhibited a mild variation of the angle from a single plane [22].
## 3 Long-Term Implications: The Question of Urban Development and Sustainability
### Cost Implications  
Further application of strategies such as intelligent transportation spaces, automated transit gateways and AVs may result in higher savings. For example, 4000 USD/year/AV from social benefits of parking, fuel efficiency, travel time and crash aversion as [PERSON] and [PERSON] [11] reported. [PERSON] et al. [71] found that the per-passenger mile cost of AVs is approximately $0.2 in the United States, provided that large-scale ride-sharing can be implemented. These results were considerably lower than the Netherlands-based findings of around $0.1 by [PERSON] et al. [72] for ride-sharing services using AV passenger cars. However, their study relied on average values and less detailed vehicle data, disregarding some stages of the vehicle life-cycle as well as the overhead cost.  
[PERSON] et al. [73] conducted a detailed study on the cost implications of the vehicle fleet automation for the Swedish traffic. They found that the life-cycle costs of electric-powered AVs for the different vehicle types (shared/taxi vehicles, bus and rails) were lower than the respective types of conventional vehicles (CVs). This was mainly due to the high purchase costs being offset by cost savings in the low fuel, maintenance and insurance costs. In general, these operational level costs (per passenger-kilometre) showed that the AV-based bus, rail and shared taxi cars were 54.7%, 6.4% and 82% lower than their CV-based counterparts, respectively. The only exception was the passenger car where the AV-based car was slightly (4%) more expensive than the conventional cars due to the significantly higher share of the initial purchase cost in the vehicle life-cycle cost. However, the study did not account for the cost externalities, such as costs of growing a PT fleet, mode choice variation, user time costs and vehicle occupancy, as well as the per-unit cost of the environmental emissions.
# Automated Measurement of Grid Cell Firing Characteristics
## 2 Design of Algorithms  
GridMet utilizes a multi-step strategy for the robust identification of grid fields that can be applied on either the firing rate maps or the corresponding spatial autocorrelograms. This approach relies on three sequential processes: global filtering, spatial clustering, and local border detection (Figure 1). Filtering relies on the key property of grid cells: the firing rates in rate maps and the correlation values in autocorrelograms are typically greater within grid fields than outside of them. This first processing step removes any \"non-field\" signal below a threshold relative to the global maximum value in the firing rate map or autocorrelogram (Figure 2A,B). The default value of 0.31 (31%) for this threshold parameter is based on our previous work [7] and can be modified by the users. GridMet then leverages the density-based  
Figure 1: Flowchart of the field detection process. Global filtering steps are in red, spatial clustering steps are in yellow, and local border detection steps are in blue. The solid arrows represent the direction of the flow of the data processing sequence. The dashed arrows represent values not retained as detected field data. spatial clustering of applications with a noise (DBSCAN) algorithm from the Statistics and Machine Learning Toolbox of MATLAB to create field firing clusters (Figure 2C). Next, GridMet calculates an initial estimate of each field centroid location. By default, this is computed with a 'center-of-mass' approach, that is, by weighing each position in a cluster by its firing rate (or correlation value) prior to averaging. As an alternative option, users can choose to compute centroids as simply the pair of mean x and y coordinates within each cluster. GridMet then incrementally checks firing rates (or correlation values) outward from the centroid through a 360-degree scan. This scan identifies where the values first fall below the same threshold used in the filtering step, but in this step the threshold is relative to the local value at the centroid position (Figure 3A). GridMet records the resultant locations as the field's boundary (Figure 3B). This processed boundary detection, which is called a radar scan, is presented in Algorithms 1 and 2. ```
Input : angle \(A\); distance \(D\) Output : distanceonx-axis : \(X\); distanceony-axis : \(Y\)
1 : \(A\leftarrow(A/360)*2\pi\)
2 :if\(A<(\pi/2)\)
3 : \(X\gets sin(A)*D\)
4 : \(Y\leftarrow\sqrt{D^{2}-X^{2}}\)
5 :elseif\(A\geq(\pi/2)\) & \(A<\pi\)
6 : \(X\gets cos(A-(\pi/2))*D\)
7 : \(Y\leftarrow-\sqrt{D^{2}-X^{2}}\)
8 :elseif\(A\geq\pi\) & \(A<(\pi*1.5)\)
9 : \(X\gets-cos((\pi*1.5)-A)*D\)
10 : \(Y\leftarrow-\sqrt{D^{2}-X^{2}}\)
11 :elseif\(A\geq(\pi*1.5)\) & \(A\leq(\pi*2)\)
12 : \(X\leftarrow-cos(A-(\pi*1.5))*D\)
13 : \(Y\leftarrow\sqrt{D^{2}-X^{2}}\)
14 :endif
```  
**Algorithm 1** Function _find_ver_hor()  
Figure 2: Filtering and clustering grid fields. (**A**) Original autocorrelogram of grid fields. (**B**) Detected grid fields with low-correlation sections filtered out. (**C**) Clustering detects grid fields (identified here by different colors).
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 4 Cryptanalysis of [PERSON] et al.'s Protocol
### Session Key Disclosure Attack  
According to Section 4.1, a \(MU_{a}\) can successfully impersonate a legitimate mobile user \(MU\) and calculate the \(SK\). According to the discussion presented in Section 2.1, \(MU_{a}\) can extract the \(\{C_{MU},R\}\) in the mobile device and obtain random number \(R_{FA}\) of \(FA\) over an open channel, and then compute \(R_{MU}=U\oplus R\). Therefore, \(MU_{a}\) can compute \(SK=C_{MU}\oplus h(R)\oplus R_{MU}\oplus R_{FA}\). Therefore, [PERSON] et al.'s protocol [16] is insecure against session key disclosure attacks.
# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 5 Limitations and Future Work  
The ability to accurately predict blasting fragmentation has direct implications for operational decision-making in mining. By anticipating block size distribution, engineers can adjust drilling parameters such as burden and spacing to achieve desired fragmentation levels. This, in turn, helps optimize explosive type and charge design to avoid over- or under-blasting. Furthermore, accurate prediction reduces the likelihood of excessive fines or oversize blocks, improving downstream processes such as loading, hauling, and crushing. These improvements not only enhance safety and efficiency but can also lead to significant cost savings in production. Although the proposed CPO-BP model has demonstrated strong prediction accuracy and practical applicability, several limitations should be acknowledged. First, the dataset used in this study, while real and relevant, includes only 97 samples, which may limit the statistical robustness of the results. Second, although the training data  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mould** & **CPO-BP** & **BP** \\ \hline Mean Absolute Error MAE & 0.014341 & 0.094344 \\ Mean Square Error MSE & 0.00025004 & 0.011837 \\ Root Mean Square Error RMSE & 0.015813 & 0.1088 \\ Mean Absolute Percentage Error MAPE & 0.037595\% & 0.25274\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: CPO-BP model and BP model prediction evaluation index.  
Figure 6: Comparison of the predicted value and the real value of the CPO-BP algorithm and the BP algorithm.  
were based on the Hudaverdi dataset and the model was validated on an actual iron ore mine, the diversity of rock mass types and blasting environments is still limited. As such, the model's performance under significantly different geological conditions remains to be evaluated.  
Future studies will aim to expand the dataset to include a wider variety of rock formations, blasting parameters, and mining operations. This will enable more comprehensive validation of the model's generalization ability and operational robustness. In addition, further investigation into ensemble learning or multi-objective optimization strategies could enhance model reliability in more complex environments.  
In future studies, we plan to apply k-fold [31] cross-validation across datasets from different geological contexts to provide more robust statistical validation and prevent data partition bias. Regularization techniques were not explicitly applied; their potential benefits will be considered in future model refinements to further mitigate overfitting risks.
## 2 Methodology
### Receptive Field Enhancement (RFE) Module  
Dilated convolution, also called expansion convolution, is simply the process of expanding the convolution kernel by adding some spaces (zeros) between the elements of the convolution kernel as a way of enlarging the sensory field [17]. [PERSON] et al. [18] proposed the receptive field enhancement module, which is divided into two parts, multi-branching based on the dilation convolution, and an aggregation weighting layer. It is characterized using 4 different scaled expansion convolution branches to capture multi-scale information and different dependency ranges, and the weights are shared among these branches; the only difference is the different acceptance domains, and its structure is shown in Figure 2.  
We draw on the idea of study [18] to introduce the RFE module into the backbone, and constitute the SPPF-RFE module with SPPF, which effectively expands the receptive field and improves the information extraction ability for multi-scale targets. In this module 1, 2, and 3 are used as different expansion rates, and they all use a fixed 3 \(\times\) 3 convolution kernel with a residual joining to prevent the problems of gradient explosion and gradient vanishing, while collecting information from different branches and weighting each branch of the feature. For the sea vessel scenario, different scales can be manipulated to fully apply each sample.