esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 6 Models of Communication in the Internet of Vehicles  
The IoV communication model focuses on communication among vehicles and with the surrounding environment. The systems of vehicular communication are interrelated and the data exchange takes place through various means and different forms of models. Each model plays a vital role in the interconnected ecosystem of vehicular communication, enabling various forms of data exchange and interactions. The IoV communication model is the communication between vehicles and their surrounding environment. The diverse range of communication models in the IoV includes vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian, vehicle-to-home, vehicle-to-roadside, vehicle-to-everything, and vehicle-to-grid models as shown in Figure 6.
# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 3 Material and Method
### Evaluation Metric
#### 3.7.1. Dice Similarity Coefficient (\(DSC\))  
The dice similarity coefficient (\(DSC\)), which evaluates the overlapped results from two independent regions [32; 33], was used to examine the liver or liver tumor segmentation of CT scan images. If true positive \(TP\) is real positive, \(FP\) for false positive, and \(FN\) for negative result, the \(DSC\) can be represents as,  
\[DSC=\frac{|2 TP|}{|2 TP|+|FP|+|FN|}, \tag{3}\]
## 2 Materials and Methods  
Preparation of Double Blends EHN/SVO and Triple Blends Diesel/EHN/SVO to Be Used as Advanced or Carbon-Neutral Fuels  
2-Ethylhexyl Nitrate (purity \(\geq\) 99.5%) was obtained from Sigma-Aldrich (St. Louis, MO, USA). Castor oil was purchased from Panreac (Castellar Del Valles, Spain), and sunflower oil (used as a reference for waste cooking oils) was sourced from a local market. Fossil diesel was acquired from a Repsol service station. For the preparation of the EHN/SVO double blends, different ratios of EHN were mixed with sunflower oil (SO) and castor oil (CO) in proportions of 20%, 40%, 60%, and 80% by volume. The double blendsthat exhibited viscosity values within the range specified by the European Regulation EN 590 and ISO-3104 standards for fossil diesel (2.0-4.5 cSt) were then used to prepare triple blends by mixing them with commercial fossil diesel (D) to form the D/EHN/SVO mixtures. These triple blends are called BioXXYY, where XX is the volume percentage of biofuel added to the fossil diesel, YY is the type of vegetable oil, SO for sunflower oil, and CO for castor oil. For example, Bio6 SO corresponds to 60% sunflower oil biofuel and 40% of fossil diesel. All volumes were measured using a graduated cylinder at 25 \({}^{\circ}\)C to ensure precision. Blends were prepared by manual mixing with thorough agitation for 5 min using a vortex mixer to ensure homogeneity. Table 1 presents key physical and chemical properties of diesel, sunflower oil, castor oil, and 2-ethylhexyl nitrate relevant to their performance as fuels in modern diesel engines.
## 3 Materials and Methods
### Incremental Online Learning  
Recently, incremental online learning has received more attention, especially in the context of learning from data streams where data continually arrive, such as in IoT applications, spam filtering, attack detection, time series forecasting, etc. [5]. This evolution challenges traditional machine learning algorithms, which assume that they have access to the entire training dataset during the learning stage [3].  
Using traditional machine learning algorithms consists of collecting large historical data, with the model being trained on the training set and validated on the testing set. In this way, the model is trained on a large dataset that has been collected and labeled beforehand [23]. This approach performs well when all the data are available up front since the model is trained on a large static dataset [6].  
In contrast, incremental online learning algorithms involves building dynamic models which are trained in real-time, rather than on a fixed dataset all at once [4].  
Formally, let D be a data stream including instances denoted by  
\[\left(\overrightarrow{x}_{i},y_{i}\right),(i=1,\ldots\ldots\cdot t,\ldots) \tag{1}\]  
where t represents a timestamp, \(\overrightarrow{x}_{i}\) is a vector of features, and \(y_{i}\) is the target or the class.  
Incremental online learning allows the model to learn continuously from each new data instance. The goal here is to continue to learn from new incoming data and update the model continuously [24]. The updated model can be used to predict a label \(y_{i}^{\prime}\) for an unlabeled \(\overrightarrow{x}_{i}^{\prime}\). The primary difference between traditional and online learning is how the data are presented [25]. In the former, the dataset is static and entirely available, whereas data instances are presented sequentially over time in online learning. Incremental online learning has a number of characteristics including:  
* Incremental learning allows for the use of data as they becomes available, creating models that are constantly up-to-date instead of having to process the entire dataset at once [5]. This can be very helpful in dynamic applications like VANET networks where data are produced in a streaming way [6];
* There is a need to process a small amount of data at a time to learn from it [25]. This is in contrast to traditional machine learning techniques, which need a lot of computing resources, particularly when training on big datasets [24];
* The ability to handle streaming data in real-time [6]. This makes it particularly useful in this area of research, where data are constantly being generated and need to be analyzed in real-time [4];
* Adaptive, because it can adjust to changing data patterns over time. This suggests that it is capable of learning and improving its predictions or decisions as new data arrive [23].  
The proposed approach for detecting malicious nodes in VANETs is outlined in the following section.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### General IoT Networks  
In most studies analyzed, there is no explicit discussion of specific network infrastructures or deployment. Instead, the primary focus is on the datasets employed to train and evaluate machine learning models. This suggests that researchers prioritize data characteristics over network configurations, as robust data are essential for building accurate and generalized models.  
Several datasets are frequently used in IoT-focused intrusion detection studies. These datasets provide labeled traffic data, simulating both benign and malicious activities, which are essential for training machine learning algorithms.  
Architecturally, the datasets often mimic centralized IoT networks, with sensor nodes transmitting data to a central server for processing. However, the specifics of these simulated architectures remain secondary to data quality and distribution.  
While datasets are discussed in more detail in Section 5, some comments can be made on the infrastructure of the most popular datasets in our studies, TON_IoT, Bot-IoT, and CIC-IDS2017. The TON_IoT dataset [25] comprises diverse data sources gathered from telemetry datasets of IoT and IIoT (Industrial IoT) sensors. These datasets were collected from a realistic, large-scale network environment that was developed. The Bot-IoT dataset [26] is a network cluster connected to a public IoT hub by AWS, via the Message Queuing Telemetry Transport (MQTT) protocol. CIC-IDS2017 [27] includes the profile of 25 users working with many IP-based protocols (HTTPS, FTP, SSH, etc.) in a complete network topology that includes a modern, firewall, switches, and routers, with Windows, Ubuntu, and Mac OS X operating systems.  
The emphasis on data rather than deployment infrastructure suggests an effort to ensure machine learning solutions are broadly applicable across multiple IoT environments. Researchers appear to be developing models that generalize well rather than optimizing for specific setups, which could limit applicability. This focus on generalization is particularly relevant given the heterogeneity of IoT systems, where devices, communication protocols, and deployment scales vary widely.  
There are some studies that, while mostly focusing on the dataset and the ML methods, mention a specific type of infrastructure, which receives more consideration.  
In [28; 29; 30], there is a focus on fog computing, an approach to network architecture that delegates a larger amount of computing power near the edge devices. As [28] suggests, fog computing is a defensive strategy that improves security, while also improving network routing performance. In [30]'s case, the fog cloud layer processes data and training, and executes the IDS filtering at a lower level, at the IoT gateway.
## 2 User Needs and Requirements Analysis
### Complex Data and Metadata  
During a neutron scattering experiment, an extensive amount of data and metadata are collected. The data are mainly events detected by the large two-dimensional (2D) position-sensitive neutron detectors with timestamps of each neutron event referring to a reference time. Such event mode data provide convenience for post-collection time-slicing or synchronizing with sample environment parameters for time-resolved studies or a stroboscopic method. The metadata, also recorded with timestamps, include inherent instrument parameters such as hardware component positions, health conditions, and sample environment equipment readouts, and user-input supplementary information such as sample and background details. All of those parameters are critical for setting up the correct instrument measurement configuration, and many of them are required for correct data reduction and interpretation.  
Figure 1: The Spectrometer Instrument Control Environment (SPICE) graphic user interface (GUI) extension with a wizard-like tool to set up scans for small-angle neutron scattering (SANS); highlighted in purple is the currently selected function group.  
Most inherent parameters are motor positions and other equipment readouts (Figure 2). They can be grouped in relation to (1) beam characteristics (e.g., status of neutron guides, source aperture, attenuator, beam trap); (2) the Q-range (the momentum transfer, Q, is the most important factor in a small-angle scattering experiment, representing the size range to be probed in the reciprocal space within which SANS measures; e.g., velocity selector rotation speed and tilt angle, sample aperture size, detector motor positions); (3) sample environment devices and conditions (e.g., temperature, magnetic field, pressure, rotating cell speed); and (4) sample changer and slot number. Individual motor position and other readings are difficult for both expert and non-expert users to comprehend; therefore, it is necessary to associate and display them with more meaningful configuration descriptions. In addition, a higher-level collective setup can be used to configure an instrument without setting those parameters individually.  
User-input supplementary information is needed for users to keep track of different parameters such as slight variations in composition, concentration, matching background, and so on. Some of this information can be pulled from the centralized sample management database, inventory tracking of equipment, material and sample (ITEMS), whereas some can be provided only by users manually during an experiment. Moreover, the system needs to provide an expandable pathway for adopting emerging standards for metadata, such as Information System for Protein crystallography Beamlines (ISPyB) used by the biological small-angle scattering community [14] and the collective action for nomadic small-angle scatterers (canSAS, www.cansas.org) community.
## 2 Fabrication of Graded Index Polymer Waveguides
### Photo-Addressing Fabrication Method (Sumitomo Bakelite)  
Sumitomo Bakelite developed a fabrication process for graded index polymer waveguides, which is based on the photo-addressing method. The details of the photo addressing method are outlined in Figure 3.  
A special varnish is prepared for the polymer core and cladding layers and coated onto a substrate. Special refractive index modifiers and photo initiators are incorporated into the varnish prior to use. These refractive index modifiers and initiators catalyze the production of graded refractive index profiles in the fabricated waveguides. The coated layers are then pre-baked, exposed to UV light through a photo-mask and heated to a certain temperature to obtain graded index polymer waveguide [9]. After heat treatment, the waveguide is laminated with polyimide film to protect it from the outer environment. Figure 4 shows the cross-sectional image of GI core waveguide after fabrication.  
Sumitomo Bakelite have developed a method of terminating polymer waveguides with a parallel optical ferrule, which is compliant with a single row 12 channel MT ferrule. The Polymer MT or PMT ferrule is currently being deployed in various commercial products by Sumitomo Bakelite (Tokyo, Japan) (Figure 5).  
Figure 4: Cross section of addressed GI waveguides.  
Figure 3: Graded index waveguide fabrication.
# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 2 Data
### UAV Images and Digital Orthophoto Map (DOM)  
After the Maduo MS 7.4 earthquake on 22 May 2021, our research team used medium-sized hybrid-wing and small multi-rotor UAVs to conduct photogrammetry of the post  
Figure 1: Topography of the study area.  
Figure 2: Different forms of coseismic surface ruptures.  
earthquake surface rupture (Figure 1). We took the aerial photos using a CW-15 DAPENG UAV, which carries a 61-megapixel CA103 ortho camera. CWCommander application software is used for flight planning and monitoring. In order to improve data accuracy, we set up the RTK ground base station to calibrate the UAV Pos attitude data. Using PhotoScan software, we obtained DOMs by quickly stitching the UAV photogrammetry. The quality of UAV photography images depends on external conditions, such as the terrain of the photography area, wind directions, flight altitudes, the overlap of adjacent photos, and the number of photos in a specific area. These factors result in different resolutions of UAV images, especially on a high plateau with undulating and complex terrain and changeable weather [25]. Furthermore, we chose the densest processing for point cloud generation to obtain high-accuracy DOMs. As a result, the final DOM resolution of the entire ruptured segment was 2-7 cm/pix, and the resolution of most images was 3-5 cm/pix. The centimeter-level high-resolution DOM could clearly show the spatial distribution of the coseismic surface rupture zone, based on which the surface rupture can be accurately interpreted. Figure 3 shows the image interpretation and its corresponding field situation.
# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
## 2 Materials and Methods
### Signal Processing on Capacitively-Coupled Signals  
Figure 2a shows the signal processing steps performed on the capacitively coupled ECG. Besides standard processing steps such as bandpass filtering, alignment, beat detection etc., the lower branch indicates the specific additional quality-based steps for motion artefact handling applied in this work:  
1. A method to discard from the set of 4 ECG channels the channels with no potential valuable information. This uses a classifier described in more detail in [28];  
Figure 1: Capacitive sensor system installed in a car used for the on-road evaluation. It includes a 17-electrode array for ccECG measurement, 4 electrodes for ccBioZ measurement (plus one unused electrode), and a capacitive Driven Right Leg (DRL) plate to reduce common mode interference.  
2. The identification of artefacts using a template-based signal quality metric previously evaluated by the authors [29], in combination with an amplitude-based metric calculated as the percentage of 0.5-s sub-windows that have data within [0.05-3] mV range;
3. The selection of the channel. This selection uses the same signal quality metric as in step 2;
4. The extraction of features from the signal parts with useful information. The parts of the signal that have been identified as containing artefacts in step 2 are omitted from the feature calculation.  
Figure 2b shows the signal processing path for the capacitively coupled bioimpedance signal towards respiration metrics. No motion artefact algorithms have been included for respiration at this point.
## 3 Methodology
### System Architecture  
The proposed wind speed and direction sensing system consist of two-stage data processing techniques. As shown in Figure 3a the thermophilic and ambient temperature data are collected by the device and then transferred wirelessly to a personal computer (PC). The transferred data are then used by two ANN models for estimating wind speed and wind direction, simultaneously. The ANN models are generally used for regression, prediction and calibration tasks, as mentioned in Section 2. The major benefit of the ANN model is its ability to model non-linear and highly stochastic mathematical relationships between multiple variables. The ANN models used in this study are trained against the observed wind speed and directions that are measured by a cup anemometer and wind vane. Due to the universal approximation properties of ANN [63], it is preferred over linear regression and other machine learning models for calibration and regression tasks [55; 64; 65; 67]. In this study, we used two identical ANN models with four hidden layers. The number of neurons in each layer were 64, 32, 16 and 8 neurons respectively, these hyper-parameters were obtained using a halving grid-search [68]. The sigmoid function is applied as the activation function for each layer, except the output layer, which has a linear activation. The Adam [69] optimizer was used for optimizing the ANN. The input data are normalized using minimum and maximum value of the training dataset. For comparison, support vector regression [18] and LR using least squares are used. The maximum number of iterations for the ANN model was set to 10,000 epochs, while for the SVM regression model the stopping tolerance was set to 10\({}^{-5}\) and the squared l2 penalty was set to 0.1. All three models were programmed using the 'Scikit-learn' [70] library in the 'Python' programming environment. The data collection and transfer routine for ESP32 were written in 'C++' programming language. The data collection and transfer rate for the thermophile array were set to a 2-s interval. The down sampling of the collected data was performed using the PC before estimating the wind speed and direction. In Figure 3b, a potential application scheme is shown, where the proposed device can be used for the wind speed and direction measurements by an energy management system for solar panels  
Figure 2: Temperature distribution measurements of the heat sink at different wind speeds and directions.  
and wind turbines. Alternatively, it can also be used for potential wind energy assessments for a given residential building or community area.
## 2 Materials and Methods
### Research Method and Process
#### 2.3.9 CASA Model  
The light energy utilization model to estimate NPP is based on a resource balancing view and is one of the most well-documented inversion models for NPP remote sensing. The Carnegie-Ames-Stanford approach (CASA) model, established by [PERSON] et al. [58], is the most widely used model for light energy utilization.  
\[\text{NPP}(x,t)=\text{APAR}(x,t)\times\varepsilon(x,t) \tag{16}\]  
where \(x\) denotes spatial position, \(t\) represents time, APAR(\(x\),\(t\)) indicates the photosynthetically active radiation (gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\)) absorbed by image element \(x\) in month \(t\), and \(\varepsilon\)(\(x\),\(t\)) is regarded as the actual light energy utilization (gC\(\cdot\)M)\({}^{-1}\)) of image element \(x\) in month \(t\).  
\[\text{APAR}(x,t)=\text{SOL}(x,t)\times\text{FPRA}(x,t)\times 0.5 \tag{17}\]where SOL indicates the total solar radiation (gC\(\cdot\)m\({}^{-2}\cdot\)month\({}^{-1}\)), FPRA represents the proportion of the incident photosynthetically active radiation absorbed by the vegetation layer, and the constant 0.5 denotes the ratio of solar radiation utilized by the vegetation to the total solar radiation [34; 59].
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Convolutional Neural Networks to Spectrum Sensing  
#### 3.1.1 Convolutional Neural Networks  
A convolutional neural network is a kind of artificial neural network (ANN) and also one of the most common networks in the field of deep learning [50]. Due to its properties and advantages, such as efficient processing of grid-structured data and automatic feature learning, it has achieved excellent performance in various domains and is particularly popular in the fields of object detection, image recognition, and speech recognition.  
The convolutional neural network architecture consists of three main parts: convolutional layers, pooling layers, and fully connected layers. Its basic network model is shown in Figure 10. The role of the convolutional layer is to extract the different features of the data that were passed in the previous layer, and the pooling layer will extract the salient features and reduce the spatial dimension of the data after the convolutional layer, but it also introduces a certain information loss. Moreover, fully connected layers typically combine all the previously extracted feature data into global features in the last part of the network architecture and finally complete the recognition and classification based on different features.  
A CNN is normally used as the preferred network for image processing, which can accurately extract feature information for 2D images [51], while in the study of spectrum sensing, researchers commonly convert the received signals into 2D data for statistical feature extraction, or obtain the spectrogram of the signals through a short-time Fourier transform, which can be viewed as an image, thus transforming the spectrum-sensing problem into an image recognition problem.