esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Our proposed method works well for scenarios where semantic clustering of the scene is possible. It is based on the solid theoretical foundation of the GradCAM and CART with additional proxy geometry heuristics. It excelled in the explanation process in a virtual simulation system based on video data with relatively low resolution. It performs very well in multimedia systems, such as computer games or simulations, with a clearly defined Agent's goal and few semantic classes. It can be used to provide an in-depth analysis of NN action rules generated in the PPO process by decision tree approximation. The rules generated by the CART tree are not only characterized by a high level of accuracy but are relatively easy for humans to interpret and lead to intuitive conclusions.  
Our proposed method also has some limitations. First, if an agent can perform many possible actions (for example, a dozen quantized rotation directions), this could increase the depth of the decision tree and the difficulty of rule interpretation. Also, many semantic classes could cause difficulties in interpreting the tree due to the increasing complexity of the rules. The method proposed in the paper was tested and validated in a specific virtual environment. It has to be remembered that in many scenarios, the virtual environment is a basic and only environment where vision-based reinforcement learning algorithms operate, for example, in medical applications such as landmark detection, object tracking, image registration [63] or in computer games [35]. The generalization ability of the proposed algorithm in complex real-world environments remains an open question; however, currently used virtual reality systems have high rendering quality, creating almost photorealistic and physics-accurate simulation environments. For this reason, it can be anticipated that the application of the currently popular virtual-to-real-world transfer learning paradigm, which involves training a system used in the real world based on virtual/synthetic data [64], may allow the application of our algorithm for non-virtual objects. The issue of applying the solution proposed in this work to real-world and especially robotics applications is a promising research topic worth pursuing. Another interesting issue is the possibility of completely replacing the deep learning model with an explainable equivalent. This would be useful when an explainable equivalent would have virtually identical performance to the neural network approach. In our case, this would also require creating a fast, real-time, lightweight, and mobile GradCAM algorithm implementation, which is a technological challenge at the moment.  
Conceptualization, T.H.; methodology, T.H.; software, T.H.; validation, M.P. and T.H; formal analysis, T.H. and M.P.; investigation, T.H.; resources, T.H.; data curation, T.H.; writing--original draft preparation, T.H. and M.P.; writing--review and editing, T.H. and M.P.; visualization, T.H.; supervision, T.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### Stages in Pre-Processing
#### 5.3.2 The Step of Transformation  
Transformation converts a property's category value into a number using a one-hot encoding algorithm. For instance, it uses this function to transform several protocols, such as the transmission control protocol (TCP) and the user datagram protocol (UDP), into numerical data.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 3 Results
### Ablation Experiments
#### 3.2.2 Ablation Experiment of the Feature Engineering  
To verify the effectiveness of feature engineering, we use UNet and MAFANet to conduct ablation experiments in different feature engineering (Table 5). We performed ablation experiments on the image of NDVI, the UGS-1 image, the image of fused NDVI in UGS-1, the UGS-2 image, and the image of fused NDVI in UGS-2 to test accordingly.  
When the NDVI feature was incorporated into UGS-1 data, the MIOU of UNet and MAFANet increased by 1.95% and 1.94%, the IOU (low vegetation) increased by 0.67% and 1.14%, and the IOU (tree) increased by 1.59% and 1.70%, respectively. When the false color image features were incorporated into UGS-1 data, the MIOU of UNet and MAFANet increased by 2.56% and 2.49%, the IOU (low vegetation) increased by 1.77% and 1.90%, and the IOU (tree) increased by 2.16% and 2.08%, respectively. The integration of false color image and the NDVI index into UGS-1 data can further improve the accuracy of UGS segmentation by MAFANet from 72.15% to 74.73%.  
Figure 11: UGS-1 ablation experiment visualization results. (**a**) Input image; (**b**) Label; (**c**) baseline; (**d**) baseline+ DE; (**e**) baseline+ DE+ BFF; (**f**) baseline+ DE+ BFF+ Pyconv; (**g**) baseline+ DE+ BFF+ GAM; (**h**) baseline+ DE+ BFF+ MSPA.  
Figure 12: UGS-2 ablation experiment visualization results. (**a**) Input image; (**b**) Label; (**c**) baseline; (**d**) baseline+ DE; (**e**) baseline+ DE+ BFF; (**f**) baseline+ DE+ BFF+ Pyconv; (**g**) baseline+ DE+ BFF+ GAM; (**h**) baseline+ DE+ BFF+ MSPA.  
In general, the fusion feature engineering can effectively improve the segmentation accuracy, and the above improvement strategy proves the effectiveness of the fusion feature engineering method. At the same time, several representative remote sensing images are selected for visual analysis, and the results are shown in Figure 13. After blending false color and NDVI feature engineering, the boundary of UGS is further distinguished. MAFANet's UGS segmentation is more accurate and is close to the real situation of the surface.
## 1 Introduction
The objective of [18] was to model the Birjand aquifer (Eastern Iran) using GMS: MODFLOW to monitor the groundwater status in the Birjand region. The model's results agreed with the observed data; therefore, the model could be used to study the changes in the aquifer's water level. [PERSON] et al. [19] addressed the questions of how reliable hyporhetic groundwater models are in typical applications examining such flow exchanges, and how reliability changes with increased data availability and model sophistication. The increased model sophistication was shown not to lead to improved model reliability as the travel time predictions from the homogeneous model were equal to, or better than, the predictions from the heterogeneous models. [PERSON] et al. [20] analyzed the connection between shallow groundwater and vegetation growth, which is generally very close. The first conclusion was that the water table and salinity are the main factors controlling shallow groundwater. Secondly, regulation plans for the water table and salinity were designed based on the corresponding regulation target. Finally, the output results from the software could provide information on how to regulate shallow groundwater in different scenarios. Various studies deal with the effects piles have on groundwater and their mutual interaction. For example, in [21], pile stability due to rising groundwater levels was evaluated to propose preliminary guidelines to protect existing structures. The results showed that groundwater rises caused upward movement of the ground and the pile foundation. It also caused a loss in pile foundation capacity. As a result of the soil swelling around the pile, the pile shaft friction was reduced. The same authors in another study [22] used a numerical model to investigate pile behavior due to the rising groundwater level. The pile-soil movement, load the distribution along the pile, and the reduction in pile capacity were observed. Besides the fact that the pile load capacity was again significantly reduced by the increase in the groundwater level, it was concluded that the pile foundation showed less heave than the surrounding soil surface during the groundwater level rising. [PERSON] et al. [23] analyzed the influence of the groundwater level and cyclic load magnitude on the performance of the pile-soil system, introducing the fractal theory. It was shown that the fractal dimension could reflect the load response of the pile-soil system under cyclic loading. This study's primary goal was to gain insight into the interaction of groundwater flow and foundation piles near a river using a 3D numerical model described below. The findings suggest that the piles represent an obstacle to the groundwater flow, causing a backwater effect upstream while increasing the local flow velocity. On the other hand, a high flow velocity around the piles can cause the suffusion of the surrounding soil in the long term, thus significantly reducing the shaft resistance of the piles.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.9 Quality and Integrity of Data for ML Algorithms  
The accessibility of quality datasets is essential for training ML algorithms. To obtain a reliable model that adapts to the domain of a particular research problem requires data with high integrity in order to extract the right features. As alluded by [135], this it is a herculean task that often requires the initiative of very big hospitals or health boards to collect patient medical records or data for research due to the need to ensure that patient data remains confidential. Even when such data are obtained, they may contain outliers that can adversely influence research outcomes if not properly handled.
## 2 Literature Review and Background Studies  
### Fibre-Reinforced LWAC as a Structural Insulation Building Envelope Material  
[PERSON] et al. [12] concluded that using lightweight concrete in a building envelope is an important approach in reducing the amount of heat transfer and energy consumption because of the low thermal conductivity of lightweight concrete compared to NWC. [PERSON] et al. [11] discovered that heating energy needs are 15% lower for LWAC as compared with NWC in Europe. Thus, designing a building envelope that uses LWAC could have a significant contribution to energy savings. The thermo-physical properties of a building envelope play a critical role in the thermal comfort and energy performance of buildings [13].  
[PERSON] and [PERSON] [14] reported that the specific heat capacity for NWC is 790-960 J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\), while for structural LWAC ranges from 920-1000 J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\). LWAC has higher thermal mass capacity than NWC. [PERSON] et al. [15] studied LWAC without fibre and had thermal conductivity ranging from 0.94-1.21 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\), a specific heat range from 932-1000 J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\) and a thermal diffusivity range of 0.62-0.73 mm\({}^{2}\cdot\)s\({}^{-1}\). [PERSON] et al. [16] concluded that increasing the steel (ST) fibre does not increase necessarily the thermal conductivity because of an increase in porosity. However, limited studies on thermal and mechanical properties of LWAC with fibres have been conducted to allow for conclusive results.  
[PERSON] et al. [17] studied the thermo-mechanical properties of steel and PP fibre-reinforced lightweight expanded clay aggregate (LECA)-LWAC. The addition of ST fibre will increase its mechanical strength and worsen the thermal conductivity while the polypropylene (PP) fibre has opposite effects. [PERSON] et al. [18] determined that steel fibre at high dosage will decrease its mechanical strength and increase the thermal conductivity k-value.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### Mobile Band-Based Technologies
#### 2.1.2 Narrow-Band IoT (NB-IoT)  
Narrow-Band IoT (NB-IoT), a.k.a. LTE Cat NB1, is another derivation of the LTE standard which is also specified in 3 GPP release 13. It is designed for IoT applications that are even more constrained than the ones using eMTC. It is based on narrow-band communications, and uses a bandwidth of 180 kHz. As a result, the data rate is greatly reduced (around 250 kbps down-link and 20 kbps up-link), which makes FotA updates hard to achieve using NB-IoT. On the bright side, NB-IoT consumes less energy power, and benefits from a greater range than eMTC. NB-IoT can be used with three different modes: in-band, guard-band LTE, and standalone. In-band mode uses the LTE band, guard-band uses  
Figure 3: LTE-M and NB-IoT coverage map [13].  
the unused part of the LTE band, and standalone uses a dedicated spectrum (e.g., GSM bands). NB-IoT does not support handover, so it is hardly worth consideration for mobile IoT applications.  
NB-IoT is already available in some regions and is still being deployed in other countries, as visible in Figure 3. However, its deployment is not as easy as eMTC because NB-IoT requires a hardware upgrade of the existing LTE infrastructure.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 4 Performance Metrics
### Product of Efficiency and Jain's Fairness Index  
It was discussed earlier that efficiency and fairness are related by a trade-off. It is useful to define a performance metric which takes both metrics into account, therefore the product of the aggregate capacity and the well-known Jain's fairness index \(Alloc\cdot\gamma_{\text{Jain}}\) are considered as well in this analysis:  
\[Alloc\cdot\gamma_{\text{Jain}}=\sum_{[s,t]\in K}\varphi^{st}\cdot\frac{( \sum_{[s,t]\in K}\varphi^{st})^{2}}{|K|\cdot\sum_{[s,t]\in K}(\varphi^{st})^{2 }}. \tag{34}\]
## 8 Numerical Example
### Forced Vibration Nonlinear SDOF System with Damping  
A forced vibration nonlinear damped SDOF system is considered in this example. The mass and nonlinear stiffness are taken to be \(m=5\,\mathrm{kg}\) and \(k_{i}~{}=~{}10^{4}\Big{(}1-5\sqrt{|u_{i}-u_{i-1}|}\Big{)}\,\mathrm{N/m}\) for \(i~{}=~{}1,~{}2,~{}\dots,~{}n\). Meanwhile, the nonlinear velocity dependent damping is assumed as:  
\[c_{i}~{}=~{}a\big{|}\dot{u}_{i}-\dot{u}_{i-1}\big{|}^{b}N-s/m\ \mathrm{for}\ i~{}=~{}1,~{}2,~{}\dots,~{}n. \tag{22}\]  
In general, \(a>0\) and \(b>-1\) are generally found [36]. In this exploration, \(a~{}=~{}10\) and \(b~{}=~{}-\frac{1}{2}\) are taken. The ground acceleration is taken as \(a_{g}~{}=~{}10(\sin t+\sin 2t)\). In this numerical experiment, AAM, with the time of 0.005 s, is taken as a reference solution. CVM (0.005, 0.5) and CVM (0.005, 1) are chosen for the analysis. The displacement responses are shown in Figure 8. The CVM results match exactly the AAM and it reveals that CVM can accurately solve nonlinear velocity-dependent problems. Hence, it is proved that the CVM can be successfully used for the velocity-dependent problem, while AAM is an implicit method.  
Figure 7: Comparison of the overshoot response of CVM for the third initial conditions.
## 3 Results and Discussion
### Total Carbon Dioxide Emissions of Each Slab System
A total of 3.52% and 10.07% of the carbon dioxide emissions reduction are due to the manufacturing of building materials (\(E_{1}\)); 3.91% and 7.07% of reduction are due to the transporting of building materials to the construction site (\(E_{2}\)); and 3.62% and 10.14% of carbon dioxide emissions were reduced in the construction equipment usage on site (\(E_{3}\)). Amongst the three cases, the manufacturing of building materials contributed the largest proportion of carbon dioxide emissions reduction (see Figure 4):  
In the current study, the reductions of carbon dioxide emissions from the transporting of building materials to the construction site were 3.91% and 7.07% for the flat plate slab and the voided slab \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Emissions (kg CO\({}_{2}\) eq.)**} & \multicolumn{2}{c}{**Reduction Ratio (\%)**} \\ \cline{2-5}  & **FPS** & **VDS** & **FPS** & **VDS** \\ \hline \(E_{1}\) & \(-\)113,741 & \(-\)325,381 & \(-\)3.52 & \(-\)10.07 \\ \(E_{2}\) & \(-\)955 & \(-\)1724 & \(-\)3.91 & \(-\)7.07 \\ \(E_{3}\) & \(-\)3758 & \(-\)4913 & \(-\)18.43 & \(-\)24.10 \\
**Total reduction** & \(-\)118,452 & \(-\)213,565 & \(-\)3.62 & \(-\)10.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reduction of the total carbon dioxide emissions. Figure 4: Total carbon dioxide emissions (unit: kg CO\({}_{2}\) eq.). systems, respectively. Moreover, 18.43% and 24.10% reduction of the carbon dioxide emissions for flat plate slab and the voided slab systems, respectively, were achieved by the utilisation of construction equipment on site. Table 6 shows that the major contributor of reduction of the total carbon dioxide emissions was manufacturing of the building materials in all cases. On the other hand, the largest reduction ratio was shown in the utilisation of construction equipment on site for the flat plate slab and the voided slab systems. From the perspective of the reduction in each emissions source when replacing the ordinary reinforced concrete slab by alternative methods, columns 4 and 5 in Table 6 indicate that the voided slab system would be the most beneficial approach to the reduction of CO\({}_{2}\), compared to the ordinary reinforced concrete slab method. In the case of the voided slab system, a reduction of 10.07% of the emissions from the manufacturing of building materials, 7.07% of the emissions from the transporting of building materials, and 24.10% of the utilisation of construction equipment were indicated when replacing the ordinary reinforced concrete slab by the voided slab system. Similarly, 3.52%, 3.91%, and 18.43% of reduction for the manufacturing of building materials, transporting of building materials to the construction site, and utilisation of construction equipment, respectively, were noted from the adoption of the flat plate slab system. Based on these results, the most effective method to mitigate the impact of the carbon dioxide emissions from the slab system of buildings would be the voided slab system as indicated in Figure 5.
## 4 Engineering Example
### Analysis of Algorithm Dependence on the Number of Measured Values  
To investigate the dependence of the algorithms on the number of measured values, we employed the last 10 measured points as the data support for parameter inversion. The parameters obtained through inversion were then used to predict the values for the first 12 measured points. We calculated the error between these predicted values and the actual measured values of the first 12 points. This analysis aimed to assess the sensitivity of the four algorithms to variations in the number of measured values. To minimize the impact of algorithmic fluctuations, we conducted 20 experiments for each of the four algorithms.  
Figure 10 illustrates the minimum values and the average results from these 20 experiments. Notably, the root mean square error of the improved modular vector method is consistently  
Figure 9: Inversion fluctuations in each algorithm’s parameters and mean value of fitting root mean square error.  
smaller than that of the genetic algorithm, particle swarm algorithm, and simulated annealing algorithm. This finding underscores that the improved modular vector method exhibits less dependence on the number of measured values than the other three algorithms. Consequently, it possesses a superior capability of withstanding the loss of measuring points. In practical situations with challenging observation conditions, reducing the number of observation points can alleviate workload while still yielding accurate inversion results.
# GE-YOLO for Weed Detection in Rice Paddy Fields
###### Abstract  
Weeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. The current research has yet to address these issues adequately. Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network's ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. Multiple experiments demonstrate that GE-YOLO achieves 93.1% mAP, 90.3% F1 Score, and 85.9 FPS, surpassing almost all mainstream object detection algorithms such as YOLOv8, YOLOv10, and YOLOv11 in terms of detection accuracy and overall performance. Furthermore, the detection results under different lighting conditions consistently maintained a high level above 90% mAP, and under conditions of heavy occlusion, the average mAP for all weed types reached 88.7%. These results indicate that GE-YOLO has excellent detection accuracy and generalization performance, highlighting the potential of GE-YOLO as a valuable tool for enhancing weed management practices in rice cultivation.  
weed detection; YOLOv8; Gold-YOLO; EMA; GIOU loss Article
## 2 Theoratical Description  
[PERSON]'s action should be restricted to prevent Trojan horse attacks. She is physically limited by Raman scattering when retrieving quantum states. Obviously, the Trojan horse pulses will increase the excess noise, so the secret key rate will be reduced. The source of the increased excess noise is blind for legitimate parties. Therefore, when the excess noise increases, [PERSON] and [PERSON] do not know where the excess noise comes from. This may be caused by a defect in the device or a mutation in the communication environment. In this case, Trojan horse attacks will not be easily discovered. The only requirement to consider during Trojan horse attacks is the secret key rate should be positive. In the case of the collective attack, the reverse reconciliation CVQKD without the finite length effect, the key rate can be expressed as:  
\[K=\beta I_{AB}-\chi_{BE}, \tag{1}\]  
where \(\beta\in[0,1]\) represents the efficiency of information reconciliation, \(I_{AB}\) and \(\chi_{BE}\) represent the mutual information between [PERSON] and [PERSON] and the Holevo bound representing [PERSON]'s possible information on [PERSON]'s key. The upper bound on an eavesdropper's information, the Holevo information \(\chi_{BE}\), (assuming reverse reconciliation) reads:  
\[\chi_{BE}=S_{E}-S_{E|B}=S_{AB}-S_{A|B}, \tag{2}\]  
where \(S_{E}\) is the von Neumann entropy of the state accessible to [PERSON] for collective measurement and \(S_{E|B}\) is the von Neumann entropy of the same state after a projective (homodyne or heterodyne) measurement has been performed by [PERSON]. Obviously, from the above analysis, we can see that in order to extract the key information, the secret key rate should be positive. Only in this case can the key sharing between [PERSON] and [PERSON] be successfully realized, and [PERSON] can effectively obtain the key information by attacks. When the transmission distance of the system is constant, there is a maximum excess noise value \(\epsilon_{max}\) satisfies this condition.  
In Figure 2 above, we simulated the variation of the secret key rate with distance under different excess noises. The modulation variance \(V_{A}\) is optimized, the quantum efficiency \(\eta_{B}\) is 0.6, the electronic noise \(v_{el}\) is 0.01 (in shot noise units), the practical reconciliation efficiency \(\beta\) is 93%. When the transmission distance is 50 km, as long as the excess noise satisfies \(\epsilon\leq 0.0145\), [PERSON] and [PERSON] can think that the key has been successfully transmitted.