esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 3 System Description
### Control Loop Developed for Experimental Testing  
The illustration in Figure 4 gives an overview of the test benchtop design. It is basically composed of three microcontrollers, represented by: sensor, controller and actuator, in accordance with the system shown in Figure 5.  
When the system is running, the sensing element receives a voltage signal referring to the level of the tank to be controlled (\(Y(k)\)) from the plant by wired communication. The analog-to-digital converter (A/D) with a resolution of 10 bits, present in the sensor and configured with a conversion period of \(t_{s}\), accomplishes the conversion of that signal, obtaining \(L_{2}(k)\). After the conversion, the sensor itself performs the wavelet encoding of the \(L_{2}(k)\) signal. Each conversion is carried out for a period \(t_{s}\). The encoded symbols are transmitted through the radio frequency module NRF24L01 to the controller, which decodes these symbols and executes a PID control routine with reference to the value of the setpoint. The control signal obtained by the PID algorithm (\(v_{p}\)) is coded, then these symbols are transmitted by the radio frequency module to the actuator. The actuator decodes the received symbols by wireless communication and produces a PWM signal to be injected into the circuit.  
Figure 4: Benchtop developed for experimental tests.  
Figure 3: (**a**) System coupled tanks; (**b**) diagram of the control system adopted in this work.  
This paper adopts a wireless communication scenario, in which the nodes are in an outdoor environment, with the Doppler effect; for example, this scenario is the control of the turbine used in wind power systems [10; 25].
# Biosynthesis of Nature-Inspired Unnatural Cannabinoidss
## 2 Olivetolic Acid Derivatives
### Precursor-Directed Combinatorial Biosynthesis  
Type III PKS enzymes have been shown to be highly promiscuous. [PERSON] et al. showed that the stilbene synthase mutant, 18 xCHS, was able to utilize acyl-CoA esters derived from butanoic acid, hexanoic acid, heptanoic acid, and even 3-hexenoic acid as starter units to form novel polyketides using malonyl-CoA as extenders, although the identities of the polyketides were not characterized [39]. The type III PKS from rice _Oryza sativa_ (OsPKS) was screened against 70 starter and 12 extender acyl-CoA thioesters which yield a total of 840 different combinations, of which 315 combinations were found to produce a new potential polyketide (38% of total matrix) [30]. The authors attributed this promiscuity profile to substrates with a degree of \"likeness\" to its native substrates, 4-hydroxycinnamyl-CoA and malonyl-CoA. Out of the 315 combinations, the authors characterized two polyketides, bisonyangonin and 26 OH, which were shown to exhibit antimicrobial activities. [PERSON] et al. characterized OLS from _C. sativa_ to be able to accept four different substrates as its starter unit, the native hexanoyl-CoA, butyryl-CoA, isovaleryl-CoA, and octanoyl-CoA, as they were able to observe a triketide pyrone product from using these starter units [22]. However, a resorcinol product was observed only from using hexanoyl-CoA and butyryl-CoA as starter units, producing olivetol and divarinol. This meant that when isovaleryl-CoA and octanoyl-CoA were used as the starter units, its reaction catalysis ended with only two rounds of extension, producing only the corresponding triketide pyrones of these substrates. However, there have been no reports to date of attempts at a more exhaustive screen of OLS with a larger library of acyl-CoA thioesters as starter or extender substrates. Understandably, the process in making OLA in nature requires the synergistic action of both OLS and the cyclase enzyme, OAC. Screening OLS against a library of potential substrates also requires a compatible cyclase enzyme such as OAC to cyclize the corresponding tetraketide intermediate via a C2-C7 aldol condensation; is also required to produce corresponding analogues of OLA. The task at hand is limited not just by the substrate promiscuity of OLS, but also OAC. However, such work is important in elucidating the structural aspects of the substrate selectivity of OLS. This will, in turn, pave the way for novel cannabinoids to be produced through a library of OLA analogues with different side chains on C6 of the aromatic ring.
## 3 The Proposed Method
### Feature Extraction and Classification Based on the Dual-CNN Model
#### 3.2.1 The Forward Propagation of the Dual-CNN Model  
In the 6 Ch-CNN, the input polarization data are a 6 Ch whose size is fixed. The polarization feature \(F_{1}(p_{n})\) can be obtained by Equation (1) and pooling. For the PauliRGB-CNN, the spatial feature \(F_{2}(s_{n})\) can be obtained in the same manner, and the input Pauli RGB image is a fixed size slice with three channels. Next, two kinds of data are input into the 6 Ch-CNN and PauliRGB-CNN separately to obtain the respective features. Then, the obtained two kinds of features are fed into a fully connected layer to combine with each other, and the P-S features \(F^{(n)}\) can be represented as:  
\[F^{(n)}=\partial[W_{1}*(F_{1}(p_{n})\odot F_{2}(s_{n}))+b_{1}] \tag{11}\]  
Figure 3: The generation of the fixed-size slices based on the center pixel.  
where \(W_{1}\) and \(b_{1}\) represent the weights and bias matrix in the last fully connected layer, and the joint operator \(\odot\) stacking the former and the latter items to be input of the last fully connected layer. At last, \(F^{(n)}\) is put into the Softmax to produce a probability vector for each class:  
\[P^{(n)}=\frac{1}{\sum_{k=1}^{N}e^{W_{k}F^{(n)+b_{k}}}}\begin{bmatrix}e^{W_{1}F^{ (n)+b_{1}}}\\ e^{W_{2}F^{(n)+b_{2}}}\\ \vdots\\ e^{W_{N}F^{(n)+b_{N}}}\end{bmatrix} \tag{12}\]  
where \(N\) is the total number of the classes. \(P^{(n)}_{max}\) is the max probability in the \(N\)-dimensional vector \(P^{(n)}\), and it is recognized as the predicted result.
## 4 The Calculation of Reliability Based on Fuzzy Mathematics
### Fuzzy Seismic Spectra
#### 4.3.2 Fuzziness of Site Category  
The construction site is divided into four classes (Class I, II, III, IV, respectively) in the Chinese seismic design specifications [21] based on the mean shear wave velocity and the thickness of the overburden. Furthermore, Class I is also divided into two sub-classes, I0 and I1. The classification approach mentioned above is convenient for engineers to use, but it is quite difficult to accurately identify the class of a site based on the site definition, especially for sites with high fuzziness that are near the boundary of two adjacent classes.  
The fuzziness of site classification is determined by the complexity of the site, and cannot be eliminated by mathematical methods. However, in this paper, fuzzy grade vector \(W\) was first obtained on the basis of fuzzy comprehensive evaluation, before a comprehensive evaluation value \(T_{g}\)[21] was obtained; this is a non-fuzzy variable and is related to site classification.  
Only when the mean shear wave velocity of a site is \(v_{s}>800\), and the overburden thickness is 0 does the site belong to Class I0, so there was no need to do a fuzzy comprehensive evaluation of site I0 [21].
### State of the Art in Proximal Sensing Sensors for SWC  
Non-invasive sensors are placed externally and without contact with the process to measure. They are called proximal sensors, where the instrument is placed within 2 m of the target, or remote sensors if the distance is greater [10]. The main advantage against invasive techniques is the possibility of reading from different locations within a reasonable timeframe and naturally, not to touch nor affect the process. This ability to move the sensor allows overcoming the disadvantage of the invasive types with the small volume of the sample being measured. Figure 1 shows the classification of Soil Moisture (SM) measurement methods. Non-invasive sensors are also easier to deploy and result in more stable results in harsh environments such as rocky or vertical soil, not depending on surface contacts.  
Apart from RADAR, there are some other techniques for contactless measurement of the amount of moisture in the soil, each of which has its strengths and weaknesses.  
Most non-invasive techniques to measure soil moisture imply using ElectroMagnetic (EM) radiation and measuring the soil's reflected radiation. Common difficulties with non-invasive approaches include limited penetration depth (L-band microwave [11]), difficulty in separating readings from different soil layers or depths (EMI, Cosmic Ray), soil moisture impacts the depth of the soil's penetration (L-band microwave, EMI, Cosmic Ray, GPR), and the labor and effort required to perform such surveys (EMI and GPR) [12].  
Figure 1: Classification of soil moisture measurement methods: Ground sensing (left), mobile or static proximal sensing (middle), and airborne or spaceborne remote sensing techniques (right).
## 2 Fundamental Theories  
### Convolutional Neural Network  
CNN is a typical feed-forward neural network model which is composed by a trainable feature extraction part and a classification part [16; 25]. The feature extraction part mainly includes convolutional layers and pooling layers [21], and the classification part is a multilayer perceptron consisting of several fully connected layers [26].  
In the convolutional layers, the input samples are convolved with a bunch of learnable convolution kernels to extract feature vectors [16]. Usually a convolution layer has multiple convolution kernels and a class of features learned from a convolution kernel are called mapping graphs. The operation can be expressed as follows.  
\[x_{i}^{k}(j)=w_{i}^{k}\star x_{i-1}(j)+b_{i}^{k} \tag{1}\]  
where \(\star\) donates the convolution operator, \(i\) donates the layer number of the network, \(k\) is the index of the output feature maps, \(x_{i-1}(j)\) is the input feature map of \(j\)-th local region, and \(x_{i}^{k}(j)\) is the \(k\)-th output feature map after the convolution calculation. \(w_{i}^{k}\) and \(b_{i}^{k}\) are the weight and the bias of the \(k\)-th group filter in the \(i\)-th layer.  
In recent years, the rectified linear unit (ReLU) has been widely used as an activation element to accelerate the convergence of neural networks [26]. ReLU model can be described by Equation (2).  
\[A_{i}^{k}(j)=f(x_{i}^{k}(j))=\max\!\left\{0,x_{i}^{k}(j)\right\} \tag{2}\]  
where \(x_{i}^{k}(j)\) is the output value of the convolution operation, \(A_{i}^{k}(j)\) is the activation of \(x_{i}^{k}(j)\).  
A pooling operation is adopted to reduce the computational complexity, and effectively control the risk of overfitting [27]. Max-pooling is defined as follows.  
\[P_{i}^{k}(j)=\max_{(j-1)S+1<t\leq jS}\!\left\{A_{i}^{k}(t)\right\} \tag{3}\]  
where \(A_{i}^{k}(t)\) denotes the value of \(t\)-th neuron in the \(k\)-th output feature map of the \(i\)-th layer. \(S\) is the pooling length, and \(P_{i}^{k}\) is the output feature map after the pooling operation.  
In a traditional convolutional neural network, after several convolutional layers and pooling layers, the feature maps learned by each kernel function are flattened into a one-dimensional array as the input of the fully connected layer. The output of the fully connected layer is a one-dimensional vector, and the mathematical model of the fully connected layer is described as follows.  
\[f(x)=\sigma(w\star x+b) \tag{4}\]where \(\sigma(\cdot)\) is the activation function, \(x\) is the input of a fully connected layer, \(f(x)\) denotes the output of a fully connected layer, and \(w\), \(b\) are the bias and the weight.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Characteristics of Underwater Acoustic Communication
#### 3.1.3 Low bandwidth and data rate  
The bandwidth of acoustic waves depends on the transmission distance. The bandwidth ranges from 1 kHz to 50 kHz [28]. In addition, considering the fact that acoustic waves have high power absorption in the underwater environment, only a few frequencies can be used to communicate over a long distance. The frequencies range that the acoustic wave can work under the water is from a few Hz to tens of kHz, thus the transmission rate can hardly exceed 100 kbps which cannot compare to RF waves in air. Therefore, this represents a great limitation in UASN routing protocol design, especially considering that the routing protocols need large amounts of information exchange during routing discovery and routing maintenance.
# LPWAN-Based Vehicular Monitoring Platform with a Generic IP Network Interface
## 5 Test-Bench
### Scenario  
As explained before, the designed and implemented system is devoted to monitor and report the current status of road vehicles. Concretely, our validation test has been contextualized in the task of monitoring the vehicles employed for providing public services within the University of Murcia (Spain) campus, e.g., postal service or internal transport. As shown in Figure 6, the main road in the campus is a ring that links the different faculties or service buildings. The major axis of this ring is 1 km long. This scenario presents high tree areas, buildings of up to five floors, and other obstacles for the wireless propagation, which convert this scenario in a realistic test-bench for our real-time vehicle monitoring platform. Please, note that the gateway location has been indicated as well in Figure 6, which was affixed on the roof of an annex building of the Computer Engineering Faculty. This is an elevated position with respect to the ring area, which was the main region of study in our tests.  
Following the architecture presented in Figure 4, during the tests the data transmitted from the vehicle were received and forwarded by the gateway towards a central data server in the cloud, which processed and presented this information by means of a web-based dashboard. Moreover, the signal strength of the LoRa channel, in terms of Received Signal Strength Indicator (RSSI), was evaluated along the testing path.  
Finally, regarding system scalability, please note that this work aims to present a proof of concept of the developed and deployed architecture, by using one transmitting vehicle at a time. In the case of having many vehicles concurrently transmitting data-packets to a common gateway, a planning study should be carried out, in order to estimate the number of simultaneous vehicles that can be hosted by a single gateway, depending on the frequency of the transmissions and LoRa parameters, such as SF, CR, BW, and packet length. For further information regarding LoRaWAN scalability, please refer to the detailed analyses presented in Refs. [27; 30].  
Figure 6: Test scenario.
## 2 Experimental Details
### Materials
#### 2.1.1 Adhesive  
The adhesive under study is a one-part, heat curing epoxy adhesive, doped with conductive microspheres [11]. The adhesive and its properties were sourced from the manufacturer's datasheet, and from previous experimental studies carried out by the authors [11] and shown in Table 1. Regarding the cure cycle, the one used in this work was the same as that used by the authors [11], consisting of a stage at 150 \({}^{\circ}\)C for 40 min. The clamping pressure applied in RJS specimen manufacturing was obtained through the usage of a screw.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Adhesive** \\ \hline Tensile strength/MPa & 65.6 \\ Young’s modulus/MPa * & 3881 \\ Shear strength/MPa * & 41.6 \\ Shear modulus/MPa * & 1447 \\ Mode I fracture toughness/N-mm\({}^{-1}\) * & 1.27 \\ Mode II fracture toughness/N-mm\({}^{-1}\) * & 7.22 \\ Electrical resistivity/\(\Omega\)-cm & \textgreater{}1 \(\times\) 10\({}^{13}\) \\ Glass Transition Temperature/\({}^{\circ}\)C & 130 \\ \hline \end{tabular}
\end{table}
Table 1: Most relevant properties of the heat curing adhesive chosen for the study.
# A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport
## 2 Smart Card Data and Public Transport Network  
The prediction of ridership on a new line requires a spatiotemporal analysis using both smart card data and the public transport network. The smart card data of Seoul city records O-D nodes (bus stop or subway station); times of check in/out; line and vehicle number; and card type, such as teenager, adult, or senior. Some examples are listed in Table 1. The card ID represents an anonymous individual. An individual's journey to their final destination is completed by connecting the continuous trips of the same card ID.  
In the examples, passengers 2 and 3 transferred once, and passenger 3 transferred at another nearby stop. Passengers 1 and 2 used the same line at different times. This allows the headway of that line to be inferred. Passengers 2 and 3 were on the same vehicle and moved to node 200. This allows the occupancy and congestion of that vehicle to be computed.  
The public transport network consists of nodes, lines, links, schedules, and walk links. Figure 1 is a simple example of a network, and Figure 2 shows the data tables of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Card** & **Departure** & **Arrival** & **Departure** & **Arrival** & **Line** & **Vehicle** & **Card** \\
**ID** & **Node** & **Node** & **Time** & **Time** & **Number** & **ID** & **Type** \\ \hline
1 & 100 & 200 & 09:00 & 09:30 & 1000 & 1 & Senior \\ \hline
2 & 100 & 200 & 09:10 & 09:40 & 1000 & 2 & Teenager \\ \hline
2 & 200 & 300 & 09:45 & 10:05 & 1500 & 5 & Teenager \\ \hline
3 & 100 & 200 & 09:10 & 09:40 & 1000 & 2 & Adult \\ \hline
3 & 250 & 350 & 09:50 & 10:20 & 2000 & 10 & Adult \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seoul smart card data sample.  
the given network. The link consists of two nodes and represents the directionality of the line containing that link. The walk link is necessary to describe the walking behavior for transfers. Based on studies of catchment areas [28], neighboring nodes within 500 m of each node are connected by walk links. The schedule indicates the time when the vehicles of each line arrive at node.
## 4 Results
### Comprehensive Evaluation  
In this study, we conducted a comparison of five universal MLRAs for cotton LAI retrieval with regard to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. In this section, we provide a comprehensive evaluation of five ML algorithms according to the obtained results, which were discussed earlier in this paper. To combine all the metrics, we performed a standardization process on the results. The mean values of the results in the sensitivity analysis (training sample size and spectral bands) were used, and we make RMSE, MAE and  
Figure 8: Distance correlation among the Sentinel-2 spectral bands and corresponding cotton LAI.  
Figure 9: Sensitivity of the ML models to the spectral bands with respect to \(R^{2}\), RMSE and MAE using the dCor and backward elimination method (the standard error is displayed). The horizontal axis (Remaining Bands) represents the number for the rest of spectral bands after each iterative removing process. (**a**) \(R^{2}\); (**b**) RMSE; (**c**) MAE.  
processing speed negative for the sake of comprehensive comparison. An alternative standardization is interval scaling, which is as follows:  
\[x^{\prime}=\frac{x-Min}{Max-Min} \tag{18}\]  
where \(Max\) denotes the maximum value of the data, and \(Min\) represents the minimum value of the data. The results are displayed in Figure 10, and the metrics used in the radar chart and its corresponding implications are displayed in Table 10. Clearly, GBRT shows an outstanding performance from a comprehensive viewpoint, however, GPR shows the worst performance. ANN and SVR show moderate performances.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
###### Abstract  
Telemedicine applications are more and more used due to the rapid development of digital imaging and information and communication technologies. Medical information which include digital medical images and patient's information are extracted and transmitted over insecure networks for clinical diagnosis and treatments. Digital watermarking is one of the main approaches used to ensure the security of medical images. Nevertheless, in some cases, the only use of digital watermarking is not sufficient to reach a high level of security. Indeed, the watermark could carry essential patient information and needs to be protected. In such cases, cryptography may be used to protect the watermark and to improve the overall secured management in the medical environment. In this paper, we propose a clone-resistant watermarking approach combining a difference expansion watermarking technique with a cryptographic technique based on secret keys generated by a clone-resistant device called Secret Unknown Ciphers (SUCs). The use of SUCs to sign the watermark enforces the security of medical images during their transfer and storage. Experimental results show that the system provides a high level of security against various forms of attacks.  
watermarking; cryptography; security; authentication; secret unknown ciphers (SUCs); telemedicine; medical images +
Footnote †: journal: Accepted: 19 June 2020; Published: 6 July 2020  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] 4
## 4 Experimental Results
### Experimental Setup
#### 4.1.2 Real-World Testbed Environment  
To validate the algorithms in a physical setting and observe their behavior with actual hardware, a real-world TSN testbed was constructed. This approach aligns with current research practices aimed at validating TSN solutions on physical automotive Ethernet testbeds [31]. The testbed utilized the ZIGGO platform, which includes ZIGGO TSN-compliant switches and the ZIGGO TSNPerf software tool for traffic generation, network configuration, and performance analysis. The software, which does not have formal version releases, was obtained from the official GitHub repository, and the version used in this study corresponds to the state of the repository as of April 2024.  
The key hardware components of the testbed were:  
* FPGA Development Boards: Xilinx Zynq-series FPGA boards were employed to function as both TSN switches and end-nodes (traffic sources and sinks). These boards were flashed with custom TSN-capable firmware developed for the ZIGGO platform, enabling features like Time-Aware Shaping and gPTP synchronization.
* Host Computers: Two host computers, with specifications similar to the simulation host (11 th Gen Intel(R) Core(TM) i7-11800H, NVIDIA RTX 3060, running Windows 10), were used for overall testbed control. Their roles included configuring the FPGA-based TSN devices, deploying computed Gate Control Lists (GCLs), initiating and monitoring traffic flows using TSNPerf, and collecting experimental data.  
Configuration of the FPGA boards, including setting parameters such as hostnames, IP addresses, and MAC addresses, was performed using MobaXTerm (Version 24.0, Mobatek, Montpellier, France) via serial port connections. Software components, including GCL files and traffic profiles, were transferred to the nodes securely using the Secure Shell (SSH) protocol [32]. The general network topology for the real-world experiments, illustrating the interconnection of emulated CUC/CNC components, traffic senders, the TSN switch, and receiver nodes, is shown in Figure 3.  
In the real-world testbed, precise time synchronization was equally critical. The CaaS-Switch component within the ZIGGO platform was configured to act as the gPTP master clock. All other TSNPerf nodes operated as gPTP slaves, ensuring that all transmissions and GCL operations were coordinated within a single, precisely synchronized time domain. This setup allowed for the direct application and observation of the GCLs generated by the proposed scheduling algorithms in a physical network.