esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The accuracy of the ONIRY device (using an impedance spectroscopy method) in comparison to ultrasound results was 93.3% and 99.6%, using the Random Forest method for raw and re-analyzed data, respectively. The convergence of impedance and clinical evaluation was at the level of 79.8% (for raw data) and 97.0% (for re-analyzed data), both using Random Forest technique. The sensitivities were higher when using the k-NN technique in all analyzed cases.  
The results of the study shown that the test performed by ONIRY appears to be an accurate and promising technique for the detection of anal sphincter injury. This tool has the potential to become a widely accessible and easy to use diagnostic method, suitable also for screening purposes.  
Conceptualization, M.M. ([PERSON]), M.R., A.S., and K.B.-K.; methodology, M.M. ([PERSON]), M.R., and K.B.-K.; software, M.M. ([PERSON]) and M.R.; validation, M.M. ([PERSON]), M.R., A.S., A.D., and K.B.-K.; formal analysis, M.M. ([PERSON]) and M.R.; investigation, E.W., G.S., M.K., P.P., M.M. ([PERSON]), and K.B.-K.; resources, K.B.-K.; data curation, M.R.; writing--original draft preparation, M.M. ([PERSON]), M.R., and K.B.-K.; writing--review and editing, M.M. ([PERSON]), M.R., A.S., A.D., E.W., G.S., M.K., P.P., L.D., M.M. ([PERSON]), and K.B.-K.; visualization, M.R.; supervision, [PERSON].  
([PERSON]) and K.B.-K.; project administration, [PERSON] ([PERSON]) and K.B.-K.; funding acquisition, K.B.-K. All authors have read and agreed to the published version of the manuscript.  
The study was financed by the European Union as part of the BRIdge Alfa program, conducted in Poland by National Centre for Research and Development (POIR01.03.01-00-0004/16).  
All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Medical University of Lodz (RNN/321/18/KE).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing is not applicable to this article due to legal and privacy issues.  
The authors would like to gratefully acknowledge [PERSON] for performing the internal manuscript revision and giving us valuable remarks.  
[PERSON]-K. is a leader of the OASIS Diagnostics project, an author of the related patent and shareholder of OASIS Diagnostics SA., [PERSON] ([PERSON]) and [PERSON] are staff of OASIS Diagnostics SA, and [PERSON] is an independent consultant of OASIS Diagnostics SA. All received no remuneration on performing this study. Other declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Particle-Velocity-Based Mixed-Source Sound Field Translation for Binaural Reproduction
###### Abstract  
Following the rise of virtual reality is a demand for sound field reproduction techniques that allow the user to interact and move within acoustic reproductions with six-degrees-of-freedom. To this end, a mixed-source model of near-field and far-field virtual sources has been introduced to improve the performance of sound field translation in binaural reproductions of spatial audio recordings. The previous works, however, expand the sound field in terms of the mixed sources based on sound pressure. In this paper, we develop a new mixed-source expansion based on particle velocity, which contributes to more precise reconstruction of the interaural phase difference and, therefore, contributes to improved human perception of sound localization. We represent particle velocity over space using velocity coefficients in the spherical harmonic domain, and the driving signals of the virtual mixed-sources are estimated by constructing cost functions to optimize the velocity coefficients. Compared to the state-of-the-art method, sound-pressure-based mixed-source expansion, we show through numerical simulations that the proposed particle-velocity-based mixed-source expansion has better reconstruction performance in sparse solutions, allowing for sound field translation with better perceptual immersion over a larger space. Finally, we perceptually validate the proposed method through a Multiple Stimulus with Hidden Reference and Anchor (MUSHRA) experiment for a single source scenario. The experimental results support the better perceptual immersion of the proposed method.  
sound field translation; binaural reproduction; particle velocity; mixed-source model; MUSHRA
## 3 Results and Discussion
### Effects of the Temperature and Microstructure
The instability would result from the change in density during phase change and the acceleration of the \(\gamma\)/\(\delta\) interface. As a result, an inertial force was imposed on the transformed material. This inertial force destabilized to the irradiate surface, while during heating, the surface tension exerted a force attempting to maintain a flat surface [22]. This could be a reason for this claim. This can be observed in Figure 6a, from the FEM analysis, which was up to temperature enhancement. The larger deformations in the samples formed at a hatch spacing of 0.5 mm and beam diameters \(\geq\)0.7 mm (Figure 6b) might be attributed to the small equiaxed dendrite grains (Figure 9e,f) instead of the columnar austenite grains observed for the sample obtained at \(D_{b}\) = 1.0 and \(h_{s}\) = 0.5 mm. This might be attributed to the complete phase transformation, as above 1,422 K and up to the melting point (1,422 K \(<\) T \(<\) 1,713 K) ferrites form in the alloy [23]. Notably, the increase in formability of the material was proportional to the decrease in grain size of the heated sample [24; 25; 26]. The homogenized equiaxed fine grains in the microstructure were also associated with a decrease in anisotropy. The decrease in anisotropy in the microstructure with the homogenization increased the work hardening capacity owing to the modified distribution of dislocations and slip systems. Thus, the sheet metal resistance to plastic instability was reduced and the formability was increased.
## 3 Results and Discussion
### Predicted Results of \(Plsr\)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{\begin{tabular}{c} **Partitioning** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{**R2**} & \multirow{2}{*}{**RMSEC**} & \multirow{2}{*}{**RMSEP**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MRE**} \\ \cline{1-1} \cline{5-7} Ash & & RS & 0.9877 & 1.4037 & 4.4775 & 3.3863 & 0.1652 \\ content & KS & 0.9986 & 0.5169 & 3.8138 & 2.9159 & 0.1512 \\ SPXY & 0.9763 & 2.0126 & 2.5834 & 2.1780 & 0.1295 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Volatile \\ matter \\ \end{tabular} } & RS & 0.9923 & 0.4046 & 2.3024 & 2.0693 & 0.0712 \\ \cline{1-1} \cline{5-7} Volatile & KS & 0.9990 & 0.1657 & 1.4518 & 1.0799 & 0.0390 \\ \cline{1-1} \cline{5-7} matter & SPXY & 0.9664 & 0.9170 & 1.6006 & 1.3498 & 0.0468 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Calorific \\ value \\ \end{tabular} } & RS & 0.9942 & 0.2768 & 1.6967 & 1.3245 & 0.0598 \\ \cline{1-1} \cline{5-7} value & KS & 0.9904 & 0.3828 & 0.9514 & 0.8578 & 0.0377 \\ \cline{1-1} \cline{5-7} value & SPXY & 0.9946 & 0.2728 & 1.1139 & 0.8344 & 0.0350 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The prediction results of PLSR model with different partitioning methods. Figure 2: The prediction results of ash content, volatile matter and calorific value of coal by PLSR based on (**a1–a3**) RS; (**b1–b3**) KS; (**c1–c3**) SPXY. Figure 3: _Cont_.
## 2 Related Works
### ICN and ICN-Based Computing Network
On the other hand, SNR methods, based on a separate name resolution system, offer better scalability and compatibility with the existing TCP/IP network, aligning more closely with the needs of evolutionary ICN architectures. Therefore, the ICN architecture discussed in this paper employs the SNR method. Additionally, it is important to note that IP addresses are utilized as locators for content providers in the ICN environment, which is compatible with TCP/IP in this paper. Regarding the integration of ICN and Computing Network, the authors in [4] proposed a scheme for constructing a named Computing Network based on ICN, emphasizing that the routing scheduling in both the Computing Network and ICN emphasize \"content\" rather than the specific location of hosts. However, this method merely presented the concept without delving into specific mechanism designs. The authors in [14] designed a distributed resource scheduling architecture for Computing Network based on ICN, utilizing Conflict-free Replicated Data Types (CRDTs) as the underlying data structure and Remote Method Invocation for ICN (RICE) as the execution environment, demonstrating advantages in simplicity, performance, and fault recovery capability. Yet, this approach, predicated on the revolutionary ICN architecture, poses significant deployment challenges. Additionally, some novel ICNs based on the SNR method, incorporating SDN principles and technologies, have implemented a new feature of readdressing or late-binding for IP in the network layer, further enhancing the flexibility of ICN data routing [45]. Consequently, they possess advantageous conditions for realizing the dynamic scheduling of computing and network resources. Finally, a review of the related works on computing resource scheduling in Computing Network mentioned above is conducted, with the most representative studies selected and summarized in Table 1 below for reference.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Comparison with Other Classifiers  
To validate and test the performance of the proposed method, the SIR-C data of Qinghe Xinjiang, China, was used. The experimental verification included two aspects, which are the classification accuracy and computational burden.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Interference Cancellation-Based Protocols  
#### 5.3.1. Contention Resolution Diversity Slotted Aloha (CRDSA)  
As the name indicates, contention resolution diversity slotted Aloha (CRDSA) is based on the DSA protocol. In addition to sending two or more copies of a packet, CRDSA iteratively resolves the collisions that occur at the receiver through the use of a successive interference cancellation (SIC) mechanism [51]. For a given frame, nodes send two or three copies of the packet in different slots. The entire frame is then stored in a digital memory at the receiver. Furthermore, each packet includes control data that indicates in what slot the \"twin\" packet has been sent. In this way, when a packet is decoded correctly by the receiver, the latter retrieves the information regarding the arriving slot for the \"twin\" packet. With this information, the receiver is able to perform the interference cancellation method. The CRDSA protocol has been developed and included in the DVB-S2/RCS standard [63].  
In terms of communication performance, when CRDSA uses two copies per packet, it achieves a maximum performance of \(S=0.52\) for a given load of \(C=0.65\). In the case of a \(PLR=10^{-3}\), the supported channel load is \(C\simeq 0.05\), improving to \(C\simeq 0.26\) when the \(PLR=10^{-2}\). Note that CRDSA achieves a high normalized throughput performance for a low PLR; consequently, the protocol is also considered energy efficient: although each node must send each packet two times, no further retransmissions are required. Nevertheless, protocols based on interference cancellation have a high dependency on a good channel estimation, which adds complexity to the implementation. In addition, CRDSA also reports high demands regarding processing and storage capabilities at the receiver, together with requiring synchronization among nodes.  
To address the drawbacks reported for CRDSA, the protocol has evolved with adaptations such as multi-frequency CRDSA (MF-CRDSA) and spread spectrum CRDSA (SS-CRDSA) [64]. The former deals with the problem of requiring power peaks to send complete messages over small time slots. By dividing the available spectrum in multiple channels, the time slots in MF-CRDSA can be longer, thus avoiding very high power peaks but at the expense of the protocol's performance. The latter protocol, SS-CRDSA, addresses the problem of \"loops\" in the original CRDSA. A loop occurs when two different sources send replicas of their packets over the same slot, making it impossible to apply a successful interference cancellation (see for example users three and four in Figure 11). To avoid the loop, SS-CRDSA uses spread spectrum techniques and randomly associates codes to each packet.
## 4 Characteristics of Different Time Distribution Systems
### White Rabbit
#### 4.3.5 Application  
At the time of writing, no literature has documented real-world applications of WR in power grids. However, extensive experiments and research on WR can be found in the literature. Additionally, WR has been implemented in real-world applications in other sectors to enhance time synchronization accuracy.  
With growing demands for precise time synchronization in sectors such as science, finance, and telecommunications, WR is being increasingly adopted for various applications. The technology is currently under evaluation by the United States Department of Transportation as a potential GPS backup, and several telecom companies have presented preliminary findings at international conferences and professional societies [95].  
An overview of White Rabbit (WR) applications is provided in [92]. WR is used at GSI (Darmstadt, Germany) as the foundation of the General Machine Timing (GMT) system for accelerator control, operational since 2015. It is also deployed in the Cubic Kilometre Neutrino Telescope, located on the Mediterranean seabed, for precise timestamping. At CERN, WR facilitates fixed-latency data transfer, enabling real-time distribution of magnetic field values in accelerator operations.  
WR's application in long-distance optical fiber links was demonstrated in [99], which realized a 950-km WR link across existing communication fiber networks between Espoo and Kajaani, Finland.  
WR is also increasingly explored in power grid applications. Reference [90] discusses WR's use in synchrophasor networks, comparing the accuracy of WR-based PMUs with other technologies such as GPS and PTP, and highlighting WR's suitability for PMU sensing networks. In distribution PMUs, WR meets the high accuracy requirements essential for these networks, with total vector error (TVE) standards at least two orders of magnitude stricter than those for transmission PMUs (TVE below 0.01%) [100].  
A case study in [101] illustrates WR's use in distributing precise timing in substation automation systems. Because WR provides both accurate time and frequency transfer, it is particularly suited for applications in high-precision sampling.  
In general, due to its strong performance, WR can be implemented in all the applications listed in Section 2. The primary factor to consider is the cost.
# Nonparaxial Propagation Properties of Specially Correlated Radially Polarized Beams in Free Space
## 2 Nonparaxial Propagation Theory of an SCRP Beam
\begin{array}{ll}\stackrel{{\leftrightarrow}}{{W}}(x_{1},y_{1},x_{2},y_{2},z)&W_{xy}(x_{1},y_{1},x_{2},y_{2},z)&W_{xz}(x_{1},y_{1},x_{2},y_{ 2},z)\\ \stackrel{{ W_{yx}(x_{1},y_{1},x_{2},y_{2},z)}}{{W}}_{{xx}}(x_{1},y_{1},x_{2},y_{2},z)&W_{yy}(x_{1},y_{1},x_{2},y_{2},z)&W_{yz}(x_{1},y_{1},x_{2},y_{ 2},z)\\ \stackrel{{ W_{zx}(x_{1},y_{1},x_{2},y_{2},z)}}{{W}}_{{zy}}(x_{1},y_{1},x_{2},y_{2},z)&W_{zz}(x_{1},y_{1},x_{2},y_{2},z)\end{array} \Biggr{)}, \tag{9}\]  
where \(W_{\alpha\beta}(x_{1},y_{1},x_{2},y_{2},z)=\left\langle E_{\alpha}^{*}(x_{1},y _{1},z)E_{\beta}(x_{2},y_{2},z)\right\rangle\), \((\alpha,\beta=x,y,z)\) denotes the coherence properties of the field components \(E_{\alpha}(x_{1},y_{1},z)\) and \(E_{\beta}(x_{2},y_{2},z)\), which satisfy the Hermitian relation [66]:  
\[W_{\alpha\beta}(x_{1},y_{1},x_{2},y_{2},z)=W_{\beta\alpha}^{*}(x_{2},y_{2},x_{1 },y_{1},z),\ (\alpha,\beta=x,y,z). \tag{10}\]Applying Equations (5)-(8), we can obtain the following generalized vectorial Rayleigh diffraction integrals for treating the propagation of a nonparaxial partially coherent vector beam in the half space \(\mathbf{z}>0\):  
\[ \begin{array}{ll}W_{\alpha\beta}(x_{1},y_{1},x_{2},y_{2},z)&=\left(\frac{kz}{2 \pi}\right)^{2}\int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty} \int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty}\frac{\exp[-ik(R_ {1}-R_{2})]}{R_{1}^{2}R_{2}^{2}}W_{\alpha\beta}(x_{10},y_{10},x_{20},y_{20},0) \\ &\times dx_{10}dy_{10}dx_{20}dy_{20}\,\qquad(\alpha,\beta=x,y)\\ \\ W_{az}(x_{1},y_{1},x_{2},y_{2},z)&=-\frac{k_{2}^{2}}{4\pi^{2}}\int\limits_{- \infty}^{\infty}\int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty} \int\limits_{-\infty}^{\infty}\frac{\exp[-ik(R_{1}-R_{2})]}{R_{1}^{2}R_{2}^{2} }[W_{ax}(x_{10},y_{10},x_{20},y_{20},0)\\ &\times(x_{2}-x_{20})+W_{ay}(x_{10},y_{10},x_{20},y_{20},0)\,(y_{2}-y_{20})]\\ &\times dx_{10}dy_{10}dx_{20}dy_{20},\\ \\ W_{zz}(x_{1},y_{1},x_{2},y_{2},z)&=-\frac{k_{2}^{2}}{4\pi^{2}}\int\limits_{- \infty}^{\infty}\int\limits_{-\infty}^{\infty}\int\limits_{-\infty}^{\infty} \frac{\exp[-ik(R_{1}-R_{2})]}{R_{1}^{2}R_{2}^{2}}[W_{xx}\left(x_{10},y_{10},x_ {20},y_{20},0\right)\\ &\times(x_{1}-x_{10})(x_{2}-x_{20})+W_{xy}(x_{10},y_{10},x_{20},y_{20},0)(x_{1 }-x_{10})\\ &\times(y_{2}-y_{20})+W_{yx}(x_{10},y_{10},x_{20},y_{20},0)(y_{1}-y_{10})(x_{2 }-x_{20})\\ &\times W_{yy}(x_{10},y_{10},x_{20},y_{20},0)(y_{1}-y_{10})\left(y_{2}-y_{20} \right)]\times dx_{10}dy_{10}dx_{20}dy_{20},\end{array}  \tag{13}\]  
Under weak nonparaxial approximation, \(R_{j}\) can be written into a series [67,68]:  
\[R_{j}=r_{j}+\frac{x_{\beta}^{2}+y_{\beta}^{2}-2x_{j}x_{\beta}+-2y_{j}y_{j0}}{ 2r_{j}}, \tag{14}\]  
where \(r_{j}\equiv\sqrt{x_{j}^{2}+x_{j}^{2}+z},\ (j=1,2)\) is the position vector on the \(z\) plane.
# GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation
## 3 Proposed Methods
### Mixing the Patches and Labels  
Let \(X_{A}\in R^{W\times H\times C}\) and \(X_{B}\in R^{W\times H\times C}\) be two randomly selected training images with labels \(Y_{A}\) and \(Y_{B}\), respectively. By cropping a patch from \(X_{B}\) and then mixing it with \(X_{A}\), we obtain the augmented image \(X^{\prime}\in R^{W\times H\times C}\) as  
\[X^{\prime}=M\odot X_{A}+M^{\prime}\odot X_{B}, \tag{5}\]  
where \(M\in\{0,1\}^{W\times H}\) is a binary mask, \(M^{\prime}\) is the complement of \(M\), and \(\odot\) represents the element-wise multiplication. We also mix their labels to get the augmented label \(Y^{\prime}\) as  
\[Y^{\prime}=\lambda Y_{A}+(1-\lambda)Y_{B}\quad\text{s.t. }\lambda=1-\frac{W_{p}H_{p} }{WH}. \tag{6}\]  
The augmented image \(X^{\prime}\) contains the information of classes \(X_{A}\) and \(X_{B}\), and the augmented label \(Y^{\prime}\) also represents this fact, where the weights of the labels depend on the size of the cropped patches as determined by \(\lambda\). Algorithm 1 outlines the whole process of selecting and mixing salient source patches in target images of a mini-batch. Additionally, please refer to Section 5.1 for an analysis of different mixing strategies.  
Figure 2: GaussianMix data augmentation pipeline. (i) Blue box: Following the receptive field theory, influential pixels are selected as the source patch based on a Gaussian distribution. (ii) Orange dashed arrow: The source patch is then pasted onto the target image while maintaining the corresponding position.
## 4 Experiment and Analysis
### The Experiment with the Enhanced YOLO Model
#### 4.2.3 Analysis of Experimental Results  
This section delineates the experimental environment and results analysis for the improvement of the YOLOv5 model. The experiments were conducted on a Windows 11 system, within a Python 3.8 environment, and using the PyTorch framework version 1.13.1, with CUDA version 11.7. To ensure consistency in hardware for each experiment, all algorithms were trained on a proprietary dataset as mentioned in Section 3.2, and all experiments were performed on a server equipped with an NVIDIA RTX 3060 (NAVID, Santa Clara, CA, USA) with 8 GB of video memory and 16 GB of GPU memory. The model weights were initialized based on pre-trained models from the VOC2007 dataset.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Parameters** & **mAP\_0.5** & **mAP\_0.5:0.95** & **fps** \\ \hline Before Improvement & 0.766 & 0.766 & 81.45 \\ After Improvement & 0.806 & 0.806 & 83.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison before and after data augmentation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameter** & **Initial** & **Weak** & **Medium** & **Strong** \\ \hline Degrees: The angle of rotation & 0.0 & 0.1 & 0.1 & 0.1 \\ Translate: The extent of translation & 0.1 & 0.1 & 0.1 & 0.1 \\ Scale: The extent of scaling & 0.5 & 0.5 & 0.9 & 0.9 \\ Shear: The extent of shearing & 0.0 & 0.1 & 0.1 & 0.1 \\ Perspective: The degree of perspective transformation & 0.0 & 0.0 & 0.01 & 0.01 \\ FlipUD: The probability of flipping upside down & 0.0 & 0.1 & 0.1 & 0.4 \\ FlipLR: The probability of flipping left to right & 0.5 & 0.5 & 0.5 & 0.5 \\ Mosaic: The probability of using mosaic data & 1.0 & 1.0 & 1.0 & 1.0 \\ augmentation & & & & \\ Mixup: The probability of using mixup data & 0.0 & 0.1 & 0.1 & 0.4 \\ augmentation & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of four data augmentation parameters.  
Figure 9: Confidence function curves for training and validation sets after intensive data augmentation treatment.  
With the training cycles maintained at 300 to prevent overfitting and to achieve the optimal effect of activation functions, Table 5 compares the following four activation function configurations.  
Therefore, the model configuration that employs the SiLU activation function and intensive data augmentation is most appropriate for detecting encroaching fallen rocks on railway slopes.
## 3 State-of-the-Art MIMO-SDR Systems
### Mimo-Sdr Waveforms  
#### 3.2.1 MIMO Waveforms Analysis  
The waveforms implemented within MIMO systems could be coarsely classified into two categories: single-carrier waveforms and multi-carrier waveforms. In addition, multi-carrier waveforms are divided into two subgroups: orthogonal and non-orthogonal ones. Table 3.2.1.  
As OFDM is a strong candidate for fifth-generation (5G) telecommunication systems [80] and it is the most popular waveform used for MIMO systems, an additional OFDM derivative waveform will be introduced in the next section.  
However, the OFDM-MIMO system model is introduced in detail in the following. The theoretical foundation corresponding to every waveform is described in the attached references.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results and their interpretation, as well as the experimental conclusions that can be drawn.