esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PiFM Measurement for a Single-Molecule
### Critical Role of Picocavity in Resolution  
In this section, we discuss the feasibility of atomic resolution measurements using PiFM. We revealed that the atomic structure on the tip surface, which has recently been called picocavity [22] in the field of TERS, plays an important role in PiFM resolution. We consider a tip model that imitates the gathering of atoms, as shown in Figure 5a,b. The alignment of the protrusions is not important for resolution. Rather, it is easier to consider only one protrusion in terms of resolution. Considering the atomic structure on the tip surface, it is necessary to consider whether ghost images caused by other protrusions can be avoided.  
Figure 4: Excitable conditions of a molecule by plasmon (top view). The hexagon and red arrows represent the molecule and molecular polarization, respectively. The black dot represents the tip position and the blue arrows are the electric field vector of the localized plasmon induced at the tip-substrate nanogap. (**a,b**) show the excitable and non-excitable tip position conditions for the allowed transition of the molecule, as shown in Figure 3f,g, respectively. (**c**) shows the excitable tip position condition for a forbidden transition of the molecule, as shown in Figure 3j.  
Figure 5: (**a,b**) show the model tip with picocavity in the cross-sectional picture of the tip center and the three-dimensional picture of the bottom view, respectively, in discretized space. The colors in (**b**) represent contour maps. (**c**) shows a map of the response field intensity from the viewpoint of (**a**). The intensity is determined as a ratio of the incident field, \(|\mathbf{E}|^{2}/|\mathbf{E}_{0}|^{2}\). (**d**) is the line profile of the field intensity at 3 Å below the tip apex.  
Figure 5c,d show the response field intensity map and line profile at the plane of 3 A below from the tip apex, respectively, irradiated with the incident laser. The intensity was determined as a ratio to the incident field, \(|E|^{2}/|E_{0}|^{2}\). The enhanced field is extremely localized in the vicinity of the picocavity. Naturally, the sharpness of the tip contributes to high-resolution imaging. However, in scanning optical microscopic techniques, such as TERS and PiFM, the enhancement and localization of the field are also determinant factors for high-resolution imaging. The force curve of the PiFM is steep [21], and the contribution from the other protrusions is negligible.  
In our previous study, we experimentally and theoretically demonstrated that the nanometer-scale structure on the tip surface enabled us to realize a resolution of PiFM measurement of less than 1 nm at room temperature [19]. In this study, we considered a tip with picocavity; however, our study is based on the assumption that the diffusion coefficient of gold atoms is small at cryogenic temperatures [22; 29].
# Real-Time, Deep Learning Based Wrong Direction Detection
## 1 Introduction  
With the development of the fourth industrial revolution, the role of the intelligent transportation system (ITS) is becoming more and more crucial for ensuring the safety and efficiency of drivers. The purpose of a vision-based ITS is to extract useful and precise traffic data, and to use surveillance technologies as efficiently as possible. According to the National Information Society Agency, there were more than eight million closed-circuit television (CCTV) cameras installed in Korea by the year 2018, which gives the country one of the highest cameras-per-capita rates in the world. CCTV footage certainly cannot be a perfect solution for all traffic violations. However, it is widely and increasingly used by government officials to identify serious violations, along with evidence, testimonies, and documents. Typically, driving the wrong way down a road happens for several reasons: the driver is distracted or confused; the driver did not notice the pavement markings or traffic signs; the driver intentionally breaks the rules; etc. Even though recent pavement delineations and signs are innovative, they might not be enough to alert drivers to wrong-way entry, and car accidents continue to happen. Even though it is almost impossible to control human driving behavior, it is important to classify and understand irregular activity in usual traffic scenarios in order to prevent serious car crashes. So far, the identification task is performed by people who work in monitoring companies. Nevertheless, with the significant increase in camera devices, the need for automated intelligent software is demanded evenmore. Wrong-direction detection technologies can be divided into three major categories: sensor-based detection, radar-based detection, and video imaging-based detection. The focus of this research is to automate the recognition of moving violations, thus requiring less human interaction, as well as to identify (using a single video camera) vehicles entering traffic the wrong way onto the highway system (Figure 1).  
While our proposed system relies on video imaging-based detection, it references three different computer vision challenges such as vehicle's localization, tracking, and determining direction. Vision-based learning is one of the cheapest methods and requires lower labor costs by saving people's time, as well as lowering costs by using fewer machines and by eliminating faulty products.
## 5 Training an Arabic Transformer Model Using the AraFast Corpus
### Results and Discussion: Training an Arabic Transformer Model
Second, the application of segmentation in the base model led to improved outcomes compared to the base model without segmentation. This finding indicates that segmentation, which involves breaking text into meaningful subunits or tokens, is a beneficial preprocessing step for Arabic text. It enables models to better capture the structure of the language and understand the context of words and phrases, which is particularly important given the morphological complexity of Arabic. Third, the high loss values of the base model trained with noise highlight the importance of using clean text for training. The presence of noise (in the form of irrelevant information, errors, or inconsistencies in the data) can significantly obstruct the learning process, making it difficult for a model to identify patterns and learn effectively. Thus, ensuring the quality of data via cleaning and preprocessing is essential for the successful training of language models on Arabic text. The above results indicate the necessity of employing the following features: a large, well-segmented, clean corpus for the development of robust and efficient models for Arabic NLP tasks. These features are crucial for advancing the NLP field and achieving high levels of accuracy in various applications, such as question-answering, machine translation, and text summarization systems.
## 2 Brief Overview of Geographic CA Modeling and Tools
### Essential Elements of GUI-Based Geographic CA Modeling Tools  
To build and implement a geographic CA model in a GIS software environment, add-in tools must have facilities for defining neighborhood functions, a structure for specifying transition rules, and a means of applying transition rules in a synchronous [62] or asynchronous manner [63]. Previous studies also outlined CA modeling tool requirements such as input and output specification options [10], interactivity [64], and user-friendly GUIs [42]. Though earlier GUI-based standalone applications had functions for displaying previously simulated timesteps [57] or recreated basic GIS functionalities to support CA modeling workflows [45], development endeavors that extend existing GIS software benefit from the built-in data visualization, input, storage, and processing functions. As such, programming efforts can be solely focused on creating CA modeling tools. In addition to implementing CA model elements, GUI-based tools often support model evaluation adhering to the broader tradition of comparing simulated outcomes and real-world data [65]. For example, prior CA modeling tools supplied small sets of map comparison metrics including overall accuracy, various Kappa metrics, and the Figure of Merit (FOM) [34; 43]. Overall, a general-purpose add-in should support users in configuring key model elements, executing model routines, and conducting basic model evaluation to facilitate end-to-end model-building activities within an established GIS software environment.
# Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer
## 5 Simulation Results and Analysis
### Comparison  
In this subsection, the proposed MOGWO exploration was compared with the original deterministic CME algorithm [22] and the hybrid stochastic exploration algorithm based on the GWO and CME [8]. The same map and experiment parameters (Table 1) were selected for the two algorithms with 60, 80, 100, and 120 iterations as it was implemented in the MOGWO exploration algorithm in Section 5.1. It should be noted that waypoints were not applied for the other two algorithms used in the comparison.  
Figure 8: Pareto optimal set of the results of the MOGWO exploration algorithm.  
Figure 7: Decision-making process of the alpha solutions: (**a**) robot 1, (**b**) robot 2, and (**c**) robot 3.  
In the experiment, the CME algorithm was run only once for each iteration class (60, 80, 100, and 120) because it does not generate any random values even when tested multiple times. By its deterministic nature, CME implements differently for every modification in the environment. For instance, the simulation runs were aborted after the 98 th iteration during our experiment when one of the robots got stuck next to the wall obstacle. For the purpose of completing the exploration, the initial position of robot 2 or \(r_{2}\) (from Table 1) was changed from (7,9) to (6,5). From these results, we can conclude that the exploration by the deterministic CME algorithm requires fine-tuning the map parameters for successful map coverage.  
The hybrid stochastic algorithm is a stochastic approach, which uses the single-based GWO algorithm. During our experiments, the simulation-runs were aborted several times due to the robot's selection of inappropriate positions among the frontier cells. This situation occurs when the GWO parameters, \(A\) and \(C\), oblige a robot to move into the wrong places, such as obstacles or another robot position. Fortunately, in this algorithm, the \(A\) and \(C\) parameters vary in each simulation-run, which allows us to obtain successful results.  
Figure 9 shows the comparison of the results obtained using the original CME, the hybrid stochastic exploration, and the MOGWO exploration with 150 waypoints. It can be seen that the deterministic CME approach has the lowest values of map coverage among all the algorithms. The proposed MOGWO algorithm does not outperform the hybrid stochastic exploration algorithm in iteration classes, 60, 80, and 100. However, it surpasses the original CME in all iteration categories and the hybrid stochastic exploration algorithm in the category of 120 iterations. Additionally, aborted simulation-runs, which are drawbacks of the CME and hybrid stochastic exploration, did not occur in the MOGWO exploration. Thus, the MOGWO exploration proved more efficient and stable compared to the other algorithms studied in this subsection.
# E-Sail Three-Dimensional Interplanetary Transfer with Fixed Pitch Angle
## 4 Numerical Simulations and Results
This situation occurs when the sail pitch angle has the value that maximizes the transversal component of the propulsive acceleration \(a_{\mathrm{TN}}\). Indeed, in the case of \(\alpha_{n}=45\,\mathrm{deg}\), the value of the flight time \(\Delta t\) substantially reaches the value closest to the minimum admissible \(\Delta t_{u}\), especially for characteristic acceleration values compatible with a low-performance E-sail. The case of \(\alpha_{n}=45\,\mathrm{deg}\) can therefore be considered as a case of particular interest in the design of an orbit-to-orbit transfer to Venus or Mars. In this regard, Figure 13 shows, for example, the optimal trajectory transfer and the time variation of the two remaining control terms \(\delta\) and \(\tau\) in the Earth-Mars scenario when \(a_{c}\in\{0.1,\,1\}\,\mathrm{mm}/\mathrm{s}^{2}\), i.e., in the case of a low or high performance E-sail. Moreover, Figure 14 shows the case of an Earth-Venus mission when \(\alpha_{n}=45\,\mathrm{deg}\) and \(a_{c}=0.5\,\mathrm{mm}/\mathrm{s}^{2}\), which confirms the possibility of performing the transfer with this specific value of the sail pitch angle. Figure 8:0 Numerical results of a sail pitch angle-constrained interplanetary transfer: minimum flight time \(\Delta t\) as a function of \(\alpha_{n}\in[35,\,55]\,\mathrm{deg}\) and \(a_{c}\in\{0.1,\,0.5,\,1\}\,\mathrm{mm}/\mathrm{s}^{2}\). The value of the flight time \(\Delta t_{u}\) in an unconstrained scenario is reported in Figure 8:1 (**a**) Case of an Earth–Venus transfer; (**b**) Case of an Earth–Mars transfer. Figure 8:2 Case of a constrained Earth–Mars interplanetary transfer with \(\alpha_{n}=45\deg\). The legend is reported in Figure 8:3 (**a**) Case of \(a_{c}=0.1\,\mathrm{mm}/\mathrm{s}^{2}\); (**b**) Case of \(a_{c}=1\,\mathrm{mm}/\mathrm{s}^{2}\). Finally, Table 2 summarizes the minimum (constrained) flight times which can be obtained by solving the optimal control problem in the two interplanetary mission scenario when \(a_{n}=45\deg\) and \(a_{c}=\in\{0.1,\,0.5,\,1\}\) mm/s\({}^{2}\). Note that the specific value of the sail pitch angle allows the designer to obtain a flight time close to the value of \(\Delta t_{u}\) by using a simplified steering law, in which only the clock angle \(\delta\) and the switching parameter \(\tau\) change during the interplanetary transfer. Interestingly, even in the sail pitch-angle constrained case, the clock angle \(\delta\) oscillates around (or remains close to) the value of 180 deg in an Earth-Venus transfer, or to the value of zero in a transfer to Mars. This aspect suggests a simplified approach to the evaluation of the optimal time of flight, which uses a two-dimensional approach and a constant setting of the E-sail in the orbital reference frame. Such a simplified semi-analytic approach, which extends the model recently proposed by the author in ref. [47] for a transfer between two close circular heliocentric orbits, can be considered as a potential extension of this work in the case of a mission towards Mars or Venus.
## 5 Fall Detection Application
### Method of Fall Detection Using Spectral Signature  
Since the system uses a CW radar, the proposed model is the most simplistic for application when the expected reflected signal frequency is up to about 10 kHz, and there are no stringent requirements for signal processing units. Therefore, the signal processing performed on a simple microcontroller can be implemented. The disadvantages of the system are that fall detection occurs only for movement perpendicular to the radar system. Therefore, the Doppler received signal will be only the distraction levy on the radial axis for movement not perpendicular to the distraction [13]. However, several mechanisms can overcome this problem. For example, installing two systems on two perpendicular walls or identifying the object's location by calculating the angle relative to the radar system [17]. The spectral signature describing a person's movement can be detected using the CW micro-Doppler MMW radar described in Section 4.1 and to generate the spectrogram described in Section 3. By examining these spectral signatures detected by the radar, it is possible to identify the kind of user movement. We measured four different kinds of motion: falling, sitting, walking and falling while walking. The differences between the spectral signatures detected using the radar could be seen for these four movements. As a  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Type of Sensing** & **Cost** & **Algorithm Type** & **Motion Classification** & **Disadvantages** & **Imaging** \\ \hline Wearable sensors-3D accelerometer & cheap & Threshold-based & Not classified & High false rate, no remote sensing & Not imaging \\ \hline Camera-based sensing & medium & Detection based on human skeleton, falling angle vertical projection & Classified & High computing resources to process data continuously, not portable, limited sensing area & Imaging \\ \hline Radar-based sensing & medium & Micro-Doppler time-frequency frequency analyze & Classified & limited sensing area & Not imaging \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of fall detection systems.  
Figure 4: Scheme of the CW micro-Doppler radar.  
result of these differences, we determined that is possible to identify and distinguish a falling person using the radar.  
In order to demonstrate the accuracy of the spectral signature results obtained using the radar, we simultaneously performed measurements with video processing. We filmed the person's movement with a simple camera, and using the \"Tracker\" program, we followed the movements of various body parts. \"Tracker\" is a free video analysis and modeling tool built on the Open Source Physics. Next, we compared the results obtained from the two different measurement modalities.
# Value of Information Based Data Retrieval in UWSNs
## 2 Related Work  
### Relationship between QoS, QoI, VoI and Routing  
The over-arching goal of this article is to improve the QoI (quality of information) that can be obtained from a UWSN. In terms of the performance objectives, QoI is to sensor networks what QoS (quality of service) is to conventional computer and wireless networks [13; 14; 15]; although, technically speaking, in terms of implementation, QoI is built upon a QoS layer in sensor networks [16; 17]. It is shown in [13; 15] that the construct of QoI is intrinsically related to VoI, therefore, we use VoI for path planning purposes so as to improve QoI garnered from the system. Design of good routing algorithms is one of the fundamental aspects that dictates QoS and QoI in conventional networks [18], sensor networks [17; 19; 20; 21; 22] and networks with mobile elements [23; 24]. Routing in networks is tailored to meet certain application-level or operational objectives. With the advent of IoT [25] and Fog-computing [26] paradigms, there continues to remain a keen interest in the development of routing algorithms to meet the new emergent objectives and constraints [27; 28]. In this article, we employ a mobile sink (AUV) as part of the routing effort to deliver a better measure of VoI and hence QoI.
The use of SAW-based UV-C sensors has been proposed and reported in the literature due to their exceptional characteristics, such as the absence of applied voltage to the sensing material, no Joule heating effect, excellent stability in harsh environments, and minimal influence from R-G centers in the sensing material [17; 18; 19; 20; 21]. In our previous studies, we utilized In\({}_{2}\)O\({}_{3}\)/Ta\({}_{2}\)O\({}_{5}\)[20], synthesized via the hydrothermal method, as a UV-C candidate, and 3D Ta\({}_{2}\)O\({}_{5}\) nanoparticles [21] with a bandgap of 4.6 eV, making them suitable for UV-C detection. However, achieving fast response and recovery times using the hydrothermal synthesis route remains a challenge. In this study, we employed HfO\({}_{2}\) (Eg-5.71 eV) as the UV-C sensing material, deposited via atomic layer deposition (ALD), to ensure enhanced response and recovery performance. HfO\({}_{2}\) was used for the first time with a SAW sensor platform operating at a center frequency of 222 MHz, offering long-term stability, fast response times, and high sensitivity. Additionally, interface electronics were designed and implemented around the sensor to enable seamless operation and facilitate accurate characteristic detection. The key novelties and advantages of the developed sensor system are as follows: for the first time, HfO\({}_{2}\), a wide bandgap insulating material, has been utilized as the sensing material for UV-C detection in SAW technology, implemented via atomic layer deposition (ALD) on a 128\({}^{\circ}\) YX LiNbO\({}_{3}\) piezoelectric substrate. Optimal structural dimensions for the sensing material, particularly thickness and mass, have been proposed during the ALD process [22], along with specific annealing temperatures designed to minimize defects without degrading the LiNbO\({}_{3}\) piezoelectric properties [23]. Given that corona discharges occur in a narrow-pulsed manner [24], ultra-fast response and recovery times are essential. The developed sensor demonstrates exceptionally rapid response and recovery times, along with high sensitivity. Additionally, the sensor system incorporates a built-in compensation feature to mitigate external environmental interferences, such as temperature and humidity, ensuring stable performance under varying conditions.
## 1 Introduction
Each controller is composed of a kinematic and a dynamic controller, where the kinematic can achieve local finite-time convergence of the tracking error to zero. [PERSON] et al. [25] proposed an adaptative high order SMC with finite-time convergence for an AUV. The authors validated the proposed controller with real experiments for depth and yaw trajectories, considering parametric uncertainties and external disturbances. Finite-time tracking controllers have been widely and successfully used to control fully actuated robot arms [26; 27], but little research has been done for underactuated unmanned underwater vehicles (UUVs), whose particular nonlinear dynamics have other challenges, for instance, the hydrodynamics effects inherent to the vehicle's geometry and water density. Considering the advantages of a controller with finite-time convergence, and that most of the controllers designed to achieve that for trajectory tracking in UUVs navigation are model-based 1 st order SMC, this paper presents the formulation and validation of a model-free 2 nd order SMC based on a Time Base Generator (TBG) that guarantees finite-time three-dimensional trajectory tracking of underactuated UUVs subject to ocean currents. The resulting control law does neither depend on the hydrodynamics nor other parameters of the vehicle; therefore, it is considered as a model-free controller. In addition, formulation of the control law of this 2 nd order SMC eliminates the chattering effect which is common in conventional 1 st order SMC [28; 29]. To achieve finite-time convergence of the tracking errors of three-dimensional trajectories, the 2 nd order SMC is parametrized by a TBG which allows the user to set an arbitrary base-time in which the vehicle will meet the desired trajectory. The performance of the proposed controller is evaluated by numerical simulations including the effects of external disturbances. All this is structured in this paper as: Section 2 describes the BlueROV2 UUV and its dynamic model, as well as the proposed control scheme including the TBG. Section 3 describes the method and parameters used to validate the proposed controller. Section 4 shows the results of the testing and validation, and Section 5 contains the concluding remarks.