esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Deep Neural Networks for Inverse Kinematics
### Supervised Learning
#### 2.1.1 Feed-Forward Multilayer Perceptron  
The feed-forward multilayer perceptron (MLP) is one of the most commonly used DNNs. For IK, it is a model representing a nonlinear mapping between an input vector (in this case, the desired Cartesian target for the end-effector \(X_{d}\)) and an output vector (the corresponding joint space positions \(\theta\)). It contains an input layer, an output layer, and several hidden layers, each of them containing a number of neurons with an activation function [32].  
Feed-forward MLPs have been proved to be useful for solving the IK problem, competing with analytical and numerical methods [33], and heuristic methods such as FABRIK [34]. In [35], an MLP is compared to other neural network approaches, achieving small error values for targets where the orientation is not considered. A study on the effect of different arm lengths, datasets, hyperparameters and optimizers is conducted in [36], comparing performance in estimating IK solutions. In [37], a training dataset is virtually generated considering only the \(x,y\) and \(z\) coordinates as inputs, and different hyperparameters and architectures are tested for MLP. The achieved distance error is low, but it proves that orientation information is needed for a better performance of the end-effector joint.  
Because of their simplicity, computational robustness and low computational cost, feed-forward MLPs are widely used. Still, when compared to other types of DNNs such as recurrent neural networks, they are proved to be less accurate [38]. Some possibilities to reduce the error are discussed in [39; 40], such as the division of the workspace in different subspaces so that a different MLP can be used for each of them. A quality function is added in [41] to detect the best solutions between all those generated for a pose in the case of a redundant robot. This idea can also be applied to manipulators with a lower number of DOF [42]. A genetic algorithm is used in [43] to improve the orientation precision of an MLP, outperforming a Generative Adversarial Network (GAN) in small workspaces.  
A recent trend for feed-forward MLPs in this area are physically inspired data-driven learning methods for inverse dynamics of a robotic manipulator [44]. However, as ref. [45] shows, they cannot yet achieve the necessary precision.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 5 Results and Discussion
#### The Main Contributions of the Research Paper  
* We developed a novel method of liver tumor segmentation for CT images with Monai in a single run.
* The main feature of using Monai in this research is that it helps us import the ResUNet architecture instead of writing all the scripts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **DSC** & **Accuracy** & **Precision** & **Specificity** \\ \hline Ref. [36] & 0.823 & 0.81 & 0.812 & 0.85 \\ Ref. [34], UNet [38] & 67.5 \(\pm\) 30.8\% & 92 \(\pm\) 3.8\% & 0.930 & 0.96 \\ Ref. [39], Ref. [40] & 0.83 & 93 & 98.9 & 98.0 \\ Ref. [39] & 0.67 & 0.89 & 0.891 & 0.90 \\ Our proposed ResUNet & 0.983 & 0.98 & 0.950 & 0.957 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the results achieved in liver tumor segmentation using the ResUNet model with state-of-the-art techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Used Technique** & **Dice Coefficient** & **Accuracy** & **Precision** & **Specificity** & **VOE** & **RVD** \\ \hline Ref. [36] & 0.823 & - & - & - & - \\ Ref. [34] & - & - & - & 15.6 & 5.8 \\ Ref. [39] & 0.83 & - & - & - & 29.04 & \(-\)2.20 \\ Ref. [40] & 0.67 & - & - & - & 0.40 \\ Our ResUNet Model & 0.87 & 0.945 & 0.930 & 0.947 & 12.09 & 6.93 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Liver tumor segmentation of ResUNet base comparison with other methods.  
Using the ResUNet deep neural network, we achieve faster testing with few details; UNet provides promising accuracy results, and ResNet extracts high-level features from an image.  
Our research evaluates the proposed technique's performance completely, comparing it to a few other fully automated techniques.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Microcolony Formation and Dispersion
#### 4.2.2 Role of Quorum Sensing  
Quorum sensing is a molecular mechanism that enables cell-to-cell communication by producing signaling molecules called autoinducers. At least two substances are crucial in quorum sensing and signaling communication: autoinducer-2 (AI-2) and diffusible signal factor. These molecules regulate gene expression upon reaching a certain threshold, influencing bacterial adherence, aggregation, spatial organization, and even dispersal from the biofilm [35]. LuxS, a key enzyme in the activated methyl cycle, produces the quorum sensing molecule autoinducer-2 (AI-2) in \(H\). _pylori_[36; 37]. During microcolony formation in \(H\). _pylori_, LuxS-mediated AI-2 signaling plays a pivotal role in coordinating bacterial communication and regulating gene expression in response to cell density [35; 38; 39]. These reports indicate that the production of AI-2 by \(luxS\) is growth phase-dependent, with maximal production occurring in the mid-exponential phase of growth. AI-2 is also involved in the structure and function of flagella. The mutant of \(luxS\) affects the flagella synthesis by _the flaA, flgE, motA, motB, flhA,_ and _fly_ genes [40]. An in-vivo study showed the involvement of \(luxS\) in motility, \(\Delta LuxS\)\(H\). _pylori_ showed less ability to colonize [41]. Further relation of autoinducer and motility also showed by the study demonstrating _fur_ (ferric uptake regulator) gene that affects the flagellar motor switch and the synthesis of autoinducer-2 (AI-2) [37].  
A diffusible signal factor (DSF) is proposed as a communication molecule in biofilm formation. DSFs are a class of fatty acid derivatives implicated in inter- and intraspecies communication in the quorum sensing system [42]. This communication affects the production of extracellular enzymes. [PERSON] et al. showed that cis-7-tetradecenic acid and lauric acid are DSF molecules in _H. pylori_. These DSFs are involved in auto-inhibitory substances, and they produce self-inhibiting substances with bacteriostatic activity and trigger _H. pylori_ to change from a spiral into a coccoid form [43; 44].  
Dispersal is important for the distribution of sources, and a gradient might exist to strat-ify protection levels after the biofilm is no longer beneficial for the H. pylori survival. Periplasmic binding proteins AibA and AibB are required for AI-2 chemorepulsion responses [35]. Autoinducer signaling also involves TlpB, a family of chemoreceptors. TlpB is a receptor of AI-2, and signaling is perceived as a chemorepellent. This activity could cause cells to disperse to find vacant space and nutrients [40]. The attachment of TlpB to the receptor would generate dispersal and determine the spatial organization of biofilm formation [35].
## 2 Background and Motivation
As a result, the PPG signal strength between 850 nm to 1000 nm is only about 1/8 th the peak amplitude at 530 nm. Since the ToF camera directly measures the body motion of a participant, we find this information useful in distinguishing intensity changes caused by body motion versus heartbeat, which would allow us to compensate for motion-induced artifacts. In addition, by measuring the motion of the chest wall using the ToF camera, we can at the same time directly measure the RR of a participant, instead of extracting RR from HR or other indirect body motion when an RGB camera is used [26; 27]. Furthermore, to our knowledge, this is the first study that investigates the performance of a ToF-based HR/RR monitor system over larger and diverse demographics [32]. A more detailed comparison between this study and previous studies using IR cameras is presented in Table 2. Since indirect ToF cameras are being widely used in many consumer electronics, our CVSM system would enable low-cost, reliable, and multi-modality physiological signal monitoring on various platforms and application scenarios in the future. For example, such a system can be mounted in a vehicle to help monitor the physiological condition of the driver in a continuous manner. If any unexpected events such as falling asleep or heart attack were to happen, the CVSM system can then alert the autonomous/assisted driving system of the vehicle to bring the vehicle to a safe stop.
## 4 Experiment
### Metrics and Simulating Parameter  
In order to demonstrate the effectiveness and superiority of the proposed RHRNet architecture, we compared it to baselines such as UNet as well as state-of-the-art works including convLSTM [47] Autoencoder [14; 47]. The details on these architectures are presented in Section 4.3. RHRNet was trained using categorical cross-entropy loss, as shown in Equation (2), and its performance was evaluated using three metrics, outlined in Equations (3)-(5):  
\[\mathcal{L}\big{(}y,y^{\prime}\big{)}=-\sum_{i}^{c}y_{i}(log\big{(}y_{i}^{ \prime}\big{)}) \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
where \(y\) represents the ground truth label, \(y^{\prime}\) represents the predicted value, \(c\) denotes the number of categories (Background, Jam, Slow and Free congestion levels), \(TP\), \(TN\), \(FP\), and \(FN\) denote True Positive, True Negative, False Positive, and False Negative, respectively  
All the aforementioned models were trained on a real-world traffic congestion data. We utilized the Adaptive Moment Estimation (Adam) optimizer with a learning rate of \(1\times 10^{-4}\) a learning rate decay rate of 0.95, a variable moving average decay of 0.999, and a batch size of 8. The models were implemented in Python 3.7 language using the Keras deep learning library and trained on an Ubuntu 18.04.4 machine equipped with NVIDIA TITAN Xp Graphics Cards.
## 4 New TIRA System Design  
With the currently ongoing upgrades, the TIRA system is facing a major overhaul concerning both radar instruments and the complete process control infrastructure. This extensive endeavor offers the unique opportunity to design a new overall system architect  
Figure 14: Bistatic polarized ISAR images of a space object.  
ture. This architecture, conceptualized to be modular and open, will enable continuous incremental upgrades of TIRA in the future. During the current phase, one key is the utilization of commercial off-the-shelf (COTS) components adapted to and interacting via robust interfaces. Figure 15 shows the prospective system architecture after completion of the major upgrades to the radar subsystems. These upgrades will enhance TIRA's capabilities as a research instrument and meet the increased requirements emerging in the context of space situational awareness (SSA).  
The future system is composed of three main subsystems: the fully movable parabolic antenna, the high-power L band radar, and the broadband imaging radar in the Ka band. A key point in the new architecture is to enable the Ka band imaging radar to track space objects in addition to the tracking capabilities of the primary tracking radar in the L band. The overall process control will be implemented as a distributed real-time system with emphasis on composability, scalability, and dependability. Real-time control of subsystems is localized as needed to achieve fast response times and autonomous subsystem functionality along parameters is defined and continuously updated by higher systems. Both radars and the antenna infrastructure are controlled by a central real-time processing system.  
This system handles orbit prediction and target tracking and automates standard measurement scenarios, while extensive customization allows users to conduct specialized experiments. Data storage and offline processing of the radar data are handled on common back-end systems. These also aggregate and record monitoring data sent by the various subsystems to maintain an overall picture of past and current system status and health.
## 3 Illustration of a Plate-Isolator System as a 2-Stage Vibration Isolation System
### Vertical (\(Y\) Direction)
Compared to optimizing the damping, reducing the stiffness of the structural plate proves to be a more effective method for improving the vibration isolation efficiency of the plate-isolator system. Nevertheless, since the structural plate serves as the mounting surface of various equipment and functions as a load-bearing structure, reducing its stiffness could lead to risks of strength issues and excessive elastic deformation. Moreover, as shown in Figure 6b, reducing the stiffness of the structural plate may also result in a large peak response, thereby increasing the risk of excessive dynamic response. Figure 5: Influence of damping on the force transmissibility of the simplified 2-stage plate-isolator System: (**a**) variation with \(C_{\text{plate}}\), \(C_{\text{isolator}}\) = 600 Ns/m; (**b**) variation with \(C_{\text{isolator}}\), \(C_{\text{plate}}\) = 60 Ns/m. To ensure the vibration isolation effect of the plate-isolator system and the reliability of the structure, the next section will adopt the structural plate with elastic boundary conditions to achieve high vibration isolation efficiency.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 3 Numerical Analysis  
Resonance conditions are not only used for judging whether the resonance occurs, but also can be utilized to determine the dangerous engine orders of excitation which can arouse the resonance of the target mode. Especially when mistuning is considered, the harmonic contents of a mode become more complex compared with the tuned mode, which makes more excitations of different engine orders capable of arousing the resonance of the impeller [36; 37]. In this situation, engine orders of these excitations need to be determined and the harmfulness of these excitations needs to be evaluated. Determination of the dangerous engine order of excitation helps to choose the appropriate number of vanes to avoid the resonance in advance.  
In this section, two cases are provided. In the tuned case, the proposed resonance conditions are used for identifying the resonance of a practical impeller and the identification based on the conventional resonance conditions are also provided as a comparison. In the mistuned case, the engine orders of excitation which can arouse the resonance of the target mode are determined by the proposed resonance conditions and the conventional ones, respectively. Further, hazard evaluation is conducted for the excitations of various engine orders.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 2 System Design  
### EOIR Module Introduction  
The EOIR module can simulate the detection, tracking, and imaging performance of electro-optical and infrared sensors for applications in Earth science and space situational awareness [33]. It supports the development, design, fielding, and operations of such systems. The module operates on a modeled Universe of Interest (MUI), which includes the Sun, the planets in the solar system, and stars. Users can design their own infrared objects and add them to it. Within the MUI, synthetic scene generator-sensor model pairs are placed and operated to produce images from various viewpoints or wavelength bands. The synthetic scene generator calculates radiation at the pupil entrance, while the sensor model adapts to spatial, spectral, optical, and radiometric aspects. In our system, we mainly used this module to obtain corresponding signal-to-noise ratio (SNR) data by customizing the infrared parameters of the target and sensor. The relationship between these components is shown in Figure 1.
## 3 The Use of the Lozi Map in Electrical Devices, Memristors, and Computers
### Memristors  
In 1971, L.O. Chua predicted the existence of a missing fourth passive circuit element in addition to the three classical ones (resistors, inductors, and capacitor) [47]. He called these new elements'memristors', referring to a resistor with memory. Memristors are characterized by a nonlinear constitutive relationship between the charge \(q\) and flux \(\varphi\)A physical device was not reported until 2008, when a physical model of a two-terminal \(hp\) device behaving as a memristor was announced [48]. This sparked intense research interest, with thousands of papers being published to date. A generalized version of Ohm's law for theorizing about such devices was published ten years ago [49].
# The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis
## 3 GDPR Implications
### Data Protection Impact Assessment  
Pursuant to Article 35 of the GDPR, the data controllers are required to undertake a DPIA prior to data processing, especially when this processing is likely to result in a high risk to the rights and freedoms of natural persons. In particular, the GDPR enlists a non-exhaustive list of risk factors to be taken into consideration, and once assessed, a DPIA must be performed:  
* A systematic and extensive evaluation of automated processing, including profiling and similar activities that have legal effects or affect the data subjects.
* Processing on a large scale of special categories of sensitive data such as racial or ethnic origin, political opinion, and of personal data relating to criminal convictions and offenses.
* A systematic monitoring of a publicly accessible area on a large scale.  
Data processing at a large scale seems more relevant to the processing of personal data by and within the ACSs. Recital 91 provides information about what large scale means. It is the processing by making reference to the number of data subjects concerned, the volume of data processed, and the duration and the geographical extent of the data processing activity. It is understood that under these circumstances a DPIA is mandatory.  
Nonetheless, even in cases where a DPIA is not legally mandated, data controllers should consider evaluating the data processing. Such practice would allow them to make a thorough assessment of the envisaged processing operations and to mitigate the risks as detailed in Article 35 [7]. In general, even in the cases where a DPIA is not required, it would be useful to carry out one as early as possible in the design process [37] under Articles 35(1) and 35(10) in combination with recitals 90 and 93 and with prior consultation of the DPAs under Article 36 of the GDPR. The DPIA should be publicly available and \"continuously reviewed and regularly reassessed\" [43].  
Finally, conducting a DPIA is a tool of controllers' accountability. As such, it is part of the data controller's responsibilities to which they must show compliance, and, as mentioned, inability to be compliant to Articles 35 and 36 holds them accountable. However, due to the rapidly evolving nature of the autonomous technologies, the time for the realization of a DPIA, and the requirement of periodical assessment, a DPIA, in practice, might not fulfill its purpose and this challenges the controller's accountability.
# Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media
## 4 Numerical Experiments
### 2D Two-Layer Acoustic-Elastic Coupled Model
To explore the effect of the Thomsen parameter on the arrival time of the Scholte wave, we test the arrival time of the Scholte wave for different \(\epsilon\) and \(\delta\), respectively (Figure 7). The velocity of the Scholte wave is positively correlated with \(\epsilon\) and negatively correlated with \(\delta\). With the increasing value of \(\epsilon\), the rate of change gradually decreases, indicating that the Scholte wave velocity gradually increases and tends to stabilize. The opposite is true when \(\delta\) is varied. In this section, we confirmed the accuracy of our algorithm by comparing it with spectral element method. We find that when the polar angle changes, the curve of arrival time of Scholte wave approaches a sinusoidal shape and exhibits pseudo periodicity. With the increasing value of \(\epsilon\), the Scholte wave velocity gradually increases. The opposite is true when \(\delta\) is varied. Figure 4: Snapshots of particle-velocity components (**a**) \(v_{x}\) and (**b**) \(v_{z}\) with spectral element method at \(t=0.5\) s. The red line denotes the fluid-solid interface. Figure 5: The vibration curve of the Scholte wave.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 2 Study Area and Data
### Himawari-8 Data  
Himawari-8 is a next-generation geostationary meteorological satellite from the Himawari series satellites designed and manufactured by Japan Aerospace Exploration Agency (JAXA) [49]. It was launched on 17 October, 2014 and began generating data on 7 July, 2015. Compared with previous generations of satellites e.g., MTSAT, the Himawari-8 offers improved spatial (500 m-2 km) and temporal (10 min) resolutions. Himawari-8 is also the world first still-weather satellite with the capability of capturing color images [49]. It has 16 observation bands distributed over the range of visible (VIS), NIR and thermal infrared (TIR), in which bands 7 (3.9 \(\upmu\)m) and 14 (11.2 \(\upmu\)m) are most useful in detecting active fire and burned area. In addition, the Himawari-8 satellite has a sound positioning accuracy, with 0.5 pixels for the VIS and NIR bands [49]. The Advanced Himawari Imager (AHI) carried by Himawari-8 satellites scan the Pacific Ocean hemispheric region every 10 min [49], which can result in the production of 142 images per day. This further enhances the capability for continued ground observation. In this study, a total of 37 scenes (Level 1 Gridded full disk) of Himawari-8 data taken by AHI on 17 November 2015 were used. The Coordinated Universal Time (UTC) time of the data is from 03:00 to 09:00, corresponding to Australia Western Standard Time (AWST) from 11:00 to 17:00. Himawari-8 data were downloaded from JAXA's website [50].
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
###### Abstract  
Wireless network devices can attain the required level of quality of service (QoS) and maintain connectivity even after detaching from a current point of access. This detachment (mobility) requires various mobility management (MM) mechanisms, which present numerous challenges due to the exponential growth of wireless devices and the demands of users. The network must be heterogeneous and dense to manage such a heightened escalation of network traffic, increased number of devices, and different types of user demands. Such factors will seriously challenge MM solutions, eventually making the networks non-feasible from the dependability, adaptability, extensibility, and power consumption points of view. Therefore, novel perspectives on MM mechanisms are desired for 5G networks and beyond. This paper introduces an innovative discussion of the functional requirements of MM mechanisms for advanced wireless networks. We present comprehensive arguments on whether the prevailing mechanisms perceived by standard bodies attempt to fulfill the stated requirements. We complete this discussion through innovative qualitative evaluation. We assess each of the discussed mechanisms in terms of their capability to fulfill the dependability, adaptability, extensibility, and power consumption benchmarks for upcoming MM schemes. Hereafter, we demonstrate the outcome and the identified gaps/challenges for the planning and deployment of 5G MM frameworks and beyond. Next, we present the capabilities and possible MM solutions to tackle the gaps/difficulties. We complete our discussion by proposing a 6G MM architecture based on defined parameters.  
Keywords:5G; QoS; 6G; MM; functional requirements +
Footnote †: journal: _Sciences_  
_Applied_  
_sciences_