esa-sceva/satcom-chunk-collection huggingface dataset:
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 6 Phase-Based Sensors  
Planar sensors based upon the measured phase-shift for tracking the glucose concentration have also been reported in the literature, although sparingly. These sensors have been proposed for multiple applications requiring the measurement of the dielectric properties of the MUT [151]. These sensors show the advantage of requiring very narrow band interrogation signals (even a single tone in some cases), and the associated driving electronics become simpler and more cost-effective. Nevertheless, the data acquisition systems for phase-based measurements can become considerably complex in comparison with other measurement approaches. In this sense, solutions based on the measurement of the magnitude of the transmission coefficient associated with a phase difference sensing have been recently proposed [152], making the phase-based approach interesting from the practical realization point of view. All these reasons have led some researchers to consider the application of this kind of sensor for glucose level identification. The working principle of these sensors is similar to the one of the insertion/return loss-based sensors, but with the focus put on the phase of the transmission or reflection coefficient. Effectively, the reflection coefficient of the lumped-element model in Figure 1, without considering the admittance inverters for simplicity, can be expressed as  
\[S_{11}=\frac{Z_{\text{in}}-Z_{0}}{Z_{\text{in}}+Z_{0}}=\frac{1-\frac{Y_{0}}{Y _{0}}}{1+\frac{Y_{0}}{Y_{0}}}, \tag{12}\]  
where \(Z_{\text{in}}\) is the input impedance seen from port 1. Then, considering the definition of \(Y_{\text{in}}=G_{\text{in}}+jB_{\text{in}}\), the phase of the reflection coefficient can be computed as  
\[\phi_{S_{11}}=\arctan\biggl{(}-\frac{2B_{\text{in}}Y_{0}}{{Y_{0}}^{2}-{G_{ \text{in}}}^{2}-{B_{\text{in}}}^{2}}\biggr{)}. \tag{13}\]  
Finally, for the model considered, applying (7) leads to  
\[\phi_{S_{11}}=\arctan\left(\frac{\frac{2Y_{0}}{\omega L_{\text{r}}}\bigl{(}1- \omega^{2}L_{\text{r}}(C_{\text{r}}+C_{\text{s}})\bigr{)}}{{Y_{0}}^{2}-\bigl{(} G_{\text{r}}+G_{\text{s}}\bigr{)}^{2}-\frac{1}{\omega^{2}L_{\text{r}}}\bigl{(} \omega^{2}L_{\text{r}}(C_{\text{r}}+C_{\text{s}})-1\bigr{)}^{2}}\right), \tag{14}\]  
where the influence of the glucose-containing sample in the finally measured phase can be seen through \(C_{\text{s}}\) and \(G_{\text{s}}\) elements.
## 1 Introduction  
Several studies dealt with the potential host rock for a repository for high-level radioactive waste in Hungary [1]. This is the Boda Claystone Formation (BCF); a formation with very low porosity and low permeability, that provides good retention properties for the repository. In addition to the intrinsic properties of the argillaceous rock, the host formation and the overlying and underlying formations must have specific favourable properties to be considered a possible site for a nuclear waste repository [2, 3]. Exploration and investigation of the adjacent rock formations are critical, especially the investigation of the fracture networks that may developed across the formations. This is because the fluid flow in a claystone body could be related to its fracture system [4, 5].  
The caprock of the BCF is the Kovagoszlos Sandstone Formation (KSF), which has markedly different rheological and hydrogeological properties than the underlying impermeable claystone. Despite the uniform structural evolution, the different rheological properties of the formations could significantly influence fracturing and the resulting fracture system. Many aspects of fracturing are influenced considerably by the rheological properties of the host rock. Different rock types respond differently to the same stress field [6, 7].  
The deep well BAF-3 aimed to explore the caprock of the BCF and reached a depth of more than 800 m with a core recovery of almost 100%. The well intersected the KSF and drilled more than 400 m of the 700-900 m thick BCF. The boundary between the sandstone and the claystone formations is 343.08 m (reported depths are in total measured depth in metres throughout the paper). This borehole allows the differences in the properties of the fracture systems of different host rocks to be investigated and compared. This work aims to model the fracture network geometry of two different rock types in one borehole, investigate the differences in their natural fracture system, and evaluate the borehole hydrogeologically.
## 2 Reliability Analysis Based on the AdaBoost Surrogate Model
### Adaptive AdaBoost Algorithm  
The adaptive AdaBoost algorithm initially employs an adaptive iteration method to obtain the optimal training sample set, which is as evenly distributed as possible on both sides of the failure curve, fully encompassing the information that describes the failure curve. By using the integration and iteration properties of the AdaBoost algorithm, a high-precision surrogate model for failure discrimination in complex structures under multi-failure modes can be achieved through simple binary classifier iteration (the dichotomy method).
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 4 Method
### Processing Algorithm  
Figure 5 shows a summary of the processing flow. Before starting cavity detection analysis, a few preprocessing steps are required. The thermal camera, used for thermogram acquisition, records the image sequence as \"*.sfmov' which is based on a file format known as the Standard Archive Format (SAF). This format is not readable directly and therefore, a code snippet is developed to convert \"*.sfmov' files into 3D arrays (x, y, t) such that x and y represent the horizontal and vertical pixels of the thermograms, and t refers to the time slice of the recorded images. The arrays are then saved as Matlab data (\"*.mat\") files.  
Raw image sequences (thermograms), representing the specimen surface temperature, are taken at 50 Hz. Recording even one minute of thermal image with the FLIR Phoenix camera can produce around 1.83 GB (640 \(\times\) 512 \(\times\) 2 \(\times\) 50 \(\times\) 60 bytes) of data. To avoid reaching the memory and computational source limits, the raw sequences are downsampled by a factor of five. We use both spatial and temporal filtering to reduce noise. The temporal filter is applied prior to downsampling to reduce the subsampling error. Then a 3 \(\times\) 3 median filter in spatial domain is used. The Median filter is effective in eliminating sharp, sudden disruptive changes in the image intensity (aka salt-and-pepper noise), including a few damaged pixels of the camera. The raw thermal images include the box and its surroundings as well as the specimen. By removing the unnecessary areas, we reduce the time and computational cost.  
To detect the cavities, we use three different methods. The first method is based on LPT [12; 46] with ATC [12]. Based on our hypothesis (Section 3), we assume that the tempera  
Figure 4: Cavity configurations used in tests. (**a**) Objects used to create cavities. (**b**) Cavity configuration 1. (**c**) Cavity configuration 2 (within the process). (**d**) Cavity configuration 2 (cannot be seen since it is covered by soil). (**e**) Cavity configuration 3 (combined 5 different cavities in one setup). (**f**) Cavity configuration 4.  
Figure 5: Processing flow. ATC: Absolute Thermal Contrast, PCT: Principal Components Thermography.  
ture decay of a sounding (normal) area should be different from those where a hollowed area is located. This can be verified by computing temperature contrast  
\[\Delta T=T_{da}-T_{su}, \tag{5}\]  
where \(T_{da}\) is the temperature of a defective area (with cavity) and \(T_{sa}\) is the temperature of a sound area (without cavity). The thermal images are also analyzed using PCT [50] and another statistical approach. These methods are discussed in detail in the following sections. To improve visualization, we normalize the thermal images (using mean and standard deviation) and apply the color mapping. The image processing algorithms are implemented in Matlab R2018a and Python.
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 2 Methodology
### Pv*sol  
PV*sol is software for planning, design, and simulation of photovoltaic systems developed by Valentin Software. In addition to PV*sol, this company has other software tools for solar thermal systems (T*sol) and geothermal solar systems (GeoT*sol). PV*sol offers the possibility of configuring different types of PV systems, i.e., stand-alone systems with or without backup generator, grid-connected systems, and grid-connected systems with electrical appliances, which can include battery systems and electric vehicles [38].  
Site-dependent climate data supplied by PV*sol is based on Meteororm database. It is also possible to load user-defined climate data measured at a specific meteorological station. There is a premium version of the program, PV*sol premium, for 3D design of the system including environmental obstacles that can influence the irradiation received on the PV module.  
When simulating the electrical production of the system and its performance, there is a choice between 8 models for the calculation of the diffuse component of the solar irradiation and 5 models ([PERSON], [PERSON], [PERSON], [PERSON] et al., and [PERSON]) [24; 31; 39; 40; 41; 42] for the estimation of irradiance received on the tilted plane. A single annual albedo value may be included or modified for each month of the year. Both annual and monthly soiling losses can also be included. The software can be used to select the temporal resolution of the simulation: hourly (faster) or each minute (greater accurate).  
The software also has an extensive database of PV modules, inverters, storage systems, and other devices. New devices can be defined for inclusion in the project. Regarding the PV modules, PV*sol allows the configuration of different types of installation, i.e., fixed, with a single-axis tracking system, and with a dual-axis tracking system. The inverter configuration includes the definition of the interconnection of the PV modules. Although not mandatory, it is also possible to consider wiring losses.  
Once all the elements of the installation are defined, the program checks that the system is correct, and the simulation can proceed. The output data includes the predicted electrical production of the system, its performance ratio, the irradiance received on the module's plane and its temperature, a general energy balance of the system, and a financial analysis of the installation. Finally, it is possible to generate a report detailing the configuration of the system, its electrical diagrams, and the results. PV*SOL is subscription software and has a 30-day trial version.
# Efficient and Sustainable Cleaning: A Comparative Analysis of Cryogenic Technology
## 4 Discussion
### Future Trends and Developments
#### 4.1.4 Optimising the Cleaning Process  
Efficiency in cryo-cleaning is directly linked to the optimisation of each stage of the process. Scientific research has analysed the ideal conditions for its application in different scenarios, examining crucial factors such as angle, distance, cryogenic gas pressure, temperature, and exposure time [3; 12; 70]. By adjusting these variables, it is possible not only to boost the effectiveness of cryo-cleaning but also to decrease the related costs. This approach includes careful management of resources, ranging from the amount of cryogenic material used to the total operation time. Applying cryo-cleaning in a methodical way, taking into account the particularities of each surface or material, not only minimises waste but also maximises the efficiency of the procedure.  
Furthermore, the optimisation of the cryo-cleaning process involves a thorough analysis of critical elements, including the temperature of the surface to be treated, the resistance of the underlying material [35; 71], possible recontamination and condensation [72]. Thisconsideration ensures the effectiveness of the cleaning, as well as the integrity and longevity of the treated surface.
# Wireless Communication: Applications Security and Reliability--Present and Future
## 3 Future  
Wireless communications are a constantly developing field. New solutions that increase communication possibilities over longer and longer distances, with higher quality of services, higher speed, and, ultimately, greater reliability, are looked for. All this requires a lot of research, new solutions, and implementation. At the same time, the search for solutions that make people's lives easier must be inextricably linked to ensuring their information security as well as their lives and health. These are challenges for the future on which work must focus. We cannot forget about the possibilities of using artificial intelligence, which is also increasingly boldly entering the area of services provided using wireless communications. Artificial intelligence is also starting to appear in intentional electromagnetic attacks on wireless infrastructure. Graphical display of processed information reconstructed based on revealing emissions is the first level of such attacks. Fast and reliable reproduction of information using electromagnetic fields can become a serious threat. Another interesting area of research is ensuring the cryptographic protection of transmitted information. Quantum cryptology using quantum computers is enjoying great interest. Using such computers, you can quickly perform certain calculations that cannot be performed in real time on ordinary computers based on silicon semiconductors.  
As we can see, wireless communication is related to a number of important issues that must be developed in parallel to ensure its security. These are tasks for the future, but not the distant future.  
**Acknowledgments:** First of all, I would like to thank all researchers who submitted articles to this Special Issue for their excellent contributions. I am also grateful to all reviewers who helped in the evaluation of the manuscripts and made very valuable suggestions to improve the quality of the contributions. I would like to acknowledge the editorial board of _Applied Sciences_, who invited me to guest edit this Special Issue. I am also grateful to the _Applied Sciences_ Editorial Office staff, who worked thoroughly to maintain the rigorous peer-review schedule and timely publication.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 1 Introduction  
### Context  
Probabilistic forecasting of power grid states promotes the management of energy use and planning. The use of renewable energy sources like solar is a measure that aims at lowering effects such as that of greenhouse gas emission which harms the climate changes. It also gives a positive impact on financial costs that are associated with the use of other energy forms like thermal or hydroelectric power. Short-term forecasting has a greater impact on the safety and financial implication of the electric grid. Since solar has a stochastic and uncontrollable nature, the current study uses Gaussian processes (GP) combined with core vector regression (CVR). Application of Gaussian processes require the computation of the covariance function which is also called a kernel, [PERSON] et al. [1], and a key element that influences deeply the forecasting results. Hence, it is critical to focus on coming up with a more accurate kernel. We adopt the minimum enclosed ball (MEB) technique, which is a branch of core vector regression that is expected to improve the forecasting results. The proposed Gaussian process regression (GPR) coupled with core vector methodology is expected to produce forecasts that are more accurate than the conventional benchmark models.
# SoK: Delegated Security in the Internet of Things
## 4 Formalisation of the Security Properties
### Access Control and/or Authorisation Delegation  
Assume that \(\exists\,\mathsf{id},\mathsf{i}:(\mathsf{id},\mathsf{i})\in M\), then, in the considered application domain, a third party element \(\mathcal{TP}_{\mathsf{id}}\) requests from a delegate \(\mathcal{D}\) access to a resource \(\mathcal{R}_{\mathsf{i}}\) provided by an RC system. \(\mathcal{D}\) replies with a token \(\mathsf{t}\). Using \(\mathsf{t}\), \(\mathcal{TP}_{\mathsf{id}}\) can access desired \(\mathcal{R}_{\mathsf{i}}\).  
The task of an adversary \(\mathcal{A}\) is to disrupt the normal operation of the system and achieve their goal of violating one or more CIA properties. To model this process, a security game described in Definition 3 is introduced.  
**Definition 3**.: _Let \(\Pi\) be an access control or authorisation protocol, and \(\mathsf{AC}\) be the following experiment:_  
1. _Initializing_ \(M\)_,_ \(F\)_,_ \(H\)_,_ \(S\)_._
2. \(\mathcal{A}\) _receives as input the access matrix_ \(M\) _or_ \(F\)_._
3. \(\mathcal{A}\) _is assigned identifier_ \(\mathsf{id}_{\mathcal{A}}\)\(\mathcal{A}\) can query the following oracles:_  
\[\{\mathsf{t},\bot\} \leftarrow\mathtt{Del}(\mathsf{id},\mathsf{i}),\] \[\{\mathsf{s},\bot\} \leftarrow\mathtt{Cor}(\mathsf{id})\text{, and}\] \[\{\mathsf{ts},\bot\} \leftarrow\mathtt{Obs}(\mathsf{id}),\]  
_where \(\mathsf{id}\) is an identifier of a protocol party \(\mathcal{TP}_{\mathsf{id}}\) which wants to obtain access to an RC system resource \(\mathcal{R}_{\mathsf{i}}\)._
5. \(\mathcal{A}\) _vins if any of the following cases hold:_ * _Confidentiality:_ \(\mathcal{A}\) _outputs a token_ \(\mathsf{t}\) _and a resource identifier_ \(\mathsf{i}\)_, such that_ 1. \(\mathcal{A}\) _is not allowed to access_ \(\mathcal{R}_{\mathsf{i}}\) _according to_ \(M\)_, but_ 2. \(\mathcal{D}\) _grants_ \(\mathcal{A}\) _access to_ \(\mathcal{R}_{\mathsf{i}}\) _when provided with_ \(\mathsf{t}\)_._ * _Integrity:_ \(\mathcal{A}\) _outputs a party identifier_ \(\mathsf{id}\)_, a resource identifier_ \(\mathsf{i}\)_, and a modified item_ \(\mathsf{h}_{\mathcal{A}}\) _such that_ 1. _there is a third party_ \(\mathcal{TP}_{\mathsf{id}}\) _that is allowed to access_ \(\mathcal{R}_{\mathsf{i}}\) _according to_ \(M\)_, and_ 2. \(\mathcal{A}\) _is not allowed to access to_ \(\mathcal{R}_{\mathsf{i}}\) _according to_ \(M\)_, but_ 3. \(\exists\mathsf{h}_{\mathcal{A}}\) _that does not match its corresponding item_ \(\mathsf{h}\in H\)_._ * _Availability:_ \(\mathcal{A}\) _outputs an identifier_ \(\mathsf{id}\) _indicating party_ \(\mathcal{TP}_{\mathsf{id}}\) _and a resource identifier_ \(\mathsf{i}\)_, such that_ 1. \(\mathcal{TP}_{\mathsf{id}}\) _has access to_ \(\mathcal{R}_{\mathsf{i}}\) _according to_ \(M\)_, but_ 2. _access using a valid token_ \(\mathsf{t}\)_is not granted._
# IoT-Enabled Smart Cities: Evolution and Outlook
## 4 Open Challenges for IoT-Enabled Smart Cities
### Overcome Application Silos  
Currently, most existing smart city applications have been developed as silo applications, often with a strong integration between applications and the underlying data sources, in particular sensors. This makes sharing and re-use of information difficult, as each application needs to be explicitly integrated with each existing information source and each change in technology, e.g., of underlying sensors or communication network requires an adaptation on the application side.  
However, the true value of IoT will be achieved if more information becomes available and can be shared across applications and whole application domains [26; 90]. It is often difficult enough to deploy a physical IoT infrastructure for one application, but it is not realistic to deploy the same or a similar infrastructure separately for the next application. By sharing infrastructure, synergies can be achieved and in some cases the deployment of infrastructure may only be feasible if it is shared.  
To achieve this, a decoupling of information sources and applications is needed. On the technical side, this can be achieved using an IoT platform and the agreement on minimum interoperability mechanisms [76], e.g., as defined by the city network Open Agile Smart Cities (OASC).  
At least as important for overcoming application silos as the technical side is the administrative and organizational side. Often applications are \"owned\" by city departments or organizations, which have paid for the infrastructure and wants to stay in control. Also, budgets are often assigned in an inflexible way, making sharing difficult. Thus, it is very important to reach a global understanding and define goals on a high level, e.g., for a whole city, and work towards achieving these goals across organizational units, often also overcoming personal rivalries in the process.
# Topology Optimization of an Aerospace Bracket: Numerical and Experimental Investigation
## 1 Introduction
Hence, they concluded that by combining topology optimization and additive manufacturing techniques, lightweight components of excellent performance can be produced, which at the same time reduce material wastage, simplify assemblies, eliminate design failures, and lead to significant production cost reductions [12]. Despite the numerous advantages, there seems to be a lack of studies focusing on the practical application of topology optimization in additive manufacturing for industrial applications, on the comparison between different techniques, and on their experimental verification. This work employs the two most prominent TO methods for the case of a bracket used in aerospace applications, with principal dimensions equaling to 107.8 \(\times\) 50 \(\times\) 67.3 mm and a plate thickness of 10 mm. This application provides practical insight into the actual mechanical performance achieved by the optimized components using each method.
## 5 Training Results  
As described earlier, a single training example corresponds to one full experimental session and consists of the individual scores achieved by a human user in a play session accompanied by the estimation of the achieved completion rate as given by a human expert (the latter one is given in a numeric mode as well as with a 'Low', 'Medium', or 'High' tag).  
We recruited four biology experts in our laboratory to evaluate 15 play sessions each (60 play sessions were played in total). All play sessions were carried out from one of the co-authors, utilizing his knowledge of the experiment itself, which he had also implemented in the software. Of those 15 sessions, 5 were played as 'Low' and classified as such by the particular expert; 5 were played and classified as 'Medium'; and the other 5 were played and classified as 'High'. We therefore have in total 20 sessions classified as 'Low', 20 as 'Medium', and 20 as 'High'.  
It is important to note here that our data collection does not aim to definitively calibrate the completion rate weights to actually usable values, but to assist the investigation where machine learning techniques seem to be suitable for this setting of the scoring mechanism. Essentially, we set the ground so that a statistically sound designed experiment can then be deployed to produce actually usable weights.  
In order to conduct the training and testing, we form various groups of our training examples. For both techniques (GA and ANNs), we ran two types of accuracy measurements to see how close we come to the score indicated by the respective expert for each data point.  
Initially, we ran an \"optimistic\" case by using re-substitution, i.e., by training our system on all data points and testing the results on these same data points and computing the Mean Squared Error (MSE) [33]. Then, we dropped the \"optimistic\" assumption and applied cross-validation, i.e., by training on some of the data points and testing the trained models on the rest of the data points (with all data points serving once as test data). Additionally, we also ran re-substitution and cross-validation experiments on a by-expert basis, three-fold cross-validation experiments on a Low-Medium-High basis (with each fold consisting of data of only one type), and four-fold cross-validation experiments among different experts (with each fold consisting of data of only one expert).