esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
In this chapter the results of data analysis will be presented. The results speak of the dependence of productivity on bottleneck, variability, processing time and the pull control mechanism, specifically, the number of control cards for each mechanism. The data gathered by simulation experimentation were processed in order to obtain a mathematical model that describes that dependence. A total of eight regression functions were generated, two for each of the mechanisms (one for the process with bottleneck and one for the process without bottleneck), respectively, for Kanban, Conwip, Hybrid and DBR. Analysis of variance (ANOVA) was performed to determine the significance of the factors and the response function was developed by regression analysis. The factors of model A, B, C and D were as follows:  
* A--coefficient of variation. * B--operation time;
* C--existence of a bottleneck;
* D--number of control cards. For every mechanism, a table of analysis of variance (ANOVA) shows that the models are significant (Tables 3-6). This is indicated by F-value, but also \(p\)-value. So, the hypothesis that variability, processing time, bottleneck and number of control cards do not affect the productivity is rejected. \(p\)-values for every parameter of the model showed significance of the parameters but also some of their interactions (Tables 3-6). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of** & **Df** &
\begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & **F-Value** & \(p\)**-Value** & **Significance** \\ \hline Model & 1896.68 & 13 & 145.90 & \(3.12\times 10^{5}\) & \textless{}0.0001 & significant \\ Factors: & & & & & & \\ A & 3.08 & 1 & 3.08 & 6594.23 & \textless{}0.0001 & significant \\ B & 1803.70 & 1 & 1803.70 & 386,000 & \textless{}0.0001 & significant \\ C & 49.09 & 1 & 49.09 & \(105\times 10^{5}\) & \textless{}0.0001 & significant \\ D & 0.0050 & 1 & 0.0050 & 10.72 & 0.0017 & significant \\ AB & 0.1545 & 1 & 0.1545 & 330.91 & \textless{}0.0001 & significant \\ AC & 0.0564 & 1 & 0.0564 & 120.90 & \textless{}0.0001 & significant \\ AD & 0.0052 & 1 & 0.0052 & 11.05 & 0.0014 & significant \\ BC & 40.52 & 1 & 40.52 & 86,813.54 & \textless{}0.0001 & significant \\ BD & 0.0056 & 1 & 0.0056 & 11.94 & 0.0010 & significant \\ CD & 0.0147 & 1 & 0.0147 & 31.54 & \textless{}0.0001 & significant \\ ABC & 0.0246 & 1 & 0.0246 & 52.70 & \textless{}0.0001 & significant \\ ABD & 0.0050 & 1 & 0.0050 & 10.82 & \textless{}0.0016 & significant \\ ACD & 0.0198 & 1 & 0.0198 & 42.38 & \textless{}0.0001 & significant \\ Residual & 0.0308 & 66 & 0.0005 & & & significant \\ Deviation & & & & & & \\ of the & 0.0009 & 2 & 0.0004 & 0.9300 & 0.3998 & not \\ model & & & & & & \\ \hline \hline \end{tabular} \(\text{Df--degree of freedom.}\)  
\end{table} 
Table 3: ANOVA-Kanban.
# Addressing the Potential of L5/E5a Signals for Road ITS Applications in GNSS-Harsh Environments +
## 2 Methods and Materials
### Materials  
In order to evaluate the impact of L5, E5a and B2a signals in GNSS-harsh environments for ITS applications, a kinematic experiment was carried out on 21 May 2022, at the NTUA campus and the greater area (Figure 1). The kinematic scenario's test trajectory was constructed such that the vehicle runs through sub-urban, urban and deep-urban areas (Figure 1). Table 1 contains information on the duration and length of each sub-trajectory.  
Two separate datasets of spatio-temporal measurements were collected. The first one contains the raw GNSS measurements captured from two _Android_ smartphones, the Xiaomi Mi 8 and the OnePlus Nord2 5G. Xiaomi Mi 8 is capable of tracking GPS, Galileo and BeiDou Signals on L1/L5, E1/E5a and B1, respectively. On the other hand, OnePlus Nord 2 5G can track L1/L5, E1/E5a and B1/B2a signals of GPS, Galileo and BeiDou, respectively. In order to realize an operational scenario, both smartphones were installed inside the vehicle, on the dashboard as shown in Figure 2. Finally, the GNSS raw measurements were retrieved from the smartphones using the _GNSSLogger_ App [12].  
The second dataset consists of the GNSS and IMU measurements collected from a tactical grade GNSS/IMU system (NovAtel(r) PwrPak7, iMAR IMU-FSAS) and a geodetic type GNSS antenna (Figure 2). The data from the tactical grade GNSS/IMU system were used to calculate the reference trajectory employing the PPK (Post Processed Kinematic) positioning technique. In this setup, the level arms among the various sensors mounted on the roof-top sensor platform and inside of the NTUA test vehicle are known thanks to the design specifications of the roof-top track. Furthermore, the level-arms among sensors are considered with respect to the IMU zero point.  
\begin{table}
\begin{tabular}{c c c} \hline
**Experimental Trajectory** & **Length (km)** & **Duration (min)** \\ \hline Sub-urban & 9.62 & 21 \\ Urban & 5.35 & 17 \\ Deep-urban & 1.24 & 7 \\ Total & 16.21 & 45 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the total experimental trajectories and the underlying sub-trajectories.  
Figure 1: (**a**) Overall test trajectory which includes sub-urban (cyan), urban (red) and deep-urban (magenta) trajectories. (**b**) Sub-urban, (**c**) urban and (**d**) deep-urban environments.
## 2 Data Description
### CALIOP and VIIRS Products and Collocation
In this study, we use the collocated daytime CALIOP and VIIRS product developed by NASA's SIPS which merges the 5 km CALIOP aerosol and cloud layer products with the collocated standard VIIRS level-1b geolocated radiance product. The collocation algorithm accounts for not only the temporal-spatial differences between the two instruments but also the parallax effects caused by the differences in viewing geometry. The details of the collocated algorithm are described in [PERSON] et al. [39]. As illustrated in Figure 1, to homogenize the spatial resolution, we first collocate the VIIRS 1 km M-band pixels with the CALIOP 5 km aerosol retrievals, and assign all the collocated VIIRS pixels within a CALIOP pixel as the same as the CALIOP aerosol classification. We would like to point out here that the original collocation product from SIPS includes only VIIRS pixels that are exactly on the track of the CALIOP (i.e., green colored pixels in Figure 1, hereafter referred to as on-track pixels). As explained in Section 3, in this study, we would like to explore whether the image context would help improve dust detection. Motivated by this consideration, we added to the collocation data four more VIIRS pixels that are adjacent to the on-track pixels across the CALIOP track (i.e., yellow-colored pixels in Figure 1, hereafter referred to as adjacent pixels). Although both instruments can operate in both daytime and nighttime, in this study, we focus only on daytime because VIIRS loses all \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Target Variables**} \\ \hline Non-Dust & No aerosol detected, \\  & Other types of Aerosols \\ \hline \multirow{2}{*}{Dust} & Pure Dust, \\  & Dust mixtures, \\  & Dust above or below other types of aerosols \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Target variables used in the study based on CALIOP algorithm. Note that all cloudy-pixels are excluded from the study. the solar-reflective bands as a passive sensor in the nighttime, which seriously limits its capability for dust detection.
## 4 Noise Reduction Analysis of the Noise Wall  
The control of bridge-borne noise is mainly implemented from the perspectives of noise source and noise transmission path. Noise reduction measures from the aspect of the sound source usually involve structural optimization or improvement, which is difficult to implement for completed bridges. Measures from the transmission paths' aspect are relatively flexible, which can effectively realize noise control in special sections and are widely used in urban transportation, and setting noise walls is the most common measure. Therefore, the effect of noise walls on reducing bridge-borne noise is analyzed in this chapter.
## 5 Validation of the Proposed Method by Comparing Inductance and Fault Current
### Fault Current Comparisons
#### 5.2.2 Inter Turn Short Circuit Fault in the Different Layers  
As shown in Figure 18, the fault current \(i_{f}\) are analyzed the FEM and NAM, when there are 10 turns shorted in the different layers of the faulty coil. Figure 18a-c indicates that the frequency waveform obtained by the NAM is coincided properly with the ones obtained by the FEM with satisfactory accuracy. Figure 18a describes the fault current waveforms, when the shorted turns are the turns from no. 8 to no. 17, in which the value of fault current acquired by the FEM and NAM are 18.330A, 17.601A, and 16.486A, respectively. Compared with the finite-element result, the values obtained by the NAM have reduced by 3.97%. Therefore, the error between the results calculated by the FEM and NAM is within a reasonable range.
## 2 System Topology  
The Power Electronic Transformer under analysis interconnects a three-phase 50 Hz/20 kV MV grid with a three-phase 50 Hz/400 V LV grid. The rated power of the considered system is 100 kVA. As shown in Figure 1, the proposed topology consists of three main blocks:  
* MV-side power converter, which transforms the LF AC voltage of the MV grid into a single-phase HF AC square-wave voltage;
* LV-side power converter, which transforms the LF AC voltage of the LV grid into a single-phase HF AC square-wave voltage;
* HF transformer, which links and guarantees galvanic insulation between the MV-side and the LV-side power converters.  
A Full Bridge-based MMC is used on the system MV-side in order to exploit its intrinsic ability to sustain high voltages with a simple increment of the number of submodules.  
The chosen LV-side converter is instead a two-stage system: one stage is a Full-Bridge Inverter (on the HF-side), while the second stage is a three-phase/four-wires VSI (on the load side).  
Figure 1: System topology of the proposed Power Electronic Transformer (PET) architecture.
## 3 Results and Discussion
### Characterization of PCL/CNC Wet-Spun Filaments
Figure 2: (**a**) TGA and (**b**) first-order derive (DTG) curves of PCL and CNC powder, and 30% PCL/3% CNC filament. In addition, a comparative analysis of the cross-sections of the filaments was conducted, distinguishing those with and without CNC, as shown in Figure 3c,d. Cross-sectional analysis of the filaments confirmed that the PCL filament exhibited a smooth core structure, while the PCL/CNC filament revealed a more textured core. However, the CNC was well-dispersed within the PCL matrix, with no visible signs of agglomeration. The cross-sections of PCL/CNC filaments displayed a dense core structure, with CNC evenly distributed throughout the polymer matrix. This uniform dispersion of CNC is critical for achieving consistent mechanical reinforcement across the filament [47; 48]. Overall, the FESEM analysis demonstrated that the incorporation of CNC into the PCL matrix leads to surface texturing and improved internal structure, which can contribute to enhanced mechanical properties and biocompatibility, which is critical for ACL tissue engineering [32; 49]. The surface roughness and textured structure that can be seen on PCL/CNC filament can provide potential sites for enhanced cell attachment and proliferation [50]. Evaluating the potential cytotoxicity of the components that make up a tissue engineering device before it is used in contact with the human body is crucial [18; 32]. The device should not display any toxicity towards cells and must not contain toxic degradation  
Figure 3: FESEM images at 500\(\times\) magnification of: (**a**) PCL filament; (**b**) PCL/CNC filament; and FESEM images of the cross-section at 1000\(\times\) magnification of: (**c**) PCL filament; (**d**) PCL/CNC filament. products in order to elicit an appropriate response in a host in a specific application [51]. The cytotoxicity of the 30% PCL filaments with and without 3% CNC was evaluated by exposing the fibroblasts to a culture medium pre-conditioned by contact with the filaments for 24 h.  
The results of the indirect contact study after fibroblast incubation for 24 and 48 h with the pre-conditioned culture media are depicted in Figure 4. The data indicated that the addition of CNC did not significantly alter cell viability because the cytotoxicity values obtained for the two types of filaments were similar and did not differ. Moreover, none of the filaments elicits significant cytotoxicity when compared with life control. After 24 h, the cell viability values obtained are all above 90% for PCL filaments and above 91% for PCL/CNC filaments. The cytotoxicity values are even lower after 48 h, with all cell viability values above 95% for PCL filaments and above 99% for PCL/CNC filaments. The reduction in cell viability is most likely caused by the presence of methanol used for coagulation purposes or by some residual chloroform in the filaments' solution.
## 5 Results
### The Effect of Directions of Waves on the Salvage System
#### 5.2.2 The Effect of Directions of Waves on the Tension of Lifting Cables
Moreover, the sunken ship's center of gravity is closer to lifting-barge A in the transverse direction and to the bow longitudinally. Additionally, the gravity on the shipw in the P1 stage is substantial, contributing to the nearly linear increase in cable tension values for cables 1-15 under waves from the 90\({}^{\circ}\) direction. To quantitatively analyze the cable tension for various wave directions, the increase in the maximum cable tension in the three stages was compared to the maximum tension for waves from the 180\({}^{\circ}\) direction. Figure 17 illustrates the increase in the maximum cable tension. Figure 17: Increases in the maximum tension values of lifting cables under the action of waves from different directions: (**a**) 155\({}^{\circ}\); (**b**) 135\({}^{\circ}\); (**c**) 115\({}^{\circ}\); (**d**) 90\({}^{\circ}\). In Figure 17, stages P1-P3 maximum tension values are highlighted, with the red dotted line representing a 0% increase. Aside from a positive increase of around 5% in cables 10-15 under waves from the 135\({}^{\circ}\) and 155\({}^{\circ}\) directions, the overall increase is negative. Generally, cable tension responses are the most severe under waves from the 180\({}^{\circ}\) direction. Additionally, under the same conditions, the absolute value of the tension increase for cables in lifting-barge A is smaller than that of the tension increase for cables in lifting-barge B (with a negative increase rate). This is because the shipwck's center of gravity favors lifting-barge A, resulting in less fluctuation in the cable tension increase on that side. Under waves from the 90\({}^{\circ}\) direction, the increase in the maximum tension for each cable typically ranges from \(-\)10% to \(-\)30%, indicating significant tension reduction. Thus, salvaging a shipwck under waves from the 90\({}^{\circ}\) direction is best for enhancing the cable safety performance.
## 4 Results
### Defects Classification  
The defect classification model results are summarized in Table 6. This table presents key metrics and performance indicators associated with the classifier's performance: accuracy rates and F1-score.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} \\  & **Test Set 1** & **Test Set 2** & **Test Set 1** & **Test Set 2** \\ \hline Defect-1 & 80.00 & 80.00 & 80.00 & 61.54 \\ Defect-3 & 50.00 & 45.45 & 58.82 & 35.71 \\ Defect-4 & 50.00 & 60.00 & 57.14 & 60.00 \\ Defect-5 + 6 & 100 & 66.67 & 87.50 & 51.28 \\ Defect-7 + 8 & 50.00 & 22.86 & 57.14 & 29.09 \\ Defect-9 & 100 & 62.86 & 57.14 & 67.69 \\ Defect-10 & 93.08 & 91.49 & 93.80 & 88.66 \\ Defect-2 + 11 & 81.48 & 53.75 & 84.62 & 58.90 \\ Defect-12 & 98.91 & 86.36 & 95.79 & 86.08 \\ \hline Macro Results & 89.97 & 77.00 & 74.66 & 59.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the defect classification model. Test set 1 refers to test images from batches 1, 2, 3, and 4; test set 2 comprises test set 1 and the entirety of batches 5 and 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{2}{c}{**mAP@.5**} & \multicolumn{2}{c}{**mAP@.5,0.95**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Recall (\%)**} \\  & **Test Set 1** & **Test Set 2** & **Test Set 1** & **Test Set 2** & **Test Set 1** & **Test Set 2** \\ \hline RetinaNet & 25.5 & 17.0 & - & - & - & - & \\ YOLOv5m & 97.2 & 56.3 & 90.9 & 50.9 & 98.2 & 97.5 & 97.0 & 51.2 \\ YOLOv5l & 97.4 & 55.8 & 90.7 & 50.9 & 99.4 & 95.4 & 96.3 & 51.6 \\ YOLOv8m & 86.6 & 52.4 & 78.7 & 46.5 & 89.4 & 82.9 & 83.9 & 47.2 \\ YOLOv8l & 88.1 & 52.3 & 80.0 & 47.8 & 92.0 & 85.5 & 83.6 & 47.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of experiments for defect detection. Test set 1 refers to test images from batches 1, 2, 3, and 4; test set 2 comprises test set 1 and the entirety of batches 5 and 6.  
Classes Defect-10 (\"Dots\") and Defect-12 (\"Cracked\") present the highest F1-scores in both test sets, indicating successful classification. Despite the efforts to handle class imbalance, this behaviour was expected as the two classes of defects are the most represented. On the contrary, classes such as Defect-2, 4, 7 + 8 and 9 underperform, suggesting challenges in accurately identifying instances of those defects.  
In terms of overall results for test set 1 and 2, the defect classification model achieved a macro accuracy of 89.97% and 77.00%, and F1-score of 74.66% and 59.90%, respectively. As explained in the previous section, the results of the first set outperform the second one because the model was trained in images acquired in the first batch of images. Nevertheless, the difference is not as abrupt as in the defect detection model, indicating more robustness and generalization capability of the classification model.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 1 Introduction  
Several issues have impeded the development of Automatic Speech Recognition (ASR) systems. The most important of these is spoken Language Identification (LID) in multi-lingual ASR systems [1]. The purpose of spoken LID systems is to classify spoken language from a given audio sample. As such, in the absence of automatic language detection, speech utterances cannot be correctly processed, and grammatical rules cannot be applied [2]. Language has no geographical boundaries in the contemporary era. More than five thousand languages are spoken worldwide, and each has distinct properties at different levels, from acoustics to semantics [3; 4; 5]. Western countries have made significant progress in using applications based on spoken language recognition. However, these applications have not gained much traction in multi-lingual and multi-dialectal Arabic countries, including Saudi Arabia, due to the complexity of the native Arabic language [6; 7]. Spoken LID systems seek to determine and classify the language that is spoken within a speech utterance [8]. The problem of identifying the spoken language of a given audio file is of considerable interest to a range of multi-lingual speech processing applications, including speech translation [9], multi-lingual speech recognition [10; 11; 12], spoken document retrieval [13], and defense and surveillance applications [14]. This article's main contribution lies in considering seven spoken language identification, including Arabic, using public audio speech corpus, and the joint use of the MFCC and GTCC under the CRNN framework. The contribution can besummarized as follows: (1) We present a hybrid CRNN that combines a CNN's descriptive capabilities with an LSTM architecture's capacity to capture sequence data; (2) We compare the effect of fine-tuning the features; (3) We conduct comprehensive tests and experiments with the proposed architecture, demonstrating its applicability in a variety of contexts, as well as its extension to new languages; and (4) We use system errors to infer the degree of similarities in considered languages.  
The article is structured as follows: in Section 2, we provide a literature review in the field of spoken LID systems; in Section 3, the chosen speech corpora are presented; Section 4 showcases the proposed system; in Section 5, we evaluate the proposed system using extensive experiments; in Section 6, we present and discuss the results; and finally, in Section 7, we present concluding remarks and recommendations for future research.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 2 Materials and Methods
### Overview of Methodology
#### 2.2.2 Difference Uncertainty
Figure 2: Density of differences between SLSTR-A and B gridded data in the S8 channel, nadir view as a function of the standard deviation of SLSTR-A grid cell pixel values, \(\text{std}(\{L_{i}\})\) (data: 3 September 2018). The solid black line shows the filtering threshold of \(\text{std}(\{L_{i}\})=2\,\text{K}\), which is used to select for homogeneous grid cells.
## 4 Experimental Results and Discussions  
The pattern effect appears as the 'eye' closure of the higher levels on an eye diagram for PAM signals. Firstly, to investigate the influence of pattern effect induced by SOA-1 and SOA-2 on the 50 Gbaud PAM-4 signal, we test the BER performance with FFE under four BtB conditions--without SOA, with SOA-1 only, with SOA-2 only, with both SOA-1 and SOA-2, and the result is presented in Figure 4a. Figure 4b-e show the received time-domain signals and corresponding FFE recovered eye diagrams at a received optical power (ROP) of \(-3\) dBm. It can be seen from the eye diagrams that both SOA-1 and SOA-2 introduce distortions, and the influence of SOA-2 is more serious than SOA-1. The BER curves also prove that when two SOAs are used, the signal suffers more distortions compared with the cases using one SOA. As a booster, the current adjustment of SOA-1 has little effect on the results, so we only focus on the performance optimization of SOA-2 in the following experiments.  
For optimizing the performance of SOA-2 as a pre-amplifier, the SOA gain and optical signal-to-noise ratio (OSNR) of 50 Gbaud PAM-4 signal versus optical output power with different bias currents are measured and shown in Figure 5a. For the 60 mA bias current, despite the high OSNR, the SOA gain is not high enough to support a high input power of PIN when the ROP is low, which will result in poor BER performance. Considering higher bias current induces higher amplified spontaneous emission (ASE) noise [25], the SOA-2 bias current is finally optimized to 80 mA with \(-0.2\) dBm \(P_{sat}\) and corresponding \(-16.5\) dBm saturated input power to strike a balance between the gain and OSNR. Figure 5b shows the BER versus SOA-2 bias current under the BtB test at \(-16\) dBm ROP. Varying the bias current from 80 mA to 300 mA, the BER shows an increasing trend.  
Figure 3: (a) BER of different roll-off factors of RRC filter under the case of BtB and without SOA; Eye diagrams of without RRC (b), and with RRC (c) with a roll-off factor of 0.4.