esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
## 2 Image Reconstruction
### PDEM Method  
Some of the authors extended the one-parameter family of the power-divergence measure [20; 21; 22; 23] by introducing an additional parameter and applying it to the objective function in optimization, therefore deriving a new iterative reconstruction method [14]. Since the extended power-divergence (EPD) measure family includes KL-divergence, the PDEM method based on EPD is a natural extension of the MLEM method. It has been shown that by setting the two parameters in accordance with the level of measurement noise, the PDEM method can reconstruct high-quality images that are robust against noise, even under significant measurement noise. In other words, the proposed method outperforms conventional methods in terms of image quality and is particularly effective in reducing radiation exposure in X-ray CT diagnostics.  
The PDEM method is an iterative algorithm that optimizes the following EPD measure family, which includes two parameters:  
\[\Phi_{\gamma,n}(y,Az):=\sum_{i=1}^{I}\int_{y_{i}}^{A_{i}z}\frac{s^{\gamma}-y _{i}^{\gamma}}{s^{\gamma n}}ds \tag{5}\]  
and is described by the following recurrence relation:  
\[z_{j}(n+1)=z_{j}(n)\frac{\sum_{i=1}^{I}A_{ij}\bigg{(}\frac{y_{i}}{\big{(}A_{i }z(n)\big{)}^{\alpha}}\bigg{)}^{\gamma}}{\sum_{i=1}^{I}A_{ij}\bigg{(}\frac{A_{ i}z(n)}{\big{(}A_{i}z(n)\big{)}^{\alpha}}\bigg{)}^{\gamma}} \tag{6}\]for \(n=0,1,2,\ldots,N-1\), where \(j=1,2,\ldots,J\), and the initial value is set to \(z(0)=x^{0}\). The parameters \(\gamma>0\) and \(\alpha\geq 0\) are included in Equation (6). The PDEM algorithm in Equation (6) coincides with the MLEM algorithm in Equation (4) when \(\gamma=1\) and \(\alpha=1\). Furthermore, when \(\alpha=1\), it becomes an iterative algorithm with the generalized Hellinger distance [24] as the objective function. Since Equation (5) encompasses many distance or divergence measures, including KL-divergence, Neyman's \(\chi^{2}\) distance, and generalized Hellinger distance, the parameter family derived through the optimization of Equation (5) is expected to exhibit diverse performance characteristics for image reconstruction. In fact, experiments have demonstrated that by selecting the appropriate parameters, the PDEM method can exhibit robustness against measurement noise and achieve higher-quality reconstructed images than the MLEM method [14]. It is important to note that the degree of improvement provided by PDEM over MLEM is likely to be dataset-dependent. Specifically, the advantage of PDEM becomes more pronounced when dealing with noisy data or under conditions where the number of projections is limited. In cases where the data quality is high, and noise is minimal, the primary advantage of increasing the parameter values in PDEM lies in achieving faster computation compared to MLEM, with no significant improvement in image quality.
## 5 Simulation and Analysis  
### Simulation Settings  
To verify the validity of our proposed CPD schemes, MATLAB and STK are used for our co-simulation. In the envisioned MLSCN, it consists of satellites, HAPs, UAVs, and ground stations. Specifically, the satellite layer uses the Iridium constellation, six polar orbits with 11 satellites in each orbit. Each satellite can connect with two adjacent satellites in the same orbit and two satellites in the adjacent orbits. The intra-track link can be established continuously, and the inter-track link will be closed above two polar regions [40].  
The HAP layer is a mesh network of 5 \(\times\) 5 nodes in the range of [29.38\({}^{\circ}\) N, 40.86\({}^{\circ}\) N], [85.55\({}^{\circ}\) E, 113.35\({}^{\circ}\) E], and its height is 20 km [37]. A total of three data transfer tasks are performed during simulation, and thus there are three UAV clusters in the network, and each of cluster has 6, 7, and 7 UAV nodes. The flight range of each cluster is a circle with a radius of 200 km, and the centers of the three clusters are (32.32\({}^{\circ}\) N, 112.371\({}^{\circ}\) E), (40.26\({}^{\circ}\) N, 112.55\({}^{\circ}\) E) and (38.11\({}^{\circ}\) N, 81.55\({}^{\circ}\) E), respectively. Three ground stations are separately located at Xi'an (34.45\({}^{\circ}\) N, 109.50\({}^{\circ}\) E), Mi'yun (40.45\({}^{\circ}\) N, 116.86\({}^{\circ}\) E), and He'tian (37.11\({}^{\circ}\) N, 79.92\({}^{\circ}\) E). For simplicity, we set the data transfer rate to 1 Mb/s and the packet size to 1 Mbit [41], which means that the transmission of a packet will occupy one second of a contact. The default lifetime of a packet is set to 4200 s. In the MPSO, the group size is set to 100. The inertia weight, the minimum inertia weight, the maximum inertia weight, the local learning factor, and the global learning factor are set to 0.6, 0.5, 0.8, 2, and 2, respectively. Other parameters of the related satellite network are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Start time** & **4 January 2018 04:00** \\ \hline Inclination (degree) & 86.4 \\ Height (Km) & 780 Km \\ Orbit Planes & 6 \\ Satellites & 66 \\ RAAN (degree) & 31.6 (Co-directional) 22 (Reverse) \\ \hline \end{tabular}
\end{table}
Table 2: Orbital parameters in satellite network.  
The 3D and 2D views of the system network in STK are shown in Figure 6.
## 2 The Characteristics of Endodontic Instruments
### Application of 3D Optical Scanning for Evaluating Instrument Design and Dimensions  
Historically, researchers have faced challenges in accurately determining the dimensions of endodontic instruments. Recently, the 3D optical scanning method has emerged as a promising approach to assess instrument dimensions and geometries independently [56]. This method eliminates the need to rely solely on manufacturer-provided information, allowing for more accurate and objective evaluations. This method utilizes a high-resolution optical scanner in a laboratory setting to capture detailed surface images of NiTi instruments, enabling the creation of reliable virtual models. Through this non-destructive process, the 3D scanning method offers precise measurements of various parameters, including perimeter, area, long axis length, and core diameter, at any cross-section level (Figure 4). Moreover, it provides valuable information on the instrument's overall volume and surface area. The utilization of this approach can be considered a valuable auxiliary methodology as it quantifies parameters that have significant relevance to the performance of the instrument.  
Figure 4: 3D models of the ProTaper Universal F3 **(left)** and ProTaper Next X2 **(right)** endodontic instruments created using the 3D optical surface scanning method. The models are artificially texturized with a metal shader to simulate real instruments and depict the complete instrument **(top)**, close-up views of the active blade **(middle)**, and the tip **(bottom)**. The yellow color highlights the details of the STL.  
Figure 3: Micro-CT evaluation of shaping ability. Illustrative images displaying the root canal system of 6 mandibular molars before (depicted in green) and after (depicted in red) preparation, highlighting the regions of the root canal walls that remained untouched by the instruments.
## 4 Aerosol Parameters in CO2 Retrieval
### Aerosol Information  
Although all 16 aerosol parameters were included in the CO\({}_{2}\) retrieval for our analysis, it is possible that TanSat NIR hyperspectral measurements only contain sufficient information for certain aspects of these parameters. Therefore, we verify the DFS of each aerosol parameter in the CO\({}_{2}\) retrieval shown in Figure 6 to determine the parameters that could be retrieved simultaneously with CO\({}_{2}\). The total DFS of the aerosol parameters for the UI, BB, and DD is six to seven, indicating that around six or seven independent aerosol parameters may be retrieved together with CO\({}_{2}\). For MA, only four to five retrievable aerosol parameters exist. A comparison of the DFS among the aerosol parameters reveals that the DFSs of the AOD, H\({}_{\text{P}}\), and H\({}_{\text{w}}\) (i.e., the first three) are large (mostly close to 1.0) for all aerosol types at any viewing geometry, while the information concerning the other parameters varies significantly according to aerosol type and observation geometry. In any case, it appears that AOD, H\({}_{\text{P}}\), and H\({}_{\text{w}}\) can be retrieved from the ACGS hyperspectral observations. Additionally, the aerosol DFS over soil only has a 0.1-0.2 difference from that of a vegetation surface. There is almost no distinction in the decision of how much aerosol parameter information could be derived from satellite measurements over different surface types. Therefore, the figures over soil are not shown in the main text, but are provided in the Supplementary Materials.  
Figure 6: (**a–p**) Degrees of freedom for signal (DFS) of each aerosol parameter in the CO\({}_{2}\) retrieval, if all aerosol parameters are retrieved simultaneously over vegetation. The x-axis represents 16 aerosol parameters, as shown from bottom to top in Figure 5a. The four rows are the four aerosol types and different columns illustrate the DFSs at different SZAs. The number over each subfigure is the total DFS of all aerosol parameters.  
Overall, four to seven pieces of independent aerosol information could be retrieved from the TanSat observations for the different aerosol types. In other words, the simultaneous retrieval of four to seven aerosol parameters during CO\({}_{2}\) retrieval is more reasonable than retrieving all 16 aerosol parameters. If the aerosol parameters in the CO\({}_{2}\) retrieval state vector are combined in different ways, the induced interference errors may also change; additionally, the forward model parameter errors from the nonretrieved aerosol parameters should be considered. Therefore, even though the number of retrievable aerosol parameters is determined, the requirement to select and retrieve four to seven aerosol parameters simultaneously is also a significant problem. The impact of changing the aerosol models used in the XCO\({}_{2}\) retrieval is analyzed in the next section.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 4 Experiment 2: Listening Effort for High-Intelligibility Oesophageal Speech
### Materials and Methods
#### 4.1.4 Cognitive Tasks  
We looked for the effect of cognitive capacities on LE and WER. We performed a Flanker task [47] to measure the selective attention ability and a backward digit span task [48] for working memory capacity.  
In the Flanker task, participants were presented 24 congruent (\"\(<<<<\)\"), 24 incongruent (\"\(>><<\)\"), and 24 neutral (\"\(--<---\)\") stimuli. They were asked to focus on the middle symbol and correctly identify it by pressing \"\(<\)\" or \"\(>\)\" as quickly as possible. Their responses and reaction times were recorded.  
In the backward digit span task, the experimenter read a lists of digits and the participant was asked to repeat them in reverse (For example, experimenter: '3 2 9 5'; participant: '5 9 2 3'). The digits were read in an even tone at intervals of approximately one second. The experimenter started with the set of three-digit lists. If the participant was able to correctly recall 5 three-digit lists out of 6, they graduated to the set of four-digit lists. This went on until the participant could no longer recall at least 5 lists in a set or until they reached the final nine-digit list set. The digit span score was the maximum number of digits in the list where the participant could recall 5 lists correctly.
## 5 Use Case of 6G-OPENSEC-Security
### Applying ML/DL and Security Policies
#### 5.3.1 Security Data Analytics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subcomponent** & **Purpose** & **Key Features** & **Interfaces** \\ \hline Data Processing \& & Prepares/reconstructs the & & **Input**: Data collected from \\ Transformation Engine (DPTE) & collected data. & Collection of data. & Security Data Collection. \\  & & & **Output**: Analytics-ready data. \\ \hline \multirow{6}{*}{Anomaly Detection Engine (ADE)} & Identifies anomalies in the data & & \\  & that might indicate security & Statistical Analysis & **Input**: Analytics-ready data. \\  & threats, breaches, or & Machine Learning Models & **Output**: Identified anomalies, \\  & other significant deviations from & Threshold Settings \& Alerts & insights, or alerts. \\  & expected patterns. & & \\ \hline \multirow{3}{*}{Real-time Analytics \& Stream Process Engine \& & **Input**: Real-time insights and \\ \& Stream Processing (RASP) & crucial for detecting ongoing & Real-time Dashboards & analysed streams \\  & security threats. & & **Output**: Dashboards and \\ \hline \multirow{3}{*}{Alert Module (AM)} & Serves as a communication & Real-time alert & **Input**: Processed data. \\  & interface with Security Decision, & Alert Generation & **Output**: Alerts. \\  & sending alerts for detected threats. & & **Input**: Analytical results and \\ \hline \multirow{3}{*}{Reporting Module (RM)} & Prepares analysed data to & Report Generation & **Input**: Analytical results and \\  & generate reports. & & **Input**: Insights. \\  & & generate reports. & **Output**: Reports and \\  & & & processed data. \\ \hline \multirow{3}{*}{Feedback \& Optimization Engine (FOE)} & Improves the analytical processes based on feedback and & & **Input**: ML model testing \\  & continuously optimises the & & **Output**: Optimised models \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Security Data Analytics modules and their functionalities. Figure 9: Security Data Analytics internal workflow.
# A Metal-Insulator-Metal Deep Subwavelength Cavity Based on Cutoff Frequency Modulation
## 1 Introduction  
Surface plasmon polaritons (SPPs), electron oscillations upon coupling with photons, appear at dielectric-metal interfaces by coupling with photons [1]. Recently, many researchers have investigated the miniaturization of photonic devices by using SPPs because of their ability to manipulate photons in subwavelength-sized cavities and waveguides beyond the diffraction limit, which is a fundamental size limit of dielectric photonic devices [2; 3; 4; 5]. In particular, plasmon cavities are exploited in deep subwavelength volume lasers [6; 4], switches [7; 8], index sensors [9; 10], and plasmonic optical filters [11; 12; 13].  
Metal-insulator-metal waveguides can strongly confine SPPs in thin insulators (even in several nanometer-scale dielectric gaps [14; 15; 16]), where it has been reported that visible light is confined in an ultrasmall dielectric layer. For this reason, MIM waveguide-based cavities were proposed to realize compact devices with various structures, such as disks [17; 18], rings [19; 20], and blocks [21; 22; 23]. As the dielectric layer between metals becomes thinner, the propagating SPP mode has a larger \(k\) (wavevector), thereby miniaturizing the physical size of the photonic device [13; 24; 21]. In MIM-based devices, there are two main optical losses: the metal absorption loss and the radiation loss. Although suppression of the absorption loss has been widely studied by introducing high-index dielectric layers inside of low-index dielectric layers [25], radiation loss into free space has not been investigated, despite the fact that it represents a large portion of the total loss.  
In this study, we propose MIM cavities made by using a cutoff frequency mechanism that only appears in the dispersion relation of the first-order waveguide mode [13]. The cutoff frequency is strongly dependent on the effective size of the waveguide mode and, therefore, it can be modulated by varying the waveguide width, dielectric thickness, and index of the dielectric. We investigated the optical properties of cavities consisting of two mirror waveguides and a sandwiched waveguide by modulating the cutoff frequency with three different techniques.  
The mode gap, which is due to the cutoff frequency difference, prevents radiation loss along the waveguide direction. Alternatively, the higher-order cavity mode observed in a longer cavity has more intensity nodes than the fundamental cavity mode, thereby suppressing radiation loss in the direction orthogonal to the waveguide by multipole cancellation [26].
# Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level
## 2 An Overview on LoRa and LoRaWAN  
A LoRaWAN network is based on two protocols: LoRa, which regulates the physical layer communication between the EDs, and LoRaWAN, the medium access control protocol used on top of LoRa. In the following section, we will investigate more deeply the protocols.
## 1 Introduction
### Related Work  
#### 1.3.1 IVR Education  
IVR has proven to be a transformative tool in education, providing intuitive and experiential learning environments that facilitate the understanding of complex scientific concepts [22]. Unlike traditional teaching methods, IVR enables active engagement with abstract principles by allowing students to interact dynamically with simulations, enhancing conceptual retention and cognitive processing [23]. The interactive nature of IVR environments surpasses many of the limitations of traditional methods, particularly in fields like physics and mathematics, where visualization plays a crucial role in understanding concepts such as motion, force, and trajectories [24]. Research has shown that IVR enhances student engagement and motivation, offering a more personalized and adaptive learning experience [25]. The ability to manipulate variables, such as velocity, acceleration, and angles in projectile motion, in real time enables students to explore cause-and-effect relationships in an immersive environment. This hands-on approach allows for deeper conceptual understanding, as students are no longer passive recipients of information but active experimenters within a virtual space. Furthermore, the integration of gamification elements in IVR has been linked to higher retention rates, suggesting that IVR-based learning could provide a sustainable alternative or complement to conventional pedagogical models [14; 26].
# Atmospheric Effects on OFDM Wireless Links Operating in the Millimeter Wave Regime
###### Abstract  
The development of millimeter wave communication links and the allocation of bands within the Extremely High Frequency (EHF) range for the next generation cellular network present significant challenges due to the unique propagation effects emerging in this regime of frequencies. This includes susceptibility to amplitude and phase distortions caused by weather conditions. In the current paper, the widely used Orthogonal Division Frequency Multiplexing (OFDM) transmission scheme is tested for resilience against weather-induced attenuation and phase shifts, focusing on the effect of rainfall rates. Operating frequency bands, channel bandwidth, and other modulation parameters were selected according to the 3 rd Generation Partnership Project (3 GPP) Technical Specification. The performance and the quality of the wireless link is analyzed via constellation diagram and BER (Bit Error Rate) performance chart. Simulation results indicate that OFDM channel performance can be significantly improved by consideration of the local atmospheric conditions while decoding the information by the receiver demodulator. It is also demonstrated that monitoring the weather conditions and employing a corresponding phase compensation assist in the correction of signal distortions caused by the atmospheric dispersion, and consequently leads to a lower bit error rate.  
Keywords:MMW; OFDM; 5G; 3 GPP; atmospheric radio propagation +
Footnote †: journal: Electronics Letters
# The Design of a High-Intensity Deuteron Radio Frequency Quadrupole Accelerator
## 5 Electromagnetic (EM) Analysis of RFQ
### Mode Separation  
The four-vane RFQ possesses many harmonics, encompassing adjacent resonant frequencies apart from the operating mode. This includes dipole modes (TE\({}_{1\text{ln}+}\), TE\({}_{1\text{ln}-}\)) and quadrupole modes (TE\({}_{2\text{ln}}\)). The quadrupole modes in Figure 21 explain the H field of TE\({}_{2\text{ln}}\) and TE\({}_{2\text{ln}}\) modes. The color of the different quadrants in the plot indicates that the left one is TE\({}_{2\text{ln}}\) mode and the right one is TE\({}_{1\text{ln}}\) mode. Given the lowest mode of fixed frequency, longer vanes reduce the spacing between the modes. The properties can be described by the following equations [22, 28], where \(l_{v}\) is the vane length:  
\[\begin{array}{c}f_{TE_{2\text{ln}}}=\frac{c}{2\pi}\sqrt{\left(\frac{2\pi}{c} \cdot f_{TE_{2\text{ln}}}\right)^{2}+\left(\frac{n\pi}{l_{v}}\right)^{2}}\\ n=0,1,2,\dots\,\,\,k_{n}=\frac{n\pi}{l_{v}}\end{array} \tag{3}\]  
\[\begin{array}{c}f_{TE_{1\text{ln}}+}\cong f_{TE_{1\text{ln}}(\text{+1})-} \cong\frac{c}{2\pi}\sqrt{\left(\frac{2\pi}{c}\cdot f_{TE_{1\text{ln}}-}\right) ^{2}+\left(\frac{(n+1)\pi}{l_{v}}\right)^{2}}\\ n=0,1,2,\dots\,\,\,k_{n}=\frac{n\pi}{l_{v}}\end{array} \tag{4}\]  
Table 7 shows the cavity frequency of different modes. The mode spacing is best when the electrode length is an even multiple of the RF wavelength. For this study, the length of the RFQ electrode was chosen to be approximately 3.7 m, which is about twice the RF wavelength. The corresponding mode separation was found to be 3.3 MHz, which is sufficient for stable operation of the cavity.  
Figure 19: Tuner distribution in the RFQ cavity.  
Figure 20: Frequency shift as a function of tuner depth.
# Exploiting Data Similarity to Improve SSD Read Performance
## 4 Design
### Content-Aware Page Allocation Scheme
#### 4.2.2 Data Content Comparison  
Due to the limited DRAM space in SSDs, performing byte-to-byte comparisons results in significant time consumption and computational overhead. In order to mitigate this, we propose dividing the data content comparison process into three parts. In the first phase, we employ the bloom filter, which is a hash-function-based algorithm used widely in data deduplication systems [28], to store written data information. During the page allocation procedure, our DS-FTL verifies whether the written data contains the same content on the target channel by employing the bloom filter. Bloom filter is a space-efficient probabilistic data structure characterized by an m-bit array and k-independent hash functions \(h_{1},h_{2},...,h_{k}\), where the value domain of the hash function is \(1,2,...,m\). Suppose the input data set \(S=X_{1},X_{2},...,X_{n}\) has n elements, each element is mapped to a different position of the array by each hash function, and the value of the corresponding position is set to \(1\), \(x\in S,Bf[h_{i}(x)]=1,i\in[1,k]\). Thus, if the element \(y\) has a value of \(1\) at all positions mapped by all hash functions, then it is highly likely that this element has existed in the set \(S\), as shown in Figure 7. On the contrary, if there is any position with a value of \(0\), the element must not be in the set \(S\).  
Figure 6: Write operation process.  
Figure 7: Bloom filter design.  
In the second phase, we conduct byte-to-byte comparison further to validate the data segment having the same content in the channel, and this time-consuming operation is performed asynchronously in idle time.  
After the fast approximate comparison using the bloom filter, DS-FTL compares the data of the write request with the data comparison set. The data comparison set consists of PPNs whose data are hashed in the same position in the Bloom filter but are not identical. It is worth noting that DS-FTL periodically performs the byte-to-byte comparison task in the background or during idle periods, consuming only limited CPU and memory resources.  
In the third phase, after the data content comparison is completed, DS-FTL updates the mapping table, which is discussed in Section 4.3.1.