esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Detection and Analysis of Non-Volatile Compounds by UPLC-MS  
#### 2.4.1 Catechin and Caffeine Analysis  
Sample Preparation: Sample preparation followed the GB/T 8313-2018 method for detecting tea polyphenols and catechins in tea. A 200 mg (accurate to 0.5 mg) sample of uniformly ground and sieved tea was weighed into a 10 mL tube. Then, 5 mL of 70% methanol solution preheated to 70 \({}^{\circ}\)C was added, vortexed, and immediately placed in a 70 \({}^{\circ}\)C water bath for 10 min (mixed every 5 min). After extraction, the sample was cooled to room temperature and centrifuged at 3500 rpm for 10 min. The supernatant was transferred to a 10 mL volumetric flask. The residue was extracted once more with 5 mL of 70% methanol, repeating the above steps, and the extracts were combined and diluted to 10 mL in the volumetric flask. A 1 mL aliquot of the supernatant was transferred to a 10 mL tube, mixed with PSA (5 mg) and C18 (40 mg) adsorbents, vortexed for 2 min, and centrifuged at 3500 rpm for 5 min. The supernatant was filtered through a 0.22 \(\upmu\)m organic membrane for analysis.  
Standard Solution Preparation: Each standard (20.0 mg) was dissolved in methanol and diluted to 10 mL in a volumetric flask to prepare individual stock solutions at 2000 \(\upmu\)g/mL. Mixed standard solutions for calibration curves were prepared fresh before use.  
Chromatographic Conditions: The analytical conditions followed the Chen S method [30]. Column: Hypersil GOLD (100 \(\times\) 2.1 mm, 1.9 \(\upmu\)m); flow rate: 0.3 mL/min; column temperature: 30.0 \({}^{\circ}\)C; mobile phase A: 0.1% formic acid in water, mobile phase B: acetonitrile with 0.1% formic acid; gradient elution: 0-12 min, 5-17% B; 12-13 min, 17-100% B; 13-16.5 min, 100% B; 16.5-16.6 min, 100-5% B; 16.6-20 min, 5% B; injection volume: 1 \(\upmu\)L.  
Mass Spectrometry Conditions: Scan mode: electrospray ionization (H-ESI); catechins in negative ion mode, caffeine in positive ion mode; detection mode: multiple reaction monitoring (MRM); positive ion voltage: 3.5 kV, negative ion voltage: 3.8 kV; sheath gas (flowing around the capillary column to protect the ion source and transport ions): 20 Arbs (Arbitrary Units), auxiliary gas (used to assist in atomization and maintain ion transport efficiency): 5 Arbs; ion source temperature: 350 \({}^{\circ}\)C, vaporizer temperature: 400 \({}^{\circ}\)C; cone and desolvation gas: N\({}_{2}\), collision gas: argon. Optimization of mass spectrometry parameters was performed using 10 ng/mL single standards of catechins and caffeine to determine characteristic parent and product ions.
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 6 Discussion and Conclusions
While the AE signals alone could not conclusively quantify damage or identify failure mechanisms, their temporal distribution and intensity offeredvaluable complementary insights, helping to contextualise the observed displacement trends. AE activity recorded in specific load step phases suggested the possible onset of internal stress redistributions or local micro-cracking phenomena not identifiable with displacement data alone. This is particularly relevant for masonry, a stiff and heterogeneous material, for which displacement analysis alone may not capture the full extent of structural behaviour. Although the test conditions did not allow for a full quantitative evaluation of the Kaiser effect or Felicity ratio, the observed AE patterns nonetheless contributed to a more comprehensive diagnostic framework. In this sense, the results may inform future monitoring strategies, guiding both the placement of sensors and the interpretation of acoustic data in similar historic masonry systems. The integrated approach adopted in this study, combining AE and displacement monitoring, demonstrates the potential of multiparametric diagnostic tools to assist engineers and conservators in assessing the current state of preservation and making informed decisions regarding further interventions or long-term surveillance efforts. Future developments should focus on expanding the monitoring network, refining attenuation laws for irregular masonry, and integrating AE results more tightly with modelling frameworks. In parallel, efforts to standardise methodologies and instrumentation for heritage structures would enhance reproducibility and foster broader adoption of AE in conservation engineering practice. Conceptualization, methodology, formal analysis, investigation, data curation: L.D.G., G.L. and C.G. ; software: C.G., validation: G.L. and C.G., writing--original draft preparation: L.D.G. and G.L., writing--review and editing: [PERSON], G.L., G.F., G.R. and C.G., supervision: [PERSON], G.F., G.R. and C.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. The authors gratefully acknowledge the support of CO.GE.P.AR. Costruzioni General sas for hosting the experimental campaign. The authors declare no conflicts of interest.
# Cutting Force When Machining Hardened Steel and the Surface Roughness Achieved
## 3 Results
### Derivation of the Structural Equation  
The derivation of the structural equation for the cutting force \(F_{c}\) for turning material in the hardened state is based on the data in Table 4 and on the following Equation (1):  
\[F_{\text{C}}=C_{F_{c}}.a_{p}^{x_{F_{c}}}.f^{y_{F_{c}}}.v_{c}^{z_{F_{c}}} \tag{1}\]  
This relationship is empirical and depends on the constant \(C_{Fc}\), the exponents \(x_{Fc}\), \(y_{Fc}\) and \(z_{Fc}\) and the variables--in this case the blade width, \(a_{p}\), the feed, \(f\), and possibly the cutting speed, \(v_{c}\). The values of the constant \(C_{Fc}\) and the exponents \(x_{Fc}\), \(y_{Fc}\) and \(z_{Fc}\), depend on the specific machining conditions and are valid within a certain range. Furthermore, the type of material and its condition have an influence, which can be expressed by the machinability class. When calculating the cutting force according to this equation, we have to consider an inaccuracy which will be proportional to the difference between our machining conditions and those used to calculate the constant \(C_{Fc}\) and the exponents \(x_{Fc}\), \(y_{Fc}\) and \(z_{Fc}\). The cutting speed term in the general formula is therefore not included and its possible influence is reflected in the value of the constant \(C_{Fc}\). From Table 4 the individual values of \(Fc\) are averaged for the combinations of feed \(f\) and cutting edge width \(a_{p}\) used and listed in Table 6 for use in the following calculation:  
\[F_{\text{C}}=C_{F_{c}}.a_{p}^{x_{Fc}}.f^{y_{Fc}}=>C_{F_{c}}=\frac{F_{\text{C} }}{a_{p}^{x_{Fc}}.f^{y_{Fc}}} \tag{2}\]  
Equation (2) in its modified form is the basic formula for determining the methodology for determining the values of the exponents \(x_{Fc}\), \(y_{Fc}\) and subsequently the constant \(C_{Fc}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Combination** & \(f\) & \(a_{p}\) & \(F_{c}\) \\ \(f\)**and**\(a_{p}\) & **(mm\(\cdot\)rev\({}^{-1}\))** & **(mm)** & **(N)** \\ \hline
1 & 0.05 & 0.2 & 124.7 \\
2 & 0.05 & 0.35 & 155.6 \\
3 & 0.1 & 0.2 & 163.9 \\
4 & 0.1 & 0.35 & 210.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Averaged values of cutting force \(F_{c}\) for combinations of feed \(f\) and cutting edge width \(a_{p}\).  
Figure 10: Contour plot of the effect of \(f\) and \(a_{p}\).
# Tests of the Influence of DAF (Delayed Auditory Feedback) on Changes in Speech Signal Parameters
## 2 Tools Used  
The study used headphones with foam ear cushions to provide participants with acoustic isolation from potential ambient sounds. These headphones are closed which feature surround sound. In addition, a foam-cushioned microphone was used separately to reduce breath sounds and suppress blast consonants. The microphone used had a cardioid characteristic, sampling at 96 kHz and a bit rate of 24 bits, with a frequency response of 20 Hz to 20 kHz. The cardioid microphone was positioned 10-15 cm from the speaker's mouth. This range was chosen so that any movement away from the microphone would be minimal and would not have a negative impact on the observations. It is worth noting that for the purposes of this study, headphones with a built-in microphone function would suffice.  
Figure 1 shows a diagram of the system used in the study. The upper part of the diagram is responsible for recording audio for later analysis for the purposes of this work, while the lower part is responsible for the auditory delay effect of feedback.  
Figure 1: Diagram of the prepared layout used to conduct the study.  
Delay simulation was performed using the DAF application, which allows fine-tuning of the desired delay. This application has the ability to set the delay and record the progress (Figure 2). By its accessibility, it was decided to use this application, as anyone interested in the DAF effect can use it anywhere. Because of its simplicity of use, it is an ideal tool for working on self-improvement in both the level of correctness and intelligibility of speech. After pressing a button, the application starts its work, and everything the user says is repeated after a certain pre-selected time. Each user has the ability to record his or her speech, which is later transferred to a library of recordings, which makes it possible to analyze speech and decide what still needs to be worked on. An additional feature of the application is the ability to record sessions, which allows for later analysis of progress.  
Audacity was used to record the speech of the study participants. It is an audio editing and recording program. It allows audio files to be saved in multiple formats and be recorded at multiple sampling rates. In addition, once the study was completed, the program had the ability to extract the marked sound and automatically measure its timing. For phonetic analysis, on the other hand, the Praat program was used, which was developed by researchers from the Institute of Phonetics at the University of Amsterdam--[PERSON] and [PERSON] [21]. The program is intuitive, easy to use, and at the same time allows the provision of much needed information about sound. It not only educates laymen who are taking their first steps on the subject of speech acoustics, but it is also a professional tool for researchers, which allows them to extract a lot of information on signal parameters [22; 23].
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 3 Adversarial Attacks on Sentiment Analysis
### White Box Adversarial Attacks  
In white box attacks, the attack requires full access to the model's information, including its architecture, parameters, loss functions, activation functions, and input and output data. White box attacks typically approximate the worst-case attack scenario. * [PERSON] et al. (2015) presented _TextFool_, a targeted attack that uses the FGSM (Fast Gradient Sign Method) concept to approximate the contribution of elements in a text in order to identify those that significantly impact the classification of the input text. Instead of using the sign of the cost gradient in FGSM, this work considers the magnitude. Specifically, the authors computed the cost gradient \(\Delta_{x}J(f,x,c^{\prime})\) of each instance in the training set by employing backpropagation, where \(f\) is the model function, \(x\) is the original data sample, and \(c^{\prime}\) is the target text class. After that, _hot characters_ were identified, which are the characters with the highest grand dimensions. The entries containing sufficient _hot characters_ were denoted as _Hot Training Phrases_ (HTP). Using HTP, the adversarial examples were created by implementing three types of modifications: (i) _insertion_, in which HTPs of the target class are inserted into \(c^{\prime}\) nearby significant entries of the original class \(c\); (ii) _modification_, in which some characters in the HTPs are replaced and _Hot Sample Phrases_ (HSPs) are created; and (iii) _deletion_, in which unnecessary adjectives or adverbs in the HSPs are eliminated. These three strategies and their combinations were evaluated using a CNN architecture. * [PERSON] et al. (2016) proposed an untargeted attack to perturb text inputs in the word embedding space of a CNN model. The proposed method addresses two main problems when performing text modifications, namely, the input space and the quality of adversarial examples. The main difficulties in generating adversarial texts are: (i) the text input's discrete space, which makes it difficult to accumulate small noise samples; and (ii) measuring the quality of adversarial texts. To deal with the input space issue, the authors perturbed the text inputs in the space of _embeddings_ against a CNN model. Specifically, they applied FGSM and DeepFool. However, applying methods from computer vision would generate meaningless adversarial texts. To address this, the authors rounded the adversarial examples to the nearest meaningful word vectors, using the word mover's distance measure (WMD) as the distance measurement. Their evaluation on sentiment analysis and text classification datasets showed WMD to be a qualified metric for controlling perturbations. * [PERSON] et al. (2018) presented an untargeted attack method called TextBugger, which provides a perturbation constraint by using similarity measures such as edit distance, Jaccard similarity coefficient, Euclidean distance, and cosine similarity. To generate
## 3 Production of Nanoparticles by Using CO2 as an Antisolvent
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **Active** & **Polymer** & **Solvent** & **Operating** & **Morphology** & **Reference** \\ \hline ASES & Itraconazole & HPMC & EtOH/ & \(P=12\)-\(15\) MPa; & NP & [39] \\ GAS & Ampicillin & - & DMSO & \(P=9\)-\(15\) MPa & NP & [116] \\ GAS & Lysozyme & - & DMSO & \(T=34\)-\(46\)\({}^{\circ}\)C & (220–430 nm) & [116] \\ GAS & Lysozyme & - & DMSO & \(T=19\)-\(35\)\({}^{\circ}\)C & CNP & [84] \\ GAS & Paclitaxel & - & DMSO/H\({}_{2}\)O & \(P=10\)–\(16\) MPa; \(T=300\) nm & [86] \\  & & & 50 \({}^{\circ}\)C & (117–200 nm) & [86] \\ GAS, PCA & Lysozyme & - & DMSO & \(P=8\)-\(15\) MPa; & NP, CNP & \\  & & & & \(T=25\)-\(50\)\({}^{\circ}\)C & (233–300 nm with GAS; & [85] \\ PCA & Gentamicin & FVM/MA & AC & \(P=10\) MPa; & NP, CNP & \\  & & & & \(T=25\)-\(50\)\({}^{\circ}\)C & (37–265 nm) & [71] \\ PCA & Hydrochlorothiazze & PVP & DMSO, \(P=8\)-\(19\) MPa; & NLP, CNP & \\  & & & DMSO/AC & \(T=30\)–\(40\)\({}^{\circ}\)C & (52–206 nm) & [87] \\ PCA & Tetracycline & - & DMF & \(P=10\)–\(20\) MPa; & NP, CNP & \\  & hydrochloride & - & DMF & \(T=5\)–\(50\)\({}^{\circ}\)C & (100 nm) & [72] \\ SAS & Atorvastatinatin & - & MeOH & \(P=10\)–\(18\) MPa; & NP & \\  & & & & \(T=40\)-\(50\)\({}^{\circ}\)C & (150–260 nm) & [92] \\ SAS & Atorvastatinatin calcium & - & AC & \(P=12\) MPa; \(T=40\) & NP & \\  & & & & \({}^{\circ}\)C & (63–180 nm) & [117] \\ SAS & Apigenin & - & DMSO & \(P=145\) MPa; & NC & [74] \\ SAS & Campothecin & - & DMSO & \(P=10\)–\(25\) MPa; & cNP & [75] \\ SAS & Cefdinitr & - & MeOH & \(P=12\) MPa; & NP & \\  & & & & \(T=45\)-\(50\)\({}^{\circ}\)C & (150 nm) & [24] \\ SAS & Dichlorone & - & DMSO & \(P=9\) MPa; & NP & \\  & sodium & - & DMSO & \(T=40\)-\({}^{\circ}\)C & (140 nm) & [82] \\ SAS & Dhydromyiretin & - & AC/DCM & \(P=8\) MPa \(T=45\)\({}^{\circ}\)C & NP & \\  & & & DMSO, EtOH, & \(P=8\)-\(20\) MPa; & \\  & & & & \(T=40\)-\(60\)\({}^{\circ}\)C & NP, AGG & [76] \\ SAS & Ginkgo biloba extract & - & EtOH & \(P=10\)–\(40\) MPa; & NP & \\  & & & & \(T=35\)–\(80\)\({}^{\circ}\)C & (100–200 nm) & [77] \\ SAS & 10- & DMSO & \(P=10\)–\(25\) MPa; & NP & \\  & & & DMSO, AC, & \(T=35\)–\(68\)\({}^{\circ}\)C & (180 nm) & [78] \\  & & & & & \(\mathrm{NMP}\), & \\ SAS & Mangiferin & - & DMSO/AC, & \(P=8\)–\(15\) MPa; & \\  & & DMSO/EDHI, & \(T=40\)–\(50\)\({}^{\circ}\)C & NP & [79] \\  & & & & & \(\mathrm{NMP}\)/AC, & \\  & & & & & \(\mathrm{NMP}\)/EOH & \\ SAS & Nalmefene HCl & - & EtOH & \(P=13\) MPa; & NP & [119] \\ SAS & Naproken & - & AC & \(P=12\) MPa; & NP & [88] \\ SAS & Resveratrol & - & MeOH/DMC, & \(P=15\) MPa; & NP, CNP & \\  & & & EtOH/DMC & \(T=40\)-\({}^{\circ}\)C & (151–209 nm) & [88] \\ SAS & Taxol & - & EtOH & \(P=10\)–\(20\) MPa; & NP & \\  & & & & \(T=57\)-\({}^{\circ}\)C & (200 nm) & [73] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview of nanoparticle production by using CO\({}_{2}\) as an antisolvent.
# Combining Keyframes and Image Classification for Violent Behavior Recognition
###### Abstract  
Surveillance cameras are increasingly prevalent in public places, and security services urgently need to monitor violence in real time. However, the current violent-behavior-recognition models focus on spatiotemporal feature extraction, which has high hardware resource requirements and can be affected by numerous interference factors, such as background information and camera movement. Our experiments have found that violent and non-violent video frames can be classified by deep-learning models. Therefore, this paper proposes a keyframe-based violent-behavior-recognition scheme. Our scheme considers video frames as independent events and judges violent events based on whether the number of keyframes exceeds a given threshold, which reduces hardware requirements. Moreover, to overcome interference factors, we propose a new training method in which the background-removed and original image pair facilitates feature extraction of deep-learning models and does not add any complexity to the networks. Comprehensive experiments demonstrate that our scheme achieves state-of-the-art performance for the RLVS, Violent Flow, and Hockey Fights datasets, outperforming existing methods.  
violence detection; deep learning; keyframes; semantic segmentation +
Footnote †: journal: Applied  
to increase the robustness of the model. For example, the wav2 vec-Switch [16] encodes noise robustness into contextualized representations of speech via contrastive learning, which feeds original-noisy speech pairs simultaneously into the network. Therefore, we use pairs of segmented and original images to train the model, forcing the network to produce consistent predictions for the original and segmented image. Specifically, if the model wants to minimize the loss, its best choice will be to match the prediction of the segmented image with the original image. This enables the development of a robust model that predicts the original image in line with the segmented image.  
However, current violent behavior recognition models have high hardware resource requirements, making them difficult to deploy on a large scale. [PERSON] et al. [17] propose a hybrid \"handcrafted/learned\" feature framework, which provides better accuracy than previous feature-learning methods. Inspired by this, we combine keyframes and deep learning to develop an approach that extracts the keyframes of the video and judges the violent behavior with reference to the keyframe recognition results. Specifically, the proposed method treats video frames as independent events and judges whether a violent event is occurring based on whether the number of keyframes exceeds a given threshold. The number of extracted keyframes can control the recognition efficiency of the system, while the judgment threshold can control the system's sensitivity.  
In summary, we propose a new keyframe-based violent-behavior-recognition scheme with low hardware requirements and a high recognition rate. Furthermore, we build a dataset of violent images with the backgrounds removed and propose a new training method.
## 1 Introduction
Based on the existing literature review, the application of vehicle-mounted TEM illustrates a new characteristic, including the whole-space in the detection surroundings, small-dimension in the transmitter coil, and short-distance in the detection target. However, this rapid detection method has not been thoroughly investigated, and the current verification methods are mainly focused on numerical simulation and theoretical inversion. Thus, the suitability and feasibility of this rapid detection method need to be profoundly investigated through different methods. To meet the technical requirements for the rapid detection of high-speed railway tunnels within a limited maintenance time, an applied detection technology called the vehicle-mounted transient electromagnetic method is proposed. Aimed at the rapid detection and location of the internal defects of the lining structure throughout the entire tunnel, the detection principle and process of vehicle-mounted TEM are deeply analyzed through the investigation of internal defects behind tunnel linings. Meanwhile, the rationality and applicability of vehicle-mounted TEM are verified by three different methods, including theoretical analysis, numerical simulation, and laboratory experiment. Finally, the inversion results of experimental data under the two conditions are comprehensively compared. The research results will provide a potential method for the rapid detection of tunnel defects.
## 2 Methods
### Statistical Analysis  
The normal distribution of continuous variables was verified using the Shapiro-Wilk test. Descriptive statistics are presented as mean and standard deviations. Analysis of variance for repeated measures with between factors (RM-ANOVA) was used to evaluate differences among arm curl, MBT, and SP. When performing the analysis, the two groups (_group_ factor: EG vs. CG) were considered between factors of the analysis, while the two time-point assessments (_time_ factor: pre-test vs. post-test) were considered within factors of the analysis. The interaction _time * group_ was also calculated. The scores obtained with each test were instead considered independent variables. In particular, the 6 independent variables were the scores obtained in the arm curl, MBT forehand, MBT backhand, MBToverhead, SP forehand, and SP backhand tests, respectively. When significant differences between pre-test vs. post-test results were detected, and significant differences between groups and/or in the interaction time * group were also detected, Student's paired t-test was used to assess the differences between pre-test vs. post-test results for the two groups separately. Finally, the partial eta square (\(\eta_{\text{p}}^{2}\)) was also calculated as an indicator of the effect size of the analysis. A partial eta-squared value between 0.01 and 0.06 indicates a small effect size, a partial eta-squared between 0.06 and 0.13 indicates a medium effect size, and a value equal to or higher than 0.14 indicates a large effect size [35]. The alpha test level for statistical significance for all variables was set at 0.05. Data analysis was performed using SPSS Statistics 21 software (IBM, Armonk, NY, USA).  
The sample size was calculated using G * Power (version 3.1.9.7; written by [PERSON], University of Kiel, Kiel, Germany). The following design specifications were considered: test family = F tests; statistical test = analysis of variance (ANOVA) repeated measures between factors; \(\alpha\) = 0.05; (1-\(\beta\)) = 0.95; effect size f = 0.4; number of groups = 2; and number of measurements = 2. The sample size estimation indicated 24 total participants with a critical F value of 4.301.