esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Microarchitectural attacks exploit target hardware properties to break software isolation techniques used by the processor. These attacks are extremely powerful and hard to detect since the determination of the program execution's impact on the microarchitecture is at the same time not precisely understood and not easily observable at the software layer. Some approaches have attempted to benefit from existing hardware to better understand and detect the microarchitectural attacks (i.e., Hardware Performance Counters or Arm CoreSight), but such hardware was not meant to be used for cybersecurity, with reduced choice on observable signals and limited throughput of information. In this paper, we propose MATNA, an open and adaptive reconfigurable hardware/software co-designed framework. Combining fine-grained analysis of microarchitectural signals and software support, MATNA allows to design and assess detection mechanisms for attacks by characterizing their microarchitectural effects--in particular, microarchitectural attacks, but also some high-level attacks such as return-oriented programming attacks. The paper also describes a prototype implementation, built with a RISC-V softcore processor Rocket running Linux 4.15 on a Virtex-6 FPGA. We successfully used MATNA to analyze cache side-channel attacks and build attack detection logic from two different perspectives: instruction-based and memory-access-based. We also successfully detected return-oriented programming attacks by exhibiting a specific behavioral pattern on the microarchitecture.  
attacks detection; hardware/software co-design; side-channel attacks; return-oriented programming; RISC-V
# Exceptional Points in Non-Hermitian Photonic Crystals Incorporated With a Defect
## 2 Non-Hermitian Photonic Crystals
Figure 2b gives the transmittance varying with the normalized frequency. Light could not transmit the PCs as the frequency of wave was in the range of the bandgap, except for the defect mode. As \(\sigma=1\), the incident wave could overall pass through the structure for the defect mode, i.e., \(T=1\). The transmittance was \(T<1\) for the defect mode as the coefficient was \(\sigma>1\), while the transmittance was \(T>1\) for the defect mode as the coefficient was \(\sigma<1\). The loss or gain in the defect can reduce or enhance the reflectance of the defect mode, respectively. Figure 2: (**a,b**) Reflectance and complex phase of reflection coefficient, respectively. (**c,d**) Complex phase of reflection coefficient varying with the normalized frequency around the coherent perfect absorption-laser point (CPA-LP) and exceptional point (EP)\({}_{1}\). (**e,f**) Transmittance and complex phase of transmission coefficient. The parameter space is composed of the normalized frequency and gain-loss coefficient. Light is incident from the left.
## 3 Results and Discussion  
### Crystal Structures and Lattice Parameters  
As shown in Figure 2a, the crystal structure of the regular Heusler alloy Ti\({}_{2}\)CoSi has a face-centered cubic structure with the following atomic positions: Ti (0, 0, and 0), Ti (0.5, 0.5, and 0.5), Co (0.25, 0.25, and 0.25), and Si (0.75, 0.75, and 0.75). Figure 2b shows that the inverse structure possesses 16 atoms in the unit cell with the following atomic positions: Ti (0, 0, and 0), Co (0.5, 0.5, and 0.5), Ti (0.25, 0.25, and 0.25), and Si (0.75, 0.75, and 0.75). From the previous study, it is known that the inverse Heusler alloy Ti\({}_{2}\)CoSi is a spin-gapless semiconductor with an integer magnetic moment of 3 u\({}_{\rm B}\) at the equilibrium lattice constant of 6.03 A. With the GGA calculations, we found that the inverse Ti\({}_{2}\)CoSi alloy evinces gapless semiconducting characteristics at the equilibrium lattice parameter of 6.02 A, which derives 0.16% less than from the previous investigation, demonstrating that our research is reasonable.  
Figure 1: Crystal cell of (**a**) a full Heusler structure, (**b**) half Heusler structure, and (**c**) inverse Heusler structure.  
For determining the magnetic ground state of these materials, we calculated the energies of the ferromagnetic (FM) and anti-ferromagnetic (AFM) states. The energy differences between the AFM and FM states are listed in Table 1. The positive values illustrate that the energy of the FM state is smaller than that of the AFM state, indicating that the FM state is the magnetic ground state of these materials. At the same time, the minimized energies were obtained by computing the total energy among a large range of lattice parameters. The equilibrium lattice parameters are 6.01 A and 6.04 A for Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)B\({}_{0.5}\) and Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)P\({}_{0.5}\), which is smaller than that of Ti\({}_{2.25}\)Co\({}_{0.75}\)Si, as the radius of Si is larger than that of the B and P atoms. The calculated values are 6.13 A, 6.13 A, 6.12 A, and 6.25 A for Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)Al\({}_{0.5}\), Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)Ga\({}_{0.5}\), Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)As\({}_{0.5}\), and Ti\({}_{2.25}\)Co\({}_{0.75}\)Si\({}_{0.5}\)Sb\({}_{0.5}\), respectively.
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
###### Abstract  
Currently, ultra-wide band (UWB) is adopted as a useful high-accuracy positioning technique in satellite-blocked areas. However, UWB's positioning performance would be limited significantly because of non-line of sight (NLOS) errors. Additionally, the truncation errors in these linearization-based adjustments such as least squares (LS) and extended Kalman filter (EKF) would also visibly degrade UWB positioning accuracy. To overcome the impacts of NLOS errors and truncation errors, this paper introduced a robust-theory-based particle filter (RPF) into UWB positioning. In such a method, the ICG-III model and PF were adopted to limit the impacts of NLOS errors and truncation errors, respectively, by introducing a weight inflation factor and particle group. For comparison, the Bancroft, LS, EKF, unscented Kalman filter (UKF), cubature Kalman filter (CKF), PF, and RPF were also presented. Here, the influences of truncation errors were analyzed by comparing the results based on LS and EKF with those calculated by UKF, CKF, and PF. The impacts of NLOS errors were evaluated by making a comparison between the results of PF and RPF. Results based on a set of simulated UWB data and a group of experiment UWB data demonstrated that the RPF can significantly avoid the positioning errors caused by both truncation errors and NLOS errors. In general, position improvements percentages of 57.2%, 52.7%, 39.6%, 38.2%, 26.6%, and 20.4% can be obtained by RPF compared to those calculated by Bancroft, LS, EKF, UKF, CKF, and PF, respectively. As a comparison, the truncation error would lead to about 8.1%, 10.1%, and 33.2% accuracy decrease in the north, east, and vertical directions on average. Such accuracy-decrease rates caused by NLOS were 6.1%, 5.2%, and 25%.  
ultra-wide band (UWB); non-line of sight (NLOS); truncation error; robust particle filter (RPF) +
Footnote †: journal: Applied Sciences
###### Abstract  
The rapid development of wireless technology has accelerated the development of Vehicular Ad Hoc Networks (VANETs) to support accident prevention and the safety of a vehicle driver. VANET is a form of Mobile Ad Hoc Network (MANET), and it differs from MANET in that the network topology of a VANET changes dynamically in response to the high mobility of a vehicle and the unstable link quality due to various types of road patterns. Since most access and merging conflicts occur due to vehicle movement patterns and traffic conditions, the collision rate can be reduced if each vehicle can predict the location, movement direction, and resource occupancy information of other vehicles. In this paper, we propose a collision avoidance method based on the vehicle mobility prediction model in TDMA-based VANET. The proposed algorithm allocates time-slots of TDMA to avoid access and merging collisions by predicting the mobility of nearby vehicles using control time-slot occupancy information, vehicle ID, hop information, vehicle movement direction, and longitude and latitude of a vehicle. Simulation results show that the proposed algorithm can reduce access and merging collision rates compared with other legacy algorithms, and the performance gain of the proposed algorithm is enhanced in road environments when traffic density is high and where vehicles have high mobility and change their travel directions frequently.  
VANET; merging collision; access collision; mobility model +
Footnote †: journal: Applied Sciences
## Abstract  
The purpose of this study is to explore the influence of introduction of the physical computing strategy of Arduino Boards in a program design course on coding literacy and the effectiveness of the application in technical high school students. This study selected two classes of twelfth-grade students enrolled in a program design course at a technical high school in Southern Taiwan as the samples. One class was the control group (43 students), and the other was the experimental group (42 students). During the 18-week course, the control group carried out a DBL (design-based learning) programming project, and the experimental group carried out the DBL programming project using the physical computing strategy of Arduino boards. Pre- and posttests and a questionnaire survey were carried out, while ANCOVA (analysis of covariance) was used for evaluation purposes. In the course, students in the experimental group were randomly selected for semi-structured interviews to understand their learning status and to perform qualitative analysis and summarization. This study proposed the physical computing strategy of Arduino boards, featuring staged teaching content, practical teaching activities, and real themes and problem-solving tasks. The results show that the coding literacy of students in the different teaching strategy groups was significantly improved. However, in the Arduino course on DBL programming, the students in the experimental group had a significantly higher learning efficiency in coding literacy than those in the control group. Moreover, according to the qualitative analysis using student interviews, Arduino boards were found to improve students' motivation to learn coding and to aid in systematically guiding students toward improving their coding literacy by combining their learning with DBL theory. Thus, Arduino technology can be effectively used to improve students' programming abilities and their operational thinking in practically applying programming theories.  
DBL; Arduino; coding literacy; physical computing; education reform
## 3 Results  
### Baseline Characteristics  
In total, 23 patients (16 males, 7 females) with RVO were included in this study. Among these 23 participants, 12 (52%) were diagnosed with CRVO and 11 (48%) with BRVO. Among the 12 individuals in the CRVO group, 4 (33.3%) had the ischemic form of CRVO and 8 (66.7%) had non-ischemic CRVO. The control group consisted of 14 healthy control subjects (3 males, 11 females). The mean age of all the RVO participants was 61.4 \(\pm\) 14.8 years (range: 36-79 years). The mean ages of the CRVO, BRVO, and control groups were 55.7 \(\pm\) 7.5 (range: 36-77), 67.7 \(\pm\) 7.8 (range: 50-79), and 57.8 \(\pm\) 13.3 (range: 41-73) years, respectively. We noticed a significant difference in the age of the participants in the CRVO and BRVO groups (\(p\) = 0.024). The morbidity rate was higher in males. There was no significant difference in the distribution of eye laterality. The basic demographics of the analyzed groups are shown in Table 1.
## 2 E-Sail Experiment Design for Solar Wind in Lunar Orbit  
We propose the ESTCube-LuNa mission to demonstrate and characterise the electric sail in an authentic solar wind environment using a lunar nanospacecraft (i.e., limiting the spacecraft mass in the range of 10-20 kg). As presented in Section 5, our current estimate for the ESTCube-LuNa mass is 15 kg within a cubesat form factor of 2 \(\times\) 4 Units (U), which is convenient for E-sail tether deployment. Figure 1 shows the ESTCube-LuNa E-sail demonstration concept in the solar wind environment around the Moon. The graphic depicts a deployed tether and the _spin plane_ it forms as the spacecraft-tether system rotates. The 2 \(\times\) 4 U side of the spacecraft is aligned with the spin plane and provides the necessary maximum moment of inertia, which increases as more tether is deployed.  
The cubesat-style design approach to limit the spacecraft mass is directly linked with the E-sail performance which, in turn, is estimated based on our previous experience--the experiments proposed in this section are reusing technologies developed for Aalto, ESTCube and Foresail missions, as discussed in the Introduction. The baseline ESTCube-LuNa E-sail experiment design includes a passive endmass at the tether's tip, which helps to generate the centrifugal force, pull out the tether and keep it stretched. An alternative remote unit solution would help in placing attitude thrusters at the end of the tether where they would be optimal for spin and spin plane control [43]. However, the endmass is preferred in the ESTCube-LuNa concept as an existing solution, while the remote unit remains on our future development roadmap to achieve trajectory control, as proposed in Section 4, where more details on the endmass-remote unit choice are provided. With the endmass solution, the spin plane control is limited; therefore, we are assuming an _inertially fixed spin plane_ in the ESTCube-LuNa experiments.  
A two-tether solution has been considered earlier in our E-sail mission designs; however, our simulations show that the stability and the control authority of the system, whose mass distribution is elongated along a single axis, does not converge (the two tethers end up drifting to the same side). Meanwhile, with a system of four or more tethers, we would be able to balance the mass distribution with respect to the centre of mass (CoM) and keep the moment of inertia low (see Section 3.2 for more details). The work of multiple-tether systems is on our development roadmap, as is the remote unit; however, the level of complexity is radically increased as compared with the development steps proposed by the ESTCube-LuNa concept.
## 6 Conclusions  
In this paper, we presented an efficient JPA method for the CoMP JDT network, aimed at maximizing the ECC in Rayleigh fading channels. In the Co2P JDT network, we introduced the LQA and LLA CTP power expressions for obtaining the optimum CTP powers. In the Co3P JDT network,  
Figure 6: ECCs of the Co3P JDT network as a function of \(\bar{\gamma}_{3}\)[dB], with \(\bar{\gamma}_{1}\)[dB] and \(\bar{\gamma}_{2}\)[dB] as parameters: (a) \(P_{S}\) = 2 W, (b) \(P_{S}\) = 4 W, (c) \(P_{S}\) = 8 W.  
we introduced a simple LLA CTP power expression for obtaining the optimum power of the worst quality-providing CTP. After obtaining the approximate CTP power for the worst quality-providing CTP, we obtained the respective LLA CTP power expressions for the two better quality-providing CTPs by invoking the LLA CTP power expressions for Co2P JDT, under the remaining power given by the TCPP minus the CTP power for the worst quality-providing CTP. From the numerical results, we verified that the LQA and LLA CTP power expressions for Co2P JDT and the LLA CTP power expressions for Co3P JDT are very efficient in terms of the simplicity for JPA and CoMP set selection, as well as the resulting ECC. When \(P_{S}\) = 2, 4, and 8 W and \(\tilde{\tau}_{2}\) = \(-\)5, 0, 5, 10, 15 and 20 dB, the approximate ECC curves of the Co2P JDT network, using the LQA CTP power expression have maximum and average percent errors of 0.0196% and 0.0017%, respectively, whereas the approximate ECC curves using the LLA CTP power expression have maximum and average percent errors of 0.3818% and 0.0114%, respectively. When \(P_{S}\) = 2, 4, and 8 W, the approximate ECC curves of the Co3P JDT network using the LLA CTP power expression have maximum and average percent errors of 0.0194% and 0.0079%, respectively. In conclusion, the proposed JDT power allocation method can efficiently determine how many CTPs participate in JDT and simply and efficiently determine the CTP power levels to be allocated over the CTPs in the resulting JDT cooperating set.  
Conceptualization, M.L. and S.-K.O.; methodology, M.L. and S.-K.O.; software, M.L.; validation, M.L. and S.-K.O.; formal analysis, M.L. and S.-K.O.; investigation, M.L. and S.-K.O.; resources, M.L. and S.-K.O.; data curation, M.L.; writing-original draft preparation, M.L.; writing-review and editing, S.-K.O.; visualization, M.L.; supervision, S.-K.O.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
4G fourth-generation Co2P coordinated two-point Co3P coordinated three-point CoMP coordinated multi-point CTP coordinated transmission point ECC ergodic cooperative capacity GNR gain-to-noise ratio JDT joint diversity transmission JPA joint power allocation LLA log-linear approximated LQA log-quadratic approximated TCPP total coordination point power UE user equipment
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 3 Proposed Method
### Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm
The layer-dependent trainable parameters of the DU-TCG detector is represented with \(\theta^{i}=\left\{a^{i},\beta^{i},L^{i}\right\}\) in the \(i\)-th layer of the network and \(\alpha\) and \(\beta\) step size, search direction coefficient, and \(L\) regularization matrix are learnt from training samples \(\left\{(y,H),x\right\}_{t=1}^{N_{T}}\) by minimizing mean square error as shown:  
\[L_{DU-TCG}^{K}\left(\theta^{1},\ldots,\theta^{K}\right)=\frac{1}{N_{T}}{\sum _{t=1}^{N_{t}}{\left\|x-\hat{x}^{K}\left(y,H;\theta^{1},\ldots,\theta^{K} \right)\right\|}^{2}} \tag{7}\]  
\(K\) denotes the number of layers, and \(\hat{x}^{K}\left(y,H;\theta^{1},\ldots,\theta^{K}\right)\) denotes the output of DU-TCG with \(y\) and \(H\) inputs.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Recurrent Neural Network (RNN)  
RNN lies under the classification of Artificial Neural Network and functions substantially effectively for training sequential or time-series data. Time-series data consist of intrinsic temporal information that a Simple Neural Network cannot capture [103]. RNN decomposes sequence data into components and maintains a state to represent the data at different time intervals [104]. Figure 5 highlights the structure of RNN, which is composed of inputs, hidden neurons, and an activation function.  
The previously hidden layer (\(h_{t}\)) is presented as shown in Equation (12):  
\[h_{t} =\tan h\ (\text{U}\!\cdot\!x_{t}\!+\!\text{W}\!\cdot\!h_{t}-1) \tag{12}\]  
where \(x_{t}\) denotes the input at time \(t\), \(h_{t}\) denotes the hidden neuron at time \(t\), \(\text{U}\) denotes the weight of the hidden layer at time \(t\), and \(\text{W}\) denotes the transition weights of the hidden layer. The input and prior hidden states are merged, as the current and past inputs flow uses the \(\tanh h\) function to provide information. The output is a new hidden state that functions as a Neural Network Memory by storing knowledge from past efforts.  
During the training phase, RNN is frequently troubled with gradient vanishing and gradient explosion difficulties. Once backpropagation has been closed at a specific point, the gradient explosion problem can be solved. However, the outcome is suboptimal, since not all the weights are updated. The initialization of weights can help to modify the vanishing gradient to decrease its chances of affecting the model. Moreover, there is another solution to the problem, which uses the LTSM and will be explained in the next section.
## 4 Model
### RAT Layer  
The input for RGISQL includes natural language (NL) questions, database schemas, and question relations. RAT-SQL [7] employs a relation-aware self-attention mechanism to facilitate global reasoning for both the schema and the questions. This mechanism is used to encode the relational structures present in the given questions and database schemas, allowing the model to effectively capture their complex interactions. The principle is illustrated in Figure 9.  
At the input stage, the three types of input are all represented as directed graphs, and a pre-trained language model (such as BERT) is employed to obtain the embeddings for nodes and relations within the input. These embeddings are then updated by using a relational graph neural network, and RAT is utilized to achieve a unified representation of the updated embeddings.  
The RAT layer employs a relation-aware self-attention mechanism to weight the node and relationship embeddings, considering the importance of different nodes and relationships. The self-attention mechanism enables the model to focus on the most critical information when generating SQL statements. The processed embedding representations are integrated into a unified representation that includes key information about the question and the database schema, providing rich context for generating SQL statements. The formula is as follows:  
\[\mathrm{P}^{(\mathrm{M})}=\mathrm{RAT}\Big{(}\mathrm{concat}\Big{(}\mathrm{X} ^{(\mathrm{N})},\mathrm{R}^{(\mathrm{N})}\Big{)}\Big{)}, \tag{11}\]  
where X is the processed node embedding, R is the processed edge embedding, M is the number of layers in the RAT layer, concat represents the operation of concatenating embeddings, and N is the number of layers in RGAT. P is the unified representation of embeddings after passing through the RAT layer, which will be used as the initial input for the decoder. This representation contains key information about the question and the database schema, enabling the decoder to generate SQL statements more accurately.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 4 Machine Learning Algorithms for SON
### ML Algorithms Taxonomy
#### 4.1.1 Supervised Learning (SL)  
Supervised learning is an ML technique based on training data used as input and test data leading to an output prediction close to reality [9]. A huge number of algorithms proposed can be found in the literature; however, the most common ones related to self-optimizing network functions are the following:  
* Bayesian: Bayesian algorithms are based on Bayes theorem and their aim is to calculate probabilities according to pre-existing probabilities. Additionally, they do not require a large training sequence.
* Neural networks: This specific category comes from biology (brain neural networks). Neural networks are trained by taking examples and generating identification characteristics from the training input that they are given. A neural network consists of a set of interconnected nodes, while the processing ability of the nodes' network is described through the node connection weights, created by the adaptation of a learning process obtained by training patterns provided as input [83]. Additional SL algorithms categories include:
* Decision trees (DT).
* Hidden Markov Models (HMM).
* Support Vector Machines (SVMs).
* K-nearest neighbors (k-NN).