esa-sceva/satcom-chunk-collection huggingface dataset:
# Meniscal Tear Management Associated with ACL Reconstruction
## 3 Management of Specific Types of Meniscal Lesions Associated with ACL Reconstruction  
### Ramp Lesions  
Described in 1988 by [PERSON], the \"ramp\" lesions are a particular type of meniscal lesion involving the peripheral portion of the posterior horn of the medial meniscus [85].  
This pathology is frequently associated with ACL ruptures (9-24%) and has been historically underestimated. Ramp lesions are often missed since they are usually located in a posteromedial \"blind spot\", visually accessible only through the trans-notch Gilchrist-visualization technical maneuver and due to the low sensitivity of preoperative MRI [86].  
If a meniscal ramp lesion is suspected, an additional posteromedial portal should be created for better evaluation [87]. Soft tissue debridement could aid the identification of hidden lesions under an intact capsule [88]. Therefore, ramp lesions should always be verified through systematic exploration, and repair should be performed if necessary. Failure to recognize and repair a ramp lesion contributes to residual anteroposterior instability in the ACL-reconstructed knees and worse results [86; 89; 90; 91].  
Ramp lesion repair is correlated with significantly better biomechanical results and outcomes (reduce our percentage of secondary meniscectomy) [92; 93], regardless of the technique [88; 89; 90]. Needling/trephination of a recent injury can be also a valuable option in short, stable lesions [94].
# Shear-Actuation and Vibrometer Reception of Penetrating Ultrasonic Guided Wave Modes in Human Tibia
###### Abstract  
The hollow long bones of the human appendicular skeleton are known to support the propagation of ultrasonic guided waves, whose potential for diagnosing bone health is being investigated. In this study, ultrasonic guided waves propagating in the diaphysis of human tibia are characterized experimentally and numerically in the frequency range around 200 kHz. The experiment involves a unique combination of omni-directional shear transducer-based excitation and detection using a 1D laser Doppler vibrometer. The cluster of phase velocities obtained from a linear array of time-history data using space-time Fourier transform is found to be in the non-dispersive low-phase velocity region of the dispersion curves obtained for a tibial cross-section. Time-domain finite element analysis revealed that the displacement components normal to the surface are significant, even though the loading is from a shear transducer. Furthermore, semi-analytical finite element analysis revealed that the wave structures of the wave modes contained within the cluster of low-phase velocity modes are consistent with the displacement profiles obtained from the time-domain analysis. The experimental results show that the low-phase velocity mode cluster has sufficient intensity to propagate axially at least 85 mm in the mid-diaphyseal region.  
ultrasonic guided waves; cortical bone; shear transducer; Fourier transform; finite element analysis; wave structure +
Footnote †: journal: Applied Physics Letters
Photonics_**2014**, \(1\), 473-487; doi:10.3390/photonics1040473  
_photomics_  
**ISSN 2304-6732**  
www.mdpi.com/journal/photonics  
Performance Analysis of a Hybrid Raman Optical Parametric Amplifier in the O- and E-Bands for CWDM PONs  
**[PERSON] 1,*, [PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 1,\({}^{\dagger}\)**  
1 Department of Electrical Engineering, City College of City University of New York, 160 Convent Avenue, New York, NY 10031, USA; E-Mails: [EMAIL_ADDRESS] (N.M.); [EMAIL_ADDRESS] (R.D.)
2 Engineering Science & Physics, The College of Staten Island of City University of New York, 2800 Victory Boulevard, Staten Island, NY 10314, USA; E-Mails: [PERSON]] (N.A.A.); [EMAIL_ADDRESS] (D.R.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 1 November 2014; in revised form: 25 November 2014 / Accepted: 25 November 2014 / Published: 1 December 2014_  
**Abstract:** We describe a hybrid Raman-optical parametric amplifier (HROPA) operating at the O- and E-bands and designed for coarse wavelength division multiplexed (CWDM) passive optical networks (PONs). We present the mathematical model and simulation results for the optimization of this HROPA design. Our analysis shows that separating the two amplification processes allows for optimization of each one separately, e.g., proper selection of pump optical powers and wavelengths to achieve maximum gain bandwidth and low gain ripple. Furthermore, we show that the proper design of optical filters incorporated in the HROPA architecture can suppress idlers generated during the OPA process, as well as other crosstalk that leaks through the passive optical components. The design approach enables error free performance for all nine wavelengths within the low half of the CWDM band, assigned to upstream traffic in a CWDM PON architecture, for all possible transmitter wavelength misalignments (\(\pm\)6 nm) from the center wavelength of the channel band. We show that the HROPA can achieve error-free performance with a 170-nm gain bandwidth (e.g., 1264 nm-1436 nm), a gain of \(>\)20 dB and a gain ripple of \(<\)4 dB.  
keywords:optical parametric amplifier; Raman amplifier; highly nonlinear fiber; coarse wavelength division multiplexing +
Footnote †: journal: Journal of Scientific and Applied Physics
## 2 Observer-Based Estimation
### GPS-Aided Estimation  
A previous extensive review of VSA estimation through GPS-aided methods has been recently carried out by [PERSON] et al. [68]. For the sake of brevity, readers are invited to refer to [PERSON]'s work. Nevertheless, some important considerations are worth highlighting.  
The majority of the GPS-aided methods studied in the literature use a simple dynamic vehicle model with linear tyres (due to its simplicity) with an EKF as an estimator aided with GPS data [75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87]. Also, the most recent works, i.e., those published after [PERSON]'s work, are focussed on this strategy [88; 89; 90; 91; 92; 93], rather than the use of a kinematic vehicle model. They all obtained promising results, especially those who have further extended the GPS use to investigate tyre-road friction [89; 93], roll, and banking angle [94], or with a dual GPS setup [74; 91].  
It is worth noticing that some major issues come into play with this approach. First of all, GPS receivers suffer from high price, low update frequencies, and sensitivity to the external environment. They are also sensitive to surroundings (e.g., trees or buildings), which can cause temporary outages. As a matter of fact, in most cases, vehicle dynamics sensors such as accelerometers and gyros operate at 100 Hz. However, due to hardware limitations, GPS receivers can operate at most at 50 Hz, whilst cheaper devices suitable for this application operate at less than 10 Hz only. Therefore, any dynamical changes in between samples are undetectable. Higher-sampled GPS are available, but their cost rises exponentially with the sample rate. The difference between the two sampling rates leads to an error during the inertial sensors (IS) and the GPS fusion process, since IS measurements are forced to be down-sampled in order to be synchronised with GPS.  
[PERSON] et al. [68; 95] reported a simple way to overcome the problem. They suggested fitting a second-order curve on the down-sampled VSA estimation in real-time in order to provide a continuous estimation between GPS samples as well, without affecting the computational burden required. This method showed some overshooting problems during sudden changes in vehicle lateral velocity, but it is helpful to solve the issue regarding discontinuity in the VSA estimation.  
A different solution to overcome the low sampling rate issue has been proposed by [PERSON] [70; 71; 81]. This method uses a KF, together with the data fusion from GPS and IS, to predict the IS biases when GPS data are available, and integrate the \"corrected\" IS signals when such data become unavailable. Hence, this allows the estimation of IS biases as well as the attenuation of the errors (drifts) in the sensor signal.  
However, [PERSON] et al. in [70; 71; 81], proposed a single GPS receiver is used, resulting in the ability to predict the gyro bias only when the vehicle is travelling straight. A dual GPS antenna setup is therefore introduced by [PERSON] et al. in [82] and [PERSON] et al. in [83] that allows the yaw angle to be measured at any instant.
###### Abstract  
This study focuses on the remotely operated underwater vehicle (ROV) and addresses key issues in existing simulation systems, such as neglecting the influence of ocean currents on the ROV's trajectory or only simulating the impact of ocean currents instead of combining wake flow and ocean currents. Additionally, the visualization capabilities of current simulation systems still have room for improvement. This paper develops a three-dimensional path simulation system for ocean inspection robots to tackle these challenges based on MATLAB and Simulink. The system optimizes the drag matrix of the original simulation model by decomposing the sea current into three directional components in three-dimensional space and simulating the relative velocity in each direction separately; it introduces the influence of the current wake, thus more accurately realizing the trajectory simulation of the ROV under the current perturbation. Experimental results demonstrate high consistency between the optimized model's simulation outcomes and theoretical expectations. The proposed system significantly improves trajectory evolution stability and consistency, compared to traditional models. The findings of this study indicate that the proposed optimized simulation system not only effectively verifies the applicability of control algorithms but also provides reliable data support for ROV design and optimization. Additionally, it lays a solid foundation for further developing intelligent underwater robots based on Internet of Things (IoT) technology.  
three-dimensional path simulation; MATLAB; ocean currents +
Footnote †: journal: Applied system  
0000-0001-5000-0001-5000][PERSON]. [PERSON]
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 4 Numerical Evaluation  
In this section, we evaluate the performance of the designed communication system invoking SVM-based allocation with the OVA strategy and the Gaussian kernel function in terms of sum rate and computational complexity via computer simulations. For comparison, we also consider three benchmark systems: (i) OPT, which is an optimization-driven method that maximizes the sum rate, i.e., exhaustive search, discussed in Section 2.3, (ii) RAND, which selects antennas randomly, (iii) and \(k\)-NN, an antenna allocation system based on the \(k\)-NN algorithm instead of SVM. Unless otherwise stated, we take into account full permutations of an antenna set (i.e., full classes), e.g., \(L=60\) when \(N_{t}=5\) and \(U=3\). We also evaluate the performance with \(L^{\prime}<L\), that is, the reduced number of classes for computational efficiency. In our simulations, the number of training samples is set by \(4.9\times 10^{4}\), i.e., \(D=4.9\times 10^{4}\), which is the number of rows in the training data matrix \(\mathbf{T}\). For a \(k\)-NN algorithm, we set \(k=\frac{D}{100}\) and use a Euclidian distance metric for the best classification accuracy of the antenna allocation system. For ease of presentation, we evaluate the performance by limiting the number of users, \(U\), to certain values (e.g., \(U=3\)). However, we can adopt any \(U\) in our system by generating multiple machine learning models offline according to various \(U\)s and then choosing one trained model for a given \(U\).
# Black Truffle Aroma Evaluation: SPME-GC-MS vs. Sensory Experts
## 3 Results and Discussion
### Truffle sensory Evaluation by the Trained Panel
#### 3.2.2 Fresh Truffle Evaluation  
The sensory evaluation of fresh truffles was conducted by the trained panel (Table 2). The evaluation was divided into two parts: visual and olfactive phases. In the visual phase, differences among some of the samples were found for the attributes' shape and uniformity, with sample 3_S1_T1 showing scores significantly lower than samples 1_S1_T1 and 3_S1_T2 (Table 2). These results indicate that the trained panel was able to discriminate truffles using these attributes with low deviation (0.7-1.3). No significant differences were found for the attribute firmness, peridium color, and sharpness of the gleba pattern, which the experts were not able to discriminate among the truffles (Table 2). In the olfactive phase, the attributes' equilibrium, intensity and complexity showed no significant difference in the truffles studied (Table 2). Differences among some of the samples were found for the attributes sulfur aroma and mushroom aroma, whereas attributes such as leather, butter, fermentation, nuts, and straw did not show statistical differences (Table 2). In agreement with the results previously obtained with the training data, those samples showing highest scores in sulfur and black olives attributes reported the lowest mushroom and leather values, i.e., samples 1_S1_T3, 2_S2_T3, 3_S2_T1. Only 2_S2_T1 showed mushroom values (5.9) higher than sulfur (3.1) and black olives (3.9).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{6}{c}{_Visual and texture descriptors_} \\ \hline
**Sample** & **Firmness** & **Shape** & **Uniformity** & **Peridium** & **Peridium** & **Cleba** & **Cleba Sharpness** \\  & & & & & & & & \\  & & & & & & & & \\  & & & &  
A correspondence analysis was used to explore the correlation between truffle maturity (during harvest time) and aromatic attributes evaluated by the trained panel (Figure 3). This analysis was carried out with only the aromatic attributes in order to detect similarities with the VOCs study. The PCA explained 76.1% of the variability with the first two components. The attribute that showed the most positive loading with the first PCA component was sulfur, whereas the mushroom attribute showed the most negative (Figure 3). The second PCA component clearly separated the truffle samples in three clusters corresponding to harvest time. The first harvest time (December) was associated with sulfur, fermentation, and straw aromas. Truffles from January were associated with mushroom, black olives, and butter aromas. This was the harvesting period with the highest variability among samples. The third harvest time (February) was associated with leather and nuts aromas. The latter aromatic attributes have been associated with truffles preserved via freeze-dried and canned treatments ([PERSON], 2016). By contrast, black olives, butter, and sulfur attributes are considered key aromas for fresh black truffle ([PERSON], 2016).
# Probabilistic Analysis as a Method for Ground Freezing Depth Estimation
## 2 Material and Methodology
### Estimation of Gumbel Distribution Parameters
#### 2.3.3 Method of Moments  
The moment method is simpler. In order to determine the parameters (\(\alpha\), \(U\)) using the method of moments, certain sample moments referring to measured values \(Z_{i}\) were compared to the corresponding moments of the Gumbel distribution [27; 28]. The arithmetic mean \(Z_{m}\) and variance \(\sigma^{2}\) as properties of the Gumbel distribution are expressed by the following formulas:  
\[Z_{m}=U+\frac{1}{\alpha}\cdot\gamma,\;\sigma^{2}=\frac{\pi^{2}}{6\cdot\alpha^ {2}} \tag{8}\]  
where: \(\gamma\) = 0.5772 is the Euler constant.  
Comparing the Formulas (8) to corresponding quantities related to the n-element sample of winter maxima of freezing depth, we obtain the values \(\alpha\), \(U\) of the Gumbel distribution parameters estimated by the method of moments (MM).
###### Abstract  
Healing agent carriers play a significant role in defining the performance of the autonomous self-healing system. Particularly, the ability to survive during the mixing process and the release of the healing agent when cracks occur without affecting the mechanical properties of the cementitious composite. Up to now, these issues are still a concern since glass capsules are unable to survive the mixing process, while some types of microcapsules were reported to cause a decrement in strength as well as limited strength recovery. Therefore, this study was twofold, addressing the surface treatment of polystyrene (PS) capsules and the evaluation of the compatibility of the modified capsules for cement-based applications. Secondly, assessing the healing performance of modified PS capsules in cementitious composites. Furthermore, the study also evaluates the potential healing performance due to the synergic effect between the encapsulation method and the autogenous self-healing mechanism. The investigation was carried out by measuring the changes in the pH of pore solution, FTIR analysis, survival ratio, and bonding strength. For self-healing assessment, the compression cracks on the cement paste were created at an early age and the strength recovery was measured at the age of 28 and 56 days. To identify the chemical compounds responsible for the healing process, SEM-EDX tests were conducted. Moreover, the effect of silica fume (SF) on bonding strength and self-healing was also evaluated. Based on the results, the modified PS capsules by roughing approach showed promising performance in terms of survivability, bonding, and recovery. The modified PS capsule increased the strength recovery by about 12.5-15% for 100%OPC and 95%OPC + 5%SF, respectively. The finding observed that the combining of modified PS capsules and the inclusion of SF gave high strength recovery of about 20% compared to 100%OPC without capsules. Thus, the modified PS capsule has a good potential for self-healing of cementitious-based applications.  
self-healing; modified polystyrene capsule; sodium silicate; silica fume; autonomous; autogenous; pH; FTIR; SEM; strength recovery; surface treatment +
Footnote †: journal: Applied Sciences
# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
## 3 The Dataset and Learning Task
### Evaluation Metric  
Unlike traditional bounding box-based object detection tasks, which use the Intersection over Union (IoU) thresholds to determine true/false positives, this task utilizes the mean average precision (mAP) between the predicted and ground truth pose information as the evaluation metric. To calculate the mAP, we need to obtain the translation distance and the rotation distance between the predicted objects and the solution objects. Let \(P\) and \(P^{\prime}\) denote the ground truth and predicted center point of an object, respectively. The translation distance between \(P\) and \(P^{\prime}\) is the Euclidean distance given by \(d_{trans}(P,P^{\prime})=\sqrt{(P_{x}-P_{x}^{\prime})^{2}+(P_{y}-P_{y}^{\prime })^{2}+(P_{z}-P_{z}^{\prime})^{2}}\), and the rotation distance \(d_{rot}(P,P^{\prime})\) is calculated using the Euclidean distance between the Euler angles. The set of equations are omitted here due to the space limit. Readers with interests can refer to Section 3.2 of [54] for a detailed calculation process.  
The resulting distances between all pairs of objects are taken to find out the closest predicted objects to the solution objects. Ten levels of thresholds are then applied to determine a true/false positive. Specifically, given a threshold level that includes a translation and a rotation threshold, if the calculated translation and rotation distances are less than the corresponding translation and rotation thresholds, then the predicted object is said to be a true positive for that threshold level. Otherwise, it is counted as a false positive. Lastly, we  
Figure 4: Vehicle object distribution in the dataset from the camera’s view (**left**) and viewed from the sky (**right**).  
Figure 3: The original sample images (**left**) and the ones with ground truth labels (**right**).  
can calculate an mAP across all predictions in all of the images for each threshold level and use Top i to represent the mAP for the threshold level \(i\). Note that the higher is a threshold level, the higher are the threshold values and the less accurate is the prediction. For this dataset, the ten rotation threshold levels are 5-50 with a step size 5, and the ten translation threshold levels are 0.01-0.1 with a step size 0.01.
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 4 Experimental Results and Analysis
### Performance Comparison with the Existing Methods  
Due to predicting the coal seam thickness through the proposed STTP-GPR algorithm with data filling at the edge of structural elements moving interpolated by the proposed RBF-Kriging algorithm, we compare our predicting algorithm with the BP neural network (BPNNs) algorithm, SVR algorithm, GPR algorithm [8; 22] in this Section, and BPNNS, SVR, and GPR still predict the coal seam thickness without transition probability and interpolated data. A 5-6-8-1 four-layer BP neural network was constructed. The SVR algorithm uses the Gaussian kernel function as the data mapping method to predict the training set data. The Mean Absolute Error (MAE) was used to measure the performance of the four algorithms in prediction of the coal seam thickness. The above real coal seam data of 30 training sets was used in this experiment.  
Table 1 shows the average absolute error that was predicted by using the four prediction algorithms. From Table 1, it can be seen that the proposed STTP-GPR algorithm is superior to the other algorithms. The prediction value of the SVR algorithm changes with the change of kernel function, and the kernel function usually depends on empirical selection. The neural network algorithm may over fit in processing small sample data, and because of a lack of sample data, the prediction error is usually worse. The proposed STTP-GPR algorithm that includes the local spatial attributes highlights the influencing factors of local data for unknown data, and the predicted values have little difference with the actual coal seam data, which reflects the changing trend of coal seams. At the same time, it enhances the ability within a certain error range for the local prediction lag that is caused by the coal seam geology change.