esa-sceva/satcom-chunk-collection huggingface dataset:
# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 4 Experimental Results and Discussion
### S3 DIS Indoor Scene Segmentation  
S3 DIS [13] is an indoor dataset with high quality, recorded by a Matterport camera. The whole dataset has around 273 million points annotated with 13 semantic labels. It consists of six large areas. Area 5 is used for validating and testing, which follows common practice [12; 28]. The experimental results are compared with baselines and some classic studies in Table 3. The results of methods other than KPConv and RandLA-Net were  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Configuration**} \\ \hline \multirow{2}{*}{Hardware} & CPU & AMD Ryzen 9 5900X 12-Core Processor 3.70 GHz \\  & GPU & NVIDIA GeForce RTX 3080 Ti \\ \hline \multirow{3}{*}{Software} & Python IDE & Pycharm \\  & Deep learning library & Tensorflow \\ \cline{1-1}  & Visualization & Cloud Compare \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hardware and software configurations for the experiments.  
Figure 4: Overall architecture of segmentation network embedded within the BIDEL framework.  
directly cited from public reports. It can be seen that KPConv improved the mIoU by 0.8% and RandLA-Net improved the mIoU by 2% after being integrated with BIDEL, showing the effectiveness and generalizability of BIDEL in different LAOs. With BIDEL, KPConv obtained the leading performance of 94.9% for ceiling, 83.3% for wall, 75.7% for bookstore, and 61.1% for clutter. Notably, considerable gains were achieved over RandLA-Net for small objects such as sofa (+9.4%), column (+9.3%), and board (+5.7%). Although the improvements of BIDEL were inferior to those of other boundary-related methods such as JSENet [7] (+2.3%) and CBL [12] (+2.9%), BIDEL can be considered superior due to its simplicity without increasing model parameters. For example, JSENet designs a semantic edge detection stream to explicitly predict the edge, which greatly increases the number of parameters; CBL applies contrast boundary learning to the input point cloud and each subscene point cloud. However, if contrast boundary learning is only applied to the input point cloud, as performed in BIDEL, the relative improvement compared to baseline is much lower than that of BIDEL.  
Furthermore, the benefits of BIDEL for KPConv and RandLA-Net are qualitatively demonstrated in Figures 5 and 6, respectively. Misclassification usually appears in transition areas. For example, in Figure 6, in the second row, third column, points of the \"clutter\" category and \"ceiling\" category are poorly separated; in the fifth row, third column, the chair cannot be identified accurately when put together with the table. By contrast, BIDEL performs well in these transition areas. The overall improved areas are consistent with the semantic boundaries.
# Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
## 5 Sensitivity Analysis
#### 5.1.2 Changing SLA Value  
Different values for the SLA were tested. The results show the lower the SLA value, the higher the penalty cost will be or the need to rent additional processors to avoid delay and the opposite in case of higher SLA. The values tested are for SLA = 16, 17, 19, and 20. It is recommended that, when deciding on the SLA time between the client and the service provider, both sides study the data batch network carefully to decide on the right SLA terms that serve both sides and achieve the goal of the batch process.
## 2 Materials and Methods
### Camera Calibration Parameters  
Photogrammetric calibration is an important process for metric reconstruction in UAV flights because it guarantees the precision and quality of the images in short-range photogrammetric measurements [63]. The strips at different heights aid in the correct calibration of the camera [37]. This calibration uses image functions to determine the parameters' values for the camera's internal orientation. Pix4 Dmapper takes the metadata of each image to obtain the camera's internal parameters, which define the geometry of the camera's lens and sensor. The correlation between these parameters describes how closely they are related [31]. Optimally, these parameters should be independent, especially when characterising the interior orientation of a new camera. However, the correlation between these parameters helps detect problems occurring in the project. The self-calibration process is based on self-consistency functions over several views of a 3D scene [34].  
The following parameters were considered during the autocalibration process carried out by PIX4 Dmapper [31]:  
* \(f\): Calibrated principal or focal distance (in pixels);
* \(R1\), \(R2\), \(R3\): Radial distortion coefficients (dimensionless), generally shallow values;
* _cx, cy_: Principal point coordinates, that is, coordinates of lens optical axis interception with the sensor plane (in pixels);
* \(T1\), \(T2\): Lens tangential distortion coefficients generally have a lesser degree of magnitude than radial distortion (dimensionless).  
Pix4 Dmapper uses the initial camera calibration to adjust internal parameters such as focal length, principal point, and radial and tangential distortions, and external parameters such as camera position and orientation. A calibration model must be within 5% of the optimised value [31]. These parameters were calculated separately for each studied scenario, in order to analyse the incidence of the oblique perimeter images in the process of camera self-calibration. Once these parameters are obtained, indirect georeferencing is applied by adding the GCPs to the project.  
Worth noticing is that a substantial improvement in the photogrammetric adjustment would have been achieved by employing 10 internal parameters (10-parameter camera model with 2 affine factors) instead of the 8-parameter model used herein, and an even better improvement would have been achieved by applying the two-step approach presented in [64], depending on the block geometry. In addition, as stated previously, to mitigate potential distortions caused by the rolling shutter effect during image acquisition, Pix4D's rolling shutter correction algorithm was tested. This algorithm models the camera's motion during image capture, assuming constant translational and rotational velocity [35].
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
###### Abstract  
We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate a probability density function (PDF) of a single variable. Sampled data is partitioned into blocks using a branching tree algorithm that minimizes deviations from a uniform density within blocks of various sample sizes arranged in a staggered format. The block sizes are constructed to balance the load in parallel computing as the PDF for each block is independently estimated using the nonparametric maximum entropy method (NMEM) previously developed for automated high throughput analysis. Once all block PDFs are calculated, they are stitched together to provide a smooth estimate throughout the sample range. Each stitch is an averaging process over weight factors based on the estimated cumulative distribution function (CDF) and a complementary CDF that characterize how data from flanking blocks overlap. Benchmarks on synthetic data show that our PDF estimates are fast and accurate for sample sizes ranging from \(2^{9}\) to \(2^{27}\), across a diverse set of distributions that account for single and multi-modal distributions with heavy tails or singularities. We also generate estimates by replacing NMEM with kernel density estimation (KDE) within blocks. Our results indicate that NAPS(NMEM) is the best-performing method overall, while NAPS(KDE) improves estimates near boundaries compared to standard KDE.  
nonparametric density estimation; adaptive data partitioning; branching tree; load balancing; heavy tails; divergent distributions; multi-modal distributions; adaptive kernel density; maximum entropy
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 4 Discussion  
### Yield Estimation  
Since the 1970s, the employment of satellite remote sensing has been crucial in the assessment of key agricultural crops by scrutinizing agro-climatic conditions, crop health, and yield predictions, with certain systems also evaluating food security to deliver early warnings of possible food deficits. The integration of free access to medium to high-resolution satellite data with cloud-computing platforms like Amazon, Google Earth Engine, and Microsoft AI for Earth has significantly enhanced crop monitoring capabilities by overcoming previous constraints related to data processing and information extraction [6].  
Pre-harvest yield estimation insights provide farmers and experts involved in farming with essential information to guide critical decisions, such as determining the ideal quantity of fertilizers to apply [60]. The predictive efficacy of various vegetation indices (VIs) in relation to wheat yield exhibits considerable variability across research investigations, with each index presenting distinct benefits contingent upon the specific context and methodological approach utilized. The Normalized Difference Vegetation Index (NDVI) is frequently used due to its simplicity and suitability for predicting winter wheat yield, particularly when combined with meteorological data [61]. Other indices such as the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Green Normalized Difference Vegetation Index (GNDVI) have illustrated improved correlation coefficients with real yield metrics, especially during critical growth periods including Flowering [62]. The Enhanced Vegetation Index (EVI) has been competently used together with machine learning methodologies, including Random Forest (RF), to estimate winter wheat yields, demonstrating a strong relation with yield estimation (an R\({}^{2}\) of 0.81 and an RMSE of 1250 kg/ha) and providing substantial insights for winter wheat yield forecasting, especially when data from various growth intervals are factored in [63].  
For durum wheat, GNDVI outperformed all other tested indices, consistent with findings from previous studies [64]. Furthermore, GNDVI also demonstrated superior predictive capabilities over NDVI, which ranked as the second most effective predictor [65]. For soft wheat, LAI, derived from EVI, demonstrates superior performance over all other examined indices. However, its predictive capability shows only a marginal difference when compared to NDVI, as corroborated by findings in previous studies [66].
## 3 Experimental Analysis
### Measuring Position and Experimental Test  
#### 3.5.1 TGIB-fw  
For the first of 18 specimens, strains at glass webs and timber flanges, and vertical and relative displacements between glass and timber were measured simultaneously. Strains were measured in two sections located in close proximity to the loading points (sections A and C in Figure 8). On the glass track, the strain measurement points were positioned at the top and bottom edges where the highest strain values were expected. The strain measurement points on the timber were distributed on the bottom of the top flange and on the bottom and top of the bottom flange. All strain measurement points were equipped with strain gages (SGs) from HBM (for measurements on glass, the length of the SG measurement grids was 6 mm and for strain measurements on timber, SGs with 100 mm long measurement grids were used). All strain gages on the specimen were connected as a quarter Wheatstone bridge with four wires [41]. All vertical displacements at mid-span were measured using inductive displacement transducers. They were also used to measure relative displacements between glass and wood in the horizontal direction at one end of the composite beam. Measurements were made using the HBM MGCPlus data acquisition system controlled by a laptop running Catman 4.5 licensed software. The strains of the glass and wood were also measured at mid-span of the subsequent specimens (section B in Figure 8). The intensity of strain was measured directly on the hydraulic piston using a set of four strain gages connected in a full Wheatstone bridge. Analytical and experimental calibration was performed to determine the strain intensity from the measured strains.  
A hydraulic piston was used for loading (Figure 15a). The load was applied symmetrically via a weight yoke (Figure 15b), which allowed us to transfer the force via the hydraulic piston to the TGIBs as a two-point load (\(P/2\)).  
Figure 14: (a) wire spacer to secure the correct thickness of epoxy adhesive; (b) weighting of the already bonded TGIB; (c) TGIB with epoxy adhesive during testing.  
Steel plates (Figure 16) were placed under the yoke to prevent the yoke from digging into the timber during the test.  
The specimens were placed in prepared steel frames (Figure 17) that prevented the beams from slipping. They were loaded with a hydraulic piston with a force of \(1\) kN\(/\)100 s until final failure. The specimen during loading is shown in Figure 18, where the first cracks in the glass are already visible.  
Figure 16: Steel plates to prevent the weight yoke from pushing into the timber.  
Figure 17: Positioning the specimen between the steel profiles and attaching the weight yoke.  
Figure 15: (**a**) hydraulic piston; (**b**) weight yoke through which the force of the piston was transmitted to the specimen.
## 2 Preliminaries  
To facilitate the presentation, the paper introduces the following notations: \(\text{Sig}^{\xi}(E)=|E|^{\xi}\text{sign}(E)\), where \(\text{sign}(E)\) is defined as follows: \(\text{sign}(E)=\left\{\begin{array}{rl}1&\text{if}\quad E>0\\ 0&\text{if}\quad E=0\\ -1&\text{otherwise}\end{array}\right.\) with \(\xi>0\). The derivative of \(\text{Sig}^{\xi}(E)\) is given by \(\frac{d}{dt}\text{Sig}^{\xi}(E)=\xi|E|^{\xi-1}\dot{E}\). In addition, \(\text{diag}(\cdot)\) represents the diagonal matrix, and \(\lambda_{\min}(\cdot)\) and \(\lambda_{\max}(\cdot)\) denote the minimum and maximum eigenvalues, respectively. Additionally, \(\|\cdot\|\) denotes the Euclidean norm.
## 2 Related Works
### Network Model  
In the general architecture of vehicular networks, the communication of vehicles among the other vehicles or with the road side units (RSUs) is based on dedicated short-range communication [20], where the vehicle-to-Infrastructure (V2I) communication is the external network among the vehicles and RSUs.  
Our proposed network model is based on [PERSON] et al.'s network model, but it addresses the problems regarding flaws in communication and authentication. Under their protocol, the process of transmitting the session key between RSUs and vehicles is unclear. Therefore, we propose a network model, in which vehicles and RSUs register at a trusted authority. The key agreement consists of all entities, including the vehicle, RSU, and trusted authority. Figure 1 illustrates our proposed network model and gives a detailed description of the entities.  
* Vehicle: vehicles have embedded devices, sensors and wireless communication device, such as velocity or location measurement equipment, Bluetooth, Wi-Fi, and OBU. In particular, the OBU collects information generated by sensors or devices. However, the OBU has relatively restricted memory. Therefore, the OBU sends the collected information to RSUs; subsequently, RSUs transmit the data to the vehicular cloud.
* RSU: RSUs are intermediary devices to transmit data between vehicles and the vehicular cloud. RSUs register with the trusted authority to generate a session key with vehicles. RSUs have more  
Figure 1: Proposed network model.  
memory and computing performance than OBUs. Therefore, RSUs can obtain data from many vehicles. However, RSUs cannot store data from multiple vehicles. Therefore, RSUs send specific data to the vehicular cloud.
* Trusted authority: a trusted authority is the top-level entity that an attacker can never attack. RSUs and vehicles should register with the trusted authority to generate the session key, and then, the trusted authority, RSUs, and vehicles perform mutual authentication.
* Vehicular cloud: a vehicular cloud is a storage server used to save a huge amount of data of different kinds within a VANET system. Each vehicle needs to collect and share the data with other vehicles. Therefore, the OBU collects data and communicates with other OBUs. However, OBUs have low computational performance and small storage space. Thus, vehicles send the data securely to RSUs and RSUs forward it to the vehicular cloud.
# Edge Couplers in Silicon Photonic Integrated Circuits: A Review
## 4 Structural Transformations of Edge Couplers in Vertical Direction
### Edge Couplers Based on Three-Dimensional Taper
It is necessary to control the reaction speed and reactant dose accurately and precisely to reach the desired vertical taper slope and to avoid the thick wedge from collapsing. The challenges and difficulties in fabrication underlie the reduced feasibility and ability to integrate 3D taper edge couplers.
# Multimodal Attention-Based Instruction-Following Part-Level Affordance Grounding
## 4 Experiments and Evaluation
### Ablation Study (RQ2)  
To verify the contribution of each component, we conducted extensive ablation experiments on the two datasets and analyzed the results of different operations. As shown in Table 6, the components of the network include the following:  
1. Baseline: In the baseline model, the encoder extracts visual and textual features, and the fused multimodal features are fed into the decoder for affordance segmentation. This model serves as a foundational comparison point without the task-specific context and VSMA.
2. Baseline\(+\)TaskContext: This model incorporates task-specific context into the baseline, enhancing the multimodal fusion process.
3. IAG-Net: The full model integrates both the multihead VSMA (MH-VSMA) and the task-specific context, offering advanced affordance-grounding capabilities.  
For the baseline, we fused the visual and textual features in the encoder, and the decoder outputs the affordance segmentation mask. For Baseline\(+\)TaskContext, we added the task-specific context and the multimodal fusion to the encoder. In this case, for all five measurements (overall accuracy, mean accuracy, frequency accuracy, mIoU, and \(F_{\beta}^{\omega}\)), improvements are observed to varying degrees. After adding MH-VSAM, the full model significantly improves in both the mIoU (+3.5%) and \(F_{\beta}^{\omega}\) (+2.1%). The results are presented in Table 6. The introduction of a task-specific context significantly improves the overall and frequency accuracies, demonstrating its effectiveness in capturing task-relevant information. The inclusion of MH-VSAM further improves the mean accuracy, mIoU, and \(F_{\beta}^{\omega}\), underscoring its role in refining affordance segmentation. The performance of overall accuracy and frequency accuracy have declined slightly. While mIoU and F-score focus on segmentation quality and boundary delineation, overall accuracy and frequency accuracy consider pixel-level classification correctness. The increase in mIoU and F-score suggests an improvement in segmentation quality, which may come at the cost of overall accuracy as the model might prioritize correctly segmenting complex regions over accurately classifying all pixels. The sample imbalance issues could also cause results where certain classes are underrepresented in the dataset. Since the frequency accuracy is computed by averaging the frequency accuracies for all classes in the dataset, the sample imbalance will cause a decrease if less frequent classes are misclassified more often. The ablation study indicates that both the task-specific context and the MH-VSAM benefit the affordance-grounding task. These enhancements validate our two-stage fusion strategy, which combines sentence-level fusion and task context fusion for more accurate affordance grounding.  
Figure 8: Example of a failure case of hand occlusion.
## 4 Results and Discussion
Combining power domain NOMA with a UFMC can address the high demands of 5G technology. NOMA uses energy-domain synchronization to accommodate many customers, whereas UFMC compensates for scheduling and bandwidth discrepancies. However, the high peak-to-average power (PAPR) problem associated with the multicarrier modulations of NOMA-UFMC waveform can negatively impact it, rendering it unsuitable for the next-generation mobile and wireless networks. Hence, 6G technology will improve the NOMA power-domain multiplexing, supporting many users, and the multicarrier modulations will be utilized well. The cellular Internet of Things (IoT) is regarded as the de facto paradigm for enhancing the systems for processing and communication. Utilizing cellular networks, cellular IoT links a sizable number of real-world and virtual things to the Internet. The newest cellular networks, such as 5G, use cutting-edge and evolutionary technology to enhance wireless network performance significantly. However, in light of the anticipated new use cases, such as holographic communication and the IoT's growing use of large smart-physical endpoints, the amount of network traffic has significantly increased, making it impossible for the present generation of mobile networks to satisfy the rising needs fully. Therefore, the next generation, 6G networks, is expected to be crucial in addressing these IoT concerns by offering uRLLC network capacity and new communication services. Technically, it was observed from the literature reviewed and evaluated that some procedures, such as deep learning (DL), were adopted and utilized to reduce computational overhead in NOMA-enabled MEC in a 6G network. This is demonstrated in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Technology** & **Objectives** & **Contribution** & **RQ1** & **RQ2** & **RQ3** \\ \hline \multirow{4}{*}{[PERSON] et al. [123]} & \multirow{4}{*}{5G wireless network, NOMA, UFMC} & This study attempts to provide a distinct Harley transform (DHT) pre-coding-founded NOMA-assisted multicarrier (DHT-NOMA-UFMC) waveform structure to mitigate the excessive PAPR. & Due to power sector multiplexing on the broadband internet connection, the recommended & Yes \\  & & & & & \\ \cline{1-1} \cline{3-6}  & & & & & \\ \cline{1-1} \cline{3-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._