esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of Indoor Air Velocity and Temperature Distributions  
### Summer-Cooling Condition  
The distribution of the air temperature and velocity obtained with numerical simulations is illustrated in Figures 5 and 6, respectively. When the minimal inlet air velocity was set to 1 m/s and no solar load was considered, the discharged air from the fan-coil unit tended to move towards the floor due to lower density after flowing around a meter (Figure 5, Case A). By increasing the inlet velocity, the forced convection condition was prevalent, and the discharged air from the fan coil moved directly until it had hit the opposite wall, which is demonstrated in Figure 5, Case B. When solar radiation was present, the temperature difference between inlet air and surrounding environment was rather large; thus, the air around the fan coil was mostly driven by buoyancy force upwards. In this case, the inlet air of the fan coil tended to move towards the floor sooner than other cases did, and less fresh air was delivered to Positions 1 and 2 (see Figure 1, Case A) than that in the other cases (Figure 5, Case C). The streamlines of Case D showed a rather similar trend to that in Case B with an augmented natural convection effect due to solar radiation.  
The resulting temperature profile inside the office was characterised by important nonuniformity along the different working stations, as shown in Figure 6. When the solar load was taken into account, the case with minimal inlet air flow could not satisfy the required thermal field to cool the environment, and this resulted in the worst possible situation. The following mean static air temperature and mean operative temperature values were obtained for each case: \(T_{mean}=24.1\ ^{\circ}\)C, \(T_{op}=24.6\ ^{\circ}\)C for Case A, \(T_{mean}=22.8\ ^{\circ}\)C, \(T_{op}=23.6\ ^{\circ}\)C for Case B, \(T_{mean}=27.6\ ^{\circ}\)C, \(T_{op}=28.1\ ^{\circ}\)C for Case C and \(T_{mean}=26.1\ ^{\circ}\)C, \(T_{op}=27.0\ ^{\circ}\)C for Case D. Thus, an incident radiation impact of about 4 \({}^{\circ}\)C in mean temperature was obtained in the room.  
Figure 5: View from the entrance of the streamlines obtained for summer cooling with low input air velocity and no solar load (Case A), high input air velocity and no solar load (Case B), low input air velocity and solar load (Case C), high input air velocity and solar load (Case D).
###### Abstract  
This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor-critic (AD-SAC) algorithm with discrete-continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms.  
mobile edge computing; computation offloading; resource allocation; deep reinforcement learning 2025  
2
## 3 Results and Discussion
### Validation of the Quantitative Analytical Method  
The HPLC-ESI-MS/MS quantitative analysis method was validated. The regression equations, coefficient of determination, linear ranges, LODs, and LOQs for the quantitative analysis of the 16 reference ginsenosides are shown in Table 4. The calibration curves for all 16 reference substances showed good linear regression (r > 0.999) within the test ranges. The LODs of the 16 reference compounds were estimated to be 0.13-2.22 ng/mL, whereas the LOQs were 0.31-5.90 ng/mL. The precision, repeatability, stability, and recovery are listed in Table 5. The precision of the quantitative method was determined; the validation studies showed that the relative standard deviation (RSD) was less than 4.87%, and the repeatability of the method was very good (RSD < 4.93%). The RSD of the storage stability was less than 4.60% in 24 h. The recovery was in the range of 99.25-104.10% with an RSD of less than 3.45%. The established HPLC-ESI-MS/MS method was accurate and reliable and is therefore appropriate for quantitative analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{\begin{tabular}{c} **Regression** \\ **Equations** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Correlation** \\ **Coefficients (r)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Linear Range** \\ **(ng \(\times\) mL\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **LOD** \\ **(ng \(\times\) mL\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **LOQ** \\ **(ng \(\times\) mL\({}^{-1}\))** \\ \end{tabular} } \\ \hline
1 & N-R1 & Y = 22.1X + 29.4 & 0.9991 & 1.56–1560 & 0.61 & 1.22 \\
2 & G-Rg1 & Y = 7.7X + 193 & 0.9999 & 3.91–3910 & 1.91 & 3.81 \\
3 & G-Re & Y = 18X + 103 & 0.9998 & 6.25–6250 & 0.38 & 1.22 \\
4 & G-Rf & Y = 53.4X + 20 & 0.9998 & 0.78–780 & 0.19 & 0.38 \\
5 & G-F3 & Y = 12.3X + 8.91 & 0.9997 & 1.71–1170 & 0.36 & 0.91 \\
6 & G-Rg2 & Y = 71.7X + 50.6 & 0.9994 & 7.03–7030 & 0.21 & 0.86 \\
7 & G-Rh1 & Y = 2.92X  – 1.56 & 0.9992 & 6.24–1560 & 2.22 & 5.90 \\
8 & G-Rb1 & Y = 7.5X - 105 & 0.9996 & 3.15–3130 & 1.53 & 3.05 \\
9 & G-Rg3 & Y = 27.8X + 3750 & 0.9992 & 9.38–9380 & 0.29 & 0.88 \\
10 & G-Rc & Y = 7.97X + 65.1 & 0.9998 & 1.56–1560 & 0.61 & 1.22 \\
11 & G-Rb2 & Y = 12.3X + 78.7 & 0.9998 & 1.56–1560 & 0.41 & 1.22 \\
12 & G-Rb3 & Y = 15.1X + 56.6 & 0.9996 & 1.56–1560 & 0.41 & 1.38 \\
13 & CS-IV & Y = 79.3X + 2610 & 0.9997 & 6.25–6250 & 0.16 & 0.31 \\
14 & CS-IVa & Y = 13.4X + 844 & 0.9991 & 7.81–7810 & 0.13 & 0.38 \\
15 & G-Rd & Y = 14X + 188 & 0.9993 & 1.56–1560 & 0.41 & 1.07 \\
16 & G-Rg3 & Y = 24.1X - 17.1 & 0.9995 & 0.78–780 & 0.25 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The regression equations, linear range, limit of detection (LOD), and limit of quantification (LOQ) of the 16 analytes.
## 4 Discussion
### Change Trends of Vegetation NDVI  
From 2000 to 2020, the annual mean NDVI in the Aksu region generally showed an increasing trend, particularly in Aksu City, Shaya County, Xinhe County, and Kuqa County. The significant vegetation growth in these areas is primarily attributed to the expansion of cultivated land due to population growth and urban expansion. Additionally, advancements in agricultural technology, such as water-saving irrigation, have substantially improved the efficiency of water resource use, reducing limitations on vegetation growth and subsequently promoting increases in vegetation cover. Furthermore, a series of ecological conservation and restoration projects initiated in 2000, such as the Tarim River Comprehensive Management Project and the Aksu River Basin Ecological Management Project, have provided strong support for vegetation improvement [48; 54; 55; 56; 57; 58]. These projects have played a critical role in enhancing the local environment, improving water resource utilization, and combating desertification. However, the slowed NDVI growth from 2011 to 2015 might indicate a reduced effectiveness of these ecological restoration projects, as well as a slowdown in urban expansion and infrastructure development.
## 2 Data Description
### Approximate Positional Accuracy
The empirical cumulative distribution functions (CDF) were determined for \(SD_{r}\) and \(\sim ACC_{r}\) for the full dataset, FLOAT data subset, and FIX data subset, as shown in Figure 6. Additionally, the corresponding frequency distribution histograms are shown within each corresponding CDF plot. \begin{table}
\begin{tabular}{c c c c c} \hline
**Metric** & **Average** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline \(SD_{r}\) & 1.01 & 0.48 & 0.12 & 2.84 \\ \(SD_{x}\) & 0.66 & 0.40 & 0.01 & 1.85 \\ \(SD_{y}\) & 0.67 & 0.44 & 0.09 & 2.74 \\ \(\sim ACC_{r}\) & 174.82 & 0.82 & 173.28 & 177.99 \\ \(\sim ACC_{z}\) & 198.63 & 2.02 & 196.13 & 206.76 \\ \hline \end{tabular}
\end{table} 
Table 6: The average, minimum, maximum, and standard deviation of the precision and the approximate \(ACC_{r}\) and \(ACC_{z}\) calculated for the subset of 177 points with a solution status as FLOAT, reported in centimeters. \begin{table}
\begin{tabular}{c c c c c} \hline
**Metric** & **Average** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline \(SD_{r}\) & 0.80 & 0.68 & 0.12 & 9.21 \\ \(SD_{x}\) & 0.53 & 0.59 & 0.01 & 8.44 \\ \(SD_{y}\) & 0.53 & 0.43 & 0.04 & 3.69 \\ \(\sim ACC_{r}\) & 174.46 & 1.17 & 173.28 & 189.02 \\ \(\sim ACC_{z}\) & 198.09 & 4.47 & 196.13 & 268.48 \\ \hline \end{tabular}
\end{table} 
Table 4: The average, minimum, maximum, and standard deviation of the precision and the approximate \(ACC_{r}\) and \(ACC_{z}\) calculated for the entire set of 320 points, reported in centimeters. \begin{table}
\begin{tabular}{c c c c c} \hline
**Metric** & **Average** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline \(SD_{r}\) & 0.54 & 0.79 & 0.13 & 9.21 \\ \(SD_{x}\) & 0.37 & 0.72 & 0.06 & 8.44 \\ \(SD_{y}\) & 0.36 & 0.36 & 0.04 & 3.69 \\ \(\sim ACC_{r}\) & 174.02 & 1.37 & 173.31 & 189.02 \\ \(\sim ACC_{z}\) & 197.43 & 6.24 & 196.27 & 268.48 \\ \hline \end{tabular}
\end{table} 
Table 5: The average, minimum, maximum, and standard deviation of the precision and the approximate \(ACC_{r}\) and \(ACC_{z}\) calculated for the subset of 143 points with a solution status as FIX, reported in centimeters.
## 3 Proposed Architecture for NR-IQA
### Spinal Network  
A spinal network is an artificial neural network with a structural architecture inspired by the human spinal cord, thus its name, SpinalNet [28]. The inputs are provided step by step and over regular intervals, just as with the architecture of the spinal cord. Two factors--local and global outputs--are computed, i.e., every layer in the network generates a local output and the amplified version of inputs is forwarded to the global output. Training data are utilized to perform configuration of the weights of the artificial neural network [29].  
The traditional SpinalNet divides each layer of the neural network (NN) into three parts: input, intermediate, and output. Each input split of the NN layers receives a piece of the input data. Except for the intermediate split of the first layer of NN, all intermediate splits acquire two components [30]. The components are the outcomes of the preceding intermediary layer split and the outcome of the current layer's existing input split. The weighted outputs of all the intermediate splits are fed into the output split of each layer where they are added together. A total of two neurons exist in each hidden layer of the intermediate splits.The output \(v_{n}\) of a neuron \(n\) in the hidden layer is:  
\[v_{n}=\sigma(\sum_{i=1}^{P}(y_{ni})u_{i}+R_{n}^{knl}), \tag{1}\]  
where \(\sigma\) is the activation function. \(P\) is the number of neurons, \(y_{ni}\) is the weight of a neuron, \(u_{i}\) is the input of a neuron and \(R_{n}^{knl}\) is the threshold value of neurons.  
The architect can change the number of neurons that exist in a hidden layer. However, because of the computational expense of multiplications, both the number of intermediate layer neurons and the number of inputs sent to each layer are reduced to a minimum [31]. The activation function utilized can be a sigmoid function as:  
\[\sigma(t)=\frac{1}{1+\exp(-t)}. \tag{2}\]  
Since the SpinalNet takes the inputs repeatedly and at a gradual pace, all the input features will have an impact on the output. A nonlinear activation function, such as Relu, sigmoid or softmax, can be deployed in the intermediate splits of each layer. A linear activation function is deployed at the output layer, which is basically an identity or no activation function where the input is equivalent to the activation [32]. Relu activation function is illustrated as:  
\[E(m)=max(0,m). \tag{3}\]  
The equivalence is drawn between the SpinalNet and a traditional neural network by comparing the two network architectures. A simplified SpinalNet consisting of four hidden layers where each layer is comprised of two neurons, is equivalent to a traditional neural network with only a single hidden layer where four neurons exist [33].  
Figure 2: Spinal-Net-based architecture for quality assessment of distorted images.
## 4 The Smart Shipping Use Case
### Description of the Training Programme  
The maritime sector is under an on-going process of digitalization in all aspects of operation. For a long period, seafarers have been trained with computer-based training programs on-board according to regulated training models. These days they are consuming training courses offered by sophisticated e-learning platforms. No doubt that maritime personnel are considered skilled enough to navigate properly in a web environment.  
A typical topology of the on-board information technology (IT) and operation technology (OT) infrastructure [53] which is exposed to cyber threats and to risks in the format of environmental, crew safety or financing negative uncertainties is portrayed below in Figure 5.  
Ships are becoming more and more integrated with shore-side operations because digital communication is being used to conduct business, manage operations, and stay in touch with head office. Furthermore, critical ship systems essential to the safety of navigation, power and cargo management have been increasingly digitalized and connected to the Internet to perform a wide variety of legitimate functions (e.g., updates, versioning upgrades, remote maintenance, voyage or ship performance monitoring from ashore, etc.). The ship-shore interface is conducted with several communication methodologies and protocols whistle cyber threats could be applicable to the full range of networking.  
A schematic approach on the aforementioned networking for consumption of services between two distinct partners (shore and ship, supplier and vessel, third-party OS system provider and vessel, etc.) is following. The next figure is displaying and describing the configuration of DNAAOS' communication protocols (web services, emails, telco, calls etc.) and security protections. Firewalls applied at each side of junctions between network components and data protection is secured with not storing data in centralized repositories but with controlling from a tailor-made and internally developed service platform (DANAOSone platform [54]). The overall platform modules are depicted in Figure 6.  
Cyber threats are raised where vulnerabilities in the system exist. A cyber-attack involves the attacker who in turn is motivated to trigger the attack in order to achieve a certain objective and the victim, who in turn faces the consequences of the attack. Protective barriers either in the form of technical protection or human awareness are set forward to prevent attack from impacting the system network components and cause negative consequences [55]. A schematic flow of cyber threat mechanism is given in Figure 7.  
Figure 5: Smart shipping system architecture.  
Figure 6: DNAAOS configuration of communication protocols.  
Along that cyber threat mechanism, training and awareness is the key supporting element and an important barrier along with technical and physical protection to an effective approach to cyber safety and security.
# Discovery of an Intact Quaternary Paleosol, Georgia Bight, USA
## 1 Introduction  
Relatively intact paleosols have been found in diverse marine settings. Recent discoveries include Quaternary paleosols in Haifa Bay [1] and the U.S. Pacific coast [2]. Paleosols are an important resource for terrestrial environmental and climatic reconstructions.  
Paleosols have not been previously identified off the U.S. Atlantic coast. This study describes our initial discovery of a paleosol in the Georgia Bight (Figure 1). Recent paleosol research has introduced semiquantitative and quantitative measures for environmental and chronometric reconstructions that provide insight into major regional to global changes in temperature, precipitation, and atmospheric \(p\)CO2 [3]. Paleosols are essential tools in sequence stratigraphic analyses. Geologically, paleosols are common in ancient shallow marine carbonate sequences, a function of the offtimes frequent oscillations at sea level which affect shallow or basin-edge seas. Such paleosols have been used as indicators of subaerial exposure, but as our knowledge of past ecologies and paleoclimate has increased, they provide insight into the Earth's system variation from deep time to the Holocene.  
Drowned forests located on marine paleosols have been reported on the Canadian continental shelf of British Columbia [4; 5]; off Prince Edward Island [6]; the Baltic Sea [7]; in Murrell's Inlet, South Carolina, on the Atlantic continental shelf [8]; and off the Gulf Coast of Alabama [9].  
A recent four-year research study of Georgia's Atlantic continental shelf investigated large artificial reef structures such as barges, vessel hulls, etc., also known as \"scour nuclei\", for evidence of paleo-land surfaces and associated paleosols. Turbulence produced by tidal and wind-generated currents in the Georgia Bight has produced scour pits termed by others as \"moats\" and \"comets\" [10]. This methodological approach differs from the use of sediment coring to collect and study marine deposits conducted in the early 1980s and again in the early 2000s in the Gulf of Mexico/America [10]. Larger samples of exposed sea floor can be directly collected and studied as demonstrated herein.  
The modern palimpsest sand sheet, at these locations, was observed to erode to depths of over a meter, forming \"moats\" immediately adjacent to an artificial reef structure. Relatively strong tidal currents operate twice daily in the Georgia Bight, with ebb tide currents typically stronger than those associated with the flood tide. [PERSON] et al. [11] and [PERSON] [12] provide the most recent, comprehensive studies of sediment composition and stratigraphy for the nearshore continental shelf off Georgia. Our current study differs from these earlier studies in that we have sought and located extant paleosols on Quaternary landforms exposed by scour processes [10; 13]. At Reef SAV (Figure 1), a Marine Isotope Stage 3 paleosol, dated to 42,015 to 43,417 calibrated years before present (cal yrBP), was exposed. We describe this soil and relate its paleoecology to our current understanding of past sea levels and paleoclimates.
## 3 Results and Discussion
### Comparison of ITS2 Secondary Structures of Shi-Liang Tea Species and Closely Related Species  
Previous studies showed that the secondary structures of ITS2 are conserved and can provide useful biological information for alignment [24; 25; 32]. These structures can be considered as molecular morphological characteristics to identify species. We compared the secondary structures of Shi-Liang tea species and closely related species. The secondary structures of 71 ITS2 sequences had four similar helices: Helices I, II, III, and IV. The secondary structures of species in _Chimonanthus_ were relatively more conserved in Helices III and IV, but the positions, sizes, and numbers of loops in Helices I and II were different. By contrast, the secondary structures between _Ca. floridus_ and _Ca. chinensis_ were highly conserved in Helices II, III, and IV, but had fewer differences in Helix I. These results show that these helices can be used as molecular characteristics to identify species [25; 26] (Figure 2). Therefore, Shi-Liang tea species could be clearly distinguished from closely related species based on their secondary structures.  
Figure 1: The NJ-tree of Shi-Liang tea and its closely related species.  
_Ca. chinensis_  
Figure 2: The ITS2 secondary structure of Shi-Liang tea and its closely related species.  
Two-Dimensional DNA Barcodes to Monitor Market Circulation and Promote the Modernization of Shi-Liang Tea Species and Closely Related Species  
The two-dimensional DNA barcode is the most appropriate tool for DNA sequences, with the largest coding capacity and relatively high compression ratio [27]. In this study, ITS2 sequences of Shi-Liang tea species and closely related species were converted into colored barcode images based on our software code (Figure 3) [31; 33]. This system consisted of the DNA barcode sequences and the cross-platform conversion of DNA barcode information. The application of two-dimensional DNA barcodes is beneficial to effectively monitor the quality and regulate the market circulation of Shi-Liang tea.  
The image list in the order of _C. salicifolius, C. zhejiangensis, C. praecox, C. nitens, C. campanulatus, Calycanthus floridus,_  
Figure 3: ITS2 sequences barcode and two-dimensional DNA barcode image of Shi-Liang tea and its closely related species (\(\blacksquare\)\(\Delta\blacksquare\)\(\mathsf{T}\overline{\mathsf{\Box}}\)\(\mathsf{G}\)).
## 3 Results and Discussion
### DPPH Radical Scavenging Activity of GPPP  
DPPH\({}^{\bullet}\) is a stable free radical which has been widely used to estimate the free radical scavenging ability of antioxidants [16]. The DPPH free radical is a stable radical with a maximum absorption at 517 nm that can readily undergo scavenging by an antioxidant. The principle of DPPH analysis is based on the color change of the solution from purple to yellow as radicals are quenched by antioxidant compounds, which can be quantitatively measured from the changes in absorbance [23]. On the basis of this principle, the antioxidant activity of polysaccharides can be expressed as their ability to scavenge DPPH free radicals. The scavenging activity of GPPP on the DPPH\({}^{\bullet}\) radicals is shown in Figure 4A and compared with ascorbic acid as a positive control in a concentration-dependent manner. The DPPH\({}^{\bullet}\) radical scavenging activity of GPPP increased at the concentration range (0.1-0.9 mg/mL) of the sample from 76.55% to 91.03%. The highest scavenging activity of GPPP was 91.03% \(\pm\) 1.54% at 0.9 mg/mL. However, scavenging activity of ascorbic acid was 95.2% \(\pm\) 1.7% at the same concentration. Therefore, it is clear that GPPP had a noticeable effect on scavenging DPPH\({}^{\bullet}\) free radicals, especially at high concentrations. The possible mechanism by which GPPP acts as an antioxidant may be attributed to the existence of numerous hydroxyls in a polysaccharide molecule, which could provide electron or hydrogen donation power to the free DPPH radicals and neutralize them [43].  
Figure 4: Antioxidant activities of GPPP and ascorbic acid: (**A**) DPPH free radical scavenging activity; (**B**) reducing power and (**C**) FRAP.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Degree of esterification (\%)** \\ \hline Titrimetric method & 53.28 \(\pm\) 0.08 \\ FT-IR spectroscopy & 51.36 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The degree of esterification of GPPP obtained from different methods.