esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### HPLC/UV/MS Analysis  
HPLC analysis was conducted using a Waters Acquity Arc system equipped with a photodiode array (PDA) detector and a QDa mass detector. Chromatographic separation was performed using a CORTECS C18 column (4.6 \(\times\) 50 mm, 2.7 um particle size) maintained at 30 \({}^{\circ}\)C. Mobile phases included water with 0.01% formic acid (A) and acetonitrile with 0.01% formic acid (B). The HPLC gradient method began with a mobile phase composition of 99% A, at a constant flow rate of 0.8 mL/min. This initial condition was held for one minute. From 1 to 13 min, the proportion of mobile phase A decreased to 70%. At 13.10 min, the mobile phase composition remained steady at 70% A. Subsequently, from 13.60 to 17.60 min, the composition shifted to 20% A, which was maintained for column cleaning and to remove any strongly retained compounds. After this phase, at 18.10 min, the mobile phase returned to the initial condition of 99% A, and this composition was held until 21.10 min for re-equilibration and preparation for the next injection. Between each injection, the column was equilibrated for 10 min to ensure stability and reproducibility. Samples were maintained at 8 \({}^{\circ}\)C during the analysis to preserve their integrity.  
Quantification was carried out at 265 nm for gallic acid, protocatechic acid, vanillic acid, and syringic acid and at 325 nm for chlorogenic acid, caffeic acid, \(p\)-coumaric acid, and ferulic acid, while mass confirmation was performed in negative mode using specific mass-to-charge ratios (\(m/z\)). These included protocatechic acid (\(m/z\) 153), \(p\)-coumaric acid (\(m/z\) 163), vanillic acid (\(m/z\) 167), gallic acid (\(m/z\) 169), caffeic acid (\(m/z\) 179), ferulic acid (\(m/z\) 193), syringic acid (\(m/z\) 197), and chlorogenic acid (\(m/z\) 353).
# Evaluation of Ultrasonic Bonding Strength with Optoacoustic Methods
## 5 Finite Element Analysis  
### Model  
We carried out eigenvalue analysis using the commercial finite element analysis (FEA) solver LS-DYNA (R 7.1, Livermore Software Technology Corp., Livermore, CA, USA). Figure 18 shows the specimen model in FEA. The size of the upper and lower plates was commonly 50.0 mm in length, 11.5 mm in width, and 0.8 mm in thickness. Two sheets were lapped in such a way that the total length of the bonded specimen was 85 mm. The 8-node hexahedron element was employed. The element size was 0.125 \(\times\) 0.125 \(\times\) 0.1 mm. The numbers of nodes and elements were 671,142 to 670,313 depending on the bonded area and 588,800, respectively. The specimen was modeled as an elastic material. Physical properties of A6061-T6 are shown in Table 1. The 10 mm edge of the lower plate was fixed. Because the specimen was fixed by the screw of a jig at a point 10 mm away from the edge in the experiment, it was considered that the clamp force was applied at this area. The other side of the specimen was a free edge. Assuming that the contact area with the bonding tip was 10 \(\times\) 10 mm and the micro-bond was preferentially formed in the bonding interface under the knurled edges on the bonding tip, 13 \(\times\) 13 bonded areas were arranged on the bonded interface. As shown in Figure 19, the bonded area was expressed by merging nodes on the contact surfaces of two plates. In addition, enlargement of the bonded area was modeled by an increase in number of the merged nodes shown as black points in Figure 19b-d. The total bonded area was set to five conditions: 2.06 mm\({}^{2}\), 2.64 mm\({}^{2}\), 5.90 mm\({}^{2}\), 6.89 mm\({}^{2}\), and 12.51 mm\({}^{2}\).  
Figure 17: Relation between resonant frequency detected by ESPI and bonding strength. (**a**) 1 st resonant frequency (173 Hz), and (**b**) 2 nd resonant frequency (626 Hz).
# An Application of Android Sensors and Google Earth
## 8 Conclusions  
This research proposes a simple low-cost PMMS utilizing a smartphone app and Google Earth for small to intermediate cities in developing countries. The system uses an Android application installed on a smartphone to measure the road surface roughness based on GPS and accelerometer sensors. Google Earth is utilized for the visual illustration of the road conditions and other analyses.  
The proposed system was implemented in Hajiah, a city in Yemen. The results reveal that IRI measurements performed by the app and Google Earth can provide a low-cost solution for cities such as Hajiah to manage their road network at an acceptable level. Google Earth is an excellent tool that can display road network conditions and all other attributes related to road maintenance management.  
The following conclusions can be drawn from these results:  
* Smartphones with built-in accelerometers, gyroscopes, and a GPS may be used to gather road data and assess road conditions. Smartphones are being used as probe devices due to their low cost and ease of use, as well as their potential for a large population coverage.
* Google Earth, which is user-friendly, is an excellent application for presenting maintenance data and subsequently making maintenance strategy decisions.
* A priority index can be established for each road segment in order to identify which segments should be implemented initially.
* Thematic maps of the output maintenance information can be visually shown using Google Earth.
* Any country, especially developing countries with limited budgets for producing maintenance studies, can employ this approach, as it allows for the easy and cost-effective production of road maintenance studies.  
Conceptualization, formal analysis, funding acquisition, investigation, methodology, project administration, and resources, A.I.A.-M.; visualization, A.I.A.-M. and A.H.A.-Q.; writing--original draft, A.I.A.-M. and A.H.A.-Q.; writing--review and editing, A.H.A.-Q. and A.I.A.-M. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support provided by the Researchers Supporting Project Number (RSP2022R424), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
Figure 11: Arrangement of priority indexes for selected segments.
## 4 Fingerprinting-Based Positioning with Spatial Side Information
### Positioning Process  
The process for positioning using spatial side information proposed in our work is summarized as follows.  
* The training phase: In a given positioning area, we create an ideal grid-like arrangement of fixed tiles. Each fixed tile corresponds in its size to the size of the 2D sensor array and in the number and size of the sub-tiles to each sensor in the 2D sensor array. The 2D sensor array is aligned with a fixed tile to sample fingerprints with adjacent side information. Fingerprint samples are collected from each sensor in the 2D sensors array and assigned to a vector of fingerprints. In turn, the vector of fingerprints is associated with a sub-tile. The elements in the vector of fingerprints are the fingerprints obtained from each fingerprint source. For a number \(N_{F}\) of fingerprint sources, the vector has \(N_{F}\) elements. The fingerprints from the whole 2D sensor array are associated with a tile and assigned to a tensor of fingerprints, with the dimensions \(N_{SS}\times N_{SS}\times N_{F}\). Fingerprint samples from fixed tiles, along with the reference ground truth positions, are used to train a regression-based FFNN model with a given pattern-matching method. Alternatively, fingerprint samples from fixed tiles, along with the tile-class labels, are used to train a classification-based CNN model with a given pattern-matching method. The training of all of the models is based on supervised learning.
* The testing phase: A positioning device implementing a 2D sensor array is placed at some random position within the positioning area. The position of the 2D sensor array determines what is thought of as a moving tile. The 2D sensor array samples fingerprints with adjacent side information. As in the case of fixed tiles, fingerprint samples are collected from each sensor in the 2D sensor array, which are associated with a sub-tile and assigned to a vector of fingerprints, with \(N_{F}\) elements obtained from each fingerprint source. The fingerprints from the whole 2D sensor array are associated with a moving tile and assigned to a tensor of fingerprints, with the dimensions \(N_{SS}\times N_{SS}\times N_{F}\). Finally, the position estimate is calculated using all of the pattern-matching methods constructed in the training phase, using as the input the tensor of fingerprints associated with the moving tile.
* Comparison of the results: This consists of comparing the performance among all of the proposed pattern-matching methods considered in terms of the error distance, \(\varepsilon_{F}\), given by Equation (1).
# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 3 Proposed Framework of LSTGINet
### Overview  
Figure 1 shows our proposed LSTGINet age prediction framework. The framework mainly consists of three parts: spatial branch (SPM), temporal branch (TEM), and local attention-enhanced graph reasoning module (\(LAGIM\)). The SPM uses the residual network (\(ConvNeXt\)) to learn brain information's multi-scale local spatial details and establish correlations between multi-scale features. Then, the patch attention module (\(PA\)) is utilized to enrich the global semantics. The temporal branch (TEM) uses \(BlndRNN++\) with convolution operations to model the temporal characteristics of brain information from both forward and reverse directions and through the cross-layer transmission of information flow and the independent functions of neurons to learn long-term dependencies. Then, the position path attention module (\(PA+\)) enhanced by average pooling is used to squeeze the temporal information, reduce redundant information, and enrich the representation of contextual semantics by assigning higher weights to salient features. The local attention-enhanced graph inference module (\(LAGIM\)) first uses the local attention embedding module (\(LAM\)) to establish a stable correlation between spatial features and temporal semantics and form a complementary relationship between different levels of features, such as spatial and temporal semantics. This alleviates the difference in the spatial distribution of spatial features and temporal semantics and further improves feature representation performance. This method of embedding features at different levels retains the semantic representation of another level to the greatest extent without disturbing one set of features. Then, the fused feature information is input into the graph inference module (\(GIM\)), and the transfer and aggregation functions of the graph inference nodes in the neighborhood are used to learn the graph representation, which helps the framework learn the hidden relationships in the brain information, thereby more effectively recognizing the correlation between changes in external information and age.
# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 4 Experimental Setup
### Optical Flow  
A comparison between the use of optical flow vectors and CSP features has been made, in order to analyze which features provide the best information about the action of the videos.  
There are some algorithms which compute the optical flow. In this paper the OpenCV implementation of [PERSON]'s algorithm [30] is used. It provides a dense optical flow, which means that it calculates the optical flow for every point of the image. Dense techniques are slower but can be more accurate than sparse ones, which only compute the optical flow for some points of interest of the image. The result of [PERSON]'s method is a two dimensional array containing the vectors which represent the displacement of each point of the scene.  
After having calculated the optical flow for every pixel, the vectors are divided into 10 bins and, according to the gradient direction of each pixel, a histogram is created. To create the histogram, the directions are separated in 8 stripes as can be seen in Figure 5. The features that are then trained are taken from this information.  
Once the features for each video have been obtained, the classification is performed.  
Figure 4: Selected pixels example.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 4 Experimental Setup
### Experimental Details  
We utilized the ESPnet [34] toolkit for data processing, resulting in enhanced channel audio. Our experiments were based on the Whisper model, including several different versions: Whisper-Small (244 M), Whisper-Medium (768 M), Whisper-Large-v2 (1550 M), and Whisper-Large-v3 (1550 M), all with English as the prompt language. Given the immense size of our dataset, fine-tuning every model comprehensively poses significant computational challenges. Hence, we adopted an efficient fine-tuning strategy, using the LoRA method to fine-tune some parameters. For LoRA, we set \(r=4\), which resulted in 2.21 M training parameters for the large version of model. We fine-tuned the model for 5 epochs using a batch size of 8, utilizing the Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-3}\) and Word Error Rate (WER) as the evaluation metric. The default \(\alpha\) for the Beta distribution was set to 2, and the default mixing maximum value \(\epsilon\) was set to 1. The proportion of one batch data to be trained on using Mixer is denoted as \(\tau\), which was set to 15%. And we set the default mixed layer \(k\) to 0.
## 5 Result and Discussion
### Machine Learning Model
The fibers act as crack-bridging agents, dissipating energy and reducing the rate of crack propagation. Additionally, fibers can bear more stress than the surrounding matrix, distributing the thermal loads more evenly and enhancing the coating's overall resilience. SHAP analysis further supports these findings, revealing that fiber content has a strong negative correlation with mean SDEG, while porosity and coating thickness exhibit weaker positive correlations. This highlights how higher fiber content significantly enhances thermal shock resistance by improving toughness and stress distribution within the material.
# Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset
## 2 Background
### Machine Learning (ML)
#### 2.2.2 Support Vector Machines (SVM)  
A Support Vector Machine (SVM) is a machine learning technique that, in its most basic version, consists in finding a line that separates two classes of (training) data points, in such a way that future data points can be accurately classified, depending on which side of the line they end up on. In most cases, the line will be an hyperplane because the data will often be in an N-dimensional (N denotes the number of features) space [17]. Additionally, among the possible hyperplanes that could separate the data into two classes, the hyperplane which is the furthest from the two data classes it is separating is preferred because it reduces risks of overfitting the model to the current training data. Furthermore, it is not always possible (or even desirable) to neatly separate all the data into two perfectly clean classes. A certain amount of mis-classification can be allowed in order to account for outliers or erroneous data. Finally, the boundary between the two classes of data may not be linear, in which case, there is a need to involve mathematical kernels that can handle those situations. In our study, we use a nonlinear SVM with a Gaussian radial basis function (rbf) kernel, which is a well established and robust version of SVM. When constructing such a classifier, two parameters must be passed as arguments. The parameter C accounts for the intolerance to mis-classification of the training data; the higher it is, the more the training data points will have to be correctly classified. The other parameter gamma can be understood as controlling the influence of a single training data point; the  
Figure 2: General architecture of a Deep Learning model.  
higher it is, the lower the reach of a single data point. The parameters C and gamma work together and have to be carefully chosen.
## 2 Theoretical Considerations
### Ammonia Stripping  
In aqueous solution, the free ammonia can be stripped through transfer from liquid to gas. Thus, the concentration ratio of the free ammonia to total ammonia is a major factor in stripping and is defined as the ammonia fraction (\(f_{NH_{3}}\)) in Equation (8) [13].  
\[f_{NH_{3}}=\frac{\left[NH_{3,L}\right]}{\left[NH_{3,L}\right]+\left[NH_{4}^{+} \right]}, \tag{8}\]  
The ammonia fraction is also expressed by Equation (9) from Equations (4) and (5).  
\[f_{NH_{3}}=\frac{K_{H_{2}O}}{K_{H_{2}O}+K_{NH_{3}}[H^{+}]}=\frac{1}{1+\left(K_ {NH_{3}}/K_{H_{2}O}\right)\cdot 10^{-pH}}, \tag{9}\]  
Since the ionization constants is described as a function of absolute temperature in Equation (6), the ammonia fraction is dependent on both temperature and pH.  
The percentage of ammonia removal (\(\eta\)) is calculated as the amount of ammonia removed compared to the initial ammonia and is expressed as Equation (10):  
\[\eta=\frac{\left[NH_{3,L}\right]_{0}^{T}-\left[NH_{3,L}\right]_{t}^{T}}{\left[ NH_{3,L}\right]_{0}^{T}}, \tag{10}\]
# Independent System Calibration of Sentinel-1B
## 6 In-Flight Antenna Model Verification
### Verification in Elevation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement Date** & **Swath Type** & **Transmit Polarization** & **Model-Measured over Main Beam/1\(\sigma\) [dB]** \\ \hline
29 June 2016 & SM2 & H & 0.035 (3 transponder average) \\
6 July 2016 & SM1 & H & 0.031 (1 transponder) \\
11 July 2016 & SM2 & V & 0.023 (3 transponder average) \\
18 July 2016 & SM1 & V & 0.032 (3 transponder average) \\
30 July 2016 & SM1 & H & 0.019 (3 transponder average) \\
1 August 2016 & SM5 & H & 0.026 (3 transponder average) \\
4 August 2016 & SM2 & H & 0.015 (1 transponder) \\
11 August 2016 & SM1 & H & 0.042 (1 transponder) \\
13 August 2016 & SM5 & H & 0.008 (3 transponder average) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of all results achieved for StripMap azimuth pattern verification. approximately 0.7 dB (e.g., EW1/EW2 for HH-Pol). In VV-polarization, a significant, systematic gain variation can be observed over all sub-swaths with absolute gain differences varying more than 1.0 dB over elevation. This variation leads to the so-called smiley effect observed in the channel imbalances. This will be discussed in more detail in Section 8.1. Since a similar effect has been observed for VV-pol in Sentinel-1A, the antenna model outputs and their application during SAR data processing requires further investigation. Figure 19: Averaged IW gamma profiles measured after star tracker realignment shown without antenna pattern compensation (solid lines) for co-pol (**top plot**) and cross-pol (**bottom plot**) and the corresponding antenna model predictions (dashed lines). The antenna model prediction has been adjusted using an offset defined by the difference between the mean modeled and mean measured IW3 gain. Hence, the beam-to-beam differences can be compared with respect to IW3 for both model and measurement. Figure 20: IW3-mean-removed difference between averaged measured IW data and the corresponding antenna model prediction after star tracker realignment.
# Pitfalls in the Detection of Hearing Loss via Otoacoustic Emissions
## 2 Materials and Methods
### Measurements  
The audiological testing included an otoscopic assessment, pure tone audiometry (PTA), tympanometry measurements, and OAE recordings.  
Hearing thresholds better than 25 dB HL between 0.125-8 kHz were taken to represent normal hearing. All subjects presented normal middle ear function, as assessed by otoscopic examination and tympanometry (tympanometric peak pressures were between -100 and +100 daPa and peak compensated static acoustic admittance values were approximately 0.2-1.0 mmhos).  
TEOAEs were measured by three systems: the ILO-292 (software version 5.61, Otodynamics Ltd., Hatfield, UK) and two versions of the Madsen Accusscreen (Otometrics, Natus Medical Denmark ApS). All OAE testing was conducted inside an audiometric booth, and the testing order was randomized.  
The ILO-292 was selected as the gold standard for two reasons: (i) Results from this system have been reported in the majority of papers in the literature; (ii) the system has been tested extensively by the authors and has proved to provide good separation of normal and impaired ears [22; 23; 24; 25]. Signal-to-noise ratios (SNRs) were collected using the standard nonlinear protocol (stimulus of 80 dB peSPL with a 50 Hz click rate and a 20 ms recording window), providing information at frequencies of 1.0, 1.4, 2.0, 2.8, and 4.0 kHz. The recording was qualified as a pass when the SNR in 3 of 5 frequency bands exceeded 3 dB [26].  
In the case of Accusscreen, two versions were used. The older device, Accusscreen Pro (firmware: 1.07E1M, GN Otometrics, Denmark)), which was produced between 2005 to 2010, will be referred to as Accu 1. The newer device, the Accusscreen OAE and ABR Screener (touchscreen, Type 1077, firmware 1.12.04877 SEU, GN Otometrics A/S, Denmark), which has been produced since 2010, will be referred to as Accu 2. The Accusscreen stimulus protocol uses 70-84 dB SPL click stimuli, in a nonlinear mode and with an approximately 60 Hz click rate. The Accusscreen manual states that the TEOAE waveform is evaluated by counting the number of significant peaks in a certain statistically generated graph; however, the user has no detailed insight into how this is performed. When the TEOAE measurement is complete, both devices provide a pass/refer evaluation of the OAE response. Since this evaluation is based on statistical criteria, there is no information on SNR parameters or any frequency-band analysis.
## 2 Materials and Methods
### Volatile Compound Analysis  
Two grams of ground dry-cured sausage or five grams of grape pomace was weighed into a 20 mL vial, which was then screw-capped with a Teflon-silicone septum. In total, 20 \(\mu\)L of a 4-methyl-1-pentanol methanolic solution (306 \(\mu\)g mL\({}^{-1}\)) was added to each sample (final concentration: 1.22 mg kg\({}^{-1}\)). Each sample was equilibrated for 5 min at 37 \({}^{\circ}\)C using a CombiPAL autosampler (CTC Analytics, Zwingen, Switzerland). A 1 cm 50/30 \(\mu\)m DVB/CAR/PDMS SPME fiber (Supelco, Bellefonte, PA, USA) was then inserted into the vial through the septum and exposed to the headspace for 30 min at 37 \({}^{\circ}\)C (a temperature sufficient to increase the headspace volatile compound concentration without causing protein denaturation).  
The SPME fiber was inserted into the injection port (set at 270 \({}^{\circ}\)C) of a Varian CP-3800 gas chromatograph coupled to a Varian Saturn 2200 MS mass spectrometer (Varian Inc., Palo Alto, CA, USA) equipped with an HP-5 capillary column (30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\mu\)m; Agilent Technology, Santa Clara, CA, USA) and with helium (1 mL min\({}^{-1}\)) as the carrier gas. The oven temperature was held at 35 \({}^{\circ}\)C for 10 min, raised to 250 \({}^{\circ}\)C (7 \({}^{\circ}\)C min\({}^{-1}\)), and held for 5 min, with a total running time of 45 min. The temperatures of the transfer line, trap, and manifold were 280 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C, respectively. The mass spectra were obtained by electronic impact at 70 eV, with one scan s\({}^{-1}\) over the 40-300 \(m/z\) range.  
The compounds were identified by comparing their mass spectra and linear retention indexes (LRIs) with those of standards (Sigma-Aldrich, St. Louis, MO, USA) or with mass spectra contained in the NIST library by using Agilent MSD Chemstation E.02.01.1177 software and/or LRI reported in the literature [34; 35; 36]. The concentration was estimated by using the internal standard and expressed as \(\upmu\)g kg\({}^{-1}\).