esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ALEXI/DisALEXI Model Inputs  
Input shared between the ALEXI and DisALEXI modeling frameworks include meteorological forcing information, including wind speed, air temperature, solar radiation, air pressure, and vapor pressure, all of which are obtained from the Climate Forecast System Reanalysis (CFSR) dataset at 0.25\({}^{\circ}\) resolution and hourly to 3 h time steps [69]. ALEXI uses landcover classification from the University of Maryland (UMD) global land cover dataset at 1 km resolution, based on observations from AVHHR [70]. Landcover classification for higher resolution Landsat is determined using the 30m National Land Cover Dataset (NLCD) [71].  
Remote sensing inputs specific to ALEXI include LST computed from 11 \(\upmu\)m brightness temperature observations from GOES-East and GOES-West Imager Instruments. Brightness temperatures are atmospherically corrected using atmospheric profiles of temperature following the procedure in [72]. LAI, used to partition LST between canopy and soil compo nents (Equation (1)), is obtained from the MODIS LAI product (MCD15A3H), aggregated to GOES resolution (4 km), and interpolated to daily timesteps.  
DisALEXI-specific inputs include Landsat 8 Collection 2 TIR and surface reflectance (SR) band imagery as well as Harmonized Landsat and Sentinel-2 SR datasets (HLS) (Table 1). Data were acquired during the year 2021 for available cloud-free days (Table 1). Sites located within the overlap of two Landsat scenes have additional imagery available for analysis (SLM, RIP, OLA). High-resolution LST maps were generated using Landsat 8 Collection 2 Surface Temperature (ST) sharpened from a native resolution of 100 m to 30 m using a data mining sharpening (DMS) approach developed by [73]. LAI and NDVI at 30 m resolution are created using HLS band information following a procedure in [74] based on machine learning, which has been recently modified to include ground-based LAI measurements taken at the GRAPEX vineyard sites [75].
## 4 BILSTM-Transformer Based Bems Model
### Based on the BiLSTM-Transformer System Architecture  
In this study, we propose a system architecture based on BiLSTM-Transformer to optimize the forecasting of power change after charge-discharge scheduling of electric vehicles in parking lots of commercial buildings. In order to achieve fast and efficient forecasting, the system adopts a design that separates training and execution.  
The network-training stage is completed at the central management level of the system, which is mainly responsible for learning and modeling the total historical data of the entire electric vehicle charging and discharging scheduling. The BiLSTM-Transformer network is trained on the sum of the time series data of the charging and discharging history of electric vehicles and the historical data of the basic demand power of the shopping mall and has the generalization ability to map the input state to the optimal scheduling decision. After training, the BiLSTM-Transformer model can generate an optimized scheduling strategy and obtain the trained network parameters for the online scheduling stage.  
In the execution phase, the charging pile of each electric vehicle can transmit the charging and discharging power to the BEMS, and the system can predict the change in the load of the mall in the next time period according to the real-time status and grid load of each electric vehicle.  
According to the system structure, the training part of the BiLSTM-Transformer model can be completed offline, while the online execution part can be run together in the central system and each charging station. This design approach not only allows the central system to continuously optimize the model but also allows each charging station to respond in real time to the dynamic charging and discharging needs of the electric vehicle. Through the separation of offline training and online execution, the system can adapt to the complexity of large-scale electric vehicle charging and discharging scheduling and achieve millisecond-level scheduling response in actual operation.  
Different from the traditional methods, the system structure based on BiLSTM-Transformer can make full use of the global modeling ability of Transformer to improve the computational efficiency and response speed of the system in dealing with large-scale electric vehicle charging and discharging scheduling while capturing the long-term and short-term dependencies in the time series. As new data are generated, the central system can periodically retrain the BiLSTM-Transformer model to ensure that the system always makes optimal scheduling decisions based on the latest data.
## 2 Related Work
### Spacecraft Telemetry Data Anomaly Detection  
During the spacecraft's on-orbit operation, the sensor parameter information obtained by its internal operating status monitoring system is encoded and transmitted to the ground through the telemetry system. This telemetry data is the only basis for ground-based spacecraft operators to understand the spacecraft's on-orbit operating status. If the on-orbit spacecraft is abnormal, the corresponding telemetry parameters' trend will change. Hence, anomalies in telemetry data can reflect problems such as acquisition equipment failure, transmission link damage, quality problems, and mechanical and electronic failure.  
Anomaly detection of the telemetry data is the key to realize the early warning of abnormal changes and effectively avoid possible failures. Anomaly detection is to use a certain method to realize the discovery of abnormal components in telemetry data. The anomaly types of telemetry data include point anomalies, contextual anomalies, collective anomalies, and correlated anomalies [13]. Recently, data-driven anomaly detection technology for spacecraft telemetry data has become a hot research topic, it does not require prior knowledge of expert experience, and it can detect anomaly through data analysis. Data-driven anomaly detection uses statistics, traditional machine learning or deep learning to model and characterize telemetry data to identify anomalous patterns that do not conform to normal data.  
Figure 1: Framework of spacecraft telemetry anomaly detection based on parametric causality and double-criteria drift streaming peaks over threshold.  
With the increasing number of on-orbit spacecrafts, the dimension of telemetry data that needs to be monitored in real time is expanding. Moreover, due to the different characteristics of anomalies and complex interaction in telemetry parameters, using data-driven methods to detect anomalies in telemetry data in a timely and effective manner has become more and more challenging. The problems of time-consuming methods, false negatives, and false positives commonly exist. Hence, detection methods with a high detection rate, low false detection rate, and strong interpretability have become a pressing need.
## 3 Global Horizontal Irradiance Models
### Error Metrics for Model Validation  
Determining statistical accuracy is necessary to quantify whether a model underestimates or overestimates the GHI. This study validated the accuracy through MBE, RMSE, rRMSE, and rMBE determination. The formulas were defined, respectively, as follows:  
\[\text{MBE}=\
icefrac{{1}}{{n}}\sum_{i=1}^{n}(\text{GHI}_{\text{model}}-\text{ GHI}_{\text{measurement}}) \tag{49}\]  
\[\text{RMSE}=\
icefrac{{\sqrt{1/n}}}{{\sum_{i=1}^{n}(\text{GHI}_{\text{model}} -\text{GHI}_{\text{measurement}})}^{2}} \tag{50}\]  
\[\text{rRMSE}=\
icefrac{{\sqrt{1/n}}\sum_{i=1}^{n}(\text{GHI}_{\text{model}} -\text{GHI}_{\text{measurement}})^{2}}{\
icefrac{{1}}{{n}}\sum_{i=1}^{n}\text{ GHI}_{\text{measurement}}}\times 100 \tag{51}\]  
\[\text{rMBE}=\
icefrac{{\frac{1}{n}}\sum_{i=1}^{n}(\text{GHI}_{\text{model}} -\text{GHI}_{\text{measurement}})}{\
icefrac{{1}}{{n}}\sum_{i=1}^{n}\text{GHI}_{ \text{measurement}}}\times 100, \tag{52}\]  
where \(n\) denotes the total number of data points. The RMSE indicated how close the predicted values were to the observational data with help from a regression line. The RMSE always produces a positive value. On the other hand, the MBE demonstrates the average bias in a model. The MBE is a sum of predicted data minus the observations. Hence, a positive MBE value indicates an overprediction, whereas a negative one represents an underprediction.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 4 BCSO Application to UC  
The process of handling the constraints conditions must be carried out in the unit commitment. In this section, the constraint handling process and the proposed BCSO for UC problem will be demonstrated. The constraints handling includes the power balance limit, generation limit and minimum up- and down-time limit, etc.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 2 Background and Related Work
### Resource Sharing in MEC Servers  
When running the vBBU on a MEC system managed by the Linux RT-Kernel, vBBU's processes may share computing resources with either collocated user-space processes or Kernel threads. This situation could potentially result in collocated workloads causing processing interference for RT processes. Such interference may stem from the sharing of physical resources [42; 43] or Kernel space processing [44; 45], both of which could have a detrimental impact on the performance of RT processes [46].  
Sharing physical resources, such as CPU, I/O, and memory, among applications with various execution time requirements (e.g., mixed time-critical services--MCS), on general-purpose servers has been extensively studied in the academic literature [47]. However, only a few pieces of research have explored the processing interference resulting from Kernel/user space processing. For instance, a study by [PERSON] et al. in [45] analyzed the processing interference caused by different Kernel subsystems under a variety of workloads related to MCS.  
In the context of MCS in embedded systems, a common strategy to mitigate processing interference with collocated applications entails running delay-sensitive services as RT processes on a set of isolated CPUs [43; 48]. However, this approach may not align seamlessly with the shared computing resources and multi-tenancy characteristics of Mobile Edge Computing [49]. According to a recent study [50], CPU utilization by a vBBU during peak times does not go beyond 60%, suggesting the possibility of resource sharing. As such, the authors suggested a CPU allocation mechanism for vBBUs that share CPU resources with collocated workloads, intending to minimize CPU underutilization.  
Despite these findings, further research is needed to fully understand the performance implications of resource sharing in edge servers, particularly when running RT applications concurrently with other workloads. While existing studies have examined vBBUperformance across different OS and virtualization environments, our research provides a comprehensive investigation by delving into the effects of resource sharing on vBBU performance within the Linux RT-Kernel. This methodology enables us to evaluate not only the overall mobile system performance but also the individual procedures within the vBBU.
## 4 Results
### Land Cover and Displacements  
The progression of subsidence differs among the ALT measurement sites. Rates are higher at VD-3 (Figure 7, 5 mm in 100 DDT). This site differs with respect to land cover and soils (sandier and dryer than CALM, VD-1, and VD-2). Such deviations can be confirmed by comparison of year to year differences with the land cover data over the Sentinel-1 scene extent (Figure 8). Sandy soils and dry cryptogamic crust areas showed much larger differences between 2016 and 2017 than all other main land cover types. Lowest differences can be observed for barren (artificial surfaces) and wet to waterlogged tundra.  
the end of the unfrozen period in all years. This agrees with the average values obtained from the overlap area of COSMO-Skymed and Sentinel-1 (Figure 6) as this is the dominating land cover in the area. Temporal evolution differs for wetter soils (Figure 9), especially in case of organic layers on the top of the soils. The continuous increase of displacement only applies until the DDT reach about 400-800 \({}^{\circ}\)C (up to the long-term average of total DDT, Table 3). Displacement does not increase further for the remaining season in 2016. This results in a lower total subsidence at the end of season and lower overall rate if the non-linear behavior is neglected. The rate before the long-term average is reached, however, is similar to other, dryer land-cover types with 4.3 mm over 100 DDT (COSMO-Skymed). The continuously high rates at dryer sites as indicated by the observation over VD-3 (Figure 7) are also confirmed by the time-series records (Figure 9). Especially the early season data from COSMO-Skymed show that the high rate applies to the full season.  
The beginning of the thaw period is characterized by heave in all years (Figures 6 and 9). Negative displacements can be recorded only starting from DDT 95 (DOY 180 & 29 days of thaw) in 2013 and 183 (DOY 188 & 42 days of thaw) in 2016.  
The spatial patterns of maximum displacement agree also with the land-cover distribution over the area (examples for COSMO-Skymed in Figures 10 and 11). Increased subsidence in 2016 corresponds to patches of classes such as dry to moist shrub tundra, as characteristic for the CALM grid. Comparably large displacements occur over lacustrine floodplains (including drained lake basins which get inundated in spring) in all years. Wet to waterlogged areas show similarly low displacements in all years.
# A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts +
## 3 A Robotic Arm-Based Food Processing System  
### Problem Descriptions  
The target system of this study is inspired by a completely autonomous robotic arm-based food batching system, which consists of three main components: an infeed conveyor belt, tray lanes next to the infeed conveyor belt and robotic arms. The conveyor belt moves continuously at a fixed speed. It transports food items with different weights arriving from a preceding food production cell. On each side of the conveyor belt, there is a single moving lane and trays, in which food items picked from the conveyor belt are batched in a tray on the lane. The tray lane can both move and stop. When it advances, it moves incrementally and the tray at the end of the lane will be disposed of from the system, being handled by a following package cell. Usually, the advancement will automatically be triggered when the weight of the last tray exceeds a threshold. The third component is the robotic arms. Each one can reach a specific area of the conveyor belts and the tray lanes. Figure 2 illustrates the target robotic arm-based food batching system.  
Given the description of the target robotic arm-based system, the following decision variables can be identified:  
* Which robotic arm will pick up an item;
* When and where an item on a conveyor belt will be picked up;
* On which tray on which side of the conveyor belt an item will be placed.  
The identified decisions will be made to minimize the give-away of the food items. Give-away is the positive deviation from the target tray weight. Suppose that an order requires trays with target weigh 500 g and the contract will contain a fixed price per tray delivered. If a tray comes to weigh 510 g, then 10 g (or 2%) as a free product is given away.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 3 Applications Across Domains
### Cloud Masking in Satellite Imagery  
Cloud masking is critical for improving the quality of satellite-based Earth observation. Recent advancements include Auto-CM [22], which uses unsupervised deep learning to outperform existing cloud masking methods across diverse geographic regions. Additionally,deep learning models like U-Net and Mask R-CNN have been employed to detect and replace cloud-contaminated pixels, improving the accuracy of climate variable retrievals [31].
## 2 Materials and Methods
### Ex Vivo Experiment with PAI System
The received PA signals were filtered and amplified by two serially connected low-noise preamplifiers (ZFL-500 LN, Mini-Circuit, Brooklyn, NY, USA) and finally caught by a high-speed digitizer (NI PXI-5124, National Instruments, Austin, TX, USA). The raw data were directly processed using Labview (Labview 2014, National Instruments, Austin, TX, USA) to reconstruct the 2D PA image. The ROI of PA images and 3D structure according to the color intensity of the treated area was also reconstructed in ImageJ. The photoacoustic amplitudes of the thermally denatured areas were also measured five times (N = 5) by using ImageJ. Figure 5: Ex vivo HIFU experiment integrated with PAI system for artificial breast tumors treatment (**a**) System schematic diagram. HWP: Half-wave plate; FC: Fiber Coupler (**b**) Self-made PAI probe (**c**) Close-up of the blue-dash operating region.
## 4 Empirical Quasi-Static Model of Single cable
### Empirical Relationship between \(b_{\pi}\) and \([\mathbf{B}^{d}\ \rho]\)
#### 4.2.2 Relationship between \(h_{b}\) and \(\rho\)  
In Figure 7, the values of \(h_{b}\) are plotted with given discrete \(\rho\in(0,2)\) and fixed \(\beta\). It is easy to state that \(h_{b}\) and \(\rho\) are linearly decreasing in the considered open set. Furthermore, when \(\beta\) is variated, the relationship between \(h_{b}\) and \(\rho\) does not change, i.e., \(h_{b}\) does not depend on \(\beta\). Thus, the relationship between \(h_{b}\) and \(\rho\) can be expressed as \(h_{b}=k_{h}\cdot\rho\), where \(k_{h}\) is a constant.  
Based on our taken data and proposed procedure, we have \(k_{h}\approx-4.845\). This parameter can be approximately equal to negative half of gravitational acceleration. Following this assumption, the relationship between \(h_{b}\) and \(\rho\) can be expressed as (28).  
\[h_{b}=-\frac{\rho g}{2} \tag{28}\]  
The obtained parameter \(h_{b}\) based on our procedure coincides with the simplified cable model introduced in [20].
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control in UAV Development
#### 5.2.4 Robust Control and Hybrid Systems  
Research shifted towards robust control methods to handle uncertain environments and disturbances. Robust and adaptive control techniques were integrated with nonlinear control approaches to enhance the performance and safety of UAVs. The development of hybrid control systems that combine different control techniques gained attention [98].
## 3 Positional Inverted Self-Index
It is easy to see that the entropy coder encodes \(N^{\prime}\) separators using \(-\sum_{j=1}^{N^{\prime}}\lceil\log_{2}p_{j}^{\prime}\rceil\) bits where represents a probability of the \(j\)-th separator. We conclude that the number of bits needed to encode PISI presentation layer is bounded above by \(\mathcal{O}(-\sum_{i=1}^{N}\lceil\log_{2}p_{i}^{n(i)}\rceil-\sum_{j=1}^{N^{ \prime}}\lceil\log_{2}p_{j}^{\prime}\rceil+N)\). The PISI ensures the operation locate in worst-case time \(\mathcal{O}(\lfloor\frac{N}{P}\rfloor+occ\times\beta\times\alpha)\) where \(occ\) is the number of occurrences of a queried term, \(\alpha\) is the step/period of the back pointers in the inverted list and \(\beta\) is the step/period of the synchronization points synchronizing the inverted list with the presentation layer. Proof. : The simple structure of PISI inverted list ensures that it can be traversed in \(occ\) steps. To determine a position of a term and possibly decompress its neighbourhood it is necessary to synchronize every occurrence with the presentation layer. The synchronization points are stored as the offsets in \(FP\) array and \(\sum_{i=1}^{k}FP_{i}\) represents a position of the entry corresponding to the stem at position \(k\times\beta\) in the inverted list. Suppose the search performed from left to right and the achieved synchronization points are cached during the search. Then the time needed for summation of all necessary synchronization points is bounded by \(\mathcal{O}(\lfloor\frac{N}{P}\rfloor)\). One has to decompress \(\beta\) entries from the corresponding synchronization point to achieve a position of the wanted term in the worst case. Decompressing of one entry is composed of two steps. First, to determine the stem. Second, to decompress the separators preceding the stem and decompress the stem variant. Suppose the encoded separators, stem variants and the gaps can be decoded in the constant time. Then the first step can be performed in \(\alpha\times\mathcal{O}(1)\) in the worst case. The second step can be done in \(\mathcal{O}(1)\) time considering standard natural language text with a low constant number of separators between two following stems. Thus, locating one occurrence takes \(\mathcal{O}(\beta\times\alpha)\) time. We conclude that the time needed to locate all occurrences of one term is bounded above by \(\mathcal{O}(occ+\lfloor\frac{N}{P}\rfloor+occ\times\beta\times\alpha)= \mathcal{O}(\lfloor\frac{N}{P}\rfloor+occ\times\beta\times\alpha)\).
# Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks
## 3 SQ Evaluation Model of EVs
### GA-BP ANN Model
#### 3.2.2 Construction of the GA-BP ANN  
The SQ evaluation model of the EVs was constructed using a three-layer neural network of the input layer, output layer, and single hidden layer. The network input layer had seven nodes, which corresponded to the physical-psychoacoustical parameters. There was only one node in the network output layer, and it corresponded to the subjective evaluation scores of the SQ. Combined with the selection rule of the hidden layer, and through experiments, the number of the hidden layer nodes was selected as 11. The final network topology structure is 7-14-1, as shown in Figure 13. The hidden layer of the network adopts the log-sigmoid function, and the output layer adopts the pure line function.  
Before the formal training of the network, the GA algorithm was introduced to optimize the initial weights and thresholds of the BP-ANN. The population size was set to 50, the genetic algebra was 150,  
Figure 12: The flow diagram of genetic algorithm back propagation artificial neural network (GA-BP ANN).  
Figure 13: Network topology structure of genetic algorithm back propagation artificial neural network (GA-BP ANN).  
and the mutation probability was 0.05. In this paper, the weights and thresholds were coded using the real number encoding method. After obtaining the individual optimal solution, it was used as the initial weights and thresholds of the BP-ANN.  
The subjective and objective evaluation data of the 76 sound samples of the EV were used as training samples, and the remaining 15 sound samples were used as model test samples. The obtained error curve is shown in Figure 14. The training accuracy was revealed to approach 0.001 after 5000 training times, and the important properties of the evaluation model including the convergence speed and the prediction precision were demonstrated and analyzed in the prediction model.  
Figure 15 is a scatter diagram of the correlation between the output value of the GA-BP ANN and the actual score, where the correlation coefficient reached 0.928. The result shows that this model can well map objective parameters to the subjective scores of the SQ. Figure 16 shows the relative error between the predicted value of the GA-BP ANN model and the actual subjective score value. The prediction error of the GA-BP ANN model is within 8%, which fully demonstrates the high prediction accuracy of the GA-BP ANN model.  
Figure 14: The training error curve.  
Figure 15: Correlation between the prediction value of the model and the scores of the jury test.