esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 1 Introduction
### Motivation and Contributions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Definition** \\ \hline NOMA & Non-orthogonal multiple access. \\
3G & Third generation. \\
4G & Fourth generation. \\ CDMA & Code-division multiple access. \\
5G & Fifth generation. \\ HetNet & Heterogeneous network. \\
5 GB & Beyond 5G. \\ SC & Sub-channel. \\ OMA & Orthogonal multiple access. \\ BS & Base station. \\ SIC & Successive interference cancellation. \\ CU & Cellular user. \\ AWGN & Additive white Gaussian noise. \\ SINR & Signal-to-noise-plus-interference ratio. \\ KKT & Karush–Kuhn–Tucker. \\ RSMA & Rate splitting multiple access. \\ OFDMA & Orthogonal frequency division multiple access. \\ \hline
**Notation** & **Definition** \\ \hline \(\mathcal{K}\) & The set of cellular users in the proposed NOMA network. \\ \(\mathcal{M}\) & The set of sub-channels in the system. \\ \(U_{m}\) & The set of cellular users on a sub-channel at one time. \\ \(U_{\max}\) & The maximum number of cellular users on a sub-channel at one time. \\ \(\text{C}U_{i}\) & Represents the cellular user \(j\). \\ \(SC_{j}\) & Represents the sub-channel \(m\). \\ \(p_{j,m}\) & The transmit power of BS for \(\text{C}U_{j}\) over \(\text{SC}_{m}\). \\ \(h_{j,m}\) & The channel gain of \(\text{C}U_{j}\) over \(\text{SC}_{m}\). \\ \(P_{BS}\) & The total power of BS. \\ \(y_{j,m}\) & The received signal of \(\text{C}U_{j}\) over \(\text{SC}_{m}\). \\ \(\omega_{i,m}\) & The data symbol of \(\text{C}U_{i}\) over \(\text{SC}_{m}\). \\ \(x_{j,m}\) & The AWGN of \(\text{C}U_{j}\) over \(\text{SC}_{m}\). \\ \(n_{0}\) & The noise variance of AWGN. \\ \(C_{j,m}\) & The capacity of \(\text{C}U_{j}\) over \(\text{SC}_{m}\). \\ \(\gamma_{j,m}\) & The SINR of \(\text{C}U_{j}\) over \(\text{SC}_{m}\). \\ \(\beta_{j,m}\) & The binary variable for sub-channel allocation. \\ \(S_{\text{sum}}\) & The sum capacity of the system. \\ \(\tilde{\text{C}}_{\min}\) & The minimum user capacity to achieve the QoS requirements. \\ \(\alpha\) & The power allocation factor. \\ \(P_{m}\) & The total transmit power on \(\text{SC}_{m}\). \\ \(H\) & The channel matrix. \\ \(R\) & The matrix of user minimum capacity for QoS requirements. \\ \(P_{r}\) & The remaining power after capacity requirements. \\ \(\mathcal{L}\) & The Lagrangian function. \\ \(\lambda,\eta\) & The non-negative Lagrangian multipliers. \\ \(\phi\) & The iteration index. \\ \(\delta\) & The non-negative step size. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of different abbreviations and notations used in this paper.
## 4 Results
### Structural Framework  
#### 4.2.1 Lineament Analysis  
The lineaments extracted from the SRTM-DEM and ALOS-DSM data (Figure 6) were analysed and rose diagrams were constructed. Two main fracture sets were oriented northeast-southwest, approximately 40-220, and northwest-southwest, approximately 120-300, with local maximum stress (\(\sigma_{1}\)) in the north-south direction and minimum stress (\(\sigma_{3}\)) in the east-west direction. The fractures in the northeast and northwest directions are thought to be transtensional, with sinistral shear on the northeast trend and dextral shear on the northwest trend motion. The interpretation of stresses is constrained by the localised data in the study area.  
The northeast-southwest trending lineaments follow the hinge line of the anticline and the Trusan Fault, while the northwest-southeast trending lineaments follow the regional fault trends of the Tinjar Line and West Baram Line. The large cavern and passages of the Niah Great Cave Complex are believed to have developed on the northeast-southwest-trending anticline hinge line (Figure 1a) and orientated in the northwest and east-west directions.  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Subis Limestone** \\ \hline Age of host limestone & Late of Oligocene to Early Miocene \\ Age of karst development & Pleistocene to recent \\ Influence by sea-level fluctuations & Yes (third order sequence) \\ Influence by tectonic (uplifting) & Yes \\ Host lithology & Coralline limestone \\ Morphology and geometry & Flat-top and layer cake architecture \\ Dominant foraminifera & Massive/branching corals, benthic foraminifera, \\  & and algae \\ Depositional environment & Quiet, clean-water, shallow inner-sea setting \\  & under normal salinity \\ \hline \end{tabular}
\end{table}
Table 1: Properties of the Subis Limestone build-ups as an analogue for offshore Central Luconia carbonates [24,27,29].  
Figure 6: Lineament analysis and rose diagrams from a dataset of (**a**) SRTM-DEM and (**b**) ALOS-DSM with a resolution of 30 m. Both lineaments showed similar main fracture sets that were oriented in northeast-southwest and northwest–southeast directions, with north–south-trending compressional stress and extensional stress in the east–west direction. The previously interpreted northeast–southwest dextral Trusan Fault and interpreted faults are shown in black lines [(33; 65)]. The interpretation is strictly based on the localised data in the study area.
## 2 Related Works
Implementing this mechanism negates the need for a minimum \((\lambda/2)\) distance between network terminals, therefore enhancing the efficiency of V2I applications. However, a key challenge in the performance of key extraction remains the dependence of its performance on the SNR value of the received signal. In certain scenarios, such as shadowing or long-distance communication, the SNR can be very low, presenting a significant obstacle in the effective extraction of keys. This highlights the need for further research to address this issue and develop practical solutions to enhance key-extraction performance under such conditions. This paper aims to assess the efficacy of using the RIS in improving key-extraction performance while operating under the effect of DoS attacks in which a large volume of traffic or data is sent to a targeted system, overwhelming its resources and causing it to become unavailable to legitimate users [25]. It is worth mentioning that this type of attack is significant for high-complexity cryptographic-based key-agreement techniques.
# Remote Sensing Applications in Agricultural, Earth and Environmental Sciences
###### Abstract
Urbanisation studies have also significantly benefited from remote sensing technologies, which aid in spatial planning and sustainable development [21, 22]. Earth observation data have been utilised to assess and predict urban expansion patterns in Maseru, and the effectiveness of satellite imagery in guiding urban planning initiatives has been demonstrated. Additionally, soil properties and agricultural sustainability have been evaluated through the spatial modelling of agrochenozem properties, which support soil reclamation and sustainable land management. Statistical algorithms have further been applied to estimate multi-frequency, multi-polarisation backscattering coefficients over bare agricultural soils, which improve remote sensing applications in precision agriculture. Emerging technologies continue to enhance the security and efficiency of remote sensing applications. A fog computing framework has been developed to detect energy-based attacks on UAV-assisted smart farming systems, which bridges the gap between cybersecurity and agricultural remote sensing [23]. Moreover, researchers have distinguished between human-induced and climate-driven land degradation in the Greater Sekhukhune District Municipality, which provides critical insights for land restoration and conservation efforts. Hydrological and climatological studies increasingly rely on remote sensing for comprehensive environmental assessments [6]. A study employing multisource remotely sensed data and Google Earth Engine facilitated inter-seasonal estimations of grass water content indicators and offered valuable information for water resource management. An enhanced water quality index developed through remote sensing and machine learning has also introduced a novel approach to aquatic ecosystem monitoring. A comprehensive review of leaf area index estimation methods further highlights the significance of remote sensing in vegetation monitoring and sustainable land management. Collectively, these studies emphasise the expanding capabilities of remote sensing in addressing contemporary challenges in agricultural, earth and environmental sciences. From precision agriculture and climate change analysis to urban planning and ecological assessments, advancements in remote sensing technologies continue to enhance our understanding of the environment and support sustainable development efforts across multiple disciplines.
# Evaluating LLMs for Automated Scoring in Formative Assessments
## 3 Results
### Similarity Between LLM and Human Evaluator  
To assess the practical similarity between human and LLM-generated grades, equivalence testing was conducted using thresholds of \(-\)5 and 5 to establish acceptable ranges of difference. Since we could not reject the null hypothesis (H\({}_{0}\): There is no difference between LLM-generated grades and human evaluations), equivalence testing provides an additional perspective on whether the observed differences fall within an acceptable range of practical similarity.  
For the open-source LLM, the \(-\)5 equivalence threshold resulted in a mean difference between grades of 3.64 (standard deviation = 10.85), with a t-statistic of 4.83 (degrees of freedom = 206) and a two-sided \(p\)-value of \(<\)0.001. The 95% confidence interval ranged from 2.15 to 5.13, and the effect size, calculated as [PERSON]'s d, was 0.336. For the 5 equivalence threshold, the mean difference was -6.36 (standard deviation = 10.85), with a t-statistic of \(-\)8.43 (degrees of freedom = 206) and a two-sided \(p\)-value \(<\) 0.001. The 95% confidence interval ranged from \(-\)7.85 to \(-\)4.87, and [PERSON]'s d was \(-\)0.586.  
Figure 4: Analysis of scoring agreement between human and AI evaluators (premium and open source) across code assessment questions, shown in three panels: **(a)** Bland–Altman plot showing score differences between human vs. premium and human vs. open-source evaluators across mean scores; **(b)** Raincloud plot displaying score distributions for question ID 13 across human, premium, and open-source evaluators; **(c)** Raincloud plot showing score distributions for question ID 14 across the same evaluator groups.  
Regarding the premium LLM, equivalence testing showed that for the \(-5\) equivalence threshold, the mean difference between grades was 8.67 (standard deviation = 11.57), with a \(t\)-statistic of 10.78 (degrees of freedom = 206) and a two-sided \(p\)-value of \(<\)0.001. The 95% confidence interval ranged from 7.08 to 10.25. [PERSON]'s d was 0.749. For the 5 equivalence threshold, the mean difference was \(-1.33\) (standard deviation = 11.57), with a t-statistic of \(-1.66\) (degrees of freedom = 206) and a two-sided \(p\)-value of 0.099. The 95% confidence interval ranged from \(-2.92\) to 0.25, and [PERSON]'s d was \(-0.115\).  
Following the equivalence testing, scoring trends across short-answer and code-based questions were analyzed, with the variability in scores for each evaluator summarized in Table 2, which presents the standard deviation and variance (Var). Across both question types, the premium LLM consistently exhibited the lowest variability in scores, followed by the open-source LLM with moderate variability. Human evaluators demonstrated the highest variability for both short-answer and code-based questions.
## 1 Introduction
Using the previous relations, the Fourier Transforms of the electromagnetic field components along (\(r\), \(l\), \(\beta\)) can be expressed as functions of their equivalent \"voltages\" and \"currents\" functions with the auxiliary relations  
\[\overline{H_{r}}=\frac{i\underline{I_{M}}}{r},~{}\overline{E_{r}}=\frac{I_{E} }{n^{2}r}\] \[\overline{H_{\varphi}}=jIV_{M}~{}/r-\frac{\beta}{F}I_{E}\] \[\overline{E_{\varphi}}=IV_{E}/r+j\overline{F}I_{M}\] \[\overline{H_{z}}=\frac{l}{F_{r}}I_{E}+j\beta V_{M}\] \[\overline{z}=-j\frac{l}{F_{r}}I_{M}+\beta V_{E}\]  
It becomes evident by inspection that the final Equation (8) represents two coupled electric transmission lines.
## 4 Experimental Evaluation
### Vehicle Configuration  
Two Kia Soul cars, one in white and the other in yellow as shown in Figure 6a, were used for the experiments. The experiment setup consisted of a combination of hardware and software components that enabled the host vehicle to gather information on the vehicle surroundings by means of on-board remote sensors and V2X communications. As shown in Figure 6b, the host vehicle (Kia Soul in white) was equipped with a Delphi ESR 2.5 radar  
Figure 5: Illustration of collision prediction based on the circle model [23].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Stage** & **Warning Type** & **Color** \\ \hline No collision detected & No threat (Level 0) & Visual & Gray \\ \(\text{TTC}>2.6\) s & Threat detected (Level 1) & Visual & Green \\ \(1.6\) s \(<\text{TTC}\leq 2.6\) s & Inform driver (Level 2) & Visual and audible & Yellow \\ \(\text{TTC}\leq 1.6\) s & Warn driver (Level 3) & Visual and audible & Red \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vehicle collision warning conditions [23].  
and a Mobileye 630 camera for detection of surrounding objects, and each test vehicle was equipped with a Cohda MK5 OBU, a DSRC antenna, a u-blox F9P GNSS receiver, and a GNSS antenna, all of which together enabled the vehicles to exchange BSM via V2X communications and share vehicle position information with a lane-level accuracy. The u-blox F9P device is a low-cost GNSS module with built-in support for standard RTCM corrections.  
For real-time data acquisition and processing, the host vehicle was equipped with a dSPACE SCALEXIO AutoBox, which is an in-vehicle prototyping system with a real-time processor and interfaces for CAN and Ethernet communications. The signals transmitted from the radar and camera systems as well as the in-vehicle ECUs were acquired via CAN interfaces, and the signals transmitted from the DSRC OBU were acquired via Ethernet interfaces. RTCM corrections were acquired with an NTRIP client running on a smartphone and streamed via a Bluetooth connection to the u-blox C099-F9P application board. In order to obtain ground truth positions for the test vehicles, both vehicles were equipped with an Aseen AKT980R, which is a highly accurate GNSS receiver that provides GNSS carrier phase measurements with a horizontal positioning accuracy of 0.008 m and a heading accuracy of 0.09 deg with a 2-m baseline. The overview of the hardware interface for the proposed system is presented in Figure 7. The driving experiments were conducted at the proving ground available at Korea Automotive Technology Institute which is shown in Figure 8.  
Figure 6: Test vehicles used for the driving experiments. (**a**) The host vehicle (white) and the remote vehicle (yellow); (**b**) experimental equipment installed on the test vehicle.
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
###### Abstract  
Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global \"double carbon\" objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.  
vehicle path optimization; genetic algorithm; large-scale neighborhood search algorithm; low-carbon logistics Article
## 4 Comparison of Load Spectrum  
### Classification of Load Spectrum  
The data sets of 25 extrusion cycles collected by 1# measuring point in the extrusion process of 5 MN metal extruder are obtained by rain flow counting method. However, it is difficult to analyze and process the continuous distribution of load amplitude and mean data. Therefore, the loads that are not much different are divided into a group and the unified value in the group is used to replace each load value to achieve classification. The continuous load cycle frequency accumulation curve is transformed into a stepped frequency accumulation curve, which can generally be divided into four levels, eight levels, sixteen levels, and thirty-two levels. German engineer [PERSON] proposed to divide the load spectrum amplitude into eight levels, and [PERSON] also found that dividing the load spectrum into eight levels can accurately reflect the fatigue effect, which has now been applied to engineering practice [22].  
In this paper, the load spectrum is divided into eight levels, which can be divided according to the equal interval method and amplitude ratio coefficient method. Considering that the damage caused by large amplitude load is much greater than that caused by small amplitude load. Therefore, the division density of large amplitude value should be greater than that of small amplitude value. The amplitude ratio coefficient is set as 1, 0.95, 0.85, 0.725, 0.575, 0.425, 0.275 and 0.125, and the mean ratio coefficient is set as 1, 0.875, 0.75, 0.625, 0.5, 0.375, 0.25 and 0.125. The eight-level load spectrum is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** \\ \hline RNN & 4.807 & 2.193 & 1.144 \\ LSTM & 0.405 & 0.636 & 0.502 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of prediction performance between LSTM and RNN.  
Figure 8: Comparison of forecast results and original data.
## 3 Model Development
### Decision Variables and Model Linearization
By introducing binary integer variables \(\beta_{u,t,h}^{\text{OP,RPR}}(h_{1}=1,\ldots,H_{1})\) and \(\beta_{u,t,h_{2}}^{\text{OP,DR}}(h_{2}=1,\ldots,H_{2})\), piecewise linear constraints can be established as follows:  
\[\sum_{h_{1}=1}^{H_{1}}\beta_{u,t,h_{1}}^{\text{OP,RPRPR}}=1, \tag{19}\]  
\[f_{u,t}^{\text{OP,RPRPR}^{\prime}}\geq\left(\beta_{u,t,h_{1}}^{\text{OP,RPR}}- 1\right)M+f_{u}^{\text{OP,RPR}}\left[P_{u,t}^{\text{TPU}}(h_{1})\right]+ \tag{20}\]  
\[P_{u,t}^{\text{TPU}}(h_{1})-M\Big{(}1-\beta_{u,t,h_{1}}^{\text{OP,RPR}}\Big{)} \leq P_{u,t}^{\text{TPU}}\leq P_{u,t}^{\text{TPU}}(h_{1}+1)+M\Big{(}1-\beta_ {u,t,h_{1}}^{\text{OP,RPR}}\Big{)}, \tag{21}\]  
\[\sum_{h_{2}=1}^{H_{2}}\beta_{u,t,h_{2}}^{\text{OP,DPR}}=1, \tag{22}\]  
\[f_{u,t}^{\text{OP,DPRPR}^{\prime}}\geq\left(\beta_{u,t,h_{2}}^{\text{OP,DPR}}- 1\right)M+f_{u}^{\text{OP,DPR}}\left[P_{u,t}^{\text{TPU}}(h_{2})\right]+ \tag{23}\]  
\[P_{u,t}^{\text{TPU}}(h_{2})-M\Big{(}1-\beta_{u,t,h_{2}}^{\text{OP,DPR}}\Big{)} \leq P_{u,t}^{\text{TPU}}\leq P_{u,t}^{\text{TPU}}(h_{2}+1)+M\Big{(}1-\beta_ {u,t,h_{2}}^{\text{OP,DPR}}\Big{)}. \tag{24}\]  
Through the incorporation of binary integer variables, the model can effectively enforce piecewise linear constraints, facilitating a more precise representation of cost dynamics associated with varying levels of power output. The piecewise linearized functions \(f_{u,t}^{\text{OP,RPR}^{\prime}}\) and \(f_{u,t}^{\text{OP,DPR}^{\prime}}\) derived from the above process replace the corresponding terms \(f_{u}^{\text{OP,RPR}}\left(P_{u,t}^{\text{TPU}}\right)\) and \(f_{u}^{\text{OP,DPR}}\left(P_{u,t}^{\text{TPU}}\right)\) in Equation (5), the linearization of the cost function for TPU can be achieved. The operation state of TPU in Equation (14) can be linearized by the big-M method as follows:  
\[\left\{ \begin{array}{l}\beta_{u,t}^{\text{RPR}}+\beta_{u,t}^{\text{DPR}}=1 \\ P_{u}^{\text{TPU,min}}-M\big{(}1-\beta_{u,t}^{\text{RPR}}\big{)}\leq P_{u,t}^{ \text{TPU}}\leq P_{u}^{\text{TPU,max}}+M\big{(}1-\beta_{u,t}^{\text{RPR}} \big{)}\\ P_{u}^{\text{TPU,min}}-M\big{(}1-\beta_{u,t}^{\text{DPR}}\big{)}\leq P_{u,t}^{ \text{TPU}}\leq P_{u}^{\text{TPU,min}}+M\big{(}1-\beta_{u,t}^{\text{DPR}} \big{)}\end{array} \right.. \tag{25}\]  
The minimum operation time and downtime constraints of TPU can be linearized as follows:  
\[\left\{ \begin{array}{l}\sigma_{u,t}^{\text{TPU}}\geq\sigma_{u,t+1}^{\text{ TPU}}-\sigma_{u,t}^{\text{TPU}},\mathbf{t}^{*}\in\left[t,\min\Big{(}T,t+D_{u}^{ \text{TPU,on,min}}\Big{)}\right]\\ \sigma_{u,t}^{\text{TPU}}\leq 1-\sigma_{u,t}^{\text{TPU}}+\sigma_{u,t+1}^{\text{ TPU}},\mathbf{t}^{*}\in\left[t,\min\Big{(}T,t+D_{u}^{\text{TPU,off,min}}\Big{)}\right] \end{array} \right..,t=1,\ldots,T-1. \tag{26}\]