esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation of Daily, Seasonal, and Annual NEE Estimations
It resulted in all 4 simulations falling within \(\pm 0.5\) normalized standard deviation from the reference point, and \(R>0.9\). Again, 3D-CMCC-PSM performed worst in EBF, with FRPue still \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **DEHai** & **ITCoI** & **FIHyy** & **ITRen** & **ITCpz** & **FRPue** & **Mean** \\ \hline \multicolumn{8}{c}{Daily NEE} \\ \hline R & 0.84 & 0.76 & 0.67 & 0.65 & 0.24 & 0.65 & 0.64 \\ NSE & 0.67 & 0.5 & 0.34 & 0.21 & -0.26 & 0.35 & 0.3 \\ RMSE & 1.84 & 2.7 & 1.48 & 2.32 & 1.8 & 1.39 & 1.92 \\ MAB & 0.39 & 0.5 & 0.53 & 0.56 & 1.15 & 0.76 & 0.65 \\ \hline \multicolumn{8}{c}{Monthly NEE} \\ \hline R & 0.93 & 0.92 & 0.96 & 0.97 & 0.42 & 0.59 & 0.8 \\ NSE & 0.81 & 0.76 & 0.9 & 0.87 & 0.12 & 0.2 & 0.61 \\ RMSE & 1.15 & 1.58 & 0.45 & 0.72 & 1.24 & 1 & 1.02 \\ MAB & 0.28 & 0.32 & 0.21 & 0.24 & 1.25 & 0.86 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Daily and Monthly Validation statistics calculated on the test-set. As stated in Table 3, \(R\) and NSE are dimensionless; RMSE and MAB are gC m\({}^{-2}\) day\({}^{-1}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(R\) & **NSE** & **RMSE** & **MAB** \\ \hline \multicolumn{4}{c}{DBF} \\ MAM & 0.43 & \(-\)0.36 & 2.77 & 0.72 \\ JJA & 0.36 & \(-\)0.02 & 2.96 & 1.17 \\ SON & 0.82 & 0.58 & 1.9 & 0.58 \\ DJF & 0.2 & \(-\)0.93 & 0.7 & 1.58 \\  & & EBF & & \\ MAM & 0.65 & 0.28 & 1.83 & 0.62 \\ JJA & 0.11 & \(-\)0.92 & 2.76 & 1.01 \\ SON & 0.51 & 0.03 & 1.56 & 0.74 \\ DJF & 0.38 & \(-\)1.59 & 0.51 & 0.82 \\  & & ENF & & \\ MAM & 0.18 & \(-\)0.41 & 1.95 & 1.2 \\ JJA & 0.32 & \(-\)0.34 & 1.58 & 1 \\ SON & 0.45 & \(-\)0.19 & 1.3 & 0.79 \\ DJF & 0.47 & \(-\)6.13 & 1.45 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Seasonal validation statistics calculated on the test-sets and aggregated by ecosystem type. As stated in Tables 3 and 4, R and NSE are dimensionless; RMSE and MAB are gC m\({}^{-2}\) day\({}^{-1}\). MAM stands for March, April, and May. JJA stands for June, July, and August. SON stands for September, October, and November. DJF stands for December, January, and February. DBF stands for Deciduous Broadleaf Forests, EBF for Evergreen Broadleaf Forests, ENF for Evergreen Needle leaf Forests. inside \(\pm 1\) normalized standard deviation region, and ITCpz falling outside the \(\pm 1.5\) normalized SD region. The consistently worse predictability in ITCpz and FRPue confirm a systematic weakness in 3D-CMCC to represent fluxes for these sites as already described in [37]. Model performance on annual scale showed a different pattern, mostly because of some sites' consistent biases in seasonal NEE, and the difference in NEE magnitude. Delay in spring phenology, and the consistent underestimation of summer NEE, resulted in significant underestimation and scarce predictability of ITCol annual NEE (\(R<0.2\)). ITCPz and FRPue resulted among the sites with higher annual predictability, partly because of the low seasonal variance in NEE, partly because winter and spring bias tends to compensate each other.
###### Abstract  
Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2-3 m in the north-eastern sector of the landslide) to 20-21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset.  
Digital Image Correlation; sub-pixel accuracy; landslide monitoring; Montescaglioso, COSI-Corr; SAR amplitude imagery 17 July 2018; Accepted: 4 September 2018; Published: 8 September 2018
## 6 Discussion
### Feasibility and Applicability of DRL-AST in High-Dimensional NWP Systems
For high-resolution applications, such as extreme precipitation nowcasting, more localized observational datasets, including in-situ station data, radar reflectivity, and radar-derived precipitation estimates, are commonly used to approximate the true state at fine spatial and temporal scales [74; 75]. These data sources serve as high-quality reference signals for constructing reward functions in DRL settings, enabling training under realistic observability constraints. By leveraging such datasets, DRL-AST can be trained in a way that respects the observational constraints of real-world NWP systems while maintaining physical realism, thereby improving its transferability from idealized testbeds (e.g., Lorenz-96) to more complex models such as SPEEDY and WRFDA. 5. **Scalability of DRL-AST in the 4D-Var framework:** following the validation of DRL-AST within the WRF model, its scalability to the 4D-Var framework warrants further investigation. In 4D-Var, while **B** is implicitly adjusted through the assimilation window, **B** remains static at the initial time of each assimilation window. Thus, the adaptive variance scaling strategy learned in 3D-Var could be applied to optimize the specification of **B** at the beginning of each assimilation window in 4D-Var. Moreover, future studies may involve fine-tuning the RL policy within the 4D-Var context, leveraging the properties of error propagation to achieve optimal assimilation performance across extended assimilation windows. However, to fully adapt to the unique characteristics of 4D-Var, the further fine-tuning of the RL policy may be required to enhance assimilation performance. In summary, extending DRL-AST from low-dimensional systems to high-dimensional NWP models is feasible, yet this necessitates targeted adaptations. Overcoming these challenges will pave the way for DRL-AST to enhance intelligent DA techniques, expand its operational applicability, and ultimately improve the accuracy and reliability of numerical weather predictions.
# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 4 Experiments  
We present the experimental results in this section, where we test our algorithm on the large-scale benchmarks of LaSOT [19] and GOT-10k [20], representative of the performance evaluations on long-term and short-term videos, respectively. Quantitative evaluations are conducted where the proposed tracker is compared with other recently proposed trackers, and we conduct ablation experiments with different settings for feature bottleneck hyperparameters, with an attribute-wise ablation analysis of the LaSOT test set. Additionally, we provide qualitative results and a comparison to further visualize the effectiveness of our proposed method.
## 2 Problem Description
### Multi-Path Path Selection  
The traffic conditions of urban road networks have significant differences in time and space. Therefore, there are multiple paths between different nodes \((i,j)\) to choose within an actual urban distribution network. Depending on the road conditions, delivery vehicles, in this way, can flexibly select the travel route to avoid urban congestion. In this paper, an optional path (OP) is defined as \(G(N,H)\), a multi-path network, where node N represents a collection of customer and depot locations, and H represents a collection of road sections in a route formulation. Figure 1 shows an n-nodes graph with multiple edges to represent the path conditions under multiple paths.  
Node 1 is the depot location, and the remaining are customer nodes; \(w_{i,j}\) is the total number of edges between nodes \((i,j)\). For example, \(w_{3,5}=2\) indicates that there are two edges between nodes (3,5), that is, two existing road connections between them. As seen in Figure 1, multiple optional paths exist in any two nodes. According to the real-time data of the traffic information management system, logistics companies can obtain real-time traffic information and then use it to select the appropriate travel route. There may be multiple paths between any two points in Figure 1 because of many edges existing between them (each edge represents an optional travel path), one of which has time, distance, cost and fuel consumption properties. In this paper, our goal is to find the optimal travel route under different conditions based on the capacity and time constraints and then to meet the distribution needs of different logistics companies.  
Figure 1: Schematic diagram of multi-path network.
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 5 Conclusions  
To address the insufficient interaction between local and global features, as well as the inadequate extraction of fine-grained features in existing Camera-LiDAR mid-fusion methods, this paper proposes MPVF, a multimodal 3D object detection algorithm based on pointwise and voxelwise fusion. The algorithm effectively enhances the interaction between local and global multimodal features by designing new fusion and feature extraction modules, thereby improving the performance of the detection model in complex scenarios. Specifically, the proposed PVWF fusion module leverages the advantages of local pointwise features and global voxelwise features, enhancing the model's performance in detecting small objects and operating in intricate environments. Additionally, the design of the IRFP module significantly improves feature extraction efficiency through a grouped convolution strategy in the IR module, while the FP module captures fine-grained features across multiple scales, further enhancing the model's detection accuracy and robustness. Experiments on the large-scale KITTI dataset demonstrate that MPVF exhibits excellent performance across various object categories, particularly achieving significant improvements in the detection tasks for pedestrians and cyclists. Overall, MPVF illustrates the immense potential of multimodal fusion in the field of 3D object detection, providing an effective solution to enhance detection accuracy and system robustness. Future work will focus on further optimizing fusion strategies and exploring applications in more complex and diverse scenarios, advancing the practical use of 3D object detection technologies in autonomous driving and other cutting-edge fields.  
Conceptualization, W.W. and P.S.; methodology, W.W. and P.S.; software, W.W.; formal analysis, W.W.; writing--original draft preparation, W.W.; visualization, W.W.; validation, W.W., P.S. and A.Y.; investigation, P.S. and A.Y.; resources, P.S.; data curation, W.W. and A.Y.; project administration, A.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Central Guided Local Science and Technology Special Project-Yangtze River Delta Science and Technology Innovation Community Joint Tackling Program Project (2023 CSJGG1600); Natural Science Foundation of Anhui Province (2208085 MF173); Wuhu City \"Red Casting Light\" Major Science and Technology Project (2023 zd01, 2023 zd03).  
Data Availability Statement: The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.
## 3 Results
### EDOF CL Use Improved VA  
In terms of VA with the CL, a statistically significant improvement (\(p<0.001\)) was observed over time, both monocularly (right eye) and binocularly (Table 2 and Figure 1). After the CL testing visit, an improvement trend was observed in the monocular and binocular far and near VA, but without reaching statistical significance (\(p\geq 0.168\)). However, significant improvements were detected in all VAs measured at 15 and 30 days of CL wear compared to the baseline value (\(p\leq 0.042\)) (Table 2). There was a significant correlation (\(p<0.001\)) between monocular far VA with CL and monocular near VA with CL on the day of CL fitting (r = 0.644), after 1 day of CL wear (r = 0.790) and after 15 days of CL use (r = 0.730).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\
\begin{tabular}{c} **Mean \(\pm\) SD** \\ **Median (Range)** \\ \end{tabular} } & \multicolumn{1}{c}{**CL Testing Visit**} & \multicolumn{1}{c}{**1 Day**} & \multicolumn{1}{c}{**15 Days**} & \multicolumn{1}{c}{**30 Days**} & \multicolumn{1}{c}{
\begin{tabular}{c} \(p\)**-Value** \\ **(CL Testing- \\ **30 Days)** \\ \end{tabular} } \\ \hline Monocular far VA & 0.03 \(\pm\) 0.05 & 0.01 \(\pm\) 0.06 & \(-\)0.03 \(\pm\) 0.05 & \(-\)0.07 \(\pm\) 0.05 & \\ with CL & 0.00 (\(-\)0.10 to 0.15) & 0.00 (\(-\)0.10 to 0.10) & 0.00 (\(-\)0.10 to 0.05) & 0.05 & \\ \hline Binocular far VA & 0.00 \(\pm\) 0.04 & \(-\)0.03 \(\pm\) 0.07 & \(-\)0.05 \(\pm\) 0.05 & \(-\)0.08 \(\pm\) 0.04 & \\ with CL & 0.00 (\(-\)0.10. 0.10) & 0.00 (\(-\)0.10. 0.05) & \(-\)0.10 (\(-\)0.10. 0.00) & \(-\)0.10 (\(-\)0.10. 0.00) & \\ \hline Monocular near & 0.06 \(\pm\) 0.06 & 0.03 \(\pm\) 0.05 & \(-\)0.03 \(\pm\) 0.06 & \(-\)0.05 \(\pm\) 0.06 & \\ VA with CL & 0.10 (\(-\)0.10. 0.15) & 0.05 (\(-\)0.10. 0.10) & 0.00 (\(-\)0.10. 0.05) & \(-\)0.10 (\(-\)0.10. 0.10) & \\ \hline Binocular near VA & 0.02 \(\pm\) 0.06 & \(-\)0.01 \(\pm\) 0.06 & \(-\)0.05 \(\pm\) 0.05 & \(-\)0.07 \(\pm\) 0.05 & \\ with CL & 0.00 (\(-\)0.10. 0.10) & 0.00 (\(-\)0.10. 0.10) & 0.00 (\(-\)0.10. 0.00) & \(-\)0.10 (\(-\)0.10. 0.00) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Visual acuity data measured at the different visits of the current clinical study. Abbreviations: SD, standard deviation; CL, contact lens; VA, visual acuity.  
Mean monocular and binocular best spectacle-corrected pre-fitting visual cavities were \(-\)0.02 \(\pm\) 0.04 and \(-\)0.04 \(\pm\) 0.06 logMAR, respectively, when measured at a distance. For near, mean monocular and binocular best spectacle-corrected pre-fitting visual cavities were \(-\)0.02 \(\pm\) 0.05 and \(-\)0.06 \(\pm\) 0.05 logMAR, respectively. There was a statistically significant reduction in the visual acuity when comparing these spectacle-corrected values with those obtained with the CL in the first testing (\(p\leq\) 0.004). However, there were no significant differences in monocular and binocular distance and near visual cavities when comparing those values measured with spectacle before the fitting and those measured with the CL at the end of the follow-up (\(p\geq\) 0.34).
## 2 Jamming Attack Models
### Jamming Attack Models from Academia
#### 2.2.4 Models of [PERSON] _et al._[2]  
[PERSON] _et al._[2] have described four models: _Single-Tone Jammer_, _Multi-Tone Jammer_, _Pulsed-Noise Jammer_, and _Electronic Intelligence (ELINT)_. The _Single-Tone Jammer_ attacks one channel at a time (akin to _Spot Jammer_), the _Multi-Tone Jammer_ can attack some or all the channels of a multi-channel receiver, while the _Pulsed-Noise Jammer_ is a wide band jammer, sending pulsed jamming signals by turning on and off periodically at a slow or fast rate. _ELINT_, as they describe, is typically a passive system that tries to break down or analyze radar or communication TCF signals, and thus, strictly speaking, is not a jamming attack model.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Sage-Husa Model  
Traditional arithmetic average as previously mentioned is not applied to the time-varying system because the coefficient is same. In [81], a statistical estimation model named as Sage-Husa is employed to estimate the measured noise online. The Sage-Husa model estimates the measurement noise and process noise from the statistics of obtained measurements based on the concept that the new data should be attached more importance to while the old one should be weakened gradually. The model is demonstrated to reduce the tracking error significantly and make the following filter process converge with a quick speed, especially when the interference noise is unknown. Due to the dynamic estimation about the noise using the Sage-Husa model, the following filter can be adaptive to any tracking environments.
# Decision Support Application for Energy Consumption Forecasting
## 2 Material and Methods
### Forecasting Methods
#### 2.1.2. Svm  
The first algorithm for pattern recognition was created by [PERSON] in 1963 [27]. The first running kernel of SVM was designed in the sequence of [27] by [PERSON], implementing a generalization of the nonlinear algorithm Generalized Portrait and only for classification and linear problems. Furthermore, in 1979, the statistical learning theory was developed by [PERSON]. However, the current form of the SVM approach was presented with a paper at the COLT conference, in 1992 [14]. The structure of the data to be used in an SVM must follow the format suggested in Equation (1).  
\[(y_{1},x_{1}),\ldots,(y_{i},x_{i}),\ x\in R^{n},\ y\in R \tag{1}\]  
Where each example \(x_{i}\) is a space vector example; \(y_{i}\) has a corresponding value; \(n\) is the size of training data. For classification: \(y_{i}\) assumes finite values; in binary classifications: \(y_{i}\in\{\)+1,\(-\)1\(\}\); in digit recognition: \(y_{i}\in\{\)1,2,3,4,5,6,7,8,9,0\(\}\); and for regression purposes, \(y_{i}\) is a real number (\(y_{i}\in R\)). The implementation of SVM requires considering some important aspects, namely:  
* Feature Space
* Loss Functions
* Kernel Functions  
The most applicable kernels for time series forecasting, as in the problem considered in this work, are the Radial Basis Function (RBF) and the exponential Radial Basis Function (eRBF) [28]. These two kernels are specifically directed to regression in time series data. The SVM approach takes as parameters:  
* training Limit--limit number of training data;
* kernel--kernel that is used in the regression process;
* \(\varepsilon\)-insensitive--the error that is permitted, i.e., the lower the value, the higher the exigency of the regression process;
* limit--limit of the kernel function;
* \(\sigma\)--the angle of the kernel function;
* offset--offset of the kernel function.  
A suitable combination of these parameters is essential to achieve quality results. The most suitable combination is highly dependable on the characteristics and particularities of each distinct problem. Therefore, an exhaustive sensitivity analysis must be performed for each application, to achieve conclusions on the best combinations of parameters that should be used.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 5 Discussion on Current Solutions for Automated Data Acquisition
Plus, by having a time-stamped location information of the digital camera, it would be possible to know from what perspective (or position) each photo frame has been taken in order to compare with the same position in the 3D models (e.g., BIM) which has applications for a more automated and real-time progress reporting [8]. Regarding the studies reviewed in this paper, improving the data collection process to acquire a complete set of as-built data with more efficiency as a project progresses and gets more complex would be an area of interest in the future. This can help to move from manual as-built modeling to an automated 3D modeling. However, investigating the use of the various RS technologies over manual techniques for automated data collection needs more attention in terms of providing economic justification for different applications. As a solution, the integration of the various RS technologies was investigated in this paper in order to address part of these issues. Suggestions for future research in this field include: (1) a more comprehensive data acquisition system in which a larger number of activities and objects are addressed, (2) improving the strategies for better fusion and registration of the sensing data acquired on site to benefit more efficiently from them. It can be done by deciding on the level of data fusion required (low to high) based on each application and the accuracy demanded, (3) deviation estimation from as-planned models to identify changes in a project more accurately and timely, (4) enhancing the compatibility and connection between these automated data acquisition technologies with the available software (e.g., Revit, Navisworks, etc. ).
## 2 Beidou-Binocular Cooperative Navigation Model
### Beidou Navigation Positioning Module
#### 2.1.4 Function Integration of Beidou Navigation Module  
In the BBCN model, the Beidou navigation positioning module is responsible for providing the robot's global position and state information, including the following:  
* Real-time calculation of the robot's current global position coordinates and attitude.
* Providing initial position references for the binocular vision module to assist in local path planning.
* Collaborating with visual and IMU data in the data fusion module to enhance the overall system's robustness and precision.  
By optimizing filtering, redundancy design, and multi-source data fusion, the Beidou navigation positioning module proposed in this paper effectively improves positioningaccuracy and robustness in the complex environment of substations, providing a stable global reference for binocular vision perception and path planning.