esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Combined Technologies  
The integration of VR, AI, and IoT is driving a new wave of technological innovation, enabling novel applications across diverse sectors beyond the capabilities of each individual technology. This article further delves into this exact combination. An aspect of this synergy is AIoT (Artificial Intelligence of Things), where AI processes data collected by IoT devices to generate actionable insights [61; 62]. Another aspect is the integration of VR with IoT sensors, where real-time data from IoT devices enhance the realism and interactivity of virtual environments by feeding real-world data into the virtual counterpart [63]. The combination of VR with AI further expands the potential for intelligent, adaptive virtual environments. AI-driven VR systems can personalize user experiences by analyzing behavior and preferences while their application is ranging from gaming to healthcare [64]. Finally, these three technologies can converge to create intelligent systems that surpass previous iterations which incorporated only a subset of these technologies, and then provide new and more robust capabilities. For example, IoT devices gather data about a user's actions or environmental conditions, AI analyzes these data to identify patterns or trends, and VR visualizes the outcomes in an intuitive, immersive environment. This synergy is particularly relevant for applications such as virtual training environments, smart city planning, and personalized healthcare solutions. Unlike previous research that often focuses on these technologies in isolation or limited pairings, this review adopts a holistic approach to explore their combined applications in real-life implementations without being limited to a certain application domain.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.6 Carlini and Wagner  
The Carlini and Wagner (C&W) attack is an optimization-driven technique based on the L-BFGS optimization algorithm. As proposed by [PERSON] et al. in [53], the C&W attack introduces modifications to the objective function and removes the box constraints typically used in L-BFGS. The authors evaluate three varieties of attacks according to three distance metrics, \(L_{0}\), \(L_{2}\), and \(L_{\infty}\). Furthermore, they use an alternative loss function, namely hinge loss, instead of the cross-entropy loss used by L-BFGS. Additionally, they introduce a novel variant denoted as \(k\), transforming the problem from optimizing the perturbation \(\delta\)to optimizing \(k\) to circumvent the box constraints. The optimization problem is formulated by below Expressions (7) and (8).  
\[\underset{\delta}{min}D(X,X+\delta)+c.f(X+\delta)\quad\quad s.t.\ \ X+\delta\in\ [0,1] \tag{7}\]  
\[X+\delta=\frac{1}{2}[tanh(k)+1] \tag{8}\]  
where \(c>0\) is a suitably selected constant, \(\delta\) denotes the adversarial perturbation, \(D(.,.)\) denotes the \(L_{0}\), \(L_{2}\), and \(L_{\infty}\) distance metrics, and \(f(X+\delta)\) define the loss function such that \(f(X+\delta)\leq 0\) if and only if the model's prediction matches the attack target. \(k\) is the new variant substitute \(\delta\) as per the above Expression (8).  
C&W attack is a white-box adversarial attack. However, this technique shows the ability to transfer from unsecured networks to secured networks. This allows an adversary with limited knowledge of an ML-based security system to carry out a black-box attack. This method outperforms the L-BFGS method in crafting adversarial examples and has demonstrated its efficacy in defeating state-of-the-art defense mechanisms like adversarial training and defensive distillation; however, from a computation cost perspective, it is more expensive than FGSM, JSMA, and others.
## 3 Method for Improving the Pointing Error
### Pre-compensation for Ship Platform Attitude
The running cycle of the software is 20 ms, and the attitude measurement data sent to the controller for execution needs three frames, a total of 60 ms. Areal-time lag compensation of 60 ms was determined by testing the lag frame number on the swing table, so that the precision of the line-of-sight disturbance compensation reached the angular second level.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### LoRa Sensor Nodes  
#### 3.4.1 Infrastructure Node (IN)  
The IN is the base module usually with the optional internal high quality inclination sensor installed. This sensor (Murata SCL3300) has a precision of 0.003\({}^{\circ}\), and thereby, even very slight changes in inclination can be measured (e.g., if a 1 m high retaining wall tilts by more than 0.1 mm, this movement should be detected). The PCB is placed in a waterproof housing, still allowing optional sensors to be attached, as stated earlier.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 8 Micromegas for UV Detection
### Tmae  
An alternative method of converting photons to electrons is using an additive to the gas mixture. One commonly used gas with a quantum efficiency of >50% is Tetrakis-(dimethylamine)-ethylene (TMAE). It has an ionization energy of \(E_{\mathrm{i}}=5.4\) eV and is sensitive to UV photons [111]. The base gas and quenchers used in a Micromegas with TMAE should have a low UV absorption coefficient to keep high the quantum efficiency, like argon with methane or isobutane [112].  
However, the mean free path length for photons in TMAE is on the order of 15-20 mm. A longer conversion region of several millimeters to centimeters is required causing uncertainty of the ionization position. The usage of TMAE as a photon/charge converter results in a worse time resolution than a transmissive photocathode due to the uncertainty of the ionization position. On the other hand, a higher detection efficiency can be reached with TMAE compared to solid photocathodes, as more photons can reach the detector [108].  
Figure 24: Sketch of UV-photon detection with Micromegas in (**a**) reflective- and (**b**) transmissive mode.
## 5 Increasing Robustness with Location Verification and Computation
### Robust Location Computation for Attack Source Positioning  
This section concerns methods for locating attack sources and attackers. With the pervasive deployment of wireless systems, it is quite easy for an attacker to launch network attacks from a wireless terminal against remote critical network infrastructure--including national, financial, energy, transportation and military systems. Traditional trace-back techniques for wired networks can only identify the wireless network access point used by an attacker. Such rough identification is far from sufficient in wireless networks, as the attack source can be any node in the coverage range of the access point. The highly mobile, anonymous, and stealthy nature of wireless communication demands that finding the true location of an attacker be done using wireless localization schemes.  
Traditional localization methods [75,76] rely on passive observation of the attacker's signal characteristics, such as its connectivity with neighboring access points, signal strength, _etc._, to make a position estimation. An intelligent attacker equipped with advanced radio technologies, like directional antennas and software defined radios (SDRs), can change its beam (or directional radio) direction and radio parameters to distort these signal features as well as significantly limit the measurements of its signal. In this way, it becomes very difficult for a localization system to compute the accurate location of an attacker and hence such an attacker can hide his/her position with great ease and anonymity.  
A method for enabling the robust location computation of an intelligent attack source is proposed in [77]. In this method, the network proactively coordinates the wireless access points around the attacker to lure the attacker to change its beamforming (or directional signal filtering) direction. In this way, the attacker unintentionally allows more access points to measure its signal features, such as angle of arrival, Received Signal Strength Indicator (RSSI), and connectivity to its neighboring nodes. With enough measurement of network topology and wireless transmission features, the attacker can be correctly positioned.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
###### Abstract  
In this paper, we proposed an ultra-thin composite metasurface for broadband sound absorption, in which a compound Helmholtz structure and porous materials are coupled in a parallel-series arrangement. The Helmholtz structure comprises multiple compound cells with hybrid-damping modes, in which the over-damping and matched-damping impedance are integrated for a lower and broader absorption spectrum. By coupling the porous materials, the metasurface obtains above 85% average absorption over 750-10,000 Hz with a thickness of 31 mm, and the performance below 1600 Hz is significantly enhanced compared to the pure porous materials. This metasurface could possess broad applications in modern equipment considering its extraordinary absorption and compact structure.  
acoustic metamaterial; composite metasurface; hybrid-damping modes; broadband absorption +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### Interactions Prior to the Seismic Event
No solely negative correlations were observed. The signal on channel 1 correlated positively with the signals from probes 5 and 6, and the signal from channel 2 correlated with the signal from probe 3. The durations of the signal correlations differed. In the case of the signal from probe 6, the time was 24 h. For the two remaining probes, the times were 12 h (probe 5) and 7 h (probe 3). In the last interval block, significant correlations were observed between the signals on channel 2 and the signals on channel 4. In both cases, the signals were correlated with those from probes 2, 5, and 6. The correlations have similar characteristics for each of them. The signals from the probes were positively correlated prior to events no. 1, 3, and 4. Negative correlations, however, were observed prior to event no. 2. The signal from channel 1 was negatively correlated with the signals from probes 2 and 3 prior to events 1 and 3 and positively correlated prior to event no. 4 (Figure 26A). Figure 24: **(A)** Comparison of the correlations between radon signals recorded by the probes and signals of tectonic activity in the rock mass observed on individual channels from 97 to 120 h before four seismic events, excluding correlation coefficients at a statistically insignificant level, i.e., \(<\)0.4. Correlation indicated with red line. (**B**) Comparison of the correlations between radon signals recorded by the probes and signals of tectonic activity in the rock mass observed on individual channels from 97 to 120 h after four seismic events, excluding correlation coefficients at a statistically insignificant level, i.e., \(<\)0.4. Correlation indicated with red line. Figure 25: **(A)** Comparison of the correlations between radon signals recorded by the probes and signals of tectonic activity in the rock mass observed on individual channels from 121 to 144 h before four seismic events, excluding correlation coefficients at a statistically insignificant level, i.e., ¡0.4. Correlation indicated with red line. **(B)** Comparison of the correlations between radon signals recorded by the probes and signals of tectonic activity in the rock mass observed on individual channels from 121 to 144 h after **(B)** four seismic events, excluding correlation coefficients at a statistically insignificant level, i.e., ¡0.4. Correlation indicated with red line. Figure 26: (**A**) Comparison of the correlations between radon signals recorded by the probes and signals of tectonic activity in the rock mass observed on individual channels from 145 to 168 h before four seismic events, excluding correlation coefficients at a statistically insignificant level, i.e., <0.4. Correlation indicated with red line. (**B**) Comparison of the correlations between radon signals recorded by the probes and signals of tectonic activity in the rock mass observed on individual channels from 145 to 168 h after four seismic events, excluding correlation coefficients at a statistically insignificant level, i.e., <0.4. Correlation indicated with red line.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 6 Models of Communication in the Internet of Vehicles
### Vehicle-to-Grid (V2G)  
Vehicle-to-grid technology systems enable bi-directional communication and energy exchange between EVs and the electrical grid, essentially turning EVs into mobile energy storage units. In a V2G setup, EVs can not only draw power from the grid for charging their batteries, but can also discharge excess electricity back into the grid when needed. V2G systems, in conjunction with plug-in electric vehicles (PEVs), have the capacity to reduce energy costs for both consumers and contribute to more efficient energy management [87; 88]. V2G smart charging coordination systems enable intelligent and optimized charging of EVs, allowing them to respond to grid demands and fluctuating electricity prices while reducing the overall cost of charging [89]. The use of AI technology transforms EVs from simple transport devices to valuable assets in energy management systems [90]. This approach will also help to significantly accelerate the spread of EVs and build a greener and more reactive energy ecosystem. V2G is a promising solution for smart grids and renewable energy generation as it helps to define the future clean and reliable energy distribution.
## 5 Results
### Simplified Solis Results  
From Figure 3, Simplified Solis overestimated GHI in five stations, namely Marrakech (\(-5.4\%\)), Tangier (\(-5.2\%\)), Ouarzazate (\(-13.0\)), Fes (\(-2.1\%\)), and Agadir (\(-8.7\%\)), whereas it underestimated GHI at Tantan (11.5%). From Figure 4, which gives the hourly rRMSE, rRMSE was less than 15% at Agadir (12.7%), Tangier (4.2%), Marrakech (4.2%), and Fes (3.0%), which shows average agreement in this station and between the McClear and Simplified Solis clear sky model, while it was more than 15% at Tantan (25.7%) station which indicate poor agreement in this station and between McClear and Simplified Solis clear sky model.  
From Figure 5, which indicates rMAE, all of the stations showed an rMAE of less than 15%, which shows good agreement, except for the Tantan (19.6%) station where rMAE was greater than 15%, thus indicating poor agreement as indicated by high bias. From Table 5, which indicates the coefficient of determination (R\({}^{2}\)) at six stations, all stations showed R\({}^{2}>0.99\), indicating excellent performance based on Table 4 with the exceptions of the Agadir (<0.99) station, where the coefficient indicates average correlation, and Tantan (0.95), where the coefficient indicates poor performance. From Table 6, Solis was feasible at Fes station, and it has a maximum R\({}^{2}\) of (0.99) at Fes and Ouarzazate. From Figure 6, Solis was closer to McClear, which shows good agreement that varies at different stations. The performance of Solis clear sky model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S7.
## 3 Results
### Practical Application and Spatial Accuracy Evaluation of the Prototype System
As for the latter, overlapping analysis results of two kinds of entities represented by line and polygon geometry were used to evaluate the relative accuracy. In particular, only the clipped parts of lines and polygons inside the background polygon are considered. The main reason is that it can be used to verify the relative precision, correctness, and reliability of the GIS overlapping analysis algorithm developed by the authors. The true values (length and area) were obtained by the spatial overlapping analysis tool in ArcGISsoftware (Version 10.2) and denoted as \(D_{\text{Arc}}\) (length) and \(S_{\text{Arc}}\) (area), respectively. The corresponding measured results automatically computed and returned from the system are denoted as \(D_{\text{web}}\) and \(S_{\text{web}}\), respectively. The absolute differences (\(\Delta X\), \(\Delta Y\), \(\Delta H\)) and relative differences (\(\Delta D\), \(\Delta S\)) were calculated between the true and observed values. The absolute precision utilizes the \(RMSE_{\text{S}}\) as indexes, and the calculation method can refer to Equations (8) and (9). Regarding the relative accuracy of length and area, a deviation ratio (\(DR\)) is first defined to evaluate the length and area errors. It can be calculated by \(\Delta D/D_{\text{Arc}}\) and \(\Delta S/S_{\text{Arc}}\), respectively, after the average \(DR\) (\(ADR\)) is computed and used as a relative accuracy index. For both types of validation data, 31 pieces of data were collected, and the accuracy results are tabulated in Tables 6 and 7; Table 6 indicates that the absolute accuracies of plane and height coordinates represented by \(RMSE_{\text{S}}\) are 2.3 cm and 7.6 cm, respectively. As for the relative accuracies defined with ADR, Table 7 demonstrated that the accuracies of both length and area are very high; the corresponding values of ADR are 0.0059% and 0.0074%, respectively. These results exhibit much smaller deviations between the true and the observed data and demonstrate the reliability of the GIS overlapping analysis algorithm and the web platform and can meet the practical engineering requirements. Figure 16: MIDA results. (**a**) Subsidence (w) with a value of 10 mm and (**b**) horizontal movement along the strike direction (U90) with a value of \(-10\) mm.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 2 Related Work
### Routing-Based Schemes  
In order to improve the time-sensitive transmission capability of services with different requirements in LEO satellite networks, various approaches have been proposed for traffic scheduling. For instance, [PERSON] et al. proposed an AI-aided intelligent multipath traffic-scheduling approach for the autonomous and efficient communication of LEO satellite networks [17]. This approach formulated the multipath traffic scheduling problem into a pheromone-incentivized Markov decision process to derive an intelligent scheduling strategy. [PERSON] et al. proposed a learning-based approach for intradomain QoS routing [18] that uses a learning-based scheduling algorithm to reduce the packet loss rate. [PERSON] et al. presented an SDN-based LEO satellite networking architecture and proposed a load-balancing-based traffic scheduling scheme that transforms scheduling problems into modified maximal-flow problems [19]. Here, a deep reinforcement-learning model is utilized to make global optimal scheduling decisions. Additionally, other studies investigated routing-based schemes [11; 12; 13]. These studies improved network resource utilization, but lacked guarantees for high-priority services.
## 2 Methods  
### Optical Setup of the Exposure System  
An orthogonal two-axis Lloyd's mirror interferometer was designed and assembled, as shown in Figure 1, which mainly includes a light source, beam shaping unit, polarization adjustment unit, and two-axis Lloyd's mirror unit. For the light source, with consideration of laser source quality (stability of power, polarization, and wavelength), photoresist sensitivity against laser wavelength, and optical alignment low-complexity related to laser visibility, we employed a commercial, well-used He-Cd laser of the KIMMON KOHA company (Tokyo, Japan) with the wavelength of 442 nm and power output of 180 mW. Two optical elements are contained in the beam-shaping unit: the spatial filter element and collimating lens element. The two-axis Lloyd's mirror unit, as shown in Figure 2a, consists of three elements: X-mirror, Y-mirror, and the jig of substrate. Besides, the three elements are placed perpendicularly to each other. In addition, the two-axis Lloyd's mirror unit possess the function of rotation so that it's easy to manipulate the grating period. We designed the azimuth angle \(\varphi\) equals to 45\({}^{\circ}\), and the angle \(\theta\) (0-90\({}^{\circ}\)) called the incident angle in this paper.  
We selected the mirror with the coating of enhanced aluminum for the purpose of adjusting arbitrarily the grating pitch. The mirror with the dielectric interface, by contrast, has a limited angle of incidence, usually within a range of 0 to 45\({}^{\circ}\). Two half-wavelength plates were employed in the polarization adjustment unit that controlled the polarization state of corresponding incidence beams, as shown in Figure 2b. Section 2.2 introduces the mechanism of polarization adjusting.  
Figure 1: The optical configuration of the orthogonal type two-axis Lloyd’s mirror interferometer-based laser interference lithography (LIL) system.  
Figure 2: The global coordinate system of exposure system. (**a**) Two-axis Lloyd’s mirror unit and definition of incident angle \(\theta\) and azimuth angle \(\varphi\). (**b**) Photograph of half-wavelength plates (HWPs) setup.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 6 Benefits
While the two currently integrated datacenters are OpenStack-based, other datacenter or cloud technologies such as Amazon Web Services (AWS), VMware vCloud, Kubernetes public and private clusters, and OpenNebula can also be supported under the same management and orchestration plane. Regarding the IIoT slice and the deployed WSN, one of the main benefits of this architecture is that sensors can serve multiple applications, meaning that they do not need to be statically assigned to a single process. Although in the use case presented in this work the whole WSN is shown to be associated with the broaching process, it is possible to set up additional processes that also make use of the services offered by the WSN in different ways. This effectively enables a scenario where sensors, and, more specifically, the services they provide, can dynamically float across one or more processes on demand, adjusting their assignments based on the needs of each process at any given moment. Each of these processes can leverage the authentication and authorization mechanisms provided by Hidra and can either make use of the previously discussed IIoT slice or set up their own local Hidra service thanks to the Hidra VNF available in the service catalog. Moreover, with such an expressive access control mechanism and with the use of the security policies, it is possible to grant or revoke access to any resource in the CDSs according to the evaluation of some local context conditions. As previously described, this access control decision is made based on a security policy, being the use of highly expressive security policies essential in order to obtain a fine-grained access control procedure. For example, the request of a preliminarily authorized user can then be denied if the battery level is not enough to perform the requested operation. In this context, sensors can be considered easily reconfigurable, since, through the access control, the values can be set and accessed depending on the result of the security policy evaluation. Therefore, the flexible IIoT integration is made possible as the behavior of the sensors can be personalized or configured by means of conveying the proper security policy to be applied in every particular case. A summary of the benefits provided by this work, including their impact on industrial environments, as well as their impact on the specific use case presented in Section 5, is presented in Table 1.