esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Transition to Automated Methods (2010-2018)  
In 2010, [PERSON] et al. introduced a method for automatic kidney segmentation from 2D abdominal CT images. The complexity of kidney segmentation arises from the similar grey levels of adjacent organs, the effects of contrast media, and the high variability in organ positions and shapes within abdominal CT scans. Traditionally, kidney segmentation has been performed manually or semi-automatically. [PERSON]'s team proposed an enhanced connected component labeling algorithm based on intensity values to estimate kidney positions. This was followed by a method that combines multiscale mathematical morphology and labeling algorithms to delineate fine kidney regions accurately [8].  
In 2017, [PERSON] et al. developed a fully automated Bayesian-based method for 3D liver segmentation. Their technique achieved high accuracy, demonstrated by Dice and Jaccard similarity coefficients of 93.5% and 87.9%, respectively, highlighting its effectiveness in precise volumetric analysis of the liver [9]. Simultaneously, in 2017, [PERSON] et al.  
presented an approach for the automatic detection of new tumors and quantification of tumor burden in horizontal liver CT scans. Recognizing the challenge of identifying small new tumors that radiologists might overlook during routine scans, they utilized baseline and follow-up CT scans, along with baseline tumor delineations and a tumor appearance prior model. By employing a convolutional neural network classifier, they enhanced new tumor detection rates significantly, achieving a true positive rate of 86% compared to the previous 72% [10]. In 2018, [PERSON] et al. proposed a method for automated kidney segmentation that utilizes ensemble learning coupled with active contour modeling. This approach begins with the detection of an initial mask within the kidney region, which is then refined by evolving its boundary. Their technique demonstrated high efficacy, achieving an average recall score of 92.6% and a Dice similarity value of 88.9% [11].
## 2 Control Architectures for WSN: A State of the Art
### Historical Control Techniques in WSNs  
Traditional control techniques are mainly based on predefined solutions to reduce or balance the energy during the use of the WSN (workload balancing, role rotation, node sleeping, etc.). Nodes can take reconfiguration decisions with local interactions (1 to few hops exchanges). This kind of approach often leads to local optima solutions.  
Several works have been carried out to develop efficient control methods in WSN, each contributing to improved resource management and network efficiency. One pioneering example is the Low-energy Adaptive Clustering Hierarchy (LEACH [11]), a protocol that introduces periodic rotation of cluster heads (CHs). At each defined period, all nodes in the network participate autonomously in the election of CH. Each node chooses a random value according to a probabilistic distribution. The node with the highest value in each cluster is elected as the CH. A prolongation of this idea funded on a game theoretic approach to optimize and balance energy consumption is proposed in [9].  
In [12], a local topology reconfiguration mechanism is proposed for energy balancing. This mechanism is activated after a node has transmitted data. The node then evaluates the strength of the next node. If this strength is less than the average strength of all the sensors, a reconfiguration is initiated, first removing the link to the low-strength node and then seeking to establish new connections with higher-strength nodes in the vicinity.  
The authors in [13] propose a content-based adaptive and dynamic scheduling scheme for WSN. This scheme, centered on the use of an analysis module located at the base station (BS), enables the content of received data packets to be processed and informed decisions to be made about the state management of network nodes. Based on the data analysis, the BS sends control messages to the sensor nodes, guiding them to change their state between different modes such as active, standby, and sleep.  
In [14], a new routing algorithm for WSN using a virtual ring infrastructure with a mobile sink is proposed. When a node in a ring detects the proximity of the sink, it informs the other nodes in the ring. These nodes then transmit their data to the sink via the nearest node. In addition, the network dynamically adjusts its routing according to the position of the sink. When the sink changes position, the nodes reconfigure their routing behavior, thus maintaining the network's efficiency in terms of energy consumption.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Power Optimization Process
#### 3.3.2 Genetic Algorithm Modeling  
The genetic algorithm (GA) is a metaheuristic algorithm that was proposed by [PERSON] and his contributors in the 1970s [29] and is inspired by the Darwinian theory of natural selection processes [30].  
Genetic algorithms are commonly used to produce high-quality solutions to search and optimization problems relying on biological-inspired operators such as selection, crossover, and mutation. The basic elements of GAs are chromosome representation, fitness selection,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Description** \\ \hline \(p_{m}\) & mutation probability \\ \(p_{c}\) & crossover probability \\ _population_ & number of chromosomes \\ _generation_ & number of generations \\ _Decimals_ & decimal precision of the \\ \(f(\mathbf{V_{gen}})=-V_{gen}\cdot I_{gen}(V_{gen})\), \(f\,:\,[0,V_{\infty}]\rightarrow\mathbb{R}\) & fitness function \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the genetic algorithm.  
and the biologically inspired operators mentioned above [31]. Usually, chromosomes are represented by a string of bits. Every chromosome contains a sequence of bits or genes, where each gene has two variant forms, 0 and 1. All chromosomes form the size of the population, and they represent different points in the solution space. The population is repeatedly replaced by the processed chromosomes using genetic operators. When all the genetic operators are applied, the population forms a new generation, and that happens constantly until the algorithm reaches the iterations that were specified by the user at an initial phase. The fitness function is a performance indicator used to evaluate how well the chromosomes are [32].  
To set up the GA, all the operators are individually examined and designed depending on the solution of the problem, which is to calculate the MPPT of a PV system according to some weather conditions as well as the current and voltage that lead to the maximum power. The pseudocode for the total GA procedure is presented in Algorithm 1.  
```
1:Initialize population \(P\) with random solutions
2:Evaluate the fitness of each solution in \(P\) using (22)
3:while termination condition not met do
4: Select parents from \(P\) based on their fitness
5: Apply crossover to create offspring
6: Apply mutation to the offspring
7: Evaluate the fitness of the new offspring
8: Replace old population with a combination of parents and offspring
9:endwhile
10:Output: Best solution found in \(P\)
```  
**Algorithm 1** Genetic Algorithm for Minimizing \(f(V)=-V\cdot I_{gen}(V)\)
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 4 Field Study
### Hardware  
Both partners utilize MLS systems from different manufacturers that are commercially available (cf. Figure 8 right). In principle, both use the same sensors: two Z + F 9012 profile scanners, one multi-frequency GNSS antenna, a tactical-grade IMU with three fiber optic gyros and three accelerometers, and an odometer. Due to different philosophies of the manufacturers and system operation by the partners, the results vary. Table 1 highlights the differences between the systems that are relevant for our processing and analysis.  
Although the laser scanners of both systems operate at 1 Mhz, they produce differing point clouds due to the different rotational speeds. The point density inside a profile rises when the scanner's rotation rate is reduced, yet the separation along the trajectory increases.  
It is worth noting that commissioning differs for both systems. The Pegasus: Two Ultimate remains mounted on a platform while the individual components of the Road-Scanner 4 system are assembled each time before deployment. Consequently, the Road-Scanner system requires an in-field calibration with ground control points (GCPs) after its installation, whereas the mounting parameters (cf. Equation (8)) of the Pegasus: Two Ultimate are updated during annual maintenance. Information on the date and quality of the last calibration might be beneficial when evaluating the co-registration of the two scanners.  
The Road-Scanner system provides a symmetrical two-scanner configuration with respect to the driving direction. The asymmetric configuration of the Pegasus:Two Ultimate system generates point clouds (cf. Figure 6) with different angles of incidence and distances to a wall that is parallel to the driving direction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Road-Scanner 4** & **Pegasus:Two Ultimate** \\ \hline Scanner rotation speed & 200 Hz & 150 Hz \\ Setup & assembled before operation & fixed \\ Calibration & on-site & during maintenance \\ Scanner configuration & 45\({}^{\circ}\)/45\({}^{\circ}\) & 60\({}^{\circ}\)/30\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main differences in the operation of the MLS systems used.  
Figure 8: Map of the study area, location of retaining walls (RW1 and RW2), and the urban test field with coordinates referring to UTM33N (**left**); MLS systems used in our study, from left to right: Siteco Road-Scanner 4 [44] and Leica Pegasus:Two Ultimate Dual Head [45] (**right**).
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion
### Administrative Controls  
In the GIS security model, more emphasis is now placed on the _DBFS administrator_ because of the elevated privileges. The DBFS administrator poses a threat as he/she can access all files and folders within the DBFS. Similarly, the database administrator (DBA) can perform many damaging actions: deleting or replacing the spatial files stored as LOBs in the database table or copying them as soon as they are decrypted. Moreover, the DBA can mimic a privileged user session creation to access the decrypted LOBs in the database table. The DBA can insert a false entry in the ODMZ_SESSION table, which would fire the session-based trigger that would execute the decryption procedure to decrypt the files. Nevertheless, these administrative accounts are by far the most trusted positions in an organisation. To protect valuable data from unwanted disclosure, organisations can limit granting these account privileges to only trusted users.
# Modifications to ArduSub That Improve BlueROV SITL Accuracy and Design of Hybrid Autopilot
## 3 Open-Loop Experimental Testing and Hydrodynamic Characterization
### Viscous Drag Analysis  
Given a constant yaw torque, the BROV2 starting from rest eventually reaches an equilibrium state after enough time has elapsed, where the drag torque balances with the thruster torque. While in equilibrium, acceleration largely ceases and a steady-state velocity is maintained. Equation (10) is reduced to Equation (A2) or  
\[N(\dot{r}=0)=N_{r}r+N_{r|r|}r^{2}. \tag{11}\]  
Previous work in [38] tested a slightly smaller ROV similar to the BROV2 at low torques; in this work, a variety of test torques above this range were tested.  
It was found that all open-loop responses at the various torques reached a steady state within two and a half seconds after the input torque was initiated. Because the experimental velocity data collected were noisy, a two-second interval spanning the range \(2.5\,\mathrm{s}\leq t\leq 4.5\,\mathrm{s}\) was averaged to obtain the steady-state velocity. Mean and variance measurements of the interval were taken and are recorded in Figure 8. Using the experimental results, a least squares curve was fit to Equation (A2) and plotted with the open-loop experimental data as well as the data from [38]. It can be seen that there is good agreement between the fitted curve across the entire range of experimental torques and velocities. The resulting hydrodynamic coefficients for this curve found that the linear term \(N_{r}\) was zero and the quadratic term \(N_{r|r|}=2.42\,\mathrm{Ns}^{2}/\mathrm{rad}^{2}\).  
Figure 8: Experimental results from the drag analysis of the open-loop yaw testing; the complete drag function is actually symmetric about the origin. Error bars were calculated as two sample standard deviations to the left and right of the mean.
## 1 Introduction
### Related Work
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|} \hline \multirow{3}{*}{**Techniques**} & \multicolumn{8}{c|}{**Considers Service Delay**} & \multicolumn{8}{c|}{**Considers Energy Consumption?**} & \multirow{3}{*}{**Task**} & \multirow{3}{*}{**Multi-User**} & \multirow{3}{*}{**Deep**} \\  & **Transmission** & & & & & & & & & & & \\ \cline{2-11}  & \(d_{ii}\) & \(d_{ii}\) & \(d_{ii}\) & \(d_{ii}\) & \(d_{ii}\) & & & & & & \\ \hline MDP-based VTA Technique & Yes & Yes & Yes & No & No & No & No & No & Yes & No \\ \cline{2-11}  & [26] & & & & & & & & & & & \\ \hline Reliability-aware Offloading & Yes & Yes & Yes & No & No & Yes & Yes & No & No & Yes & No \\ \cline{2-11}  & [7] & & & & & & & & & & & \\ \hline Traditional Optimization Techniques & Yes & Yes & No & No & Yes & No & No & No & No & No \\ \cline{2-11}  & [18; 19] & & & & & & & & & & & \\ \hline Energy Harvesting Techniques & Yes & Yes & No & No & Yes & No & No & No & No & No \\ \cline{2-11}  & [20; 21; 22; 25; 26; 27] & & & & & & & & & & \\ \hline Genetic Algorithm & Yes & Yes & No & No & Yes & No & No & No & Yes & No \\ \cline{2-11}  & [26] & & & & & & & & & & & \\ \hline Offloading of DNN-driven & Yes & Yes & No & No & Yes & Yes & No & No & Yes & No \\ \cline{2-11}  & Applications & [31] & & & & & & & & & \\ \hline Offloading for OCR Case & Yes & Yes & No & No & Yes & No & No & Yes & No & No \\ \cline{2-11}  & [22] & & & & & & & & & & \\ \hline Game Theoretic & No & No & No & No & No & Yes & Yes & No & Yes & No \\ \cline{2-11}  & Approach [54] & & & & & & & & & & \\ \hline Energy Efficacy-based Offloading & Yes & Yes & No & No & No & No & No & No & Yes & Yes \\ \cline{2-11}  & [35; 37] & & & & & & & & & & \\ \hline Cost Function-based Offloading & Yes & Yes & No & No & Yes & Yes & No & No & Yes & Yes \\ \cline{2-11}  & [36; 38] & & & & & & & & & & \\ \hline Cost Function-based Offloading & Yes & Yes & No & No & Yes & No & No & No & No & Yes \\ \cline{2-11}  & [39; 40] & & & & & & & & & & & \\ \hline Our Proposed Technique & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ (GEDOT) & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the related work.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Medical Prediction  
Medical prediction is currently the main application of deep learning in EHR, and the performance of neural networks trained on big data is usually satisfactory. Although there are many different prediction objects, medical outcome prediction can be broadly thought of as static prediction and temporal prediction.
## 3 Results and Discussion
### Kinetic Modelling  
#### 3.5.1 Model Proposal  
The study of kinetics in heterogeneous catalysis is key to the design, scale-up and operation of chemical reactors, and helps in the study of mechanistic details when phenomenological models are proposed and fit to a wide range of experimental data. The first task is to propose the kinetic model itself, based on the observed effects on the initial reaction rate and the conversions and yields at high reaction time values. In this case, we can appreciate a hyperbolic trend of the initial reaction rate with the amount of catalyst, suggesting a reaction taking place in an interface that is saturated with one of the reagents [48,49]. As for these reagents, we assume that the reaction is similar to a esterification or transesterification, so it is an elemental bimolecular reaction and the model is potential in relation with the reagents, being the partial order for the two reagents equal to one, their molecularity or stoichiometric coefficient.  
We have found that, in similar reacting systems, we needed to consider the zero order for the carbonate used, since this compound is in large excess compared to glycerol and glycerol carbonate. We appreciated that the carbonate concentration was very high at the liquid-liquid interface, due to mass transfer to the glycerol phase, and that the reaction takes place there, so the order of EC is zero [50,51]. In this case, the proposed model involves a reversible reaction system, but the most notable difference was due to the temperature. When working over 76 \({}^{\circ}\)C, EC and glycerol turn to be miscible and the contact between both reagents is ensured. In this case, the concentration of both affects the observed reaction rate in a proportional way, as befits an elemental reaction. As the catalyst is heterogeneous, a model as simple as this one also includes the possibility of no restriction on the reagent adsorption onto the active basic sites, so it is purely the chemical reaction itself as the controlling step. However, the hyperbolic trend of r\({}_{0}\) versus catalyst concentration suggest a non-homogeneous distribution of the reagents on the surface of the catalyst. The kinetic model proposed on the premises of our experimental findings is the following:  
\[r=\frac{-dC_{glycerol}}{dt}=k\cdot\frac{C_{cat}}{K_{cat}+C_{cat}}\cdot C_{glycerol}\cdot C_{EC} \tag{7}\]  
\[\frac{dx}{dt}=k\cdot\frac{C_{cat}}{K_{cat}+C_{cat}}\cdot C_{gly0}\cdot(1-X) \cdot(M-X) \tag{8}\]
# Effect of Methylammonium Iodide (MACI) on MAPbI\({}_{3}\)-Based Perovskite UV-C Photodetectors
###### Abstract  
In this study, we fabricated deep ultraviolet (DUV) photodetectors based on perovskite thin films doped with halide materials using formamidinium bromide (FABr) and methylammonium iodide (MAI). The device was fabricated using a simple surface engineering technique by post-treating the MAPbI\({}_{3}\) perovskite film with an FABr solution. This film acts as a light absorption layer, like a depletion layer with a p-i-n (PIN) structure, with n-type of SnO\({}_{2}\)-SDBS and p-type of spiro-OMeTAD. Adding 0.10 M MACI to the MAPbI3 precursor solution during the manufacturing process could effectively reduce the trap density compared with existing films. Films with MACI added in the two-step process can control a wide band gap and improve crystallinity. In addition, the CI atom has a smaller atomic radius than iodine and a higher electronegativity of 3.16, which can improve phase stability, and the effect of the added CI\({}^{-}\) increases the electron mobility of the perovskite, showing a fast response.  
deep-ultraviolet (UV) photodetectors; perovskite; MAPbI\({}_{3}\); MACI; FABr +
Footnote †: journal: Applied
## 3 Results and Discussion
### Emissions Characteristics
#### 3.2.2 Unregulated Gaseous Emissions-VCCs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**PD0**} & \multicolumn{2}{c}{**PD10**} & \multicolumn{2}{c}{**PD30**} & \multicolumn{2}{c}{**PD100**} \\ \hline
**Name** & **Formula** & **Name** & **Formula** & **Name** & **Formula** & **Name** & **Formula** \\ \hline Ethyl alcohol & C\({}_{2}\)H\({}_{6}\)O & 1-Butene & C\({}_{4}\)H\({}_{8}\) & Ethyl alcohol & C\({}_{2}\)H\({}_{6}\)O & 1-Butene & C\({}_{4}\)H\({}_{8}\) \\ Tetrahydrofuran & C\({}_{4}\)H\({}_{8}\)O & Ethyl alcohol & C\({}_{2}\)H\({}_{6}\)O & Tetrahydrofuran & C\({}_{4}\)H\({}_{8}\)O & Ethyl alcohol & C\({}_{2}\)H\({}_{6}\)O \\ Benzene & C\({}_{6}\)H\({}_{6}\) & Tetrahydrofuran & C\({}_{4}\)H\({}_{8}\)O & Benzene & C\({}_{6}\)H\({}_{6}\) & Tetrahydrofuran & C\({}_{4}\)H\({}_{8}\)O \\ Toluene & C\({}_{7}\)H\({}_{8}\) & Benzene & C\({}_{6}\)H\({}_{6}\) & Toluene & C\({}_{7}\)H\({}_{8}\) & Benzene & C\({}_{6}\)H\({}_{6}\) \\ Octane & C\({}_{8}\)H\({}_{10}\) & Toluene & C\({}_{7}\)H\({}_{8}\) & Octane & C\({}_{8}\)H\({}_{18}\) & Toluene & C\({}_{7}\)H\({}_{8}\) \\ Ethlybenzene & C\({}_{8}\)H\({}_{10}\) & Ethlybenzene & C\({}_{8}\)H\({}_{10}\) & Ethlybenzene & C\({}_{8}\)H\({}_{10}\) & Octane & C\({}_{8}\)H\({}_{18}\) \\ m\({}_{p}\)-Xylene & C\({}_{9}\)H\({}_{20}\) & m\({}_{p}\)-Xylene & C\({}_{8}\)H\({}_{10}\) & m\({}_{p}\)-Xylene & C\({}_{8}\)H\({}_{10}\) & Ethlybenzene & C\({}_{8}\)H\({}_{10}\) \\ o-Xylene & C\({}_{8}\)H\({}_{10}\) & Nonane & C\({}_{9}\)H\({}_{20}\) & o-Xylene & C\({}_{8}\)H\({}_{10}\) & m\({}_{p}\)-Xylene & C\({}_{8}\)H\({}_{10}\) \\ Decane & C\({}_{10}\)H\({}_{22}\) & o-Xylene & C\({}_{8}\)H\({}_{10}\) & Decane & C\({}_{10}\)H\({}_{22}\) & Nonane & C\({}_{9}\)H\({}_{20}\) \\ Tetradecane & C\({}_{14}\)H\({}_{40}\) & Decane & C\({}_{10}\)H\({}_{22}\) & Tetradecane & C\({}_{14}\)H\({}_{30}\) & o-Xylene & C\({}_{8}\)H\({}_{10}\) \\ Tetradecane & C\({}_{14}\)H\({}_{30}\) & Tetradecane & C\({}_{14}\)H\({}_{30}\) & Decane & C\({}_{10}\)H\({}_{22}\) \\ Tetradecane & C\({}_{14}\)H\({}_{30}\) & Tetradecane & C\({}_{14}\)H\({}_{30}\) & Tetradecane & C\({}_{14}\)H\({}_{30}\) \\  & & & & & Undecane & C\({}_{11}\)H\({}_{24}\) \\  & & & & Tetradecane & C\({}_{14}\)H\({}_{30}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: List of VOCs observed under an engine load of 0 Nm.
## 2 Transient Model of a Doubly Fed Induction Generator (DFIG)  
In order to study the control strategies for frequency stability, a detailed model of DFIG has been extracted. The proposed model can be divided into electrical and mechanical parts. The electrical part model describes the procedure in which the electrical output power of DFIG is generated according to its set point values dictated by WF control unit. The mechanical part model describes how the torque and speed of the WTG are controlled, according to the control strategy, so that the WTG can participate in the system's frequency control.