esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 6 Future Work  
This research concentrates on the optimal method for achieving precise satellite PE. In future work, we will expand in two research directions. First, the satellite PE method will be tested in other environments, such as the moon, to benefit future space exploration endeavors. Second, satellite movements will be predicted based on satellite PE. For example, the alternative state estimation method can be implemented by using the PE-PSO method as a model for measuring the position and orientation of a satellite for prediction or estimation, which would be useful for satellite operations.  
Conceptualization, P.K. and C.C.; methodology, P.K.; software, P.K.; validation, P.K.; formal analysis, P.K.; investigation, P.K.; resources, P.K.; data curation, P.K.; writing--original draft preparation, P.K., W.B., L.T. and P.B.; writing--review and editing, P.K.; visualization, P.K.; supervision, P.K., C.C., Y.Z. and P.B.; project administration, P.K.; funding acquisition, P.K. All authors have read and agreed to the published version of the manuscript.  
This research is the result of the project entitled \"Impact of air traffic on air quality by Artificial Intelligent Grant No. RE-KRIS/FF67/012\" by King Mongkut's Institute of Technology Ladkrabang, which has received funding support from the NSRF.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors would like to acknowledge the administrative and technical support received from Satang Space Company Limited, the Air-Space Control Optimization and Management Laboratory (ASCOM-LAB), the International Academy of Aviation Industry, and King Mongkut'sInstitute of Technology Ladkrabang, which contributed suggestions and infrastructure during the experiments.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### RTK-GNSS Data Processing  
Mounting a GNSS rover onto a bicycle made for efficient topographic measurements, but, unlike a rover set atop a pole kept static and vertical during data acquisition, measured points' coordinates may well be affected by errors due to pitch and roll whereby the bike is subject to both the local relief in the direction of travel and its own instability. In order to minimize these effects, particularly pitch-related errors, which were easier to detect, survey points were cleaned according to (i) the confidence level with which they were obtained, and (ii) systematic errors due to the uneven terrain topography.  
Where survey points were sufficiently close to each other, point confidence was estimated as the standard deviation of elevations within a circular window with 0.2 m radius, which was considered a proxy of the local bed complexity. For the points evaluated, only those with a confidence score below 0.035 m (the theoretical vertical precision of RTK-GNSS) were retained, hence filtering less reliable points due to a locally complex bed morphology and/or unnaturally large deviations among surrounding points.  
Pitch-related errors due to the uneven terrain were estimated, assuming the bicycle followed a straight line between two consecutive survey points, that the antenna of the GNSS unit was vertically aligned with the real wheel center point when the bike is horizontal and that the system was well equilibrated at all times (i.e., no roll). Under these conditions, the bed slope in the travel direction, the pitch angle of the bicycle and that of the GNSS antenna are same. We calculated the horizontal (dx) and vertical (dz) point displacements due to a sloping bed using a distance between the two wheels of 1.2 m and a GNSS antenna height of 0.6 m. With the arrangement used, horizontal errors were predominant over vertical errors. As an indication, a 6\({}^{\circ}\) slope (equivalent to a beach gradient of 1 in 10) resulted in horizontal and vertical errors of approximately 0.062 m and 0.007 m, respectively. Similar to filtering by point confidence, points with pitch-related errors exceeding 0.035 m were systematically filtered to only retain data in permissible terrain (here, terrain with forward slopes below approximately 3\({}^{\circ}\)).
## 3 Test Results and Analyses
### The Effect of Corrosion Thickness on the Bearing Performance of the Pipes
#### 3.1.2 Comparative Analysis of the Pipes' Load-Bearing Performance after Rehabilitation  
The load-displacement curve of pipe sections with the same repair thickness at different corrosion depths is shown in Figure 4. From the figure, it can be seen that when repairing pipe sections with different corrosion thicknesses, the load-displacement curves of the whole structure formed using a repair lining layer of the same thickness are basically the same. The load-displacement variation was approximately linear before the cracking load was reached during the early stage of loading, and when the pipe sections cracked, the slope of the load curve gradually decreased as the load increased and the crack continued to expand. However, the greater the corrosion thickness, the smaller the slope of the load curve. As shown in Table 6, with the initial increase in the corrosion thickness of the pipe sections, the ultimate load of the overall structure decreased. Considering the initial thinning of the pipe sections' thickness, to reduce the load-bearing capacity and increase the lining layer between the structural stiffness gap of the pipe sections themselves, a certain thickness of the lining layer was unable to effectively inhibit the overall structure of the deformation. Combined with the damage of specimen F-8, when the corrosion depth reached a thickness that impacted the load-bearing capacity, the existing pipe sections underwent structural deformation, resulting in a significant weakening of the interfacial bonding. The laminated structure then quickly transformed into a composite structure, and the lining layer of the bearing role could not carry out its full role, resulting in a decline of the performance of the repaired pipe sections.  
Figure 3: Load–displacement curves of the corroded pipe sections.  
The results show that when the corrosion depth did not reach the thickness of the protective layer, the spraying repair of the pipeline effectively reduced the occurrence of a local rupture, leakage, and other defects caused by structural deformation of the pipeline. Simultaneously, timely repairs were conducted on the corroded pipeline with a relatively small spraying thickness, thereby ensuring the flow capacity of the existing pipeline.
## 3 Results and Discussions
### Microstructure Analysis  
Surface analyses were performed for both composite mixtures and for the interface between the two components to identify and analyze the pores of the structures and the compatibility between the matrix and components. The SEM images of the microstructure of composite 1 are shown in Figure 17a,b, where the surface morphology indicates a homogenous material and the presence of both constituents, of which the dark gray area represents the polyester matrix. In addition, it was observed that calcium carbonate particles were integrated into the matrix, while cracks were not detected on the surface of composite 1.  
All the constituents used in the manufacturing process can be identified within the microstructure of composite 2 (Figure 18a,b), where polyester resin covers all the constituents. It can be seen that the glass fiber monofilaments are randomly positioned, where it was observed that these monofilaments are glossy and sleek on the exterior, without impregnation from a blinder. The polymer is seen to be attached and bonded on the surface of the composite. The same thing can be observed for quartz particles, where the matrix is present on the particle surfaces.  
Open porosity structures were identified (Figure 18), allowing a good connection with the adhesive cement. The bonding of ornamental panels can be realized by mechanical adhesion, where the adhesive enters into the porous material of the panel and forms a strong adhesive connection.  
The interface between the surface layer and the structure material can be approximated as a linear shape, as shown in Figure 19a, where all the constituents are presented (shown in Figure 19b). This can be explained by a simplification hypothesis because the surface between composite 1 and composite 2 is not planar. The surface is determined by the stone breaking surface. For a small piece, such as a sample, this can be considered a planar surface. Calcium carbonate particles can be seen to be integrated into the polyester matrices, and quartz particles are covered by the matrix and connected by the surface layer material. A good connection between composite 1 and composite 2 was detected.  
Figure 15: Panel 1: the intervals plot of (**a**) the number of pores, (**b**) pore diameters, (**c**) and the surfaces of the pores versus pore volume classes. Bars are one standard error from the mean.  
Figure 16: The sphericity (Sp) parameter of closed pores for: (**a**) sample S1; (**b**) sample S2; (**c**) sample S3; (**d**) sample S4; (**e**) sample S5.  
Figure 17: Scanning electron microscopy (SEM) images of composite 1: (**a**) 1:5 \(\times\) 10\({}^{-4}\) m scale (**b**); 1:1 \(\times\) 10\({}^{-5}\) m scale.
# Trusted GNSS-Based Time Synchronization for Industry 4.0 Applications
## 3 Time Distribution Network Attack Vector
### Attacks against the SW Integrity and Configuration of the Nodes
In detail, all the Slave nodes reach the PTP_SLAVE status [18; 19], acquire the control and start correcting their internal clocks. It is known that ptpd can achieve microsecond level of time coordination, even on limited platforms [46]. The protection of the integrity of the SW stack in Figure 2 is of paramount importance to avoid that nodes share intentionally modified timing information over any secure version of PTP. Aiming to detect possible intentional changes in the work logic of the Master and/or the Slave node, the next section proposes a software integrity architecture based on Trusted Computing principles and customized for embedded devices.
# Aortic Hemodynamics with Accelerated Dual-Venc 4D Flow MRI in Type B Aortic Dissection
The velocity (v) aliasing may occur for unpredictable high blood flow velocities (v > venc) such as in TL in TBAD and additionally, increased noise for slow flow regions (v < venc) may also be observed such as in FL in TBAD. The venc used in the SV 4D flow MRI acquisitions is usually adjusted to the estimated peak velocity (PV) to avoid velocity aliasing, limiting the evaluation of the slow flow velocities [20; 21; 22]. However, there is a direct positive correlation between the velocity-noise-ratio (VNR) and the measured velocity and inverse relationship between the VNR and the venc; consequentially, the higher the venc, the lower VNR. This is especially important in regions with a slow flow such as in FL in TBAD [23]. Ideal hemodynamic evaluation of cardiovascular diseases should be performed by a technique that maintains high VNR across the encountered range of velocities. However, this range is very broad in TBAD as there are large differences in TL and FL velocities. It is also likely that poor VNR will lead to reduced accuracy of more advanced hemodynamic parameters such as flow stasis and kinetic energy (KE) which may be important markers of adverse outcome risk in TBAD [18]. To address this problem, low-venc (LV) and high-venc (HV) images are being acquired in the same scan with dual-velocity encoded (dual-venc--DV) 4D flow MRI using methods such as Bayesian analysis where the aliased data can be recovered using the HV data while preserving the favorable VNR of the LV data [24; 25; 26]. Combining DV 4D flow MRI method with compressed sensing (CS) acceleration technique may provide additional critically important benefits for TBAD management such as shorter scan times, as CS can significantly accelerate MRI acquisitions utilizing the inherent sparsity of MRI data [27; 28; 29; 30]. In our previous study, we investigated the applicability of the CS-accelerated SV 4D flow MRI in TBAD with no impact by the DV methodology [31]. Here, in this study, we aim to systematically evaluate the potential utility of CS-accelerated DV 4D flow MRI with an acceleration level of 7.7 (R = 7.7) in aortic hemodynamics, including KE, PV, forward flow (FF), reverse flow (RF) and flow stasis in a cohort of chronic TBAD patients and volunteers. We have provided the detailed comparisons of the quantitative flow parameters in TBAD between the SV GRAPPA-accelerated 4D flow MRI and CS-accelerated DV 4D flow MRI acquisitions and discuss our results, taking into account the results from the previously published studies available in the literature. We hypothesize that DV 4D flow MRI acquisition improves the characterization of flow hemodynamics relative to SV 4D flow MRI acquisition by better capturing the full dynamic range of velocities in TBAD while maintaining a high VNR.
## 3 Results and Discussion
### Influence of Shear Temperature
#### 3.2.2 MSCR Test  
In order to investigate the influence of shear temperature on the SBS-modified asphalt's high-temperature performance, the above samples were sent for MSCR testing, and the calculated R and Jnr are shown in Figure 6.  
For the SBS-modified asphalt prepared at 170 \({}^{\circ}\)C, due to its poor uniformity and weak network structure strength, it shows the lowest R with the highest Jnr. SBS-modified asphalt prepared at 180 \({}^{\circ}\)C and 190 \({}^{\circ}\)C show similar performance at the testing temperature  
Figure 5: Fluorescence microscopy test of SBS-modified asphalt at different shear temperatures (100 times).  
Figure 6: MSCR test results of SBS-modified asphalt under different shear temperatures (**a**) R, (**b**) Jnr.  
of 70 \({}^{\circ}\)C, but when the testing temperature reaches 82 \({}^{\circ}\)C, the performance of SBS-modified asphalt prepared at 180 \({}^{\circ}\)C is superior to that of SBS-modified asphalt prepared at 190 \({}^{\circ}\)C.  
These results indicate that the preparation temperature of 170 \({}^{\circ}\)C may not be high enough to facilitate the swelling and blending of the SBS polymer, while 190 \({}^{\circ}\)C might be too high to avoid thermal oxidative degradation of the SBS polymer. When SBS-modified asphalt is prepared at 180 \({}^{\circ}\)C, the best MSCR results are recorded [6]. For this reason, a shearing temperature of 180 \({}^{\circ}\)C is recommended.
# Adaptive Versioning in Transactional Memory Systems +
## 4 Basic Adaptive Versioning  
We now describe our approach, Adaptive, that runs transactions using either eager or lazy versioning, switching between them dynamically at runtime. Figure 4 compares Adaptive with eager and lazy versioning. The pseudocode of Adaptive is given in Algorithm 1.  
Figure 4: An illustration of (**a**) eager, (**b**) lazy, and (**c**) basic adaptive versioning. The time gap \(\sigma_{*}\), while switching from eager (lazy) to lazy (eager) is to let finish executing in-flight transactions. This helps in avoiding potential data versioning inconsistencies.  
```
1\(N_{Ecommit}\leftarrow\) number of transaction commits until \(t\) executed using \(Eager\) \(N_{Lcommit}\leftarrow\) number of transaction commits until \(t\) executed using \(Lazy\) \(\text{versioning}\); \(N_{Eshort}\leftarrow\) number of transaction aborts until \(t\) executed using \(Eager\) versioning; \(N_{Lshort}\leftarrow\) number of transaction aborts until \(t\) executed using \(Lazy\) \(\text{versioning}\); \(N_{Commit}\gets N_{Ecommit}+N_{Lcommit}\); \(N_{abort}\gets N_{Eabort}+N_{Labort}\); \(V_{cur}\leftarrow\) current versioning method for transactions running in a thread; // \(V_{cur}\in\{Eager,Lazy\}\) \(tms\leftarrow\) Transactional Memory System type; // \(tms\in\{persistent,non\_persistent\}\) /* Initially, at \(t=0\), choose the versioning method based on workload size (if available), otherwise randomly */ if\(N_{commit}+N_{abort}=0\)then ifinformation on read and write sets is availablethen \(Wset(T)\leftarrow\) write set of transaction \(T\); \(Rset(T)\leftarrow\) read set of transaction \(T\); if\(|Wset(T)|>|Rset(T)|\)then \(V_{cur}\gets Lazy\); Execute \(T\) using \(Lazy\) versioning; else\(V_{cur}\gets Eager\); Execute \(T\) using \(Eager\) versioning; else Execute \(T\) using either eager or lazy versioning; /* At any time \(t>0\), choose the versioning method based on abort-to-commit ratio (ACR) */ elseif\(tms==\) persistentthen // ADAPTIVE for persistent memories \(AAR\leftarrow\frac{N_{abort}}{N_{commit}+N_{abort}}\), \(AAR_{Eager}\leftarrow\frac{N_{abort}}{N_{Excommit}+N_{Export}}\); \(ACR_{Eager}\leftarrow\frac{N_{Eabort}}{N_{Excommit}}\), \(ACR_{Lazy}\leftarrow\frac{N_{labort}}{N_{Lcommit}}\); if\((AAR\geq\frac{2}{3})\vee((ACR_{Eager}>ACR_{Lazy})\wedge(AAR_{Eager}\geq\frac{2}{3}))\)then  Execute \(T\) using \(Lazy\) versioning; else  Execute \(T\) using \(Eager\) versioning; elseif\(tms==\) non_persistentthen // ADAPTIVE for non-persistent memories \(ACR_{Eager}\leftarrow\frac{N_{Eabort}}{N_{Excommit}}\), \(ACR_{Lazy}\leftarrow\frac{N_{labort}}{N_{Lcommit}}\); if((\(V_{cur}=Eager\)) \(\wedge(ACR_{Eager}>\frac{1}{2})\)) then \(V_{cur}\gets Lazy\); elseif((\(V_{cur}=Lazy\)) \(\wedge(ACR_{Lazy}<2)\)) then \(V_{cur}\gets Eager\);  Execute \(T\) using \(V_{cur}\) versioning method;
```  
**Algorithm 1**Adaptive for a transaction \(T\) at any time \(t\geq 0\)
## 5 Performance Analysis and Optimization
### Ergodic Capacity  
The channel capacity, in [PERSON]'s sense, is a core performance measure, since it provides the maximum achievable transmission rate in which the errors are recoverable. The ergodic capacity of the SSC in wireless fading channels can be expressed as [1]:  
\[\overline{C}_{\gamma_{sec}}=\frac{B}{\ln 2}\int_{0}^{\infty}\ln(1+\gamma)f_{ \gamma_{sec}}(\gamma)d\gamma, \tag{84}\]  
where \(B\) denotes the bandwidth of the channel. By inserting (1  
\[\overline{C}_{\gamma_{sec}}=\frac{BG_{3,3}^{3,2}[\Lambda\Big{|}_{m,0,0}^{1-n,0,1} ][F_{\gamma}(\gamma_{\tau})+1]}{\ln 2\Gamma(m)\Gamma(n)}-\frac{B\Lambda^{m}\gamma_{ \tau}^{m}}{\ln 2\Gamma(m)\Gamma(n)}G_{1,12,22,1,1}^{0,1:1:2,1,1}[\gamma_{\tau _{\tau}},\gamma_{\tau}\Lambda\Big{|}_{-m}^{1-m}\Big{|}_{1,0}^{1,1}\Big{|}_{0}^{ 1-(m+n)}]. \tag{92}\]  
To obtain the optimum switching threshold so as to maximize the ergodic capacity \(\overline{C}_{\gamma_{SCC}}\) in the i.i.d. case, we use (91) and set \(\overline{\partial C}_{\gamma_{SCC}}/\partial\gamma_{\tau}|_{\gamma_{\tau}^{2 }=\gamma_{\tau}}=0\). After some algebraic manipulation, the optimum switching threshold can be yielded as \(\gamma_{\tau}^{*}=\exp[_{4}]-1\), where:  
\[\gamma_{4}=\exp\left(\frac{G_{3,3}^{3,2}[\Lambda\Big{|}_{m,0,0}^{1-n,0,1}]}{ \Gamma(m)\Gamma(n)}\right)-1.\]  
For the multiple-branch SEC receiver, by substituting (45) into (84), the closed-form expression of ergodic capacity in the i.i.d. case can be expressed as:  
\[\begin{array}{ll}\overline{C}_{\gamma_{SEC}}&=\frac{BR_{1}}{\ln 2}\int_{0}^{ \infty}\ln(1+\gamma)f_{\gamma}(\gamma)d\gamma+\frac{BP_{1}}{\ln 2}\int_{ \gamma_{\tau}}^{\infty}\ln(1+\gamma)f_{\gamma}(\gamma)d\gamma\\ &=\frac{BG_{3,3}^{3,2}[\Lambda\Big{|}_{m,0,0}^{1-n,0,1}](P_{1}+P_{3})}{\ln 2 \Gamma(m)\Gamma(n)}-\frac{BP_{3}\Lambda^{m}\gamma_{\tau}^{m}}{\ln 2\Gamma(m) \Gamma(n)}G_{1,12,22,1,1}^{0,1:1,2:1,1}[\gamma_{\tau_{\tau}},\gamma_{\tau} \Lambda\Big{|}_{-m}^{1-m}\Big{|}_{1,0}^{1,1}\Big{|}_{0}^{1-(m+n)}].\end{array} \tag{93}\]  
Similar to (78), by plugging (45) into (84), the closed-form expression of ergodic capacity for the multiple-branch SECps receiver in the i.i.d. case can be written as:  
\[\begin{array}{ll}\overline{C}_{\gamma_{SECps}}&=\frac{B(\Lambda\gamma_{ \tau})^{i\infty}}{\Gamma^{i}(m)\Gamma^{i}(n)\ln 2}H_{1,3,2;2;1,1,1;2,2;1,1;1,2}^{0,1:1,1:2,2,2;1,1,1;1,2}(\overline{\Lambda\gamma_{\tau_{\tau}}},\ldots\wedge\gamma_{ \tau_{\tau_{\tau}}})[(1-m;\tau_{1})]_{i=1,1:1,1}[(1-m;\tau_{1})]_{i=1,1:1,1}[(1 -m;\tau_{1})]_{i=1,1:1,1}[(1,1),(1,1)]\\ &+\frac{P_{1}B}{\ln 2\Gamma(m)\Gamma(n)}G_{3,3}^{3,2}[\Lambda\Big{|}_{m,0,0}^{1-n,0,1} ]-\frac{B\Lambda(\gamma_{\tau})^{i}}{\ln 2\Gamma(m)\Gamma(n)}G_{1,12,22,1,1}^{0,1:2,1,1,1}[\gamma_{\tau_{\tau}},\gamma_{\tau}\Lambda\Big{|}_{-m}^{1-m}\Big{|}_{1,1}^{ 1,1}\Big{|}_{0}^{1-(m+n)}].\end{array} \tag{94}\]
## 3 Results
### Comparative Analysis of S. deserticola JD052 Extracts by HPLC and UPLC-Q-TOF-MS Analysis  
In a previous study, loliolide, which has been reported to exhibit antibacterial, anticancer, skin-moisturizing, antioxidant, and even hair-inductive properties, can be found from _S. deserticola_ JD052 [21; 66; 80; 81; 82]. We investigated whether the hair-growth-promoting effects of HE are mediated by loliolide. Analysis of the chemical composition of PE and HE was conducted using HPLC. As shown in Figure 6, there was no significant variation in the content of loliolide in both PE and HE; however, a specific peak for HE was observed around the retention time of 18 to 19 min, indicating an increased content. To further elucidate this peak, UPLC-Q-TOF-MS analysis was performed. The results, presented in Figure 7, revealed that the peak at 9.51 min corresponded to pheophorbide A, while the peak at 9.33 min was identified with an \(m/z\) of 277.2192 and an elemental composition of C\({}_{18}\)H\({}_{30}\)O\({}_{2}\), indicative of linoleicidic acid or linoleic acid. These results suggested that the representative bioactive components of HE, which were increased compared to PE, were expected to have hair-inductive effects in HDPCs.  
Figure 6: HPLC/DAD chromatogram at 217 nm corresponding to (**a**) loliolide standard, (**b**) PE, and (**c**) HE. The arrow indicates the HE-specific peak. PE, photoautotrophic-cultured _S. deserticola_ JD052 extract; HE, heterotrophic-cultured _S. deserticola_ JD052 extract.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 1 Introduction
### Methods and Scope of Study
#### 1.2.4 Application of the AI Model in Architectural Environments  
To assess the applicability of EEG-based emotion recognition in adaptive environments, the trained models were deployed in a 360\({}^{\circ}\) virtual reality (VR) simulation. This experimental setup facilitated real-time EEG signal processing, enabling an empirical evaluation of how EEG-based emotion recognition can contribute to adaptive spatial configurations [13].  
Architectural adaptations were made in response to detected emotional states. When a positive emotion was identified, the system increased natural lighting and introduced warm ambient tones, promoting user comfort [7]. When a neutral emotion was detected, the environment remained unchanged, ensuring stability [8]. Conversely, when a negative emotion was recognised, the system adjusted lighting, spatial configurations, and acoustic properties to enhance the user's emotional well-being [9].  
This study aligns with the principles of sensing architecture, wherein built environments dynamically adapt to users' affective states in real time. The findings provide valuable insights into the feasibility of integrating EEG-based emotion recognition with AI-driven architectural adaptation, contributing to research in emotion-responsive spatial design [10].
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 2 The POSEIDON System  
The POSEIDON system is supported by a team of researchers, scientists and engineers with different expertise and scientific backgrounds from the Institute of Oceanography (IO) of HCMR. There is a combination of practical science and engineering experience acquired over the years, as well as the addition of new specialized personnel that has supported the POSEIDON multistage development. (Figure 1). The scientific mission and the technological complexity of the current multiplatform observing system proved to be a challenge demanding the cooperation of the different personnel in all activities.  
Figure 1: The evolution of the POSEIDON system with the addition of new platforms and technologies for 2000–2022.