esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Post-prostatectomy incontinence (PPI) is a significant functional complication that can negatively affect a patient's quality of life following radical prostatectomy (RP) [1, 2, 3].  
Although improved surgical techniques have been introduced, the incidence of PPI remains high, particularly during the early postoperative period [1]. Approximately 20% of patients experience consistent PPI as long as 1 year after RP [4]. In addition to surgical techniques, various demographic and anatomical factors reportedly influence PPI recovery [5; 6; 7; 8; 9; 10; 11; 12; 13]. Advanced age, high body mass index (BMI), a large prostate size and shape, the presence of comorbidities, a history of transurethral resection of the prostate (TURP), or a history of lower urinary tract symptoms have been associated with delayed continence recovery [14; 15]. Surgical factors, including surgery type and neurovascular bundle-sparing, are also associated with recovery rates from PPI [16]. Regarding anatomic features, previous studies have reported that preoperative membranous urethral length (MUL), which is related to urethral sphincter function, is strongly associated with PPI after RP [6; 7; 17]. Other anatomical pelvic structures around the urethra and postoperative bladder neck sparing have shown potential as predictors for recovery from PPI [5; 18; 19]. While multiple related factors are known, a complex relationship among them makes it difficult to predict an individual's likelihood of recovery from PPI, and there is a paucity of reports on predictive models for recovery from PPI following RP [16; 20].  
In recent years, machine learning approaches have become popular tools for constructing prediction models in medicine, primarily in complex multi-variable systems [21; 22]. Magnetic resonance imaging (MRI) is now a standard method for detecting significant prostate cancer using the Prostate Imaging-Reporting and Data System [23]. The diagnostic value of MRI means it can be used to delineate the anatomic pelvic structure around the urethra, preoperatively.  
Here, we describe a system to predict early recovery from PPI using machine learning algorithms and compared its diagnostic performance with that of conventional logistic regression (LR) using only preoperative clinical data and preoperative anatomical features.
# High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images
## 4 Experimental Results  
### The Environment and Parameters  
We performed extensive experiments to determine the efficacy of our proposed data-hiding technique. An assessment was conducted to determine the hiding capacity and imperceptibility. We utilized the common test grayscale images, known as airplane, baboon, boat, Lena, and peppers, as shown in Figure 9a-e, respectively. All of the test images are 512 \(\times\) 512 pixels in size. We performed binary confidential data which were produced by a pseudo-random number generator. The experimental test runs on Windows 10 Home Single Language with an Intel(R) Core(TM) i7-8550U CPU @ 1.80 GHz 1.99 GHz. Furthermore, all programs were generated and executed in MATLAB 9.8 (R2020a). The hiding capacity is identified by the number of bits, while the imperceptibility is identified by image quality. To assess image quality, the commonly used parameter is the peak signal-to-noise ratio (PSNR) as defined in Equation (11).  
\[PSNR=10\log_{10}\frac{255^{2}}{MSE} \tag{11}\]  
The mean square error (MSE) is defined in Equation (12).  
\[MSE=\frac{1}{N\times N}{\sum_{i=1}^{N}{\sum_{j=1}^{N}{\left({TI_{ij}-TI_{ij}} \right)^{2}}}} \tag{12}\]  
where \(TI_{ij}\) is the pixel in position of the \(i\)-th row and the \(j\)-th column of image \(TI\), and \(TI_{ij}\) is the pixel in the \(i\)-th row and the \(j\)-th column image \(TI\prime\)  
\(1\).
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 2 Recent Advances in Speech Recognition
Their experiments demonstrated the great potential of using large pre-training models estimated with unlabelled data and the impact of employing a different amount of labelled data for fine-tuning the model. For instance, using a large model pre-trained on 60,000 h and fine-tuned on 10 min of labelled training data, they reached Word Error Rates (WER) of 4.8 and 8.2 on test-clean/other of Librispeech, respectively, using a Transformer language model (LM). Increasing the labelled data to 100 h and using the same LM, they achieved a WER of 2.0 and 4.0 on the same test set. Nevertheless, self-supervised approaches are challenging mainly because there is not a predefined lexicon for the input sound units during the pre-training phase. Moreover, sound units can be variable in length since an explicit segmentation is not provided [21]. In order to deal with these problems, Facebook AI released HuBERT (Hidden-Unit BERT), a new approach for learning self-supervised speech representations [3]. Unlike Wav2 vec2.0, the HuBERT model learns not only acoustics, but also language models from continuous inputs. First, the model encodes unmasked audio inputs into meaningful continuous latent representations, which map to the classical acoustic modelling problem. In a next step, the model captures the long-range temporal relations between learned representations to reduce the prediction error. This work mainly focuses on the consistency of the \(k\)-means mapping from audio inputs into discrete targets, which enables the model to focus on modelling the sequential structure of input data. HuBERT has shown that it can overcome other SoA approaches on speech representation learning for speech recognition. In the work presented in [21], WER values of 1.8 and 2.9 were reported on test-clean/other of the LibriSpeech dataset, respectively, using a Transformer LM. On the other hand, at the language model level, deep Transformers or LSTM-RNN based language models have shown better performance than the traditional \(n\)-gram models especially during the re-scoring of the initial lattices [22]. More recently, the novel network architecture Self attentive simple Recurrent Unit (SRU) shows interesting improvements on the rescoring of the initial hypothesis [19]. The ASR engines presented in this work were built following the hybrid DNN-HMM, Quartznet, and Wav2 vec2.0 architecture basis, in order to compare the performance of the systems trained with the same corpora as well as their feasibility to be deployed in different platforms, from high-performance servers to embedded systems.
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 2 Theoretical Model
### LRC Model  
The eddy current sensor is connected to the current source via a coaxial cable. For the case of high frequencies, the capacitances from the coaxial cable and adjacent coil turns need to be considered. The  
Figure 1: Eddy current sensor configuration and working principle.  
Figure 2: (**a**) inductor–resistor (LR) model and (**b**) equivalent model of eddy current sensor.  
eddy current sensor system can be approximately modeled as the combination of electronic components consisting of an inductor, a resistor and a parallel capacitor, known as an inductor-resistor-capacitor (LRC) circuit model. It is shown in Figure 3.  
The impedance of the coil in Figure 3 can be calculated as:  
\[Z_{LRC}=\frac{(R_{0}+aR_{1})(1-\omega^{2}C_{0}(L_{0}-\alpha L_{1})+\omega^{2}C_ {0})+j\omega\Big{[}(L_{0}-\alpha L_{1})(1-\omega^{2}C_{0}(L_{0}-\alpha L_{1}) )-C_{0}(R_{0}+\alpha R_{1})^{2}\Big{]}}{[(R_{0}+aR_{1})\omega C_{0}]^{2}+[1- \omega^{2}C_{0}(L_{0}-\alpha L_{1})]^{2}} \tag{6}\]  
\[\alpha=\frac{\omega^{2}k^{2}L_{0}L_{1}}{{R_{1}}^{2}+\omega^{2}{L_{1}}^{2}} \tag{7}\]  
When a swept frequency signal passes through the sensor coil, there occurs a frequency known as electrical resonance, at which the impedance magnitude becomes a maximum and impedance phase is zero. The general expression for the resonant frequency of LRC model can be given by:  
\[\omega_{0}=\frac{1}{(L_{0}-\alpha L_{1})}\sqrt{\frac{(L_{0}-\alpha L_{1})-C_{ 0}(R_{0}+\alpha R_{1})^{2}}{C_{0}}} \tag{8}\]  
The impedance of coil is dependent on factors such as the gap between the sensor coil and the test specimen \(h\), the resistivity of the test specimen \(\rho\), the magnetic permeability of the test specimen \(\mu\) and the frequency of the excitation signal \(f\)[23; 24; 25; 26].  
\[Z=f(h,\rho,\mu,f) \tag{9}\]  
The tension in the test specimen changes the induced eddy current resulting in the measurable impedance variation in the sensor coil, which is acquired by an impedance analyzer.
## 2 Related Works
In [38; 39], the RACH resources are divided into M2M section, H2H section and a sharing section. Then a mixed strategy game method in [38] and a continuous time Markov chain in [39] are used to determine the optimal allocation parameters. The authors in [40] introduce a power prioritized random access scheme. The H2H devices send preambles in a higher power level while the M2M devices in a lower power level. The BS can distinguish the requests from H2H devices from M2M devices based on two detection levels and a timing alignment matching mechanism. Obviously, most existing works draw their attentions to RACH congestion problem without considering the following data transmission requirement. Taking signaling overhead into consideration, a theoretical analysis about supportable arrival rate against packet size is derived in [19]. The conclusion is that one stage protocol can accomodate more traffics with small size packets than two stage protocol. The advantages and drawbacks of various MAC protocols have been discussed in [13; 14]. Due to the differentiated QoS requirements between M2M traffic and H2H traffic, the traditional four-way handshake random access procedure may be not applicable to such a complex scenario. Therefore, it's a reasonable idea to design a hybrid MAC protocol to combine the strength of different MAC protocols. In [20], M2M devices can send data on the granted channels for Msg3 immediately after preamble transmissions. The authors in [41] divide the resources into contention only period and transmission only period. In contention only period, the M2M devices contend for the collision free access opportunities in the transmission only period in the way of carrier sense multiple access (CSMA) mechanism. In order to reduce the signaling overhead, the authors split preamble set into H2H part and M2M part [42]. The reserved preamble sequences for M2M devices are used in a CSMA/CA manner. However, the H2H traffic is not considered in [20; 41] while the data transmission is not considered in [42]. The HSTMAC proposed in [21] divides reserved uplink resources into S-ALOHA part for M2M small data transmission and TDMA part for traditional H2H traffic. A joint dynamic ACB and resource allocation algorithm has been designed to maximize the resource utilization under a fairness constraint. In this paper, we further analyze the equilibrium performance of HSTMAC under arbitrary retransmission limit. Based on the equilibrium analysis, a dynamic pre-backoff algorithm is designed for load balance of beta distributed M2M traffic and QoS provision for H2H traffic.
# An Innovative Recompression Scheme for VQ Index Tables
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & & \(n=2\) & & \\ \hline
**Images** & **SOC**[24] & **Improvement Rate** & **State Codebook**[26] & **Improvement Rate** & **Ours** \\ \hline Elaine & 0.4043 & 15.35\% & 0.3880 & 11.78\% & 0.3423 \\ Lena & 0.3662 & 10.28\% & 0.3538 & 7.13\% & 0.3286 \\ Pepper & 0.3726 & 12.27\% & 0.3602 & 9.25\% & 0.3269 \\ Wine & 0.3545 & 10.44\% & 0.3393 & 6.43\% & 0.3175 \\ Woodland & 0.4322 & 15.41\% & 0.4088 & 10.56\% & 0.3656 \\ Zelda & 0.4007 & 15.86\% & 0.3901 & 13.58\% & 0.3371 \\ \hline Average & 0.3884 & 13.41\% & 0.3733 & 9.92\% & 0.3363 \\ \hline \multicolumn{5}{c}{\(n=3\)} \\ \hline
**Images** & **SOC**[24] & **Improvement Rate** & **State Codebook**[26] & **Improvement Rate** & **Ours** \\ \hline Elaine & 0.3940 & 11.39\% & 0.3858 & 9.51\% & 0.3491 \\ Lena & 0.3697 & 6.52\% & 0.3644 & 5.16\% & 0.3456 \\ Pepper & 0.3760 & 8.43\% & 0.3704 & 7.04\% & 0.3443 \\ Wine & 0.3678 & 7.27\% & 0.3597 & 5.18\% & 0.3411 \\ Woodland & 0.4113 & 11.18\% & 0.3988 & 8.40\% & 0.3653 \\ Zelda & 0.3939 & 11.88\% & 0.3887 & 10.70\% & 0.3471 \\ \hline Average & 0.3854 & 9.52\% & 0.3780 & 7.73\% & 0.3487 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the bit rates of other schemes with the proposed scheme using a VQ codebook with 256 codewords. The same as for the metric when the codebook size is 256, Table 4 represents the experimental results for codebook size 512, and Table 5 represents the experimental results for codebook size 1024. It is worth noting that the conventional VQ compression bit rates are 0.5625 and 0.625 for codebook sizes 512 and 1024, respectively. From the experimental results, it is evident that the proposed method consistently outperforms the previous methods, demonstrating a significant improvement rate. Figures 12-14 show the bit rate comparison of the proposed scheme for different values of \(r\) with \(n=2\) and \(n=3\) when the VQ codebook size is 256, 512, and 1024, respectively. We can observe that when the VQ codebook size is 256 and \(r=4\) (this means 16 matches), the bit rate is the lowest. Similarly, when the VQ codebook size is 1024 and \(r=5\) (this means 32 matches), the bit rate is also the lowest. This is due to the increased number of codewords, resulting in more possible interference items being matched. Therefore, it becomes necessary to expand the scope of matching to increase the likelihood of finding a matching index. Exploiting this characteristic, we conducted an experiment using a
## 5 Results and Discussion
### Validation Test for Angular Momentum
Both experiments are based on the same wave and there is only difference in the presence or absence of angular momentum. For this experiment, the initial angular momentum of the water particles was randomly set in the first frame, and the water particles were designed to move while its value was maintained. As can be seen from the experimental results, if angular momentum is not applied, the foam particles only follow the motion of the water particles, so a monotonous pattern that moves depending only on the water particles on the liquid surface appears (see Figure 9b). However, if angular momentum is applied to the advection of the foam particles, it can be confirmed that the foam particles continuously show dynamic movement due to the angular momentum of the water particles (see Figure 9a). In particular, the angular momentum is different according to the change in altitude such as wave crest, and this causes the foam particles to be dispersed in various shapes than in Figure 9b. Similar experimental results were also shown in Figure 2. As shown in Figure 2, different intrinsic patterns are created depending on the location of the foam particles. However, while the  
Figure 8: Comparison of 2D particle-based smoke simulation results. The related video has been submitted as Supplementary Material. (**a**) Our method. (**b**) Without angular momentum. Figure 9: Comparison of movement and pattern change of foam particles. The related video has been submitted as Supplementary Material. (**a**) Our method. (**b**) Without angular momentum. previous methods are static methods like a mask map that does not consider the motion of water, our method reflects the motion of water better. This characteristic is more evident in the animation of successive frames than in single frame. In the 2D validation test shown earlier, the position of water particles was updated using angular velocity to show the effect of angular momentum. The angular momentum technique proposed in this paper reliably expresses the rotational motion of particles in various scenarios. In the next section, it can be seen that the turbulent flow of the foam is improved by applying angular momentum to the foam advection process.
## 4 Verification
### Comparison with the In Situ Test Results from the Kielder Experimental Tunnel  
The results calculated using the proposed solution were compared with the measurements carried out in the Kielder experimental tunnel. The data were mainly from Oreste and Peila [8]. The rock mass in which the tests were carried out was a mudstone (RMR (Rock Mass Rating) = 32 and Q (Quantitative Classification of Rock Mass) = 0.33) with a strata thickness of 8 m. The main properties of the rock mass and passive bolts are listed in Table 3. The values of \(K_{s}\) and \(K_{ep}\) were assumed to be 70 MPa and 30 MN/m, respectively. The tension force and radial displacement after 10 days from the initial bolt are potted in Figure 10. It can be observed that the calculation results agree well with the measured data.  
Figure 9: Comparison of the results calculated using the proposed model and numerical model (Hoek–Brown (H–B) rock mass): (**a**) \(\sigma_{r}\) and \(\sigma_{\theta}\), (**b**) \(u_{r}\), (**c**) \(F_{n}\), and (**d**) \(\tau_{s}\).
## 2 Materials and Methods
### Signal Separation Metrics  
#### 2.6.1 Statistical Analysis of Class Separation  
In order to assess the degree of class separation, analysis of variance (ANOVA) was performed, with a significance level of \(p<0.05\). ANOVA allows us to compare the variances of the means of different groups [45]. The F-ratio measures the difference between variance within and variance between groups. The entropies of each signal within the class were calculated, and then the F-ratio between the classes of pair A and pair B of synthetic signals, as well as between the control group and the group of patients with Alzheimer's disease, was calculated (Section 3.4). As a result, the larger the F-ratio, the stronger the separation between two classes of signals.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### ElGamal Public-Key Cryptography
#### 2.2.3 Digital Signature  
Assume that the user \(U_{1}\) wants to send a signed message to the user \(U_{2}\). A message \(m\) is signed by \(U_{1}\) as follows.  
1. \(U_{1}\) chooses a random number \(k\) such that \(\gcd(k,p-1)=1\);
2. \(U_{1}\) computes \(r=g^{k}\bmod p\);
3. \(U_{1}\) finds \(s\) satisfying \(m\)= (\(x_{1}r+k\)s) \(\bmod(p-1)\);
4. \(U_{1}\) attaches the signature (\(r,s\)) to the message.  
When \(U_{2}\) receives the signed message (\(r\), \(s\)) and \(m\), \(U_{2}\) can use \(U_{1}\)'s public key \(y_{1}\) to authenticate the signed message. The signature is valid if and only if \(g^{m}\equiv y_{1}r^{s}\bmod p\).
## 5 Results and Analysis
### The Number of Living Node  
The number of living nodes is also evaluated to examine the efficiency of each protocol. Figure 15 shows our simulation results. Under each scanning angle, the number of living nodes in the simple-link protocol is larger than that indicated by both RRP and DRS. Due to the special topology formed by simple-link, the death of a node is likely to bring about a dramatic decrease of connectivity. The successors of the dead node, which are supposed to relay packets from the dead node as well as the dead node's predecessors, may probably have a much smaller number of packets to transmit in this case. It helps decelerate their energy consumption rate, prolonging their lifetime. Nevertheless, both RRP and DRS aim to maintain the network connectivity for packet delivery. Though some nodes are out of energy during data transmission, other living nodes will continue transmitting packets to the BS without being affected by those dead ones. Thus, the number of transmission per node in RRP and DRS is greater than that in simple-link, eventually leading to a smaller value of living nodes. To a certain extent, the better performance achieved by the simple-link is a strong evidence of its inefficient data transmission. Note that within the same simulation time, as the scanning angle increases, the number of living nodes shows a decreasing trend in each protocol. This can be explained by the expended energy per transmission in the case of a larger scanning angle.
## 4 Finite Element Numerical Simulation of Casing Damage Well
### Von Mises Stress Analysis of Casing under Mudstone Creep  
#### 4.2.1 Selection of Creep Model  
Due to the presence of some hydrophilic clay minerals in the interstitial material of the reservoir in this block, as the injection and production parameters increase, the water content of the reservoir increases, and the clay minerals hydrate, causing rock sliding, compression, and shear of the casing. Therefore, it is necessary to analyze the stress on the casing under water immersion of the mudstone.  
Mudstone undergoes elastic deformation after compression, and its elastic modulus decreases when encountering water. Therefore, its elastic modulus is inversely proportional to its water content; Afterwards, the creep of mudstone reaches a steady-state creep process, and its deformation will gradually increase. Therefore, the creep of mudstone when encountering water should contain elastic and viscous elements, so the Maxwell model is mostly used to represent the creep of mudstone, and the constitutive expression is shown in Equation (17):  
\[\varepsilon=\frac{\sigma}{E}e^{\mu w}+\frac{\sigma^{N}}{\eta}te^{(\omega- \omega_{0})^{-1}} \tag{17}\]  
In the equation, \(\omega\) is the water content of mudstone (%); \(\omega_{0}\) is the saturated water content of mudstone under geological conditions (%); \(E\), \(\eta\), \(a\) is the rheological parameter of the rock; \(N\) is the non-linear index.  
Although the Maxwell model can describe the creep process of mudstone with water content, it cannot reflect the transitional stage of the mudstone creep process. Therefore, the Maxwell model has defects. In the later stage, the Maxwell model was modified and improved by adding a component related to water content to the component, as shown in Figure 10. If the water content of mudstone exceeds the natural water content in the  
Figure 9: [PERSON] stress curve of (**a**) X1 well and (**b**) X2 well.  
formation, the elastic modulus and compressive strength of the mudstone will change with changes in the water content, and its creep equation is:  
\[\left\{\begin{array}{l}\varepsilon=\frac{\sigma_{0}}{\eta}t+c,\,\sigma_{\omega_ {0}}<\sigma_{\omega}\\ \varepsilon=\frac{\sigma_{0}}{\eta}e^{\textit{arcu}}\Big{(}1-\exp\Big{(}- \frac{E}{\eta}\Big{(}\frac{1}{\omega-\omega_{0}}-\textit{a}\textit{w}\Big{)} \Big{)}\Big{)},\,\sigma_{\omega_{0}}\geq\sigma_{\omega}\end{array}\right. \tag{18}\]  
where w is the water content of mudstone in a stable state (%).
## 1 Introduction
The algorithm can select the reference node dynamically according to the network's predicted topology and mobility pattern which is more suitable for DUANs with mobile sensor nodes. The rest of this paper is organized as follows. In Section 2, we will describe the network model including the network architecture, the mobility model, and the time model. Then, in Section 3, we will describe the proposed dynamic reference selection-based self-localization algorithm for DUANs in details. Following that, we will analyze the simulation results of the proposed algorithm in Section 4. Finally, we conclude the paper in Section 5.