esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The interaction between moisture and GPF level factor significantly (\(p\) < 0.01) positively influenced pasta firmness. [PERSON] et al. [14] reported an increase in pasta firmness when grape pomace was incorporated. On the other hand, pasta firmness decrease may be related to GPF ability to bind water, similar to gluten proteins. Polyphenols may also contribute to pasta texture behavior as a result of interactions with other components, a fact supported by the positive correlation (\(r\) = 0.42, \(p\) < 0.01) between firmness and TPC. Our data were in agreement with those reported by [PERSON] et al. [47] for noodles with treated wheat flour, which presented a firmness of 3397.76 g. The gumminess parameter was significantly decreased (\(p\) < 0.01) by HMT conditions and GPF level increase. [PERSON] et al. [48] also obtained lower gumminess values for pasta made of HMT amaranth starch noodles. Using a small particle size of GPF (<180 \(\upmu\)m) might have favorable effects on pasta texture by lowering gumminess values, similar to findings being reported by [PERSON] et al. [49] for Chinese noodles with wheat bran addition. RS is a fraction resistant to hydrolysis in the digestive system under the action of enzymes, having a physiological behavior similar to DF [50]. HMT and GPF addition significantly (\(p\) < 0.01) increased RS content of wheat pasta. Starch resistance to enzyme action could have been determined by the crystalline areas' perfection and more dense amorphous areas caused by HMT [5]. Similar to our results, [PERSON] et al. [5] observed that RS formation is directly proportional to the moisture content of the sample. The interactions between flour components, especially starch, lipids, and proteins, could possibly be responsible for the higher RS content, since proteins films could have been formed on starch surface and lead to difficult amylase attack [4]. In addition, the presence of polyphenols from GPFcould have a decisive role in starch digestion. It was stated that polyphenols may interact with starch to form complexes through non-covalent linkages, which are not accessible for hydrolysis [51]. Furthermore, polyphenols may inhibit the digestive enzymes by means of proteins-polyphenols interactions [52]. Thus, the synergistic effects of HMT and GPF led to an improved functional value of pasta by increasing RS content. The optimization of HMT regime and GPF level allowed the establishment of a formulation with maximum nutritional and functional benefits and with minimum quality impairment. Thus, at 87.56 \({}^{\circ}\)C with a moisture of 26.01% for 3 h and an addition level of 4.81% GPF, pasta presented high TPC content (11.36 mg/100 g) and RS content (2.80%). [PERSON] et al. [4] reported an RS content of HMT wheat starch at 25% moisture of 2.64%. [PERSON] et al. [53] obtained a TPC value of 9.41 mg/100 g of cakes with 4% grape po-mace powder.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion
### Limitations of Analog Methods vs. Advantages of 3D Standardization  
Our analysis demonstrates that digital cheiloscopy achieves significantly higher repeatability than traditional analog methods, a finding consistent with recent studies advocating digital tools to improve forensic data quality [13; 15]. Unlike analog techniques, where variability arises from methodological challenges such as pressure inconsistencies during impression transfer, material degradation, and operator-dependent handling, 3D scanning eliminates these sources of error by directly capturing the lip surface topography.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Operator** & \begin{tabular}{l} **Mean Wrinkle** \\ **Count** \\ \end{tabular} & \
\begin{tabular}{l} **Standard** \\ **Deviation** \\ \end{tabular} &
\begin{tabular}{l} **Coefficient of** \\ **Variation (\%)** \\ \end{tabular} \\ \hline Analog & Expert & 42.6 & 6.35 & 14.9 \\ Analog & Novice & 31.2 & 6.96 & 22.3 \\ Digital & Expert & 66.89 & 0.93 & 1.39 \\ Digital & Novice & 66.20 & 0.92 & 1.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operator-dependent variability in analog vs. digital cheiloscopy.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **Analog Method** & **Digital Method** \\ \hline Usable prints (\%) & 60\% (12/20) & 100\% \\ Surface deviation (SD) & N/A & 0.229 mm \\ Variance & N/A & 0.052 mm\({}^{2}\) \\ Operator variability & High (CV: 14.9–22.3\%) & Low (CV: 1.39\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reproducibility and technical performance.  
This approach preserves intricate morphological details, such as groove branching and depth, with minimal human intervention. Quantitative validation via surface deviation analysis revealed a 70% average similarity between repeated 3D scans, with a standard deviation of 0.229 mm, confirming superior reproducibility. These results underscore that digital methods enhance not only analytical accuracy but also enable the development of standardized, long-term forensic databases for reliable comparative analyses.
## 3 Conditional Compression  
One option we have to increase compression is to use the knowledge of similar messages that Sender and Receiver have compressed in the past from the same source and to design algorithms that efficiently compress and decompress given this previous knowledge: by doing this the conditional entropy is the new theoretical limit and it allows for better compression.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 1 Introduction
[PERSON] et al. implements optimization technique for parameter extraction of PV module. The performance efficiency of any optimization technique can be judged by applying on standard mathematical test functions [20]. Many efficient optimization algorithm are proposed and tested by researcher such as Hybrid Water Cycle--Moth Flame Optimiza  
Figure 1: Single-diode PV model. tion algorithm (WCMFO) [21], Artificial Bee Colony (ABC) algorithm [22], Cuckoo Search (CS) [23], Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) [24], Moth Flame Optimization (MFO) [25], Gravitational Search Algorithm (GSA) [26], Water Cycle Algorithm (WCA) [27], Dragonfly Algorithm (DA) [28], and Whale Optimization Algorithm (WOA) [29] for finding solution to many real world problem. Finding solution to problem based on multi-objective optimization process and single objective optimization process. It is found that multi-objective optimization process has better problem-solving ability as compared to single objective optimization process [30]. Researchers all over the world suggest that [16] there are three methods for improving optimization techniques. These are: (i) putting forward new optimization techniques, (ii) improving present techniques, and (iii) the hybridization of optimization techniques. The third one, hybridization of optimization techniques [16], is one of the most popular and efficient methods. In this paper, a multi-objective PV module parameter extraction scheme depending upon the Hybrid Bald Eagle Search Simulated Annealing technique (Hybrid BESSA) is discussed. This technique is a hybridization of the Bald Eagle Search and Simulated Annealing techniques. Problem-solving with a multi-objective optimization process has many advantages in comparison to solving problems with a single-objective problem formulation [17]. It is reported in [10] that multi-objective optimization can deal with many objectives at one time. Multi-objective optimization-solving schemes output better results when the objectives are correlated to each other as compared to a single-objective optimization problem-solving method. To reiterate, the single-objective optimization problem-solving method takes more time and is also a cumbersome process for finding the correct parameters, whereas a multi-objective optimization problem-solving method takes less time but is difficult to frame and requires a greater depth of optimization knowledge. Section 2 focuses on the analytical modeling of the PV module. Section 3 focuses on the BES technique. Section 4 explains the Simulated Annealing technique. Section 5 discusses the Hybrid BESSA technique and the improvement in the results obtained by implementing the Hybrid BESSA technique as compared to the BES for 23 benchmark functions. Section 6 discusses a multi-objective parameter extraction scheme and problem formulation based on the Hybrid BESSA technique. Results are discussed in Section 7. The conclusion and future scope for research are discussed in Section 8.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 4 Property Analysis of Moving Target in the Complex Image Domain
### Azimuth Displacement  
Besides the range displacement, the motion of the target will also lead to an azimuth offset in SAR image. According to (37) and the properties of FT, the target's azimuth displacement can be expressed as  
\[\begin{array}{rl}a_{shift}&=v_{g}*\left(\frac{l_{1}}{2l_{2}}+\frac{3l_{1}^{2 }l_{3}}{8l_{2}^{3}}+\lambda\frac{1}{4l_{2}}M\cdot PRF+\lambda\frac{3l_{1}l_{2 }}{8l_{2}^{3}}M\cdot PRF+\frac{l_{3}\lambda^{2}}{3l_{2}^{3}}3M^{2}\cdot PRF^{ 2}\right)\\ &=v_{g}*\left(\frac{1}{2l_{2}}\left(l_{1}+\frac{\lambda}{2}M\cdot PRF\right)+ \frac{3l_{3}}{8l_{2}^{3}}\left(l_{1}+\frac{\lambda}{2}M\cdot PRF\right)^{2} \right),\end{array} \tag{41}\]  
where \(v_{g}\) is the velocity of the beam sweeping along the surface of earth.  
Substituting the expressions of \(l_{1}\), \(l_{2}\) and \(l_{3}\) into (41), \(a_{shift}\) can be rewritten as  
\[\begin{array}{ll}a_{shift}&=\frac{v_{g}(3a_{aa}v_{00}-j_{sr}R_{0}-3a_{aa}v_{0 0})R_{0}^{2}}{2(v_{00}^{2}-2v_{hu}v_{00}+v_{00}^{2}-v_{tr}^{2}+a_{tr}R_{0}-a_{tr} R_{0})}(v_{tr}+\frac{\lambda}{2}M\cdot PRF)^{2}\\ &+\frac{v_{g}R_{0}}{(v_{00}^{2}-2v_{hu}v_{00}+v_{00}^{2}-v_{tr}^{2}+a_{tr}R_{0} -a_{tr}R_{0})}(v_{tr}+\frac{\lambda}{2}M\cdot PRF)\\ &\approx\frac{-v_{g}3a_{ta}v_{0}R_{0}^{2}}{2(v_{00}^{2}-2v_{hu}v_{00}+v_{00}^{2 }-v_{tr}^{2}+a_{tr}R_{0}-a_{tr}R_{0})}(v_{tr}+\frac{\lambda}{2}M\cdot PRF)^{2} \\ &+\frac{v_{g}}{(v_{00}^{2}-2v_{hu}v_{00}+v_{00}^{2}-v_{tr}^{2}+a_{tr}R_{0}-a_ {tr}R_{0})}(v_{tr}+\frac{\lambda}{2}M\cdot PRF)\\ &=\frac{-v_{g}3a_{ta}v_{0}R_{0}^{2}}{2(-2v_{tu}v_{00}+v_{00}^{2}+a_{tr}R_{0}-a_ {tr}R_{0})^{3}}\frac{\lambda}{4}^{2}f_{b}^{2}-\frac{v_{g}R_{0}}{(-2v_{hu}v_{00} +v_{00}^{2}+a_{tr}R_{0}-a_{tr}R_{0})}\frac{\lambda}{2}f_{b}.\end{array} \tag{42}\]  
Note that since the value of \(v_{g}(3a_{ta}v_{0}-j_{sr}R_{0})R_{0}^{2}\) is much smaller than \(3v_{g}a_{ta}v_{00}R_{0}^{2}\), it can be ignored.  
From (42), one can see that the azimuth displacement is also caused by the target's radial velocity and is dependent on the baseband Doppler center frequency rather than the absolute Doppler center frequency. Moreover, the azimuth displacement is also dependent on the target's along-track velocity, accelerations, and distance. In order to reveal the variation of the range displacement with these parameters, numerical experiments are carried out, with the results presented in Figure 5.  
From Figure 5a it can be seen that the azimuth displacement varies with \(v_{tr}\) periodically and gets the maximum value when the baseband Doppler center frequency equals to \(PRF/2\)In addition, one can see that the range displacement is almost independent of \(v_{ta}\). The variation of the azimuth displacement with the target's distance and accelerations are shown in Figure 5b,c. It can be seen that the range displacement increases with \(R_{0}\) approximately linearly. From Figure 5c one can see that the azimuth displacement is inversely proportional to \(a_{tr}\) and almost independent of \(a_{ta}\).
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Gateway Discovery Process  
In the case where the \(RL_{eNB}\) of the selected eNB is below the threshold \(RL_{eNBth}\) and the DT application is requested, the IV should start to initiate the gateway discovery mechanism. The IV broadcasts GWreq to all NVs within its coverage area as detailed in Equation (4) [28].  
\[A_{c}=R_{max}.(1-\epsilon) \tag{4}\]  
where \(A_{c}\) is the coverage area of the IV, \(R_{max}\) is the maximum transmission range of IEEE 802.11p, and \(\epsilon\) represents the channel fading condition of the IV.  
Apart from other standard information such as source identification (_sourceID_), source node directions (_sourceDir_), location coordinate (_LOC_), and speed (\(V\)), the GWreq containstwo other important points of information, which are the time to live (TTL) and message sequence number (_seqNo_). The TTL value is used to restrict the number of hops for searching for the gateway vehicle. The AQAGS scheme uses a single hop broadcast mechanism. Therefore, TTL is set to 1, meaning only a one-hop NV must receive the message. The NV checks if the _seqNo_ of the incoming GWreq message is greater than any previously received GWreq message: if yes, the NV computes its receive signal strength (\(RSS_{NV}\)), available load capacity (\(AVC_{NV}\)), the separation distance between the IV and NV (\(D_{IVNV}\)), and the link lifetime between the IV and NV (\(LLT_{IVNV}\)), and inserts the GWrep message in the send back to the IV using the _sourceID_ obtained from GWreq. If the _seqNo_ of the GWreq message is less than any previously received GWreq message (preSeqNo), the NV discards that message.  
Furthermore, in the event that the IV does not receive a GWrep message from the particular neighbor vehicle within a specific timeframe, the connection is considered down, and the NV is discarded. Moreover, if the IV received a GWrep message, it extracts the \(RSS_{NV}\), \(AVC_{NV}\), \(D_{IVNV}\), and \(LLT_{IVNV}\) parameters, which become the criteria in selecting the best NV to be the MGW.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 3 System Model
### Problem Formulation  
The container migration optimization objective for edge network load balancing is defined as the weighted sum of all the above influencing factors and is expressed as follows:  
\[F=\theta\left(U_{balance}^{total}+V_{balance}^{total}\right)+\gamma Mig_{cos }^{total} \tag{9}\]  
Here, \(\theta\) and \(\gamma\) are the weighting factors of the load-balancing degree of the edge network and the container migration cost, respectively, where \(\theta\) and \(\gamma\) are in the range of \([0,1]\) and \(\theta+\gamma=1\).
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 5 Conclusions  
CMAC is a new AC method developed to serve the smallsat remote sensing industry. From this investigation using S2 data as the testbed, it is apparent that CMAC has an accuracy that exceeds the existing state-of-the-art software, while requiring a fraction of the processing time. In other tests, CMAC utility has proven readily transferable to smallsat data through automated calibration.  
CMAC is new to remote sensing's AC science. The following points are contributions by CMAC and this investigation:  
1. Application of quasi-invariant areas (QIAs) for proofing atmospheric correction: QIAs are large-scale spectrally diverse targets whose surface reflectance remains sufficiently stable for testing methods across variable levels of atmospheric effects from clean to extremely hazy conditions through time.
2. The CMAC method in its entirety: Based on the analysis results presented here, CMAC correction agrees well with the accepted method for S2 AC and showed greater statistical stability, particularly for visible band reflectance below 2000 DN, the region of reflectance distribution that defines image quality and utility.
3. The CMAC conceptual model: The atmospheric correction algorithm is based upon empirical observations represented by a linear conceptual model. Statistical dispersion measured for 28 images across six QIAs provided evidence supporting that the simple CMAC conceptual model mathematically captures the atmospheric effects upon transmitted light.
4. Treatment of atmospheric effect as a single lump-sum variable: RadTRAN methods attempt to correct for multiple effects from the atmosphere; however, their interactive effects are largely unknown. CMAC is a simplification that performs AC based upon a single estimated variable: scatter in the S2 blue band calibrated to vegetation responses. This simplification resulted in comparatively higher accuracy and stability than the state-of-the-art Sen2 Cor.
5. The simplicity of CMAC is promotional--nothing is black box: The simplification for estimation of surface reflectance through the pathway of reflectance, not radiance, enables rapid calibration of any smallsat and accurate results. The resulting CMAC workflow led to the discovery of the important role that forward scatter plays in image effects from the atmospheric transmission and promises to solve reliable AC over the ocean for SZA-Att-I interactive effects as well as for other issues.  
Figure 19: TOAR (**a**) and CMAC (**b**) views of 8-18-2021 Planet Labs Dove satellite image over Fargo, North Dakota (20210818_175123_23_105a_3B_AnalyticMS_clip.tif cohort PS2.SD).
# Corrected Tilt Calculation for Atmospheric Pressure-Induced Seismic Noise
## 1 Introduction  
In the process of reviewing the literature of wind-generated pressure fluctuation coupling into the ground as part of a research effort [1, 2], the paper \"_A preliminary investigation into the relationship between long-period seismic noise and local fluctuations in the atmospheric pressure field_\" by [PERSON] [3] was studied. It was noted that [PERSON]'s expression for the ground tilt due to passage of a pressure wave was not complete for sensors below the surface. A complete review of peer-reviewed literature which referenced [PERSON] revealed that [PERSON] and [PERSON] [4] had identified the correct expression but had not used it in their research. In this paper, we use the complete expression to analyze apparent subsurface ground motion due to plane wave propagating harmonic pressure fluctuations caused by the wind, and highlight the frequency and depth regimes where the complete expression must be used.  
Section 2 first reviews the theory of coupling of slow-moving plane wave pressure fluctuations into vertical and horizontal ground displacements, as presented by [PERSON] [3]. Then, expressions for the correction of apparent horizontal displacement due to the complete term of the tilting of the sensor in the medium are derived and compared to [PERSON]. For discussion purposes, the analytic approximations corresponding to [PERSON]'s approximation are derived.  
Section 3 presents numerical calculations of the corrected total measured displacements at the surface for [PERSON]'s models 1, 2b, and 4 of the ground, and the attenuation with depth for [PERSON]'s Model 4. The results for the measured horizontal displacement display significant changes from calculations using with the incomplete tilt expressions.  
Section 4 reviews the impact of the use of the correct complete expression for the tilt on the literature which reference [PERSON]'s [3] paper. Many of the papers are unaffected since they study near-surface displacements or investigate very low-frequency excitations. A few papers for which the correction may affect the results are examined in detail.  
Section 5 summarizes the findings of the research and discusses possible research which is motivated by the more complex dependence of the horizontal displacement with depth of the complete expression for tilt.
## 5 Discussion
### Limitation of RR Measurements  
In this study, the RRs are acquired by directly measuring the displacement of the chest wall during respiration. In reality, it is often the case when the users' bodies are involuntarily moving while the system is measuring the respiration rate. Therefore, similar to the HR measurement, the RR measurement could also suffer from motion-induced artifacts. Even though such an issue is less pronounced in this study as the participants are asked to sit in front of the camera, we still discuss possible methods to mitigate the effects of body motion that is unrelated to respiration. Since the neck region usually moves with the chest but does not show a strong respiration motion, we could use the depth signal on the neck as a reference region to separate the respiration motion from other body motions. Therefore, we could view the depth signal from the chest region as a superposition of the general body motion and the chest wall motion from respiration. A blind source separation method such as Independent Component Analysis (ICA) can then be applied to the raw depth signals to extract the respiration motion. For example, Figure 20a shows the raw depth signal from the neck and chest region, respectively. The depth signal from the neck region records mainly the motion of the body since respiration does not cause significant muscle contraction on the neck. As a comparison, the signal from the chest region (Figure 20a bottom figure) is also affected by the overall body motion as the periodic chest wall motion from respiration is masked. The signal after ICA separation is shown in lFigure 20b. A clear pattern of the chest wall moving back and forth from the respiring motion can now be seen in the top row of the separated signal. The respiratory rate, derived from the first component in Figure 9, is measured as 15 BPM, while the reference pulse oxim also measures 15 BPM.  
It is also worth noticing that since the method of acquiring the respiration rate relies on directly measuring chest wall movement, any type of clothing, such as a loose sweatshirt or down jacket, that obfuscates the chest wall movement, will prevent the system from acquiring RR. To overcome this limitation, some complementary methods such as deriving RR from HR variability or using a thermal sensor to measure respiration rate can be used in combination with the ToF camera to provide RR measurements in more versatile environments [5; 7; 26; 27].  
Figure 19: Bland-Altman plot for HR measurements within groups of: age > 35 (**left**) and age <= 35 (**right**).