esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Synthesis Mechanism  
In the first step of obtaining the initial solution, polyvinyl pyrrolone (PVP) works as a stabilizer for dissolving metallic salts through steric and electrostatic stabilization of the amide groups of the pyrrolone rings and the methylene groups. By dissolving titanium (IV) isopropoxide in PVP solution, the Ti\({}^{4+}\) ions are bound by the strong ionic bonds between the amide group in a polymeric chain and metallic ions. The uniform immobilization of metallic ions in the cavities of the polymer chains favors the formation of nanoparticles with uniform distribution [26; 27]. During calcination, the nucleation and growth of nanoparticles is performed by reacting Ti ions and available oxygen in the air to form TiO\({}_{2}\) particles. The organic matter is decomposed in this stage, but the carbon residual that bonded on the surface of nanoparticles protects them from uncontrolled growth and agglomeration.
###### Abstract  
Fifth-generation (5G) New Radio (NR) systems are expected to support multiple traffic classes including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) at the same air interface. This functionality is assumed to be implemented by utilizing the network slicing concept. According to the 3 rd Generation Partnership Project (3 GPP), the efficient support of this feature requires statistical multiplexing and, at the same time, traffic isolation between slices. In this paper, we formulate and solve a mathematical model for a class of Radio Access Network (RAN) slicing algorithms that simultaneously include resource reservation and a priority-based service discipline allowing us to incur fine granularity in the service processes of different traffic aggregates. The system is based on a queueing model and allows parametrization by accounting for the specifics of wireless channel impairments. As metrics of interest, we utilize \(K\)-class session drop probability, \(K\)-class session pre-emption probability, and system resource utilization. To showcase the capabilities of the model, we also compare performance guarantees provided for URLLC, eMBB, and mMTC traffic when multiplexed over the same NR radio interface. Our results demonstrate that the performance trade-off is dictated by the offered traffic load of the highest priority sessions: (i) when it is small, mixed reservation/priority scheme outperforms the full reservation mechanism; (ii) for overloaded conditions, full reservations provides better traffic isolation. The mixed strategy is beneficial to traffic aggregates with short-lived lightweight sessions, such as URLLC and mMTC, while the reservation only scheme works better for elastic eMBB traffic. The most important feature is that the mixed strategy allows resource utilization to be improved up to 95%, which is 10-15% higher compared to the reservation-only scheme while still providing isolation between traffic types.  
5G NR; network slicing; radio access network; mathematical modeling; queueing theory; pre-emptive priority; resource reservation  
11 January 202311 February 202  
conditions may deteriorate statically provisioned performance guarantees [7; 8]. In addition, according to the unified opinion of the 3 rd Generation Partnership Project (3 GPP), ITU-R, and the Groupe Speciale Mobile Association (GSMA) [9; 10], the utilized slicing scheme should satisfy two contradictory requirements--traffic isolation and efficient usage of radio resources. The former requirement can be implemented by utilizing static or dynamic resource allocation policies [11; 12], while the latter can be implemented via traffic prioritization schemes [13; 14]. The joint dynamics of schemes accounting for both types of requirements is rather complex. To this end, mathematical models are needed to understand the trade-offs between user-centric key performance indicators (KPI), such as session loss and pre-emption probabilities, and system-centric ones, such as the efficiency of system resource utilization.
## 3 Results  
Figure 1 depicts the mycelium growth at the beginning (Day 0) and at the end (Day11) of fermentation. Table 1 illustrates the results of the assessment of IHERs' moisture and crude protein content as well as TSS and RSS concentration.  
As shown in Table 1, the moisture content recorded a statistically significant increase (_p_\(\leq\) 0.05) ranging from 65.47% to 85.91% between Days 0 and 11. The concentration of TSS was decreased by 22.19% without significance (_p_\(\leq\) 0.05), whereas RSS content followed a similar decrease pattern of 61.58%, which was statistically significant (_p_\(\leq\) 0.05) between Days 0 and 11. The crude protein content was increased for the examined substrates, displaying a statistically significant increase (_p_\(\leq\) 0.05) of 53.05% between Days 0 and 11 of the fermentation period.  
Table 2 shows the results concerning CFS, cellulose, lignin, and ash presence in the examined substrates at Day 0 and Day 11. Specifically, the CFS concentration ranged from 12.10% to 11.14% during the fermentation process, while the presence of cellulose remained practically unaffected, although a slight increase of 5.25% was observed. On the other hand, the determined lignin content afforded a statistically significant increase (_p_\(\leq\) 0.05), exceeding 2-fold between Days 0 and 11. Finally, the amount of ash content recorded a statistically significant increase (_p_\(\leq\) 0.05) ranging from 3.14% to 8.80% between Days 0 and 11 of fermentation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Day 0** & **Day 11** \\ \multicolumn{3}{c}{**(g/100 g)**} \\ \hline
**Moisture** & 65.47 \(\pm\) 0.79  \\
**TSS** & 3.65 \(\pm\) 0.29 [FOOTNOTE:]Footnote : TSS, total soluble sugars; RSS, reducing soluble sugars. Statistical analysis was performed between Day 0 and Day 11. Different superscripts between the same letters indicate statistical significance, _p_\(\leq\) 0.05.[ENDFOOTNOTE]** & 2.84 \(\pm\) 0.16 \\
**RSS** & 3.54 \(\pm\) 0.08  \\
**Crude Proteins** & 15.89 \(\pm\) 0.03  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of fermentation time on physicochemical composition in the examined substrates.  
Figure 1: Representative image of mycelial growth at the beginning (Day 0) and at the end (Day 11) of fermentation.  
Figure 2 illustrates the 1,3-1,6 \(\beta\)-glucan concentration of IHER on Days 0 and 11. In particular, \(\beta\)-glucans' presence presented a statistically significant increase (\(p\leq 0.05\)) by 32.51% during the fermentation process (Days 0 to 11).
## 6 Prototype Trial Production
### Performance Test  
The minimum output resolution of the mechanism is critical for its actual working performance within the synchrotron radiation device. To assess the motion performance of the designed mechanism, an experimental setup was established. The device is secured on an optical vibration isolation platform using bolts at a room temperature of 25 degrees Celsius. This setup effectively constrains the movement of the device base in each degree of freedom and isolates potential disturbances from external sources. The piezoelectric actuator used is a high-precision model manufactured by Piezosystem Jena (model PAHL40-20 SG, with a travel distance of 42 \(\upmu\)m and a closed-loop resolution of 0.4 nm, Piezosystem Jena GmbH, Thuringia, Germany). A grating measurement system from Renishaw (model TONiC series Ti20 KD, with a resolution of 1 nm, Renishaw PLC, New Mills, UK) was employed to collect the motion displacement data of the mechanism.
## 3 Results and Discussion
### Smoke Particles Size Distribution (SPSD)  
The SPSD values of the POSS bio-char combustion measurement, by using an eight-state Andersen sampler, from four samples with varying moisture content of 15, 30, 45, and 60% d.b are presented in Figure 6. The results showed that the size distribution indicated a single-mode behavior. The MMAD values were found to be 0.44 to 1.05 micron at various moisture contents and combustion periods. For this reason, the reduced moisture content and rise in the combustion period reduced the smoke particles' size distribution. This agreed with the results reported by [17; 20; 22], which studied the particles' size distribution from para rubber wood combustion.
# Surface Subsidence Modelling Induced by Formation of Cavities in Underground Coal Gasification
## 1 Introduction
[14] studied the rock movement characteristics and surface settlement prediction method during underground coal gasification (UCG) process. [PERSON] et al. [15] used surrogate models to predict settlement above foundation caves and conducted a reliability analysis. [PERSON] et al. [16] studied the mechanical mechanism and prediction method of the settlement mechanism through numerical simulation and theoretical analysis. [PERSON] et al. [17] used numerical analysis (NA), neural network (NN), fuzzy logic (FL), and statistical analysis (SA) models to estimate the vertical displacement of the middle and key points of the roof and floor of a power station cave under different conditions. [PERSON] et al. [18] discussed the influence of voids in the strata on soil deformation during shield tunnel construction and established a three-dimensional symmetric calculation model to calculate soil deformation and surface settlement. [PERSON] et al. [19] used FLAC3D for numerical modeling and studied the mechanism of surface subsidence during underground coal gasification, taking into account the influence of thermal effects on soil rock mechanical properties. The impact of mining on surface streams was studied by [PERSON] et al. [20] based on self-developed SDP software. In addition, some people have studied the gasification process of underground coal, explored the development of underground cavities and their sustained effects on surrounding rock layers [21; 22; 23]. In order to more accurately predict the process of surface subsidence, some scholars continuously improve the dynamic prediction model of underground mining subsidence in order to achieve better prediction results [24; 25; 26; 27; 28; 29; 30]. The scope of this work is to model, under the elasticity regime, the surface subsidence when multiple gasification cavities are formed within a coal seam at a depth of 300 m. Four cavities (C1, C2, C3, and C4) of size 30 m \(\times\) 30 m \(\times\) 15 m (13,500 m\({}^{3}\)) separated by 15 m wide pillars are modelled. It is also intended in this work to investigate the influencesof the gasification sequence on the surface subsidence based on two different scenarios: (I). C1 and C2 first, followed by C3 and C4; (II). C1 and C3 first, followed by C2 and C4. These cavities are within an 18-m-thick coal seam overlain by carbonaceous mudstone with occasional siderite bands.
## 3 Results
### Capability of MTVDI Seasonality in Representing Canopy Photosynthesis Seasonality  
We further compare MTVDI with photosynthesis proxies, the Enhanced Vegetation Index (EVI) and Solar-Induced Chlorophyll Fluorescence (SIF), which were found to have the capability of representing the Amazon dry-season green up [14; 67]. The seasonality of MTVDI, SIF and EVI of the whole study area are presented in Figure 8. From Jan to Jun, both EVI and SIF decline and reach the lowest values in Jun. After Jun, EVI and SIF begin to rebound and reached the initial level of about Jan. On the contrary, the MTVDI shows an approximately opposite variation, which corresponds to the fact that the growth of vegetation is inhibited during the drought period (dry season) and is enhanced during the wet season. Therefore, the approximately opposite seasonality between MTVDI and EVI (NSE = 0.75), SIF (NSE = 0.35) indicate that MTVDI has a good capability in reflecting the photosynthesis seasonality of tropical forests in Amazon. Especially in the SA2 (NSE of EVI and SIF are 0.95 and 0.79, respectively) and WA regions (NSE of EVI and SIF are 0.75 and 0.39, respectively) (Supplementary Materials Figure S9, Table S2).  
The spatial patterns of linear correlations at the pixel level are presented in Figure 9. In northern parts (NA1 and NA2) of the Amazon tropical forests, the MTVDI shows a positive correlation with both photosynthesis proxies, while in central (CA) and south parts (WA, SA1 and SA2) their correlations are negative. This result exactly coincides with [PERSON] et al. [67]. [PERSON] et al. [67] investigated the differences of the wet-season minus dry-season quantities of simulated \(\Delta\)EVI and \(\Delta\)SIF (Figure 9c,d), respectively, and found that those forests which green up during the dry season were located in wet regions with mean annual precipitation (MAP) higher than 2000 mm per year (blue pixels in Figure 9c,d). That  
Figure 7: The spatial pattern of correlation between seasonality of MTVDI and seasonality of (**a**) Vapor Pressure Deficit (VPD), (**b**) Climatological Water Deficit (CWD), (**c**) Terrestrial Water Storage (TWS), and (**d**) Palmer Drought Severity Index (PDSI).  
is, the red pixels in Figure 9a,b with a positive correlation between EVI, SIF and MTVDI, where the forests show a dry-season green-up. In contrast, for the green pixels that show a negative relation, the forests, however, show canopy loss during dry-season periods. The MTVDI shows good capability in detecting the forest areas, which green up during the dry season in wet regions.  
Figure 8: The seasonality of MTVDI, Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI). The seasonality represents the mean values of each month from 2003 to 2010. The gray shadings represent standard error of the mean (SEM).  
Figure 9: The spatial pattern of the correlation between seasonality of MTVDI and seasonality of (**a**) Enhanced Vegetation Index (EVI) and (**b**) Solar-Induced Chlorophyll Fluorescence (SIF). Multiyear average of wet minus dry season differences of simulated (**c**) \(\Delta\)EVI and (**d**) \(\Delta\)SIF.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
## 4 Results and Discussion  
### Experimental Results  
The point target polarization matrix was extracted from the image using the following steps: first, locate the point target local region [51], extract the 32 \(\times\) 32-size matrix around the point target in the SLC data, perform FFT interpolation on this part of the data, find the peak point of the interpolated data, and then extract the magnitude and phase, respectively. The measured values of the polarization matrix of each ARC extracted from the image were substituted into Equations (10), (12), (14), and (16) to obtain the distortion matrix results. As discussed, four ARCs were deployed in this experiment for the comparison of different range-direction polarization distortion parameter calculations; the combinations of ARC-1, ARC-2, and ARC-3 (Group 1) and ARC-1, ARC-2, and ARC4 (Group 2) were set up according to the experimental requirements. The results obtained using the different combinations are shown in Table 4.  
The image was polarization calibrated using the computed \(R\) and \(T\) matrices, and then the scattering matrices of the trihedral CRs were extracted from the calibrated image, as shown in Tables 5 and 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Distortion** & **Group 1** & **Group 2** \\ \hline \(R\) & \(\begin{pmatrix}1.420\angle-141.36^{\circ}&0.0004\angle 14.00^{\circ}\\ 0.0001\angle-151.05^{\circ}&1.0/2.00^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}1.479\angle-141.26^{\circ}&0.0004\angle 14.10^{\circ}\\ 0.0001\angle-151.05^{\circ}&1.0/2.00^{\circ}\\ \end{pmatrix}\) \\ \(T\) & \(\begin{pmatrix}1.0\angle 0.0^{\circ}&0.0008\angle 103.99^{\circ}\\ 0.00003\angle-16.57^{\circ}&0.913\angle-81.77^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}1.0\angle 0.0^{\circ}&0.0008\angle 103.99^{\circ}\\ 0.00003\angle-16.75^{\circ}&0.913\angle-81.94^{\circ}\\ \end{pmatrix}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Distortion matrix obtained from different equipment combination options.
# JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)
## 3 Materials and Methods
### Experimental Design
#### 3.5.1 Quantitative: Final Grades  
An initial analysis was conducted to assess the similarity between the grades assigned by each LLM system, based on the evaluation rubric, and those assigned by the instructor. To this end, Pearson's correlation coefficient was employed to measure the strength of the relationship between both sets of assessments.  
Once the most similar LLM-generated scores were identified based on the results of the previous tests, a linear regression analysis was performed to evaluate the feasibility of adjusting the instructor-assigned grades using only the AI systems that produced statistically significant results. The initial model was trained using a 5-fold cross-validation approach on a subset of 547 observations, ensuring robustness and minimizing overfitting in model evaluation.  
To assess the performance of the linear regression model, both the adjusted coefficient of determination (adjusted R\({}^{2}\)) and the Mean Absolute Error (MAE) were computed. The adjusted R\({}^{2}\) provides a measure of the proportion of variance in the dependent variable explained by the model, penalizing the number of predictors to avoid overfitting. The MAE quantifies the average magnitude of prediction errors in the same units as the response variable, which is measured on a scale from 0 to 10.  
In addition, an analysis of variance (ANOVA) was carried out to determine which LLM systems had a statistically significant impact on the prediction of grades. This analysis resulted in the identification of a subset of models that meaningfully contributed to the regression. Based on their relative importance, alternative regression models were constructed excluding non-significant variables, with the objective of defining a final model that uses the fewest possible LLM systems without compromising predictive accuracy.  
Once the final regression model was selected, a residual analysis was conducted to assess whether the residuals behaved as white noise, whether they were normally distributed with zero mean and constant variance (homoscedasticity). This step is essential because the classical assumptions of linear regression models require residuals to be independently and identically distributed, with no patterns left unexplained by the model. Confirmingthat the residuals follow these assumptions ensures the validity of the inference drawn from the model.
## 3 Results
### Glacier Mass Balance and Uncertainty  
We used the uncertainty assessment method in Section 2.3.4 to calculate the uncertainty of the glacier surface elevation change, the uncertainty of the seasonal correction, and the uncertainty of the radar penetration difference correction for the six regions (Table 4).  
Altogether the 12 image pair were merged to produce results for six areas: Jiali, Bianba, Luolong, Bomi, Basu, and Chayu. Based on the rate of glacier surface elevation change and the weight of the glacier area covered by bi-static InSAR data in these six areas, the overall glacier surface elevation changes in the entire SETP was calculated, which was \(-\)0.505 m/yr. According to an assumed overall ice density of 900 \(\pm\) 17 kg m\({}^{-3}\) adopted by [PERSON] et al. [5], the glacier mass balance in the entire SETP from 2000 to 2014 was \(-\)454.5 mm w.eq.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Acquisition Date**} & \multirow{2}{*}{**Covered Area**} & **Glacier Surface** & **Seasonal** & **Corrected Glacier** & \multirow{2}{*}{**Years**} & **Corrected Glacier** \\  & & **Elevation** & **Correction** & **Surface Elevation** & & **Surface Elevation** \\  & & **Change (m)** & **Value (m)** & **Change (m)** & & **Change Rate (m/yr)** \\ \hline
15 January 2014 & Jiali\_1 & \(-\)9.990 & 0 & \(-\)9.990 & 14 & \(-\)0.714 \\
17 February 2014 & Jiali\_2 & \(-\)5.744 & 0 & \(-\)5.744 & 14 & \(-\)0.410 \\
6 February 2014 & Jiali\_3 & \(-\)7.332 & 0 & \(-\)7.332 & 14 & \(-\)0.524 \\
20 December 2011 & Bianba\_1 & \(-\)5.840 & 0 & \(-\)5.840 & 12 & \(-\)0.487 \\
4 February 2011 & Bianba\_2 & \(-\)6.925 & 0 & \(-\)6.925 & 13 & \(-\)0.533 \\
20 December 2011 & Bianba\_3 & \(-\)7.491 & 0 & \(-\)7.491 & 12 & \(-\)0.624 \\
20 August 2011 & Luolong & \(-\)8.248 & 1.908 & \(-\)6.340 & 13 & \(-\)0.488 \\
22 September 2011 & Bomi & \(-\)8.757 & 1.908 & \(-\)6.849 & 13 & \(-\)0.527 \\
16 September 2014 & Basu\_1 & \(-\)10.125 & 1.908 & \(-\)8.217 & 14 & \(-\)0.587 \\
5 September 2014 & Basu\_2 & \(-\)11.832 & 1.908 & \(-\)9.924 & 14 & \(-\)0.709 \\
14 August 2014 & Chayu\_1 & \(-\)9.472 & 1.908 & \(-\)7.564 & 14 & \(-\)0.540 \\
25 August 2014 & Chayu\_2 & \(-\)14.408 & 1.908 & \(-\)12.500 & 14 & \(-\)0.893 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Seasonal correction values and corrected glacier surface elevation change rate.
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 5 Security Protocols in SENTINEL
### Drone Authentication Protocol
In the near future, drone-to-drone authentication would be necessary for some situations. For example, each drone should avoid collisions with other drones during flight. Therefore, it needs to interact and share its GPS information with other drones in real-time. From the perspective of cybersecurity, however, faked drone information can be intentionally generated and exchanged between drones [27]. To prevent such malicious activities, each drone should check the authenticity of other drones that communicate with. SENTINEL can also be a neat solution for drones to communicate with each other securely. Figure 6 shows the authentication procedure between a drone \(d_{1}\) and another drone \(d_{2}\). Because \(d_{1}\) and \(d_{2}\) do not share a flight session key, the verifier drone \(d_{2}\) checks the validity of the authentication code delivered from \(d_{1}\) via a ground station \(gs\). 1. The drone \(d_{1}\) creates a message \(msg_{d_{1}}\) that contains the information about the drone \(d_{1}\)'s (GPS) location and timestamp. The message also contains the drone \(d_{1}\)'s identifier \(id_{d_{1}}\) as the message sender's identifier ('Source ID') and the other drone \(d_{2}\)'s identifier as the message recipient's identifier ('Destination ID'). Then, \(d_{1}\) computes the authentication code \(H_{d_{1}}\) using a HMAC (hash-based message authentication code function) with \(k_{d_{1}}\) and \(msg_{d_{1}}\) and sends \(H_{d_{1}}\) with the original message \(msg_{d_{1}}\) to the drone \(d_{2}\). 2. On receiving the message from the drone \(d_{1}\), the drone \(d_{2}\) relays the message to a ground station \(gs\). 3. On receiving the message from the drone \(d_{2}\), the ground station \(gs\) fetches the drone \(d_{1}\)'s record with \(id_{d_{1}}\) from the flight information database and extracts the flight session key \(k_{d_{1}}\) from the record. For verifying the validity of the received \(H_{d_{1}}\), \(gs\) computes \(H^{\prime}_{d_{1}}=HMAC(k_{d_{1}},msg_{d_{1}})\) using received message \(msg_{d_{1}}\) and the retrieved flight session key \(k_{d_{1}}\). After computing \(H^{\prime}_{d_{1}}\), \(gs\) compares \(H^{\prime}_{d_{1}}\) with the received authentication code \(H_{d_{1}}\). If \(H^{\prime}_{d_{1}}=H_{d_{1}}\), \(gs\) believes that \(msg_{d_{1}}\) is not compromised and the drone \(d_{1}\) holds the flight session key \(k_{d_{1}}\). After verifying the authentication code, \(gs\) makes a decision on whether or not to allow the drone \(d_{1}\)'s flight entering its fly zone by checking whether this fly zone is contained in \(d_{1}\)'s flight plan. The ground station \(gs\) sends this result to the drone \(d_{2}\). Figure 6: Authentication protocol between drone and drone.