esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Its Applications: A Comprehensive Survey
## 5 IoT Applications
### Industrial Applications
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Focus Area** & **Application** & **Protocol** & **Device** \\ \hline \multirow{2}{*}{[126]} & QoS-aware service & \multirow{2}{*}{Ecosystem} & \multirow{2}{*}{SoA} & \multirow{2}{*}{Smart devices} \\  & composition & & & \\ \hline \multirow{2}{*}{[127]} & Semantic-aware service & Smart homes & 6 LoWPAN & \\  & composition & Smart devices & CoAP & \\ \hline \multirow{2}{*}{[128]} & QoS-aware multi-objective & Composite service & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & service composition & Optimization service & & \\ \hline \multirow{2}{*}{[129]} & QoS-aware service & \multirow{2}{*}{Optimization service} & \multirow{2}{*}{IP} & \multirow{2}{*}{-} \\  & composition & & & \\ \hline \multirow{2}{*}{[130]} & QoS-aware multi-agent & \multirow{2}{*}{Web services} & \multirow{2}{*}{XMPP} & \multirow{2}{*}{-} \\  & composition & & & \\ \hline \multirow{2}{*}{[131]} & Service accuracy & IoT Mashup application & RTM and FM & IoT sensors \\ \hline \multirow{2}{*}{[132,133]} & Finance data flow system & Financial and banking sector & NFC & - \\ \hline \multirow{2}{*}{[134]} & Etherum BC & Smart grid & BC & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: IoT commercial applications. (IIoT), focusing on Low-Power Wide-Area Networks (LPWANs). The idea was to design a realistic simulation model of LoRaWAN that allowed the performance of network designs typically employed for industrial monitoring to be examined. A comparison to a design of a similar WPAN, for example, IEEE 802.15.4, has also been conducted and offered promising outcomes. [PERSON] et al. [143] defined the execution of a transferable, platform-agnostic, and reliable Industrial Blockchain Tokenizer (IBT) for IoT applications. Its function is to gather industrial data in the attainment center, which is able to collect information from both new and conventional systems while also precisely communicating with sensors. Table 7 summarizes the industrial applications for IoT.
## 6 PMMA-AgNP Composite Properties  
PMMA is used in different fields due to properties that enable it to be used in biomedical applications. The use of silver nanoparticles promotes the antibacterial properties of those PMMA-based materials applied in different ways in the biomedical field. In this review, we focused on dental applications, although the use of PMMA-AgNP composites as bone cement is also widely reported (Figure 1).  
[PERSON] et al. [15] evaluated the effects of silver nanoparticles on the tensile strength of self-cured PMMA. They reported that the presence of 5% silver nanoparticles significantly decreased the tensile strength of commercial-brand heat-cured acrylic resin Triplex (Ivoclar Vivadent). These results are  
Figure 3: Example of a PMMA–AgNP composite application. (**a**) Culture obtained from a patient’s oral mucosa. The green colonies correspond to _Candida albicans_ and the purple colonies correspond to _Candida tropicalis_ cultured on CHROMagar Candida (CHROMagar, 4, Place du 18-Juin-1940, Paris, France, 75006). (**b**) PMMA–AgNP discs on cell culture plates. With this technique, we could not observe an inhibition zone because AgNPs could not be released from the PMMA discs. Silver nanoparticles embedded in the PMMA act as an agent that inhibits the growth of microorganisms on the surface of the discs. Author’s own images.  
consistent with those reported by [PERSON] et al. [16]. In their study, they observed that the addition of 0.05% silver nanoparticles to the commercially used acrylic resins Rapid Repair and Selecta Plus (Dentsplay, Weightge, UK) significantly decreased the flexural strength. The authors mentioned that this effect could be due to the silver nanoparticles' dispersion, and they also mention that the use of 0.02% silver nanoparticles increased the flexural strength, but not significantly.  
In contrast, a study reported by [PERSON] et al. [17] reports higher values of flexural strength and modulus, which represents an enhancement of the mechanical properties of PMMA-AgNP acrylic resins. Dental prostheses are made of PMMA due to its better mechanical properties, dimensional stability and minimal inflammatory response. It is a biocompatible polymer and, due to its properties, is a suitable material for use in the patient's oral environment. According to this review, the ideal material for dental applications would be one that is biocompatible, with better mechanical properties (flexural modulus, fracture resistance and compression modulus) and resistance to corrosion, as well as one that provides an antimicrobial and antifungal effect to prevent oral infections.
## 2 Materials and Methods
### EIS Measurement System  
EIS measurements were conducted using the ISX3 EIS analyser Version 4 (Sciospec Scientific Instruments GmbH, Bennewitz, Germany). The BNC ends of the four-point probe were connected in linear order to the W (work), WS (working sense), R (reference), and C (counter) connectors on the ISX3 EIS analyser, respectively, as shown in Figure 1a. The excitation voltage was applied between electrodes W and C, while EIS spectra were acquired between WS and R. A representative Nyquist plot of the sample EIS data is shown in Supplementary Figure S2. Prior to EIS measurements, the exposed cores of the four-point probe were inserted 1 cm deep into the samples.  
To select an appropriate frequency range for our EIS measurements, we referred to the accuracy contour plot [ScioSpec ISX-3/ISX-3 mini manual, version 87, 2021], which indicated an 0.01% error for 1 k\(\Omega\) impedances from 1 Hz to 1 MHz and an 0.1% error for frequencies above 1 MHz. Also, coaxial cable capacitance, determined by shielding quality, sets an upper frequency limit, while cable inductance, influenced by length, defines a lower frequency limit for accurate impedance measurement [50]. All EIS scans, including calibration scans, were performed over a frequency range of 1 Hz to 5 MHz, using 2048 logarithmically spaced data points per sweep. An excitation voltage of 250.0 mV was applied. Mean and repeat count were set to 1.  
The complex impedance (\(Z\)) at each frequency point was determined based on the measured real (\(Re\)) and imaginary (\(Im\)) components of impedance, expressed as:  
\[Z=Re(Z)+j\ Im(Z) \tag{1}\]where \(j\) is the imaginary unit. The equivalent circuit of the sample and electrode system was modelled as a resistor in parallel with a capacitor, assuming negligible contact resistance and inductance between the sample and the EIS probes [65; 66]. For a parallel circuit, the admittance (\(Y=1/Z\)), defined as the inverse of impedance, is given by:  
\[Y=G+jB=G+j\,\omega C \tag{2}\]  
Here, \(G\) represents the conductance contributed by the resistor, while \(B\) denotes the susceptance contributed by the capacitor with capacitance (\(C\)), proportional to the relative permittivity of the sample (\(\epsilon_{r}\)). The angular frequency is defined as \(\omega=2\pi f\), where \(f\) is the frequency [47].  
For the sample modelled as a parallel RC circuit, the conductance (\(G\)) and susceptance (\(B\)) were calculated from the measured impedance components as  
\[G=\frac{Re(Z)}{Re(Z)^{2}+Im(Z)^{2}}\text{ and }B=\frac{-Im(Z)}{Re(Z)^{2}+Im(Z)^{2}} \tag{3}\]  
The sample's (real-valued) conductivity (\(\sigma\)) and relative permittivity (\(\epsilon_{r}\)) were further derived using the following relations: \(\sigma=K_{Cell}\cdot G\) and \(\epsilon_{0}\)\(\epsilon_{r}=K_{Cell}\cdot C\). Here, \(\sigma\) is the conductivity of the sample, \(\epsilon_{0}\) is the permittivity of free space (\(8.85\times 10^{-12}\) Fm\({}^{-1}\)) and \(K_{Cell}\) (in m\({}^{-1}\)) is the cell constant that depends on the geometry and properties of the electrode [67].
## 3 Interoperability Issues
### HBIM and GIS Integration: Semantic and Technical Interoperability
#### 3.3.1 HBIM-GIS Integration Using a Recent Function in a GIS Commercial Solution
The characteristics were compared with those of CityGML. It considers all the geometric features, but does not concern with information of building design and construction phase such as state of maintenance, materials and so on. Figure 10: Three-dimensional visualization of LoD 3 in ArcGIS Pro. In a second evaluation phase, it has been observed that the only parameter corresponding both to IFC and CityGML classifications is the _Year of construction_, identified in the XML schema (Figure 11). The difference of this parameter in the two standards concerns the elements it identifies: in CityGML it is assigned at the whole building, in IFC it is a property of each part of the construction. After having analysed the semantic interoperability, the IFC missing parameters were added into the GIS environment editing the attribute table of the HBIM model geometry. In this way, by querying the entities in the GIS model it could be possible to obtain all the useful information required in case of intervention on the artefact. Regarding information present in the CityGML standard but not in IFC, as well as the _Year of Maintenance, Intervention Typology_ and _Years of Intervention_, useful in risk or hazard situation, it was decided to provide them in the model in the Revit workspace, in the BIM environment. To assign these not-geometric information to the model, it is possible to use, in the Revit software, the _Shared parameters_, adding a.txt file that contains the new information. Figure 12 shows the new parameters connected to the damage event in Revit. Figure 11: Structure of a simple building in CityGML [37]. Figure 12: Population of new parameters connected to the damage event in Revit.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 4 Forecasting of Chaotic Time Series in Various Applications
### Power and Energy
#### 4.1.1 Wind Farms  
Many applications of wind power and speed forecasting approaches have been developed based on chaotic characteristics or chaotic time series and applied on various wind farms. For instance, the statistical type of forecasting approaches for predicting wind power, speed, and load for short-term and long-term wind power, speed, and load prediction in Beijing in China are as follows:  
* ITSM with wavelet decomposition method [74];
* SVM [16];
* Rough set neural network [80];
* BFA tuned double-reservoir ESN [166].  
The approaches, as mentioned earlier, showed good short-term and long-term performance, but the computational complexity is high to complete the task. Similarly, many other works have been attempted to predict weather conditions for wind farms whose operations are more complex.  
Various researchers have proposed hybrid prediction methods to enhance accuracy. These approaches were made by integrating the following:  
* Wavelet transforms with chaotic time series and grey model [81];
* Hilbert-Huang transforms with Hurst analysis [108];
* Hybrid neuro evolutionary [175].  
The hybrid approaches mentioned above with multistep chaotic characteristics were validated for short-term forecasting of wind power at the Dongtai wind farm and Hebei province in the east of China. The work highlights that EMD-based combined forecasting methods can improve short-term forecasting accuracy based on their characteristics. The surrogate data technique and spectral analysis methods are applied to forecast wind wave height, period, and direction for three-hourly chaotic time series from three stations in the Caspian's southern, central, and northern parts of the sea [109]. The hybrid approach developed using ensemble EMD-sample entropy and the full parameters continued fraction model were developed for predicting the wind power of farm location at Xinjiang, China [119]. Moreover, the Markov chain switching regime model developed in [144] used hourly, short-term, and long-term chaotic time series data for predicting wind speed and direction of the farm located at Bonneville Power Administration control area in the Northwest USA. It is to be noted that these proposed approaches can also be used for the  
Figure 5: List of forecasting parameters categorized into the different applications.  
proper planning and scheduling of wind power. The self-adaptive and artificial intelligence type forecasting techniques for predicting the wind speed are as follows:  
* Fractal dimension-Lorenz stenflo-Ensemble EMD;
* GA tuned BPNN model;
* Empirical dynamical model [152; 165].  
For short-term prediction of wind speed considers the atmospheric motion and fractal feature at Abobstford in Canada, and Kansas and Missouri in the USA. It is to be noted that better results can be generated using exogenous variables in the ANN approach.
###### Abstract  
In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches.  
Keywords:antenna design; radial basis function neural networks (RBFNNs); particle swarm optimization (PSO); multi-objective optimization +
Footnote †: journal: Applied Sciences
## 3 Thin-Film Materials and Their Applications
### Complex Metal Oxides
presented a review where new piezoelectric thin films without Pb were studied (such as piezopolymers, piezoceramic BaTiO\({}_{3}\) films, and (Na,K)NbO\({}_{3}\)), which were able for biocompatible, flexible, and compliant piezoelectric energy harvesters for monitoring the environment, an individual's health, wearable, implantable and portable devices, etc. [113]. Finally, traditional perovskite structure thin films can be used in fast oxygen exchange, which means it is somewhat similar to the binary oxide thin films, the CO, NO, or other gas can obtain oxygen ions when passing through the film, after which, oxidized into harmless gases. The typical structure of this kind is La\({}_{0.6}\)Sr\({}_{0.4}\)Co\({}_{0.2}\)Fe\({}_{0.8}\)O\({}_{3}\) and Bi\({}_{1-x}\)Sr\({}_{x}\)FeO\({}_{3-\delta}\)[111]. Generally speaking, the excellent properties of perovskite structure polyoxide thin film in the field of light and heat make it possible to be used in the electrodes of photovoltaic power generation or photovoltaic cells, which provides the material foundation for the development of the next generation environmental-friendly energy storage system. At the same time, as an oxide thin film, this kind of film can also embody the characteristics of general oxide films, which have been widely used in environmental sensors such as gas detection or the disposal of waste. More importantly, metal oxide thin-films can be used for dissolvable/transient electronics.
## 3 Results and Discussion
### System Identification
#### 3.1.1 Data-Based Mechanistic Modelling of CIVC
Figure 5: The parallel configuration of the interconnection between the two first-order TFs resulting from the decomposition of the second-order system (Equation (3)). equation representing the heat flow (W) between the finger and the colder Peltier's element can be given by:  
\[\frac{dT_{skin}}{dt}=\frac{h_{vain}A_{vain}}{C_{skin}}\left(T_{blad}-T_{skin} \right)+\left\{\frac{h_{cap}A_{cap}}{C_{skin}}(T_{blad}-T_{skin})-\frac{K_{skin} A_{skin}}{C_{skin}}\left(T_{skin}-T_{\infty}\right)\right\} \tag{5}\]  
where \(C_{skin}\) (J\({}^{\circ}\)C\({}^{-1}\)) is the skin thermal capacitance, \(T_{skin}\) is the finger skin temperature, \(h_{vain}\) (W m\({}^{-2}\)\({}^{\circ}\)C\({}^{-1}\)) is the convictive heat transfer coefficient of flowing blood in the superficial vein, \(A_{vain}\) is the cross-section area of the superficial vein, \(T_{blad}\) is the blood temperature, \(h_{cap}\) (W m\({}^{-2}\)\({}^{\circ}\)C\({}^{-1}\)) is the convective heat transfer coefficient of the blood in the superficial capillaries network, \(K_{skin}\) (W m\({}^{-2}\)\({}^{\circ}\)C\({}^{-1}\)) is the skin conductance. Under normal temperature (normothermia), before applying the step cooling using the Peltier's element, there is a baseline level of vasoconstrtive tone [33], where the blood velocity fluctuates, between a high level and a very low level, with about 2-3 vasoconstrtictions per minute [17]. The high level corresponds to a state when the AVAs were open. However, when the local environmental temperature (Peltier's element temperature) drops, the AVAs close, causing the direct blood flow from the artery to the superficial vein to stop quite abruptly. Hence, the convective heat transfer (\(\dot{Q}_{vain}=h_{vain}A_{vain}(T_{blad}-T_{skin})\)) from the blood in the superficial veins (Figure 6) decreases dramatically, causing a fast reduction in the skin temperature, which can be represented by the transfer function \(TF_{2}\). On the other hand, the convective heat transfer from capillaries (\(\dot{Q}_{cap}=h_{cap}A_{cap}(T_{blood}-T_{skin})\)) together with the conductive heat exchange (\(\dot{Q}_{skin}=\ K_{skin}A_{skin}(T_{skin}-T_{\infty})\)) between the skin and colder Peltier's element are dissipating the heat much slower. This is mainly due to the effect of skin thermal capacitance and the high resistance of the capillaries to blood flow. Hence, TF1, with the slow time constant, is suitable for describing the dynamic response of both the convective heat transfer from capillaries and conductive heat exchange between the skin and the Peltier's element.
# Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices
## 5 Hardware Infrastructure
### Sensor Unit  
Sensors are used for monitoring the environmental conditions in the field. In the introduced system, the sensors are selected to measure soil moisture and soil temperature. The sensors are shown in Figures 6 and 7, along with some of the sensor data collected through initial testing. These are not data from the field, but data collected during initial testing at an indoor environment to examine the feasibility of the sensor node. The data are plotted for demonstration only.  
Since after a heavy rainfall water can accumulate in a certain spot in the field, it would be difficult to find wet spots if the field is not checked regularly. Sparkfun soil moisture sensor [26], showing in Figure 6a, was selected because of each sufficient accuracy (accuracy 3% volumetric water content (VWC), is sufficient to characterize the soil reading in the four general categories), low energy requirements and low-cost. Other soil moisture sensors that have better accuracy exist in the market, however, that would increase significantly the cost per monitoring node. Initial testing with the soil moisture sensor was conducted and the results are shown in Figure 6b. The sensor needed some initial calibration, however, the accuracy of the sensor is sufficient for the proposed application.  
For most diseases, the air temperature has a specific range where it is most likely to manifest. However, soil temperature can also give important insight regarding the manifest of the diseases. By monitoring when the temperature for an area is in the high-risk range of a disease developing, proper protection practices can be applied well in advance. DS18B20 waterproof temperature sensor [27], showing in Figure 7a, was selected for the proposed system. Similar to the soil moisture sensor selection, this DS18B20 was preferred over other temperature sensors due to its low-cost and low energy requirements. The results of initial testing with this sensor are shown in Figure 7b.  
Figure 6: Soil moisture (**a**) sensor and (**b**) experimental data readings.
# Food Waste Detection in Canteen Plates Using YOLOv11
## 2 Related Work  
### YOLO: You Only Look Once  
YOLO was first introduced to the CV community by [PERSON] through an article published in 2015, where it reassessed the object detection paradigm [6]. It has been evolving at an extremely high pace [7], with YOLO currently in its 11 th version. During the last years, various versions of YOLO have been presented [7; 8; 9], adding objectness score to bounding box predictions and making predictions at three distinct levels of detail to improve small object detection performance (YOLOv3) [8]; improving the accuracy of the detection without impacting the inference time (YOLOv4) [10]; changing the implementation approach (YOLOv5 using PyTorch 1.6.0) [11]; using a neural network framework (YOLOv6) similar to Google TensorFlow [11]; improving fast and real-time accuracy object detection (YOLOv7) [12]; directly predicting the center of an object instead of the offset from a known anchor box (YOLOv8) [11], including Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN), both of which significantly improve feature extraction, gradient flow, and network efficiency (YOLOv9) [13]; improving the use of Deep Convolutional Neural Networks (DCNNs); and the new Multidimensional Feature Fusion (MFD) engine (YOLOv10) [14; 15].  
Figure 1 presents the model key architecture in YOLOv11 [16], which incorporates Spatial Pyramid Pooling-Fast (SPPF), Cross Stage Partial with Spatial Attention (C2 PSA), and Cross Stage Partial with kernel size 2 (C3k2) modules as core components. These elements collectively enhance both the efficiency and accuracy of the model.  
YOLOv11 [17] is the latest version of YOLO, which provides a more capable and adaptable model that pushes the boundaries of CV. The current model supports CV tasks like posture estimation and improved instance segmentation, representing a significant advancement in real-time object detection technology [16].  
In this project, which aims to identify FW on plates in a school canteens, we chose to use YOLOv11. This version was selected due to its contemporaneity at the time of development, its comprehensive documentation directly applicable to the project's requirements, and superior benchmarking results compared to other versions, as detailed in Section 3.2.3. YOLOv11 proved to be essential due to its significant improvements in detecting smaller objects, which was crucial for identifying small food portions or leftovers on the plates and segmenting them using the instance segmentation technique. Furthermore, its ability to adapt to varying lighting conditions and dynamic environments, such as those found in school canteens, ensured a more effective and accurate solution. The extensive documentation also played a key role, as it facilitated the necessary implementation and adjustments, leading to successful outcomes in FW identification and providing efficient, high-quality results.