esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Limitations and Practical Implications  
Although the YOLOv11-OAM model achieved strong overall performance, several limitations remain that warrant further research:  
1. Class-level confusion. The model exhibited persistent misclassification between morphologically similar species. For example, saxau trees often showed relatively low recall due to their small canopy size, indistinct boundaries, and visual similarity to apricot or prune. These factors led to frequent confusion or missed detections, especially under conditions of crown overlap and variable illumination. Future work will explore the use of multimodal features, attention-based modules, and class-balanced loss functions to specifically mitigate such misclassification issues.
2. Data modality limitations. The current study relies solely on UAV RGB imagery. While practical and cost-effective, this restricts the model's ability to capture structural and spectral cues necessary for distinguishing visually similar or underrepresented classes. Incorporating multimodal data--such as hyperspectral imaging, LiDAR point clouds, or time-series UAV imagery--could provide complementary spatial, spectral, and structural features for fine-grained tree species discrimination.
3. Deployment challenges. Although the proposed model achieved real-time inference (65+ FPS with \(\sim\)20 M parameters on an RTX 4090 GPU), practical deployment on UAVs or edge devices remains constrained by hardware limitations, including memory, power consumption, and onboard computing capacity. Nevertheless, the lightweight design of YOLOv11-OAM suggests that with further optimization, real-time deployment on embedded GPUs (e.g., NVIDIA Jetson Orin or Nano) is feasible. In future work, model compression techniques such as pruning, quantization, and knowledge distillation will also be explored to further reduce computational cost while maintaining accuracy.  
From a practical perspective, the YOLOv11-OAM framework is well-suited for UAV-based orchard monitoring and precision agriculture tasks, such as targeted irrigation, pruning, and pest management. Future research will address the above limitations by improving robustness to seasonal phenological changes, variable lighting conditions, and unseen orchard layouts.
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 2 Related Works  
Image retrieval can be seen as a ranking problem: the goal is to sort gallery images by their relevance to the given query. This has led researchers to train end-to-end neural networks by pushing the visual representations of highly relevant images closer and vice versa. We have witnessed the development of network structures and loss functions specifically designed for image retrieval for a long time [10; 11; 12]. Recently, representation learning by directly optimizing the average precision is believed to be one of the state-of-theart methods. We therefore adopt image features learned by using APLoss [10] throughout our experiments.  
Once the image features are obtained, the most used ranking protocol is the well-known k-nearest neighbor (k-NN) search. However, since k-NN is not always suitable for all types of feature distributions, some re-ranking approaches are developed to achieve better retrieval performance. Besides the famous query expansion [13], diffusion is also a competitive candidate. The origin of diffusion can be traced back to the PageRank developed in the late 1990s [14]. In 2004, ref. [15] proposed a novel method to rank on data manifolds by capturing the local structure of underlying data manifolds, which indeed becomes the first version of diffusion even though it was not called diffusion at that time. A lot of variations of diffusion are later developed based on the random walk mechanism proposed by [15]. For example, ref. [16] proposed a variant of the diffusion process and explained why the diffusion process on tensor product graph can better reveal the intrinsic relationship between objects. Ref. [4] proposed to conduct diffusion not only on global features but also regional features. Even though diffusion is known for its performance boost, its computational complexity has become the major obstacle to practical applications. After realizing that, some researchers began to tackle the inefficiency. Ref. [5] proposed to decouple diffusion into online and offline processing, making the online search a simple linear combination on top of a k-NN search.  
Another technique related to this work is local anchor embedding (LAE). LAE was originally proposed to address the scalability issue in graph-based semi-supervised learning [17]. Its basic idea is to reconstruct any data point as a convex combination of its nearest cluster centers. This is undoubtedly inspired by locally linear embedding (LLE) [18], where each data point is approximated by a linear combination of its nearest neighbors. In this work, we utilize LAE to bind the gallery images to a set of anchors.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Scalability and System Constraints  
Device limitations, in terms of resources (computation power, battery power, storage, connectivity, and bandwidth), restrict the integration of resource-hungry security mechanisms. When security features are incorporated, the next challenge is the scalability issue,  
Figure 7: User as Admin for Authorisation.  
because this relates directly to performance, both in terms of device computation ability and network tolerance. The sensors dealing with multimedia information are resource-demanding, and adding security features strains resources further. The amount of IoT data that a robot can handle and manage, to meet real-time response requirements with a high accuracy rate, is a research domain to explore. Considering the number of sensors that would be used, Bluetooth technology might not be the best way forward compared to Wi-Fi technology, because this allows a higher number of client connections. Incorporating blockchain technology to maintain transparency and traceability will again be resource-draining, because of its demands on computation, energy, storage, and bandwidth. That is the reason why it has been proposed that the raw data should not be stored within the BC system, but only the hash and related key indexing, with identifying values stored in the blockchain blocks and not the data. In fact, it would be impossible to manage and maintain, in terms of resources, especially the storage requirement, if a copy of all data was also distributed across the nodes of the blockchain network. Thus, measures should be taken so that scalability is not an issue when IoT and Blockchain technology are integrated with a robot, and such a secure framework is proposed in Figure 2.
## 3 Our Approach
### Rank Selection Mechanism  
Selecting the rank for tensor decomposition benefits our model in terms of compression ratio and storage cost. Therefore, in this work, we explored the following three different methods for rank selection:  
**Random Rank Selection:** This method assigns a rank to each layer in a stochastic manner, typically based on the tensor size. This approach provides a simple and computationally efficient solution but does not consider the relative importance of different layers in the model's overall performance. Owing to its random nature, this model may achieve suboptimal compression in critical layers or excessive compression in less important layers.  
**Sensitivity-Based Rank Selection:** We introduce a novel sensitivity-based rank selection method that selects the rank of the each layer based on its importance to the overall model performance. This approach assigns higher ranks to layers that have a significant impact on overall model accuracy, ensuring minimal accuracy loss during decomposition Algorithm 1. This method balances between the compression ratio and performance preservation by reducing the redundancy in layers that have a minimal impact on overall model accuracy while preserving the the integrity of the layers that have a large impact on overall model accuracy.  
```
1:procedureSelectRank(\(\mathcal{X},L\))
2: Initialize ranks \(R_{l}\) for each layer \(l\)
3:for each layer \(l\) in \(L\)do
4: Compute sensitivity \(S_{l}\) based on validation accuracy
5:if\(S_{l}<\) threshold then
6: Reduce rank \(R_{l}\)
7:else
8: Maintain or increase rank \(R_{l}\)
9:endif
10:endfor
11:return optimal ranks \(R=\{R_{1},R_{2},\ldots,R_{L}\}\)
12:endprocedure
```  
**Algorithm 1** Sensitivity-Based Rank Selection  
**Variational Bayes Matrix Factorization(VBMF):** VBMF methods uses a probability approach to determine the rank. This methods unfolds the tensor and applies matrix factorization to find the optimal rank that minimizes the overall reconstruction error. VBMF balances model complexity and the fidelity of the original tensor structure [74; 75].  
We apply and study these three rank selection mechanism in order to identify the most effective approach for our ultimate compression method, considering both compression efficiency and model performance preservation.
# An Assessment Scheme for Road Network Capacity under Demand Uncertainty
## 3 Sensitivity-Analysis-Based Approximation Method
### Solving the Capacity Model with Nominal Demand  
The capacity model with the nominal O-D demand is solved as the first stage of the SAB approximation method. The Algorithm 1 is a description of the process [27].  
```
Step 0: Initialization. Determine the initial values of the O-D demand, \(\mathbf{q}^{0}\in Q\). and initial value \(\mu^{(0)}\). Set \(n:=0\). Step 1: Solving the PSL model. Solve the PSL model based on \(\mu^{(n)}\), and obtain the equilibrium solution \(v_{a}\). Step 2: Sensitivity analysis. Use the sensitivity analysis approach to obtain the partial derivatives \(\
abla_{\mu^{0}}(\mu^{(n)})\) for the PSL model. Step 3: Local linear approximation. By employing the derivatives to create local linear approximations of the upper-level capacity restrictions, the approximate linear programming problem can then be solved to create an auxiliary multiplier \(\hat{\mu}^{(n)}\). Step 4: Updating solution. Let \(\mu^{(n+1)}=\mu^{(n)}+\alpha(\hat{\mu}^{(n)}-\mu^{(n)})\), where \(\alpha\) is a predetermined step size, e.g., \(\alpha=1/n\). Step 5: Convergence criterion. Stop if the convergence requirement has been met; if not, return to Step 1 and set \(n:=n+1\).
```  
**Algorithm 1**Solving the capacity model with the nominal O-D demand.  
See Appendix A for a derivation of the sensitivity analysis of the PSL model.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
###### Abstract  
The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth's population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature _Pinus radiata_ D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.  
tree health; precision forestry; sensor fusion; RPAS; drone; RapidEye; plantation forest; radiata pine; forest management; forest productivity +
Footnote †: journal: Remote Sensing  
17 June 2018; Accepted: 1 August 2018; Published: 3 August 2018
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 4 Improved LDPC Decoder Hardware Design and Implementation
### Variable Node Information Processing Module and Decision Module  
According to Figure 3, the base parity-check matrix \(H_{\mathbf{b}}\) includes 8 columns with three non-negative elements each, 11 columns with two non-negative elements each, and 5 columns with six non-negative elements each. Each variable node processing unit handles not only the messages passed through the confidence message matrix but also includes one updated channel data input and one decision signal output. Therefore, the design requires 8 four-input, four-output variable node processing units, 11 three-input, three-output units, and 5 seven-input, seven-output units.  
After receiving the enable signal from the control module and in conjunction with the shift address update module, each variable node processing unit reads the check node  
Figure 8: Check node processing unit.  
messages stored at the same address in H_ram for their respective columns in the base matrix, along with the channel information from llr_ram at the corresponding position.  
After processing, the updated variable node messages are returned to the corresponding column in H_ram, and the processed decision information is output. Every two clock cycles, the dual-port RAM completes a read-and-write operation, incrementing the storage address. Within 56 clock cycles, all decision information is output, and the 28-depth data in all H_ram blocks is updated.  
After all 24 variable node processing units have updated the 672 rows of variable node information, the decision information is also fully output, and both the variable node and decision modules simultaneously send completion signals to the control module.  
The design of the variable node processing unit is depicted in Figure 9. The updated channel data and check node information read from the column are summed up using an addition tree. To prevent overflow due to excessively high input amplitudes during addition, a limiter module prevents result overflow. The sign bit of the sum result is used as the decision output. The sum result is subtracted from each check node message input using two's complement addition, and the limited output is returned to the corresponding H_ram depth as the variable node information.
## 4 Protocol Description
### EAR Performance When Sink Number Increases  
We simulate EAR performance when increase the sink number from 1 to 4 as the delay bound is set from 10 to 100. Figure 5 shows the end-to-end delay as delay bound increases.  
We could see that end-to-end delay decreases as the sink number increases, because more sinks incur more packet delivery within the bound. For a given number sink, the end-to-end delay increases slowly as we relax the bound. For one sink, the end-to-end delay is very small as the bound is 10 ms, because very seldom packet can be delivered within the bound. Figure 6 shows the miss ratio when we decrease delay bound. The packet miss ratio according to delay bound decreases as the sink number increases from 1 to 4. Because more sinks increases the real-time packet delivery probability. Figure 7 illustrates the packet dismiss ratio according to delay estimate. From the result, the dismiss ratio decreases as the sink number varies from 1 to 4. And we can see that EAR provides a good delay estimate and guide packet delivery towards the real-time direction when compared with miss ratio results in Figure 6. Figure 8 shows the average residual energy for node in the simulation time from 0 to 300 s when the delay bound is 70 ms. The average node energy does not vary greatly as we  
Figure 5: End-to-end delay as delay bound increases.  
increase the number of sinks. Since increase the sink number, more packets are delivered by more energy consumption and also less routing trials with increased power. The node energy decreases as we relax the bound because more packets are delivered within the given delay bound.  
Figure 6: Miss ratio percentage as delay bound increases.  
Figure 7: Dismis ratio percentage as delay bound increases.
## 2 Models and Assumptions
### Security Model  
Identity-based cryptography, which is elaborated in [1; 5; 12; 13], is a rationale in this paper. Each node is pre-allocated an unique identity \(\mathrm{ID}_{i}\), one public and private key pair, and a hash function \(\mathrm{H}(\cdot)\). We assume that an authentication node could be captured and replicated in the network. These replicas of the compromised nodes have all the legitimate key materials of the captured nodes. This means that the replicas of the compromised nodes could access the network by pretending to be legitimate nodes. However, though nodes can be replicated, the new legitimate node IDs could not be created. It is also impossible to decrypt the contents of the network by capturing nodes' key materials.
## 4 Experimental Results
### Datasets  
After data preprocessing, three publicly available IIoT-related intrusion detection datasets were employed in this study:  
* **CIC-IDS2017**: The CIC-IDS2017 dataset simulates benign and attack traffic during normal working hours, capturing common threats such as DoS, PortScan, and Brute Force attacks. The total number of flow records after preprocessing is 692,703.
* **CSE-CIC-IDS2018**: The CSE-CIC-IDS2018 dataset contains traffic from various realistic attack scenarios, including infiltration, botnets, and web attacks. It comprises 1,048,574 flow records and was processed using CICFlowMeter to extract standardized network features.
* **CIC-DDoS2019**: The CIC-DDoS2019 dataset is designed to capture DDoS attack patterns targeting multiple protocols (e.g., HTTP, TCP, UDP); this dataset reflects high-volume traffic characteristics. After preprocessing, it includes 431,371 records.  
The distribution of class labels across these datasets is summarized in Table 2. In this context, the Benign label denotes normal traffic, whereas all other labels are considered anomalous. To address the prevalent issue of class imbalance, where benign traffic significantly outweighs attack samples, the SMOTE (Synthetic Minority Oversampling Technique) [13] algorithm was applied. Post-processing, the number of samples in each class was balanced to match the count of the majority class, ensuring an equal representation of all categories during model training and evaluation.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 2 Materials and Methods
### Link between Meteorological and Hydrological Droughts
#### 3.2.2 Moving Average Analysis and Lag Time Identification  
A moving average analysis was performed at multiple time scales to better assess the correlation between SPI and SDI, see Figure 7. Results show that the variations of SPI and SDI were quite comparable; however, the differences between them do exist. In general, a strong correlation was observed between SPI and SDI at the upstream sub-basin 1, i.e., CC = 0.66. Overall based on the correlation analysis with different lag times, it was observed that at both sub-basins, the SDI was 1 month lagging behind SPI at 3-month and 12-month time scales and 2 months lagging for 6-month and 9-month time scales. This period of lagging time was in accordance with the prior study [7].  
Figure 6: The linear relationship between SDI and SPI at the four selected time scales.  
Figure 7: Moving average analysis for lag time identification between SPI and SDI at sub-basins 1, and 2 for different time scales. (**a**,**b**) 3 months, (**c**,**d**) 6 months, (**e**,**f**) 9 months, (**g**,**h**) 12 months.
## 3 Results and Discussion
### Geometric Evaluation
#### 3.3.1 Reference Plate  
The reference plate was defined as an unstiffened plate with dimensions \(a\) = 2000 mm, \(b\) = 1000 mm, and \(t\) = 20 mm; made of AH-36 steel, with material properties \(E\) = 210 GPa, \(\
u\) = 0.3, and \(\sigma_{y}\)= 355 MPa; and subjected to a lateral loading \(P_{z}\) = 0.16 MPa. The application of \(P_{z}\) resulted in an out-of-plane maximum displacement of \(U_{z}\) = 10.40 mm located at the center of the plate (Figure 16a). Thereafter, with the subsequent application of the biaxial compressive loading, this displacement achieved \(U_{z}\) = 40.19 mm (Figure 16b), resulting in \(\sigma_{uR}\) = 44.37 MPa (Figure 16c).
## 2 Related Work
### Minimum Cross-Entropy Thresholding (MCET)  
MCET, introduced by [PERSON] et al. [3], is an entropy-based method for thresholding-based image segmentation. It features an independent objective function capable of employing any distribution for mean estimation. This flexibility arises from the original formulation of cross-entropy functions which was initially proposed by [PERSON] [15]. The optimization process hinges on the mean values within each region of the given image. Numerous entropy-based thresholding models have emerged to address data separation challenges [16; 17; 18]. The application of cross-entropy approaches has paved the way for advancements in image segmentation within the literature. MCET, for instance, was devised to separate agricultural data using previously estimated satellite imagery information [19]. [PERSON]'s entropy-based thresholding method found an application in water body segmentation [20]. To mitigate computational costs, a Particle Swarm Optimization (PSO)-based multilevel thresholding algorithm was proposed as an enhancement for MCET [21]. Additionally, the minimum cross-entropy method introduced improvements by utilizing the Gamma distribution to determine optimal thresholds [22]. Furthermore, the method was extended to multi-level threshold selection through an enhanced human mental search algorithm [23]. Concerning mean estimation for objective functions, mean-based thresholding methods, including MCET, have been enhanced through the integration of homogeneous and heterogeneous mean filter approaches [9; 24]. MCET has been further developed by incorporating hybrid distributions in a parallel algorithm for image segmentation, including the utilization of Gaussian and Gamma distributions for skin lesion segmentation [25].  
In this paper, we introduce the utilization of a Maximum Likelihood Estimation of the Gumbel distribution for mean-based thresholding methods, specifically for mean estimations within objective functions.