esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D-Printed Accessories and Auxiliaries in Orthodontic Treatment
## 4 Discussion
### Benefits of 3D-Printed Accessories in Orthodontics  
When it comes to 3D-printed accessories and appliances in orthodontics, there are many benefits to consider. Making use of the digital workflow processes that enable dentists to maintain full control of the designed appliance and to completely bypass dental technicians in many cases, the in-house production of orthodontic appliances transforms and optimizes patient care as well as reducing the costs [92; 93; 94]. Patients can be provided with fully customized 3D-printed orthodontic brackets or other dental or maxillofacial devices that may better reflect the patient needs; this concept is also known as biological customization [95; 96]. Being able to produce removable appliances without any dental technician, without compromising the final result, brings new possibilities to orthodontists working in areas with scarce dental technicians, whose services may take too much time [97]. In fact, that is a reason why working in private practices and being exposed to an increasing patient load are both associated with the likelihood of possessing a 3D printer in an orthodontic office [98].  
In fact, 3D printing of various appliances is often only one component of digital workflows in orthodontic practices that need to be appreciated as a whole. Making use of 3D printing is but a terminal step; at first, digital data acquisition takes place, then data processing (often using Artificial Intelligence algorithms) is performed, and only then are countless appliances to be 3D-printed designed [1].
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 2 Fault Detection Method
### Feature Construction
#### 2.1.1 Fundamental Electrical Features (U1-U4)  
These feature vectors represent fundamental electrical parameters extracted from voltage and current waveforms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Notation** & **Formulation** \\ \hline U1 & \(In(t)\) & current magnitude \\ U2 & \(Vn(t)\) & voltage magnitude \\ U3 & \(\varphi In(t)\) & current angle \\ U4 & \(\varphi Vn(t)\) & voltage angle \\ U5 & \(d/dt(In(t))\) & derivative of current magnitude \\ U6 & \(d/dt(Vn(t))\) & derivative of voltage magnitude \\ U7 & \(d/dt(\varphi ln(t))\) & derivative of current angle \\ U8 & \(d/dt(\varphi Vn(t))\) & derivative of voltage angle \\ U9 & \(\left\|\mathbf{V}_{n}\right\|_{1}\) & \(\sum\limits_{j=1}^{360}\lvert\mathbf{S}^{j}\rvert\) \\ U10 & \(\left\|\mathbf{V}_{n}\right\|_{2}\) & \(\left[\sum\limits_{j=1}^{360}\mathbf{(S}^{j})^{2}\right]^{1/2}\) \\ U11 & \(\left\|\mathbf{V}_{n}\right\|_{\infty}\) & \(\max\limits_{j}\left|\mathbf{S}^{j}\right|\) \\ U12 & \(\left\|\mathbf{I}_{n}\right\|_{1}\) & \(\sum\limits_{j=1}^{360}\lvert\mathbf{S}^{j}\rvert\) \\ U13 & \(\left\|\mathbf{I}_{n}\right\|_{2}\) & \(\left[\sum\limits_{j=1}^{360}\mathbf{(S}^{j})^{2}\right]^{1/2}\) \\ U14 & \(\left\|\mathbf{I}_{n}\right\|_{\infty}\) & \(\max\limits_{j}\left|\mathbf{S}^{j}\right|\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature vectors for fault detection.  
* **U1--**Represents the absolute magnitude of the current waveform in each phase. Changes in this value can indicate the presence of a fault.
* **U2--**Represents the absolute magnitude of the voltage waveform in each phase. A significant drop in voltage is often a sign of a fault.
* **U3--**The phase angle of the current relative to a reference point, providing insights into power flow and fault-induced distortions.
* **U4--**The phase angle of the voltage, which can shift significantly during a fault event, particularly in unbalanced conditions.  
These fundamental features are critical in distinguishing between normal and faulty operating conditions in the distribution network.
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
## 1 Introduction
The local oscillator (LO) signal required for the down conversion of the received signals, as well as the calibration signal required for the precise characterization of the multiple receiver chains have to be connected to the distant sub-arrays. This paper compares different options for the distribution of the LO and calibration signals and contrasts their respective advantages and disadvantages for the example of a four-element array consisting of two identical sub-arrays mounted in the side-mirrors of a passenger car. For this installation scenario, design parameters like mechanical dimensions and the cost of the solution are the most important, along with electrical performance. If the achievable electrical performance dominates the optimization goal, the system design can lead to different conclusions.
## 5 Discussion
For example, applications such as edge-based video analytics use the resource-aware task allocation method; the distributed method is used in edge computing for IoT systems; and dynamic traffic management systems are using the dynamic task allocation method; predictive maintenance systems are based on machine learning task allocation methods; the energy-aware methods are usually used in IoT systems; and finally, the QoS-aware allocation method is used in real-time video streaming applications. The optimization algorithms and network types are depending on the application and the topology that needs addressing. Future research [91; 92; 93; 94; 95] in the area of task allocation in edge computing must deal with the difficulties of adjusting to the dynamic nature of each network and establishing a balance of trade-offs between several competing objectives such as energy efficiency, data privacy, security, latency, and QoS. In addition, it is necessary to create new techniques and algorithms that can manage the growing complexity of edge computing networks. Further study is required on hybrid strategies that mix centralized and decentralized methods to maximize work distribution while taking into account the special features of edge computing settings. Also, further research is needed in the field of task allocation optimization using machine learning methods. Lastly, research must continue to concentrate on enhancing the task allocation's efficacy and efficiency so that edge computing systems can manage workloads that are getting more complicated.
## Appendix A Stray Light Correction
Assuming a background proportional to the incident intensity:  
\[B(x,y)=\kappa I_{0}\] (A6)  
we can rewrite (4) as:  
\[T_{m}(x,y)=\frac{I(x,y)+B(x,y)}{I_{0}(x,y)+B(x,y)}=\frac{T(x,y)+\kappa}{1+\kappa}\] (A7)  
In a reference sample, i.e., a sample with a known absorption spectrum, in absence of effects related to the beam polarization and the crystals orientations [32], the absorption has to be the same everywhere except for a constant factor depending on the thickness (see Equation (A1)):  
\[T_{m}^{thick}(x,y)\propto T^{thin}(x,y)\ ;\ A_{m}^{thick}(x,y)\propto A^{thin}(x,y)\]  
Let us rewrite then Equation (A5) explicitly for a thick region on the reference sample:  
\[T_{m}^{thick}(x,y)=\frac{T^{thick}(x,y)+\kappa}{1+\kappa}=\frac{c\times T^{ thin}(x,y)+\kappa}{1+\kappa}\]  
In a thin region \(I(x,y)\gg B(x,y)\) and therefore \(\ A^{thin}(x,y)\cong\ A_{m}^{thin}(x,y)\). This allows us to write:  
\[A_{m}^{thick}=-log\Big{(}T_{m}^{thick}\Big{)}=-log\Bigg{(}\frac{c\times 10^{-A^{ thin}(x,y)+\kappa}}{1+\kappa}\Bigg{)}\cong-log\Bigg{(}\frac{c\times 10^{-A^{ thin}_{m}(x,y)+\kappa}}{1+\kappa}\Bigg{)}\] (A8)Measuring the absorbance both in a thick and in a thin region of a reference sample we can then estimate the two parameters c and \(\kappa\) fitting \(A_{m}^{thick}\) as a function of \(A_{m}^{thin}\). This has been done on a MnO\({}_{2}\) reference sample particle. The fit and its result are reported in \[T(x,y,\ E)=\frac{I(x,y,E)}{I_{0}(x,y,E)}=e^{(-\int u(x,y,E)dt)}=e^{(-\int \sigma(x,y,E)n(x,y)dt)} \tag{2}\]0 In the inset, the selected MnO\({}_{2}\) particle with the thin region in red is also reported. The thin region of the particle was selected using a measured absorbance threshold \(<\)0.43 on the maximum absorbance peak (\(\approx\)529.1 eV). At this value of the measured absorbance (0.43), the deviation with respect to the theoretical one was assumed to be small enough to be neglected (see Figure 10). Following this simple model, the background in the measured intensities is about 7.3% the incident intensity on the sample. Using this estimation, we have corrected all the measures reported in this work. The comparison of the phase map with and without background correction is reported in Figure 11 for P05 data. The absorbance peak value at \(\sim\)530.3 eV versus the one at \(\sim\)528.1 eV is plotted using a color-coded scale to represent the corresponding density of points in different regions of the plot. It is clear that the background correction redistributes the plot values along an almost straight line and expands towards bigger values the accessible measurable absorbance, as expected. Looking at Figure 11, we could speculate that if the linearity between the two components is maintained up to absorbance \(\sim\)1 without the background correction, this linearity is maintained at least up absorbance \(\sim\)1.5 after the correction is applied. It is easy to verify that this corresponds to expanding the accessible thickness by 30%.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 3 Proposed Scheme
### Limited CSI Feedback Sequence  
Figure 2 shows the proposed frame sequence, including limited CSI feedback, MU-MIMO-OFDM transmission, and acknowledgement subsequences. It is assumed that the signaling complies with the frame formats of IEEE802.11 ac [6]. In the limited CSI feedback subsequence, the AP first broadcasts a null data packet announcement (NDPA) and NDP frames to STAs for estimating channel responses on a subcarrier basis; each STA estimates channel responses from the received NDP frame and calculates the spatial correlation value between receiving antennas for each subcarrier, \(\rho_{n}(k)\), as:  
\[\rho_{n}(k)=\frac{\left|\boldsymbol{h}_{1,n}(k)\boldsymbol{\cdot}\boldsymbol{ h}_{2,n}(k)^{H}\right|}{||\boldsymbol{h}_{1,n}(k)||_{F}||\boldsymbol{h}_{2,n}(k)|| _{F}}. \tag{8}\]  
Figure 2: Proposed frame sequence, including limited channel state information (CSI) feedback, MU-MIMO-OFDM transmission, and acknowledgement subsequences based on frame formats of IEEE802.11 ac [6].  
Then, each STA feeds back just the channel response of the antenna with maximum norm to the AP via a compressed beamforming frame (CBF) when the spatial correlation value between the receiving antennas exceeds a threshold, \(\rho_{th}\). Note that this threshold is a parameter that determines the tradeoff between spatial diversity gain and CSI feedback reduction. The CSI feedback quantity is up to half that of the conventional value. Moreover, it is generally known that the channel capacity in MIMO transmission depends on the received signal power and spatial correlation. Since it can be expected that using the antenna with a higher received signal power improves the channel capacity, this paper selects the channel response of the antenna with maximum norm. Otherwise, each STA feeds full channel responses back to the AP. This paper assumes unitary matrix compression feedback, so the channel response is sent in the form of angles representing compressed beamforming feedback matrices, \(\mathbf{V}\), per subcarriers, as supported by IEEE802.11 ac [6]. The AP lets other STAs feed the channel response back to the AP in response of the beamforming report poll (BRP) frame. After receiving the channel responses, the AP calculates its pre-coding weights and allocated antenna combination using Equations (2)-(7). In the MU-MIMO-OFDM transmission subsequence, the AP then transmits the data frames to multiple STAs for the downlink MU-MIMO-OFDM transmission. Finally, in the acknowledgement subsequence, the AP and STA check for the existence of errors by block acknowledgment (BA) and BA request (BAR).
## 5 Experiments
### Implementation and Results  
Knowing that in order to obtain the reward it is imperative for the agent to interact with the environment, a significant part of the network is the reward function R. Keeping that in mind, it is requisite to put up a flawless reward function. This function should be able to respond correctly to the robot's arm to make sure that the parameters are suitable to resolve the robotic's arm inverse kinematics.  
In order to reach the targeted object, the actor-network takes the 3D position of each joint and the distance between the joints and the object as input and outputs the joint angles. The critic network then evaluates the last actor by taking as inputs the end effector 3D position calculated with the joint angles found and the object 3D position, which was found with the backward projection to determine the distance between the object position and the end-effector. The smaller the distance, the more significant the reward. Therefore, the reward function is represented as follows:  
\[R=-\sqrt{(P_{x}^{2}+P_{y}^{2}+P_{z}^{2})} \tag{7}\]  
\(P_{x},P_{y},P_{z}\) represent the positions needed to calculate the distance between the end effector and the object along the axis \(x,y\) and \(z\).  
In DDPG, the critic network and the actor network help the learning of the Q-value function and the policy, respectively. Both of these networks utilize target networks, which are updated using the trajectory \(\tau\). The Algorithm 1 below states the pseudo-code for training. For the exploration noise, a usual distributed decreasing random noise N is employed. It is noted to deliver fine results in training. As given in Equations (4) and (5), the critic and actor networks are, respectively, updated.  
```
1 Initialize Actor and Critic Networks randomly
2\(TargetActorNetwork\gets ActorNetwork\)
3\(TargetCriticNetwork\gets CriticNetwork\)
4 for\(i=1\)to EpMaxdo
5\(s\leftarrow\mathit{Reset}()\)
6 for\(j=1\)to StepMaxdo
7\(action\leftarrow Policy(s)\)// ActorNetwork gets action
8\(action\gets action+N\)// Exploration Noise added
9\(s^{\prime},r,done\gets step(action)\)\(ReplayBuffer\gets Store(s,a,s^{\prime},r)\)
10 ifReplayBuffer\(>\)BSizethen
11\(batch\gets RandomSample(ReplayBuffer,\mathit{Bsize})\)\(Q\gets Update(CriticNetwork,batch)\)// Updates Q-value function
12 Update(ActorNetwork, batch, Q) // Target networks updated by policy using T
13 ifdonethen
14 break
```  
**Algorithm 1**DDPG algorithm for inverse kinematics learning  
From the Table 2, it can be noticed that the accuracy is increasing with the training. The highest mean accuracy obtained is 95.5% at 400 episodes.
# Improved Text Summarization of News Articles Using GA-HC and PSO-HC
###### Abstract  
Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchical Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.  
Automatic Text Summarization (ATS); genetic algorithm; Hierarchical Clustering Technique (HCT); agglomerative clustering; extracted summary; Single Document Summarization Article
# Multi-Line Prefetch Covert Channel with Huge Pages
## 3 Background
### The Cache Architecture and Coherence Protocols: MESI  
Modern x86 processors feature a hierarchical cache architecture consisting of L1, L2, and L3 caches. The L1 and L2 caches are fast and private to each CPU core and handle requests rapidly. The L3 cache, or the last-level cache (LLC), is shared among cores, slower, and larger, operating on fixed-size data blocks known as cache lines.  
Memory access in this hierarchical architecture begins with the CPU checking the L1 data cache for the requested data. If the data is found (a cache hit), it is retrieved rapidly. If the data is not in the L1 cache (a cache miss), the search proceeds to the L2 cache and subsequently to the L3 cache if necessary. When the data is not available at any cache level, it is fetched from the main memory, incurring significant latency. This process highlights the critical role of the cache hierarchy in reducing memory access times and enhancing the overall system performance.  
Many modern Intel processors use an extension of the MESI protocol, such as MESIF for an Intel(R) Core(TM) i5-6500 CPU. Cache coherence protocols like MESI (Modified, Exclusive, Shared, Invalid) are crucial for maintaining data consistency across caches in multi-core processors. This protocol helps to ensure that multiple cores can manage shared data without integrity or consistency issues by transitioning cache lines through various states based on access patterns and data ownership changes:  
Modified (M): The cache line is present only in one core cache, has been modified (dirty), and is not in sync with the LLC.  
Exclusive (E): The cache line is present only in one core cache, has not been modified, and is exclusive to that cache.  
Shared (S): The cache line is present in multiple core caches but has not been modified, reflecting uniformity across caches.  
Invalid (I): The cache line is not valid in any core cache.  
This hierarchical architecture works in tandem with the MESI protocol to optimize both the performance and consistency, ensuring efficient data sharing and synchronization across multi-core systems.
###### Abstract  
Wide-bandgap gallium nitride (GaN)-based semiconductors offer significant advantages over traditional Si-based semiconductors in terms of high-power and high-frequency operations. As it has superior properties, such as high operating temperatures, high-frequency operation, high breakdown electric field, and enhanced radiation resistance, GaN is applied in various fields, such as power electronic devices, renewable energy systems, light-emitting diodes, and radio frequency (RF) electronic devices. For example, GaN-based high-electron-mobility transistors (HEMTs) are used widely in various applications, such as 5G cellular networks, satellite communication, and radar systems. When a current flows through the transistor channels during operation, the self-heating effect (SHE) deriving from joule heat generation causes a significant increase in the temperature. Increases in the channel temperature reduce the carrier mobility and cause a shift in the threshold voltage, resulting in significant performance degradation. Moreover, temperature increases cause substantial lifetime reductions. Accordingly, GaN-based HEMTs are operated at a low power, although they have demonstrated high RF output power potential. The SHE is expected to be even more important in future advanced technology designs, such as gate-all-around field-effect transistor (GAFET) and three-dimensional (3D) IC architectures. Materials with high thermal conductivities, such as silicon carbide (SiC) and diamond, are good candidates as substrates for heat dissipation in GaN-based semiconductors. However, the thermal boundary resistance (TBR) of the GaN/substrate interface is a bottleneck for heat dissipation. This bottleneck should be reduced optimally to enable full employment of the high thermal conductivity of the substrates. Here, we comprehensively review the experimental and simulation studies that report TBRs in GaN-on-SiC and GaN-on-diamond devices. The effects of the growth methods, growth conditions, integration methods, and interlayer structures on the TBR are summarized. This study provides guidelines for decreasing the TBR for thermal management in the design and implementation of GaN-based semiconductor devices.  
keywords: thermal boundary resistance; thermal management; GaN; SiC; diamond +
Footnote †: journal: Journal of Applied Physics
## 2 Design of Proposed Frequency Selective Surface with Harmonic Suppression
### The Reflection Coefficient and Transmission Coefficient Results of the Proposed FSS
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(h\) & 0.25 & \(w_{3}\) & 2 \\ \(d\) & 10.5 & \(w_{4}\) & 0.3 \\ \(l\) & 0.3 & \(w_{5}\) & 0.91 \\ \(w_{1}\) & 5.9 & \(w_{6}\) & 0.68 \\ \(w_{2}\) & 2.3 & \(g\) & 3.12 \\ \hline \end{tabular}
\end{table} 
Table 1: Optimized design parameters of the proposed FSS (unit: mm). Figure 4: Photographs of the FSS structures: (**a**) top view (grid + patch) (**b**) bottom view (loop). Figure 5: Simulation setup: (**a**) CST unit cell (**b**) ADS s-parameter. Figure 3: Equivalent circuit model for a band-pass filter and band-stop filter. a higher frequency. When the thickness of the substrate was increased, the overall resonant frequency shifted to a higher frequency. On the other hand, reducing the thickness of the substrate caused the resonant frequency to shift to a lower frequency. Through this parameter sweep study, we aimed to minimize transmission loss by using a substrate with the lowest possible dielectric constant and reducing the substrate thickness as much as possible.
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
###### Abstract  
Many forms of air pollution increase as science and technology rapidly advance. In particular, fine dust harms the human body, causing or worsening heart and lung-related diseases. In this study, the level of fine dust in Seoul after 8 h is predicted to prevent health damage in advance. We construct a dataset by combining two modalities (i.e., numerical and image data) for accurate prediction. In addition, we propose a multimodal deep learning model combining a Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). An LSTM AutoEncoder is chosen as a model for numerical time series data processing and basic CNN. A Visual Geometry Group Neural Network (VGGNet) (VGG16, VGG19) is also chosen as a CNN model for image processing to compare performance differences according to network depth. The VGGNet is a standard deep CNN architecture with multiple layers. Our multimodal deep learning model using two modalities (i.e., numerical and image data) showed better performance than a single deep learning model using only one modality (numerical data). Specifically, the performance improved up to 14.16% when the VGG19 model, which has a deeper network, was used rather than the VGG16 model.  
weather forecast; time series data; cropped image data; LSTM; CNN; VGGNet; multimodal deep learning +
Footnote †: journal: Applied Sciences
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 4 Simulation Analysis and Results
### FOP Parameters: Fill Factor and Fiber Diameter
Therefore, we choose two FOP schemes for subsequent analysis: **(1)** scheme one: \(d_{f}=4\)\(\upmu\)m, \(K=0.7\) and \(CLA=0.031\) pixel; **(2)** scheme two: \(d_{f}=6\)\(\upmu\)m, \(K=0.7\) and \(CLA=0.037\) pixel. Both FOP schemes meet the above conclusions. Compared with FOP scheme two, FOP scheme one can achieve higher CLA value of 0.031 pixel.