esa-sceva/satcom-chunk-collection huggingface dataset:
# Updates on Impact Ionisation Triggering of Thyristors
###### Abstract  
High voltage (HV) generators are used in multiple industrial and scientific facilities. Recent publications have demonstrated that triggering industrial thyristors (relatively slow switching devices) in overvoltage mode, also called impact ionization mode, significantly enhances their dU/dt and dI/dt characteristics. This novel triggering methodology necessitates the application of substantial overvoltage between the thyristor's anode and cathode, delivered with a swift slew rate exceeding 1 kV/ns. The adoption of compact pulse generators constructed from commercially available off-the-shelf components (COTS) opens up avenues for deploying this technology across various domains, including the implementation of high-speed kicker generators in particle accelerators. In our methodology, we employed commercially available high-voltage SiC MOSFETs along with a custom-designed fast gate driver. This driver was conceptualized based on the recent development of gate boosting techniques, featuring a driving voltage exceeding 600 V. The gate driver for these MOSFETs comprises three key components: a level-shifter with NMOS and PMOS transistors, a compact Marx generator with two avalanche transistors, and a GaN HEMT in a high input and low output impedance configuration. The proposed gate-boosting driver achieves a slew rate exceeding 1 kV/ns for the driving pulse. Furthermore, we demonstrate that with this driver, a 1.7 kV rated SiC MOSFET can produce an output pulse of 1.45 kV and a maximum slew rate of \(\approx\)2.5 kV/ns. This gate-boosting driver aims to minimize commutation times, achieves a slew rate of over 1 kV/ns, and handle higher loads, making it ideal for impact ionization triggering of industrial thyristors.  
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## 2 Materials and Methods
### Surface Characterization
#### 2.2.3 Analysis of Surface Roughness  
The roughness study was performed using the Sensofar S NEOX confocal-interferometric microscope (Sensofar Medical, Terrasa, Spain). SensoMAP Premium 7.4 was the software used. Measurements were made in accordance with ISO 25178: Geometric Product Specifications (GPS)-Surface texture: areal. A 20\(\times\) epi-illumination lens was used at a focal length of 4.50 mm and a green optical resolution of 0.32 \(\upmu\)m. Five measurements were made at the apical third of the implant, with a pre-established area dimension of 0.87 \(\times\) 0.66 mm\({}^{2}\) and a cut-off correction of 250 \(\upmu\)m (Figure 2). The quantitative roughness parameters used were profile mean arithmetic roughness (Ra), mean square deviation of the roughness profile (Rq), maximum peak height of the roughness profile (Rp), maximum valley depth of the roughness profile (Rv) and three-dimensional surface roughness (Sa), three-dimensional root mean square height (Sq), three-dimensional maximum peak height (Sp), three-dimensional maximum pit height (Sv). Mean and standard deviation were expressed in microns (\(\upmu\)m).  
Figure 1: Regions of interest of the surface characterization. Morphological and compositional measurements zone (blue rectangle). Roughness analysis zone (green rectangle).
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### Summary of State-of-the-Art and Our Proposed Model at a Glance  
In Table 1, we present a summary of the reviewed ionospheric models. Our new model Interpolated and Averaged Memory Model (IAMM), to be discussed in Section 3, is also presented and compared in this table for the sake of compactness. The IAMM concept is based on the periodicity of the solar cycle. Assuming that a large part of the ionospheric-related errors are influenced by the solar cycle [19; 21], it is possible to predict an averaged delay based on measurements from the previous solar cycle. Each solar cycle lasts for 11 years, and therefore, our model is based on 11 years worth of IONEX files from the IGS database, from the 1 January 2008 to the 31 December 2018. Details on the proposed IAMM methodology and creation will be reviewed in Section 3. The models in Table 1 are discussed based on their type (mathematical, empirical, or data-driven), whether they  
Figure 2: Illustration of IONEX file content.
# A Review of Oil Spill Remote Sensing
## 3 Use of Active Sensors for Oil Spill Detection and Mapping
### Radar
Radar imagery is susceptible to look-alikes and has wind speed limitations (1.5-10 m/s). Radar is now the standard for mapping oil offshore. Many satellite options with extensive coverage are now available to the user. Figure 7 shows radar imagery that was used for the Deepwater Horizon spill.
# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 2 The ANFIS Architecture for Mechanical Properties Prediction
### Subtractive Clustering  
Subtractive clustering [26] is a data clustering technique commonly used in ANFIS to generate an optimal number of fuzzy rules by identifying potential cluster centers within the input-output data. This method is particularly advantageous for ANFIS as it automates the selection of cluster centers, which serve as the basis for creating fuzzy rules, without requiring a predetermined number of clusters. Subtractive clustering calculates the density of each data point in a multidimensional space by measuring its distance to all other points.  
Points with higher density values are considered potential cluster centers, as they are surrounded by more data points. Once a cluster center is established, the algorithm reduces the density of nearby points, preventing the formation of closely spaced clusters. This process continues iteratively until all relevant clusters are identified, yielding an efficient structure for the fuzzy inference system. In ANFIS, subtractive clustering enables the model to efficiently capture nonlinear relationships in complex datasets, making it highly suitable for applications in predictive modeling, such as in additive manufacturing contexts where various parameters impact mechanical properties.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 2 System Model
### Simulation of Physical Layer Attacks
\[y_{\text{faded}}=h_{f}y\]  
where \(h_{f}\) represents the fading factor (typically \(0<h_{f}<1\)). **Denial of service**: Denial of service is a type of attack where the communication channel is overwhelmed with unwanted traffic to the point where the legitimate user cannot access the network. The result may vary from degraded network performance to complete interruption of communication. It could also be modelled by simulating a high volume of noise or irrelevant packets injected into the communication channel that actually congests the bandwidth. \[y_{\text{DOS}}=\sum_{i=1}^{N}n_{i}\]  
where \(N\) is the number of noise signals or irrelevant packets overwhelming the channel. **Man-in-the-middle (MitM)**: Man-in-the-middle (MitM) is an attack that involves an adversary who intercepts the communication between two legitimate parties. The interceptor may or may not alter the transmitted data before forwarding it. This type of attack poses a threat to data integrity and confidentiality because the communication can be altered or eavesdropped upon without any noticeable issues by the sender or receiver. In the case of a MitM attack, the received signal \(y\) is intercepted and changed by the attacker, then retransmitted to the intended receiver. \[y_{\text{MitM}}=f(y)+n_{\text{MitM}}\]  
where \(f(y)\) represents the modification of the original signal by the attacker, and \(n_{\text{MitM}}\) is any additional noise introduced during the attack.
## 3 Results
### Supportive Periodontal Treatment  
Six systematic reviews were identified, which analyzed the use of APWJT during supportive periodontal therapy [37; 38; 40; 42; 43; 44]. The systematic reviews for which no meta-analysis was performed reported similar results in terms of clinical outcomes compared to conventional mechanical debridement with hand instruments or ultrasonic devices [40; 43]. The four reviews including meta-analyses show heterogenous, statistically non-significant results when APWJT was used as a stand-alone device or as an adjunct to conventional treatment measures [37; 38; 42; 44]. All of the articles, which also dealt with patient-related outcomes, concluded that APWJT devices correlated with better to significant patient comfort and less treatment pain [37; 38; 40; 42; 43; 44] (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Test Measures** & **Control Measures** & **Clinical Outcomes** & **Microbiological Outcomes** & **Patient-Related Outcomes** \\ \hline \multirow{7}{*}{[PERSON] et al. [37]} & \multirow{7}{*}{2022} & \multirow{7}{*}{2022} & M-a APT (\(N=4\)) & \multirow{7}{*}{} \\  & & & & _PPD red:_\(\uparrow\) & \\  & & & (0.03 mm, & \\  & & & WMD; \(p=0.71\)) & & \\  & & 1. EPAP + SRP (HI & 1. SRP (HI or US) & \(\text{ROI}\); \(p=0.71\)) & & No m-a available: \\  & & or US or HI/US & & \(\text{ROI}\); \(p=0.62\),\(\uparrow\) & & _Patient comfort (\(N=3\))_: \\  & & 2. SPPT + EPAP + SRP (HI/US) & (HI/US) & \(\text{Rec.}\) n.a. & & \(\text{=}/\uparrow\) \\  & & & & _PPD red:_\(\uparrow\) & \\  & & & & (0.03 mm, & \\  & & & & WMD; & \\  & & & & _PPD_: = & \\ \hline \multirow{7}{*}{[PERSON] et al. [40]} & \multirow{7}{*}{2021} & \multirow{7}{*}{2021} & \multirow{7}{*}{2022} & No m-a available: & \multirow{7}{*}{} \\  & & & & _PPD_: = & \\ \cline{1-1}  & & or + HI + US & & _BOP_: = & \\ \cline{1-1}  & & 2. TPAP & & _Rec_: n.a. & \\ \cline{1-1}  & & & _CAL:_\(=\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the included systematic reviews on active periodontal therapy.(BOP: bleeding on probing reduction; CAL: clinical attachment level gain; EPAP: erythroid powder air polishing; GPAP: glycine powder air polishing; HI: hand instruments; M-a: meta-analysis; N: number of included studies; n.a.: not available; PPD red: periodontal pocket depth reduction; TPAP: trehalose powder air polishing; Rec: gingival recession; SPPT: simplified papilla preservation technique; SRP: scaling & root planing; US: ultrasonic scaler; WMD: weighted mean difference; \(=\): no difference; \(\uparrow\): (* significant) better results in the test group; \(\downarrow\): (* significant) worse results in the test group).  
The reviews reported that [PERSON] et al. showed no additional effect if APWJT was used as an adjunct [46]. On the contrary, it was reported that [PERSON] et al. showed a significant additional effect on BOP and PPD reduction [47]. The results on the reduction of clinical probing depth from the two studies could be included into a meta-analysis that resulted in no significant differences between the test and control groups [41].
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 1 Introduction
OAM detection can be classified into three categories, mode analysis based on field data [68; 69; 70], observation of OAM induced effects such as the rotational Doppler shift [71; 72; 73] and the beam reforming by adopting holographic technology [74]. The first approach needs to acquire all the three components of electric and magnetic fields so that the OAM can be calculated explicitly [68]. Alternatively, one can measure the phase gradient that is equal to the OAM index \(l\)[75]. In the third approach, by using a hologram, the OAM modes can be projected into a detectable Gaussian mode [76]. Geometric-phase metasurfaces can be used to implement the holograms and have the advantages of low profile and high tuning flexibility. In this review article, we concentrate on the current research of the OAM generation and detection by geometric-phase based metasurfaces. We start by the introduction of geometric phase. Then, we go over several novel metasurfaces for OAM generation. The induced geometric phases on the metasurfaces can be in both discrete and continuous formats. Their geometries, working principles and novel functionalities have been reviewed and discussed in detail. At last, a method for OAM detection is reviewed.
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 4 Algorithm Implementation for Surround System  
The algorithm for determining the INA channel impulse responses presented in Section 2 for the five-channel surround system with cardioid masking functions shown in Figure 7 implements the operations expressed by Equation (2):  
\[h_{INA\_Vi,m}=h_{i}\cdot C_{m}(arg(SI_{i})) \tag{2}\]  
where \(i=1\ldots N\) and denotes the sample number (\(N\) is the vector length of the recorded impulse response), \(m=1\ldots M\) and denotes the channel number (\(M\)--the number of speaker channels in a given playback system; in the experiment described here, \(M=5\)), \(h\) is the registered omnidirectional room impulse response, \(SI()\)--a complex function representing  
Figure 5: Components of the sound recording system and their mutual arrangement—real setup (**a**) and diagram (**b**). Figure 6: Spatial layout of the recording system and positions of the test sound source. the 2D intensity impulse response in the time domain. The operator \(arg()\) returns the vector angle on the plane. The cardioid masking function \(C_{m}\) has the following form:  
\[C_{m}(\alpha)=\frac{1}{2}(1+cos(\alpha+\varphi_{m})) \tag{3}\]  
The \(\varphi_{m}\) angle determines the initial spatial orientation of the function. In the present experiment, this was related to the direction of the individual microphones in the INA 5 system. It is \([-90^{\circ},0^{\circ},90^{\circ},-150^{\circ},150^{\circ}]\), corresponding to the channels [_Left, Central, Right, LeftSurround, RightSurround_], respectively. The resulting five-impulse responses are hereafter referred to as the INA_V (Virtual INA) response set and used for convolving channel signals. The processing steps are shown in Figure 8.  
Figure 9 shows the input data for the proposed algorithm obtained for loudspeaker positions #2 and #5. Figure 9a,c show the omnidirectional pressure responses from the MK5 microphone, and Figure 9b,d show the 2D intensity responses from the mechatronic probe for positions #2 and #5, respectively. Figures 10 and 11 show sets of five-channel impulse responses recorded directly by the SPL ATMOS INA5 microphones (Figures 10a and 11a) and those obtained from the proposed algorithm (Figures 10b and 11b). Figure 8: The proposed algorithm’s flow chart. Figure 7: Spatial distribution of cardioid masking functions corresponding to the angular arrangement of microphones in the INA 5 system. Table 1 lists the values of the maxima of the correlation function of the channel responses from the actual INA 5 with the omnidirectional and channel responses obtained after applying the proposed algorithm. In addition, the percentage changes in these indicators were calculated. The results are presented for all five speaker positions. Figure 9: Input data for the proposed algorithm for locations #2 an #5: omnidirectional pressure impulse responses (**a**,**c**); directional intensity impulse responses (**b**,**d**).
## 4 Sparse Signal Transmission Framework
### Background  
#### 4.1.1 Lossy Wireless Link  
The propagation of wireless signals with low-power radios is affected by several factors, such as space, time and and asymmetric connectivity [2], that contribute to the degradation of its quality. Consequently, wireless links in WSNs are often unpredictable and unreliable, especially lossy links in the transitional region, where the spatial characteristic of the wireless link presents three different communication regions, which are the connected region, the transitional region and the disconnected region [28].  
To verify the spatial characteristic of the wireless link, we conduct extensive experiments by using a pair of STM32W108 chips [29] putting them in our teaching building corridor for link testing. The transmitting power for the sensor node is \(-10\) dBm. The sender node transmits 256 packets to the receiver node in each contest, with the packet length being 13 bytes. The experiment is conducted 10 times on each distance, and we take the typical packet reception rate (PRR) value to show the spatial characteristic.  
As shown in Figure 1, taking the PRR as the index to indicate the link's quality, a higher PRR means better link quality. Figure 1 verifies the existing results for three different communication regions.  
Traditionally, high quality links in the connected region are used to ensure communication reliability. However, a high quality link also implies shorter physical distance and, hence, denser sensor node deployment with higher transmission power and, hence, a shorter network lifespan. On the other hand, using an unstable lossy link in the transitional region invokes error-correcting measures, such as ARQ, to retransmit lost data packets. ARQ-based error-correcting measures will increase transmission times and latency. To this end, both approaches are expensive and imperfect solutions. In this section, we will introduce a novel use of the lossy link by using the compressive sensing technique.  
Figure 1: The spatial characteristic of the wireless link.
## 5 Discussion
### Two Main Directions of Anisotropy Varying with Depth
#### 5.2.2 Origin of Electrical Anisotropy and the Role of Stress Field  
There is no published scientific work until now that studied crustal and mantle anisotropy in the Asal rift. However, in cases where consistent correlation between the geoelectric strike direction and the anisotropy direction exist as demonstrated in this study, it is likely a reasonable argument to suggest the presence of both paleo-stress field and actual stress direction in a geodynamic context such as the Asal rift where dynamic plates motion and magmatic activities co-exist and generate different direction of stress field [3; 13; 15; 45]. In other words, stress field can change as plates motions can change their direction. Thus, it is likely possible to advance that electrical anisotropy direction in the Asal rift is mainly associated with the dominant stress field of plates motion. Indeed, the Asal rift is located at the junction of the three diverging plates (Somali, Arabia and Nubia), which result the westward propagation of Aden ridge, the jump of the Red Sea ridge into Afar depression and their interaction can change, rotate or alter the direction of plate motion over the geological time. The existence of a relic strain, possibly resulting from ancient continental collision processes, is interpreted to have induced a general alignment of olivine down to mantle depths beneath the continental rigid plate which in turn generally produce electrical anisotropy direction coincident with the surface trend deformation pattern that was preserved since the last tectono-thermal event responsible for the deformation [46]. Additionally, chemical and petrological analysis of Asal rift lavas indicated a presence of olivine with size up to 1 cm [47] and it is likely possible that hydrous olivine may exist in the upper mantle of the Asal rift that can cause electrical anisotropy as suggested in similar geodynamic context by [48]. Thus, the preserved traces of past and remanent directions of magmatic-tectonic activities can be determined from joint analysis of the geoelectric strike and anisotropy direction as proposed previously [46; 48; 49]. Moreover, combined magmatic-tectonic activities play an important role in defining the stress field direction where tectonic activity accommodates the extension, the preferred emplacement direction of recent volcanic materials coming from the cooling magmatic intrusion. Moreover, both magmatic-tectonic activities can likely change the orientation of old subsurface geological materials to the direction of the existing maximum stress that can be parallel to the maximum conductivity direction.
## 4 The Architecture of the ASI (Figure 1)  
This paper proposes an ASI whose functions are to interpret the on-line data being monitored and relate this data to the record of system performance under adverse conditions which has been recorded off-line. From its embedded algorithms, the ASI predicts the performance of the system under similar stresses. Those operations are all carried out on a selected location termed an Area of Vulnerability.  
The concept of an ASI as monitor, interpreter and predictor is realized as shown in Figure 1 by means of the following three components, namely:  
- The Input Module  
- The Predictive Module  
- The Output Module.  
These modules are described in some detail in this section.
## 3 Results and Discussion  
### Material Characterization  
#### 3.1.1 SEM-EDS Analysis  
The electron microscopic morphology of the SiO\({}_{2}\) carrier (75-150 \(\upmu\)m, Figure 2a,b) revealed that it exhibited a high degree of sphericity and a smooth spherical surface. The SiPMA surface was also smooth, as shown in Figure 2c. The morphology of the SiPMA-TEPA adsorbent is depicted in Figure 2d; the surface of SiPMA-TEPA appeared slightly rougher than that of SiPMA.  
Figure 2: SEM–EDS images of the surfaces of SiO\({}_{2}\) (**a,b**), SiPMA (**c**), SiPMA-TEPA (**d**), and SiPMA-TEPA-U (**e**).  
The elemental distribution results, as shown on the right side of the energy spectra in Figure 2d,e, and the elemental analysis results (Table S1) indicated that the elements C, N, O, and U were evenly distributed within the silica particles. This suggests that SiPMA-TETA successfully adsorbed uranium. These findings confirm the successful preparation of the silica-based polyamine composite resin.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 3 Proposed Localization Algorithm
### TDOA Localization Algorithm
#### 3.1.1 TDOA Positioning Principle  
In order to achieve localization, nodes in the WSN can be divided into two types: beacon nodes and unknown nodes, where beacon nodes are also called anchor nodes or reference points, and unknown nodes are also called ordinary nodes. Beacon nodes are nodes with known location information, and unknown nodes are nodes without location information. The principle of TDOA localization is shown in Figure 1, where an unknown node broadcasts a signal to the surrounding area to anchor nodes, and the anchor nodes in the surrounding area will successively receive the signal sent by the unknown node. Then the distance difference from the unknown node to the anchor nodes can be calculated through the time difference. For example, if we know the distance difference between the unknown node MS and the anchor nodes BS1 and BS2, we know that the unknown node is on the hyperbola with BS1 and BS2 as the focal points, and similarly, MS is on the hyperbola with BS2 and BS3 as the focal points, and the coordinates of the unknown node can be determined by solving for the intersection of the hyperbolas according to some known conditions.  
Figure 1: TDOA positioning principle.