esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Training and Modelling  
#### 2.4.1 Modelling Process  
Machine learning is a powerful tool that automates the creation of models and is categorized under artificial intelligence. Using the concept that algorithms can learn from data, recognize patterns, and make decisions with minimal human intervention, machine learning has become necessary for analyzing larger and more complex datasets, producing accurate results at a faster rate, even when dealing with massive amounts of data [46].  
There are two primary types of models in supervised learning, a subcategory of machine learning: regression and classification. Classification models accurately assign test data to specific categories or classifications and are suited for non-numerical data types such as texts, boolean values, and dates. Regression models identify the relationship between dependent and independent variables in numerical data.  
Machine learning algorithms make predictions based on data by constructing mathematical models from multiple datasets, including training, validation, and test sets. The three-step process of utilizing training, validation, and test data sets is essential for developing accurate and reliable machine learning models. The training data set is used during the learning process and for fitting the model parameters. The validation data set is used to evaluate the model and provide an unbiased assessment of its fit to the training data, while also tuning the hyperparameters of the model [47].  
Commonly used parameters for evaluating the performance of trained models include accuracy rates, coefficient of determination (R\({}^{2}\)), and root mean square error (RMSE). Depending on the type of model, these evaluation parameters or correlation structures are utilized differently. For classification models, accuracy rates are commonly used as a performance metric, whereas for regression models, the coefficient of determination and root mean square error are frequently used.  
In the process of training and modeling, collected data are used to train multiple regression and classification machine learning models. After that, their performance parameters are compared to determine which model is superior. To further optimize the performance of the winning model, hyperparameters are modified through an iterative process of tuning hyperparameters to achieve the best results possible. Overall, machine learning is an indispensable instrument for automating data processing and developing reliable and precise models.
## 3 Proposed Method
### DAS Beamforming Algorithm  
Beamforming is a versatile technology used for directional signal enhancement with sensor arrays [99]. Letting \(y(t)\) be a beamformer output signal at time \(t\) and letting \(M\) be the number of microphones, then for \(z_{m}(t)\) and \(w_{m}(t)\), which respectively denote a measured signal and a filter of the \(m\)-th sensor, \(y(t)\) is calculated as:  
\[y(t)=\sum_{m=1}^{M}w_{m}(t)\otimes z_{m}(t), \tag{17}\]  
where symbol \(\otimes\) represents convolution.  
Based on our previous study [96], we considered DAS beamforming in the temporal domain. Assuming that single plane waves exist and letting \(s_{m}(t)\) be a set of acoustic signals, then delay \(\tau_{m}\) as expressed for the formula below occurs for incident waves observed for the \(m\)-th sensor.  
\[z_{m}(t)=s_{m}(t-\tau_{m}), \tag{18}\]  
where \(M\) represents the total number of microphones.  
Figure 3: Delay-and-sum (DAS) beamforming in the time domain [96].  
The delayed \(-\tau_{m}\) of incident waves was offset by the advanced \(+\tau_{m}\) of incident waves for a filter. Signals from the direction of \(\theta\) are enhanced because of the gathered phases of signal \(s_{m}(t)\) in all channels. The temporal compensated filter \(w_{m}(t)\) is defined as:  
\[w_{m}(t)=\frac{1}{M}\delta(t+\tau_{m}), \tag{19}\]  
where \(\delta\) is Dirac's delta function.  
Letting \(\theta\) be an angle obtained using beamforming, then, for the comparison of acoustic directions, the relative mean power level \(G(\theta)\) of \(y(t)\) is defined as:  
\[G(\theta)=\frac{1}{T}\sum_{t=0}^{T}y^{2}(t), \tag{20}\]  
where \(T\) represents the interval time length.  
We changed \(\theta\) from \(-90^{\circ}\) to \(90^{\circ}\) with \(1^{\circ}\) intervals. Let \(P_{1}(\theta)\) and \(P_{2}(\theta)\) respectively denote \(G(\theta)\) obtained from M1 and M2. Using \(P_{1}(\theta)\) and \(P_{2}(\theta)\), one can obtain \(\theta_{1}\) and \(\theta_{2}\) as:  
\[\theta_{n}=\underset{-90\leq\theta\leq 90}{\text{arg max}}P_{n}(\theta),\hskip 28.452756 ptn =\{1,2\} \tag{21}\]
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 5 Cloud Services  
The objective of developing a cloud system is to provide autonomous mission planning, monitoring, and control as well as to enable a global data space where data can be easily transferred and managed within the system. The data gathered throughout the mission can then be used for the analysis and optimization of the autonomous mission.  
The exchange of information between the drone swarm and the cloud services is carried out over the internet. A common data object model ensures interoperability between applications running in the drone swarm system on air, the GCS, and the cloud infrastructure. The interactions between the drone system and the cloud are characterized by being asynchronous with loosely coupled entities such as clients and servers of a client-server programming model. A global path plan, which is determined by a service in the cloud, is communicated to the drones in the form of locations to be inspected with tasks that define the inspection itself. Drones report their status to the cloud by sending their current location, velocity, charge level, etc., along with other relevant telemetry data. A mission operator can plan, control, and monitor the mission by engaging cloud services through the web interface. The web interface is based on a 2D map where the user selects the mission targets, determines inspection routes, sends routes to the drones, and monitors the mission progress. During the mission, drones send telemetry data and inspection images to the cloud.  
The cloud services are deployed in a Kubernetes cluster on one or multiple servers, depending on the user demand, and the architecture is designed as microservices. The microservice architecture provides better code organization and facilitates the expansion of the system with the implementation of new features. It furthermore enables individual scaling of the parts of the system that are in high demand, resulting in the optimized usage of the cloud resources [34]. Services are designed to provide mission planning, control, and monitoring, as well as data storage and analysis. In the microservice application architecture (Figure 5), services are grouped by their high-level purpose. Communication between services and services with drones is achieved through synchronous and asynchronous HTTPS requests, respectively. For a detailed insight into the system's architecture and development, the reader can refer to [35].
# Crop Land Change Detection with MC&N-PSPNet
## 3 Results
### Change Detection  
Change detection is carried out by different methods based on the results extracted from the agricultural area in this paper. The graph of the results of the experimental detection part of the change detection is shown in Figure 9.  
Based on the method in this paper, the OA and Kappa values for change detection reached 93.24% and 92.29%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Model** & **Backbone** & **IBN** & **NAM** & **OA\%** & **Kappa** & **Training Time** \\ \hline B1 & ResNet50 & & & 91.24 & 0.7999 & 2.522 \\ B2 & MobileNetV2 & & & 94.61 & 0.8728 & **1.993** \\ B2 + IBN & MobileNetV2 & \(\surd\) & & 94.75 & 0.8761 & 2.116 \\ B2 + NAM & MobileNetV2 & & \(\surd\) & 94.86 & 0.8784 & 2.418 \\ B2 + IBN + NAM & MobileNetV2 & \(\surd\) & \(\surd\) & **95.03** & **0.8815** & 2.109 \\ \hline \hline \end{tabular}  
*Baseline uses the original PSPNet model, the original network. Values in bold font are the best, B1: Baseline 1, B2: Baseline 2, IBN: improved backbone network.  
\end{table}
Table 3: Ablation experiment results.  
Figure 9: Chart of change detection results.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 4 Simulation
### Effect of Communication Distance
#### 4.4.4 BER at Different Communication Distances  
The effectiveness of optimizing the L2A ratio was verified through examining the BER of a VOCC system. In this simulation, the communication distance ranged from 30 to 70 m. At each communication distance, two sets of LED frames were replicated. In the optimal set, the LED luminance was adjusted to obtain the average optimal L2A ratio given in Figure 21b. In the non-optimal set, the LED luminance was adjusted to a random L2A ratio in the range from 0.2 to 4. In both sets, the LED interval is set randomly from 0.5 to 2. The ambient light is set randomly in the range from 50 to 16,000 lux. The LED luminance is set based on the ambient light and the L2A ratio. The LED surface reflectance is set randomly in the range from 0.3 to 0.5. The ISO is set based on the ambient light. The remaining parameters are set to their fixed values given in Table 1.  
The results are shown in Figure 22: The BER of the optimal set is seen to be lower than that of the non-optimal set at all communication distances. Because of L2A ratio optimization, this improvement in BER comes without the costs of reducing the data rate or increasing the size of the LED panel on the vehicle.  
Figure 21: Optimal L2A ratio. (**a**) Optimal L2A ratio at different LED intervals and distances. (**b**) Optimal range of L2A ratio corresponding to LED intervals.
## 4 Discussion
Training a smaller network on scarce annotated data can potentially avoid the overfitting problem because larger networks are more flexible on data fitting and tend to overfit the limited training data. The conventional VGG16 network [27] has over 138 million parameters which is much more than the 22 M parameters of the Xception network. As we compared our Xception-based approach with the VGG16-based approach [19], an obvious improvement of classification accuracy was observed. More importantly, the improvement becomes bigger when more phenotypes are involved as the target classes. That means the VGG16 network has imperfect generalization ability for more classes. The Xception network successfully alleviated the overfitting issue without losing the accuracy of classification. Our study has explored an effective way to cope with scarce annotation data for biological image classification. As we further compare our method with more CNN networks, it seems that deeper network tends to yield more accurate results. An intuitive explanation is that deeper networks are better at learning deep features from the large ImageNet dataset and thus performs better at transfer learning. However, the Xception network that we use is not the deepest, but it yielded the most accurate results for eight- and all-phenotype combination tests. This might be attributed to the residual connections (i.e., the shortcut connections of \(1\times 1\) convolution in Figure 3) which does not exist in AlexNet, VGG16, VGG19 or GoogleNet. As demonstrated in the original paper of the Xception network [25], such a residual connection is essential for increasing the classification accuracy since it improved the convergence performance on a large pretraining dataset and helped the network to learn more distinctive image features for transfer learning. As a limitation of this study, we only tested our method on a publicly available dataset. However, the appearance of zebrafish larvae microscopic images may vary between institutes, acquisition devices and sample preparation procedures. It is important to test our method with more diverse datasets. Moreover, our method also needs to be implemented in an on-site experimental environment to produce real-time classification results. Fortunately, the Xception model has small file size which can be implemented easily in embedded operating systems.
# Corrected Tilt Calculation for Atmospheric Pressure-Induced Seismic Noise
## 2 Theory  
### Displacement  
The theory of coupling of plane pressure waves into a homogenous elastic half-space is summarized in this section as background for the research presented herein. The basic setup follows [PERSON] [3] in the notation of [PERSON] [5].  
Consider the two-dimensional problem where x and z are the Cartesian coordinates in directions parallel and normal to the surface of the ground, as shown in Figure 1. The normal component z denotes the depth into the ground. The wind moving at a speed c in the x-direction creates pressure fluctuations in local space defined by:  
\[\mathrm{P(x,\ t)=P_{o}e^{i(kx-\omega t)}} \tag{1}\]  
where \(\mathrm{P_{0}}\) is the amplitude of the local pressure. The angular frequency is \(\omega\) and the horizontal component of wave number, k, is given by  
\[\mathrm{k=\frac{\omega}{c}} \tag{2}\]  
where c is the speed of the wind, and is much less than the acoustic velocity and the seismic velocity.  
In general, a displacement vector can be expressed in terms of a scalar potential, \(\varphi\), and a vector potential, \(\psi\), as  
\[\overset{\rightarrow}{\mathrm{a}}=\
abla\varphi+\
abla\ x\overset{\rightarrow} {\psi}. \tag{3}\]  
These potentials obey the following wave equations:  
\[\
abla^{2}\varphi=\frac{1}{\alpha^{2}}\frac{\partial^{2}\varphi}{\partial t^{ 2}} \tag{4}\]  
Figure 1: Coordinate systems and displacements (u. w) of an elastic half-space.  
The boundary conditions at the ground surface (z = 0) are given by  
\[Z_{\mathrm{z}}=\ \lambda\bigg{(}\frac{\partial\mathrm{u}}{\partial x}+\frac{ \partial\mathrm{w}}{\partial z}\bigg{)}+2\mathrm{\mu}\bigg{(}\frac{\partial \mathrm{w}}{\partial z}\bigg{)}=-P_{\mathrm{o}}\mathrm{e}^{\mathrm{i}(kx- \omega t)}\mid_{\mathrm{z=0}}\] (15a) and \[Z_{\mathrm{x}}=\ \mathrm{\mu}\bigg{(}\frac{\partial\mathrm{u}}{\partial z}+\frac{ \partial\mathrm{w}}{\partial x}\bigg{)}=0\big{|}_{\mathrm{z=0}}. \tag{15b}\]  
Solving Equation (15) for specified ground properties (\(\lambda\), \(\mathrm{\mu}\), or \(\mathrm{\alpha}\), \(\mathrm{\beta}\), and \(\mathrm{\rho}\)), frequency (\(\omega\)), wind speed (c), and the associated pressure (P\({}_{\mathrm{o}}\)), we obtain values for the potentials \(\varphi_{0}\) and \(\psi_{0}\). Substituting this solution into Equations (12b) and (13b) provides the horizontal and vertical displacement at a depth, z, below ground surface. The induced waves are in the form of Rayleigh waves.
###### Abstract
It could lower the capital costs by up to 37% and annual costs by up to 11% comparatively. Hence, the current system layout has been developed by focusing on the costs and taking into account expediency and feasibility assurance. Investment in an irrigation project must be profitable for farmers with a reasonable break-even point to compensate for initial and operational expenses. This is more important for small state owners to return the investment costs in the shortest possible time when farms are the only income resource for a family. Therefore, it can help yeoman and small farms, especially when external subsidies are not accessible, so low cost systems are preferred. For the present work, a corner pivot lateral layout sprinkler irrigation system (CPL), is designed based on a quadrant movement of a center-pivot system, but it moves manually. In this layout a considerable length of required pipe will be diminished (compared to traditional semi-permanent system) which will lower the capital and consequently operational and annual expenses. The theory was put forward according to a small plot requirement as an integration of both semi-permanent and center-pivot techniques. In this novel design, the lateral is directly connected to the water resource at the corner of the farm and it will rotate around the pivot point 90 degrees in square shape plots (and less or more according to the plot shape) manually. It also needs a smaller number of fittings, outlet valves, and coupling joints if properly designed, which will consequently reduce the grand total costs. In a conventional semi-permanent system which is normally designed as split-lateral (Figure 1A), the two removable laterals are on the both ends of the sub mainline in a homolographic pattern and are transferred lengthwise regularly to cover the entire farm. Hydrants will be placed isochronally to supply the laterals along the main or sub main pipe. In a CHL semi-permanent design [21], the mainline is shortened to the plot center to feed the lateral (Figure 1B), however unlike for CHL, in a CPL system (Figure 1C) the main or sub mainline is converted to a lateral with sprinklers installed on it and indeed retrofitted to a semi-portable system. In fact, for a CPL layout, there will only be one hydrant required which uses a rotation elbow at the corner where the lateral is connected to the water supply. The below diagrams (Figure 1) shows all 3 concepts comparatively. To evaluate and measure the system feasibility, CPL layout implementation was done in a 2300 m\({}^{2}\) farm. System was operated during one month (from 1 st to 30 th May 2018) after installation in an irrigation test center. A split lateral semi-permanent layout was designed and evaluated for the same plot. DU coefficient or water distribution uniformity examination of the lower quarter, via catch can test method, was monitored and measured [22,23]. A cost comparison for the 2 systems was carried out on the components as well as annual costs to identify the influencing factors [24,25].
## 3 Results
### Analysis of the Critical Defects
Except for one data (\(\sqrt{a_{c}}=207\)\(\mu\)m, \(N_{f}=~{}9.8\cdot 10^{5}\)), the scatter associated to the \(N_{f}\) reduced, with respect to the S-N plot shown in Figure 3, thus confirming that the defect size controls the VHCF response. The deviation of the datapoint (\(\sqrt{a_{c}}=207\)\(\mu\)m, \(N_{f}=~{}9.8\times~{}10^{5}\)) could be due to a local variation of the microhardness (Section 3.1). The defect size range (\(130:286\)) \(\mu\)m is close to the range found for SLM AlSi10 Mg specimens [20] horizontally built. In particular, \(\sqrt{a_{c}}\) is smaller than \(207\)\(\mu\)m in 9 out of 12 defects (\(75\%\)), whereas it is larger than \(250\)\(\mu\)m in 3 out of 12 defects (\(25\%\)). In order to investigate the influence of defects on the VHCF response, the stress intensity factor threshold (SIF threshold, \(K_{th}\)) is also analysed. According to [21], for failures in the VHCF region, the SIF associated with the defect must be equal to the SIF threshold. The onset of the VHCF regime is assumed to be equal to \(2\times 10^{6}\) cycles [21]. Therefore, only specimens which failed above \(2\times 10^{6}\) cycles were considered for computing the SIF threshold. Figure 7 plots the SIF threshold, with respect to the number of cycles to failure, together with the mean SIF threshold, \(K_{th,~{}mean}\). Figure 5: Types of critical defects: (**a**) Defect due to incomplete fusion; (**b**) defect due to improper bonding; (**c**) non critical pores found within the specimen volume. Figure 6: Defect size \(\sqrt{a_{c}}\), with respect to \(N_{f}\). According to Figure 7, SIF threshold values are close to the average value, with limited differences (apart from one data). By considering the SIF threshold, the scatter is significantly reduced, with respect to the S-N plot in Figure 3, further confirming that, for a proper analysis of the VHCF response of AM parts, the interaction between the local stress near the defect and the defect size must be taken into account.
# Mini-Review: Potential of Diatom-Derived Silica for Biomedical Applications
## 5 Silica for Biomedical Applications: Advantages
### Employment of Genetically Engineered Diatom Frustules for Protein Immobilization  
Diatom particles can be considered as useful scaffolds for enzyme immobilization that could enhance protein properties. Genetic engineering represents a viable alternative to in vitro immobilization systems, as it does not require protein purification and is carried out under physiological conditions [74]. Since sialaffins and cingulins are involved in silica condensation becoming part of diatom frustules, the fusion of an exogenous protein to these frustule-associated proteins can result in the strong binding of exogenous proteins to the silica cell wall.  
Transformation of diatom genomes with recombinant genes is a useful tool to allow the fusion between enzymes and cell wall proteins. This technology is mentioned in a recent study as living diatom silica immobilization (LiDSI), and has been mostly performed on the model species _T. pseudonana_[98; 99]. To our knowledge, the pioneer studies focused on enzymes immobilised on diatom biosilica were aimed at inserting and blocking the bacterial enzyme hydroxylaminobenzene mutase (HabB) on the sialaffin tpSil3 of _T. pseudonana_ frustule [98]. Aside from the potential of this specific genome modification, this study paved the way for the genetic manipulation of diatom species to enhance protein immobilization on frustules for biomedical purposes.  
The genome of _T. pseudonana_ has been recently modified with the insertion of exogenous genes encoding the fusion of two enzymes, glucose oxidase and horseradish peroxidase, with cell wall proteins, enabling a regioselective functionalization, and suggesting that silica morphology could influence the effectiveness of the enzymes reactivity [99]. The frustule of this species has been also antibody-functionalised, in order to test its effectiveness in binding large and small antigen molecules [100].
# A Brief Survey of Fixed-Parameter Parallelism
## 3 Parameterized Parallel Complexity Classes  
The notion of parameterized parallel complexity was first introduced in [1], where the concepts of \(PNC\) and \(FPP\) were formally defined. More recently, the class \(FPPT\) was defined in [2] as a refined version of \(FPP\). Formal definitions of these complexity classes follow.  
Definition 1: A parameterized problem is in \(PNC\) if it can be solved by a parallel deterministic algorithm whose running time is in \(\mathcal{O}\left(f(k)\cdot(\log n)^{h(k)}\right)\) using \(\mathcal{O}\left(g(k)\cdot n^{\beta}\right)\) processors, where \(f\), \(g\), and \(h\) are arbitrary computable functions, and \(\beta\) is a constant independent of \(n\) and \(k\).  
Definition 2: A problem is fixed-parameter parallel (FPP) if it can be solved in \(O(f(k)*log^{c_{1}}(n))\) time using \(O(g(k)*n^{c_{2}})\) processors, where \(c_{1}\) and \(c_{1}\) are independent fixed constants.  
Definition 3: A problem is fixed-parameter parallel tractable (FPPT) if it can be solved in \(O(f(k)*log^{c_{1}}(n))\) time using \(O(n^{c_{2}})\) processors, where \(c_{1}\) and \(c_{1}\) are independent fixed constants.  
It was shown in [1] that \(FPP\) is a subset of \(PNC\) and that \(PNC\) is a subset of \(FPT\). It was also shown that an \(FPT\) problem can be \(PNC\) if the work done at every step (of a corresponding \(FPT\) algorithm) can be performed in \(PNC\)-time. In fact, it is clear from the definitions that \(FPPT\subseteq FPP\subseteq PNC\subseteq NC\) and \(FPPT\subseteq FPP\subseteq PNC\subseteq FPT\). What still needs to be clarified is whether some classes are proper subsets of, or equal to, their successors.