esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX)  
Figure 4a-d shows SEM images at 50,000\(\times\) magnification of bio-calcium and bone powders calcined at 900 \({}^{\circ}\)C 6 h. Nano-particles in the range of a few tens of nano-meters that agglomerated into larger clusters were observed for both Bio-cal-A and Bio-cal-H (Figure 4a,b). After calcination of these powders, larger crystals or grains with the sizes of a few hundred nano-meters were formed (Figure 4c,d) as a result of crystal growth at an elevated temperature. The results were in agreement with XRD data that showed significant peak narrowing along with a sharp increasing in peak intensity of the calcined bone powders. The crystal sizes of samples with 9-h calcination (not shown in this article) were similar to that of the 6-h calcination samples, which were supported by their nearly identical full width at half maximum (FWHM) of diffraction patterns shown in Figure 3.  
Figure 3: X-ray diffraction patterns of bio-calcium and 900 \({}^{\circ}\)C- calcined bone powders at 6 and 9 h with alkaline treatment (**a**) and without alkaline treatment (**b**).  
Nano-particles in the range of a few tens of nano-meters that agglomerated into larger clusters were observed for both Bio-cal-A and Bio-cal-H (Figure 4a,b). After the calcination of these powders, larger crystals or grains with the sizes of a few hundred nano-meters were formed (Figure 4c,d) as a result of crystal growth at an elevated temperature. The results were in agreement with XRD data that showed significant peak narrowing along with sharp increasing in peak intensity of the calcined bone powders. The crystal sizes of samples with 9-h calcination (not shown in this article) were similar to that of 6-h calcination samples, which were supported by their nearly identical FWHM of diffraction patterns shown in Figure 3.  
A relative abundance of elements in all the samples was observed using SEM-EDX. Elements such as Ca, P, C, O, Na, and Mg were found in both the bio-calcium and calcined powders as depicted in Figure 5. It was noted that the detection limit of SEM-EDX could vary from 1-10%wt as a result of spectral resolution and difficulty in detecting low-Z elements [29]. Consequently, other trace elements could not be detected in the bio-calcium and calcined powders. Despite the instrumental limitation, the results show that the alkaline treatment of fish bones led to an increase in Ca and P contents of the bio-calcium powder. This was in line with higher contents of Ca and P determined by ICP-OES (Table 1). In addition, for all calcined samples, more pronounced and sharp peaks indicating a higher intensity of Ca and P were observed. Simultaneously, the contents of C were decreased, re-affirming the removal of organic matters such as lipids and meat proteins as a result of calcination.  
Figure 4: SEM images at 50,000\(\times\) magnification for (**a**) Bio-cal-A, (**b**) Bio-cal-H, (**c**) Cal-A 900 \({}^{\circ}\)C 6 h and (**d**) Cal-H 900 \({}^{\circ}\)C 6 h.
# Polyisocyanurate Foam Pyrolysis and Flame Spread Modeling
## 2 Materials and Methods
### Modeling
#### 2.4.1 Modeling of Milligram-Scale Experiments  
During the modeling of TGA, DSC and MCC tests, all samples were treated as zero-dimensional and represented by a single spatial element. The element temperature was forced to follow the experimental temperature profile by defining a sufficiently high convection coefficient, \(1\times 10^{5}\) W m\({}^{2}\) K\({}^{-1}\), at the boundary. The experimental temperature profile was prescribed by expressing the heating rate via an exponentially-decaying sinusoidal function. This function was employed to account for the variation in the instantaneous experimental heating rate with time, as explained in detail elsewhere [24]. The mass flow boundary conditions were defined such that the gaseous pyrolyzate instantaneously escaped the element.  
The PIR foam thermal decomposition mechanism was assumed to consist of consecutive reactions; the number of reactions was determined from the inspection of the MLR data obtained in the 10 K min\({}^{-1}\) TGA experiments performed in nitrogen. The kinetic parameters of these reactions were determined through fitting of the TGA data using a multi-parameter optimization with a hill-climbing algorithm. Details of this algorithm implementation can be found in an earlier publication [30]. The heat capacities of the  
Figure 1: Schematic of the full-scale experiment.  
condensed-phase components of the foam and heats of decomposition reactions were determined through a manual fitting of the corresponding (10 K min\({}^{-1}\)) DSC data, including the data collected in a separate set of tests on the decomposition residue. Details of this well-established fitting routine can also be found in earlier publications [16; 17; 24; 25]. The heats of combustion of the gaseous pyrolysis products defined in the reaction mechanism (derived from the TGA data) were determined via a simulation of the MCC test. The heats of combustion were first assumed to be the same, equal to the mean heat of combustion of the gasified material obtained through the integration of the MCC HRR curve and were subsequently adjusted (if needed) to capture the shape of the MCC curve.
## 9 Detection over Longer Tx-Rx Distances  
Having demonstrated that keystroke recognition depended on the hand's location and movement, we proceeded with a final investigation into the importance of device placement. We identified that prior work on gesture recognition relied on high-proximity WiFi devices, which may not be realisable in a commercial application. Hence, we iteratively increased the sensor device separation and examined its influence on keystroke recognition accuracy.  
Experiment Setup and Results  
We conducted the final experiment with the setup of the devices as in Figure 7 with the aim of examining the impact of the sensing range. We introduced an additional independent variable, changing the separation between Tx and Rx from 1 m to 3.5 m. The CSI was sampled at 1300 Hz by the Rx and processed with the DWT/KNN architecture described in Section 6.  
In Figure 21, we present a plot of the keystroke recognition accuracy as a function of the Tx-Rx distance. The x-axis represents the distance between Tx and Rx, varying between  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Modality** & **Power Use** & **Hardware Cost** & **Privacy** & **Integration Complexity** \\ \hline This work & Low & Low & High & Low \\ \hline Optical cameras & High & Medium & Low & Medium \\ \hline Floor sensors & Medium & High & Low & Low \\ \hline RFID (backscatter) & Very low (tags) & Medium & High & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the proposed method with other methods.  
1 m and 3.5 m. The y-axis represents the accuracy of a keystroke recognition system setup with said device separation. Once again, key details are summarised in Table 9.  
We observed that the accuracy was clearly highly negatively correlated with the device separation. Firstly, the accuracy with 1m separation was 88%, a result similar to that in Section 6. Beyond this, there was a huge drop in accuracy when the distance increases from 1 m to 2 m, with decreases of about 10% to 80% at 1.5 m and then another 10% to 72.5% at 2 m. After that, we further increased the distance from 2 m to 3.5 m. Within 2-3 m, average accuracy still dropped but in a much smaller range (from 72.5% to 68%). When the distance was more than 3 m, the value significantly dropped again, which indicates that the system can perfectly fit in indoor sensing scenarios. This result demonstrates that the efficacy of fine-grained gesture recognition using CSI hinges on the proximity to the sensing devices.  
The results show that keystroke recognition is highly dependent on device proximity. Accuracy drops significantly as Tx-Rx distance exceeds 2 m, supporting the conclusion that WiFi sensing for fine-grained gestures is best suited for close-range indoor environments.
## 3 Results and Discussion
### Gel/Space Ratio and Compressive Strength
In contrast, LF-NMR is a fast, non-invasive technique, allowing the study of the cement hydration in real-time. Thus, according to the continuous monitoring of the LF-NMR signal of the evaporable water of the cement paste, the degree of hydration and the gel/space ratio can be accordingly calculated to further determine the initial and final setting times and to predicate the development of the early compressive strength.
## 5 Experimental Results
### Performance and Computational Complexity of the Low Complexity Demodulation Method  
We simulated the case where the antenna of the reader is 1, 2, 4, and 8. Currently, the number of antennas supported in mobile terminals is up to four and research on 8- and 12-antennas array is actively ongoing [14; 15]. However, because there were no basic results depending on the number of antennas simulation for eight antennas is performed.  
Figure 5: Throughput for existing and the proposed backscatter method.  
Figure 6: BER performance for the proposed backscatter method depending on the number of antennas of the reader. (**a**) Proposed MIMO encoding/decoding algorithm for increasing the data rate, (**b**) proposed MIMO encoding/decoding algorithm for improving the reliability.
## 1 Introduction
However, most ECMO imaging NDT systems based on the Faraday effect have low response to magnetic fields, mT is the normal level that can be reached, and only a few studies can reach less than the mT level [23]. Therefore, the EC excitation usually requires a large excitation current of one ampere or even a hundred amperes [24]. This may cause additional interference with other electronic devices and affect the promotion and application of ECMO imaging NDTs. Refs. [25; 26] specialize in the magnetic responsivity and frequency range of MO detection, which can reach the levels of nT, pT, or even dozens of fT and have a wide frequency response range. However, most ECMO imaging NDT systems are not responsive enough to magnetic fields. This is mainly because in the high-sensitivity test, photodetectors are used for point detection, followed by filtering and amplification processes, which can obtain a higher signal-to-noise ratio so that weak magnetic field signals can also be identified. In real ECMO testing applications, in order to improve the efficiency of detection, cameras are primarily used for imaging. It is difficult to achieve point detection sensitivity due to camera bit limit and noise. In particular, traditional ECMO detection, considering the skin effect of ECs [27], requires a large excitation current and a lower excitation frequency when detecting metal materials, usually within 100 kHz [28], and neither the high magnetic field resolution ability of MO detection nor its wide frequency response range can be adopted. In order to overcome the shortcomings of traditional ECMO imaging to it can be applied to the defect detection of CFRPs with weak conductivity, the magnetic field response of a traditional ECMO system is improved by point detection, lock-in amplification, and scanning imaging. The crack defects of weakly conductive CFRPs are detected with higher excitation frequency and smaller excitation current, and the relevant influencing factors are analyzed. (_TmBi_)\({}_{3}\) (_FeGa_)\({}_{5}\)\(O_{12}\) film is used as an MO sensor [29], and a reflective MO detection system is built, which uses a photodetector for detection, a lock-in amplifier to amplify the detection signal, and high-precision two-dimensional scanning for MO imaging.
## 6 Dynamic Responses of Floating OWTs
### UMaine TLP  
Since the surge and pitch motions could be the most two dominant modes of the dynamic responses of the UMaine TLP, a series of regular wave cases (\(H_{0}\) = 5 m, \(T_{0}\) = 2.5 \(\sim\) 20 sec) are implemented to capture the response characteristics of these two motions by comparing hydrodynamic simulator with CRAFT (Coupled Response Analysis of Floating wind Turbine) [3]. CRAFT accounts for the nonlinear tendon dynamics and viscous drag force imposed on the horizontal pontoons. As shown in Figure 21a,b, the surge and pitch RAOs obtained from these two codes are comparable and consistent with each other. Specifically, it is evident in Figure 21a that the influence of the second-order drift force could be insignificant because the excitations and motions of the surge RAO are small. Figure 21b exhibits that the pitch resonant frequency (0.323 Hz) predicted by hydrodynamic simulator is very close to the result (0.319 Hz) obtained from FAST. The peak value predicted by hydrodynamic simulator is also very close to the one predicted using FAST, but a small discrepancy is caused by the damping effect of the viscous drag force on the main column and pontoons.  
It is clearly shown in Figure 22a that the wave load is mainly dominated by the wave exciting force and radiation added-mass force at the surge natural frequency because of their large cross-sectional areas of the pontoons in the surge mode. The effect of the taut-leg mooring system on the dynamic responses as illustrated in Figure 22b is relatively evident at the pitch natural frequency than that at the surge natural frequency. It appears that the taut-leg mooring system has an increasing dominant effect on the pitch motion, but has a decreasing effect on the surge motion for increasing wave frequencies.  
Figure 20: Time histories of wave-load components at (**a**) the surge natural frequency (\(f_{p}\) = 0.037 Hz), and (**b**) the heave natural frequency (\(f_{p}\) = 0.015 Hz) for the MIT/NREL TLP.  
Figure 21: The performance of (**a**) surge, and (**b**) pitch RAOs as a function of wave frequency for the UMaine TLP by comparing hydrodynamic simulator with FAST.  
Figure 22: Time histories of wave-load components at (**a**) the surge natural frequency (\(f_{p}=0.053\) Hz), and (**b**) the pitch natural frequency (\(f_{p}=0.323\) Hz) for the UMaine TLP.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 2 Problem Description and Proposed Solution
### Solution Setup  
This work aims at developing an APG strategy to acquire images of multiple ground targets. By having as input the required starting acquisition time (\(t_{start}\)) and the \(ECEF\) ground targets positions (expressed in terms of latitude (\(\mu\)), longitude (\(\lambda\)), and altitude/elevation (\(h\)) above mean sea level), the \(AGP\) task is to generate the reference guidance. The reference guidance brings at first the satellite to the right initial position and then makes it follow the right pointing path. Therefore, the entire guidance can be split into two phases, and each phase can be solved with a different approach. The two phases are briefly explained below  
**First phase:**: the problem is to modify the attitude from any initial condition to the required initial condition to start image acquisition. This is translated into a boundary value problem that can be solved with a point-to-point solution. From now on we refer to this part as the _attitude point-to-point generator_ (_APPG_).
**Second phase:**: the problem focuses on the generation of the reference guidance path ensuring that the camera view axis constantly remains pointed towards the ground target. This phase starts at \(t_{start}\) and lasts the time of image acquisition, \(t_{ac}\). We will refer to this phase as the _attitude target pointing generator_ (_ATPG_).  
The scheme of the proposed solution is shown in Figure 2. Here one can see how the satellite maneuvers from the initial attitude to the target acquisition attitude during the _APPG_ phase, while the target tracking is performed during the _ATPG_. Furthermore, it is relevant to note that the solution is evaluated starting from the _ATPG_ phase, i.e., the maneuver is planned in the opposite direction with respect to the time evolution. This is why, in the following sections, the _ATPG_ is described in Section 3 and the _APPG_ is described in Section 4.
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
## 5 Proposed PS-CARA Scheme for mPC
For low priority non-PS users, the remaining resources are allocated randomly between the non-PS users by using multi-channel slotted ALOHOA (MCALOHA) protocol. To improve the efficiency of resource allocation for non-PS users, MCALOHA is combined with energy sensing (MCALOHA-ES) if interference is very high [39]. In MCALOHA-ES resource allocation, user \(u\) considers received energy levels to allocate RBs among non-PS users. **PS and non-PS users fpc implementation**: Initially, all users are deployed in a system by allocating full transmission power. This may result in high interference from the neighboring users. To control this situation, fpc is implemented that allocates appropriate power to users by considering their locations, which in turn will reduce the interference. Before implementation of the PS-CARA scheme, it is important to analyze the complexity to prove its suitability. The PS-CARA scheme summarized in Figure 4, where initially the proposed scheme allocates the dedicated resource to AL, SH, and AL links. After the environment selection phase, we have iterations that decide which two users share the current RB. The complexity of the proposed PS-CARA scheme is reduced as compared to the existing schemes because we separately allocate resources for PS and non-PS users, and hence the resource selection competition is separately among PS and non-PS users. Therefore, the proposed PS-CARA is a sub-optimal scheme that requires a modest complexity, and it has a linear relationship with the number of RBs as compared to the huge number of possible combinations regarding the NP-hard problem. Since we have a distributed network and each node decision is independent of the other node, this results in less system complexity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User Priority** & **User Identification** & **Traffic Class** & **Barring** & **Establishment Cause** \\ \hline PS First Responders & PS Emergency; PS1 to PS5 & 12-14 & Barring for Special & High Priority Access \\ Commercial User Emergency & Commercial User Emergency & 10 & Barring for Special & Emergency \\ Commercial User Non-Emergency & Commercial User Non-Emergency & 0-9 & Low Barring Factor & Mobile Originating \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Users’ priority table for PS LTE system.
## 3 Methodology
### Criteria Weight Determination Using AHP-MEREC Approach
#### 3.1.2 Objective Weight Calculation Using MEREC Approach  
MEREC (Method Based on the Removal Effects of Criteria) is an objective criteria-weighting method that analyses the impact of each criterion on the overall performance of the alternatives [55]. It can be described by the following steps:  
1. Identify the criteria to be used in the decision-making process. Create the decision matrix \(D\) consisting of the values \(d_{ij}\) in Equation (3). Here, \(i=1,\ldots,n\) denotes the number of alternatives, and \(j=1,\ldots,m\) denote the index of the criterion. \[D=\begin{bmatrix}d_{11}&d_{12}&\ldots&d_{1n}\\ d_{j1}&d_{ji}&\ldots&d_{jn}\\ \vdots&\vdots&\ddots&\vdots\\ d_{m1}&d_{m2}&\ldots&d_{mn}\end{bmatrix}\] (3)
2. Calculate the concordance coefficient \(W\) and Pearson \(\chi^{2}\) for the values presented in the decision matrix \(D\).
3. If \(W>0.1\) and \(\chi^{2}>\chi^{2}_{Table}\), normalise \(D\) by applying linear normalisation and calculate the overall performance of the alternatives (\(S_{i}\)). This can be calculated by Equation (4). \[S_{i}=\ln\biggl{(}1+\biggl{(}\frac{1}{m}{\sum}\Bigl{|}\ln\Bigl{(}n^{d}_{ij} \Bigr{)}\Bigr{|}\biggr{)}\biggr{)}\] (4) Here, \(n^{d}_{ij}\) denotes the elements of the normalized matrix and \(m\) defines the number of the criteria.
4. Remove each criterion, one by one, and recalculate the overall performance of the \(i\)th alternative with respect to the removal of \(j\)th criterion (\(S^{\prime}_{ij}\)).
5. Determine the objective weights \(a_{j}\) of the criterion \(j\) based on the removal effects \(E_{j}\) (Equation (5)). \[a_{j}=\frac{E_{j}}{\sum_{k}E_{k}},\ E_{j}={\sum_{i}}\Bigl{|}S^{\prime}_{ij}-S _{i}\Bigr{|}\] (5)
## 4 Discussions
Another novelty of our experiment is to show the effect of HA on oral MSCs from SSc, as to our knowledge, extremely limited information has been provided on this topic. Data from our study allowed the identification of some stimulating effects of HA on MSC functions. Adding HA in the culture medium appeared to significantly stimulate the migration potential of hMSCs. Moreover, HA seemed to positively influence SScMSCs compared to SScqtMSCs. Additionally, our review of the literature reveals that infusions with HA and adipose-derived MSCs in the treatment of affected skin areas of SSc patients offer definite clinical benefits [14]. The ready-applicable MSC-based clinical approaches addressing regeneration of oral structures are an extremely appealing tool of regenerative medicine [61]; however, they have not, to date, been made available [10]. Periodontal regeneration of affected tissues is a relatively unpredictable phenomenon [62], especially in the case of SSc associated with an impaired local environment. A positive point that needs to be taken into account is that HA molecules are already commercially available and, thus, could be used in periodontal interventions in SSc patients, providing wound healing, bacteriostatic, and anti-inflammatory effects [17,19,23], and contributing to ameliorating the behavior of local MSCs. Reduction of inflammation could be the consequence of the antimicrobial effect of HA as well as the direct interference/effect of inflammation pathways. As for wound healing, it has been demonstrated that initially, low-molecular-weight hyaluron binds fibrinogen, promoting clot formation, and provides anti-angiogenic and immunosuppressive effects [63]. Moreover, the low-molecular-weight hyaluron protects against apoptosis based on a pathway associated with nuclear factor kappa-B (NF-\(\kappa\)B). In an inflammatory environment, the low-molecular-weight hyaluron is broken into low-molecular-weight molecules [64], which stimulate angiogenesis [65] as well as phagocytosis and chemotaxis of neutrophils [66], local secretion of pro-inflammatory cytokines,and matrix metalloproteinases [67]. However, the exact mechanisms of hyaluron are currently unknown [16]. Positive influences of topical applications of HA in gingivitis [24] and nonsurgical [16; 68] or surgical treatment of periodontitis [69] have been reported in healthy patients. The isolation and characterization of oral MSCs from SSc along with an analysis of their behavior in relation to normal MSCs and HA constitute the novelty of this study; however, given the limited number of samples, further verification and studies are needed in order to draw firm conclusions on this topic. Oral tissues are an accessible and abundant source of MSCs and a promising alternative for regenerative medicine. More research on oral MSCs from SSc is needed to provide useful information for clinical translation in the complex treatment of SSc. HA could be used as an adjunctive of periodontal treatments in SSc patients for stimulating progenitor pools and increasing repair and regeneration.