esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Future Aspects  
#### 4.2.1 Advancements in AI Algorithms  
Future AIoT-based solar energy systems will increasingly leverage advanced AI techniques to boost accuracy, adaptability, and real-time decision-making. Hybrid AI models that combine deep learning, fuzzy logic, and evolutionary algorithms will enhance fault detection, energy forecasting, and system optimization under dynamic and uncertain conditions. GenAI is set to transform PV monitoring and diagnostics by improving anomaly detection, automating fault classification, and generating predictive insights. Its ability to model complex patterns enables early detection of system degradation, more accurate maintenance scheduling, and reduced false positives in fault alerts. Explainable AI (XAI) will be essential for building trust and transparency, allowing operators to interpret and validate AI-generated insights. Reinforcement learning and self-optimizing models will support adaptive control by continuously tuning parameters such as panel orientation, battery usage, and grid interactions based on real-time feedback. Furthermore, the adoption of physics-informed AI--combining domain knowledge with data-driven methods--will improve model generalization and reliability, especially in data-scarce environments. Collectively, these innovations mark a shift toward intelligent, autonomous, and resilient energy management in next-generation solar systems.
# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 4 Experimental Evaluation  
In this section, we assess our proactive and reactive approaches for NDN path selection, targeting to the efficient operation of NDN over WMN. Our evaluation includes real experimentation over an indoor wireless environment, considering small-scale SDN-NDN deployments, utilizing the w-iLab.1 Fed4 FIRE+ testbed. In summary, the evaluation metrics include (i) the NDN performance in terms of delay; (ii) the SDN communication management overhead; (iii) the frequency of dynamic path changes (path selection), and (iv) NDN reliability (i.e., percentage of Interest-Data exchange failures). More precisely, we consider two experimental scenarios:  
* Scenario 1, which evaluates the reactive approach over different types of network conditions, i.e., stable and unstable WMN conditions.
* Scenario 2, which assesses the effectiveness of a proactive (clustering-based) approach in this context, also comparing its outputs with these of the reactive approach.
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 3 Proposed Approach
### Model Training  
Training a machine learning model entails instructing the algorithm to distinguish between authentic emails and phishing emails using a predetermined set of criteria. A conventional approach involves employing a training dataset, which is then partitioned into distinct training and testing sets, to assess the model's performance. Regarding the dataset division, the original dataset included a total of 18,000 emails, with 15,000 classified as non-spam and 3000 as spam. For evaluation, we utilized 70% (12,600 emails) of the data as a training set, while the remaining 30% (5400 emails) were employed as a testing set. To improve the model's accuracy and its capacity to generalize across various text patterns, data augmentation approaches were utilized. The purpose of incorporating random phrases in the emails was to diversify the training data, hence enhancing the model's ability to detect phishing patterns, including potential variants. Data augmentation is an essential component in the domain of machine learning, particularly when dealing with datasets that have a restricted size. By incorporating random phrases, the model is exposed to a broader array of situations, which could potentially improve its ability to identify phishing attributes in actual email communications. This technique mitigates the risk of overfitting and improves the model's capacity to generalize effectively. After expanding the dataset, the next step was to train the phishing classifier [34].  
In the previous section, a set of five distinct machine learning classifiers were utilized, which included ensemble learning-based Two-Class Boosted Decision Tree, Two-Class Neural Network, Two-Class Support Vector Machine, Two-Class Logistic Regression, and lastly, Two-Class Bayes Point Machine (BCPM).  
Throughout the training phase, the model can differentiate between legitimate and phishing emails by discerning patterns and features present in the input data. This process plays a crucial role in equipping the model with the capacity to make informed decisions when faced with novel, unseen data.
## 2 Data and Methods  
### Data  
We used the ERA-Interim reanalysis data (1979-present), version 2.0 of the ECMWF (European Centre for Medium-Range Weather Forecasts) [30; 31] to analyse CAPE.  
CAPE is defined as [32]:  
\[\text{CAPE}=\int_{LFC}^{LNB}R_{d}(T_{vp}-T_{ve})d\ln(P). \tag{1}\]  
It is the integral between the level of free convection (_LFC_) and the level of neutral buoyancy (_LNB_). \(T_{vp}\) and \(T_{ve}\) show the virtual temperature of the air parcel and surrounding environment, respectively. \(R_{d}\) is the gas constant, and \(P\) is pressure.  
For the calculation of CAPE in ERA-interim there are two key assumptions: First, there is no mixing of the parcels with the surrounding air. Second, there exists a pseudo-adiabatic ascent in which all condensed water falls out as precipitation [33]. The ERA-Interim reanalysis data used in this study have a spatial resolution of 0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\) and a temporal resolution of 6 h. ERA-Interim reanalysis data are interpolated to the station points using a nearest-neighbour interpolation method.  
We used GNSS-integrated water vapour data from the Argentine Continuous Satellite Monitoring Network (RAMSAC) for two stations: San Miguel de Tucuman (TUCU, 1999-2013) located at 65\({}^{\circ}\)13\({}^{\prime}\) W and 26\({}^{\circ}\)50\({}^{\prime}\) S and San Fernando del Valle de Catamarca (CATA, 2008-2013) located at 65\({}^{\circ}\)46\({}^{\prime}\) W and 28\({}^{\circ}\)28\({}^{\prime}\) S (Figure 1A). The distance between these two GNSS stations is about 189 km. These two stations were selected as they have the longest data availability for this region.  
The RAMSAC network was created in 1998 and has grown to include around 100 continuously operating GNSS stations in north and central Argentina [34]. The GNSS-IWV data used in this study have a temporal resolution of 30 min. There were several missing data (3% from CATA and 14% from TUCU) in the GNSS-IWV data set that were removed from the associated CAPE and rainfall time series for these hours or days.  
TRMM (Tropical Rainfall Measuring Mission) data [35], product 3B42 [36; 37] (Version 7) with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and hourly temporal resolution have been used to analyse rainfall. TRMM rainfall data are interpolated at the station location using nearest neighbour interpolation method. TRMM data have been indicated to be a reliable dataset for investigating rainfall in South America [4; 38; 39].
## 4 Application to HHs Selection  
As schematized in Figure 10, the high vacuum chamber containing the entire monochromator set-up, made up of three optical elements, can be used for high-resolution applications dedicated to pump-probe IR-XUV experiments. After HHs generation, a preliminary toroidal mirror TM\({}_{1}\) with equal entrance and exit arms (1:1 configuration) in order to minimize aberrations, was used for the XUV-beam focusing on the entrance slit in the tangential (dispersion) direction. Similarly, after the monochromator exit slit, the toroidal mirror TM\({}_{2}\) in the XUV-IR recombination chamber acts as a focusing mirror to the experimental apparatus. The output arm has to be long enough to accommodate such a chamber. In addition, this mirror is operated in the 1:1 configuration. The total length of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Energy (eV)** & **Grating Type** & **W\({}_{\text{OUT}}\) (\(\upmu\)m)** & \(\Delta\)**\(\varepsilon\) (meV)** \\ \hline
13.48 & G\({}_{1}\) & 60 & 2.5 \\
16.67 & G\({}_{1}\) & 70 & 4.0 \\
21.22 & G\({}_{1}\) & 60 & 5.0 \\
26.91 & G\({}_{2}\) & 65 & 5.4 \\
40.82 & G\({}_{2}\) & 60 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Widths of the spectral lines measured on the exit slit plane.  
Figure 9: Experimental total efficiency of the monochromator for s-polarized light (dashed) and theoretical for p-polarized light (solid). For each measurement, the subtended angle has been selected to satisfy the tangential focus condition.  
high-resolution monochromator set-up (between entrance and exit slit) is about 3 m. The focus in the plane parallel to the slits, i.e., the sagittal plane, is assured by a suitable choice of the sagittal radii of the two toroidal mirrors--the first mirror TM\({}_{1}\) collimates the source beam in the vertical direction and the second mirror TM\({}_{2}\) focuses it. These mirrors are assumed to be operated at incidence angles grater than 86\({}^{\circ}\) in order to have high reflectivity and high insensitivity to the polarization of the incoming light. The arms are chosen typically equal to 700 mm for both mirrors, in order to avoid a degradation of TM\({}_{1}\) caused by a high XUV flux.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Laser-Based SLAM  
Laser sensors are an emerging technology in underwater SLAM, offering a novel approach through laser-based acoustic generation and detection. These sensors are particularly valuable for underwater applications as they combine laser measurements' high accuracy with acoustic waves' propagation advantages. Fibre laser-based sensors, for example, provide robust solutions for underwater surveillance, offering lightweight and deployable options that enhance mapping capabilities. Such sensors are increasingly used underwater for high-resolution mapping and loop closure [44; 69].
# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 3 Materials and Methods  
There are two innovative aspects in this research. The first consists of distance assessment using low-cost cameras based on special sign detection (not used until now). The second is the development of a customized technical solution for a perception subsystem using a laser RPLIDAR sensor and a photo camera.  
Each of the abovementioned systems was independently calibrated and the results evaluated.  
The research is based on the concept of the blockchain scheme for a mechatronic system (virtual prototype) designed to improve the quality of life of people with visual impairments (see Figure 2).  
The collaboration of the equipment of the perception subsystem, the HQ camera and laser beam sensor, is supervised by the actuator controller that monitors the evolution of the factual situation, data fusion, route and action planning, optimization, and execution of commands so that the missions can be fulfilled.  
Figure 2: Diagram of device for visually impaired people.  
There are some basic hypotheses considered for this research that mainly focus on the customized object detection and recognition techniques for an assistive device for visually impaired people. These hypotheses are mentioned next.  
**Hypothesis 1 (H1).**_The software used for visualizing the environment is RViz, installed under the operating system for robots ROS Melodic, enabling work on the NVIDIA Jetson Nano platform. This was the choice due to the customized hardware system (RPLIDAR and HQ camera)._  
**Hypothesis 2 (H2).**_The authors propose the use of specially designed signs for a new technique in distance assessment._  
**Hypothesis 3 (H3).**_There is no need for spatial consistency of the assistive devices for visually impaired people, as the possibility of receiving complex information is, in most cases, limited. The visualization study is only focused in one direction--to the detected object._  
**Hypothesis 4 (H4).**_In the case of motion control, especially for the robotic arm of the designed assistive device, the target is to grab an object, close a window, or open a door standing in the user's way and that could harm them. It is not the case and, moreover, it would be a stress factor to have the robotic arm monitored throughout the motion._  
**Hypothesis 5 (H5).**_The experiments are carried in the daytime, in a closed space with windows, in natural lighting (for the RPLIDAR and HQ camera subsystem). There will also be additional sources for lighting, such as lamp and reflector (for the low-cost camera subsystem). The proposed scenarios are similar to real-life situations. More specifically, the research laboratory conditions (similar to a hall or a classroom) are considered for this research stage. In fact, one of the issues of this research is that of studying the influence of lighting on the object recognition accuracy._
###### Abstract  
Wind turbine blades are easily affected by the working environment and often show damage features such as cracks and surface shedding. An improved convolution neural network, ED Net, is proposed to identify their damage features. An EAC block based on the improved asymmetric convolution is introduced which strengthens the feature extraction during convolution. A DPCL_SC block, which is improved based on the attention module, is embedded to enhance the ability to obtain spatial location information of the damage. GELU is used as the activation function. The loss function is smoothed and labeled during training. Finally, three sets of experiments were conducted. Experiment 1 confirmed the efficacy of the ED Net for identifying damaged wind turbine blades. Experiment 2 confirmed the efficacy of the relevant improvements proposed in this work. Experiment 3 compares the recognition of wind turbine blade damage by commonly used lightweight networks and shows that the ED Net model proposed has a better performance with an accuracy range of 99.12% to 99.23% and a recall of 99.23%  
computer vision; damage identification; convolutional neural network; asymmetric convolution; attention mechanism +
Footnote †: journal: Applied  
using a telescope to monitor the damage status of the blades. This type of inspection is time-consuming and laborious. The blade image in the telescope is frequently blurred when observed with the human eye, and the inspection effect is not satisfactory due to the influence of weather and light. On the other hand, the timelier the maintenance and care of blades, the better. When there is light wear, a timely repair can prevent the wear from deteriorating. However, if the problem is not addressed promptly, the wear on the blade may gradually degrade into fractures, resulting in blade fracture and fall off, causing major economic losses and possible safety accidents. Therefore, investigating a quick and accurate approach for detecting damage to the wind turbine blade has important practical engineering significance and application value.
## 1 Introduction  
Near-field communication (NFC) has proven to be a reliable data transfer technology in a series of biomedical implants and devices: the communication interface, based on inductive coupling of two coils, is hereby complementing the actual sensor front-end by establishing the connection to the corresponding data processing unit. Among others, inductive communication technologies have been applied in biomedical sensor systems to monitor physiological parameters such as heart rate, blood pressure, or gastrointestinal activity [1, 2], to analyze biochemical fluids [3, 4], to improve hearing in cochlear implants [5], to reestablish vision in retinal implants [6], and to analyze and record neural signals in the context of brain-computer interfaces [7].  
Improved methods and materials in electrical and biomedical engineering are recently driving the need for near-field interfaces with very high data rates in the range of several megabits per second: on the one hand, sensor systems store data over extended periods of time, which need to be acquired in regular time intervals. Here, a fast communication speed to extract extended datasets will increase convenience of patients and physicians. On the other hand, there are technical systems in which data are not only sampled with high resolution, but simultaneously at hundreds of channels. Examples of such systems are neural implants, which record the electrical signals of the brain's neurons with high spatial and temporal resolution [8; 9], in order to detect seizures of epilepsy or Parkinson's disease, which is useful in understanding the electrochemical processes and in mitigating the symptoms by subsequent electrical stimulation. Taking the highly integrated silicon neural probes of [9] as an example, the raw data output stream from implant to the reader unit of such a device using 144 channels, a sampling rate of 20 kHz and a resolution of 11 bit per channel can be as high as 32 MBit/s. This certainly requests a powerful extracorporeal data processing unit, which must be connected wirelessly in order to avoid any source of inflammation emerging from wired connections. The concepts of closed-loop neural interfaces even require fast bidirectional data communication, as they apply high-resolution and multi-channel electrical stimulation controlled by the extracorporeal data processing unit in order to suppress the symptoms of detected neural seizures [10]. Hence, very high bit rate NFC systems are a key technology to enable these applications.
## 2 Model of Ship-Radiated Noise and Sample Generation
### Design of Samples and Datasets
#### 2.2.2 Generation of Positive and Negative Sample Pairs  
A pair of samples was used as training data for a Siamese network. The difference between them was obtained, making it possible to recognize targets from a small sample.  
When the sample pairs were from the same target, they were called positive sample pairs. There were 3000 samples in dataset A for each type of target. According to mathematical knowledge, combined formulas can be written as  
\[C_{m}^{n}=\frac{m!}{n!(m-n)!} \tag{10}\]  
where \(m\) is the total number of samples, and \(n\) represents the number of samples arbitrarily selected from \(m\). Positive sample pairs were formed by randomly selecting two samples from the 3000 samples, so the number of positive sample pairs was \(N_{A}^{+}=7\cdot C_{3000}^{2}\). The training dataset AA+ for the Siamese network was generated by selecting 7000 random pairs from \(N_{A}^{+}\).  
There were 500 samples in datasets B, C, and D for each type. Their positive sample pairs were generated in the same way as dataset A. Consequently, the number of positive sample pairs was \(N_{B}{}^{+}=41\cdot 7\cdot C_{500}^{2}\), \(N_{C}{}^{+}=16\cdot 7\cdot C_{500}^{2}\), and \(N_{D}{}^{+}=7\cdot 7\cdot C_{500}^{2}\). Datasets BB+, CC+, and DD+ were generated by selecting 500 pairs for \(N_{B}{}^{+}\), \(N_{C}{}^{+}\), and \(N_{D}{}^{+}\).  
In the same way as datasets B, C, and D, the number of positive sample pairs for datasets E, F, and G were \(N_{E}^{+}=41\cdot 10\cdot C_{500}^{2}\), \(N_{F}^{+}=16\cdot 10\cdot C_{500}^{2}\), and \(N_{G}^{+}=7\cdot 10\cdot C_{500}^{2}\), respectively. Datasets EE+, FF+, and GG+ were generated by selecting 1000 pairs for each type.  
Similarly, when the sample pairs were from different targets, they were called negative sample pairs. Datasets AA\(-\), BB\(-\), CC\(-\), DD\(-\), EE\(-\), FF\(-\), and GG\(-\) were generated by the same method.  
Positive sample pairs were labeled \"1\", and negative sample pairs were labeled \"0.\" The numbers of positive and negative sample pairs for training, validation, and testing are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Datasets** & **Ship Status** & **No. Ships** & **Sample Dimension** & **Total No. Samples** \\ \hline Training & A & 1 & 7 & 1 \(\times\) 2048 & 1 \(\times\) 7 \(\times\) 3000 \\ \hline \multirow{3}{*}{Verification} & B & 41 & 7 & 1 \(\times\) 2048 & 41 \(\times\) 7 \(\times\) 500 \\  & C & 16 & 7 & 1 \(\times\) 2048 & 16 \(\times\) 7 \(\times\) 500 \\  & D & 7 & 7 & 1 \(\times\) 2048 & 7 \(\times\) 7 \(\times\) 500 \\ \hline \multirow{3}{*}{Test} & E & 41 & 10 & 1 \(\times\) 2048 & 41 \(\times\) 10 \(\times\) 500 \\  & F & 16 & 10 & 1 \(\times\) 2048 & 16 \(\times\) 10 \(\times\) 500 \\ \cline{1-1}  & G & 7 & 10 & 1 \(\times\) 2048 & 7 \(\times\) 10 \(\times\) 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dimensions of datasets A to G.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### Influence of Dominant Features on Fatigue Behavior  
#### 7.3.1 Radius of Curvature: The Most Significant Factor  
The radius of curvature emerged as the strongest predictor of fatigue life in both models. A smaller radius results in higher localized bending stress, accelerating fatigue  
Figure 11: Feature importance impact on fatigue life of CT for best ML models.  
crack initiation and propagation. This aligns with the elastic-plastic fatigue theory, where stress concentration increases exponentially as the radius decreases, leading to early failure.  
The ML models identified radius as the most critical feature, confirming established fatigue mechanisms in coiled tubing operations.