esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 3 Method
### Optimization of the Extraction of Power Transmission Components  
After PL and tower detection, a few single tall trees, signal poles (happened to be in the corridor) and low-voltage PLs were still present in the scene as interference factors. However, in the transmission corridor, any PL was hanging in midair in the 3D space and was connected with HV pylons. This constraint was used to refine the pylon and PL detections. As seen in Figure 7, pylon and PL points were connected to each other, which is a unique feature that can be used to distinguish HV towers from interference elements. Note that HV transmission lines were different from other linear interference factors such as low voltage conductors or supporting wires. Additionally, as a basic criterion, the principal direction of the candidate PL points was approximately parallel to the line connecting their two adjacent towers [69].  
Thus, the extractions of those two transmission objects were optimized simultaneously, considering the physical connections between them. This included the following constraints: (a) the transmission tower and the PLs were connected to each other [27], meaning the PLs in neighbor spans were connected through the HV towers; (b) the dominant orientation of the PLs was approximately parallel to the line connecting the towers at both ends; (c) the neighboring grid of the tower must contain PLs, and there should be a tower at the position of the derivative extremum of each adjacent line.  
* First, the extracted HV towers were taken as searching objects. For each HV tower, the neighboring grids were searched for PL points.
* Secondly, the PL point set and the tower were confirmed to be connected to each other. If a tower was connected with a PL, it was determined as HV tower area, and the PL connected with it was labeled as transmission wires.
* Lastly, the directions formed by every two adjacent pylons were taken as references to remove the linear segments with large angles from these directions.  
The above steps eliminated tall trees, signal poles, and other remaining interferences of low-voltage transmission wires and other linear targets. Illustrated in Figure 8b,c, tree points with a similar distribution of pylons, and most low-voltage transmission lines and cross-over transmission lines (see in Figure 8a), were excluded. As depicted in Figure 8d, the result was the clean and complete detection of PLs and pylons in a span.  
Figure 7: The spatial relationship between PLs and towers.  
Figure 8: Refined extraction results of pylons and PLs: (**a**) preliminary results with cross-over PLs and isolated tall trees; (**b**) example of extracted PLs; (**c**) example of detected transmission towers; (**d**) the refined result within a span.
## 3 Methodology
### ECA-CBAM Attention Mechanism
The improved E-CAM and SAM are utilized to construct a new channel-spatial attention structure, the ECA-CBAM, as shown in Figure 5.  
The module first utilizes the E-CAM and SAM to obtain the channel attention weight \(M_{c}\) and the spatial attention weight \(M_{s}\), respectively. Then, \(M_{c}\) and \(M_{s}\) are expanded to the size of \(R^{W\times H\times C}\), summed element-wise  
Finally, the hybrid attention weight matrix \(M_{cs}\) is element-wise multiplied with the input feature map \(F\), resulting in the refined feature map \(F_{cs}\), which is calculated using the formula below:  
\[F_{cs}=F\otimes M_{cs} \tag{4}\]  
The attention mechanism guides the model in allocating more computational resources to emphasize the region of interest, enhancing its expressiveness. The concept of the E-CBAM is to obtain attention weight matrices \(M_{c}\) and \(M_{s}\) from the input feature map \(F\) in both the spatial and channel dimensions, which improves the adequate flow of feature information in the network. The module emphasizes focusing on meaningful features in both channel and spatial dimensions, focusing on essential features and suppressing ineffective ones. For small objects, individual feature regions will receive more significant weight and contain more valid objects, allowing the model to focus more on learning the features of that region, thereby improving feature extraction under limited computational resources.
## 4 Results
### Comparison between FGWR and MFGWR
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{4}{c}{**FGWR**} & \multicolumn{4}{c}{**MFGWR**} \\ \cline{2-9}  & **Mean** & **STD** & **Min** & **Max** & **Mean** & **STD** & **Min** & **Max** \\ \hline intercept & \(-\)3.813 & 5.910 & \(-\)25.417 & 37.085 & \(-\)3.778 & 1.196 & \(-\)5.708 & \(-\)1.735 \\ distance & \(-\)0.010 & 0.007 & \(-\)0.068 & 0.032 & \(-\)7.9e-3 & 1.1e-7 & \(-\)7.9e-3 & \(-\)7.9e-3 \\ Topp & 1.403 & 0.733 & \(-\)5.563 & 6.120 & 1.412 & 0.073 & 1.136 & 1.695 \\ GDP & 1.208 & 0.699 & \(-\)4.865 & 4.912 & 1.169 & 0.188 & 0.809 & 1.532 \\ \hline t\_intercept & \(-\)1.382 & 1.769 & \(-\)8.307 & 8.264 & \(-\)5.751 & 1.653 & \(-\)9.992 & \(-\)2.756 \\ t\_distance & \(-\)2.978 & 1.815 & \(-\)10.15 & 3.878 & \(-\)7.375 & 0.0001 & \(-\)7.375 & \(-\)7.374 \\ t\_Tpop & 4.202 & 2.240 & \(-\)8.050 & 13.51 & 15.485 & 4.241 & 9.095 & 28.341 \\ t\_GDP & 4.327 & 2.483 & \(-\)7.873 & 14.36 & 16.543 & 4.495 & 8.726 & 27.540 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of parameter estimations for FGWR and MFGWR.
## 1 Introduction
One approach to mitigating these issues is the use of multiple-input, multiple-output (MIMO) techniques, which transmit data in parallel from multiple sources [16; 17; 18; 19]. MIMO, originally developed for wireless communication systems, addresses signal degradation caused by fading and other environmental impairments [20; 21]. By employing spatial diversity using multiple spatially separated antennas for transmission and/or reception, MIMO enhances signal reliability and mitigates the effects of fading and interference, ultimately improving overall system performance. Furthermore, multiple users can be supported efficiently [22; 23]. Therefore, spatial multiplexing can reduce the interference caused by ambient light. Several alternative methods have been proposed for driving light sources (LEDs or LASER) in VLC systems. On-off keying (OOK) is one of the simplest modulation techniques used in VLC, but it requires detection threshold or logic level classification methods to estimate information bits for user mobility, which is a major drawback [17]. Additionally, the bit error rate (BER) performance of OOK is susceptible to interference from ambient light, as it causes fluctuations in the threshold level. This highlights the need for modulation schemes that can improve data bit detection without relying on a threshold. In this paper, we focus on non-return to zero (NRZ), return to zero (RZ), and quadrature phase shift keying (QPSK) to identify the most effective modulation schemes for improving the performance of MIMO-VLC systems [24; 25; 26]. In this paper, we study the performance of MIMO-VLC systems using various modulation techniques, with a focus on their transmission capabilities over different distances and resilience to ambient light interference. The spatial multiplexing inherent in MIMO systems helps reduce interference from ambient light, enabling MIMO to outperform SISO consistently in terms of distance coverage. Among the modulation schemes, NRZ and QPSK prove to be the most effective, demonstrating superior performance in mitigating ambient light interference. MIMO systems also exhibit greater robustness in challenging lighting conditions. The insights gained from this study provide valuable guidance for designing communication systems that meet specific performance requirements in diverse environments.
## 3 Results
### Relationship between the Population Density and Residential Imperviousness  
The range of residential imperviousness and population density used in this study is within 0.7-51% and 365-4762 persons/km\({}^{2}\). Linear and three nonlinear regression models were developed between population density (PD) and residential imperviousness (I\({}_{\rm r}\)) (Table 1). Based on R\({}^{2}\), MAE, and RMSE, residential imperviousness in the 154 suburbs can be estimated with a higher accuracy by linear regression model than power regression model. Therefore, the linear regression model which leads to lower error is selected as the best model in this study (Figure 6). Exponential and logarithmic models, however, significantly estimate the residential imperviousness with lower correlation and accuracy compared to the linear and power models. Figure 6 also shows predicted residential imperviousness by the best model and observed data.  
Results reveal a stronger relationship between population density and residential imperviousness than that between the population density and total imperviousness (Figures 5 and 6). This is mainly owing to removal of important non-residential impervious areas such as service buildings (hospitals, universities, shopping centers), industrial areas, and major highways in each suburb. If the current land use map could separate other non-residential impervious areas such as streets and pedestrian pathways, a stronger model than the recent linear regression model (Table 3) can be developed. In this model, errors related to building structures are the major source of error which reduces accuracy of the developed model between population density and residential imperviousness. Having said that, information about height of building, as an important characteristic of building structure at suburb scale, is not available in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Regression** & **Equation** & **R\({}^{2}\)** & **MAE** & **RMSE** \\ \hline Linear & I\({}_{\rm r}=0.0108\) PD + 0.16 & 0.77 & 4.4 & 5.4 \\ \hline Nonlinear (power) & I\({}_{\rm r}=0.0039\) PD\({}^{1.13}\) & 0.76 & 4.5 & 5.55 \\ \hline Nonlinear (exponential) & I\({}_{\rm r}
# Ultra-Wideband Antenna Design for 5G NR Using the Bezier Search Differential Evolution Algorithm
###### Abstract  
As the energy crisis is leading to energy shortages and constant increases in prices, green energy and renewable energy sources are trending as a viable solution to this problem. One of the most rapidly expanding green energy methods is RF (RadioFrequency) energy harvesting, as RF energy and its corresponding technologies are constantly progressing, due to the introduction of 5G and high-speed telecommunications. The usual system for RF energy harvesting is called a rectenna, and one of its main components is an antenna, responsible for collecting ambient RF energy. In this paper, the optimization process of an ultra-wideband antenna for RF energy harvesting applications was studied, with the main goal of broadening the antenna's operational bandwidth to include 5G New Radio. For this purpose, the Bezier Search Differential Evolution Algorithm (BeSD) was used along with a novel CST-Matlab API, to manipulate the degrees of freedom of the antenna, while searching for the optimal result, which would satisfy all the necessary dependencies to make it capable of harvesting RF energy in the target frequency band. The BeSD algorithm was first tested with benchmark functions and compared to other widely used algorithms, which it successfully outperformed, and hence, it was selected as the optimizer for this research. All in all, the optimization process was successful by producing an ultra-wideband optimal antenna operating from 1.4 GHz to 3.9 GHz, which includes all vastly used telecommunication technologies, like GSM (1.8 GHz), UMTS (2.1 GHz), Wi-Fi (2.4 GHz), LTE (2.6 GHz), and 5G NR (3.5 GHz). Its ultra-wideband properties and the rest of the characteristics that make this design suitable for RF energy harvesting are proven by its \(S_{11}\) response graph, its impedance response graph, its efficiency on the targeted technologies, and its omnidirectionality across its band of operation.  
ultra-wideband antenna; optimization methods; Bezier Search Differential Evolution Algorithm; RF energy harvesting; 5G new radio 2025  
3
# Getting Young People to Farm: How Effective Is Thailand's Young Smart Farmer Programme?
## 2 The Young Smart Farmer Programme Background and Evaluation Framework
### Evaluation Framework  
We outlined the theory of change for the YSF programme, as implemented in the Prachin Buri province in Thailand between 2014 and 2018, using a result chain to clearly demonstrate the link between inputs, activities, outputs, outcomes, and aims of the programme (Figure 1). This is an important first step in any programme impact evaluation, as it helps to define a clear evaluation question, in which the answer clearly benefits future policy and an understanding of why a programme might succeed or fail its desired outcomes [29]. We focused on the measurable desired outcomes of the programme from our collected data, that is, to evaluate whether they have been achieved or not. We measured the outcome on increased financial independence by net farm income and the enhanced innovative methods adoption by examining whether participants adopted innovative methods other than common machinery and chemicals. As per [PERSON] and [PERSON] [2], this enabled us to indicate the probability of the programme's achievement regarding its final aim (in particular, the increase in the number of young people continuing, returning to, or entering farming), although we did not directly focus on this aim. Two hypotheses could, therefore, be formulated:  
**Hypothesis 1 (H1).**_Participation in the YSF programme increases the financial independence of the participants._  
**Hypothesis 2 (H2).**_Participation in the YSF programme helps to adopt innovative farming methods._  
Satisfaction is an important proxy for the success or failure of a development programme's outcome [30], as it can show whether a programme's support has fulfilled participants' expectations and improved them in a desired outcome [30; 31]. Participating farmers who are satisfied with an agricultural extension programme and their situation are more likely to continue farming [32]. We, therefore, also focused on evaluating participants' levels of satisfaction with the programme. A third hypothesis could, therefore, be formulated:  
**Hypothesis 3 (H3).**_Participants in the YSE programme are satisfied with the programme._
## 5 The Proposed Hybrid Scheme
### Application Scenarios and Architecture  
In this subsection, we describe the possibility of integrating our proposed scheme with the FANET application scenario. In order to deploy FANET in different application scenarios, the UAV can be equipped with cameras, data storage, sensors, an IMU, a GPS unit, and other embedded devices such as a flight controller, on-board processing units, and short-ranged radio transceivers (i.e., Bluetooth and Wi-Fi). In surveillance applications, such as patrolling a specific region, UAVs play an important role in minimizing human involvement. These missions involve information collection from battlefield to earthquake regions. Surveillance tasks may include collecting images and video streaming from the mission area. In our proposed system, the mission area is divided into zones, where a multi-layer UAV ad-hoc architecture is deployed as shown in Figure 5. In this architecture, multiple groups of heterogeneous UAVs are placed in different zones. The lower layer is used for communication between the BM-UAV and GW-UAV, while the upper layer is concerned with communication between the BM-UAV and BS-UAV. The GW-UAV will be further connected with the ground station. The ground station only holds the information, which is exclusively routed to it, thus reducing the computation costs on the ground station.
## 2 Materials and Methods
### BMP_Net: A Point Cloud Deep Regression Network Incorporating Neighbor Attention
#### 2.3.3 N2P Attention: Neighborhood Point Attention Network  
N2P Attention is a key attention mechanism specifically designed for point cloud data processing [34]. Its structure includes three main components: query (Q), key (K), and value (V), and it works by calculating the correlation between a point and its neighbors to capture local information. Since point cloud data typically has complex structures and high-dimensional features, traditional methods struggle to effectively capture local information and relationships. N2P Attention, on the other hand, can capture the information from the neighbors surrounding each point, allowing for a better understanding of the local structures and features within point cloud data. This results in improved accuracy and performance in biomass prediction tasks. By introducing N2P Attention, the network can more fully leverage the relationships between the points in the cloud, enabling the model to make more accurate predictions of biomass while maintaining efficiency and flexibility when processing large-scale and complex point cloud data. The structure of N2P Attention is illustrated in Figure 5.  
Figure 4: Architecture of the Point Cloud Transition Downsampling (PTransDown) module. It employs “FurtherSample” selection, local region “Query and Group”, “hierarchical set mixing’ for feature learning, ‘Channel Shrinking’, ‘Softmax’ normalization, and a ‘linear’ transformation. This reduces point density while preserving key structural information for biomass prediction.  
Figure 5: Diagram of the Neighborhood Point Attention (N2P Attention) mechanism. Input features (Nxd) are processed using k-nearest neighbors (knns) to form query (Q), key (K), and value (V) projections. Attention weights, derived from Q and K, are applied to V, and the result is combined with original features via a residual connection and a feed-forward network to capture salient local geometric details.
## 2 Materials and Methods
### Data Processing
#### 2.4.1 Data Reduction
(**A**) the linear distance between the COP and _XCOM_ in mediolateral (M/L) and anteroposterior (A/P) directions, i.e., _COP–XCOM M/L_ and _COP–XCOM A/P_, respectively, (**B**) the linear distance between U\({}_{\rm MAX}\) (i.e., head of the fifth metatarsal) and _XCOM_ in the mediolateral direction, i.e., \(U_{MAX}\)–_XCOM M/L_, and (**C**) the linear distance between U\({}_{\rm MAX}\) (i.e., between the heads of the first and second metatarsal) and _XCOM_ in the anteroposterior direction, i.e., \(U_{MAX}\)–_XCOM A/P_. Based on the rigid body and inverted pendulum model assumptions, the extrapolated center of mass (\(XCOM\)) was computed as follows:  
\[XCOM=COM+(V_{COM}/\omega_{0}) \tag{1}\]  
Where \(COM\) and \(V_{COM}\) are the instantaneous position and velocity of the whole-body \(COM\), respectively, and \(\omega_{0}\) is the angular eigenfrequency of the pendulum, which is calculated as follows:  
\[\omega_{0}=\sqrt{g/l} \tag{2}\]  
where \(g\) = 9.81 m/s\({}^{2}\) and \(l\) is the leg length (defined as the distance from the \(COM\) to the ankle joint center in meters) [21]. The boundaries of the BOS were defined by the foot markers, with the toe marker as the anterior limit (_A/P U\({}_{MAX}\)_) and the fifth metatarsal marker as the lateral limit (_M/L U\({}_{MAX}\)_). The \(MOS\) in the A/P and M/L directions (in the virtual foot coordinate system) were determined relative to these boundaries. Dynamic stability during turning was calculated using the \(MOS\) and \(XCOM\), with the following equation:  
\[MOS=U_{MAX}-XCOM \tag{3}\]  
where \(MOS\) is the shortest distance between the boundaries of the BOS and the \(XCOM\), expressed in millimeters. The distances between both the COP and \(XCOM\) and the COP and COM were also determined in the A/P and M/L directions. The COM-COP inclination angle was defined by the 3D angle between the vertical projection on the ground of the COM and a line connecting the COM to the most lateral position of the COP. Our secondary variables of interest included spatiotemporal parameters (ST): velocity, stride width, stride length, step length, cadence, stance time, and swing time. Following Visual3D data reduction, data were exported to Excel and SAS_JMP (JMP, Cary, NC, USA) for descriptive and statistical analyses.
# A Fairness-Aware Peer-to-Peer Decentralized Learning Framework with Heterogeneous Devices
## 3 A Distributed Learning Framework
### Security Constraints for Distributed Learning in IoT  
For distributed learning architectures, a malicious user has the ability to inject poisoned data or compromised parameters to undermine the model [19; 26]. In the data poisoning attack, the training datasets are manipulated, and the model is optimized with incorrect features [27; 28]. For example, [PERSON] et al. [29] demonstrated that the trained model for reading comprehension could be compromised with only a short sentence added to the sample. In distributed machine learning scenarios, the adversarial attacks could pose a serious risk due to the loose control of the participants [30]. Moreover, federated learning-based systems are prone to single-point failure [31]. In such a case, if the parameter server is down, the whole system will halt. To tackle such a problem, decentralized learning frameworks have been proposed [20; 32]. Because the central server is not available and clients have no prior knowledge of other peers (i.e., distribution), the client itself has to guarantee its own performance during the aggregation process.  
While the emerging decentralized learning solves the aforementioned issues in federated learning systems, it also raises other security concerns. Among them, Byzantine and Sybil attacks are two of the most common and critical attacks. Byzantine adversaries are the users who follow the system protocol and yet disseminate malicious information in the system, thereby misleading or even controlling the system performance. In the decentralized system, a Byzantine adversary may report bogus model information so that the normal model aggregation process in the system can be affected. Such a bogus model, being aggregated into the global or neighbor's model, leads the aggregated model to perform faulty results on some deliberately designed data samples. On the other hand, Sybil adversaries are the malicious users who create multiple identities to disrupt the system operations. In the decentralized learning system, a Sybil adversary may create multiple identities and submit a series of bogus information so that a certain Byzantine-tolerant aggregation method in a regular learning paradigm could be bypassed.  
Decentralized machine learning frameworks can improve stability by reducing the dependency on the single node. In addition, a fully decentralized framework has to consider the heterogeneous problem (data distribution, unbalanced dataset, resource constraints, etc.). To ensure the performance, clients need to adopt an adaptive aggregation mechanism to establish the optimized model when receiving the information from its peers.