esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures
#### 2.2.3 Thermographic Measurements  
Immediately before and after the PBC procedure, football players were subjected to 4 thermal imaging scans in anatomical position in the Anterior-Posterior (AP) projections: frontal plane front upper body, frontal plane front lower body, frontal plane back upper body, and frontal plane back lower body (Figures 1 and 2). Each of the thermograms taken was subjected to a detailed analysis using FLIR ResearchIR 4 Software, USA, that enabled the determination of specific, symmetrical areas of the body on thermograms, which were the basis for assessing the temperature distribution. Subsequently, the following areas of the right and left side of the body were selected for a detailed analysis: UL--upper limb, LL--lower limb, A--arm, Fr--forearm, H--hand, Th--thigh, K--knee, S--shank, Ch--chest, Ab--abdomen, UB--upper back, and LB--lower back. A FLIR T1030c High Performance HandHeld Thermal Imaging Camera with a detector resolution of 1024 \(\times\) 768 (786,432 pixels) and accuracy of \(\pm\)1 \({}^{\circ}\)C (\(\pm\)1.8\({}^{\circ}\) F) or \(\pm\)1% at 25 \({}^{\circ}\)C for temperatures between 5 \({}^{\circ}\)C and 150 \({}^{\circ}\)C was used. Using the FLIR ResearchIR 4 Software V. 4.30.0, the parameters were the minimum temperature (Tmin), the maximum temperature (Tmax), and the average temperature (Tmean), and these parameters of the selected body areas on recorded images were calculated. The mean temperature in the region of interest (ROI), marked as Tmean, was used to analyze the results. The tests were performed in accordance with the standards of the European Association of Thermology [52], under thermal comfort conditions after 15 min of acclimation. The subjects were positioned so that the optical axis of the lens was normal to the frontal plane, thus ensuring the optimal measurement angle. The skin emissivity was set to 0.98. The camera was placed onto a tripod. Thermogramswere taken in a room with a humidity of 50% and a temperature of 23 \(\pm\) 1 \({}^{\circ}\)C, from a distance of 1.5 m, which meets the criteria for thermal imaging tests. Air temperature and relative humidity were monitored on an ongoing basis by a thermohygrometer (digital thermo-hygrometer 30.5023, TFA Dostmann, Wertheim-Reicholzheim, Germany) and taken into account when configuring the thermal imaging camera.  
Figure 1: Sample thermographic photos before (1 and 3) and after (2 and 4) partial body cryostimulation for the front view. Legend: Ch—chest; Ab—abdominal; AL—left arm; AR—right arm; FrL—left forearm; FrR—right forearm; ThL—left thigh; ThR—right thigh; SL—left shank; SR—right shank; KL—left knee; KR—right knee.
# GPR Data Interpretation Approaches in Archaeological Prospection
###### Abstract  
This article focuses on the possible drawbacks and pitfalls in the GPR data interpretation process commonly followed by most GPR practitioners in archaeological prospection. Standard processing techniques aim to remove some noise, enhance reflections of the subsurface. Next, one has to calculate the instantaneous envelope and produce C-scans which are 2D amplitude maps showing high reflectivity surfaces. These amplitude maps are mainly used for data interpretation and provide a good insight into the subsurface but cannot fully describe it. The main limitations are discussed while studies aiming to overcome them are reviewed. These studies involve integrated interpretation approaches using both B-scans and C-scans, attribute analysis, fusion approaches, and recent attempts to automatically interpret C-scans using Deep Learning (DL) algorithms. To contribute to the automatic interpretation of GPR data using DL, an application of Convolutional Neural Networks (CNNs) to classify GPR data is also presented and discussed.  
Ground Penetrating Radar; archaeological prospection; data interpretation; Convolutional Neural Networks; AlexNet +
Footnote †: journal: Medical  
Geographic Information System (GIS), where the important reflections can be digitized manually. The latter allows the production of 2D maps to visualize the most important findings. The above-mentioned process can produce useful insights on each GPR survey applied. However, several drawbacks and pitfalls exist that might have a negative impact on data interpretation.  
One pitfall is related to the fact that B-scans are often bypassed during interpretation. This may lead to losing information that cannot be projected in the plan view of the final images. Another pitfall is that the standard processes applied on GPR data focus only on the amplitudes of the reflection. Hence, the interpretation is limited only to information that can be described by the reflected signal's amplitude, which cannot entirely describe the complexity of the GPR data. An additional issue to consider is that the targets might be invisible to the GPR instrumentation, meaning that the contrast of their physical properties compared to the ones of the surrounding media is not detectable by the GPR method. With the downsides mentioned earlier in mind, this article reviews approaches that help improve the data interpretability, overcoming limitations of the conventional interpretation approach. An overview of the approaches is presented in Figure 1. Last, the automatic data interpretation with Deep Learning (DL) is discussed as an approach to assist GPR data interpretation and an example of Convolutional Neural Networks' (CNNs) application to classify GPR C-scans collected from two archaeological sites is presented.
# Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
## 2 Analyze the Influence Law of Forming Difficulty of Complex Curved Hull Plates
### The Influence of Heating Speed on the Forming Difficulty of Complex Curved Hull Plates  
The heating speed \(v\) in this paper is obtained by calculating the ratio of heating line length \(l\) to heating time \(t\). The heating line length \(l\) can be obtained according to the plate forming scheme. The heating time is obtained by a timer during the processing.  
Temperature is a very important factor in the process of forming complex curved hull plates. When the heating speed is too fast, the surface temperature of the curved plates will be too low, resulting in the deformation value of the curved plates being too small or even zero. When the heating speed is too slow, the surface temperature of the curved board will be too high, resulting in the material performance of the curved plate not meeting the use requirements.  
In order to obtain the influence law of heating speed on the forming difficulty of complex curved hull plates, it is necessary to control the plate surface temperature and set the heating speed within a reasonable interval.  
In the limited and reasonable temperature range, except for the heating speed, other influencing factors keep constant. The plate length is 3 m. The radius of curvature is 3 m. The heating line length is 0.3 m. The heating line position \(l_{p}\) is 0.5. As shown in Figure 8, the influence law of heating speed on deflection deformation at different plate thicknesses are obtained. The faster the heating speed, the smaller the deflection of the curved plate. The sensitivity of the deflection deformation to the heating speed decreases with the increase of the heating speed. Therefore, when other influencing factors keep constant, and the temperature is controlled within a reasonable interval, the faster the heating speed, the smaller the deflection deformation produced, the greater the forming difficulty. The heating speed is positively correlated with the forming difficulty.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### Nanofluid Stability
#### 2.2.2 Zeta Potential  
Another method of stability determination of a nanofluid is using the zeta-potential test in which mutual repulsion happens between nanoparticles with the same charge. Thus, there is little agglomeration tendency between the particles with high surface charge, in their collisions [66]. This method can help describe the differences in some experimental data, where the structure of the suspension and the nanoparticles' surface charges are altered by using surfactants [67]. The zeta potential technique is a basic test for determining nanofluid stability, which considers the electrophoretic behavior of the fluid [68]. According to the theory of electrophoresis, the zeta potential measures the repulsion force between two particles [69]. Based on the stabilization theory, a high value of zeta potential is an indication of a high solubility of particles in the suspension, which is due to increased electrostatic repulsion between particles [70]. It is suggested that suspensions with 5 mV zeta potential or less are not stable and experience agglomeration, those with 20 mV have limited stability, but the suspensions with >30 mV have physical stability and if their zeta potential is greater than 60 mV they will have excellent stability [71].
## 3 Results and Discussion
### Adsorbents Characteristics
#### 3.1.4 Surface Chemistry of Adsorbents
In this case, the sample C3 contains (as %At) a larger share of individual oxygen groups compared to the sample C4. The characteristics of the individual components of the OIs signal after its deconvolution indicate the presence of quinones (\(\sim\)530 eV), C=O or O=C-O- groups (\(\sim\)532 eV), C-OH and Si-O groups (\(\sim\)533 eV), as well as O=C-O-/Si-OH groups (\(\sim\)534-535 eV) for all carbon samples. All identified groups are acidic and fully reflect the strongly acidic nature of the adsorbent, which was experimentally determined using potentiometric titration. This confirms the adsorption potential towards cationic adsorbates (selected dyes). Figure 12: (A) Survey scan XPS spectra of the samples C1–C4. In the main figure, the XPS spectra have been moved apart on the ordinate scale for better visibility. In the inset, the 0–300 eV region has been moved apart on the ordinate scale for better visibility. been enlarged and the spectra are in the original intensity scale. (**B**) High-resolution core-level spectra from the C1s region for sample C3, and (**C**) high-resolution core-level spectra from the C1s region for sample C4 with the corresponding deconvolution components for the investigated samples. The model of the C1s region for the samples C3 and C4 was based on [PERSON]'s work [68]. (**D**) High-resolution core-level spectra from the O1s region for sample C3, and (**E**) high-resolution core-level spectra from the O1s region for C4. (**F**) Survey scan XPS spectra of the samples P1 and P2, and (**G**) a high resolution of C1s signals. XPS analysis of polymer materials revealed the presence of oxygen (O1s), nitrogen (N1s), and carbon (C1s) as primary elements on the surface of the polymer materials. The total atomic content of each element is consistent with the expected structure resulting from the combination of monomers shown in Figure 2 (e.g., the highest oxygen content for the sample P1 (19.6 %At) obtained from oxygen-rich monomers (VP and TRIM) and a higher carbon content (% At) for the sample P2 (92.3 %At) obtained from carbon-rich monomers (VP and DVB)). The confirmed presence of \(\pi\)-\(\pi\)- forms can determine the basic nature of the polymeric materials. To evaluate the acid/base properties of the carbon and polymer materials, potentiometric titration experiments were performed. Figure 13 shows the dependence of the surface charge density on the pH value of the solution for the adsorbents investigated. The values obtained for the zero point of the charge pHpzc are in the range of 5.1-6.2 for carbon samples. It can be seen that the surface of the carbons was negatively charged under the test conditions (neutral pH solutions). Of all the carbons, C1 and C3 had the strongest charge, which is due to a larger number of acidic moieties on their surface. The data analysis for the polymers shows their basic character with pHpzc values of 8.1 and 9.6 for P1 and P2, respectively. The higher pHpzc value for polymer 2 is related to the strong basicity of the DVB monomers, which are absent in P1.
## 3 Materials and Methods
### Simulation Model Calibration
#### 3.4.2 Simulation Model Calibration Method
Subsequently, we calculate the variation in demand by determining the difference between the new and original demand quantities. Based on this variation, we conduct a random sampling of the original travel demand set to generate a collection reflecting the changes in demand. This modified set is then merged with the existing travel demand set for the current time period. Notably, due to the limited coverage of LBS data, the calibration process infrequently involves the reduction in original LBS travel records. Instead, the focus of the demand reduction process is primarily on addressing excessive resampling on links. In such scenarios, all original LBS travel records and their corresponding demands are preserved, and the random sampling is solely applied to the modified demand set. This comprehensive approach ensures the accuracy and reliability of the travel demand data, thereby enhancing the effectiveness of the simulation model. Additionally, we generally need a warming-up period before the initial calibration time period. This warming-up period does not require any calibration or statistical analysis.
## 3 Particle Filter with Weight Adjustment Design for the Compensation of Sensor Noise and Time Delay
### Tracking Performance of the Particle Filter Design in Overall Control System  
It should be noted that the control system design incorporating the particle filter with weight adjustment can still guarantee its tracking performance using the tracking controller described in Section 3.1, which illustrates that the incorporation of the particle filter avoids redesigning the controller.  
Proof.: Tracking performance using controller \(C\).  
Figure 2 can be re-expressed as Figure 3, where the uncertainty caused by state estimation and measurement prediction using particle filter and the structural uncertainty are summarized into \(\Delta N\). Figure 4 shows the equivalent block-diagram of stabilizing system \(\widetilde{P}\).  
From Figure 4, the error \(e_{2}\) can be written as  
\[e_{2}(t)=(I+\widetilde{P}C)^{-1}(r_{2})(t), \tag{23}\]  
where \(I\) is identity operator. From the exponential iteration theorem, \((I+\widetilde{P}C)^{-1}(r_{2})(t)\) exists [26]. Furthermore, we have [31]\[y(t) =r_{2}(t)-e_{2}(t) \tag{24}\] \[=r_{2}(t)-(I+\widetilde{P}C)^{-1}(r_{2}(t))\] \[=r_{2}(t)-\Big{(}r_{2}(t)+\widetilde{P}C(r_{2}(t))\Big{)}^{-1}\] \[=r_{2}(t)-\Big{(}r_{2}(t)+K_{\alpha}\widetilde{P}(r_{2}(t))+K_{ \beta}\int_{0}^{t}\widetilde{P}(r(\tau))d\tau\Big{)}^{-1}.\]  
Considering the first condition that \(\widetilde{P}\) satisfies, as stated in [26], it is assumed that for all \(t\) in \([0,T]\), \(C\) is stable and \(\widetilde{P}(u)\geq K_{l}\) as \(T\geq t\geq_{1}\geq 0\), \(u>0\), then  
\[K_{\alpha}\widetilde{P}(r_{2}(t))+K_{\beta}\int_{0}^{t}\widetilde {P}(r(\tau))d\tau \tag{25}\] \[\geq K_{\alpha}K_{l}+K_{\beta}\int_{0}^{t}\widetilde{P}(r_{2}( \tau))\] \[\geq K_{\alpha}K_{l}+K_{\beta}\int_{0}^{t_{1}}\widetilde{P}(r( \tau_{1}))d\tau_{1}+K_{\beta}K_{l}\int_{t_{1}}^{t}d\tau_{2}.\]  
\(K_{\beta}K_{l}\int_{t_{1}}^{t}d\tau_{2}\) can be made arbitrarily large by making \(t<T\) large enough and selecting an appropriate \(K_{l}\), then \(\Big{(}r_{2}(t)+K_{\alpha}\widetilde{P}(r_{2}(t))+K_{\beta}\int_{0}^{t} \widetilde{P}(r(\tau))d\tau\Big{)}^{-1}\) can be made arbitrarily small, indicating that \(y(t)-r_{2}(t)\) can be made arbitrarily small, so \(y(t)\) can track \(r_{2}(t)\).
# Random PWM Technique Based Two-State Markov Chain for Permanent Magnet Synchronous Motor Control
###### Abstract  
On the basis of the space voltage vector pulse width modulation (SVPWM) technique, the random pulse width modulation (RPWM) technique, which can reduce harmonics, is investigated based on the vector control system of permanent magnet synchronous motor (PMSM) to address the problem of generating a large number of high-amplitude harmonics at the carrier frequency and its multiplier frequency. Firstly, the root causes of the large number of high-amplitude harmonics at the carrier frequency and its multiplier frequency are analyzed in depth, and the RPWM technique is explained in detail on how to reduce the amplitude of these harmonics effectively. Secondly, to address the problem of insufficient random performance in the traditional RPWM technique, an innovative optimization scheme is proposed, i.e., the introduction of a two-state Markov chain and, based on the immune algorithm for transition probability and random gain, the optimization of two key parameters. Ultimately, through experimental verification, the proposed method significantly improves the spectral distribution of the current waveform compared with the traditional RPWM, which makes the distribution more uniform and effectively reduces the high-amplitude harmonics concentrated near the carrier frequency and its octave frequency, thus enhancing the overall performance of the system.  
space voltage vector pulse width modulation; random pulse width modulation technique; Markov chain; current harmonics; immune algorithm Article
## 5 Results
### Option Price for EGARCH Price Model
#### 5.4.3 Decision Frequency  
The resulting decision policies using LSM and RL, illustrated in Figure 13, were applied to 10,000 price paths sampled from the calibrated EGARCH price model with a spot price of \(S_{0}=USD70\) and strike price \(K=USD80\). The frequency of exercise times for each method is shown in Figure 14. In both methods, approximately 35% of the paths are not exercised early. However, the RL method exhibits more early exercises before time step 25, as indicated by its decision boundary (discussed in the previous section).
## 5 Discussions
### Evaluation and Interpretation of Survey Results
#### 5.1.2 Notable Results from Opposite Filter Groups  
Overall, the privacy concern in group ALL with regard to data types Q3.2 (74.48%), Q3.4 (87.5%), Q3.8 (77.08%), and Q3.20 (73.96%) is high, as Table 5 shows. At the same time, a look at Table A6 reveals that between 18.75% and 6.25% disagree that the data types Q3.2 (18.75%), Q3.4 (6.25%), Q3.8 (14.58%), and Q3.20 (17.19%) are privacy-compromising, which further confirms our findings.  
Furthermore, interesting is that 93.75% of those in subgroup LOB are concerned about the car license number (Q3.15), but only 76.04% of the overall group ALL and 66.28% of subgroup LRS, respectively, are concerned about the same data type. The reason for this could either be that people who lived on their own property are more aware of what can be revealed or what harm can be performed through a car license number, or the meaning of car license number was misunderstood for something other than a license plate.  
As for data type full name of previous owners (Q3.5), 62.5% of those in the LOB and LOS groups are concerned, whereas in the ALL group 47.92% are concerned. Even significantly lower is the concern in subgroup ALP with 25.0%, and 40.7% for the LRS subgroup. An indication of a decrease in concern could be that property owners are more aware of potential risks that the name of previous owners can pose in comparison with people who live in rented accommodation.  
Another subgroup of interest is LRB where 83.67% are concerned about Q3.19 (security measures against burglars) but only 62.5% of LOB are concerned whereas in the overall group ALL 71.88% are concerned. A possible explanation for this is that people who live on their own property have full power over installation and can also choose on their own to implement concealed and potentially strong measures against burglars, whereas people who live in rented accommodation need the approval of the landlord and will not get compensated in case they move to different housing. This reasoning could lead to a decision for a cheaper movable, and thus non-concealed measures against burglars.  
The results that surprised us the most were that subgroup SUC has fewer concerns in each of the most concerned data types. Moreover, compared with group ALL, the concern is lowered on each data type except for current state or country (Q3.1) 47.62%, blood type (Q3.6) 23.81%, size of property (Q3.16) 42.86%, and property tax (Q3.17) 42.86%. A supermajority within the group SUC is concerned about full address (Q3.4) 66.67%, social security number (Q3.7) 66.67%, information on relatives (Q3.8) 71.43%, phone number (Q3.9) 71.43%, and the car license number (Q3.15) 71.43%.
## 3 Results and Discussion
### Determination of Design Horizontal Response Spectra and Site Coefficient
#### 3.3.2 Site Coefficients for Acceleration and Velocity (Ca and Cv)  
When dealing with a shallow foundation, Site coefficients known as Ca and Cv values are dependent on the site; Sa was predicted for 0.2 s and 1.0 s periods [26; 28]. The response spectrum analysis was found to be an effective linear analysis and cost-effective when dealing with the seismic analysis [28]. DHRSa can be presented in the form of a graph, or it can also be plotted based on equations proposed by ISO19901-2 if the values of Ca and Cv are available. The third objective of this study was to propose Ca and Cv values of soil types D and E for the Malay Basin. The proposed Ca and Cv values for this study, along with proposed values by ISO19901-2, are presented in Table 5.  
Referring to Table 5, it can be concluded that values of Ca and Cv for soil type E are higher compared to soil type D. Even though seismic events from far-field have a higher magnitude compared to near-field, the distance traveled caused energy loss; thus, the Ca and Cv values from far-field seismic events were found to be the lowest in this study. In contrast with the near-field seismic events, a closer distance led to higher Ca and Cv values even though the magnitude was lower than far-field seismic events. Thus, the combination of far-field and near-field was proposed to avoid overestimating or underestimating Ca and Cv values. Note that the site coefficients were proposed by referring to the methodology in ISO19902-1 [26]. The designed horizontal response spectral acceleration can be presented in the form of a graph or it can be constructed using site coefficients and equations suggested in ISO19902-1.