esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Macroscopic Weathering Indicators
#### 3.1.4 P-Wave Velocity  
P-wave velocity is an important indicator of fracture development in rock, which, as a measure, is characterized by convenient operation and high sensitivity. During the test, both ends of the probe should be held tightly to ensure that the probe is tightly fitted to the rock and the test error is therefore reduced. Figure 9 presents the variation curves of P-wave velocity and its loss for limestone and sandstone. The curves demonstrate that the initial P-wave velocities of fresh limestone and sandstone are similar, at 3800 m/s and 2330 m/s, respectively, with limestone exhibiting a higher initial P-wave velocity than sandstone by 1500 m/s. As the number of cycles increases, both rock types experience a linear decrease in P-wave velocity. The P-wave velocity of sandstone decreases from 2331 m/s to 1736 m/s, resulting in a total decrease of 700 m/s. Conversely, the P-wave velocity of limestone changes from 3839 m/s to 3322 m/s, representing a cumulative decrease of 500 m/s; the sandstone's wave velocity is 200 m/s less than that of the limestone. Due to the substantial disparity in the initial P-wave velocities of the two rock samples, the loss of P-wave velocity in sandstone is markedly greater than that in limestone. By the 25 th cycle, the loss of P-wave velocity in the sandstone sample reached 34%, while in the limestone sample, it was only 13%, indicating a 20% difference between the two.  
Figure 9: Variation curves of P-wave velocity and its loss for sandstone and limestone. (**a**) Variation curves of P-wave velocity for the two rock samples; (**b**) variation curves of P-wave velocity loss for the two rock samples.
# The Impact of Drilling Parameters on Drilling Temperature in High-Strength Steel Thin-Walled Parts
###### Abstract  
High-strength steel has high strength and low thermal conductivity, and its thin-walled parts are very susceptible to residual stress and deformation caused by cutting heat during the drilling process, which affects the machining accuracy and quality. High-strength steel thin-walled components are widely used in aerospace and other high-end sectors; however, systematic investigations into their temperature fields during drilling remain scarce, particularly regarding the evolution characteristics of the temperature field in thin-wall drilling and the quantitative relationship between drilling parameters and these temperature variations. This paper takes the thin-walled parts of AF1410 high-strength steel as the research object, designs a special fixture, and applies infrared thermography to measure the bottom surface temperature in the thin-walled drilling process in real time; this is carried out in order to study the characteristics of the temperature field during the thin-walled drilling process of high-strength steel, as well as the influence of the drilling dosage on the temperature field of the bottom surface. The experimental findings are as follows: in the process of thin-wall drilling of high-strength steel, the temperature field of the bottom surface of the workpiece shows an obvious temperature gradient distribution, before the formation of the drill cap, the highest temperature of the bottom surface of the workpiece is distributed in the central circular area corresponding to the extrusion of the transverse edge during the drilling process, and the highest temperature of the bottom surface can be approximated as the temperature of the extrusion friction zone between the top edge of the drill and the workpiece when the top edge of the drill bit drills to a position close to the bottom surface of the workpiece and increases with the increase in the drilling speed and the feed volume; during the process of drilling, the highest temperature of the bottom surface of the workpiece is approximated as the temperature of the top edge of the drill bit and the workpiece. The maximum temperature of the bottom surface of the workpiece in the drilling process increases nearly linearly with the drilling of the drill, and the slope of the maximum temperature increases nearly linearly with the increase in the drilling speed and feed, in which the influence of the feed on the slope of the maximum temperature increases is larger than that of the drilling speed.  
high-strength steel; thin-walled parts; drilling amount; cutting temperature +
Footnote †: journal: Applied Sciences  
Article
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year (Ref.)**} & **Biomedical** & **Best Overall** & \multirow{2}{*}{**Sample Size**} & \multirow{2}{*}{**ACC (\%)**} & \multirow{2}{*}{**SN (\%)**} & \multirow{2}{*}{**SP (\%)**} \\  & **Application** & **Classifier** & & & & \\ \hline
2015 [44] & Breast cancer & ANN & 22 (11 normal, 11 cancer) & 90.9 & 81.8 & 100 \\ \hline
2015 [26] & Breast cancer & ANN & 240 & 92.8 & & \\ \hline
2015 [63] & Dry-eye disease & k-NN & 104 (83 ill, 21 healthy) & 99.8 & 99.8 & 99.8 \\ \hline
2016 [23] & Breast cancer & AdaBoost & 67 & 87.4 & & \\ \hline
2016 [24] & Breast cancer & Fuzzy C-Means & 67 & 75 & & \\ \hline
2016 [46] & Breast cancer & Decision Trees & 50 (25 malignant, 25 normal) & 98 & 96.6 & 100 \\ \hline
2016 [32] & Breast cancer & SVM & 80 (40 abnormal, 40 abnormal) & 90 & 87.5 & 92.5 \\ \hline
2016 [39] & Breast cancer & k-NN & 80 (40 normal, 40 abnormal) & 100 & 100 & 100 \\ \hline
2016 [81] & Breast cancer & Fuzzy active & 60 & 91.8 & 85 & \\ \hline
2016 [67] & Thyroid tumour & Decision Trees & 51 (30 abnormal, 21 normal) & 98 & 95 & 99 \\ \hline
2017 [68] & Breath analysis & ANN & 1 subject (25 experiments) & 100 & & \\ \hline
2017 [69] & Drunkenness state & SVM & 41 & 100 & & \\ \hline
2017 [33] & Breast cancer & SVM & 240 (100 normal, 66 Benign, 78 Cancer) & 94.8 & & \\ \hline
2017 [70] & Exercise & Ensemble classifier & 19 & 81.5 & & \\ \hline
2017 [71] & Hypertension & ANN & 28 (14 hypertensive, 14 normal) & 89 & 85.7 & 92.9 \\ \hline
2017 [7] & Rheumatoid arthritis & k-means & 60 (30 arthritis rheumatoid, 30 healthy) & 83 & 86.6 & 79 \\ \hline
2017 [72] & Schizophrenia & SVM & 35 (18 moderately ill, 17 markedly ill) & 94.3 & & \\ \hline
2018 [73] & Burn wounds & Random Forest & 34 & 85.3 & & \\ \hline
2018 [74] & Facial recognition & ANN & 3561 from 22 subjects (2124 positive, 1437 negative) & 85.5 & & \\ \hline
2018 [53] & Diabetic Foot & k-NN & 107 (51 healthy, 66 diabetic-33 non-neuropathatic) & 93.1 & 90.3 & 98.0 \\ \hline
2018 [35] & Breast cancer & SVM & 407 (117 abnormal, 290 normal) & 98 & 98 & 98 \\ \hline
2018 [58] & Skin neoplasms & k-NN & 85 (68 malignant, 17 benign) & 60 & & \\ \hline
2018 [45] & Breast cancer & Ensemble classifier & 725 (219 cyst, 371 benign, 235 malignant) & 73.3 & 78 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\chi^{2}\) & \(p\)**-Value** & \(I^{2}\) **(\%)** \\ \hline All studies & 185.559 & \(<\)0.001 & 86.66 \\ \hline Breast cancer & 96.474 & \(<\)0.001 & 82.81 \\ \hline Diabetic foot & 0.520 & 0.771 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 6 Lessons Learned
### Signature Based Scanning vs. Anomaly Detection  
Signature based malware detection is important, as it is the most effective way of detecting known malware; however, Stuxnet, Duqu, Flame, Gauss, and other recent targeted attacks clearly show that it is not sufficient against targeted attacks. In fact, the creators of high-profile targeted attacks have the resources to fine-tune their malware until it passes the verification of all known anti-virus products; therefore, such threats will basically never be detected by signature based tools before they are identified and their signatures are added to the signature database.  
A solution could be heuristic anomaly detection. As we have shown in Section 5, anomalies caused by Duqu and Flame could have been detected by manual inspection or by general purpose rootkit detector tools in a semi-automated manner. In addition, some anti-virus vendors have already started to extend their signature based tools with heuristic solutions. While these techniques are not yet reliable enough, they are certainly a first step toward an effective approach for detecting unknown malware.  
A basic problem with anomaly detection based approaches is that they may generate false alarms, and it is difficult to filter those false positives. More work on white-listing techniques and collaborative information sharing may improve the situation. Academic research could contribute a lot in this area, because the problems require new, innovative solutions. We should also mention that, in some application areas, false positives may be better tolerated, because false negatives (_i.e._, missing a real attack) have devastating consequences. In particular, we believe that in critical infrastructures (such as nuclear power plants, chemical factories, certain transportation systems) where a successful logical attack on the connected IT infrastructure may lead to a fatal physical accident, false positives should be tolerated, and there should be expert personnel available to handle them.
# Influence of Soil Heterogeneity on the Contact Problems in Geotechnical Engineering
## 5 Conclusions
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* Zeng et al. (2020) Zeng, M.H. ; Wu, Z.M. ; Wang, Y.J. A stochastic model considering heterogeneity and crack propagation in concrete. _Constr. Build. Mater._**2020**, _254_, 119289. [CrossRef]
* Amato et al. (2017) Amato, F.; Moscato, V.; Picariello, A.; Sperli, G. Diffusion algorithms in multimedia social networks: A preliminary model. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia, 31 July-3 August 2017; pp. 844-851. * Fenton and Griffiths (2003) Fenton, G.A. ; Griffiths, D.V. Bearing capacity prediction of spatially random \(c-\phi\) soil. _Can. Geotech. J._**2003**, _40_, 54-65. [CrossRef]
* Cho and Park (2010) Cho, S.E. ; Park, H.C. Effect of spatial variability of cross-correlated soil properties on bearing capacity of strip footing. _Int. J. Numer. Anal. Methods Geomech._**2010**, _34_, 1-26. [CrossRef]
* Griffiths and Fenton (1993) Griffiths, D.V. ; Fenton, G.A. Seepage beneath water retaining structures founded on spatially random soil. _Geotechnique_**1993**, _43_, 577-587. [CrossRef]
* Liu et al. (2017) Liu, K.; Vardon, P.J. ; Hicks, M.A. Sequential reduction of slope stability uncertainty based on temporal hydraulic measurements via the ensemble Kalman filter. _Comput. Geotech._**2017**, _95_, 147-161. [CrossRef]
* Yetbarek et al. (2020) Yetbarek, E.; Kumar, S.; Ojha, R. Effects of soil heterogeneity on subsurface water movement in agricultural fields: A numerical study. _J. Hydrol._**2020**, _590_, 125420. [CrossRef]
* Hicks and Li (2018) Hicks, M.A. ; Li, Y. Influence of length effect on embankment slope reliability in 3D. _Int. J. Numer. Anal. Methods Geomech._**2018**, _42_, 891-915. * ElGhoraiby and Manzari (2020) ElGhoraiby, M.A. ; Manzari, M.T. The effects of base motion variability and soil heterogeneity on lateral spreading of mildly sloping ground. _Soil Dyn. Earthg. Eng._**2020**, _135_, 106185. [CrossRef]
* Fenton (1994) Fenton, G.A. Error evaluation of three random-field generators. _J. Eng. Mech._**1994**, _120_, 2478-2497. [CrossRef]
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* Samy (2003) Samy, K. _Stochastic Analysis with Finite Elements in Geotechnical Engineering_; University of Manchester: Manchester, UK, 2003. * Spencer (2007) Spencer, W.A. _Parallel Stochastic and Finite Element Modelling of Clay Slope Stability in 3D_; University of Manchester: Manchester, UK, 2007.
# Application of Image Recognition Methods to Determine Land Use Classes
## 4 Results
### Pre-Processing Results  
The results of the pre-processing stage are not easily tested or measured. It was observed that the satellite image layers were combined into a single image, indices were calculated, clouds were removed based on the Scene Classification Layer (SCL) and cloud interpolation was performed to obtain the best possible satellite images before proceeding  
\begin{table}
\begin{tabular}{c c} \hline
**Region** & **Sentinel-2 Tiles** \\ \hline Zemaitija & 34 UDG, 34 VDH, 34 UEG, 34 VEH \\ Aukštaitija & 34 VFH, 34 UFG, 35 VLC, 35 ULB, 35 UMB, 35 VMC \\ Suvalkija & 34 UFF, 34 UFE, 35 ULA, 34 UGE \\ Dzukija & 35 ULV, 35 UMA, 35 UMV \\ \hline \end{tabular}
\end{table}
Table 2: Sentinel-2 tiles used in the experiment.  
Figure 8: S2 band 6 (B6)—vegetation red edge.  
to classification. It is therefore concluded that the outcomes of the aforementioned pre-processing are of a high standard and meet the objectives set out at the project's inception.  
The most verifiable pre-processing stage is the cloud interpolation stage, whereby an optimal satellite image is generated for each S2 geographic unit (tile) from all images acquired within a defined period (no longer than one month). To facilitate comprehension, the selection of interpolation cases was based on exceedingly high cloud cover, with the objective of enhancing the visibility of pixel swapping. For illustrative purposes, Figures 9 and 10 present examples of the satellite images before and after cloud interpolation, respectively.
## 3 Results and Discussion
### Antimicrobial Activity of PLA- Co-cit Films against L. monocytogenes in Culture Medium  
The effect of 20 \(\upmu\)m thick PLA film with and without 23.3% \(w/w\) Co-cit on the growth of _L. monocytogenes_ on absorbent pads soaked with nutrient broth is shown in Figure 2.  
The selection of the film thickness and the Co-cit concentration was based on preliminary experiments with films of 20 and 50 nm thickness and various Co-cit concentrations (data not shown). The thickness/concentration selected gave close to optimum antimicrobial activity while keeping the Co-cit concentration as low as possible. Growth was minimal in all samples for the first 6 h of incubation at 37 \({}^{\circ}\)C. At 8 h, the _L. monocytogenes_ count in samples containing no Co-cit was 2.0 log CFU/cm\({}^{2}\) higher than the time 0 count (log(N/N\({}_{0}\)) = 2.0, or a 100-fold increase in count), whereas in samples with membranes containing Co-cit the increase was only 1.3 log CFU/cm\({}^{2}\) (approximately 20-fold) at the same time point. No significant growth (\(p\) > 0.05) occurred in the samples with Co-cit between 8 and 24 h, while the _L. monocytogenes_ counts in the control samples increased by a further 2.1 log CFU/cm\({}^{2}\) over the same period.  
Figure 2: Growth of _Listeria monocytogenes_ on paper pads soaked in TSBYE broth and covered with PLA films with 23.3% \(w/w\) Co-cit (red, circles) and PLA films without Co-cit (blue, squares). Results are combined from two separate experiments and expressed as the count at time t (N) divided by the count at time 0 (N0). Different letters within the same time point indicate significant (\(p\) < 0.05) differences; where there are no letters, there were no significant differences (\(p\) > 0.05).  
In these experiments, Co-cit incorporated into PLA films was shown to be bacteriostatic after a delay of around 6 h, during which growth occurred. This delay in bacteriostasis may be due to the time needed for the Co-cit to diffuse out of the membrane and through the pads.
# Scaled Modeling and Measurement for Studying Radio Wave Propagation in Tunnels
## 2 Properties of Tunnels and Systems and Their Effect on Propagation  
Radio wave propagation along a tunnel depends on the position, radiation pattern, and the emission polarization of the transmitting and receiving antenna [22; 23; 24]. The attenuation can be lowered by using a directional antenna with a suitable radiation pattern. While omnidirectional antennas do offer wider coverage for areas without line of sight (LOS) rays in a tunnel, their performance is surpassed by directional antennas in areas with LOS rays [19; 25]. The optimal position of the transmitter is at the center of the cross section of the tunnel, while a transmitter positioned near the walls has the worst propagation properties [24; 26].  
In empty, straight, narrow, high, rectangular tunnels, as long as the dielectric constants of the side walls, ceiling, and floor are approximately equal, when the width of the tunnel is smaller than its height, vertically polarized waves are shown to be attenuated less than horizontally polarized waves [27]. When the width is greater than the height, the opposite is true [24; 28]. This can be understood using modal expansion of the propagating field while considering the dimensions of the horizontal cross section [22]. The attenuation decreases when the dimensions are increased in relation to the wavelength. In addition, the reflection coefficients of the floor and ceiling are larger than that of the walls, which contributes an additional large factor of attenuation for vertically polarized electromagnetic waves [22]. In general, to achieve minimal attenuation in a rectangular tunnel, the polarization of the electric field must be coincident with the largest side-to-side dimension of the tunnel.
## 1 Introduction
### Operation Modes for Diode Lasers  
In diode laser technology, different operating modes are possible. Usually, the continuous wave mode (CW) and the \"classic\" pulse mode (also called \"chopped\" or \"gate\" mode) with pulse durations in the millisecond (ms) range are used. However, the use of diode lasers in the so-called \"over-pulsing\" mode is unusual. In this case, the standard operating current of the diode is briefly exceeded, i.e., it is operated above its destruction threshold. As a result, pulses are generated whose output power is in the higher watt range (>30 W), with pulse durations in the order of microseconds (\(\upmu\)s). Figure 1 illustrates this situation. Initial applications can be found in dentistry at a wavelength of 810 nm [17; 18; 19], but applications have not been realized for 445 nm diode lasers yet.  
This paper presents and characterizes a diode laser system with a wavelength of 445 nm, peak-pulse powers > 30 W, pulse repetition rates in the order of kilohertz and pulse durations of down to 1 \(\upmu\)s. Initial laser cuts on porcine tissue were performed in contact mode and histologically analyzed.  
Figure 1: Illustration of the different operating modes for diode lasers.
## 2 Materials and Methods
### Characterization  
The morphologies of FGi and FGi/PTFE composites were investigated via scanning electron microscopy (SEM, JSM-5610 LV, JEOL Ltd., Tokyo, Japan). The crystalline phases of the FGi/PTFE composites were characterized by X-ray diffraction (XRD) using CuK\({}_{\alpha}\) radiation (\(\lambda\) = 0.15406 nm) in a Bruker D8 Advance Diffractometer (40 kV, 40 mA) (Bruker, Billerica, MA, USA). The orientation of the molecular chain was characterized by wide-angle X-ray scattering (WAXS, Xeuss 3.0 SAXS/WAXS, Xenocs, Grenoble, France). Furthermore, the melting temperature (\(T_{m}\)) of the composites was determined by differential scanning calorimetry (DSC, DSC8500, PerkinElmer, Waltham, MA, USA) from room temperature to 380 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\) in a nitrogen atmosphere. Next, in-plane and out-of-plane thermal diffusivities (TD) were obtained by the laser flash method (LFA 467 NanoFlash, Netzsch, Selb, Germany) at room temperature under atmospheric pressure. During the test, in-plane and out-of-plane fixtures were used to measure the in-plane and out-of-plane TD of the composites, respectively. The test samples were circular disks with diameters of 25.4 mm and 12.7 mm, and the laser voltage was set at 260 V with the pulse width of 100 ms. To avoid interference from the orientation of samples, the raw data in the tests were directly used without correction. The thermal conductivity (TC) was calculated from the following equation: \(\lambda=\alpha\times\rho\times\)Cp, where \(\lambda\) is the TC, \(\alpha\) is the TD, \(\rho\) is the measured density, and Cp is the specific heat capacity of the sample. Subsequently, the tensile properties of the composites were assessed by an electronic universal testing machine (HLD HLT, Instron  
Figure 1: Schematic diagram of the preparation of oriented FGi/PTFE composites.  
5967, Instron, Norwood, MA, USA) with a drawing speed of 10 mm min\({}^{-1}\). The dielectric properties of the composites were measured by an Agilent HP8722 ET microwave network analyzer (HP8722 ET, Agilent, Santa Clara, CA, USA @ 1-40 GHz) with the microstrip line method according to ICP-TM-650 2.5.5.
## 4 Simulation and Analysis  
In order to validate the effectiveness and superiority of the proposed method, a piece of simulated train acoustic signal taken from Matlab 2015b Signal Processing Toolbox (8.6, MathWorks Inc., Natick, MD, USA) was extracted as the original pure signal. Then, Gaussian white noise with different signal-to-noise ratio (SNR) was added into the pure signal. Three evaluation parameters were introduced to make a comprehensive comparison amongst wavelet threshold denoising, traditional EMD denoising, EMD-PSO denoising, EMD-FOA denoising and the proposed EMD-IFOA.
# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 4 Discussion
### Current State of the Art Limitations  
Healthcare DTs require diverse data from different sources, such as electronic health records, wearable devices, and genetic information. Integrating these different types of data into a coherent model remains a significant challenge. While AI can generate DTs, ensuring that they adapt (including through self-learning) in real time to reflect the changing condition of the patient remains difficult, especially in the case of unpredictable health events. Using sensitive personal data (including health data) to generate DTs raises privacy concerns. Ensuring that patient data are securely managed and compliant with regulations such as the EU GDPR is a critical limitation. The accuracy of AI-generated DTs depends on the quality and quantity of the data used. Inaccurate or incomplete data can lead to unreliable models, potentially resulting in incorrect diagnoses or health prognoses. Creating and maintaining complex DTs, especially those that simulate entire physiological systems, requires significant computing power that may not be readily available in all healthcare settings. In addition, there is no universal standard for how DTs should be built or used in healthcare, especially in the case of AI/ML, leading to inconsistencies in their development and application across organizations and systems. The use of DTs in healthcare raises ethical questions, such as who is liable if a DT makes an incorrect prediction and how to ensure equitable access to this technology. While DTs may work well at a small scale or for specific use cases, scaling these models to widespread use across patient populations is challenging due to the variability of individual health profiles. The complex algorithms behind AI-based digital twins can be difficult to interpret, making it difficult for healthcare providers to trust and act on the predictions and insights generated by these models. The regulatory framework for AI-based digital twins in healthcare is still evolving, and the approval processes, accountability, and long-term validation of these technologies are uncertain [65; 66; 67].