esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Data  
The HF radars used in this study are OSMAR-071G high-frequency surface wave radars developed by Wuhan University of China. OSMAR-071G HF radars operate at a frequency of 9.355 MHz and detect ocean surface currents and wave heights in the ranges of 100-150 km. A pair of HF radars were deployed at Shangchuan and Wanshan Islands to the west of the PRE outlet in southeastern China. Their total detection range is 112.4\({}^{\circ}\) E-114.7\({}^{\circ}\) E, 20.2\({}^{\circ}\) N-22.0\({}^{\circ}\) N, with a detection area of nearly 40,000 km\({}^{2}\). The radar data used in this study span from 1-30 March 2020, with a time interval of 10 min and a spatial resolution of 5 km.  
The standardized winds at 10 m were obtained from the ERA5 dataset, which is the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5 | ECMWF). The wind data had a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). Tidal data were taken from the TPXO7.2 global tidal model developed by [PERSON] and [PERSON] [22] of Oregon State University. Model data were simulated using a regional PRE model with a minimum model grid size of 100 m in the radar observation area. The model-simulated currents were validated against the radar and ADCP measurements in our previous study [23]. In order to coincide with the radar data, wind, tide, and model data from 1-30 March 2020 were used in this study. Note that all the above data were averaged to hourly data and smoothed using a four-hour running mean to eliminate very-high-frequency signals.
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 1 Introduction
In our research, we implemented an anomaly-based IDS for the identification of attack messages injected on the CAN bus using both supervised and unsupervised SOM networks [19] combined with a K-means clustering algorithm. In this paper, we present only the results for the supervised SOM network comparing two different approaches. The first was a supervised X-Y fused Kohonen network (XYF), the second was a supervised XYF Kohonen network combined with a K-means clustering algorithm (XYF-K). The two methods were tested on an open source dataset concerning traffic data messages on a CAN network 2.0B and containing more than 2000 different attack types sent totally at random. The dataset contains a large volume of traffic data messages with a low number of features and highly imbalanced data distribution. Despite the complex structure of the CAN bus dataset, the proposed methods showed a high performance in the accuracy of detection of attack messages. This paper presents the following contributions:  
* We propose an anomaly-based IDS to detect attack injections on a CAN bus. To the best of our knowledge, this is the first work which tests the Kohonen SOM neural network as IDS for detection on in-vehicle networks. The SOM network can be implemented both as supervised and as unsupervised learning. * We tested the proposed IDS on real data concerning in-vehicle network attacks. Data consists in four datasets, each containing a different attack type, in particular, _Denial of Service Attack (DoS), spoofing the drive gear, spoofing the Revolution per Minute (RPM) gauge_, and _fuzzy_ attacks. Data were obtained by injecting and logging CAN messages on a real vehicle [20]. Moreover, we present the effectiveness of the proposed method showing the performance metrics. * Most of the researches proposed in the literature present detection methods able to identify only periodic malicious attacks, but not aperiodic violations [18]. The proposed IDS show a high performance in detecting attacks sent on the CAN bus with any kind of period, also aperiodic attacks. * We compared the results obtained in terms of the system's accuracy to the other studies that tested different IDS approaches on the same dataset. * This is the first work which tests an IDS not only on single datasets containing different attack types but also merging datasets into a unique dataset containing all attack types. This paper is organized as follows: Section 2 illustrates the related works; Section 3 describes the CAN message structure; Section 4 explains the theoretical framework used in the work; Section 5 shows the experimental process; in Section 6 are pointed out the results and discussions; Section 7 sets out the conclusions.
# Modes Control of Lamb Wave in Plates Using Meander-Line Electromagnetic Acoustic Transducers
## 4 Verifications
### Experimental Setup  
Next, the modes controlled by using paired structure EMATs are validated by various experiments (as shown in Figure 7), where a paired structure EMAT consists of two traditional EMATs. The parameters of the coils and magnets used in the experiments are the same as those used in simulations, which are given in Table 1. The coils are fabricated by a printed circuit board (PCB). The upper coil and lower coil are series connected. Figure 8 represents the photograph of the transmitter, where two cross slides are installed on the shelves, which are made of aluminum alloy profiles. Each slide can be moved both in the X and Z direction by turning the handwheels. Both the lower EMAT and upper EMAT are fixed on the cross slides. The magnets and coils are installed on a seat. The position and lift-off height of EMATs can be adjusted by turning the handwheel, which is installed on the terminal of the lead screws. The dial scale on the handwheel is used to precisely locate the EMATs' positions. Accurate alignment of upper and lower EMATs can be achieved by adjusting the positions of X and Z handwheels.  
A function signal generator is used to generate a narrow bandwidth 5-peak signal, which is a cosine signal modulated by the Hanning window. An impedance matching circuit is connected between the transmitting EMAT and pulse power amplifier to enhance the efficiency of power conversion. The receiver is connected to both a filter and a programmable amplifier, which are integrated in the power amplifier. The output signals are displayed on the digital storage oscilloscope. The transmitter and the receiver are separated with a distance of 400 mm between them. The receiver is instead by a traditional EMAT.  
Figure 8 shows the experimental results, where Figure 8a,c are the ultrasonic signals generated by the traditional symmetric and antisymmetric EMATs. The amplitude ratio of the S0 to A0 is 11.8 dB for the conventional symmetric mode EMAT. The amplitude ratio of the A0 to S0 is 25.7 dB for the conventional antisymmetric mode EMAT. Figure 8b shows the S0 mode ultrasonic signals generated by the paired structure EMATs, and the ratio of S0 mode to A0 mode is 36 dB. Similarly, in Figure 8d, the ratio of A0 mode to S0 mode is 36.9 dB. However, the signal to noise ratio (SNR) is about 36 dB in the experiment. In other words, the amplitudes of unwanted modes are as same as the noise.  
The results of simulations and experiments verify that the paired structure EMATs can control the generation of single S0 and A0 mode Lamb waves. These EMATs will help improve the application of Lamb waves in nondestructive testing and evaluation.  
Figure 7: Experimental setup. The upper EMAT and lower EMAT are installed on the cross slides symmetrically, and printed circuit board (PCB) coils are series connected by conductor wire.
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT
### Adaptive Handover Decision Using Fuzzy Logic  
In this study, we propose an FL-based handover algorithm with dynamic HOM and dynamic TTT (abbreviated as FLDHDT) for 5G UDNs. The proposed FLDHDT can configure the appropriate handover parameters for UEs on the basis of their individual inputs. An FL controller is used to dynamically adjust both the HOM and TTT. The inputs to the FL controller are the signal strength and horizontal moving speed of user devices, and the outputs are the HOM and TTT values of each piece of UE. FLDHDT consists of three stages: data pre-processing, inference using FL, and handover decision.
# An Integrated Testbed for Power System Cyber-Physical Operations Training
## 5 Discussion and Future Perspectives  
In this paper, we performed a detailed analysis of CPS using a real-time simulator, with a focus on power system applications. To showcase the efficacy of the methodology and a description of the CPS security landscape, we examined four different modules--a distribution system operation and a SCADA system, and three cyber-attack scenarios. Each scenario is accompanied by pertinent background details, a mathematical formulation, and a discussion of the threat model and attack configurations involved. These case studies underwent simulation in both typical and atypical operating conditions to reveal their comprehensive effects on the entire system. The paper provides a comprehensive approach and valuable studies that provide guidelines for modeling threats in CPS. It also offers insights into designing, simulating, and evaluating detailed CPS models. The framework introduced in the paper is a valuable tool for conducting rigorous security analyses of CPS, ultimately contributing to a deeper and more thorough understanding of CPS security.  
By building upon the existing foundation, our objective is to push the boundaries and unlock new possibilities for the framework, enabling more comprehensive and sophisticated assessments of CPS security.  
* Module 4.1 can be expanded to include dynamic reconfiguration and self-healing capabilities during faults and/or power blackout events.
* Module 4.2 can incorporate multiple cyberattack scenarios, such as man-in-the-middle (MitM) attacks; false data injection attacks; vulnerability attacks; software-based attacks; passive attacks such as eavesdropping, port scanning, and network scanning; and signal intelligence attacks.
* Module 4.3 can add wide-area synchro-phasor monitoring, protection, and control to simulate the loss of generation or load due to cyberattacks on power system frequency and/or voltages.  
Figure 18: Comparison of the active and reactive power: (**a**) without attack; (**b**) with attack.  
* Module 4.4 can be further studied to develop simulation-aided risk assessments, real-time intrusion detection systems, and cyber defense mechanisms on DER-integrated smart grids.
## 4 Discussion  
The effect of the granulometry and the percentage of diatoms a given DE powder has and whether their modification can alter its insecticidal efficacy, was verified in this study. Formulations of DE originated from the same location, but with different particle size and diatom content, showed a variation in their insecticidal efficacy against the tested species. [PERSON] (2018) and [PERSON] (2018) were the first who suggested that the size of DE particles may play an important role in the insecticidal value of a given DE. Later, other authors added more data that confirmed this observation (2018; 2018; 2018). Through their experiments, the authors claimed that DE particles smaller than 45 \(\upmu\)m were significantly more effective against the targeted species. On the contrary, [PERSON] and [PERSON] (2018) through their experiments, stated that a mean particle size below 15 \(\upmu\)m and diatom shape were not correlated with insecticidal activity. It was found that DE formulations originated also from Elassona, Greece, had large particles (45-150 \(\upmu\)m) that were in some cases more effective than smaller ones in other tested DE (2018). This indicates that the insecticidal value of a DE cannot be predicted only from its particle size but from additional factors, such as larger available active surface and oil adsorption, the diameter of inner pores of particles, moisture content, SiO\({}_{2}\) content and tapped density (2018). However, in any case, large particles (\(>\)150 \(\upmu\)m) usually correspond to rocks, sand and very large diatoms, which should be removed during the formulation process, as they can reduce DE performance (2018). Hence, a first step in mined DE processing and purification is the removal of the larger particles as completely as possible. The next step should be the separation in such a way to reduce the fracture of the diatoms as little as possible and then to carefully separate the intact from the fractured diatoms--all in a cost-effective way (2018). In contrast with the large number of published studies of the insecticidal effect of DE against different pests, there are serious bibliography gaps for methods in diatom separation from other materials; therefore, more research must be carried out towards this direction. In the current study, the aforementioned separation was achieved by drying the mined material, grinding it into fine powder and using air to separate the diatoms mainly from clay and quartz. DE5 and DE6 formulations had a smaller mean particle size to DE1 and DE2, which can partially explain their higher insecticidal efficacy. DE1 was the formulation which contained mostly physically intact diatoms but had no effect on most of the tested species; DE2 and DE5 had semifractured diatoms, but only the later formulation was efficient; DE3 and DE6 contained only fractured diatoms with the later formulation to cause high mortality rates in the target species. Based on these data, we assume that the presence of fractured or no-fractured diatoms in the DE formulation has no, at least direct, effect on its insecticidal value.
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 3 Problem Reduction and Surrogate Model Building
### Can Problem Reductions Be Meaningful?  
The possibility of obtaining interesting insights from simplification depends on the structure of the problem--if there is any structure in it at all. If eliminating even one of the most superficial elements leads to a completely different outcome, there would be no hope for interpretability. Fortunately, real-world optimization problems exhibit an inherent structure that enables simplification without a significant loss of accuracy [30].  
This is exemplified by the Pareto principle [31], which suggests that, in most real-world cases, a small fraction of inputs often explains most of the variance in a system. If this principle holds for optimization problems, then structured reductions can preserve most of the decision-relevant information while improving transparency. Interpretable surrogates can then be built based on the extracted elements; even if they do not lead to the same optimal solutions, they can be used as a complement to build comprehensible explanations that can guide and support the decision process.  
This is confirmed by empirical observations across multiple domains, where only a small subset of variables and constraints tends to drive decision outcomes. In power systems planning, for instance, large-scale transmission expansion problems often contain thousands of constraints, but only a handful of them critically influence the final investment decisions [32]. Similarly, in portfolio optimization, a small subset of risk factors typically dominates asset allocation decisions, allowing for simplified models that retain most of the original problem's predictive power [33]. The structure hypothesis suggests that interpretable optimization can significantly reduce model complexity while preserving the essential characteristics of the decision problem.  
A long stream of literature has already addressed building simplified optimization problems, primarily to reduce computational complexity without sacrificing accuracy,rather than to improve interpretability. These methods are shown in Figure 3, and they will be discussed in the remainder of this section.
## 4 Discussion
Several studies have applied these methods during the evaluation of various motor assessments or primitive reflexes [43, 44, 45] where the results supported the consistency of these procedures. A similar approach was used when evaluating the reliability of the TASHUN test as well, where four independent experts have assessed the same 20 children based on video recordings. The RM ANOVA test did not show any significant differences for any of the reflexes, meaning that there was a high level of agreement between the assessments given by different evaluators. The intraclass correlation resulted in values of at least 0.980 and above for all TASHUN assessments. With ICC \(>\) 0.9, the agreement can be considered excellent [46]. Thus, it can be concluded that the ICC values for all TASHUN tests fall in the \"excellent\" category. In other words, the four independent evaluators provided highly consistent assessments. The 95% confidence intervals are very narrow, and their lower threshold is also above 0.9. This indicates that the ICC values are stable, further supporting the reliability of the tests. The TASHUN is a much-needed tool that is suitable for the assessment of three retained primitive reflexes in neurotypical children. Both its reliability and validity indicators make it a well suited measurement tool to be used by researchers and practicing professionals as well. Limitations of the assessment  
Our study had some limitations that need to be addressed. The most important limitation of this study is that it only included girls. It would be useful to perform a measurement in the opposite sex and compare the results. A larger sample size is needed to provide standardized scoring by age groups. Another limitation is that this test is not suitable to assess the changes in muscle tone during head movements; it only scores movements and shifts. It would be helpful to perform the measurement under laboratory conditions, for example, using a ZEBRIS (ZEBRIS, CMS-HS10, Medizintechnik GmbH, Isyn, Germany) 3D-movement-analysis system that can accurately determine the movement of body parts based on kinematic points and uses EMG to measure the changes in muscle tone. Thus, a measurement with the Zebris device could provide a basis for a more thorough understanding of the role of retained reflexes in motor control. However, future studies could fill these gaps and increase the reliability and validity of TASHUN test.
# Split-And-Delay Unit for FEL Interferometry in the XUV Spectral Range
## 4 Results and Discussion  
### Analysis of the SDU Displacement Jitter  
Using the WLI, we found that the movable grating is affected by environmental vibrations transmitted to the experimental chamber. The vibrations cause the grating to oscillate around the set position with amplitudes up to 100 nm. The frequency spectrum of this displacement jitter was analyzed as follows. A movie of the jittering interference pattern was recorded by the WLI camera at a high frame rate of 1380 Hz. In order to increase the frame rate from the maximum 60 Hz available for the full frame data acquisition, the frame was sized down to a small area of interest comprising  
Figure 3: Surface fragment of the SDU seen through the WLI camera. The image clearly shows horizontal facets of the gratings with white light interference fringes on their surface. Neighboring facets belong to different gratings. One of the gratings is displaced along the surface normal (\(z\)-direction) as is indicated by the shift of the interference pattern.  
only two 100 \(\upmu\)m wide lamellae (one from each grating). The positions of interferogram maxima \(x_{0}\) corresponding to \(s=0\) were found for each grating in every movie frame. The relative displacements \(\Delta z\) between the gratings were derived from these data as described in Section 2. The Fourier transform of \(\Delta z(t)\) directly gives the vibrational spectrum shown in Figure 4. As seen from the figure, the major frequency components lay below 100 Hz. The spectrum demonstrates that typical jitter periods (>10 ms) are much longer than the typical exposure time of the camera (-1 ms). In an experiment with femtosecond laser pulses, this allows the SDU displacement to be determined on a single-shot basis at a repetition rate limited solely by the camera characteristics (up to 60 Hz for the full frame acquisition in our case).
# UCMAC: A Cooperative MAC Protocol for Underwater Wireless Sensor Networks
## 3 System Description  
We assume that all nodes, which are equivalent in terms of capability and physical specification, are fixed on the seabed without timing synchronization and that they acquire propagation delay information from/to one-hop-distance neighbors through network initialization [35]. In addition, the nodes configure a distributed and partially connected mesh topology where they are connected to their one-hop distance neighbors. Each node can communicate directly with any of its one-hop distance neighbor. Regarding the means of cooperation, the DF signaling strategy [6] is applied, and a cooperator selectively joins the retransmission of erroneous packets as a replacement of source. We also assume that bit errors happen only in the data payload, not in control messages such as header and control packets, which are much smaller than the payload. In regard to the channel model, the empirical underwater acoustic channel model [36] is considered, which is in wide use in the literature. In this model, the underwater channel is characterized by attenuation that increases with signal frequency, noise of which power spectral density decays with the frequency, and signal-to-noise ratio (SNR) that varies over the signal bandwidth, etc.
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 3 Taxonomy of Input Data Types
### Taxonomy Presentation
Not to overcomplicate the taxonomy, we decided to classify the three-dimensional trajectory as spatial data as well. This way, we extended this group with solutions processing readings from tracked Inertial Measurement Units (IMUs), mobile devices, or mobile scanners. A specific case that we assigned to this group was a way of depth-aware RGBD image processing. In the solution found, images were not analyzed as matrices of data (pixel-examination was abandoned) but were used to construct a point cloud instead. The **Graph Structures** group combines methodologies processing input data represented as graphs of dependencies. In this case, the most popular solution was to use the spatial instances of building structures (rooms, corridors, areas) as graph nodes and their adjacency or connections as graph edges. The aspect determining if a found solution should be a member of this group was the usage of graph theory. If the data provided were formatted in such a way that graph theory could be used, the method was classified as processing graph structures. The **Feature Set** group is the most diverse. Because of this diversity, we separated it into six different subtypes: CAD-like data, energy consumption measurements, laser range measurements, mixed input data, radio signal fingerprints, and the combined group of sound, echo, chirping, and radio frequencies-based input. Subtypes differ significantly from one another. The Computer Aided Design (CAD) data were much like the models typically used by architects, e.g., exported from an already existing BIM project. Energy consumption measurements were based on home energy meters and the individual energy profile of the device. Laser range measurements represented all the input data that were not an occupancy grid and came from laser object-to-obstacle distance measurements. Mixed input data combined situations where multiple, not directly related parameters, such as a picture of the building facade and expected room sizes, were provided. Radio signal fingerprints were used as distance determinants between a signal source and reference stations. The last subtype combined relatively uncommon data--measurements obtained via sound analysis. An example was a smartphone's microphone readings after a chirp generation from the smartphone's loudspeaker.