esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Potential Cytotoxicity Associated with the Use of Ozonation to Degrade PPCPs  
The present study is, to our best knowledge, a pioneer in addressing the effects of municipal wastewater treatment (using synthetic effluents as a proxy) in human cell models, whereas there are studies focused on the efficiency of treatment of metal-loaded electroplating industrial wastewaters [25; 26] and on cytotoxicity assessment of phosphogypsum transport water from waste ponds of a fertilizer factory [27]. This is somewhat surprising given the likelihood of human exposure, particularly of skin cells, during the use of reclaimed water. For example, the use of reclaimed water in pools leads to direct exposure of pool-users skin cells; also, its use for irrigation may imply occupational exposure. Technical issues may have been the reason for this scarcity of studies. Indeed, the cell testing set up has high requirements in terms of sterilization of all elements involved, and the testing of effluents poses challenges concerning the preparation of test solutions in such a way that the assessment over full-strength solutions can be made without constraining the nutrient supply to the cells. The methods applied herein were successful in both preventing contaminations (prioritization of sterile conditions while preparing the test solutions) and allowing testing of full-strength effluents (lyophilization and resuspension in cell culture medium), thus, the overcoming of these technical difficulties will hopefully stimulate the investigation in the field. Considering the above, although some studies examine the cytotoxicity of various PPCPs used here in different cell lines, it should be noted that the results are not fully comparable with those of the present study, yet some parallels can be made, feeding several discussion items in the following sections.  
The cytotoxicity associated with the application of ozonation for effluent treatment was assessed by recording the mitochondrial metabolic rate (MTT) following HaCaT cell exposure to the synthetic effluents before and after ozonation treatment. In this context, three different exposure times were tested (24, 48, and 72 h) to empirically understand whether the elongation of the exposure period could modulate the toxic response. In general, extending the exposure period led to an increase in toxicity from 24 to 72 h (compare Figures 2 and 3 with Figures S1 and S2), providing a larger breadth for interpretation of effects and thus driving most of the discussion below.
# Inter-Sentence Segmentation of YouTube Subtitles Using Long-Short Term Memory (LSTM)
## 1 Introduction
Because these studies were focusing on speech recognition and translation, they relied on some acoustic features, which could not be provided by YouTube scripts. Chinese sentence segmentation [16] created a statistical model using data derived from Chinese Treebank, and predicted the positions of periods based on the model. This research was different from other mentioned research studies because it was using only text data. This paper presents a new sentence segmentation approach based on neural networks and YouTube scripts that is relatively less dependent on word order and sentence structure. We measures the performance of this approach. We used the 27,826 subtitles included in the online lectures provided by Stanford University for this study. These lecture videos provide well-separated subtitle data in sentence units. Therefore, this subtitles can be converted into the format of automatic subtitles provided by YouTube, and can be used as training data for the model to classify whether the period is present or not. We use Long-Short Term Memory (LSTM) [17] of Recurrent Neural Network (RNN) [18], which has excellent performance in natural language processing, to build a model with data and predict the position of the punctuation mark. LSTM showed potential to be adapted to punctuation restoration in speech transcripts [19], which combines textual features and pause duration. Although RNN has shown good performance with various input lengths, we sacrificed some of this benefits by making \begin{table}
\begin{tabular}{c c} \hline \hline
**Example Sentence** & **I told him eat [PERSON] called imp followed the command** \\ \hline Case 1 & I told him eat. [PERSON] called imp followed the command. \\ Case 2 & I told him eat [PERSON]. [PERSON] called imp followed the command. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of distinction (segmentation) ambiguity in STT-generated sentences without periods and capital letters. the data length similar to YouTube subtitles. In this study, we build the input as closely as possible to YouTube scripts, and try to locate the punctuation marks using only text features. This paper is composed as follows. Section 2 describes the experimental data used in this study and their preprocessing process. Section 3 explains neural network-based machine learning and explains algorithms and models used in this study. In Section 4, the process of the period prediction and the sentence segmentation experiment are explained in detail. Finally, in Section 5, the present study is summarized and conclusions are presented.
## 3 Results and Discussion  
### FT-IR Analysis  
The FT-IR spectra of Fe\({}_{3}\)O\({}_{4}\) MNP\({}_{5}\) and [Poly(aniline-co-o-toluidine)/Fe\({}_{3}\)O\({}_{4}\)] MNP\({}_{5}\) are shown in Figure 1a,b, respectively. It can be seen from Figure 1a that the characteristic absorption of the Fe-O bond is at 570 cm\({}^{-1}\), whereas in the copolymer, it appears at 582 cm\({}^{-1}\). As shown in Figure 1b, the characteristic peak at 3321 cm\({}^{-1}\) is related to the N-H stretching of the primary amine, and proves the amino group copolymer [43]. The peak at 2920 cm\({}^{-1}\) is attributed to the C-H stretching vibration of the methyl group of the copolymer. The absorption peak at 1589.40 cm\({}^{-1}\) is assigned to the quinoid ring stretching for Poly(aniline-co-o-toluidine) MNPs. The peak observed at 1496 cm\({}^{-1}\) corresponds to the presence of the C=C stretching vibration in the benzenoid ring in the copolymer. The sharp peaks at 1149.61 cm\({}^{-1}\) and 813.99 cm\({}^{-1}\) correspond to the C-H bending in-plane and out-plane, respectively, in the copolymer. The peak at 1300 cm\({}^{-1}\) corresponds to the C-N stretching vibrations of the primary aromatic amine in the Poly(aniline-co-o-toluidine) MNPs. The peak at 1242 cm\({}^{-1}\) is attributed to the C-N stretch of the benzenoid ring C-N-C.  
Figure 1: The FTIR spectrum of (**a**) as-prepared (Fe\({}_{3}\)O\({}_{4}\)) MNPs, and (**b**) as-prepared [Poly(aniline-co-o-toluidine)/Fe\({}_{3}\)O\({}_{4}\)] MNPs.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 2 Materials and Methods
### Overall Design of the System
#### 2.2.3 Design of the BDS/IMU Integrated Navigation Program  
The integrated navigation program is shown in Figure 5. The on-board navigation system is powered on to initialise the industrial computer, F-V111 receiver, 100D2, etc. (1) The industrial computer checks the motherboard, graphics card and memory, and initializes peripherals such as the USB port; (2) The F-V111 receiver and 100D2 are configured; (3) The parameters of the integrated navigation system are configured, including the initial zero deviation and installation error angle of the 100D2, the bar arm between the F-V111 receiver antenna and 100D2, and the initial state vector \(X_{0}\), the initial error estimation covariance matrix \(P_{0}\), the initial system noise variance matrix \(Q_{0}\), the initial measurement noise variance matrix \(R_{0}\); (4) The system enters polling and the performs static coarse alignment under the static state of the sprayer. The initial pitch angle and roll angle are calculated by the accelerometer of IMU, and the initial heading angle and longitude and latitude are calculated by F-V111 receiver; (5) The data of 100D2 is collected and the data of F-V111 receiver is waiting to be updated; (6) The longitude and latitude are obtained, and the integrated navigation solution is carried out to obtain the heading angle and position of spraying robot; (7) The system polls for the data of 100D2 and waits for the next data of F-V111 receiver update.
## 1 Introduction
### Intrinsic Metal-CFRP-Hybrids  
Due to the growing importance of metal-CFRP hybrids, a lot of different approaches can be found in literature. One is the use of pin structures on the metal surface, which penetrate the fibre fabric before the infusion and cure of the resin [8]. This leads to a big improvement of the mechanical properties, while the manufacturing effort is too high for serial production. Another approach is \"laser direct joining\" of CFRP to metal [9]. A strong physical bonding can be achieved, but the method is limited to thermoplastic matrix materials.  
An upcoming new concept is based on the intrinsic hybridisation. The general concept as well as different approaches are discussed in [10]. Intrinsic hybridisation means that the manufacturing of the hybrid takes place in one process step [10; 11], which makes it suitable for serial production and allows the use of thermoplastic as well as thermosetting polymers. For the manufacturing of intrinsic metal-CFRP hybrids, different processes are suitable. In [12], rotational moulding is used to produce shaft connections while in [13], automated fibre placement (AFP) is used to build up multilayer inserts.  
The specimens investigated in this work are manufactured in a resin transfer moulding (RTM) process and are a geometrical optimisation of the specimens that are discussed in [3; 4; 5; 6]. The characteristic of this approach is the thermoplastic interlayer (PPA-GF30), which prevents contact corrosion and lowers the stiffness gap between aluminium and CFRP leading to enhanced mechanical fatigue properties [3]. This generates two different interfaces that should be considered in further observations.  
Figure 1: Illustrations of (**a**) the hybrid (**b**) the PPA-GF30 overmoulded insert and (**c**) the aluminium insert.
## 2 Methodologies
### PPP-B2b Orbit Recovery  
BDS-3 PPP-B2b orbit and clock products are corrections with respect to the broadcast ephemeris. The orbit correction coordinate is related to the satellite-fixed coordinate system in radial, along-track, and cross-track directions. The satellite positions used for PPP, however, are located in the ECEF frame. Thus, the orbit corrections should be transformed into the ECEF system to correct the satellite positions from the broadcast ephemeris [26]. The corrected real-time precise satellite position can be expressed as,  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}_{t}=\begin{bmatrix}X_{brdc}\\ Y_{brdc}\\ Z_{brdc}\end{bmatrix}_{t}-\begin{bmatrix}\Lambda_{r}\\ \Delta_{a}\\ \Delta_{c}\end{bmatrix}_{t} \tag{1}\]  
With  
\[\mathbf{R}=\begin{bmatrix}\dot{\mathbf{r}}&\times&\frac{r\times\dot{r}}{|r\times r|}& \dot{\mathbf{r}}&\frac{\dot{\mathbf{r}}}{|r|}&\frac{r\times\dot{r}}{|r\times r|}\end{bmatrix} \tag{2}\]  
where \(\Delta_{r}\), \(\Delta_{a}\) and \(\Delta_{c}\) represent the position corrections in the radial, along-track, and cross-track directions, respectively; \(\begin{bmatrix}X&Y&Z\end{bmatrix}^{T}\) and \(\begin{bmatrix}X_{brdc}&Y_{brdc}&Z_{brdc}\end{bmatrix}^{T}\) represent the corrected satellite coordinates in the EFEC system and the satellite coordinates calculated from the broadcast ephemeris, respectively; \(\mathbf{R}\) is the transformation matrix from the satellite-fixed coordinate system to the ECEF system [26; 27]; \(\mathbf{r}\) and \(\dot{\mathbf{r}}\) represent the satellite position and velocity calculated from the broadcast ephemeris, respectively.  
It should be indicated that the orbit products provided by IGSACs in this article are with respect to the Center of Mass (CoM) of satellites. In contrast, the orbits of PPP-B2b products are with respect to the satellite Antenna Phase Center (APC); the BDS is based on the APC of B3I frequency, while GPS is based on the APC of IF combination [26]. Therefore, the antenna phase center correction should be considered. The relationship between APC and CoM can be described by,  
\[\begin{bmatrix}X_{APC}\\ Y_{APC}\\ Z_{APC}\end{bmatrix}=\begin{bmatrix}X_{CoM}\\ Y_{CoM}\\ Z_{CoM}\end{bmatrix}+\mathbf{A}^{T}\cdot\begin{bmatrix}N_{PCO}\\ E_{PCO}\\ U_{PCO}\end{bmatrix} \tag{3}\]  
where \(\begin{bmatrix}X_{APC}&Y_{APC}&Z_{APC}\end{bmatrix}^{T}\) and \(\begin{bmatrix}X_{CoM}&Y_{CoM}&Z_{CoM}\end{bmatrix}^{T}\) represent the APC and CoM satellite position in the EFEC system; \(\begin{bmatrix}N_{PCO}&E_{PCO}&U_{PCO}\end{bmatrix}^{T}\) represent the PCO correction of the satellite from the latest \"igs14.at\" file released by IGS; \(\mathbf{A}\) is the satellite attitude matrix.
## 4 Discussion

Table 1: Five beam width measurements were taken each at two distances from the fiber tip to yield an effective numerical aperture measurement for SMF-28e (control) and the reduced-diameter sapphire optical fiber. Using the effective \(NA\) measured for the 6.5 \(\upmu\)m-diameter sapphire fiber, this calculation predicts that only the fundamental mode would be supported at wavelengths greater than \(\sim\)760 nm, which is in fact what was observed experimentally; the higher-order LP\({}_{11}\) mode profile was observed at a laser wavelength of 532 nm, while the LP\({}_{01}\) mode was observed at laser wavelengths of 783 nm and 983 nm. As it appears the effective numerical aperture of sapphire is highly dependent upon fiber diameter in this low modal volume regime, \(NA_{\text{eff}}\) measurements would need to be taken at any given fiber diameter to improve the accuracy of predictions at other fiber diameters (i.e., it is not likely that this relationship is linear). Extrapolation using the measured \(NA_{\text{eff}}\) at a diameter of 6.5 \(\upmu\)m for the diameter range of 0-9 \(\upmu\)m is certainly within a reasonable window of accuracy for purposes of demonstrating this approximation, however. Greater predictive accuracy may be afforded through continued development of wave-based models for low modal volume sapphire optical fiber, but the reported experimentation suggests that legacy ray-based weakly-guiding approximations may be used with considerable accuracy if the effective numerical aperture is measured and utilized in calculations.
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 4 Metabolic Pathways Associated with Pathogenicity
### Virulence Factors
#### 4.1.1 Secretion Systems
aeruginosa_, there are three loci encoding T6 SS components (HSI-I, HSI-II and HSI-III), which constitute a set of 15-20 genes involved in interaction with other bacteria and the environment, similar to the T3 SS [93]. At least six effector proteins (Tse1-6) are secreted through the T6 SS. Tse1 and Tse3 are inserted into the periplasm and hydrolyse peptidoglycan, consequently causing the lysis of bacterial cells. Tse2 arrests the growth of both prokaryotic and eukaryotic cells. Tse4, Tse5 and Tse6 function as antibacterial effectors [94; 95]. The HSI-I needle is a dynamic contractile structure, much like a phage tail, projecting from the cytosol onto the bacterial surface; it is regulated by two-component systems and sRNAs [95; 96]. HSI-II increases bacterial internalization in epithelial cells, while HSI-III plays important roles in pathogenesis [97]. Regulators and expression of the HSI-II or HSI-III are affected by transcriptional factor MvfR, a quorum sensing (QS) regulator [90]. In addition, in _P. aeruginosa_, HSI-II and HSI-III play important roles in the infection process in animals and plants [97]. This diversity of secretion systems may have resulted from an evolutionary process that optimized various biological functions in _P. aeruginosa_. Although this pathogen lacks some secretion systems, such as T4 SS, it still has a great diversity of systems. All of these systems enable _P. aeruginosa_ to cause various types of infections in various human and animal tissues, since they endow it with numerous virulence factors.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 2 Regional Geology and Tectonics
### Geological and Tectonic Framework  
The Khyber range formed in the western portion of the lesser Himalayas. The fold and thrust belt of the Khyber range extends eastward up to the western margin of the Basham and Swat crystalline nappes after covering by alluvial deposits of the Peshawar basin [39]. Convergent tectonics transverse these ranges, which are highly deformed and are characterized by tight, asymmetrical, or isochinal folds implicated by several thrusts [40]. The Silurian and Devonian systems are reported from the axial belt of the Khyber and Nowshera-Swabi areas. The Punjal-Khairabad Fault System, which extends west from Gari Habibullah to the Khyber Pass region, is located to the north of the Khyber range [41]. The Khyber range constitutes a transition zone with an igneous-metamorphic complex in the north and sedimentary cover in the south [40]. To the north and south of the Khyber range are the Kohistan Island arc and Kalachitta-Margalla range bounded by MMT and MBT, respectively [42]. In the Late Triassic, postrift thermal subsidence led to marine sedimentation [43]. Late Paleozoic to early Paleozoic E-W-trending doleritic dikes were intruded into the Precambrian to Paleozoic rocks in the fold and thrust belt of the Khyber,  
Figure 2: Geological formation map of the study area, and the study area location [38].  
Attock-Cherat, and Hazara ranges [41; 44]. All the dikes and sills are found in the upper section of the Khyber limestone and are therefore younger than the Paleozoic section of the Khyber area.
# Skin Cancer Disease Detection Using Transfer Learning Technique
## 3 Materials and Methods
### MobileNetV2 Architecture  
In the current study, deep transfer learning MobileNetV2 [71] architecture is to tackle the issue of melanoma classification. Several different factors influenced the selection of the MobileNetV2 model. The dataset used for training a model was relatively tiny, making it susceptible to over-fitting, and using a small but more expressive system, like MobileNetV2, mitigated this effect significantly. MobileNetV2 is a framework that optimizes execution speed and memory consumption at a minimal cost with respect to the error [71]. Due to the high execution speed, parameter tuning and experimenting are considerably more manageable, while minimal memory consumption is an additional attractive feature. The main structure of MobileNetV2 is based on its previous version, MobileNetV1. Two significant notions explaining the MobileNetV2 framework are the depthwise separable convolution, linear bottleneck, and the inverted residual, which are discussed further.
## 3 Measurement Results and Discussion
### On-Wafer Characterization
Figure 7: Small signal TIA measurements: (**a**) measured S-parameters (where \(S_{21}\) and \(S_{31}\) correspond to the forward transmission coefficients from the input port to each of the differential TIA outputs, cf. Figure 2) and transimpedance response calculated from measurements, compared to simulation results; (**b**) effect of the gain control functionality on the S-parameter response for one of the TIA outputs. only parasitic capacitance extraction. The compression point, however, occurs at a similar output power. From the measured response, a differential output swing of 896 \(mV_{pp}\) is obtained at 1 dB compression. This large output swing is facilitated by the use of emitter followers as output buffers, where the output swing is determined by device linearity and not constrained by power consumption, as would be the case for a 50 \(\Omega\) matched common emitter stage.
## 3 Results
### Adjacency Matrix  
#### 3.2.1 Adjacency Matrix of Left and Right Helix Deviation  
In this section, the correlation coefficient of each tooth is then calculated and listed as an adjacency matrix. In this study, the injection-molded plastic gear used is a gear consisting of 48 teeth. Therefore, the adjacency matrix formed is a 48 \(\times\) 48 dimensions matrix. Figure 6a shows the adjacency matrix enclosed with the correlation coefficient of left helix deviation. Due to the enormous size of the adjacency matrix, only the information of the tooth numbers 1 st to 8 th are shown here. To study the network of the helix deviation, we then visualize the adjacency matrix by generating a pixel plot, and a color bar is added to express the strength of the correlation coefficient. Figure 6b shows the pixel plot corresponding to the adjacency matrix shown in Figure 5a. From the pixel plot, the pixels of tooth number pairs 3 rd and 4 th, 3 rd and 5 th, and 4 th and 5 th are filled with the color yellow as the correlation coefficients of the 3 pairs are 0.797, 0.773, and 0.773, respectively. On the contrary, the pixels of tooth number pairs 2 nd and 5 th and 5 th and 7 th are filled with the color blue as the correlation coefficients of the pairs are \(-\)0.002 and 0.032, respectively. With the visualization of pixel plots, the helix deviation network of gear with an enormous number of teeth can be understood easily.  
The adjacency matrix and its visualization allow the expression of the entire tooth helix deviation curve from a bird's-eye view, which is a significant improvement over the conventional chart diagram that organizes the deviation curves tooth by tooth. The conventional chart diagram poses difficulty in expressing an overview of the tooth helix deviation curve, which is addressed by the utilization of this method. By employing this approach, the entire curve can be visualized with ease, providing a comprehensive representation of the network of helix deviation.  
Figure 5: Helix deviation curves after elimination of DC and slope component: (**a**) Left helix deviation where the helix deviation curves of tooth number 1 st–48 th are shown accordingly in the graph from top to bottom; (**b**) Right helix deviation where the helix deviation curves of tooth number 1 st–48 th are shown accordingly in the graph from top to bottom.