esa-sceva/satcom-chunk-collection huggingface dataset:
# Noninvasive Magnetic-Marking-Based Flow Metering with Optically Pumped Magnetometers
## 3 Results
### Short RF Pulsing
To make the result of \(S(x_{0})\) comparable to experimental OPM data, the spatial distribution was converted to a temporal distribution \(S(t)\). This was done by converting the starting point argument \(x_{0}\) to the time \(t\) it takes for the water to arrive in front of the OPM sensor: \(t=(\Delta L-x_{0})/v\). \(\Delta L\) is the distance between the RF coil and the OPM (cf. Figure 1). The resulting expected signal according to (7) and an extract from the experimental data are shown in Figure 8 for comparison. For both curves the x axis was centered at t = 0. The y axis of the modeled signal was rescaled to match the peak height of the observed pulse. While the observed polarization \"pulse width\" (FWHM = 0.34 s) was longer than the duration of the applied RF pulse (0.25 s), it was clearly shorter than the width derived from our simple model (0.67 s). This, as well as the asymmetry of the observed \"pulse\" shape, indicates that the assumed B field distribution along the x axis may considerably differ from our simple model assumption. Figure 7: Spatial distribution of the magnetic field of the Helmholtz coil according to Equation (8). The distribution is calculated using a current of 200 mA. The green lines mark the positions of the Helmholtz coil pair. The outermost layer of the magnetic shield is positioned at x = 6 cm. As a consequence, the field distribution in the hatched region will differ from the simple model.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 2 Related Work  
Previous methods for TUAB EEG normal abnormal classification have predominantly focused on feature extraction techniques [20] and deep learning models [23; 29]. Some studies [30; 31] trained and tested models on the same patient data, without evaluating performance on unseen datasets. Additionally, many classification approaches [20; 25] used only the first 60 s of EEG recordings or excluded this period due to artifacts [31], potentially limiting their generalizability to full EEG recordings or other datasets. Furthermore, many deep learning-based methods lack interpretability, which can lead to distrust from researchers.  
Despite the widespread use of the TUAB dataset, existing studies vary considerably in preprocessing pipelines, channel selection, model design, and the amount of EEG data utilized. Table 1 provides a structured comparison of representative studies, highlighting these methodological differences.  
As shown in Table 1, previous studies demonstrate substantial variability in their use of TUAB EEG data, particularly in terms of selected channels and recording duration. To address these inconsistencies, our study utilizes the full-length EEG recordings and evaluates performance on an independent test set to ensure robust and generalizable results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Model Type** & **Channels** & **EEG Duration** & **Accuracy** \\ \hline
[31] & Deep neural networks & 21 & \textless{}15 min & 86.16\% \\ \hline
[2] & ChronoNet (RNN) & All & First 1 min & 86.57 \\ \hline
[30] & CLBP + SVM & 24 & First 1 min & 93.84–98.19\% \\ \hline
[20] & KNN/RF & T5–O1 & First 1 min & 58.2\% (KNN), 68.3\% (RF) \\ \hline
[23] & CNN & T5–O1 & First 1 min & 79.34\% \\ \hline
[29] & WaveNet + LSTM & T5–O1 & First 30 s & 88.76\% \\ \hline
[21] & CNN & All & Up to 11 min & 76.90\% \\ \hline
[32] & Deep Learning & All & Up to 20 min & RMSE: 0.47–1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing studies using the TUAB dataset for EEG classification.
# Study on Dominant Frequency Attenuation of Blasting Vibration for Ultra-Small-Spacing Tunnel
## 6 Discussion  
The most critical measure to control blast vibration is establishing a threshold for PPV, which can be selected according to the characteristics of frequency distribution in different areas. As shown in Figure 6, the dominant frequency of the blast vibration wave undergoes a fluctuating decrease at a certain distance, only the high-frequency part decays rapidly with the distance, and the dominant frequency shifts from a higher band to a lower one (upper bond shown in Figure 6). The main reason is that, for multi-sequences delay blast, the spectrum of the vibration waves is multi-structured (as shown in Figure 9); and variant delay sequences produce different frequencies of vibration waves (as shown in Figure 10); and different frequency components in the propagation with different attenuation rate  
Figure 12: Relationship between ZCF and DF.  
decay (as shown in Figure 6). On the other hand, the abrupt change of the propagation medium leads to a significant difference in the distribution of the dominant frequencies of vibration on the lining and the primary support (as shown in Figure 7).  
On the basis of Chinese Safety Regulations for Blasting (GB6722-2014), a safety PPV criterion for traffic tunnel considering dominant frequency has been proposed (as shown in Table 6). Its definition of dominant frequency is corresponding to the maximum amplitude of the particle velocity, which means ZCF.  
Therefore, the way to select the right critical PPV for the tunnel structure is to choose a frequency band based on frequency-based vibration criteria. For the range of SD greater than 5.0 kg/m\({}^{3}\), a safe PPV less than 50 Hz band is recommended; while the SD is below 5.0 kg/m\({}^{3}\), it is determined by experience; and a more stringent PPV is more secure for the structure. For a point at the lining is exceeds 20 m away from the blast, because the distribution of the frequency is generally greater than 50 Hz, the safe PPV can be selected appropriately larger compared to the primary support.  
However, it should be noted that the ZCF and DF exhibit a non-linear relationship, and the blast vibration wave frequencies obtained by different methods may lead to a different selection for safe vibration frequency bands.
# Mechanisms and Models of Attenuation of Shock Waves through Rock Formations
## 3 Numerical Simulation Study  
### Model Building  
The Universal Distinct Element Code (UDEC) is a common numerical simulation software for geotechnical engineering. The special calculation unit divides the material into blocks and presents a cloud of fracture and stress distribution in the blocks. In the numerical simulation of dynamics experiments, it is necessary to consider whether cracks are generated during the loading process.  
According to the distribution characteristics of the working face and rock formation, the UDEC trigon numerical simulation model is established. A numerical model of the Hujiahe mine recovery area is shown in Figure 4, with the properties of each rock formation in the model on the right. The seven points from A to G in the figure represent the test points added to the model. The location of the impact shock source was set at the roof of the model based on the results of the field monitoring. The recovery roadway and drainage roadway are laid out on the coal seam floor. Table 1 shows the physical and mechanical parameters used for the mechanical analysis of each rock formation of the model.  
Figure 5 shows the characteristics of the support construction arrangement for the recovery roadway. The support material is all anchor cable, with lengths of 3.5 m and 6.3 m, respectively. The longer anchor cables have a spacing and row spacing of 1600 mm and 1600 mm, respectively. Those of the other anchor cables are 800 mm and 800 mm. The two types of anchor cables are paired with a pallet made of 300 mm carbon steel and an M4-type steel belt, with 70 mm \(\times\)\(\varphi\)6 mm metal mesh selected underneath the belt. The shock wave was set as a sine wave function with a peak of 100 MPa and a loading time of 0.2 s.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Location** & **Lithology** & **Thickness (m)** & **Bulk (GPa)** & **Density (kg/m\({}^{3}\))** & **Shear (GPa)** & **Frictional (GPa)** & **Cohesion (MPa)** & **Tension (MPa)** \\ \hline Roof & mudstone & 5.58 & 15 & 2300 & 7 & 30 & 2 & 1.84 \\ Main roof & sandstone & 35.95 & 22 & 2600 & 14 & 39 & 10.2 & 1.2 \\ Roof & mudstone & 6.03 & 15 & 2300 & 7 & 30 & 2 & 1.84 \\ Immediate & carbonaceous & 0.45 & 15 & 2500 & 4 & 36 & 4.98 & 2.01 \\ roof & mudstone & 23 & 10 & 1290 & 5 & 28 & 1.5 & 0.55 \\ Local seam & carbonaceous & 1.1 & 15 & 2500 & 4 & 36 & 4.98 & 2.01 \\ Immediate & carbonaceous & 4.79 & 15 & 2300 & 7 & 30 & 2 & 1.84 \\ floor & mudstone & 2.03 & 10 & 1290 & 5 & 28 & 1.5 & 0.55 \\ Floor & mudstone & 1.1 & 15 & 2500 & 4 & 36 & 4.98 & 2.01 \\ Floor & mudstone & 4.79 & 15 & 2300 & 7 & 30 & 2 & 1.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the mechanical parameters of the coal rock mass.  
Figure 4: Numerical simulation model.
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 3 Results and Discussion  
### Chemistry  
Betulin, a pentacyclic lupane-type triterpene, occurs in many plant species. The largest amounts, of up to 20-30%, of this natural bioactive substance are isolated from the outer bark of birches [38]. Obtaining the target compounds required a preliminary modification of the triterpenoid skeleton of betulin at the C3, C28, and C30 positions (Scheme 1). Inthe first step, the C3 and C28 hydroxyl groups of betulin were subjected to an acylation reaction with acetic anhydride ((CH\({}_{3}\)CO)\({}_{2}\)O) in pyridine, leading to 3,28-diacetylbetulin **1**[28]. Then, compound **1** was transformed into 3-acetylbetulin **2** as a result of the alkaline hydrolysis reaction in a 0.2 M solution of sodium hydroxide in CH\({}_{3}\)OH/THF/H\({}_{2}\)O (1:2:1, \(v/v\)) [29]. 3,28-Diacetylbetulin **1** was also reacted with 3-chloroperbenzoic acid (_m_-CPBA) in chloroform at the 60 \({}^{\circ}\)C to yield 3,28-diacetyl-30-hydroxybetulin **3**[30]. Betulin was also a substrate for the synthesis of 28-tetrahydropyranyl ether **4** and 28-acetylbetulin **5** according procedures described by [PERSON] and [PERSON] [31; 32] (Scheme 1). Changing the conditions of the betulin acylation reaction allowed us to obtain 28-acetylbetulin **5**. Acylation with acetic anhydride in pyridine medium enables the reaction of both the primary (C28) and secondary (C3) hydroxyl groups (obtaining compound **1**). On the other hand, the use of chloroform as a solvent in the acylation reaction and the presence of imidazole as a catalyst allowed us to obtain monoacetylated derivative **5**. The chemical structures of substrates **1-5** were confirmed on the basis of NMR spectra (\({}^{1}\)H, \({}^{13}\)C) and compared with the relevant data described in the literature [28; 30; 31; 32; 39].  
The \(\alpha\)-lipoic esters of triterpenoids **6-10** were obtained using the Steglich method. In this procedure, betulin and triterpenoids **2-5** were treated with \(\alpha\)-lipoic acid in the presence of DCC and DMAP in dry dichloromethane.  
After purification by column chromatography, target compounds **6-10** were obtained in a 48-88% yield. The chemical structures of \(\alpha\)-lipoic acid conjugates with a triterpenoids **6-10** were confirmed based on \({}^{1}\)H NMR, \({}^{13}\)C NMR, IR, and HRMS spectra (Supplementary Materials, Figures S1-S20). The synthesis (reagents and conditions) of compounds **6-10** is presented in Scheme 2.
# Role of Kir4.1 Channel in Auditory Function: Impact on Endocochlear Potential and Hearing Loss
## 4 Discussion
### Overview of Potassium Channels  
Potassium channels, involved in the regulation of inflow and ionic efflux in cell membranes, are diffused in the human organism and structurally consist of a domain of the formation of pores assigned to the ion transport and a regulatory domain that detects the various stimulations [17]. Potassium channels, which have several transmembrane helices that cross the lipid double layer, are divided into four classes based on their structure and function, as reported in Table 1[18,19].  
In particular, in the inner ear, there are several potassium channels involved in K\({}^{+}\) recirculation, among which  
* Kir channels are responsible for maintaining the cochlear EP [14];
* Are voltage-gated channels Kv 7.1 (KCNQI) and Kv 7.4 (KCNQ4), whose mutations have been associated with hearing loss, such as DFNA2 [15]. The resting potential and membrane potential in internal hair cells are regulated by these channels. When opened, they allow for the release of K\({}^{+}\) from the cell, which supports the maintenance of the membrane potential of the internal ciliate cell, which is fundamental for hearing transduction [20];  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Acronyms** & **Channel Type** & **TM Number 1** \\ \hline Kir & inwardly rectifying & 2 \\ K2P & tandem pore domain & 4 \\ Kv & voltage-gated & 6 \\ Kligand & ligand-gated channels & 2 or 6 \\ \hline \hline \end{tabular} \({}^{\dagger}\)TM = transmembrane helices.  
\end{table}
Table 1: Vertebrate K\({}^{+}\) channel classification [18,19].  
Figure 2: The detailed structure of the organ of [PERSON] and the stria vascularis.  
* Large-conductance calcium-activated potassium channels, named BK channels, are capable of allowing significant K\({}^{+}\) efflux due to the binding of Ca\({}^{2+}\) ions to specific sites in the channel protein, which can hyperpolarize the cell membrane and regulate cellular excitability. BK channels play a role in the transduction of the auditory signal in hair cells by responding to changes in sound intensity [21].  
Other proteins involved in auditory function include Na\({}^{+}\)K\({}^{+}\)Cl\({}^{2-}\) and Na\({}^{+}\)K\({}^{+}\)ATPase pumps. Among the proteins mentioned, firstly, there is NKCC1, which is found in the marginal cells of the stria vascularis. It transports sodium, potassium, and chloride ions across the cell membrane at once. It is involved in the maintenance mechanisms of ionic homeostasis necessary for sensory cell functionality by regulating the concentration of potassium in the scala media [9]. Na\({}^{+}\)K\({}^{+}\) pumps, on the other hand, are enzymes expressed along the vascularis stria; they have a role in the maintenance of EP by transporting, against the gradient of concentrations, the sodium ions out of the cell and the potassium ions inside [9].
## 3 Results
### Helix Deviation Curve
#### 3.1.2 Elimination of Direct Current (DC) and Slope Component  
The helix deviation curves of left and right gear teeth after the elimination of DC and slope components are shown in Figure 5a and Figure 5b, respectively. In contrast to the helix deviation curves of the original data, both left and right helix deviations display a relatively uniform gradient. Nonetheless, certain curves exhibit a slightly convex shape, with a minor dip in the central region of the curves.
## 3 Preliminaries
### Design Goals  
#### 3.2.1 Privacy  
When considering aerial drones in urban environments, personally identifiable information (PII) includes the faces captured in the live video feed and the relevant image features extracted by a model during drone deployment. Sensitive information such as faces can be used by adversaries to derive a database of where people reside, exploit the information for systems that rely on biometric authentication, etc. Protecting image/video features and any other intermediate representations is just as important as protecting the original faces. Extracted features contain semantic information related to the original image frames. These features can be used to reconstruct faces and recover the identities of people in the video feed.  
As such, drones tasked with traveling in urban environments will often encounter bystanders, and detecting them may either be the primary goal or a necessary step as part of a downstream computer vision task. The system must guarantee that the frames containing faces and relevant image features remain protected from the cloudlet. We understand that, depending on the extra knowledge of an attacker on a person caught in the camera, information other than faces, e.g., clothes, may also be used to identify the person. However, at this stage, our research only focuses on protecting face-based PII.
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 3 Methodology
### Developed Algorithms
#### 3.4.1 Temporal Spatial Hyper Cube (TSHC)  
The hyper-cube is a quantization level that is supposed to map various values to one value in each coordinate to reduce the resolution of data storing and eliminate the effect of slight changes that do not influence the meaning of the data (type of filtering). Two aspects will feature the cube in this study; the first one is the heterogeneity, and the second is its dynamicity. The goal of the heterogeneity is to handle the heterogeneous data of the stream. The purpose of dynamicity is to handle the evolution of the data according to the values of its features or coordinates.  
The cube is calculated based on an algorithm as an adaptive hyper temp-spatial cube, and it works as the pseudocode provided by Algorithm 1. After receiving new points of the data, the algorithm starts with an initial resolution of the grid. It keeps incrementing at each dimension sequentially until obtaining the least resolution containing enough points for clustering. The algorithm can be observed as incremental neighborhood expanding for tempo-spatial hyper-cube (TSHC).  
The goal of TSCH is to change the values of the quantization or the cube granularities every time new data arrived in the buffer to enable an adaptive cube with respect to both time and spatial arrangement of points. The approach uses a step of increment of the cube from initial grid values toward the converged value based on checking the condition that the number of points in one hyper-cube (or grid) should not be lower than a certain threshold. In other words, the condition of \(min(\text{NumPoints})>\text{ cellThreshold}\) indicates the convergence case with meeting the constraint. The increment of the cube in any dimension is constrained by the inequality given in Equation (1) which assures that the increment in any dimension should not lead to exceeding the number of points than half the amount in the buffer.  
\[q(i)+delta*max(B(;i))<=max(\frac{B(;i)}{2}) \tag{1}\]
## 4 Experimental Results and Discussions  
The algorithms implemented are tested by setting up a physical real time monitoring test bed to test the ability of data collection from an ECG device and from the sensor nodes as well as to evaluate the data transmission over the WSN. Obtained sensory data is manipulated, processed, analyzed and displayed graphically on the smart phone screen. The ability to decode the personalized barcode with smart phone's built-in camera is tested as well.
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node
### Hardware Node: UCODALI Board
#### 4.1.1 Power Supply Block  
All of the board's components, except for the DALI interface block, need a power supply voltage of 3.3 VDC. We used the FC47121 switching power supply [39] to obtain this voltage from the 230 VAC network. This power supply has the following characteristics: 3.3 VDC output voltage, \(\pm\)2% output voltage accuracy (100% load), 2.5 W power and 65% efficiency. This switching power supply is a switched mode power supply based on flyback topology that provides an alternative solution to traditional power supplies used in low-power applications.  
As will be indicated below, the DALI interface uses voltage levels to represent the high logic level between 11.5 V and 22.5 V. As a result, a power supply that generates a voltage within this  
Figure 10: UCODALI block diagram.  
Figure 9: UCODALI version 2 board.  
range needs to be used. On the other hand, the control part of the node of the DALI bus needs to be isolated. To do this, we used the MEU1S0315 ZC DC/DC converter by Murata Power Solutions [40]. The MEU1S0315 ZC is an ultra-miniature through-hole 1 W DC/DC converter that is available in a ZIP-style pinout. It is ideally suited for providing local power supplies on control system boards, with the added benefit of 1 kVDC galvanic isolation to reduce switching noise. From the 3.3 VDC voltage, it generates an output voltage of 15 VDC with 81% efficiency.  
In the first version of the board, a FC47125 switching power supply was used instead of the MEU1S0315 ZC DC/DC converter. The use of this converter and the fact that version 2 has 4 layers--compared with version 1, which had just 2 layers--have allowed us to reduce the size of the board by 40% and reduce its total cost.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 2 Background
### Low Power Wide Area Network (LPWAN)
#### 2.1.4 Bluetooth Low Energy (BLE)  
BLE is considered to be an important short-range radio technology with high market penetration i.e., it is already present in billions of existing smart devices, e.g., smart phones and tablets. BLE was initially introduced by Nokia in 2006 and, later, in 2010, Bluetooth Version 4.0 was added to the Core Specification [15]. Currently, Bluetooth Special Interest Group (SIG) provides standards for newer version of BLE [16]. There are two types of channels in the newer version of BLE i.e., advertising channels that are used for the purpose of broadcast and device discovery, and data channels that are used for bi-directional data transmission [16].
# Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases
## 4 Method for 2D Shape Analysis Based on Photogrammetric Models
### Method for Detecting Contours and Extracting Plane Sections Based on Point Segmentation
#### 4.2.2 Generating Height Maps (DEMs)  
The 3D models were primarily projected to the XY-plane and the Z coordinate was exported to scalar fields in order to obtain and represent detailed values of object depths in visual form (by colors, Figure 10). The depth of the 3D model was displayed as the Z component. Creating DEM and exporting 3D point coordinates to scalar fields provides detailed depth map information for the 3D model, coloring the points in respect to their depths. In this case, rather than generating a rasterized image as an output, the point cloud with the selected coordinate values as a scalar field was created. This way, the 3D model depth was considered as the Z component. The depth information generated from DEM was stored as scalar values of the 3D cloud of colored points representing object depth. This way, the depth dimensions of complex geometry (in this case, coordinate Z) which are otherwise hard to measure can be obtained from photogrammetric models and rendered as an equirectangular projection with elevations, in which blue-green colors indicate lower object depths, and yellow -red colors indicate higher (Figure 10).  
Figure 10: DEMs of point clouds with the coordinates exported to the scalar field: (**a**) ICONB and (**b**) ICONC.  
Figure 9: Ambient occlusion for point clouds: (**a**) ICONB and (**b**) ICONC.  
The height maps were generated with the aim to split the 3D point clouds of the iconostases into their main parts concerning the depth values. To achieve this, the point cloud needs to be split by filtering points according to the scalar field display parameters. The most protruding parts of the iconostasis' structure also present the main constructive elements such as columns and moldings, while the rest of the construction contains decorative ornaments and icons which form mainly flat surfaces. The color scale histogram is presented in meters.