esa-sceva/satcom-chunk-collection huggingface dataset:
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 3 Bottom-Up Fabrication of Periodic Structures on OFs
### Other Self-Assembly Methods
#### 3.2.2 Directed Self-Assembly
This investigation demonstrated that ordered patterns with plasmonic properties can be obtained on flat substrates, while the process applied to the OFs still needs some optimization to enhance the long-range order of the nanostructures.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 6 Future Research Challenges and Opportunities
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Issues** & **Opportunities** & **Challenges** \\ \hline Most of energy efficiency and consumption model are based on old hardware and standard & Need to develop energy efficiency and consumption model incorporating current hardware and WSN nodes & Difficult to infuse various node technologies and hardware in model. \\ \hline Need dynamic and adaptive ECS to support WSN applications & Support varieties of applications, full utilisation of channel capacity and several design parameters & Design and implementation of re-configurable transmitter and receiver at energy constrained WSN node and energy analysis of re-configurable WSN nodes. \\ \hline Need to assimilate interference from other networks during simulation & Possible to replicate practical WSN situations & Hard to emulate in simulation setting and experimental research is expected. \\ \hline Lack of suitable network model and channel model to represent a practical WSN & To design a universal network model that covers various parameters including node feature, channel model, reliability, data rate, etc. & WSN nodes are using different technologies, communication channel between nodes, reliability and other parameters are varying according to application. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of ECS design issues, opportunities and challenges in WSN.
# JSON Schemas with Semantic Annotations Supporting Data Translation
## 4 Proposed Annotation Mechanisms for JSON Schemas  
JSON Schema aims to define the structure of JSON data [48], supporting its documentation and validation, among others. An example of JSON data (a JSON instance) is presented in Listing 1, and its schema is presented in Listing 2. Based on these listings, the information that can be extracted is the structure and data types of the document, and one might even suspect that the \"units\" property refers to a temperature unit from a temperature sensor \"OutTempSens\" because of our human semantic interpretation of the properties abbreviation. However, will a machine/system be able to interpret with such acuity?Systems need domain knowledge and reasoning capabilities such as humans to meaningfully extract the data from the documents. For a correct interpretation of semantic information, a previous knowledge base needs to exist, exactly in the same way as for us, humans, who can access our knowledge and associate certain keywords with certain concepts. A way of giving this knowledge to the systems is by providing them with a reference ontology, and this ontology shall have all of the required concepts and properties. By sharing this reference ontology amongst all interoperable systems, it is possible to achieve common knowledge for the SOA. However, in addition to a shared understanding of the domain, meta-data mapping property names to ontology elements are also required since the message schemas are typically developed independently from potential ontologies or even the domain of the SOA. One way to have this metadata is with the introduction of semantic annotations that refer to the shared reference ontology. If, i.e., the \"OutSenstemp\" property is properly annotated, the system can access the ontology and identify it as an outdoor temperature sensor.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 2 Micromegas Fabrication Techniques
### Resistive Anode Micromegas  
To improve the stability of Micromegas in high particle flux environments, quench the spark, and protect the electronics, a resistive overlay, or resistive strips are applied on the anode (i.e., ATLAS, T2K, ILC, CLAS12, and Muon tomography presented in the following). The resistive anode is also used to spread the charge between several pads or strips to improve the charge sharing. As the type of the resistive layer depends on the implementation, further details are given in the corresponding Sections 4 and 6.1.
## 1 Introduction
### Chemical Looping Combustion and Oxygen Carriers  
The concept of oxidizing fuels with oxygen provided by solid oxygen carrier materials, rather than with oxygen from air, has several possible applications [1; 2]. Chemical Looping Combustion (CLC) [1; 2; 3; 4] is a combustion technology that would enable carbon capture without significant costs for gas separation [5]. In CLC, fuel is oxidized with oxygen from a solid Oxygen Carrier (OC) in particle form, exemplified below by oxidation of methane with iron (III) oxide, see reaction (1).  
\[\mathrm{CH_{4}(g)+12 Fe_{2}O_{3}(s)\to 8 Fe_{3}O_{4}(s)+CO_{2}(g)+2H_{2}O(g)} \tag{1}\]  
The products are carbon dioxide, steam and iron (II, III) oxide, of which the latter is oxidized to its initial state with air in a separate air reactor, see reaction (2).  
\[2O_{2}(g)+8 Fe_{3}O_{4}(s)\to 12 Fe_{2}O_{3}(s) \tag{2}\]  
The oxidized material is then be returned to the first reactor vessel ready to oxidize more fuel. Since fuel and air are not mixed, the flue gas will be undiluted by N\({}_{2}\). The sum of reaction (1) and reaction (2) is reaction (3), that is, combustion of the fuel with oxygen.  
\[\mathrm{CH_{4}(g)+2O_{2}(g)\to CO_{2}(g)+2H_{2}O(g)} \tag{3}\]  
Some oxygen carriers, such as, for example, manganese (III) oxide, are capable of releasing gas phase oxygen directly into an atmosphere with low partial pressure of oxygen, see reaction (4).  
\[6 Mn_{2}O_{3}(s)\to O_{2}(g)+4 Mn_{3}O_{4}(s) \tag{4}\]  
The mechanism described in reaction (4) is typically referred to as Chemical Looping with Oxygen Uncoupling (CLOU) [6]. The oxygen that is released can react directly with fuels, in accordance with reaction (3). A chemical looping process could involve fuel oxidation both via reaction (1) and via reactions (4+3).  
As of this moment, Chemical Looping Combustion is not a commercially available technology. Further, there is no clear consensus in the scientific community about reactor design and choice of oxygen carrier material. However, fluidized bed reactors with oxygen carrier as the bed material is currently the most examined design principle, and there are more than 10,000 h of operational experience with such pilot reactors [7]. A large majority of studies about solid fuel applications utilizes cheap and readily available oxygen carriers such as naturally occurring minerals or industrial by-products [4].
## 3 Thin-Film Materials and Their Applications
### Organic Thin-Films
#### 3.2.1 Small Molecule and Polymer Thin-Films
The albumen-gated TFTs exhibit an ultralow operation voltage, high field-effect mobility, a low subthreshold swing, a large on-off ratio, and good reproducibility in response to the repeatedly pulsed gate voltage. Such EDL-TFTs with albumen electrolyte as the gate dielectric will be useful for the biodegradable and low-energy portable electronic products.
# Development of a Calibration Strip for Immunohromatographic Assay Detection Systems
## 2 Quantitative Detection System
### Photoelectric Detection System  
As aforementioned in Section 1, the photoelectric detection system is superior to image processing detection systems in detection speed and computational burden, which attracts many researchers. Figure 1a displays the schematic diagram of photoelectric detection system, including a mechanical module, a photoelectric module, and a central board. In the mechanical module, the test strip is placed on the mechanical stage, which is driven in and out by a driving motor. In the optical module, two LEDs irradiate light on the test strip, as indicated by green arrows. An optical fiber is placed vertically above the test strip, through which the reflective light is focused on a photodiode for photoelectric conversion, as indicated by red purple arrows. Following that, electrical signal is transferred to a digital signal by an A/D conversion unit. The central board, which is equipped with a high-performance embedded processor, controls the whole detection procedure and performs the signal processing algorithm. The 3D structure of photoelectric detection system is shown in Figure 1b, in which major components including the test strip, photoelectric module, mechanical stage, central board, and driving module.  
The selection of an LED light source is based on the principle of complementary color. By analyzing the test line absorption spectrum of the colloidal gold ICG assay test strip, the maximum absorption wavelength is found at 525 nm, which is within the green light wavelength range (500-560 nm). The complementary color of green is red-purple. Therefore, according to the principle of complementary color, the test line of the strip absorbs green light and shows red-purple. In this regard, a green LED is selected to get the highest excitation energy. Furthermore, the linear working range of photoelectric sensors, the transmission characteristics of the optical fiber, as well as the parameters of the filter and amplifying circuit in the central board, have certain differences during manufacturing. They may even drift with different rules after a long time of operation, e.g., variation of the temperature and humidness, which have a great impact on detection accuracy and repeatability of the photoelectric detection system. Therefore, calibration of the detection system plays a significant role before the detection, and how to extract the property of the ICG test strips for designing the calibration strip in the next step becomes the primary problem.
# Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses
## 1 Introduction
The first is the increased efficiency in actuator design and implementation such as the usage of systematic control software algorithm design combined with the previously mentioned innovative hardware design measures such as inclusion of possibly variable in-series/parallel compliances and fast load levelling. The second is more comprehensive usage of preview information from camera, Global Positioning System (GPS), and electronic horizon such as vehicle-to-vehicle communication, vehicle-to-infrastructure communication, vehicle localization and real-time accessing of cloud information, and crowd sourcing leading to up-to-date road profile maps\". So, there are still many areas for investigation [23; 39; 40; 41; 42; 43]. We planned to keep in check our developed (semi-)active suspension [44] using information acquired from a segment of traveled road profile [45; 46]. The central vehicle safety block would receive the road characteristics and set damper damping values to minimize the vibration of the vehicle body. The needed step is to relate the obtained road characteristics to the required optimal damping values. For that purpose, we needed a gridded matrix of damping coefficients at certain fixed values of speed and road characteristics. We found only a few papers with the results needed for this purpose; only a few points needed for the whole matrix can be found in them, and they used different car models, road types and characteristics, and optimization methods (see Table 1). \begin{table}
\begin{tabular}{c c c c} \hline
**Road Type** & **Car Model** & **Remarks** & **Refs.** \\ \hline Asphalt, concrete, rough & Quarter-car & Modified skyhook control & [47] \\ \hline Sinusoidal and random road & Full-car (7 DOF) & \begin{tabular}{c} LIDAR and diagonal recurrent \\ neural network control model \\ \end{tabular} & [23] \\ \hline ISO class B, C, D & Quarter-car & \
\begin{tabular}{c} Optimized damping using three \\ criteria or modified criteria; \\ various speeds \\ \end{tabular} & [48; 49] \\ \hline Good and poor quality & Quarter-car & Optimized damping and stiffness & [50] \\ \hline Random (max elevation 50 mm) & Full-car (12 DOF) &
\begin{tabular}{c} Optimized damping and stiffness; \\ separated engine \\ \end{tabular} & [21] \\ \hline Random (standard deviation 10 mm) & Quarter-car & Genetic algorithm & [51] \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation of road profile impact to the vehicle body. This paper provides a relationship between the detected \(w_{1}\), \(w_{2}\) waviness values for a wide range of road profiles and the optimized damping coefficient for a full-car model (12 DOF) traveling them at various speeds. To minimize the number of possible choices, here we focus on instances when the damping coefficient was optimized for the maximum level of driver comfort. In addition, there are various locations of masses, and we used the same value of damping for all four wheels. This idea is similar to the developed method of minimizing the acceleration at a selected point on the vehicle body [54].
## 2 Overall Technical Parameter Requirements and Architecture Analysis  
At present, the obstacle avoidance system of high-speed railway tunnel inspection vehicles still relies on a combination of single-line laser 2D plane scanning and manual real-time monitoring for detection and obstacle avoidance. This approach has insufficient detection capabilities for small obstacles generating sparse point clouds, and the risk warning methods are relatively singular. To adapt to the obstacle avoidance requirements of high-speed railway tunnel inspection, as shown in Table 1, according to the obstacle avoidance requirements of the tunnel inspection vehicle in many practical applications, this paper analyzes and summarizes the technical indicators required for the vehicle-mounted Ground Penetrating Radar (GPR) detection device's obstacle avoidance system. A hierarchical architecture (as shown in Figure 2) is adopted, and a comprehensive upgrade of the hardware, software, and basic interface design is conducted based on the existing system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
# Buckwheat Disease Recognition Based on Convolution Neural Network
## 2 Network Structure of CNN  
CNN is a feed forward neural network including convolution operation and with a deep structure. Its basic structure includes input layer, conv layer, pooling layer, full connection layer and output layer (classifier) [30]. A typical CNN structure is shown in Figure 1.  
In the convolution network, the transformation between layers is a process of feature extraction. Each layer is composed of several two-dimensional planes, each plane represents a feature map (FM). The input layer is the original image, and each feature extraction layer (convolution layer) in the network is followed by a secondary extraction calculation layer (pooling layer). This structure of secondary feature extraction enables the convolutional network to have a certain tolerance when there is a large deformation of the input data. Generally, there are several convolution layers and pooling layers. The specific operation process is as follows:  
1. [label=(0)]
2. Convolution process: The input image (or FM of the upper layer) is convoluted with a trainable filter and then the bias \(\mathrm{b_{x}}\) is added. The convolution layer \(\mathrm{C_{x}}\) is obtained.
3. Pooling process: Pixels in the neighborhood calculate the average value to get a pixel, which is weighted by scalar \(\mathrm{W_{x}}+1\), then offset \(\mathrm{b_{x}}+1\) is added to the weighted results. Using an activation function, a reduced feature map \(\mathrm{S_{x}}+1\) can be obtained.
4. The full connection layer: The full connection layer is equivalent to the hidden layer in multilayer perceptron (MLP). It is fully connected to the previous layer. The calculation process is to multiply the output result of the upper layer by the weight vector, add a bias, and then pass it to the sigmoid function.
5. The output layer (classification layer): It consists of Euclidean radial basis function units, and each category corresponds to a unit. The output layer uses a classifier to calculate the probability that the input sample belongs to a category.
## 4 Cryptanalysis of Son et al.'s Son et al. (2019) Protocol  
Son et al. claim that their protocol fulfills the essential security properties required for a handover authentication scheme Son et al. (2019). Nevertheless, our analysis exposes several crucial vulnerabilities in their protocol, such as susceptibility to vehicle impersonation, traceability, and susceptibility to TA circumvention attacks.These attacks can result in privacy violations and data tampering, and in severe cases, they may compromise system security, leading to crashes and system outages. To break through these vulnerabilities, the attacker \(\mathcal{A}\) initially needs to obtain secret data, \(s_{j}\) and \(k\), stored in \(RSU_{j}\), typically through power analysis attacks (like side channel attacks). As noted in [PERSON] et al. (2019), the remote placement of RSUs makes them vulnerable to physical attacks, allowing \(\mathcal{A}\) to capture and manipulate RSUs, extract cryptographic details, and even relocate tampered RSUs to facilitate further attacks.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Range-Free Localization Techniques
#### 2.3.5 Subtract on Negative Add on Positive (SNAP)  
The SNAP [55] algorithm assumes the use of binary sensors and maximum likelihood estimation for localization. The likelihood matrix is constructed by using the \((+1)\) and \((-1)\) contributions from the sensor nodes, depending on their alarm state. Each alarmed sensor adds a positive one contribution to the elements of the likelihood matrix, which correspond to the cells inside its region of coverage (ROC), whereas the non-alarmed sensors add a negative one. It is a fault-tolerant algorithm capable of identifying the event location by using only binary sensor data, as well as promoting the sensor collaborative efforts. Each sensor has a coverage area (ROC), and the source creates a corresponding area around its location, the region of influence (ROI), which is defined as the area that the sensor nodes will be alarmed with high probability (at least 0.5). For example, Figure 8 shows an example of using the SNAP algorithm with eight sensor nodes, three of which are alarmed (solid colour), and the event is correctly localized in the cell with the maximum value \((+3)\).  
Figure 8: Subtract on Negative Add on Positive (SNAP) reproduced with permission from [PERSON], SNAP: Fault Tolerant Event Location Estimation in Sensor Networks Using Binary Data; published by IEEE, 2009.  
Figure 7: Approximate Point in Triangulation (APIT) localization technique, reproduced with permission from [PERSON], An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm; published by MDPI, 2017. (**a**) The receiving nodes are determined as being inside the triangle; (**b**) the receiving nodes are determined as being outside the triangle; (**c**) “InToOut” error; (**d**) “OutToIn” error.  
The SNAP limitations include the assumptions that (i) the sensor nodes are static and uniformly spread over a rectangular area, (ii) the source of the event continuously emits a uniform signal in all directions, and (iii) no environmental changes occur. Nevertheless, compared to the centroid or classical maximum likelihood estimation, the SNAP is more fault tolerant and demonstrates robust behaviour to sensor position errors. Depending on the sensor node density, it can achieve similar accuracy with maximum likelihood estimation at a very low computational cost.
## 3 Results and Discussion
### Optimisation Step  
The software MODDE.05 allows to identify the optimal values of the factors that would generate the highest yield of phenolic compounds. It contains an optimisation routine that can analyse several responses simultaneously and which are affected by different weighting coefficients (Table 2). Given to the three obtained models, the process (i.e., maximising the phenolic compound yield of the three compounds simultaneously) is optimised for T = 39 \(\upmu\)s, n = 356, and [C] = 60%, corresponding to extraction amounts of 582, 340, and 568 mg for phenol, flavonoids, and tannins, respectively (Table 3). To the best of our knowledge, PEF has never been applied to extract phenolic compounds from _Eucalyptus_. However, other optimization techniques have been used. The comparison of our results with those of other authors is relatively unfair because the content of phenolic compounds is variable depending on the season of harvest, the age of the plant, and the environmental conditions. It is more accurate to compare the effectiveness of the optimization technique used versus the control (conventional ex-traction) in the same work. Our results revealed that treating the plant with PEF for a very short extraction time increases the phenolic compound content approximately by 94%, 2166 %, and 9% for TPC, TPC, and CTC compared to the best yields obtained by simple macceration with ethanol. [PERSON] et al. (2017) performed a study on the influence of temperature, time, and ethanol/water ratio, to analyse their effects on extracting phenolic compounds from Eucalyptus globulus leaves using the response surface methodology [9]. Their results revealed a 5% increase for TPC and a 10% increase for TFC. Another study of optimization of phenolic compounds from another Eucalyptus species (_E. robuta_) by microwave-assisted extraction showed a 100% increase in yield for TPC and TFC compared to the lowest levels obtained in this same study [11]. The microwave-assisted extraction seems to be more efficient than other optimization techniques applied on this species in other studies, such as the use of ultrasound and supercritical fluids [10; 12].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Response** & **Criteria** & **Weight** & **Min** & **Target** \\ \hline
1 & Phenol & Maximize & \(1\) & 594.433 & 622.503 \\ \hline
2 & Flavonoid & Maximize & \(1\) & 348.083 & 364.758 \\ \hline
3 & Tanins & Maximise & \(1\) & 568.68 & 591.68 \\ \hline \end{tabular}
\end{table}
Table 2: Presentation of the optimisation routine of MODDE 5.0 used for maximising the phenolic compound yield of the three compounds simultaneously.  
Note that 'iter' and log(D) are the iterations number and the log of overall distance to the target, respectively; log(D) is equal to zero when all the responses are lying between Target and Limit values [34].