esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
CaCl\({}_{2}\)-induced abdominal aortic injury offers a controlled and reproducible method to investigate the molecular and structural alterations associated with vascular diseases, providing valuable insights for developing therapeutic strategies and interventions aimed to mitigate aortic pathologies. This model has been widely employed in preclinical studies, allowing researchers to explore the mechanisms underlying aortic injuries and their implications for cardiovascular health [23; 24]. The Transient Receptor Potential (TRP) is a large superfamily of channels in mammalian that are divided into six subfamilies according to their DNA and protein sequence: TRPV (vanilloid), TRPC (canonical), TRPM (melastatin), TRPA (ankyrin), TRPV (polycystin), and TRPML (mucoliptin). Twenty-eight genes encoded for TRP cation channels are expressed on different mammalian cell types [25]. The TRPV family is subdivided into six isoforms, among them, 1-4 isoforms are clearly found to play an essential role in vascular regulation [25]. The human TRPV4 gene is located in chromosome twelve, that encodes for Ca\({}^{2+}\)-permeable non-selective cation channels. It comprises six membrane-spanning helices (S1-S6), a pore loop between S5 and S6, and intracellular N-, C-termini. TRPV4 is found to be expressed in many tissues; its messenger RNA has been detected in kidney, lung, spleen, testis, and adipose tissue using multiple tissue Northern blots. TRPV4 mRNA has been detected in the brain neurons using in situ hybridization [26]. Moreover, it is expressed in the heart, blood vessel endothelium, and some vascular smooth muscle cells [25]. The TRPV4 channel is multimodal and responds to a variety of signals, including physical stimuli like heat [27], chemical stimuli like lipid agonists [1], mechanical stimuli [27], and extracellular osmolarity changes [26]. It is found to play different biological functions due to its wide range of expression in tissues and ability to respond to multiple stimuli. TRPV4 in endothelial cells has a role in vasodilation by different independent pathways like nitric oxide, prostacyclin, and endothelium-derived hyperpolarizing factor (EDHF) [28]. Moreover, its expression in smooth muscles contributes to the activation of conductance potassium channels and hyperpolarization of smooth muscles [29]. In addition, the TRPV4 channel mediates osteoblast differentiation, which is involved in vascular calcification mechanisms, as described previously [30]. Despite previous studies, the precise role of TRPV4, a Ca\({}^{2+}\)-permeable cation channel, in the progression of vascular calcification and AAA, remains unclear. To shed light on this matter, the present study utilized TRPV4-knockout mice and subjected them to standard CaCl\({}_{2}\)-induced abdominal aortic injury to investigate the involvement of the TRPV4 channel in the progression of vascular calcification.
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
## 3 Spatial Resolution of Photoelectron Spectroscopy
### Ion Sputtering and Depth Profiling  
Information about depth-resolved concentrations of elements and morphology of products, thicknesses of surface layers of adsorbates, etc., can be obtained in XPS by several methods [15; 16]. First, atoms from outer layers can be removed by sputtering with heavy ions, usually Ar\({}^{+}\) ions, exposing deeper structures. The Ar\({}^{+}\) bombardment causes substantial disordering of crystal and electronic structures, reduction of some elements, and so on, so the information on the chemical state of atoms will be lost; moreover, elemental composition can change under the ion beam, too. For example, ion bombardment of pyrite results in preferential removal of S atoms, producing disordered layer with high-spin Fe\({}^{2+}\)/Fe\({}^{3+}\) cations and sulfide anions instead of singlet ferrous iron and disulfide groups [126; 127]. Nevertheless, the Ar\({}^{+}\) bombardment is often employed for depth-profiling down to about 100 nm, although for rough and inhomogeneous surfaces of real, oxidized sulfide minerals it provides semi-quantitative concentration distributions at best. It is more convenient in such situations to use the ion bombardment together with Auger electron spectroscopy [25; 26] because of the smaller size of an electron beam; see for example, studies of oxidized and leached pyrrhotite [76; 77; 78; 79]. In addition to profiling, mild Ar\({}^{+}\) sputtering is used to clean surfaces from casual contaminations.
## 4 Performance Evaluation
### Numerical Results
#### 4.1.1 Case Study
On the contrary, better performance can be achieved by MF-RFT-NCI, as shown in Figure 7c. Although it does not exploit the phase information, the target's energy can be effectively enhanced due to the MF-DFB in the non-coherent mode by (21). From Figure 7d, the output SNR of the proposed MF-RFT-QCI is the highest among the considered methods, since it benefits from both MF-DFB in the coherent mode and the exploitation of phase information of the target. These results confirm the effectiveness of the proposed method in the detection scenarios with MF-DFM. Next, the effectiveness of the proposed ME-PSO algorithm for phase compensation is also evaluated. The comparison of the Doppler profile of MF-RFT with and without the ME-PSO algorithm is shown in Figure 8. Herein, we consider a case when \(M=3\) (\([f_{1},f_{2},f_{3}]\) = [12.395, 12.515, 12.675] GHz) and the other settings are the same as in Figure 7. The phase of the target at different frequency bands \([\phi_{1},\phi_{2},\phi_{3}]\) are equal to 1.487, 0.593, and \(-2.080\) (in radian), respectively. The standard deviation of the target's phase exceeds 1.85 radians. From Figure 8, the output SNR of MF-RFT with ME-PSO is significantly better than that of MF-RFT without ME-PSO in this case. This result verifies the effectiveness of the proposed phase estimation method and the importance of the phase compensation in quasi-coherent MF integration in the presence of substantial residual phase error. Figure 7: Comparison of different fusion methods in the presence of MF-DFM: **(a)** MF-NCI-EL; **(b)** MF-NCI-UEL; **(c)** MF-RFT-NCI; **(d)** MF-RFT-QCI (proposed).
## 2 Seismic Profile Signal Reconstruction Model
### Swin-ReshoUnet Model with Hierarchical Convolution and Regional Coordinate Attention Enhancement
#### 2.3.1 Hierarchical Dilated Convolutions  
In the research on seismic profile data reconstruction, we adopt a hierarchical convolution approach to extract multi-scale feature information through convolutional operations at different levels. Specifically, the network architecture is divided into four stages, each employing distinct convolutional kernels and dilation rates to effectively capture features at various scales in seismic profile data [18; 19; 20]. This hierarchical convolution mechanism is schematically illustrated in Figure 7 Hierarchical convolution structure diagram, demonstrating how multi-level receptive fields enable comprehensive characterization of geological structures with diverse spatial resolutions.  
Level 1 employs 1 \(\times\) 1 convolution kernels to preserve spatial dimensions, focusing on extracting high-frequency shallow features. This operation rapidly captures detailed information, laying a foundation for subsequent feature extraction.  
Level 2 utilizes 3 \(\times\) 3 convolution kernels with a dilation rate of 6. This configuration expands the receptive field moderately without increasing computational cost, effectively capturing medium-scale geological structures.  
Level 3 maintains 3 \(\times\) 3 kernels but increases the dilation rate to 12. By further expanding the receptive field, this level captures broader contextual information, crucial for reconstructing complex geological features in seismic data.  
Level 4 uses 3 \(\times\) 3 kernels with a dilation rate of 18, providing the largest receptive field to capture low-frequency features distributed across the seismic profile, thereby enhancing overall reconstruction quality [21]. This hierarchical convolution design enables the network to extract multi-scale features efficiently, significantly improving its ability to characterize geological structures with varying spatial resolutions. Compared to traditional convolution, this approach balances receptive field expansion and computational efficiency while mitigating information dilution. Mathematically, the feature extraction process of hierarchical convolution can be expressed as follows.  
For the \(l\)-th layer, the convolution operation is formulated as  
\[F_{l}(x)=\sum_{i=1}^{k_{l}}w_{l,i}*x_{l,i}+b_{l} \tag{11}\]  
where \(w_{l,i}\) denotes the weight of the \(i\)-th convolution kernel in the \(l\)-th layer, \(x_{l,i}\) represents the input feature map, \(b_{l}\) is the bias term, and \(k_{l}\) is the number of convolution kernels in this layer [22].  
Through convolutional operations at different layers, the network can gradually extract feature information from local details to global structures, enabling efficient reconstruction of seismic profile data. This hierarchical design not only enhances the model's expressive power but also improves its adaptability to geological features at different scales, providing strong support for the accurate reconstruction of seismic profile data.
## 2 Loss Mechanism Hypergraph of Single-Stage Planetary Gear Transmission
### Loss Mechanism of Single-Stage Planetary Gear Transmission  
#### 2.2.1 Hypergraph of Single-Stage Planetary Gear Transmission  
The structure of the single-stage planetary gear transmission structure is shown in Figure 2, including the frame, sun gear, planetary gear, ring gear, bearing surface contacts, and teeth surface contacts. The topology of the system is presented as a hypergraph in Figure 2d, in which numbered vertices represent components of the gear system and edges represent kinematic pairs.  
Figure 1: Photographs of herringbone tooth drive. (**a**) Structure of herringbone gear; (**b**) herringbone gear transmission.  
A single-stage planetary gear transmission can be represented as a diamond-shaped hypergraph with four connected nodes. Each node corresponds to one main branch of the gear train including the carrier, ring gear, sun gear, and frame. The sun gear and frame, planet gear and carrier, and ring gear and frame are connected by bearings, denoted \(R_{1}\), \(R_{2}\), and \(R_{3}\), respectively. In addition, a teeth surface contact link exists between the sun gear and planetary gear and between the planetary gear and ring gear.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 1 Introduction
### Underwater Communication with Magnetic Fields  
A promising alternative for near/medium-range communication is based on magnetic fields [18]. By using a transmitter and a receiver coil, the information signal sent is reproducible on the receiver side. When compared to acoustic communication, the advantages of MI include negligible propagation loss and less susceptibility to surroundings. This technique is insensitive to water turbidity (including wave zones, tides, river flow), depth of water, reflections, and interference from sound and light. Furthermore, rapidly decaying magnetic fields are more environmentally friendly for marine mammals than sound waves [19].  
This technology is already being developed and used in various projects. For instance, it has been applied as a complement to a cooperative multiple-input multiple-output (MIMO) communication mechanism that uses a hybrid acoustic and magnetic induction technique [20]. Another example of its use is a new approach for hybrid submarine communication nodes based on magnetic induction and visible light which presents an improvement in the position estimation of autonomous underwater vehicles [21].  
The content of the article is presented as follows: in Section 2, the basic principles of the mathematical modeling of magnetic coils are presented; Section 3 presents the description of the elements of the proposed communication system, including the transmitter and receiver system; Section 4 presents the main characteristics of the test platform used to evaluate the submarine communication system; Section 5 presents the experimental results of the communication system and finally, the conclusions and references are presented.
## 4 Methodology
### The Cooperative Protocols and the Routing Algorithms
#### 4.4.2 Limited Cooperation Scenario  
In the intermediate scenario of limited cooperation, companies in the coalition are allowed to share a given proportion of their customers for each logistic service, that is, each time window and day. The cooperative mechanism consists of creating a pool of customers that may be served from other companies in the coalition. We fix the quota for transferring customers to a 50%. Note that, it is based on time-distance (time to make the delivery in the real situation of traffic in a fixed time) to the chosen supermarket. Then, the customers are iteratively assigned to the closest supermarket until the pool of customers is empty. This process is illustrated in Figure 6 for a given time window, where companies transfer a total of 30 customers to the common pool. The initial contributions of each company in the example are 3 customers from \(S\)1, 15 from \(S\)2, 4 from \(S\)3 and 8 from \(S\)4. Then, customers are transferred at different rounds until the pool of customers is empty. In this case, there are 3 rounds for assigning the customers. In the first round, the percentage of customers the biggest contributor (supermarket having the greatest number of orders in one day) may pick is bounded by the percentage corresponding to the smallest contributor. Since there are 3 rounds, the smallest contributor is assigned a 33% of customers per round, which means that each company should receive a 33% of their given customers per round. That gives 5 customers to the first picker, that is, \(S\)2. Recall that customers assignment is based on time-distance. Therefore, the 5 closest customers in the pool are assigned to \(S\)2. If the aforementioned proportions do not produce an integer number, as it is the case for \(S\)4 and \(S\)3, then the number of assigned customers is rounded up. This process is repeated until all the customers are assigned in forthcoming rounds. Then, the resulting VRP is solved using the biased randomization procedure described in Figure 5. Finally, when customers are served, they are reassigned to the chosen supermarket and this process is restarted for the next time window.
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 3 Results
### Evaluation of the Training Monitoring System
#### 3.1.2 Player Survey
When asked if they encountered any problems or difficulties using the system during the season, 72% of players stated that they encountered none, 24% stated they had difficulties with the process of logging into the system, and 4% stated they had difficulty remembering to use the system. Twenty-one percent of the surveyed players responded to the open-ended question regarding changes required to the system and two higher order themes were highlighted: (a) feedback and use of data (b) reminders. Fourteen percent of players stated they wanted to receive more detailed and frequent feedback on the data they had given and to see that data contribute to training prescription. One player reported: _\"I would like more feedback on the data collected and this to be discussed very quickly as part of our training\"_ while another stated he _\"didn't really know if the monitoring system was looked at by rugby coaches\"_. Seven percent of players stated they would like to see the addition of regular reminders to the system as highlighted by the player's quote: _\"give reminders to complete before and after training\"_.
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 2 Related Works
### Machine Learning Algorithms
#### 2.2.5 Memory-Augmented Deep Autoencoder (MemAE)  
In MemAE, an algorithm based on AE, an encoder and a decoder are used to compress and reconstruct input data. However, unlike the traditional AE, MemAE uses a memory module to store and extract the features of the input data. The encoder maps the input data to a low-dimensional space as shown in Equation (10).  
\[h_{t}=f(x_{t},m_{t-1})=\sigma(W_{xh}x_{t}+W_{hh}h_{t-1}+W_{mh}m_{t-1}+b_{h}) \tag{10}\]  
In this equation, \(x_{t}\) is the input data at time \(t\), and \(m_{t-1}\) is the external memory at time \(t-1\). \(W_{xh}\), \(W_{hh}\), \(W_{mh}\) are weight matrices, and \(b_{h}\) is the bias vector. MemAE computes the similarity between the input data and the memory to store and extract the features of the input data. Each position in memory is computed as shown in Equation (11).  
\[m_{t}[i]=(1-\alpha_{t})m_{t-1}[i]+\alpha_{t}h_{t} \tag{11}\]  
In this equation, \(i\) is the index of memory, \(\alpha_{t}\) calculates the similarity between the input data \(x_{t}\) and the memory \(m_{t-1}\), and determines the weight based on the similarity. Each location in the memory stores the extracted features of the input data, which are used as inputs to the decoder along with the low-dimensional vector \(h_{t}\) generated by the encoder. The decoder, Equation (12), takes the low-dimensional vector \(h_{t}\) and memory \(m_{t}\) as inputs to reconstruct the input data \(x_{t}\).  
\[r_{t}=g(h_{t},m_{t})=\sigma(W_{ht}h_{t}+W_{mt}m_{t}+b_{r}) \tag{12}\]  
In this equation, \(W_{htr}\), \(W_{mr}\), \(b_{r}\) are the weight matrix and bias vector of the decoder. The decoder's output \(r_{t}\) is the reconstructed result of the input data \(x_{t}\). The training is performed using the objective function as shown in Equation (13).  
\[\mathcal{L}(x_{t},r_{t})=||x_{t}-r_{t}||_{2}^{2} \tag{13}\]  
Using this structure, MemAE can store and extract the features of input data by using external memory. By using this memory-based structure, it can extract more features of data than Autoencoder, and these features can be used in various problems such as dimension reduction, classification, and clustering.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### Character-Level NOMA Scheme with Zero-Adding Interleaving  
Figure 8 illustrates a block diagram of a NOMA system that includes interleaving and zero element addition at the symbol level (from the perspective of the subscriber station). After the interference code has been processed, the bit level is entered. The Samsung IGMA (interleaved mapping multiple accesses) system employs a similar transmitting side arrangement [36].  
When data are processed at the character level in the IGMA scheme, a specialized display process is carried out. During this process, character sequences are placed through the insertion of null components and elements. As a consequence of this, sparse sequences of characters are produced, and the degree of sparseness can be adjusted by manipulating the display matrix [37].  
Figure 8: NOMA system structure with interleaving and zero elements.  
Figure 7: Design of a NOMA system’s character-level hybrid processing.
## 3 Physical Layer Improvement Proposals for PRIME and G3-PLC
### PRIME: Equalizer  
In a real propagation environment, the in-phase and quadrature (IQ) symbols extracted from the PLC signal appear distorted, due to the frequency response of the channel. This channel response may be estimated so the receiver is able to unmake that distortion and recover the transmitted data. That is the role of the equalizer, which will estimate the channel response using the pilot subcarriers in the header of the received frames. PRIME uses intercarrier differential modulations, which make it possible to decode its frames without channel estimation. Nevertheless, the equalization is intended to help in aligning the IQ symbols before soft decoding, reducing the error probability. However, using the pilot subcarriers for estimating the channel response has two drawbacks: the pilot subcarriers are not present in all the frequencies, and these samples are also distorted by the noise in the network.  
The equalizer will therefore need to extrapolate and estimate the complete channel response, based on the pilot subcarriers, which are discrete and impaired by disturbances. The channel estimation performs curve fitting, in order to estimate the parts of the channel where there is no pilot subcarrier information, and in order to smooth the effect of the noise impact in the pilot subcarriers information. The equalizer performance, its ability to represent accurately the channel response and its immunity level against noise will depend on the curve model selected for the fitting.  
For the previous PRIME study published in [10], a complex polynomial curve of degree 3 was selected to model the channel frequency response. The degree 3 was selected as a compromise between curve adaptability and stability. The coefficients are extracted through Least Square curve fitting, using the response of the pilot carriers as input. Polynomial curves, especially at higher degrees, might have negative properties. Figure 1 depicts the Runge effect, where small variations in the sampling of pilot subcarriers may cause large oscillations in the curves, increasing the resultant fluctuation with the degree of the polynomial.  
An alternative spline curve model will be studied for the channel estimation of the equalizer. This first identified improvement is an implementation option of the receiver, and as such, it does not modify the transmitted signal and is transparent to the PRIME standard.  
Studies available regarding interpolation based on spline curves are oriented to generic orthogonal frequency division multiplexing (OFDM) systems with diverse applications [16], but no studies particularizing the usage of spline curves on narrowband PLC systems have been found.  
A spline is a function defined in segments formed by pieces of polynomials. Figure 2 shows a flat channel frequency response impaired by tonal noise, so that the 25 th pilot subcarrier contains distorted information. The figure shows how spline estimation keeps a transfer function more stable
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 4 Experimental Results  
### The dSPACE Online Estimation Validation of the Proposed Method  
The proposed algorithm was verified through the following steps:  
(1) The SoC-OCV relationship curve was fitted by the interval static method, as shown in Figure 2;  
(2) The parameters of the second-order equivalent circuit model through DST test conditions were identified, as shown in Sections 2.2 and 2.3;  
(3) The MATLAB/SIMULINK block diagram of the four algorithms (UKF, CDKF, EKF, Proposed) was built;  
(4) The SIMULINK block diagram of the algorithm was compiled and downloaded to the ControlDesk of dSPACE through the RTI model of SIMULINK;  
(5) A virtual instrument for real-time estimation of lithium-ion battery SoC through ControlDesk was built;  
(6) The US06 and FUDS operating conditions were run through the battery test system, the current and voltage signals were collected in real-time through the current sensor and the DS1104 board, and the output voltage, voltage difference, SoC and SoC difference were displayed through the virtual instrument built by ControlDesk. The built-up battery test platform based on dSPACE is shown in Figure 6.