esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 3 System Model
### Data Fidelity Metric  
We use selection combining technology at the FC, i.e., we select the largest \(|h_{i}|\) among all sensor nodes denoted as \(|h_{\text{max}}|\), i.e., \(\text{max}\{|h_{i}|,1\leq i\leq N\}=|h_{\text{max}}|\). And that results in:  
\[Y=|h_{\text{max}}|X+Z_{i}. \tag{5}\]  
Accordingly, the distortion between the source \(X\) and the received signal \(Y\) is denoted as \(D\), which is expressed as  
\[D=\mathbb{E}[kY-X]^{2}=\mathbb{E}[(k|h_{\text{max}}|-1)X+kZ_{i}]^{2}, \tag{6}\]  
where \(k\) is the signal estimating coefficient.
## 4 Results
### Evaluation of the ALDA Model  
In order to examine the quality of topics in online query sessions identified by the ALDA model proposed in our paper, five typical methods for inferring user preference distributions are selected as baseline methods.  
* LDA is a generative probabilistic model in which each document is modeled as a finite mixture over an underlying set of topics and each topic is modeled as an infinite mixture over an underlying set of word distributions [21].
* Twitter-BTM aggregates user-based biterms to learn user specific topic distribution and incorporates a background topic to distinguish user's preference between background words and topical words [51].
* UCIT learns users' short-term and long-term preferences based on their followees' topic distributions, the content of current short texts, and the previously estimated distributions [52].
* HDLDA is a hierarchically dual latent Dirichlet allocation that assumes there is a semantic relation between search query documents and search result documents, and it quantitatively characterizes how consumers translate their content preferences into search queries [49].
* UATM infers topic intensities in user's preference by learning topic intensities in user's preference and topic intensities in followees' preference, which can efficiently alleviate the sparsity problem [53].  
We use the AOL query log dataset in this section. By comparing the parameter settings of the above models, we set the hyperparameters \(\alpha=50/K\), \(\beta=0.01\), \(\gamma=0.5\).
## 4 Data Analysis in Maritime IoT Applications
### Role of Data Analysis in Enhancing Maritime Operations
#### 4.2.1 Boost Operational Efficiency  
Data analysis is a powerful tool for optimizing maritime operations. It enables the discovery of patterns and trends from the vast amounts of data generated by IoT devices in maritime environments. For example, by analyzing data related to vessel speed, fuel consumption, weather conditions, and sea currents, optimal routes can be determined to save fuel and time.  
Moreover, data analysis can streamline various processes, such as cargo loading and unloading, by identifying bottlenecks and suggesting improvements. By correlating data from different sources, inefficiencies in operations can be pinpointed and resolved, thereby improving the overall operational efficiency of maritime organizations. [80].
# A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts +
## 4 The Proposed Solution Approach  
To the best of our knowledge, no standard optimization model seems to cover the target robotic batching problem of this paper in its entirety. While knapsack variations, such as the bin covering or subset sum problem, can be considered to address the target problem [27; 28], the subset sum model does not allow filling past the target, and the bin covering only seeks to fill as many trays as possible without considering the level of overfill, likely resulting in trays with high give-away.  
To address the complexity of the target scheduling problem, we propose a decomposition approach that sequentially solves a simplified scheduling problem for a single tray. Specifically, the original problem is decomposed into a series of sub-problems. Each sub-problem is to assign items to multiple robotic arms only for a specific tray, while making the tray reach a target weight. This sub-problem is formulated using a goal programming approach based on the nature of the objective of the scheduling, i.e., to minimize the deviation of a food tray from the target weight of the tray. A series of sub-problems is solved to find schedules for all trays in the system.  
In our decomposition approach for the original scheduling problem, a sub-problem for a tray (let us say, A) on a tray track will be solved after solving the sub-problems for the trays placed in the tray track ahead of tray A, based on the dynamics of a conveyor belt. Recall that a tray track will be advanced only when the tray at the end of the track exceeds or meets a target weight, and therefore, it is natural to sequentially solve the sub-problems according to the positions of the corresponding trays so that the dependency between the  
Figure 3: Simplified flow chart of the robotic batching system. _t_plan_ is the time between planning newly arrived groups of items (deterministic), _t_computing_ is the time to compute the plan (fixed threshold), _t_a_ is the arrival times of items (stochastic), _t_timestep_ is a single time step, conveyor moves all the time (deterministic), _t_movements_ is the time that the robotic arm needs to conduct a task (deterministic), and _t_adv_ is the time for the tray lane to advance (deterministic).  
sub-problems becomes uni-directional and tractable. Under this decomposition scheme, the outcome of a sub-problem solving--what items are scheduled to be picked up and when a tray track will be advanced--will be used to form the following sub-problems.  
Finally, once all the sub-problems are solved following the sequence (meaning that all trays are considered for placing the items entered into the robotic arm-based batching system), the same process will begin as a new set of food items enters the system, while keeping the solutions from the previous sub-problem solving. The proposed decomposition-based solution approach is illustrated in Figure 4.
# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 2 Materials and Methods  
Neurodegenerative diseases affect motor neurons, reducing functional mobility. Therefore, people suffering from neurodegenerative disorders could present limitations in functional mobility, especially with the progress of the disease. Everyday movements such as standing, bending and walking can be compromised, contributing significantly to the subject's quality of life. In this work, we propose a vision-based system aiming to predict whether a person is suffering from neurodegenerative disorders by observing the execution of the StS test. Patients with neurodegenerative diseases and control subjects were asked to perform the five-time StS test (see Figure 1). Specifically, the participant started the test sitting on a chair with their arms crossed at the wrists and held against the chest. Then, the participant rose from the chair by levering on both legs, reached a standing position with complete distention of the spine (where possible), and then rest and repeated the exercise. The participant was instructed to sit fully between each stand. The test ended when the participant rest for the fifth time. A video camera, placed in front of the subject, recorded the StS exercise. The camera was a low-cost RGB camera usually used in surveillance contexts. The videos were preprocessed to collect and extract information about people's postures during the exercise execution. A feature extraction phase was then applied to select the most significant features useful for training machine learning approaches. Meanwhile, machine learning approaches were applied to the acquired data and compared to gain insight into those performing better than others in this challenging context.  
Figure 1: Sit-to-stand test.
## 4 Experiment and Result Analysis
### Comparison of Other Backbone Networks  
In this experiment, YOLOv8n was used as the baseline network, replacing mainstream feature extraction backbones such as MobileNetV3, GhostNetV2, and ShuffleNet. To ensure a fair comparison, all settings except for the backbone network remained unchanged while evaluating the training performance on the blueberry dataset.  
As shown in Table 6, our backbone network incorporating the C2f-RFAConv improvement method outperformed ShuffleNetV1, ShuffleNetV2, and GhostNetV2, achieving an increase in [EMAIL_ADDRESS] of 4.3%, 2.4%, and 2.5%, respectively. While the proposed backbone achieved the same [EMAIL_ADDRESS] improvement as MobileNetV3, it reduced the number of parameters by 2.2 M, the computational complexity by 2.2 G, and the model size by 5 MB. These reductions make the model more efficient and better suited for real-world deployment.  
Figure 10: Comparison and analysis of the heatmaps for blueberries of different sizes.  
Figure 9: P-R curve comparison between the improved model and the baseline model.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
## 3 Methodology for the Elevation Angle Characterization
### Simulations Configuration  
Simulations were performed for a LEO satellite with characteristics mentioned in Table 1, and for circular orbit configurations listed in Table 2. The LEO region goes from a few hundred kilometers above the Earth's surface up to 2000 km; but the performed simulations and methodology cover just the upper part of this region since orbit perturbations effects are much lower at those altitudes than at orbital heights closer to the surface, and facilitates operation for longer periods of time without a complex propulsion system.  
Three initial values of orbital parameters not included in Table 2 are right ascension of the ascending node, \(\Omega\), argument of perigee, \(\omega\), and true anomaly, \(\
u\), which were all set to zero. From those simulations, ephemeris files with the satellite position and velocity in five-second steps were generated. Then, ES's were placed (simulated) at 18 different and arbitrarily chosen locations listed in Table 3. Finally, time series of the observed elevation angle from the ES to the satellite were calculated for each ground location using the previous generated ephemeris files and geometrical relations as in (1).  
The orbital mechanical equations required to calculate the position and velocity of the satellite as a function of time, were solved using an open source program developed and maintained by the NASA, GMAT [25]. The time series for the elevation angle discussed in this paper can be generated in GMAT using the data of Tables 1-3; also, these can be generated using other software, for example STK as in [2]. The time series mentioned in this paper were generated by the authors specially for this work, and are available at [26].  
\begin{table}
\begin{tabular}{c c c} \hline
**Dry Mass** & **Drag Area** & **Solar Radiation Pressure Area** \\ \hline
5 kg & 1 m\({}^{2}\) & 1 m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Satellite characteristics for the ephemeris generation.
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 3 Methodology
### Cross-LSTM Multi-Feature Distillation  
After identifying the significant factors influencing fuel consumption with XGBoost, the subsequent step involves forecasting fuel consumption using the Cross-LSTM Multi-Feature Distillation method. In this approach, Crossformer acts as the teacher model, while LSTM operates as the student model. These are linked through a multi-layer feature distillation mechanism. This design not only improves the precision of LSTM predictions but also minimizes parameter usage and enhances runtime efficiency. The detailed structure is depicted in Figure 1.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 3 APTs Faced by IIoT and Its Defense Methods
### Overall Architecture of IIoT  
A layered approach to researching IIoT can help deepen our understanding of the different levels and components of IIoT, refine the APT attack issues faced, improve systemic thinking, support innovation and application, and promote better protection of IIoT. However, the APT attacks faced by the entire IIoT system are very complex, and this article summarizes some means of defending against APT attacks.  
* Strengthen the security of IIoT equipment [62; 63], enhance access control and the authentication of equipment, use encryption technology to protect data transmission, regularly update firmware and software to fix known vulnerabilities, and restrict IoT equipment access to the network;
* Strengthen network security, use security equipment such as firewalls [64] and intrusion detection systems [65] to monitor network traffic, and timely identity and respond to abnormal activity. In addition, technologies such as network isolation [66] and segmentation can be used to reduce the attack surface and restrict the attack range;
* Implement security auditing and monitoring [67; 68; 69], detect and identify any security events through security auditing and monitoring, and respond to them promptly. Meanwhile, authenticate and control access for all users and entities accessing IoT equipment and networks to ensure that only authorized users can access the system;
* Enhance employee security awareness [70; 71], strengthen employee security awareness education, and make employees aware of basic security practices such as strong passwords and anti-phishing training to reduce the occurrence of internal security threats;
* Implement emergency response plans [72; 73], and develop emergency response plans to quickly identify and respond to security events. This plan should include steps to deal with vulnerabilities and malicious software, as well as how to protect critical data and information in the system.  
However, these defense methods also have shortcomings. For equipment defense [62; 63], it consumes a lot of resources and time, and may affect the performance and efficiency of the equipment, and cause conflicts or compatibility problems with other security software or systems, resulting in increased security risks for users. Network defense [64; 65; 66] may be identified and bypassed by malicious code, resulting in detection failure or false positives, thereby leaking user privacy data. Implementing security audit and monitoring [67; 68; 69] may block some legitimate software or programs. And employees [70; 71] may be deceived and interfered with by attackers, resulting in poor or worse defense results. Implementing emergency response plans [72; 73] needs to match and adapt to the security system and strategy of the enterprise, which may lead to difficulties and poor effects in information utilization. Therefore, comprehensively adopting the above means can help IIoT systems resist APT attacks.
## 3 Results
### Seasonal Sky Status  
This section provides an analysis of the sky conditions over Greece on a seasonal basis. For clarity, spring includes March, April, and May; summer consists of June, July, and August; autumn has the months of September, October, and November; and winter contains December, January, and February. Figure 4 give the frequency of occurrence (FOO, in %) for the duration of all three types of sky conditions (clear, intermediate, overcast, as defined in Section 2.2). Figure 4a represents the time occupied by DCS, DIS, or DOS conditions in a TMY. The sum of all percentages shown by the bars in the figure gives 100% (corresponding to 4108 h annually). The DCS and DIS conditions show a maximum in the summer season, while the opposite occurs for the DOS cases. The DIS conditions are favoured in spring (a transition season from winter to summer), while the DCSs prevail in summertime, as expected. Figure 4b depicts the FOOs for the DCS, DIS, and DOS conditions within the TMY. The sum of all FOOs in the same season is 100% (4108 h annually). The occurrence of DCSs is highest in the summer, while the DISs and DOSs have equal percentage occurrence in spring (transition season). The pattern of Figure 4a is repeated in Figure 4b, i.e., higher frequencies for the DCS and DIS skies in the summer and lower for the DOS ones; the DOSs are highest in winter, as expected. Figure 4c gives the frequencies for the DCS, DIS, and DOS events within the same season. It is seen that spring is dominated by DIS skies (transition period), summer by DCS cases (expected), autumn by DIS events (transition period), and winter by DOS skies (expected). The sum of the percentages within any season gives 100% (i.e., 4108 h annually = 1125 h for spring + 1221 h for summer + 939 h for autumn + 823 h for winter).  
Figure 5 show the seasonal mean DCS, DIS, and DOS values together with the \(\pm 1\sigma\) band. Best-fitted curves are also shown by the green dashed curves. Though the regression lines come out of the \(\pm 1\sigma\) band (\(\sigma\) = standard deviation) between the spring and summer mean values, they go exactly through the seasonal values; this gives them an R2 = 1. The expressions of the regression lines are given in Table 2. As also shown by Figure 5, the higher seasonal durations of the sky conditions over Greece during the summer are the DCS and DIS cases; the opposite exists for the DOS occurrences.
## 2 Models and Data
### Data  
The model input variables used NASA's Modern ERA-Retrospective Analysis for Research and Applications of Global Modeling and Assimilation (MERRA) and MODIS products. MERRA provides surface meteorological variables, such as radiation, air temperature, and atmospheric conditions, from 1979 to the present, with a spatial resolution of 0.5\({}^{\circ}\) (latitude) \(\times\) 0.6\({}^{\circ}\) (longitude) [40]. In our study, the MERRA monthly product was used, including the photosynthetically active radiation (PAR), cloud fraction (CLD), net radiation (Rn), air temperature (Ta), specific humidity (Sh), air pressure (Ps), and wind speed (Ws).  
Several global MODIS products were used, including 8 day 1 km Leaf Area Index/Fraction of Photosynthetically Active Radiation (LAI/FPAR, MOD15A2) and 500 m surface reflectance (MOD09A1, used to calculate the Land Surface Water Index, LSWI), 16 day 1 km Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI, MOD13A2), and yearly 500 m land cover (MCD12Q1). Quality control flags in these products were examined to screen and reject poor quality data. The missing and unreliable values in the sub-monthly products were filled based on their accompanying quality assessment data fields according to the method proposed by [PERSON] _et al._, [41]. The 8 and 16 day MODIS products were composited into monthly data using maximum value composite (MVC) method [42].  
Several models (_i.e._, CASA, CFix, CFlux, and EC-LUE) use ecosystem evapotranspiration (ET) to indicate water stress of vegetation production (please see model introduction in the SOM). This study used a satellite-based ET model (_i.e._, the revised RS-PM) to estimate global ET [39]. This model can simulate canopy transpiration and soil evaporation, respectively, and uses the Beer-Lambert law to exponentially partition net radiation between the canopy and the soil surface. This model has been calibrated and validated using measurements at 54 eddy covariance towers globally, and the model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively [39]. In addition, we used a global Palmer Drought Severity Index (PDSI) data to analyze the effect of drought on vegetation production [3]. A lower PDSI generally implies a drier condition. All MERRA and MODIS datasets used in this study were resampled to 10 km \(\times\) 10 km for driving models with one month temporal resolution [41, 42, 43].
## 4 Results and Discussion
### Optimization Parameters and Computational Performance  
The optimization method used Gurobi Optimiser version 11 for Windows 11 on a computer with an Intel Core i7 processor running at 2.40 GHz and having Streaming SIMD Extensions (SSE) 22. The computer had two physical cores and four logical processors that could work efficiently with four threads within ten minutes for the optimization problem-solving process. The discussed model included 164 constraints (rows), 95 decision variables (columns), and 362 zero coefficients in the constraint matrix. This model of size serves as an example of optimization frameworks that involve multiple stages and various situations that lead to increased computational complexity over time. Gurobi's pre-solve phase successfully reduced the problem size throughout the optimization process by eliminating 154 constraints and 75 variables from the model. The pre-solve phase resulted in 10 constraints and 20 variables with 88 nonzero coefficients. The significant drop in numbers showcases how Gurobi's innovative problem-solving methods boost efficiency by spotting and removing redundancies and honing in on boundaries without compromising the model's accuracy. The optimization process went through six rounds of refinement. The system solved the problem in 0.12 s, requiring minimal work units. The solver precisely identified the best objective value of USD 1,772,500, demonstrating both the model's practicality and the solver's ability to tackle optimization challenges within set computational constraints.  
Table 9 delineates the principal optimization parameters and computational performance metrics recorded during the execution of the multi-stage stochastic optimization model. The table emphasizes the solver configuration, computational environment, model size before and after the presolve phase, and the performance metrics achieved during optimization.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Optimizer & Gurobi Optimizer v11.0.3 (build v11.0.3 rc0) \\ \hline Operating System & Windows 11.0 (22000.2) \\ \hline CPU Model & Intel® CoreTM i7-5500U CPU @ 2.40 GHz \\ \hline Instruction Sets & SSE2, AVX, AVX2 \\ \hline Physical Cores & 2 \\ \hline Logical Processors & 4 \\ \hline Solver Parameters & TimeLimit: 600 s \\ \hline \multicolumn{3}{c}{Threads: 4} \\ \hline Model Size (Pre-Presolve) & Rows: 164 \\ \hline \multicolumn{3}{c}{Columns: 95} \\ \hline \hline \end{tabular}
\end{table}
Table 9: Key Optimization Parameters and Computational Performance.