esa-sceva/satcom-chunk-collection huggingface dataset:
### Research Questions and Article Structure  
Based on the research gaps identified above, this paper aims to study the impact of designing alarm sounds combining local characteristics on task performance and user experience in the case of automatic driving L3 ([PERSON] et al., 2020). Therefore, a design criterion combining local characteristic alarm sounds is proposed to improve task performance and driving safety. To achieve this goal, we need to answer the following research questions:  
**RQ:**: _Can local alarm sound design improve task performance in driving?_
**SQ1:**: _Do there exist differences in the perception of sound of users in different regions?_
**SQ2:**: _How can ADAS alarm sounds that incorporate local characteristics be designed?_
**SQ3:**: _How can the evaluation and validation of the scheme guided by the design criteria be verified?_  
Addressing these problems will help to better apply effective auditory feedback to AD users, provide users with a better AD experience, and thus promote the widespread adoption of AD. Based on the above problems, the work of this paper is carried out from the following levels:  
To address SQ1, we conducted local research through user perception profiles to conduct an extensive sound analysis.  
To address SQ2 issues, we combined local user preference information with locally optimized specifications to develop a recommended guide for ADAS audible alarm systems.  
To solve the SQ3 problem, we recruited participants, built a dynamic simulation driving platform, and conducted controlled experiments and within-subject experiments to evaluate and verify the design scheme.  
The structure of this paper is as follows: In the second section, we combine the local user's information preference and local optimization format to form the ADAS sound alarm system guide and put forward the design scheme. In Sections 3 and 4, we build a dynamic driving simulator platform, complete the test of the design scheme in the L3 automatic driving scenario, obtain experimental data, and carry out the analysis. In Section 5, we discuss the experimental results and point out the limitations of this study. Finally, the work of this paper is summarized.
### 1.1 Article
###### Abstract  
The Einstein Telescope will be a gravitational wave observatory comprising six nested detectors, three optimized to collect low-frequency signals, and three for high frequency. It will be built a few hundred meters under Earth's surface to reduce direct seismic and Newtonian noise. A critical issue with the Einstein Telescope design are the three corner stations, each hosting at least one sensitive component of all six detectors in the same hall. Maintenance, commissioning, and upgrade activities on a detector will cause interruptions of the operation of the other five, in some cases for years, thus greatly reducing the Einstein Telescope observational duty cycle. This paper proposes a new topology that moves the recombination and input-output optics of the Michelson interferometers, the top stages of the seismic attenuation chains and noise-inducing equipment in separate excavations far from the tunnels where the test masses reside. This separation takes advantage of the shielding properties of the rock mass to allow continuing detection with most detectors even during maintenance and upgrade of others. This configuration drastically improves the observatory's event detection efficiency. In addition, distributing the seismic attenuation chain components over multiple tunnel levels allows the use of effectively arbitrarily long seismic attenuation chains that relegate the seismic noise at frequencies farther from the present low-frequency noise budget, thus keeping the door open for future upgrades. Mechanical crowding around the test masses is eliminated allowing the use of smaller vacuum tanks and reduced cross section of excavations, which require less support measures.  
gravitational waves; Einstein Telescope; Newtonian noise; observatory; seismic attenuation; tunnel configuration; observational efficiency +
Footnote †: journal: Applied Sciences  
0
Footnote 0: 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)]([https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)) 4.0/).
## 1 Introduction
[22] investigated the MFD on expressways in Shanghai for one week (26 September to 1 October 2009, including the Chinese National Day) of different days. They also found two hysteresis loops in the morning and evening peaks on weekdays, while during the National Day, the hysteresis of the MFD had larger loops or different types. Literature shows that the traffic flow characteristics and hysteresis types on public holidays are significantly different from those on weekdays and weekends. It is necessary to analyze the traffic characteristics from the macroscopic perspective of networks and develop control strategies to alleviate congestion during holidays. In many studies, the heterogeneity of density distribution is perceived as a critical factor that causes the hysteresis pattern [23; 24]. For the heterogeneous network, [PERSON] et al. [15] introduced different control strategies with different levels of coordination for metropolitan transportation networks that have a hierarchical structure which consists of freeways and urban roads. Furthermore, some researchers proposed hierarchical control frameworks to investigate the dynamics of heterogeneity in urban regions, aiming at decreasing system delays and congestion heterogeneity [25; 26]. In response to the issues mentioned above, this study analyzes the characteristics of regional traffic flow and the MFD on the network of Tianjin during the public holiday for Qingming Festival (1 st to 4 th April 2017), which is a traditional Chinese festival about ancestor worship and spring outings. The contribution of this paper is threefold. Firstly, the regional traffic flow characteristics are clarified based on the empirical data. The analysis involves different days, including the weekday before the holiday, during the holiday, and the day of Qingming Festival (the last day of the holiday). Secondly, the whole network is partitioned into several homogeneous subregions with different network densities and congestion levels. The calibrated dynamic models are applied to analyze the evolution process of traffic flow during the holidays. Finally, some control strategies are proposed to mitigate regional congestion during holidays based on the results of network state evolution under different model parameters.
## 4 Experimental Results
### Experiments with \"Dyjob\" Climbing a Slope  
The proposed mechanism of moving a vehicle's COG at a low level contributes to reducing the risk of turning over on irregularly inclined terrains. While _Dyjob_ was passing over the step (Section 4.1) and stairs (Section 4.2) slightly diagonally in the horizontal plane, there was a small possibility of slipping down but little chance of overturning.  
In the case that a vehicle has its COG at a high position (G\({}_{2}\) in Figure 13), similar to vehicles that can move their COG [30; 31; 32; 33; 34; 35], the vehicle turns over around P in Figure 13 at relatively gentle angles of \(\theta\). However, it is expected that _Dyjob_, which has its COG at the low and most forward position G\({}_{1}\) (\(x=0.0585\) m, \(y=0.006\) m), is stable at steep angles given by:  
\[\theta<0.99\ \text{rad}. \tag{7}\]  
Figure 12: Snapshots of “_Dyjob”_ traversing a gap.  
It is expected that _Dyjob_ does not slip down a slope on which the coefficient of kinetic friction \(\mu\) is 1.12 if  
\[\theta<\tan^{-1}\mu=\tan^{-1}1.12=0.842\text{ rad}. \tag{8}\]  
Equations (7) and (8) establish a condition \((\theta<0.842\text{ rad})\) for the vehicle to be able to steadily continue to climb the slope.  
In experiments, _Dyjob_ successfully achieved climbing slopes (\(\mu=1.12\)) with \(\theta<0.80\) rad. The movie and snapshot of _Dyjob_ climbing a slope of 0.80 rad are provided in the Supplementary Video S4 and Figure 14. This result demonstrates that our proposed mechanism of moving a vehicle's COG at a low level is effective in moving on inclined terrains.
## 4 Results and Discussion
### Wire Bond Solenoid Inductor  
Figure 7a shows the simulated result of the wire bond inductor that varies from 47 to 181 at 200 MHz. For vacuum, EMG911, EMG909, EMG905 and EMG901, the simulated inductances are 47.8, 61.5, 85.5, 107.8 and 286.3 nH at 300 MHz, respectively. The contra result observed is compared to the wire-wound inductor, where, according to Figure 7b, the maximum quality factor obtained is 70 at 350 MHz for EMG901. Figure 7c shows the simulated tuning ratio, where the maximum tuning ratio belongs to the EMG901, equal to 498% at 300 MHz. According to the inductance variations, the minimum and the maximum tuning ratio correspond to EMG911 and EMG901, respectively.  
Figure 8 shows the measurement result for the wire bond inductor. As shown in Figure 7a, a minimum inductance value of 42.6 nH at 150 MHz showed at vacuum. The maximum inductance obtained is 66.6 nH at 150 MHz, with EMG901 ferrofluids. The measured inductances for EMG911, EMG909 and EMG905 are 46.2, 49.8, 54.2 nH at 150 MHz, respectively. The measured quality factors are shown in Figure 8b. The maximum quality factor obtained with a vacuum at 39.1 for 150 MHz, while EMG901-filled channel presents the minimum achieved quality factor of 17.2 at 150 MHz. Figure 8c shows the tuning ratio. The maximum achieved tuning ratio is 81% for EMG901 at 310 MHz. Table 2 showed the summary of performance for the wire-bonded structure tunable inductor. Table 4 shows the characterization of wire-bonded structure tunable inductor with different type of ferrofluid. Table 5 shows the summary of the fabricated tunable inductor compared to the other work using solenoid structure technology. As shown in Table 5, the fabricated tunable inductor proved to be a promising and alternative tunable inductor.  
Figure 6: Measurement results of wire-wound inductor. (**a**) Measured inductance, (**b**) Quality factor, and (**c**) Tuning ratio.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Wire-Wound** & **Sim.** & **Sim.** & **Meas.** & **Meas.** & **Sim.** & **Meas.** \\
**@100 MHz** & **L** & **Q** & **L** & **Q** & **TR\%** & **TR\%** \\ \hline
**Vacuum** & 54.4 & 300 & 55 & 200 & - & - \\ EMG901 & 187.3 & 300 & 101 & 53.7 & 243\% & 83.5\% \\ EMG905 & 105.6 & 300 & 78.6 & 55 & 94.11\% & 42.5\% \\ EMG909 & 86.1 & 300 & 68.8 & 61.1 & 58.27\% & 25\% \\ EMG911 & 65.4 & 300 & 62.4 & 74.4 & 20.22\% & 13.3\% \\ \hline \end{tabular}
\end{table}
Table 3: Summary of characterization of wire-wound structure tunable inductor.  
Figure 7: Simulation results of wire bond inductor. (**a**) Simulated inductance, (**b**) Quality factor, and (**c**) Tuning ratio.  
Figure 8: Measurement results of wire bond inductor. (**a**) Measured inductance, (**b**) Quality factor, and (**c**) Tuning ratio.
## 1 Introduction
### Channel Modeling
Using the relation between irradiance, instantaneous and average SNR [4], i.e.,  
\[I=\sqrt{\frac{\gamma}{\mu}} \tag{8}\]  
the corresponding expression for the outage probability for the LN model is deduced as follows [4],  
\[P_{out}=\frac{1}{2}erfc\left(\frac{\ln(\mu/\gamma_{th})-\sigma^{2}}{2\sqrt{2} \sigma}\right) \tag{9}\]  
where _erfc(. )_ denotes the complementary error function, whereas, for the GG model, \(P_{out}\) can be expressed as [4],  
\[P_{out}=\frac{(ab)^{\frac{a+b}{2}}}{\Gamma(a)\Gamma(b)}\left(\frac{\gamma_{th} }{\mu}\right)^{\frac{a+b}{4}}G_{1,3}^{2,1}\left(ab\sqrt{\frac{\gamma_{th}}{ \mu}}|_{\frac{1-a+b}{2},\frac{b-a}{2},-\frac{a+b}{2}}^{1-\frac{a+b}{2}}\right) \tag{10}\]  
where \(G(. )\) is the Meijer's G-function.
## 3 Problem Definition
### Mathematical Modeling  
Based on the mathematical model in [2; 13], the notations for stating the problem are as follows:  
\[\begin{array}{lll}i&=&\text{Index of target points}\\ j&=&\text{Index of potential camera positions}\\ k&=&\text{Index of camera types}\\ \varphi&=&\text{Index of possible poses}\\ \lambda_{ijk\varphi}&=&\left\{\begin{array}{ll}1&\text{If can cover a target point $j$ is covered by a camera placement $i$}\\ 0&\text{otherwise}\\ c_{k}&=&\text{Cost of a camera type $k$}\\ p&=&\text{Minimum coverage (\%)}\\ x_{jk\varphi}&=&\left\{\begin{array}{ll}1&\text{if a camera of type $k$ is placed at position $j$ with pose $\varphi$}\\ 0&\text{otherwise}\\ 1&\text{if a target point $i$ is covered}\\ 0&\text{otherwise}\\ \end{array}\right.\end{array}\]  
The mathematic model for the camera placement problem for minimizing costs while satisfying the minimum coverage level is:
# Characterisation of Holographic Recording in Environmentally Stable Photopolymerisable Glass
## 3 Results and Discussion
### Environmental Stability  
In the current research, the impact of the environmental humidity and temperature on grating stability was investigated by monitoring the diffraction efficiency of transmission gratings at relative humidity up to 95% and temperature of 130 \({}^{\circ}\)C, respectively. For the humidity test, the sample was placed in a controlled environment chamber (Electro-tech system, Perkasie, PA, USA, model LV 202-E) as shown in Figure 6a. Diffraction efficiency was measured before humidity exposure at ambient humidity of 50% and temperature of 21 \({}^{\circ}\)C (Figure 6b). Then, the humidity level was increased to 70% and the temperature was kept constant at 21 \(\pm\) 0.3 \({}^{\circ}\)C. After 60 min, the sample was removed from the chamber and its Bragg selectivity curve was measured by probing with a 633 nm laser at different incident angles. The same experiment was repeated at 95% RH. As seen from Figure 6b, no changes in either the absolute value of the diffraction efficiency or the profile of the Bragg selectivity curve were observed. This confirms the insensitivity of the PHSG to humidity and broadens the range of its potential applications for the development of holographic optical elements with improved stability at different humidity levels.  
For the thermal response test, the layers were coated on a flexible polycarbonate substrate (Bayer Makrofol DE1-1 cc, 375 \(\upmu\)m thick). The choice of the substrate was governed by the requirements for roll-to-roll manufacturing and integrated optical components considered as the most feasible approach for mass manufacturing of holographic optical components [27]. The temperature resistance of the PHSG was investigated by monitoring the diffraction efficiency of transmission gratings before and after curing in the oven (Binder, model ED56) at 130 \({}^{\circ}\)C for 60 min (Figure 6c). Both the diffraction efficiency and the profile of the Bragg selectivity curve were unchanged after exposure to 130 \({}^{\circ}\)C. In addition, no defects and cracks were observed on the layer (Figure 6d) and the grating produced a highly efficient diffraction pattern (Figure 6e). These experimental results confirm the resistance of PHSG to high temperature and the capability of holographic optical elements recorded on PHSG to withstand temperatures up to 130 \({}^{\circ}\)C.  
Figure 5: (a) Normalised diffraction efficiency (DE) versus the exposure (recording) time shows the effect of UV-curing on the grating fixation. Normalised diffraction efficiency was calculated as the ratio of the diffraction efficiency obtained after UV-curing and after recording; (b) Bragg selectivity curves of the transmission grating recorded using 30 s exposure demonstrate stability of the diffraction efficiency after UV-curing.
# A Stigmergy-Based Differential Evolution
## 5 Discussion  
Stigmergic DE implements slight modifications to the original DE operators, so the computational time required to complete several iterations is practically the same, as suggested by the results in Tables 5 and 6 and after applying the Wilcoxon test to such data. Figure 4 shows a comparison between the execution time of DE and SDE for each test function, considering \(D=500\) and \(N_{p}=512\). The comparison considers the difference in percentage between the execution times of each algorithm. In most cases (with the exception of the Brent and Griewank functions in these results), the proposal has a better time performance than its DE counterpart of about 17% in the best case. This behavior is similar in all these experiments; however, as already mentioned, SDE has a marginal advantage. Moreover, both algorithms present similar problems in finding the global optimum in some functions. However, as can be seen from Tables 7 and 8, in the case of the Rastrigin, Schwefel and Zacharov functions, the difference between the final fitness values obtained for SDE and DE is very large. SDE obtains fitness values lower than DE by an order of magnitude 2, and for the case of the Trid, Dixon and Price and Rosenbrock functions, DE performs better than SDE, but the difference is less significant. If the dimension increases (Tables 9 and 10), only in the case of the Sum Powers function does DE have a better performance than SDE, but it is minimal, on the order of 0.0001. These results show the advantage of the proposal in high dimensions; empirically, this is due to the intrinsic parallelism of the stigmergy concept that was used as an inspiration to modify the original operators of a metaheuristic (DE, version best/1/bin in this case). The proposal avoids using the global best in the construction of new candidate solutions, thus simulating indirect communication among all individuals in the population. In summary, under the same conditions for both metaheuristics, our proposal can find a similar result as DE (and better outcomes for some objective functions) but using fewer iterations on average (Tables 7-10), especially when the problems are high-dimensional.
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 1 Introduction
The state-of-art in this research is to maintain QoS provisioning and prolong the network lifetime. To improve the performance of WSNs, this paper introduces the LMM. The LMM has features of handling the upward, lateral, and even downward movement. For example, a sensor node may be promoted to be a sink node, but to continue to work as a node at the same time, the node may need to take a lateral step to gain knowledge regarding other nodes for sending and receiving the data (e.g., schedules, priorities). After gaining these details, the node may then become a sink node again, this time in measuring the energy rather than distance and location. The LMM handles the limitations of existing models and makes the following contributions:  
* Handling the node and sink mobility concurrently. * Reducing control packets when detecting the location of nodes. * Decreasing extra pause time. * Determining the node's distance from the previous to the current location, on variance mobility rates. The remaining sections are organized as follows: Section 2 gives an overview on the state of the art research. Section 3 highlights the existing problem and mobility model formulation. Section 4 describes the simulation setup and the analysis of results, Section 5 gives the discussion of the results and finally Section 6 concludes the work.
# Indoor-Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches
## 3 Generating Data  
The main case for this paper is indoor-outdoor detection. For the calculations, a dataset is generated. The proposed case is in the Universal Mobile Telecommunications System (UMTS) network. The data contain two features for the machine learning step: the received power level of the active connection and the received pilot signal to interference ratio. We use the simplified model from [32] and add extra loss to emulate the indoor environment, based on a simplified propagation model. The propagation model is:  
\[P_{R}=\frac{P_{T}}{L(d)\beta\Gamma}, \tag{5}\]  
or in logaritmic (dB) terms:  
\[P_{R}^{dB}=P_{T}^{dB}-L(d)^{dB}-\beta^{dB}-\Gamma^{dB}, \tag{6}\]  
where  
* \(P_{R}\): Received power level,
* \(P_{T}\): Transmitted power level,
* \(L(d)\): Path loss/Attenuation,
* \(\beta\): Slow fading/Shadowing,
* \(\Gamma\): Fast fading/Multipath effects.  
The level of the slow-faded power (\(\beta\)) fluctuates with a log-normal distribution around the received mean power. If the linear valued \(\beta\) is log-normally distributed, then the logarithmic valued \(\beta^{dB}=10^{0.1\beta}\) is normally distributed and expressed in dB, with parameters \((\mu,\sigma)\), where \(\mu=0\). Now, for a user at a certain location \(x\) receiving the signal from cell \(c_{i}\), neglecting the fast fading effect, we obtain (again in dB):  
\[P_{R,i}^{dB}(x)=P_{T,i}^{dB}-L_{i}^{dB}(x)-\beta_{i}^{dB}, \tag{7}\]  
which is the first feature. Now we can define the second feature, the signal-to-interference ratio \(SIR\)[33; 34] of cell \(c_{i}\) at the base station, having \(M\) interfering transmitters, as  
\[SIR_{i}(x)=\frac{P_{T,i}L_{i}(x)\beta_{i}}{\sum_{j=1,j\
eq i}^{M}P_{T,j}L_{j}(x )\beta_{j}}, \tag{8}\]  
\[SIR_{i}^{dB}(x)=10\log_{10}SIR_{i}(x). \tag{9}\]  
Using this model, we generated 300 data points, using \(P_{T,i}^{dB}-L_{i}^{dB}(x)=90\) dBm, \(\sigma=8\) dB, \(M=4\) and an additional path loss for indoor settings of 15 dB. As data preparation, we scaled the data to the \([0,1]\) interval and inverted the \(SIR\) axis. The original data are shown in Figure 3, and the prepared data in Figure 4.  
Figure 4: Prepared artificial data points. Red dots are the outdoor measurements, blue dots are the indoor measurements.  
Figure 3: Original generated data points. Red dots are the outdoor measurements, blue dots are the indoor measurements.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 5 Privacy Preserving Federated Learning
### Data Structure and Protocol  
The HE scheme increases data security but has the disadvantage of incurring communication overhead. Especially since the data length after encryption is independent of the plaintext length to be encrypted, the communication efficiency is significantly reduced if only one parameter is encrypted and sent. The proposed system establishes a data structure that can transfer multiple parameters to alleviate this efficiency degradation. The data structure for a weight \(w_{i}\) consists of a bit representing the sign, a zero bit to prevent an overflow caused by homomorphic additions, and the remaining bits signifying the weight's value. The number of weights included in one ciphertext can be calculated as \(D=\left\lfloor\frac{K}{w_{o}}\right\rfloor\), where \(\lfloor\cdot\rfloor\) is a round-down operation and \(L_{o}\) is the data length used for representing weights. Then, the data format to be encrypted can be expressed as follows: \([w_{D}^{L_{o}(D-1)},w_{D-1}^{L_{o}(D-2)},\cdots,w_{1}]\). Furthermore, since the secure aggregation operations are performed in the plaintext space except for the process of adding noise, the proposed algorithm can be implemented using only homomorphic operations for integer processing. Therefore, in the proposed system, integer numbers are used for data transmission, and they are represented as floating-point numbers using a decimal point pre-agreed between the clients and the servers after decryption.