esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Techniques to Check a Possible Degradation of PCM
### Fourier Transform Infrared Spectroscopy (FT-IR)  
Fourier transform infrared (FT-IR) spectroscopy is an analytical technique based on the vibrations of a molecule that is irradiated with infrared radiation at a specific wavelength range due to selective absorption of infrared radiation by the sample material. Since the wavelengths that are absorbed by the sample material are characteristic of its molecular structure, this technique allows identifying molecular components and structures of organic and inorganic materials.  
FT-IR with attenuated total reflectance (ATR) allows analyzing the PCM chemical degradation caused by thermal cycling. The use of ATR has the advantage that the sample does not require any previous specific preparation to obtain the spectra. When the material is oxidized or degraded as a result of thermal cycling, a total or partial disappearance of the characteristic peaks and/or the appearance of new peaks are observed in the spectra.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 2 Small Satellites for VIS, TIR, and MW Remote Sensing
### Current Status of Small Satellite Payload for Earth Observation
#### 2.2.1 Optical Payloads  
The performance of an optical payload, in terms of spatial and signal-to-noise ratio (SNR), is limited by aperture size. Remote optical sensing systems mounted on small satellites usually employ aperture diameters \(\leq\)400 mm (cf. Table 2) (e.g., [68]), allowing for a spatial resolution ranging from 1 to 4 m, which is limited by light diffraction and dependent on satellite altitude. New methods have been implemented to limit sensors' volume and mass with similar resolutions to larger satellites, e.g., the high-precision telescope (HPT), a high spatial resolution multispectral sensor sized for small platforms [69], and the deployable optical systems [24].  
At the same time, small satellites provide a unique opportunity for launching affordable constellations, which can provide daily return cycles. In this respect, small satellites reach the temporal resolutions that are not practical with large satellites. Constellations such as SkySat (Terra Bella), Flock Imaging Constellation and RapidEye (Planet Labs), ICEYE (ICYE Ltd. of Espoo), NovaSar, and Disaster Monitoring Constellation Second Generation (SSTL/UKSA) are examples of small satellites working cooperatively to provide a daily coverage of the terrestrial surface (cf. Table 2).
## 3 Results
### Change of Diameter Dd  
The analysis of diameter changes indicated that when EV was 70 mL and FP was 60 min\({}^{-1}\), the mean of D\({}_{\text{d}}\) for iliac arteries was 0.83 \(\pm\) 0.016 mm (Figure 4a-d). The mean D\({}_{\text{d}}\) for silicon tubes was 0.56 \(\pm\) 0.007 mm, which was approximately 48% lower (compared to the iliac arteries (\(p\) < 0.0001)) (Figure 4a-d).  
Similarly, when EV was 115 mL and FP was 120 min\({}^{-1}\), the mean of D\({}_{\text{d}}\) for iliac arteries was 1.48 \(\pm\) 0.008 mm (Figure 4a-d). The mean D\({}_{\text{d}}\) for the silicon tubes was also about 62% lower than the mean D\({}_{\text{d}}\) for the arteries (0.92 \(\pm\) 0.011 mm; \(p\) < 0.0001).  
Next, the experimental results regarding vessel flexibility (D\({}_{\text{d}}\)) were verified with 2 DSTT data. There were no significant changes between results gathered from ACM and 2 DSTT. The mean D\({}_{\text{d}}\) for the iliac arteries for FP = 75 min\({}^{-1}\) was 0.94 \(\pm\) 0.017 mm when calculated with ACM and 0.90 \(\pm\) 0.018 mm when calculated with 2 DSTT (\(p\) = 0.083). Similarly, the mean D\({}_{\text{d}}\) for the FP = 90 min\({}^{-1}\) was 1.00 \(\pm\) 0.024 mm for ACM and 1.04 \(\pm\) 0.012 mm for 2 DSTT (\(p\) = 0.063). Additionally, according to Bland-Altman analysis of the iliac arteries, for FP = 75 min\({}^{-1}\), the difference between ACM and 2 DSTT was equal to 0.02 mm for the range equal to 0.04 mm (Figure 5a), while for FP = 90 min\({}^{-1}\), it was 0.02 mm for the range equal to 0.08 mm (Figure 5b).  
Figure 4: Changes of mean diameter D\({}_{\text{d}}\) (mm) of iliac arteries and silicon tubes for: ejection volume equal to (**a**) 70 mL; (**b**) 85 mL; (**c**) 100 mL; (**d**) 115 mL. Values are presented as mean \(\pm\) SD, \(n\) = 5. For all analyzes, \(p\) < 0.0001.  
Furthermore, [PERSON]'s rank correlation coefficients were determined between \(\rm D_{d}\) and hemodynamic parameters (Figure 6a-d). There was a positive correlation between \(\rm D_{d}\) and EV for the iliac arteries (rho = 0.964; \(p<0.05\)) and \(\rm D_{d}\) and FP (rho = 0.923; \(p<0.05\)).  
Figure 5: Comparison of ACM and 2 DSTT for the iliac arteries with the use of Bland-Altman analysis for: (a) FP = 75 min\({}^{-1}\) (b) FP = 90 min\({}^{-1}\). For all analyzes, \(p>0.05\).  
Figure 6: Scatterplot graphic representation of [PERSON]’s correlation factors rho (-) for the analyzed vessels for \(\rm D_{d}\) (mm) as function of FP and EV. \(\rm D_{d}\) as function of EV: (a) iliac arteries (rho = 0.964); (c) silicon tubes (rho = 0.911). \(\rm D_{d}\) as function of FP: (b) iliac arteries (rho = 0.957); (d) silicon tubes (rho = 0.923). Two types of vessels were analyzed, each group consisted of \(n=5\). For all analyzes, \(p<0.05\).
## 2 Methods
### Improved A* Algorithm for the Path Analysis of Restricted Networks
#### 2.1.1 Traditional A* Algorithm  
The A* algorithm must calculate the evaluation function \(F(N)\) each time a sub-node is generated to estimate the optimal path cost of the constraints of the starting node through the nodes to reach the destination node. When nodes are expanded, the nodes with the smallest \(F(N)\) value are always selected as the expansion object from the pool of nodes to be expanded. This approach ensures that the search progresses in the most promising direction. The following formula is the expression for \(F(N)\):  
\[F(N)=G(N)+H(N). \tag{1}\]  
Here, \(G(N)\) represents the estimated value of the actual cost \(G^{\prime}(N)\) of the optimal path from starting node \(O\) to any node \(N\) (\(G(N)\geq G^{\prime}(N)\)). The heuristic function \(H(N)\) is the estimated value of the actual cost \(H^{\prime}(N)\) for the optimal path from node \(N\) to target node \(D\) (\(H^{\prime}(N)\geq H(N)\)) [5]. The selection of heuristic function \(H(N)\) is rather important. It lacks a stable mathematical model and mainly depends on the prior knowledge of the characteristics of the problem to be solved, as well as strategy tricks for it is resolution.  
Generally, \(H(N)\) is commonly used as the distance formula between two points to estimate the cost of traveling from point \(N\) to ending point \(D\) in the future. \(G(N)\) represents the previous cost from starting point \(O\) to point \(N\), e.g., the sum of the distances traveled on each path. In road networks, the distance heuristic function can provide relatively reliable and effective path planning for travelers. During their travel, people are more concerned about the number of transfers, travel time, travel costs, and walking distance in public transportation networks. However, the traditional heuristic function cannot meet these specific needs. In this paper, we optimize the heuristic function and improve the traditional A* algorithm to solve the above problems.
## 5 Conclusions  
This paper outlines PEC detection based on rectangular coils in order to prevent accidents caused by shell defects in power-battery aluminum alloys. Defect shape mapping is realized by picking up the coil in both the direction of magnetic induction and the direction of the excitation current. Different defects developed in the 3003 Al-Mn alloy specimens. The experiment results showed that defect shape mapping in two directions could be obtained using C-scan contour images. The other finding made by this paper was that defect recognition in the magnetic induction flux direction was better than in the excitation current direction. We ascertained that the proposed method was also effective for subsurface defect and irregular defect shape mapping. One drawback in our results was that the estimated defects' dimensions were slightly mismatched with the actual dimensions. Other than this, the proposed method, based on rectangular coils and orthogonal _By_ pickup coils, is an effective method with which to imagine surface and subsurface defects. This may be useful in characterizing other defects with more complex geometry. The authors also acknowledge that there is still room for improvement regarding the accuracy and effectiveness of the current testing methods, which is the next step in improving the experimental system. The research team plans to use precision scanners to control the movement of sensors in subsequent experiments to reduce deviations and improve measurement accuracy. This method has potential for future applications. Moreover, this method provides a useful solution for warning of defects in the aluminum alloy shell of electric vehicle power batteries.  
Conceptualization, K.Z., Z.D. and Y.H.; methodology, K.Z., Z.D. and Y.H.; formal analysis, K.Z.; investigation, K.Z. and Z.Y.; resources, Z.D.; data curation, Z.Y.; writing--original draft preparation, K.Z. and Y.H.; writing--review and editing, K.Z.; visualization, Z.Y.; supervision, Y.H.; project administration, K.Z.; funding acquisition, K.Z., Z.D. and Y.H.  
This work is supported by the Shenzhen Science and Technology Project (Basic Research, Grant No. JCYJ20170306144608417), National Natural Science Foundation of China (Grant No. 61501483), Shenzhen Basic Research Project (Grant No. JCYJ20160523113817077) and the Shenzhen Polytechnic Research Project (Youth Innovation, Grant No. 601822K30021).  
The authors would like to extend their appreciations to the help provided by the editors and reviewers.  
The authors declare no conflict of interest.  
Figure 15: C-scan contour images of irregular defect 4.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 3 Results  
### SBAS Surface Displacement in the Quetta Valley  
Figure 6 shows the vertical displacement velocity ranging from \(-5.8\) mm/year to \(1.8\) mm/year averaged for seven years (3 April 2003-23 September 2010). There are fewer data points in the center of the footprint due to the low coherence of the images. On a descending path, the ENVISAT satellite moves south with its antenna pointed to the right. Therefore, a positive value can be regarded as an eastward shift, uplift, or both. We attributed this movement primarily to uplift for the following reasons: (1) From the general tectonics of the area, the region is subject to thrust and north-south strike-slip faults, intervening valleys, and basins. We could not resolve the north-south strike-slip Chaman Fault movement because of the Line of Sight (LOS) direction is perpendicular to the strike-slip movement direction. (2) The GPS observations from the stations deployed in the Quetta Valley during 2006-2016 indicate that the observed deformation is predominantly vertical movement (subs  
The subsidence signal in the Quetta Valley was also recorded by GPS station recordings from 2006-2016, with an average subsidence rate of 7.7 mm/year. The InSAR results match the GPS rates, but with a smaller magnitude. This small discrepancy is likely due to the limitations of the spatial and temporal coverage caused by the intermittent GPS operation. GPS operates over a shorter time frame that does not involve continuous monitoring, whereas the InSAR result value involves a longer time frame and continuous monitoring and is therefore smaller. It is worth noting that InSAR-derived deformation velocity is a spatial average within a pixel (e.g., 80 m \(\times\) 80 m), while GPS measurements represent a discrete point location. A 2D velocity profile CC\({}^{\prime}\), which crosses zones A through D, was extracted from the SBAS-InSAR results. Profile CC\({}^{\prime}\) shows zone A with 1.4 mm/year uplift, zone B with 0.5 mm/year subsidence (that could be noise), zone C with 5.5 mm/year subsidence, and zone D with 1.8 mm/year uplift (Figure 6A).
## 3 Results
### Analysis Using FTIR and SEM  
The result from FTIR analysis (LabRam ARAMIS IR2, HORIBA JOBIN YVON) confirmed the incorporation between the GO and MMT. The FTIR pattern of GO in Figure 3 shows bands at 1045 cm\({}^{-1}\) (C-O-C stretch of epoxy group), 1385 cm\({}^{-1}\)(C=C bending), 1625 cm\({}^{-1}\) (-OH bending), and 1710 cm\({}^{-1}\) (C=O stretch of carboxyl group). The pattern of MMT shows bands at 1035 cm\({}^{-1}\) (Si-O stretching band), 1630 cm\({}^{-1}\) (O-H bending band), 3450 cm\({}^{-1}\) (-OH stretch from free H\({}_{2}\)O), and 3630 cm\({}^{-1}\) (-OH stretch band). The FTIR patterns of MMT such as Si-O and O-H appeared in the GO-MMT composite. The barrier layer was fabricated via drop casting a stretchable substrate without pre-strain, as shown in Figure 4, to fabricate a barrier layer on the stretchable substrate without applying pre-strain [27]. The stretchable gas barrier film was stretched and relaxed. The surfaces of the stretchable gas barrier films fabricated via drop casting without pre-strain and after applying 10% and 30% pre-strain to the stretchable substrate were subsequently analyzed by SEM. Figure 5 shows SEM top-view images of the sample after 30 layers of PDDA (GO)/PVA (MMT) were prepared by LbL deposition at different strains (0%, 10%, and 30%). PDDA (GO) and PVA (MMT) were well deposited on the mixed Ecoflex and PDMS substrate, as shown in Figure 5a-c, which show the surface of the 30-layer laminate of PDDA (GO)/PVA (MMT) after 0%, 10%, and 30% pre-strain, respectively. After pre-strain was applied, it was confirmed that many wrinkles appeared on the surface of the substrate where the barrier layer was laminated and subsequently relaxed. The SEM image in Figure 6 shows the surface of the substrate after 100 cycles of stretching (30% strain) of the stretchable gas barrier film fabricated by applying different pre-strains (0%, 10%, and 30%). The barrier layer could not withstand 30% stretching, as indicated by the large number of cracks in the stretched samples without pre-strain (Figure 6a). The 10% pre-strain sample (Figure 6b) showed less cracks than the samples prepared without pre-strain, but a few cracks were still observed. In contrast, the 30% pre-strain sample showed high durability after 100 stretching cycles. It was confirmed that the gas barrier film was able to withstand stretching well due to wrinkles formed by applying pre-strain. Thus, it can be concluded that the barrier layers are vulnerable to stretching but show high durability during contraction.  
Figure 2: Zeta potential values of the GO, PDDA (GO), MMT, and PVA (MMT) solutions.  
Figure 4: Schematic of the multilayered films deposited on an Ecoflex/PDMS stretchable substrate with pre-strain using LBL assembly.  
Figure 5: SEM surface images of the 30 laminated PDDA (GO)/PVA (MMT) layers on a stretchable substrate applying strains of **(a)** 0%, **(b)** 10%, and **(c)** 30%.  
Figure 3: FTIR patterns of GO, MMT, and GO-MMT.  
Figure 6: SEM surface images after stretching test on substrates with **(a)** 0%, **(b)** 10%, and **(c)** 30% pre-strain applied.
## 2 Theoretical Analysis  
### Support Vector Machine  
The support vector machine is a commonly used algorithm to classify data sets with binary output values. The method derived by [PERSON] is mainly based on the basics of statistical learning theory and applies the quadratic optimization problem to distinguish two classes in a feature space [15]. The training is done with a training set \(S\) of length \(N\) and consists of the input data \(x_{i}\) and binary classified data \(y_{i}\):  
\[S\ =\ (x_{1},y_{1}),...,(x_{n},y_{n}),\ \ \ \ \ \ \ \ \ x_{i}\in\mathbb{R}^{n},y_{i}\in\pm 1 \tag{1}\]  
In order to separate the input features, a hyperplane \(h(\mathbf{x})\) is calculated by a quadratic optimization problem. To allow more general decision surfaces, the input data is mapped to a higher dimensional feature space, that is, \(\phi(\mathbf{x})\in\mathbb{R}^{m}\), where the data is linearly separable. The SVM classifies an estimation according to  
\[\mathbf{\hat{y}}=\text{sign}\{\mathbf{w}^{T}\phi(\mathbf{x})+b\}, \tag{2}\]  
where \(\mathbf{w}\) and \(b\) are an orthogonal vector to \(h(\mathbf{x})\) and a bias term, respectively, which are determined in a training process. The optimal hyperplane is found, if the margin--smallest distance between the hyperplane and any of the samples--is maximized [3; 16].  
The mentioned optimization problem distinguishes two classes whose feature vectors are located in an ideally delimitable area. If there are strong deviations of individual data points in the training set, for example, if a data point is located in the area of the contrary class, it is not possible to separate the data successfully. The problem can be solved by using a soft margin classifier. This enables a tolerance against data anomalies. For this purpose the optimization problem is extended by an error term including a weighting coefficient \(C\) and slack variables \(\xi\)[3]  
\[\min_{\mathbf{w},b,\xi}\frac{1}{2}||w||^{2}+C\sum_{i=0}^{N}\xi_{i} \tag{3}\]  
under constraints  
\[y_{i}(\mathbf{w}^{T}\phi(\mathbf{x})+b\geq 1-\xi_{i}) \tag{4}\]\[\xi_{i}\geq 0,\ \ \ \ \ i\ =\ 1,...,N. \tag{5}\]  
The basic algorithm of the SVM has been formulated in terms of scalar products in the feature space \(F\). According to [PERSON]'s theorem the intensive calculation in the higher dimensional space can be significantly reduced by a suitable kernel function \(K(\mathbf{x},\mathbf{x}_{i})\ =\ \phi(\mathbf{x})\cdot\phi(\mathbf{x}_{i})\)[16]. With the use of different kernels, diverse problems can be solved, which opens up a wide variety of SVM learning machines. In case of \(M\)-QAM transmission, the radial base function (RBF) is the most suitable kernel and is defined by  
\[K(\mathbf{x},\mathbf{x}_{i})\ =\ \exp(-\gamma||\mathbf{x}-\mathbf{x}_{i}||^{2}) \tag{6}\]  
with the kernel parameter \(\gamma>0\)[16].
## 5 Discussion and Future Directions
### Limitations of SCI Device-Based Therapies
#### 5.2.6 Regulatory and Health System Integration  
Despite promising technological advances, many spinal cord injury devices face significant regulatory hurdles before clinical use. Especially implantable devices must undergo rigorous safety testing, biocompatibility assessments, and long-term monitoring to meet the standards set by agencies such as the FDA (U.S) or EMA (Europe). These processes can delay large-scale production and may increase development costs. These regulatory processes lack global consistency, which creates more issues with global development [53].  
Implantable devices such as epidural electrical stimulators, brain-computer interfaces, and SMART implants are typically classified as Class III high-risk devices, requiring extensive premarket approval. In the United States, the FDA mandates compliance with Good Manufacturing Practices (GMP) and Quality System Regulations (QSR), while in Europe, there are rigorous conformity assessments and clinical evaluations [54]. Devices like DARPA's 64-channel brain-computer interface must undergo both neurological safety testing and software validation. These systems often integrate AI and wireless communication, triggering additional scrutiny around cybersecurity, data privacy, and human-machine interfacing. Regulatory frameworks such as ISO 13485 (Quality Management) and ISO 14971 (Risk Management) are essential but not always harmonised globally [44]. To overcome these barriers, future SCI device development should align early with international standards (ISO 13485 and ISO 14971), incorporate cybersecurity protocols (IEC 62304, GDPR compliance), engage with regulatory bodies during design and testing phases, and prioritise modular and scalable device architectures to ease certification.
## 3 Results and Discussion
### Effect of the Occupants on STI Results
The trend of the STI decreasing with an increasing number of occupants suggests that, for automotive cabins, the effect of adding occupants on the STI is mainly reflected in the detrimental effect of reducing SNR rather than the beneficial effect of reducing reverberation time; this is because the reverberation is already small enough to be ignored [7; 22]. From this perspective, in the design of car interiors, properly arranging internal reflection boundaries to obtain more (early) reflected sounds, rather than simply increasing internal sound absorption, can greatly improve speech intelligibility and is more conducive to obtaining sufficient speech intensity in different scenarios (such as when fully occupied). Compared to the results of previous studies [35; 43], it seems that the STIs in the automotive cabin considered here are less affected by adding occupants. [PERSON] et al. [43] reported STI increases of 0.050 and 0.035 with or without a public address system, respectively, in six churches, and [PERSON] [35] found an average STI increase of 0.05 for six reflective classrooms and an average STI decrease of 0.02 for six absorptive classrooms. The changes in the STI value after adding occupants in the present study (mostly no more than 0.03) were lower than those found in both of these previous studies. This further validates the understanding that the influence of occupancy on the indoor acoustic environment and speech intelligibility is contingent upon the initial acoustic environment. Nevertheless, since the internal acoustical environment and volume of an automobile cabin are highly dissimilar from those of classrooms and churches, they are actually not comparable. Nonetheless, the quantitative findings derived from the present study can offer specific insights for future investigations of speech intelligibility within automotive cabins.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 4 Linear Quantum Antenna Systems
### The Klein-Gordon Field Theory  
In relativistic quantum field theory [38; 54; 55], everything takes place in the four-dimensional Minkowski spacetime [51; 53], denoted by \(\mathbb{M}^{4}\), which is a linear vector space endowed with a special metric, the Lorentz metric \(g_{\mu\
u}\), see Table 15 in Appendix B, where the relativistic notation and key quantum formulas in the natural unit systems are reviewed. (In what follows, we work in the natural unit systems where \(c\), the speed of light, and \(\hbar\), the Planck constant, are both reduced to unity (\(c=\hbar=1\)); see Appendix C). Without loss of generality, and as mentioned earlier, the q-antenna quantum field \(\phi(x)\) is assumed tobe that of scalar massive theories.[12] To further simplify the mathematical manipulations, we focus on the neutral massive Klein-Gordon field theory, which is outlined in Appendix F. For massive particles (spin-0 particles in our scalar theory example), the dispersion relation (A27) governs the behavior of particles emitted when the q-antenna system is used in applications such as quantum molecular communications. On the other hand, massless (\(m=0\)) particles models \"scalar photons\" or photons with polarization ignored, since in this case, the Klein-Gordon equation reduces to the (scalar) wave equations.[13] In general, adding spin to the theory does not involve any major changes in the main conceptual ideas related to q-antennas and hence will be left for future work.  
We also further note that the field modal expansion (A26) is intrinsically Lorentz invariant, even though the spectral integration performed there is carried with respect to the non-Lorentz invariant volume measure \(\mathrm{d}^{3}x\) because the standard method of _normalizing factor_ has been already employed in our formulation.[14] In other words, the spacetime q-antenna theory developed here is fully relativistic. A breakdown of relativistic invariance, as exemplified by choices of spatiotemporal decomposition (slicing) of the source or receiver regions \(D_{\mathrm{s},\mathrm{r}}\), e.g., as in (3), can be introduced later as an external restriction enforced by hand to simplify the calculations and the presentation.