esa-sceva/satcom-chunk-collection huggingface dataset:
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 1 Introduction
The main contributions of this article are summarized as follows:  
* An overview of typically adopted PRBSs in PMCW radar systems, namely m-sequences, Gold sequences, Kasami sequences, almost perfect autocorrelation sequences (APASs), zero correlation zone (ZCZ) sequences, and Golay sequences, and their implications on range and velocity ambiguities for different PRBS lengths. For the aforementioned analyses, an automotive (1 GHz bandwidth and 79 GHz carrier frequency) and a gesture recognition application (12.5 GHz bandwidth and 140 GHz carrier frequency) were considered. For the Golay sequences, both the use of complementary pairs and the use of a single Golay sequence as a variation of ZCZ sequences, which to the best of the authors' knowledge has not been previously reported in PMCW radar literature, are adressed. * An analysis of the Doppler shift tolerance of the oversampled PACFs of the aforementioned PRBSs for different sequence lengths. For this analysis, a normalized Doppler shift parameter introduced in a previous article [23] was adopted and sequences that achieve similar maximum unambiguous range were compared by using peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) as metrics. The remainder of this paper is organized as follows. Section 2 outlines the system model of a SISO-PMCW radar system for a generic PRBS, showing closed-form expressions for multiple stages of the radar signal processing chain and formulating the problem of correlation-based range sensing under Doppler shifts. Next, Section 3 presents metrics for evaluating the distortion of the PACF of a PRBS caused by Doppler shifts, which ultimately yields distorted range profiles. A comparative performance analysis is then carried out in Section 4, where the relevant aspects of the considered SISO-PMCW radar system are assessed and the PPLR, PSLR, and ISLR for typically adopted PRBSs in PMCW radar are evaluated to measure the Doppler shift-induced degradation of range profiles. Finally, concluding remarks are given in Section 5.
# Curable Area Substantiation of Self-Healing in Concrete Using Neutral Axis
## 3 Self-Healing Assessment  
### Strength  
The flexural strengths of the specimens at four points were compared in this study. Strength is the most widely used and most comparable criterion in the case of a specimen under a given condition. When using flexural strength, it is important to fix the points that are the subjects of comparison, because of which the degree of evaluation stabilizes accordingly. First, the load at the unloading point of the 1 st loading was compared to the maximum load of the 3 rd loading. If the latter was found to be larger than the former, the healing effect was considered to occur, although the age effect of the undamaged part may have played a role as well. That is, the increase in flexural strength after healing can be ascribed not only to the healing effect but also the age effect. Second, the load at CMOD 0.05 mm of the 3 rd loading was compared to the load at the same point of the 2 nd loading. This comparison made it relatively straightforward to compare the flexural strength recovery before and after healing because these values were measured with the same degree of damage. This comparison may be less affected by the age effect because it was made at the point at which the crack does not advance.
# Study of Discharge Characteristics on Ignition Performance via High-Speed Imaging in a CVCC
## 3 Results and Discussion
### Simultaneous Visualization Results
Due to the little effect on the discharge channel behaviors, the initial flame kernel was smaller than that in the low dilution ratio condition, even using the high ignition energy coil C.  
When under the high flow condition, comparing the direct photographs at 0.5 ms of coil C in Figure 10a,c, it should be noticed that the discharge channel length of the high flow condition is longer than that of the basic condition. That is to say, the discharge channel stretches faster when enhancing the flow velocity. Due to the initial flame being formed along with the discharge channel, a longer discharge channel length results in the initial flame propagating farther. As shown in Figure 10c, all the initial flames detach and move downstream at 2.5 ms for all the coils, which prevented the initial flame from forming the self-sustained flame. Nevertheless, high current coil B and high energy coil C have a larger flame area at 1.5 ms than that of the standard coil A, which indicates that high current could ensure the initial flame generates under such an enhanced flow condition. While standard coil A with a lower discharge current results in a smaller initial flame kernel that disappears after the discharge process. On the other hand, the high flow velocity not only lengthens the discharge channel length, but also wrinkles the initial flame. As shown in Figure 10c of coil A, the luminance of the initial flame is lower than the basic condition (Figure 10a). Furthermore, according to Figure 10c of coil B, the initial flame starts to detach the spark plug at 1.8 ms, which is earlier than the high diluted condition (Figure 10b). However, the high energy coil C maintains the initial flame growth well for the higher luminance of the initial flame and holds the initial flame around the spark plug for more time compared to low ignition energy coils. Thus, it can be considered that increasing the flow velocity may improve the combustion speed only if increasing the ignition energy correspondingly as the flow intensity has both an influence on the discharge channel stretching and flame kernel formation. According to the discussion above, discharge coils with different discharge characteristics affect the generation of the discharge channel and also make some contribution to the initial flame. The mixture composition and flow velocity have a great influence on the initial flame growth and propagation. The quality of initial flame generation affects the combustion directly, and quantitative analysis of the initial flame helps support the explanation of the combustion results. Therefore, the value of the flame area and the mean luminance are measured to estimate the growth of the initial flame, and the results are shown in the next section.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 2 The Theory of Particle Damping Energy Dissipation
### Analysis of Particle Damping Energy Dissipation Mechanism  
Assuming that no sliding occurs between particles, the collision energy dissipation in the normal and tangent directions is expressed as  
\[\left\{\begin{array}{l}\Delta E_{n}=\frac{1}{2}\frac{m_{i}m_{j}}{m_{i}+m_{j}} \big{(}1-e_{n}^{2}\big{)}\big{|}v_{m}\big{|}^{2}\\ \Delta E_{t}=\frac{1}{2}\frac{m_{i}m_{j}}{m_{i}+m_{j}}\big{(}1-e_{t}^{2}\big{)} \big{|}v_{rt}^{-}\big{|}^{2}\end{array}\right. \tag{15}\]  
where \(e_{n}\) and \(e_{t}\) are the normal and tangential recovery coefficients between particles, which are expressed as  
\[\left\{\begin{array}{l}e_{n}=\frac{v_{m}^{+}-v_{n}^{+}}{v_{m}^{-}-v_{n}^{-}} =\frac{v_{m}^{+}}{v_{m}^{-}}\\ e_{t}=\frac{v_{rt}^{+}-v_{n}^{-}}{v_{rt}^{-}-v_{n}^{-}}=\frac{v_{rt}^{+}}{v_{ rt}^{-}}\end{array}\right. \tag{16}\]  
where \(v_{in}\) and \(v_{jn}\) and \(v_{lt}\) and \(v_{lt}\) are the normal and tangential velocities of the particles, with the superscripts '\(-\)' and '\(+\)' representing the conditions before and after the collisions, respectively.  
When the tangential force between particles is greater than the maximum static friction force, friction energy dissipation will replace tangential collision energy dissipation. The friction energy dissipation is expressed as  
\[\Delta E_{f}=\mu_{f}|F_{n}\delta_{t}| \tag{17}\]  
where \(\mu_{f}\) and \(\delta_{t}\) are the friction coefficient and the tangential relative displacement between particles, respectively. \(F_{n}\) is the normal force between particles, the expression of which is obtained from the Hertz contact theory [28]:  
\[F_{n}=\frac{4}{3}E^{*}\sqrt{R^{*}}\delta_{n}^{\frac{3}{2}} \tag{18}\]  
Due to the small contact area between the particle and the container wall, the analysis method for energy dissipation is also applicable to the energy dissipation between the particle and the container wall. Therefore, the energy dissipation for the particle damping system is expressed as  
\[E=\Sigma\Delta E_{n}+\Sigma\Delta E_{t}+\Sigma\Delta E_{f} \tag{19}\]  
where \(\sum\Delta E_{n}\), \(\sum\Delta E_{t}\), and \(\sum\Delta E_{f}\) represent the total normal and tangential collision energy consumption, and friction energy consumption of the particle collision damping system, respectively.
# Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Process
## 4 Conclusions  
An experimental method is presented to measure the bolt clamping force by acoustic signatures. The change of dynamic characteristics during bolt fastening were investigated by the vibration responses analyzed using cepstral analysis. The short-time spectrum of the vibration responses measured during bolt fastening showed the change of the natural frequency of the bolt. The increased clamping force induced the increasing natural frequency. As the tightening force increased, the vibration components occurred at higher frequencies. The frequency components inevitably induced a change in quefrency from the cepstral analysis. The change in quefrency components was used as the main feature of the k-means clustering algorithm. When the torque was used to monitor the clamping force, there were deviations of more than 30%, as reported in previous studies. When the vibration responses were used to monitor the clamping force, the deviation from the actual value measured by the load cell was smaller than 10%. To apply the proposed method in actual production lines, vibrations of the nutrunner were measured. The responses influenced by the operation of the rubber were analyzed and discarded during the estimation process. By using the components that have a strong influence on the bolt's natural mode, the estimation was achieved with similar accuracy as that using the vibration of the clamped part. Thus, measurement of the clamping force of bolts fastened in a nondestructive  
\begin{table}
\begin{tabular}{c c c} \hline
**Torque Measurement** & **Load Cell Vibration Measurement** & **Nut Runner Vibration Measurement** \\ \hline
35.83\% & 14.99\% & 9.57\% \\ \hline \end{tabular}
\end{table}
Table 1: Deviations of the clamping force for the constant torque tightening.  
Figure 8: Change of the spectral characteristics with time (the increase of the clamping force) for the vibration measured on the nutrunner.  
Figure 9: Scatter of clamping force estimated using accelerometer on nutrunner. **(a)** Estimated clamping force by vibration measurements and **(b)** relative deviations.  
and practical manner for production and with high accuracy was presented. With the use of vibrations in audio frequency ranges (20 Hz-20 kHz), the proposed method benefits big-data configurations and data augmentation necessary for unsupervised learning. In this study, the speech recognition algorithm was applied to the method only to measure the tightening force of the bolts. This method can be applied to experiments with variables that continuously change with time. The proposed methodology also has applications for experiments where the measurement response changes due to attenuation with distance.  
Writing--original draft, G.T.; Conceptualization and Methodology, G.T. and J.P.; Investigation and Validation, G.T. and J.G.; Data curation, J.G.; Writing--Review and Editing, G.T. and J.P.  
This research was supported by Hyundai Motor Group.  
The authors declare no conflict of interest.
## 5 Cluster Formation and Parameter Optimization
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & **Cluster Size** & **Performance Metric** & **Simulation Tool** \\ \hline MOBIC [1] & NA & Radio propagation & ns-2 \\ \hline [PERSON] [2] & Based on speed & Speed & No Simulation \\ \hline Su [3] & Based on traffic direction & Direction & MATLAB \\ \hline Rawsheth [4] & Based on speed & Speed, location, and direction & C testbed \\ \hline Maskelar [5] & NA & Location and direction & NCTUns \\ \hline RMAC [6] & NA & Speed, location, and direction & ns-2 \\ \hline APROVE [7] & Based on traffic direction & Distance and speed & ns-2 \\ \hline DBC [8] & Depends on density & Node density, link quality, node reputation, and traffic conditions & J\(\mathrm{ST}\)/SWANS++, VanetMobiSim \\ \hline Almalag [9] & Depends on radio propagation & lane with the most traffic & ns-3 \\ \hline Wang [10] & NA & Vehicle density, link quality, and sustainability & MOVE, ns-2 \\ \hline ALM [11] & NA & Relative mobility & SUMO, SIDE/SMURPH \\ \hline VWCA [12; 53] & Flexible & Direction & MATLAB \\ \hline AMACAD [13] & Flexible & Speed, location, and direction & JAVA testbed \\ \hline HCA [14] & Based on radio propagation & Radio propagation & OMNET++, SUMO \\ \hline ASPIRE [15] & NA & Network characteristics & ns-2 \\ \hline Zhang [16] & NA & Relative mobility & ns-2 \\ \hline Maskelar [17] & Based on radio propagation & Direction & NCTUns \\ \hline RWCP-MOFA [52] & Adaptable & PDR, no. of clusters, control & NETSIM, MATLAB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clustering overview.
# Classification of Architectural Heritage Images Using Deep Learning Techniques
## 3 Results
### Convolutional Neural Networks (AlexNet, Inception V3)
#### 3.1.2 Fine-Tuning of an Inception V3 Network  
The Inception V3 network was used [51] to fine-tune a network, in its 2015 version of Google's Inception architecture for image recognition. Inception V3 is trained using the 2012 data from the ImageNet Large Scale Visual Recognition Challenge. The \"Inception\" modules were introduced in the GoogLeNet network [18], which concatenated filters of different sizes and dimensions into a new single filter. Each \"Inception\" layer consists of six convolutional layers and one pooling layer. Although the increase in model size (number of layers, network depth) tends to translate into immediate accuracy gains for most tasks (assuming enough labeled information for training is provided), the associated computational cost is usually a limiting factor in various use cases. In this way, the GoogLeNet Inception architecture was designed to work well, even under strict memory and computational budget constraints. Thus, GoogleNet used only five million parameters, which was a reduction compared to its predecessor AlexNet, which used 60 million parameters. Although other networks, such as VGGNet [17], offer a great architectural simplicity, this has a high cost: the evaluation of the network requires a great effort of computation (VGGNet used three times as many parameters as AlexNet). Therefore, this has enabled highly efficient networks such as Inception to be used in Big-Data scenarios, where large amounts of data need to be processed at a reasonable cost, or scenarios where memory or computational capacity is inherently limited, for example in vision applications on mobile devices.  
Our training uses the pre-trained model and replaces the final layer of the network, performing a new training using the ten categories we have considered. In this way, the lower layers that have been pre-trained can be reused for our recognition task without the need to modify them.  
The hyperparameters used in this case are those shown in Table 3.  
Figure 4: (**a**) Visualization of the first layer at the beginning of the training; and (**b**) visualization of the first layer at the end of the training.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Momentum**} & **Initial** & \multirow{2}{*}{**Decay**} & **Number of** & **Weight** & **End Learning** & **Batch Size** \\  & **Learning Rate** & & **Epochs Per Decay** & **Decay Rate** & **Rate** & **Batch Size** \\ \hline
0.9 & 0.01 & 0.94 & 2 & 0.00004 & 0.0001 & 32 \\ \hline \end{tabular}
\end{table}
Table 3: Hyperparameters used in the fine-tuning of an Inception V3 network.  
In this test, we obtained an accuracy value of 0.8943 using 64 \(\times\) 64 pixels in training and a value of 0.9155 using images of 128 \(\times\) 128 pixels. As a reference, the recall for the two most likely classes is 0.9676. The time required to achieve convergence has, as expected, been lower than in the previous case.
# Mesenchymal Stem Cell Applications in Spine Disorders: A Comprehensive Review
## 3 Spinal Cord Injuries  
The management of spinal cord injuries (SCIs) represents a challenge, as patients and physicians are confronted with numerous issues ranging from sensory and motor impairment to bed sores [34]. It is estimated that anywhere from 300,000 to 1 million people currently live with chronic SCIs [35; 36], and it is thus of paramount importance to investigate possible treatment options to limit the damage to the neural structures after trauma and improve the patients' quality of life after chronic damage has set in [34]. The rationale in the use of MSCs for the treatment of SCIs lies in the known ability of these cells to regenerate damaged nervous tissue [37; 38; 39] and in the possibility of these cells to act as scaffold for the regeneration of the lost nervous tissue [40; 41]. The stem cells are deliveredintravenously, intraparenchymally, or intrathecally, with or without the use of scaffolds and adjuvants or anti-inflammatory drugs such as minocycline [34,39]. Most available data regard animal studies [42], and the clinical data currently present in the literature are very limited. Recently, seven clinical studies regarding the use of MSCs in patients affected by spinal cord injuries have been published. One clinical trial on four patients with thoracic SCIs treated with midline bilateral human spinal cord-derived neural stem cell injection showed some degree of sensory and motor improvement without any reported adverse events [43]. Two further SCI patients observed a sensory and functional improvement one year after transplantation of MSCs on a collagen scaffold at the injury site [44]. [PERSON] et al. reported the results obtained for nine SCI patients treated with intrathecal MSC administration, with an improvement in sensitivity and motor control without any adverse events [45]. In one case report, a patient with an SCI following altanto-occipital subluxation was treated with intrathecal MSCs harvested from the iliac crest. In this case, no clinical or radiological improvement could be observed [46]. Two further studies assessed the safety and feasibility of MSC therapy for SCIs [47,48], but one was discontinued as the obtained results did not prove sufficient clinical efficacy [47]. A recent meta-analysis established that MSC treatment can improve impairment, sensory scores, and electrophysiological parameters without causing major adverse events [10]. While the data from the available studies are encouraging, there is no consensus on the ideal way to administer MSCs in SCI patients, and the evidence for the effectiveness of this therapeutic approach is still lacking. Currently, 14 clinical trials on the use of MSCs for SCIs are registered on clinicaltrials.gov and will hopefully offer new insight on this topic (status: recruiting). The key information regarding the available studies is presented in Table 2.
# Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
## 4 Discussion
This could justify the time required to include DL networks in radiomics analyses by removing the user-dependence and achieving accurate prostate segmentations (DSC = 90.89%) using a few training examples. In this way, our model can be used to improve prognosis evaluation and prediction of patient outcomes, allowing the personalization of patient management. However, the results presented in this study derive from the performance of DL networks on proprietary imaging datasets; for routine clinical application, it should be mandatory to test and validate the proposed methods in multicenter studies and/or on a large set of publicly released representative training data, such as PROMISE12 [42]. Moreover, in the present study, we test DL networks for the whole prostate gland segmentation, with ENet demonstrating the best performance; however, a main clinical goal is the segmentation of related prostatic structures or substructures such as the prostatic zones (transition, central and peripheral), neurovascular bundles or seminal vesicles. The performance of DL networks, especially ENet, on this topic should play an essential role in many medical imaging and image analysis tasks such as cancer detection, patient management, and treatment planning including surgical planning, and should be analyzed in future works. Automatic segmentation of the whole prostate gland and prostatic zone (transition, central and peripheral) without inter-user variability will lead, in the future, to a correct localization of prostate cancer. This result will increase the reliability of computer-aided design (CAD) algorithms which will help automatically create PI-RADS zone maps to reduce inter-user variability among clinicians when interpreting prostate MRI images. In this scenario, radiomics analysis should be performed automatically providing information that can lead clinicians on the management of patients with prostate cancer.