esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security and Privacy-Preserving Solutions for IoE
### Quantum-Resilient Security Approaches
#### 3.3.1 Post-Quantum Cryptographic Methods  
PQC methods encompass a new generation of algorithms designed to be resistant to quantum algorithms that threaten classical cryptography [33; 34; 35]. Notable examples include lattice-based encryption, hash-based signature algorithms, multivariate polynomial cryptography, and code-based encryption [52; 53; 54; 55; 56].  
Figure 8 illustrates how PQC is integrated into IoE systems through a layered architecture. The objective is to provide protection against quantum attacks across all components, from the hardware level to the application layer.  
In the IoE, post-quantum security must be ensured through a multi-layered architecture, spanning from hardware to the application layer [18; 20; 39; 70]. The following structure explains how quantum-resistant cryptographic components are integrated into IoE systems across four fundamental layers:  
1. _Hardware Layer_: This bottom layer involves physical components such as cryptographic accelerators and secure hardware modules. These devices enable post-quantum algorithms in the upper layers to operate securely and efficiently [70].
2. _PQC Algorithms Layer_: Running on hardware, this layer includes lattice-based, hash-based, and code-based algorithms. These are designed to be resilient against quantum computers and form the cryptographic foundation of the system [33; 34; 35; 52; 53].
3. _Protocols Layer_: At this level, the aforementioned algorithms are integrated into communication and security protocols. Key exchange, public key encryption, and digital signature protocols are made quantum-resistant at this stage [33; 39; 49].  
Figure 8: Post-Quantum Cryptography layered structure for IoE.  
4. _Applications Layer_: At the top, this security structure is deployed within IoE applications. Smart cities, cyber-physical systems, autonomous vehicles, and large-scale IoT solutions benefit from the security provided by this layered approach [20; 49; 69; 88]. Each of the methods used in these layers offers specific advantages for different IoE scenarios, but they vary in terms of performance, key size, processing time, and hardware requirements. Therefore, it is crucial for system designers to select the most suitable algorithm according to the required security level and resource constraints. Table 12 presents a comparative analysis of prominent quantum-resistant cryptographic methods, outlining their technical features and evaluating them based on application areas.
###### Abstract  
The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological features from images of different lesions and the inability to resolve the issue of the same lesion, which presents significant feature variances due to grading disparities. This paper proposes a multi-disease recognition network model, Fundus-DANet, based on the dilated convolution. It has two sub-modules to address the aforementioned issues: the interclass learning module (ILM) and the dilated-convolution convolutional block attention module (DA-CBAM). The DA-CBAM uses a convolutional block attention module (CBAM) and dilated convolution to extract and merge multiscale information from images. The ILM uses the channel attention mechanism to map the features to lower dimensions, facilitating exploring latent relationships between various categories. The results demonstrate that this model outperforms previous models in classifying fundus multivoTherefore, this paper proposes Fundus-DANet with the following contributions:  
1. A dilated-convolution convolutional block attention module (DA-CBAM) is designed to feature the representation in the convolutional neural network that is enhanced using the convolutional block attention module (CBAM) [6] after extracting and fusing the multiscale image features using dilated convolution to effectively extract the multiscale features within the image.
2. An interclass learning module (ILM) is designed to adapt the channel attention method to solve problems, such as the difficulty of effectively capturing the important features between classes by the traditional feature extraction method of convolutional neural networks.
3. Asymmetric polynomial loss (APL) [7] is used to apply different weights to the positive and negative samples to improve the model's ability to recognize rare categories. Specifically, this paper enhances the model's focus on positive samples by giving greater penalty to positive samples, which improves the model's performance.  
The rest of the paper is organized as follows: Section 2 presents the related work on CNNs and graph neural networks in multi-disease fundus classification. Section 3 details the methodology and evaluation metrics. Section 4 presents the dataset, experimental information, and results. Finally, Section 5 summarizes the paper and emphasizes the importance of the method.
## 3 Results
### Sensitivity Experiments
#### 3.1.2 Parameter a Schemes  
Parameter \(a\) had a great impact on the simulated soil water content (Figure 5a). Increasing \(a\) led to a higher soil water content, improving the match with the observed values. Across the tested range of \(a\) values (0.1-3.0), the model performance metrics for soil water content were as follows: _NSE_ from \(-0.43\) to \(0.63\), _pbias_ from \(-0.83\%\) to \(-5.4\%\), and _RMSE_ from \(0.01\) to \(0.02\) cm\({}^{3}\) cm\({}^{-3}\). Root fraction was strongly affected by parameter \(a\) (Figure 5d). Smaller \(a\) values resulted in a more uniform root distribution with less allocation to shallow soil layers. As parameter \(a\) increased from \(0.1\) to \(3.0\), the cumulative root fraction within the upper 2 m of soil increased from \(49.7\%\) to \(98.6\%\), approaching the observed value of \(90\%\). The corresponding _NSE_ values ranged from \(-0.08\) to \(0.86\), and the _RMSE_ values ranged from \(10.1\%\) to \(3.6\%\). In contrast, this parameter had a minor effect on the simulated leaf area index (Figure 5b). The _RMSE_ for the leaf area index ranged from \(0.51\) to \(0.63\) m\({}^{2}\) m\({}^{-2}\), _NSE_ from \(0.59\) to \(0.73\), and _pbias_ from \(1.4\%\) to \(8.0\%\).
# Preliminary Examination of Emergent Threat and Risk Landscapes in Intelligent Harvesting Robots
## 4 Security and Safety Issues
### AI Security Issues  
Depending on the design, the field robots use at least two machine vision systems based on deep learning. The former is used for crop detection, and the latter is used for route planning/obstacle avoidance. Figure 2 illustrates the details of the general machine vision system workflow.  
As explained earlier, the AI-based systems used by IHR are developed, tuned and maintained in the cloud. The image data collected by the robot is sent to the cloud to improve the performance of the Automatic Crop Recognition module. Note that the quality of these images is evaluated by an intermediate engineer (Remote Dev Machine) before sending it to the MVS. In some cases, companies could also use third-party cloud tools for image cleaning and labeling before training the AI-based system. Additionally, this standard workflow for intelligent harvesting machines have many security concerns that could negatively impact the performance of the AI-based systems. It is wise to keep in mind that AI techniques and systems using AI can lead to unexpected results and can be modified to manipulate the expected results. Decent research has been conducted in identifying the threats to AI-based systems [22,31] and they can be summarised as detailed below:  
* Adversarial AI: The aim is to target the inference phase of the ML and deep learning systems in the machine vision system by including perturbations to the crop images that are undetectable to the human eye but maximizes the model's prediction error, forcing it to make wrong predictions.
* Data Poisoning: This involves injecting erroneous/lalsified/bad cropping images into the training/validation set by gaining legitimate or illegitimate access by exploiting poor authentication/authorization mechanisms. The purpose is to affect the operation and performance of the machine vision system.
* Input Tampering: By deliberately or unintentionally manipulating crop images in several stages stage of the AI life cycle. Actors such as AI/ML engineers can manipulate data during the storage procedure and using some processes such as feature selection or image labelling. This could interfere with model inference and introduce bias into training data and affect the performance of the machine vision system.
* Model Extraction: These attacks aim to duplicate a machine learning model through query access to a target model. The typical setup for a model extraction attack is an API, such as the ones provided by MLaaS platforms [32]. In the literature, protecting the confidentiality of DLs can be conducted by:  
Figure 2: Machine vision system workflow.  
* Change the API to limit the number of user queries and also to ignore incomplete queries,
* Introducing random and controllable noise to the model to maximise the loss of a stolen model while preserving its prediction accuracy. This deceptive perturbation can degrade or slow down the model stealing process.
## 3 Results and Discussion
### The Efficiency of Pollutant Removal
These values are lower than the permissible value (50 mg/dm\({}^{3}\)) determined in Polish regulations [47]. However, [PERSON] and [PERSON] [31] found significantly higher concentrations of TSS (55-122 mg/dm\({}^{3}\)) in the outflow from three other facilities with activated sludge but without preliminary settling tanks. **BOD\({}_{5}\). ** The efficiency of BOD\({}_{5}\) removal in the studied facilities no. 1 and 2 was diverse and amounted to 83% and 66%, respectively (Figure 2). Similar effects of BOD\({}_{5}\) removal (61-95%) were found by [PERSON] and [PERSON] [31] in three other facilities with activated sludge but without preliminary settling tanks. Quite high BOD\({}_{5}\) removal effects (92-97%) were also obtained by [PERSON] and [PERSON] [53] in household WWTPs operating with an SBR system. Very high (98-99%) BOD\({}_{5}\) removal efficiency was also obtained in two hybrid household CWs of VF/HF type operating in the RNP [36]. Moreover, [PERSON] et al. [49] in a household CW WWTP obtained an efficiency of BOD\({}_{5}\) removal greater than 95%. The average BOD\({}_{5}\) in outflow from the studied facilities no. 1 and 2 was 9.0 and 29.1 mg/dm\({}^{3}\), respectively (Tables 2 and 3). These results are lower than the permissible value (40 mg/dm\({}^{3}\)) specified in Polish regulations [47]. Significantly higher BOD\({}_{5}\) values (24.9 to 267 mg/dm\({}^{3}\)) were found by [PERSON] and [PERSON] [31] in three WWTPs with activated sludge operating without a classical mechanical stage. **COD. ** At the studied facilities no. 1 and 2, the COD removal efficiency was similar and amounted to 65% and 62%, respectively (Figure 2). Higher effects of COD removal (59-90%) were found by [PERSON] and [PERSON] [31] in three other facilities with activated sludge without preliminary settling tanks. Similarly high COD removal effects (83-90%) were also obtained by [PERSON] and [PERSON] [53] in individual SBR systems. A study of two hybrid household CW WWTPs in the RNP showed that they also provide very high (96%) COD removal efficiency [36]. In a household hybrid CW, [PERSON] et al. [49] also obtained more than 95% COD removal efficiency. The average COD values in the outflow from the analyzed facilities no. 1 and 2 were 63.0 and 95.0 mg/dm\({}^{3}\), respectively (Tables 2 and 3). These results are lower than the permissible value (150 mg/dm\({}^{3}\)) specified in Polish regulations [47]. [PERSON] and [PERSON] [31] studied some household WWTPs of similar construction to the analyzed
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 3 Experiment and Results
### Experimental Results and Discussion
The DT-based method works well for the appliances that have a long operation time, such as refrigerator in the three houses and the ones that have small fluctuations in power during their steady-state operation, such as microwave in Houses 1 and 2. In addition, it is worth noting that our proposed GSQF-APD achieves the best disaggregation performance for multi-state appliances, for example washer dryer and dishwasher. On the other hand, Table 5 presents a comparison of disaggregation accuracy of the two GSP-based methods to the three FHMM-based unsupervised methods of [31] and two FHMM-based methods of [32], as reported in [41]. As can be seen from the table, the proposed GSQF-APD algorithm provides the highest accuracy, while the other GSP-based algorithm, GLQF-APD, is more accuracy than Expectation Maximization FHMM (EM-FHMM), Factorial HDP-HMM (F-HDP-HMM) and two FHMM approaches considering or ignoring interaction, but performs more poorly than F-HDP-HSMM.
Remote Real-Time Monitoring and Control of Small Wind Turbines Using Open-Source Hardware and Software  
[PERSON]  
However, the integration of SWTs into ancillary service frameworks presents significant challenges, particularly in terms of real-time monitoring, remote control, and operational flexibility. The small size and distributed nature of these systems often necessitate low-cost, scalable solutions that ensure seamless communication and precise control. Open-source technologies, such as Arduino(r) microcontrollers and Raspberry Pi(r) platforms, have emerged as promising candidates for addressing these challenges due to their affordability and versatility.  
In this context, this study proposes a novel platform for the remote control of SWTs, enabling real-time adjustments to operating points based on grid requirements and environmental conditions. The platform incorporates an Arduino(r)-controlled DC-DC boost converter, a Raspberry Pi(r)-hosted WebSocket server for data transmission, and a desktop application developed using MATLAB(r)'s App Designer for user-friendly interaction. This approach bridges the gap between emerging DG regulatory requirements and the technological capabilities of small-scale renewable energy systems, offering a robust framework for their integration into modern power grids.
## 5 Simulation Results and Discussion  
This section demonstrates the efficacy and capability of the proposed FOFP-iPID controller for solving the LFC-based interconnected power system. The proposed FOFP-iPID controller is designed separately for each control area using the ALO algorithm. The dynamic performance of the FOFP-iPID controller is assessed by comparing the performances with a recently published article based on the FOPID controller for an identical system [17]. The simulation procedures are outlined as:  
1. Performance evaluation under different perturbations (LPs): * Performance evaluation of the suggested FOFP-iPID controller under step load perturbation (SLP) in area-1. * Performance evaluation of the suggested FOFP-iPID controller under sinusoidal load change (SLC) in area-1. * Performance evaluation of the suggested FOFP-iPID controller under random load perturbation (RLP) in area-1.
2. Sensitivity analysis is also carried out to appraise the robustness of the current controller against \(\pm 50\%\) uncertainty in system parameters.  
Figure 5: Flowchart of the ALO algorithm.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 3 Research Methodology
### Data Preparation  
The image dataset is constructed using a variety of little gem romaine lettuce images from diverse sources. This involves top-view images of lettuce grown in Allfactory 4.0, an NFT-based aquaponics facility at the University of Alberta, Canada, focusing on smart indoor farming [2]. These images are divided into two classes based on the color of foliage: green foliage (no leaf chlorosis) and yellow foliage (leaf chlorosis). To increase the model flexibility to segment lettuce foliage, irrespective of background, and to ensure it correctly determines the plant's health, the dataset is complemented with more lettuce images obtained from Ecosia, a search engine based in Berlin, Germany [22]. Figure 2 shows examples of some of the images.  
Figure 1: Research methodology outline.  
Figure 2: Image dataset: (**a,b**) acquired from aquaponics facility, and (**c–f**) are downloaded from ecosia.org.  
Next, the image augmentation process is performed to increase the dataset and reliability of the segmentation process, despite the location and orientation of the objects in the image, by generating new images from existing images. This study uses _Albumentations_, a Python library, for fast and flexible image augmentations [23]. The different augmentation techniques applied are the horizontal flip, vertical flip, 90\({}^{\circ}\) rotation, and glass noise. The new images are added to their respective classes. Figure 3 shows examples of the augmentations.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 4 Interference Analysis of LTE-M Ground System
### Power Control  
The uplink users change the transmitted power through the power control model [39], given as follows  
\[P_{t}=P_{max}*min\bigg{\{}1,max\bigg{[}R_{min},\bigg{(}\frac{PL}{PL_{x-ile}} \bigg{)}^{7}\bigg{]}\bigg{\}} \tag{16}\]  
where \(R_{min}=P_{min}/P_{max}\) is the minimum power reduction ratio to prevent UEs from good channel conditions from transmitting with very low power, \(PL\) represents the path loss from the UE to the BS, and its propagation model is given in Equation (10). \(PL_{x-ile}\) is the \(x\)-percentile path loss value, which is used to ensure the \(x\) percent of UEs with highest propagation loss transmission will transmit at \(P_{Imax}\). Additionally, \(0<\gamma\leq 1\) is the balancing coefficient of UE under different channel conditions. The power control parameter sets are shown in Table 8.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Sleep/Wake-Up Schemes
#### 2.3.2 Duty-Cycling
In a more general context, in QPPDMAC (QoSMAC Protocol for Prioritized Data) [106], after broadcasting a beacon with QoS mode on (asynchronous contention-based RI protocol), the receiver uses a limited interval and listen for senders' requests. The protocol implements a request-allocation scheme based on transmitter beacons and data priority tags: the highest priority transmitter beacon received at interval expiration, or, immediately, if received one tagged with the top level priority, determines which contender will obtain receiver assignment. From the point of view of EE/EH, the protocol adjusts the receiver duty cycle according to its current power level, working at 5% if remaining energy is below 10%, at full activity if battery state is above 90%, and with the value determined by a particular expression (continuous function) otherwise. The authors say the biggest advantage of their proposal over others (TDMA -based) is the energy savings due to the lack of synchronization. * In EEQ-MAC (Energy-Efficient and QoS-aware MAC) [107], queue length and data priority are used to adapt the node's duty cycle, increasing the length of its active period in the event of high traffic which provides less waiting time to support time-bounded delivery of priority packets. A Random Early Detection mechanism is also included to avoid starvation of low priority data.
## 3 Dataset  
In our experiment, we considered a spherical tank made of carbon steel (A283-C) as a testbed. The testbed consisted of a spherical tank, AE sensors, a data acquisition device and a computer system, as presented in Figure 4. Moreover, for a detailed description, a schematic diagram of the test bed is also given in Figure 5. In the schematic diagram, the location of the four channels (sensors) and the pinhole crack can be easily observed. A 3-mm crack was deliberately introduced on the bottom of the tank. Four AE sensors were attached to the surface of the tank at different locations. A pencil lead test was performed to generate a guided wave through the tank surface. Acoustic emission signals were recorded using AE sensors keeping engineering norms in mind set by the American Society of Mechanical Engineers (ASME) and given in boiler and pressure vessel code (PVC) [23]. A peripheral component Interconnect bus (PCI-2) based data acquisition device connected with wideband differential AE sensors (WDI-AST) was used for this purpose. These wideband AE sensors are typically used in research and other applications for signal analysis. The details about the physical sensors and the PCI board is provided in Table 2. Data acquisition system and channels (sensors) arrangement during the experiment are shown in Figure 6. The signals were recorded at a \(1\) MHz sampling frequency from the tank under a constant load condition. For each condition (i.e., normal and cracked state), 20 signals were recorded, each for 0.1 seconds. The dataset is described in Table 3.  
Figure 4: Testbed used to collect acoustic emissions in the experiment.  
Figure 5: Schematic representation of the testbed.  
Figure 6: The data acquisition system. PC: personal computer; PCI: peripheral component interconnect; WDI-AST: wideband differential; AE sensor.
## 4 Discussion  
Finally, according to the results (descriptive statistical analysis, tests, and visual analysis), it is concluded that the final best models for forecasting hourly ozone concentration were the \({}^{\mathbf{a}}\)**STLD\({}^{\mathbf{b}}_{\mathbf{c}}\)**, the \({}^{\mathbf{b}}\)**STLD\({}^{\mathbf{c}}_{\mathbf{c}}\)**, the \({}^{\mathbf{b}}\)**STLD\({}^{\mathbf{b}}_{\mathbf{c}}\)**, and the \({}^{\mathbf{c}}\)**STLD\({}^{\mathbf{b}}_{\mathbf{c}}\)** for the ATE, the CDM, the SB, and the STA, respectively. However, to verify the superiority of these final best models, we compare them with some standard baseline time series models, including parametric autoregressive (PAR), nonparametric autoregressive (NPAR), and autoregressive integrated moving averages (ARIMA) models. For example, the comparative results are presented in Table 6 for all four monitoring stations. The results show that the considered baseline time series models are significantly outperformed by the best-proposed model in each station. In addition, to confirm the dominance of the best-proposed models given in Table 6 for each station, we performed a statistical DM test on each pair of models. The results (\(p\)-values) of the DM test are listed in Table 7, indicating that the baseline time series (PAR, NPAR, and ARIMA) models performed poorly in comparison to our best-proposed models in the considered stations at the 5% level of significance. To conclude, based on overall results, the performance measures of accuracy for the proposed methods of fore  
Figure 7: Ozone concentration in four Metropolitan Lima stations (\(\mathrm{\mu g}/\mathrm{m}^{3}\)): (**a**) Ate, (**b**) Campo de Marte, (**c**) San Borja, and (**d**) Santa Anita: actual and forecasted ozone concentration values for four of the best models over three weeks. casting are comparatively better and more efficient than all other benchmark models in the competition. In addition to the above, in the literature, [PERSON] [23] explored the correlations between ozone and meteorological variables and predicted ozone concentration for the same sites and winter periods selected in this study. They used models such as linear regression, support vector regression, decision trees, random forest, and multilayer perceptron and based their arguments on R\({}^{2}\), MSE, and MAE. The linear model presented the highest prediction performance for all the places evaluated (R\({}^{2}\): 0.9849-9923), supported by the lowest calculated errors (MAE: 0.0087-0.0724 and MSE: 0.0036-0.0087). Conversely, when