esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
### Related Work
In [34], the flexible user association is proposed in NOMA-based multiple base stations (BSs) networks to maximize the weighted sum rate of the system, and a user association optimization problem is formulated. While a NOMA user pairing scheme combined with the almost blank subframe (ABS) technology, and a dynamic NOMA power allocation scheme are presented to maximize the network fairness based on the optimized throughput of the edge users in [35]. But few writers focus on various services UEs' associations with BSs. This study is aimed to improve isolation and flexibility by resolving the UE association in 5G [36]. In [37], using a matching game, UE association in a cellular virtualization network captures UE and BS preferences, QoS needs, and backhaul limitations. The Pointer Network (PtrNet) architecture implements NOMA's joint UE pairing and association schemes [38]. Recently, a matching game has been proposed for wireless UE affiliation and resource allocation [39]. In [40], the authors intend to improve the eMBB network rate and decrease eMBB loss by convincing uRLLC UEs to cohabit with eMBB UEs by superposition readiness by matching game. In [41], matching game techniques for UE association in HetNets are presented, taking both downlink and uplink properties into account. Moreover, in [42], the optimization of downlink data rates was achieved with Radio access technology (RAT) heterogeneity by ensuring fairness and lowering uplink power usage. However, none of those mentioned works above considersintra and inter-isolation problems between slices when analyzing cell association and coexistence between uRLLC and eMBB slices.
## 3 Solution Approach
### Load Flow  
Load flow studies [43] are essential for ensuring stable, reliable, and economical transmission of electrical power from generators to consumers through the grid system. We refer to the movement of both reactive and active power as \"flow\" or \"load flow\". A mathematical technique for determining distinct bus voltages, their phase angles, and the flow of reactive and active power across various branches, generators, and loads in a steady-state setting is provided by power flow studies. The backward/forward sweep method is used to evaluate load flow in radial distribution. First, voltage data on the main line and its derivatives are all adjusted to the nominal voltage. Next comes an iterative procedure where Kirchhoff's Current Law (KCL) is used to compute the branch current in the main line in a backward sweep after currents in derivative lines have been determined. Then, node voltages are calculated using Kirchhoff's Voltage Law (KVL) in a forward sweep. Until the voltage magnitudes at each node in the current iteration and the previous iterations are lower than the tolerance limit, this backward and forward sweep approach is repeated.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 4 Measurements
### Methodology
#### 4.1.1 Firmware Changes  
To perform valid measurements, the firmware code that toggles debug pins and LEDs on the OpenUSB board was disabled. Furthermore, the serial communication code was also completely disabled because even when the OpenUSB is not connected to a computer, the code would still try to output data, unnecessarily increasing power consumption.  
In order to prepare the 2.4 GHz driver for the measurements, only small adaptations had to be made to the original firmware. The firmware for the 868 MHz driver however required additional implementation effort, as there were no working drivers for the CC1200 radio chip on the OpenUSB. Based on a branch of the official OpenWSN repository [21], we implemented a working CC1200 radio driver, which can be found in [15].
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 2 Computer Vision
### Infrared Vision Sensors and Spectrometers  
An infrared vision sensor is an imaging or detection device capable of detecting, receiving, and processing infrared radiation. It captures infrared radiation emitted or reflected by objects to form visible or analyzable infrared images. It is widely applied in fields such as night vision, temperature measurement, and target tracking [50]. Near-infrared spectroscopy is a penetrating spectrum whose non-invasive and accurate characteristics make it widely used in the agricultural sector. For example, it provides non-destructive testing for agriculture and quality assessment of food interiors [51; 52]. Additionally, combining near-infrared spectroscopy with computer vision enables automatic differentiation of crop characteristics such as appearance, shape, color, and texture, meeting the needs of crop grading [53; 54]. It also facilitates crop health monitoring, pest and disease early warning, and yield optimization.  
Traditional visible light cameras have limitations in image processing under complex lighting conditions. Using infrared vision sensors to generate infrared thermal images, combined with image processing to extract features, can effectively address issues caused by strong light interference [55; 56]. Ref. [57] employs a spectrometer to dynamically collect the transmission spectra of pear fruits, enabling real-time acquisition of spectral data. Byvisualizing spectral features and analyzing their distribution using visual methods, the system achieves online non-destructive detection of pear browning. Additionally, computer vision techniques were used to fuse multi-modal data such as near-infrared spectra and thermal imaging, enabling real-time online detection of crops and rapid detection of leaks in drip irrigation systems for agricultural applications [58; 59].
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 5 Conclusions  
In this paper, we proposed a multiple parallel morphological anti-aliasing algorithm and implemented it in FPGA.  
Through experiments and analysis, we find that our algorithm achieves a better anti-aliasing performance at the edge of the image than MSAA4x. This is because our algorithm achieves better continuity at the edge of the image than MSAA4x by achieving more color gradients than MSAA4x. Our algorithm not only reduces the difference between color gradients but also enhances the anti-aliasing processing for the aliasing pattern with a length of 1 by optimizing the blend weight coefficients used by MLAA. Therefore, our algorithm also achieves a better anti-aliasing performance than MLAA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Size of the Image** & **Ours** & **MLAA** \\ \hline Height = 100 & 100 Kbit & 1600 Kbit \\ Width = 100 & & \\ \hline Height = 150 & 121 Kbit & 5625 Kbitus \\ Width = 150 & & \\ \hline Height = 200 & 144 Kbit & 10,000 Kbitus \\ Width = 200 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Memory size used to store the area texture of the two algorithms.  
Moreover, our algorithm consumes much less time than MLAA in FPGA implementation by performing edge detection of the image, classification of the aliasing patterns, acquisition of the blend weight coefficients, and calculation of the anti-aliased color in three channels of the image simultaneously. In the process of classifying the aliasing patterns and obtaining the blend weight coefficients, our algorithm is performed from the horizontal and vertical directions of each channel of the image simultaneously, which also reduces the consumption of time greatly.  
In addition, our algorithm also consumes much less memory than MLAA in FPGA implementation. This is because we scale the area texture used by MLAA. By representing all blend weight coefficients for a certain length interval of each aliasing pattern as a single datapoint, we reduce the number of datapoints stored in the area texture, thus reducing the memory required to store the area texture.  
Last but not least, although the algorithm proposed in this paper consumes less time than MLAA in FPGA implementation, the latency of the whole system is still too high, because we have not carried out more timing optimization for the hardware implementation of the algorithm, such as pipeline processing. Further timing optimization is required for future research. In addition, the memory consumption of this algorithm is still too large, which makes it impossible for us to further process larger images in FPGA. Thus, memory issues should also be discussed for future research.  
Conceptualization, M.W.; methodology, D.A.; software, D.A.; validation, D.A. and J.X.; writing-original draft preparation, D.A.; writing-review and editing, D.A. and J.X.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 5 Problem Formulation
### Computation Capacity of Mobile Devices  
Each mobile device is subject to a computation capacity denoted as \(c_{i}^{p}\) for \(p_{i}\)'s mobile device and \(c_{j}^{h}\) for \(h_{j}\)'s mobile device where \(i\in\{1,2,...,p\},j\in\{1,2,...,h\}\). When talking about the computation capacity of a device, one typically will refer to its CPU. CPU processing capacity is typically referred to in terms of megahertz (MHz) or gigahertz (GHz). Professionals talk about clock speed, which is the standard ability of the CPU to cycle through its operations over time. Therefore, a 1-GHz CPU is able to tick its clock around one billion times per second, which in turn can perform more complicated computational tasks. Without loss of generality, we regulate these computational power values in clock cycles to a scale of 0-100, i.e., \(0\leq c_{i}^{p}\leq 100\) and \(0\leq c_{j}^{h}\leq 100\ \forall i,j\), for simplicity. Each entry of the computation assignment matrix, \(M_{i,j,t}\), refers to the number of clock cycles required to perform the corresponding task section. For example, \(M_{3,4,0}=1000\) means that at \(t=0\), \(h_{4}\) is assigned to help \(p_{3}\) for 1000 clock cycles worth of computational task. If \(c_{4}^{h}=50\) Hz, then it takes \(h_{4}\ \frac{M_{3,4,0}}{c_{i}^{h}}=\frac{1000}{50\,\mathrm{Hz}}=20\) s to perform the task. Therefore, each device is subject to an amount of computational task in clock cycles at each time slot as a higher limit, that is:  
\[\sum_{i=1}^{p}M_{i,j,t}\leq c_{j}^{h},\ \forall j\in\{1,2,...,h\},t\in\{1,2,...,\frac{T}{\Delta t}\} \tag{10}\]  
for all helper devices, and:  
\[M_{i,j,h+1,t}\leq c_{i}^{p},\ \forall i\in\{1,2,...,p\},t\in\{1,2,...,\frac{T}{ \Delta t}\} \tag{11}\]  
for all requester devices as the \((h+1)\)th column of the computation assignment matrix is representing the amount of self-computing tasks.
## 3 Error Sensitivity Analysis of the TE Caused by Components of the Cycloidal Gear Transmission
### Error Compensation Model of the Cycloidal Gear Transmission  
According to the error sensitivity analysis method of the influence of error factors that affect the TE of the cycloidal gear transmission, error coefficients in Table 2 are represented as  
\[A=\left(\begin{array}{cccccccc}2\sqrt{1-{K_{1}^{\prime}}^{2}}&0&0&0&0&0&0&0&0\\ 0&-2&0&0&0&0&0&0&0\\ 0&0&1&0&0&0&0&0&0\\ 0&0&0&0.5&0&0&0&0&0\\ 0&0&0&0&2{K_{1}^{\prime}}&0&0&0&0\\ 0&0&0&0&0&2&0&0&0\\ 0&0&0&0&0&0&-2\sqrt{1-{K_{1}^{\prime}}^{2}}&0&0\\ 0&0&0&0&0&0&-{K_{1}^{\prime}}&0\\ 0&0&0&0&0&0&0&2{k_{n}}\\ \end{array}\right). \tag{24}\]  
An error distribution model can be represented as  
\[\overline{\delta}=\left(\overline{\delta_{a}}\,\overline{\delta_{b}}\, \overline{\delta_{c}}\,\overline{\delta_{d}}\,\overline{\delta_{e}}\, \overline{\delta_{f}}\,\overline{\delta_{g}}\,\overline{\delta_{h}}\, \overline{\delta_{i}}\right)^{T}. \tag{25}\]  
A mean TE vector can be represented as  
\[\overline{\Delta\theta}=\left(\overline{\Delta\theta_{a}\Delta\theta_{b} \Delta\theta_{c}\Delta\theta_{d}\Delta\theta_{e}\Delta\theta_{f}\Delta\theta _{g}\Delta\theta_{h}\Delta\theta_{i}}\right)^{T}. \tag{26}\]  
By substituting Equations (19)-(23) into Equations (11) and (12), the error compensation model can be represented as  
\[\overline{\delta}=\frac{\pi\alpha z_{c}}{180\times 60}A^{-1}\overline{\Delta \theta}. \tag{27}\]  
It can be known from Table 2 that error coefficients of the pinwheel radius error, the shifting tooth profile modification error, and the cumulative error of the circumference of the cycloidal gear are negative. These three error factors can decrease the TE of the RV speed reducer. Error coefficients of the other six error factors that have positive effects can increase the TE of the RV speed reducer. The total TE of the initial RV speed reducer is assigned to three error factors with negative TE error coefficients by using Equation (27), and the value of the corresponding component error of these three error factors of the TE can be calculated. By modifying three-dimensional (3D) models of components of the cycloidal gear transmission, a modified RV speed reducer prototype can be obtained by the TE compensation method.  
Since some errors in the actual RV speed reducer cannot be modeled by 3D modeling software, modifications of these errors are set as an equivalent process [30]. Since the sensitivity index of the circular pitch circumference error is negative, as shown in Table 2, the TE of the RV speed reducer can be decreased by modifying tooth profiles of cycloidal gears to compensate the cumulative error of the circumference of the cycloidal gear. Therefore, the cumulative error of the circumference of the cycloidal gear can be replaced by its tooth profile offset. For the circumferential position error of the pinwheel groove, the TE is increased with increments of the outward positional deviation in the radial direction of the pinwheel groove, and vice versa. Hence, the circumferential position error of the pinwheel groove can be replaced by a radially inward offset.
# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 3 Results
### Model Interpretability
On the contrary, the Bus is the least informative feature, contributing just \(\pm\)EUR 1703.87 to each house price prediction--a fact that is expected given the widespread availability of bus stops, which makes them less of an influential factor for property prices. The SHAP analysis results in Figure 4 show that our model for predicting real estate prices reveals a nuanced approach to property valuation. Key findings include the following:  
1. Primary predictors: The size of the property, indicated by the private gross area (PGA), emerges as the most significant factor, confirming that larger dwellings typically command higher prices. The property's exact location, denoted by Longitude, Latitude, and postal code, is also crucial, aligning with the well-known real estate emphasis on location;
2. Secondary factors: The condition of the property and its energy performance play important roles, although their impact might vary in different contexts. Proximity to amenities like transport, commerce, and cultural sites is significant, highlighting the value of convenience and lifestyle associated with the property;
3. Economic indicators: broader economic conditions, reflected by indicators such as GDP (Gross Domestic Product) and CPI (Consumer Price Index), are considered in the valuation but have a lesser impact compared to the direct physical and locational attributes;
4. Environmental and health attributes: features like proximity to parks and health facilities moderately affect property values, suggesting that environmental quality and access to healthcare are important to potential buyers;
5. Variability in impact: the model indicates a range of impacts for certain features, suggesting that the desirability of attributes like proximity to amenities may depend on specific market or neighborhood dynamics. Overall, while the model places the most significant emphasis on physical characteristics and location specifics, it also integrates various factors, from accessibility to amenities to macroeconomic conditions, illustrating the multifaceted nature of real estate pricing.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 2 Related Works  
### Human Pose Estimation Methods Based on RGB Images  
#### 2.1.1 OpenPose  
Carnegie Mellon University (CMU) introduced a 2D HPE model called Convolutional Pose Machine (CPM) [29], which employs a multi-stage cascaded architecture based on convolutional neural network (CNN) layers. CPM progressively expands the receptive field across stages, enabling joint detection that effectively captures both local and global features. To enhance the performance of 2D pose estimation, the CMU research team later modified the CPM network and introduced part affinity fields (PAFs), leading to the development of the OpenPose model [30]. The OpenPose network retains the multi-stage cascaded structure of CPM but is optimized to efficiently extract confidence maps--heatmaps representing body joint locations--and PAFs. PAFs are a two-dimensional vector field that encodes the unit vector direction between two connected joints along the limb. Non-limb pixels are assigned a value of zero. The final extracted confidence maps are used to detect body joints, and a bipartite graph matching algorithm identifies candidate joint pairs. The selected joint pairs are connected using straight lines, and their association scores are computed through line integrals of the PAF directional information. The skeleton is then formed by assembling the limbs selected by their association scores. The association score \(E\) for a candidate joint pair is computed as follows:  
\[E=\int_{u=0}^{u=1}\mathbf{L}_{c}(\mathbf{p}(u))\cdot\frac{\mathbf{d}_{j_{2}}- \mathbf{d}_{j_{1}}}{\left\|\mathbf{d}_{j_{2}}-\mathbf{d}_{j_{1}}\right\|}du, \tag{1}\]  
where \(c\) is a limb type that refers a specific connection between two body keypoints, \(\mathbf{L}_{c}\) is the directional information of \(c\) in the PAF, \(\mathbf{d}_{j_{1}}\) and \(\mathbf{d}_{j_{2}}\) are the candidate joint positions for \(c\), and \(\mathbf{p}(u)\) is a parametric function that interpolates the position of the two body parts as follows:  
\[\mathbf{p}(u)=(1-u)\mathbf{d}_{j_{1}}+u\mathbf{d}_{j_{2}}, \tag{2}\]  
where \(u\) is an interpolation parameter that ranges from 0 to 1.  
As the predicted unit vector directions at each pixel align more closely with the directional information from \(\mathbf{d}_{j_{1}}\) to \(\mathbf{d}_{j_{2}}\), the association score \(E\) increases. Consequently, the joint pair \((\mathbf{d}_{j_{1}},\mathbf{d}_{j_{2}})\) with the highest \(E\) value for limb type \(c\) is grouped as belonging to a specific individual. This correlation-based grouping enables efficient pose estimation with real-time processing capability.  
OpenPose adopts a bottom-up approach, maintaining stable computational complexity regardless of the number of people in the image. Its PAF-based grouping strategy achieves higher pose estimation accuracy compared to previous methods. However, in occluded scenarios, OpenPose still performs poorly due to joint misdetections and incorrect associations, indicating that further improvements are needed.
## 2 AIS Data Pre-Processing
### Data Volume and Ship Distribution  
The dynamic data used in this paper are extracted from message1 (noted as DYM1) received by the ocean satellite HY-2B from 1 November 2019 to 21 April 2020, and the ship distribution is shown in Figure 4. The static data are extracted from message5 (noted as STM5) received by ocean satellite HY-1C/D and HY-2B/C. The detailed information of DYM1 and STM5 is shown in Table 2.  
In the literature [4], the value in the Type field of passenger ships, cargo ships, tankers, fishing boats, and tugs are 60 to 69, 70 to 79, 80 to 89, 30, and 52, respectively. The ships with code 0, 90, and a code larger than 99 have no specific type definition, and these ships are not considered in ship type statistics of space-based AIS data. Figure 5a,c show the number and cumulative percentage of the top 20 types of vessels in DYM1, counted by message quantity and ship (MMSI) quantity, which account for 96.62% and 95.37% of message1. In Figure 5b,d, four major categories of ships (i.e., passenger ships, tankers, fishing boats, and cargo ships) are used for type statistics, which account for 90.65% and 88.98% of message1 by message quantity and ship quantity. In this paper, these four kinds of ships are selected as the research object, and their global distribution is shown in Figure 4.  
Figure 3: The overall dimension of a ship.  
Figure 4: Message1 received by HY-2B.  
Figure 5: Cont.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Data** & **Period** & **Number of Messages** \\ \hline HY-2B & Message1 & 1 November 2019–21 April 2020 & 10,875,328 \\ HY-2B & Message5 & 1 November 2018–18 June 2021 & \\ HY-2C & Message5 & 24 September 2020–18 June 2021 & 113,472 \\ HY-1C & Message5 & 1 November 2018–18 June 2021 & \\ HY-1D & Message5 & 13 June 2020–18 June 2021 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of message1 and message5.
###### Abstract  
The increasing need for efficient energy consumption monitoring, driven by economic and environmental concerns, has made Non-Intrusive Load Monitoring (NILM) a cost-effective alternative to traditional measurement methods. Despite its progress since the 1980s, NILM still lacks standardized benchmarks, limiting objective performance comparisons. This study introduces several key contributions: (1) the development of five new converters with 13-digit timestamp support and harmonic inclusion, improving the data collection accuracy by up to 25%; (2) the implementation of an advanced disaggregation software, achieving a 10-15% increase in the F1-score for certain appliances; (3) a detailed analysis of harmonics' impact on NILM, reducing the Mean Normalized Error in Assigned Power by up to 40%; and (4) the design of open-source measurement hardware to enhance reproducibility. This study also evaluates open hardware platforms and compares five common household appliances using NILM Toolkit metrics. Results demonstrate that open hardware and software foster reproducibility and accelerate innovation in NILM. The proposed approach contributes to a standardized and scalable NILM framework, facilitating real-world applications in energy management and smart grid optimization.  
data-driven algorithms; energy disaggregation; F1-score; MAE; NDE; non-intrusive load monitoring; non-intrusive load monitoring toolkit; RMSE; sampling rate; smart energy monitoring