esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Effect of QS on Biofilm Formation on the Microplastic Surface  
To investigate the role of bacterial signaling by QS in the biofilm formed on the surface of the microplastics, AHL signaling molecules were added to the biofilm experiment described above. Only PE, which forms the most biofilm among the four types of plastics, was used in this experiment. N-butanol-L-homoserine lactone (C4-HSL) and N-(3-oxododecanoyl)-L-homoserine lactone (3-oxo-C12-HSL), which are produced and utilized by PAO1, were dissolved in dimethyl sulfoxide (DMSO, Sigma-Aldrich, USA) to generate AHL cocktails. The AHL cocktails were injected into a 24-well plate containing PAO1 (OD\({}_{600}\) of 0.03) and PE microplastic beads (10 each) at final concentrations of 5, 10, and 20 \(\upmu\)M. The control was injected with DMSO in a volume corresponding to the injected AHL cocktails. After sealing with parafilm and incubating at 180 rpm and 37 \({}^{\circ}\)C for 6 h, the biofilm was quantified with the method described in Section 2.4.
# Method to Predict Performances of PCB Silicone Conformal Coating under Thermal Aging
## 2 Materials and Methods
### Thermal Shock and Thermal Cycling  
Silicone-coated PCBs are laid in an ESPEC TSE-11A environmental chamber for thermal shock aging and in an ESPEC EGNX 12-6 CWL environmental chamber for thermal cycling aging. Thermal shock aging conditions are \(-40\) to +125 \({}^{\circ}\)C temperature exposure with a 30 min plateau at both \(-40\)\({}^{\circ}\)C and +125 \({}^{\circ}\)C (Figure 3a). Temperature change from 40 \({}^{\circ}\)C to +125 \({}^{\circ}\)C is completed within 1 min. Thermal cycling aging conditions correspond to temperature cycles between \(-40\) to +125 \({}^{\circ}\)C, with a 90 min cycle duration (Figure 3b). Dwell time at both \(-40\)\({}^{\circ}\)C and +125 \({}^{\circ}\)C is 10 min.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Unit** & **Conformal Coating A** & **Conformal Coating B** \\ \hline Viscosity & mPa-s & 1050 & 350 \\ Tack-free time (25 \({}^{\circ}\)C) & Mins & 6 & 8 \\ Density & g/cm\({}^{3}\) & 1.12 & 0.98 \\ Hardness & Shore A & 85 & 34 \\ Elastic modulus (25 \({}^{\circ}\)C) & MPa & 77.1 & 1.50 \\ CTE & ppm/K & 300 & 490 \\ Dielectric strength & kV/mm & 13 & 17 \\ Volume resistivity & Ohm\(\cdot\)cm & \(1.9\times 10^{14}\) & \(5.5\times 10^{15}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conformal coating material properties.  
Figure 1: Prepare silicone conformal coating A and B films with an auto film applicator.  
Figure 2: Silicone conformal coating A and B dog bone for tensile test.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 3 System Model and Problem Description
### Offloading Model  
In this section, we will examine the impact of task offloading on both latency and energy consumption. Latency consists of four components: transmission latency, waiting latency, computation latency, and return latency. Typically, the return latency is negligible as the results are relatively small after computation. Energy consumption is comprised of two components: transmission energy and computational energy.  
For the \(k\)th satellite, if there are tasks offloaded at time slot \(t\), the transmission latency can be calculated as follows:  
\[T_{k}^{trans}(t)=\sum_{i=1}^{M}o_{i}^{k}(t)*\frac{D_{i}(t)}{B}*Hop_{i}^{k}(t), \tag{7}\]  
in which \(B\) represents the bandwidth of the ISL, and \(Hop_{i}^{k}(t)\) denotes the shortest hop from the \(i\)th satellite to the \(k\)th satellite at time slot \(t\). It should be noted that, unlike fixed terrestrial networks, satellites move at high speeds, causing their topology to vary at different time slots. Therefore, the shortest hop between two satellites may vary at different time slots. However, the changing topology can be represented by a connection matrix [1], which allows us to obtain the shortest hop between any two satellites at any given time slot.  
To prevent tasks from being split due to changes in the topology of the satellite network, we assume that the transmission time for a single hop does not exceed the length of a time slot \(\tau\)[18]. The propagation time for data is very short as the signal travels through space at the speed of light. Therefore, we ignore the propagation time to simplify the analysis.  
The energy consumption associated with offloading to the \(k\)th satellite is defined as  
\[E_{k}^{trans}(t)=T_{k}^{trans}(t)*P^{trans}, \tag{8}\]  
in which, \(P^{trans}\) (J/s) is the transmission power of satellites.  
\[T_{k}^{com}(t)=\frac{\sum_{i=1}^{M}o_{i}^{k}(t)*D_{i}(t)}{C_{k}(t)},i\in\{1,2,\cdots,M\}. \tag{9}\]  
The computational latency is defined as  
And the computational energy consumption can be expressed as  
\[E_{k}^{com}(t)=T_{k}^{com}(t)*P_{k}^{com}, \tag{10}\]  
in which, \(P_{k}^{com}\) (J/s) is the computing power of the \(k\)th satellite. Furthermore, the waiting time for a task in the queue is defined as  
\[T_{k}^{wait}(t)=\frac{Q_{k}(t)}{C_{k}(t)}. \tag{11}\]  
Therefore, for the \(k\)th satellite, the latency at time slot \(t\) can be expressed as  
\[T_{k}(t)=T_{k}^{trans}(t)+T_{k}^{wait}(t)+T_{k}^{com}(t). \tag{12}\]  
And the energy consumption is presented as  
\[E_{k}(t)=E_{k}^{trans}(t)+E_{k}^{com}(t). \tag{13}\]
## 1 Introduction
While CT and MRI could provide detailed images of the spine in three dimensions, radiography can only provide a basic view of the spine in two potential projections--anterior-posterior and lateral. ST is a photogrammetric technique that involves reconstructing an object's forms, sizes, and relative placements. Hence, each modality has its pros and cons, which can affect the performance of the diagnosis. Much research has investigated the assessment method in identifying scoliosis in every aspect to acquire a good diagnosis. The current body of research on the assessment methodology for diagnosing scoliosis through the utilization of image-processing techniques that can be applied in practical scenarios is restricted. What remains unknown is the optimal diagnostic mechanism that employs these approaches and that can be effectively executed in real-world scenarios. In this regard, several researchers have attempted to create novel methods that might enhance the way spinal deformity is currently assessed, such as raster stereography, the artificial intelligence (AI) scoliosis detection method, and many more. There are studies on the golden parameter which is the Cobb Angle measurement, using AI methods performed by [PERSON] [11] and [PERSON] [12] for detecting deformities. Research [13] on a new angle, a polar angle using non-invasive methods to assess scoliosis, also has been explored. Due to the recent development of these methods, there exists a scarcity of comprehensive reviews that concentrate on summarizing the assessment methods employed in diagnosing scoliosis deformities. In addition, certain reviews have a limited range that is not particularly relevant to our field of interest in image-processing approaches for scoliosis diagnosis. Previous reviews [14] focus on summarizing assessment methods and imaging modalities with chest and trunk deformation as the parameters. Considering the vast range of potential variables that can be employed in the assessment methods, including the parameters evaluated, imaging modality, plane, and software tool utilized, it is plausible that certain articles may have been inadvertently disregarded in previous reviews. Hence, the aim of this systematic review paper is to provide a summary and to gain an understanding systematically of the current and latest scoliosis diagnostic method that utilizes image processing techniques. It aims to find gaps and possible best variables that can be utilized in scoliosis deformity assessment methods that can be helpful for future works when developing accurate diagnostic methods. Therefore, the advantages and disadvantages of the diagnostic methods presented in this review paper can help to find appropriate methods to assess scoliosis severity.
## 3 Results and Discussion
### Principal Component Analysis (PCA) Analysis  
Figure 1 reports the loading plot of the PCA model computed on the physicochemical variables considered in this study. The analysis showed that about 59.84% of the total variation is explained by the first principal component (PC1) and 14.58% by the second principal component (PC2). These two PCs account for about 74.42% of the total data variance. PC1 correlated positively with shear force, cooking loss, TBARS, and pH, while it had a negative correlation with moisture and a* color value. This tendency confirms the opposite relationship between moisture content and shear force, cooking loss, TBARS, and pH. PC2 was only correlated positively with L* color value. A similar result was reported by [PERSON] et al. [53].  
Figure 1: PC1 vs. PC2: Loading plot of physicochemical variables studied in sous vide chicken fillet breasts cooked at different combinations of temperature (60, 70, 80, and 100 \({}^{\circ}\)C) and time (60, 90, 120, and 150 min).  
Figure 2 reports the score plot. The colors on the plot refer to the different temperatures (60, 70, 80, and 100 \({}^{\circ}\)C) while the numbers indicate the cooking time (60, 90, 120, and 150 min). Chicken breast samples cooked at 60 \({}^{\circ}\)C and 70 \({}^{\circ}\)C were at negative values of PC1. Conversely, the chicken breast samples cooked at 80 and 100 \({}^{\circ}\)C were at positive values of PC1. By comparing the score plot with the corresponding loading plot, it is possible to interpret the relationships between samples and variables [58]. The score plot in conjugation with the loading plot demonstrated that increasing cooking temperature caused an increase in cooking loss, lipid oxidation, TBARS, and pH. Comparing the results in Table 3 with the PCA model allowed us to conclude that the cooking temperature played a major role in measured variables while the effect of cooking times seemed to be negligible.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 1 Introduction
### Innovation and Engineering Value  
Our novel approach for autonomous, perfectly secret low-bit-rate voice communication is based on the following key innovations:  
1. Artificially Synthesized Speech as a Common Randomness Source * Unlike natural speech, synthesized speech allows precise control over its randomness properties. * The proposed system uses LSP parameters of a MELPe-like synthesizer to generate shared randomness between [PERSON] and [PERSON]. * This approach eliminates the need for external entropy sources such as radio channels or biometrics.
2. Independent True Randomness for Enhanced Security * The LSP parameters are randomly selected, ensuring unpredictability. * An independent source of true randomness (e.g., from a cryptographic random number generator) ensures entropy is sufficient for perfect secrecy.
3. SKD Over a Public Authenticated Channel * A real-time SKD protocol is executed over an authenticated but public channel, allowing [PERSON] and [PERSON] to extract a mutually secret key from their synthesized speech signals. * The key rate achieved significantly exceeds the 1.2 kb/s or 2.4 kb/s needed for MELPe encryption, ensuring continuous perfect secrecy.
4. No Prior Key Distribution or TTP * The system operates autonomously without requiring prior key exchange. * Unlike Quantum Key Distribution (QKD) or traditional key management systems, no external infrastructure is needed.
5. Real-Time Suitability for Low-Bit-Rate Voice Encryption * The key generation rate is synchronized with the encryption rate of MELPe vocoders, allowing seamless one-time-pad encryption. * vPCP-V was used for empirical verification, demonstrating an achievable secret key rate of up to 8.8 kb/s and a BER of order \(10^{-3}\) for various communications channels, including GSM 3G and GSM VoLTE networks.  
Considering all of this, the proposed system meets all the fundamental security and functional requirements that a protected system must meet.
## 2 Landsat Overview
### Landsat Legacy
In addition to red edge and water vapor bands, Sentinel-2 has a higher spatial resolution in the visible and shortwave infrared bands compared to that of Landsat, yet Sentinel-2 does not capture data in the thermal infrared band. As such, Sentinel-2 is the best complement to the Landsat program and has resulted in opportunities for international partnerships and virtual constellations [36]. With about 2.5 days of temporal resolution, the synthesis of Sentinel-2A, Sentinel-2B, and Landsat-8 data allows for the development of new applications for monitoring highly dynamic phenomenon [48]. These opportunities are possible via new methods for geometric and radiometric registration between Sentinel-2 and Landsat at pixel-level [49; 50; 51]. Permanent global imaging and data continuity are the main concerns of the Landsat Program [52]. The Landsat Data Continuity Mission (LDCM), along with the launch of Landsat-8 in 2013, was the first step towards plans for increasing data continuity [53]. Figure 1: Graphical illustration of Landsat mission’s timeline, showing previously launched satellites and Landsat-9 planned to be launched in September 2021. From 2014, the U.S. implemented the Sustainable Land Imaging (SLI) program, based on the sustainability, continuity, and reliability of Landsat data, to solidify the future of Landsat legacy into the 2030s and beyond through a partnership between NASA and USGS. This led to the design of the Landsat-9 mission with Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), planned for launch in 2021 [54], coinciding with the end-of-life of Landsat-7 and related orbital changes. At almost any time, at least two Landsat instruments have been in orbit over the Earth for global imaging every 8 days, which is expected to continue well into the future.
## 3 Results  
### Synthesis of the acyAOVT Nucleoside Phosphoramidite  
We planned to synthesize the C6-substituted 5-azacytosine by ring opening of the 5-azacytosine moiety forming guanylurea and subsequent ring closing reaction that follows a previous report (Scheme 1) [20]. In our previous study, the acyclic linkers-protected MOM or Bn group produced the coupling products in low yields. The acyclic side chain **3**-protected TBS group was synthesized from 2\({}^{\prime}\)-deoxy-p-ribose by a previous modified procedure [21]. The coupling reaction between the sodium salt of 5-azacytosine and the acyclic side chain compound **3** was done to form the N1- and N3- alkylated products. The reaction conditions were screened in order to optimize the formation of the N1-alkylated product **4** and a considerable amount of the N1-alkylation (58%) in DMSO at 60 \({}^{\circ}\)Cwas achieved with a N1:N3 ratio = 6:1. The formation of the glycosidic bond at the N1 position of 5-azacytosine was confirmed by HMQC and HMBC analyses.  
**Scheme 1.**_Reagents and conditions_: (**a**) 5-azacytosine sodium salt, DMSO, 60 \({}^{\circ}\)C, 18 h, 58%; (**b**) 7M NH\({}_{4}\)OH, MeOH-1,4-dioxane (1:1), 55 \({}^{\circ}\)C, 24 h; (**c**) orthoester, DMF, 120 \({}^{\circ}\)C, 2 h, 51% (2 steps); (**d**) AcCl, pyridine, rt, 19 h, 54%; (**e**) BF\({}_{3}\)-OEt, CH\({}_{2}\)Cl\({}_{2}\), 0 \({}^{\circ}\)C, 2 h, 52%; (**f**) DMTrCl, pyridine, 0 \({}^{\circ}\)C, 1 h, 70%; (**g**) 2-Cyanoethyl-N,\(N\)-disopropylchloropho spohoramidite, DIPEA, CH\({}_{2}\)Cl\({}_{2}\), 0 \({}^{\circ}\)C, 1 h, 72%.  
The 5-azacytidine derivative **4** was treated with NH\({}_{3}\) to give the guanylurea intermediate **5**, which was condensed with the orthoester to afford the desired C6-octylthioethyl-5-azacytidine derivative **6** in 45% yield (2 steps). After the 4-N-acetylation and the deprotection of the TBS groups by BF\({}_{3}\)OEt\({}_{2}\), the 5\({}^{\prime}\)-hydroxyl group was selectively protected with the DMTr group, then the 3\({}^{\prime}\)-hydroxyl group was phosphitylated to yield the _acy_AOVT nucleoside phosphoramidite **10**.
###### Abstract  
This article presents the possibilities of decreasing moisture sorption properties via thermal modification of Norway spruce wood in musical instruments. The 202 resonance wood specimens that were used to produce piano soundboards have been conditioned and divided into three density groups. The first specimen group had natural untreated properties, the second was thermally treated at 180 \({}^{\circ}\)C, and the third group was treated at 200 \({}^{\circ}\)C. All specimens were isothermally conditioned at 20 \({}^{\circ}\)C with relative humidity values of 40, 60, and 80%. The equilibrium moisture content (_EMC_), swelling, and acoustical properties, such as the longitudinal dynamic modulus (\(E_{L}\)), bending dynamic modulus (\(E_{b}\)), damping coefficient (\(tan\)\(\delta\)), acoustic conversion efficiency (\(ACE_{L}\)), and relative acoustic conversion efficiency (\(RACE_{L}\)) were evaluated on every moisture content level. Treatment at 180 \({}^{\circ}\)C caused the EMC to decrease by 36% and the volume swelling to decrease by 9.9%. Treatment at 200 \({}^{\circ}\)C decreased the EMC by 42% and the swelling by 39.6%. The 180 \({}^{\circ}\)C treatment decreased the value of the longitudinal sound velocity by 1.6%, whereas the treatment at 200 \({}^{\circ}\)C increased the velocity by 2.1%. The acoustical properties \(E_{L}\)', \(E_{b}\), \(ACE_{L}\), and \(RACE_{L}\) were lower due to the higher moisture content of the samples, and only the \(tan\)\(\delta\) increased. Although both treatments significantly affected the swelling and _EMC_, the treatment at 180 \({}^{\circ}\)C did not significantly affect the acoustical properties.  
acoustical properties; hygroscopicity; sound velocity; spruce; swelling; thermal treatment +
Footnote †: journal: Applied Sciences
## 4 Problem Formulation
### Probability of Link Subsistence between UAVs  
Through Equation (1), the distance between UAVs can be obtained at the time of departure. For the link subsistence between two UAVs, in-network UAVs broadcast hello packet, with the help of which we can calculate the link subsistence probability between two UAVs, UAV ij and UAV j, extracting two parameters from the packet strength and frequency. In [44], approaching and receding are two scenarios presented. Figure 7 represents the approaching scenario; here, UAV i is at a fixed position located at a, where UAV j moves from b to c. Its relative moment speed is vr. The center of the circle shows the maximum communication range of UAV i. Interruption occurs when two UAVs are at their maximum communication range.  
The link subsistence probability between two UAVs, UAV i, and UAV j, also has an impact on the relative speed and distance between them. Thus, the probability of link subsistence can be calculated as:  
\[PLS_{ij}=\left\{\begin{array}{cc}\frac{d_{ac}}{v_{r}t_{threshold}},&\frac{d_ {ac}}{v_{r}}<\textbf{{t}}_{threshold}\\ 1,&\frac{d_{ac}}{v_{r}}\geq\textbf{{t}}_{threshold}\end{array}\right. \tag{9}\]  
\(PLS_{ij}\) represents the probability of link subsistence between UAV \(i\) and UAV \(j\). The higher the value, the better link will be. dce represents the distance between \(c\) and \(e\). \(\textbf{{t}}_{threshold}\) represents the threshold value of the link subsistence. The relative speed is zero between the two neighbor UAVs. \(d_{ce}/v_{r}\) goes on for infinity. As \(d_{ce}/v_{r}=\textbf{{t}}_{threshold}\) when \(d_{ce}/v_{r}>=\textbf{{t}}_{threshold}\), therefore \(PLS_{ij}=1\).  
Figure 7: Approaching scenario.
## 3 Wireless Access-Metro Networks
### Wireless QoS Provisioning Techniques and Mechanisms  
In this section, we describe the QoS provisioning techniques and mechanisms designed for LTE, WiMAX, and WiFi networks.  
Traffic Classes  
While LTE is designed to support end-to-end QoS for any IP-based traffic [33], IEEE 802.16e defines the following five scheduling services to support different traffic classes:  
1. **Unsolicited grant service (UGS):** it provides fixed-size interval transmission opportunities without the need for requests or grants.
2. **Real-time polling service (rtPS):** it allows the BS to offer periodic request opportunities to a specific SS in order to indicate its required bandwidth.
3. **Extended-real-time polling service (ErtPS):** it is able to offer unsolicited unicast grants, similarly to UGS, and to change the size of the bandwidth grant dynamically.
4. **Non-real-time polling service (nrtPS):** it provides fairness by means of allocating the contention and regular unicast request opportunities in the uplink bandwidth request (UL-request) of each SS.  
Unicast request opportunities are offered regularly in order to ensure that the SS has a chance to request bandwidth even in a congested network environment.
5. **BE:** it is based on contention and provides non-regular unicast request opportunities.  
In WiFi networks, the amendments IEEE 802.11e and IEEE 802.11n are designed to provide QoS support for mobile STAs. In IEEE 802.11e, the hybrid coordination function (HCF) is used for providing parameterized (_i.e._, absolute) and prioritized (_i.e._, relative) QoS. HCF uses a contention-based channel access method, known as enhanced distributed channel access (EDCA), that operates concurrently with a polling-based HCF-controlled channel access (HCCA) method [34]. HCCA uses the eight traffic classes of IEEE 802.1D, while EDCA deploys four different access categories (ACs), each associated with a different channel access priority.