esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Comparative Analysis  
In this part, we compared the aggregated findings produced with q-ROFAAWA and q-ROFAAWG operators to the aggregated results obtained with Dombi WA and WG AOs for q-ROFS by [PERSON] et al. [17], q-ROF Yage WA (q-ROFYHA) and q-ROF Yager WG (q-ROFYWG) by [PERSON] and [PERSON] [39], q-ROF weighted averaging (q-ROFWA), and geometric (q-ROFWG) operators by [PERSON] and [PERSON] [36]. All the results are shown in Table 10. We also showed that most of the AOs fail to aggregate the information provided in the form of q-ROFNs. These AOs include:  
* IF Aczel-Alsina WA (IFAAWA), and Aczel-Alsina WG (IFAAWG) operators by [PERSON] et al. [40]. * Interval-valued IFAAWA (IVIFAAWA) and interval-valued IFAAWA (IVIFAAWG) by [PERSON] et al. [41]. * PyF Aczel-Alsina weighted averaging (PyFAAWA) and geometric (PyFAAWG) operators by [PERSON] et al. [42]. * PyF weighted averaging (PyFWA) and geometric (PyFSWG) operators by [PERSON] et al. [43]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Operators** & **Score Values** & **Ranking Results** \\ \hline \multirow{4}{*}{Proposed operators} & \(\mathbb{S}(c_{1})=-0.5873,\mathbb{S}(c_{2})=-0.3380\), & \\  & q-ROFAAWA & \(\mathbb{S}(c_{3})=-0.4573,\mathbb{S}(c_{4})=0.6707\), & \(a_{4}>a_{5}>a_{2}>a_{3}>a_{1}\) \\  & & \(\mathbb{S}(c_{5})=0.6489\) & \\ \cline{2-4}  & & \(\mathbb{S}(c_{1})=-0.8917,\mathbb{S}(c_{2})=-0.4557\), & \\ \cline{2-4}  & q-ROFAAWG & \(\mathbb{S}(c_{3})=-0.5138,\mathbb{S}(c_{4})=-0.8343\), & \(a_{5}>a_{2}>a_{3}>a_{4}>a_{1}\) \\ \cline{2-4}  & & \(\mathbb{S}(c_{5})=0.1223\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative analysis with existing operators. Figure 5: The graphical representation of score value, variation by \(q\). Their structure is not allowed to aggregate the data. A short review of the aggregated outcomes of this article with other existing AOs is represented below in Table 10 and its geometrical representation in Figure 6.  
The results of Table 9 are described in Figure 6 for further clarity. In Table 10 and Figure 6, we compared our results with [PERSON] et al. [17], [PERSON] and [PERSON] [39], [PERSON] and [PERSON] [36] by applying the AOs discussed in those references in our example. Since the work in [PERSON]. et al. [17], [PERSON] and [PERSON] [39], [PERSON] and [PERSON] [36] are based on Dombi, Yager, and algebraic TN and TCN, while our proposed work is based on AATN and AATCN. By keeping in mind that AATN and AATCN work significantly more than other discussed TNs, as suggested by [44]. Due to this fact, we believe that the proposed work is better than the previous work. We also discussed the limitations of several other AOs proposed by [PERSON] et al. [40], [PERSON] et al. [41], [PERSON] et al. [42], and [PERSON] et al. [43]. The AOs discussed in [40; 41; 42; 43] fail to aggregate the information provided in the form of q-ROFNs.
## 3 Survey Results
### Sensor Mote: Microcontroller  
Used microcontrollers are listed in Table 6. \begin{table}
\begin{tabular}{c l l l l l l} \hline \hline
**Nr** & **Codename** & **MCU count** & **MCU Name** & **Architecture,** & **MHz** & **RAM, KB** & **Program Memory, KB** \\ \hline
1 & Habitats & 1 & Atmel ATMega103L & 8 & 4 & 4 & 128 \\
2 & Minefield & 1 & Hitachi SH4 7751 & 32 & 167 & 64,000 & 0 \\
3 & Battlefield & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
4 & Line in the sand & 1 & Atmel ATMega128 & 8 & 4 & 4 & 128 \\
5 & Counter-sniper & 1 + Field- & Atmel ATMega128L & 8 & 7.3 & 4 & 128 \\  & & Programmable & & & & & \\  & & Gate Array & & & & & \\  & & (FPGA) & & & & & \\
6 & Electro-shepherd & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
7 & Virtual fences & 1 & Intel StrongArm & 32 & 206 & 65,536 &? \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deployments: used microcontrollers (MCUs). Figure 5: **Custom, adapted and off-the-shelf mote usage in deployments—Almost half of deployments adapt off-the-shelf motes by custom sensing and packaging hardware, 32% use custom platforms and only 20% use commercial motes with default sensing modules. Only a few deployments use motes with more than one MCU. Therefore, OS support for multi-MCU platforms is an interesting option; however, the potential usage is limited. Multi-MCU motes are a future research area for applications running simple tasks routinely and requiring extra processing power sporadically. Gumsense mote is an example of this approach [68]. The most popular MCUs belong to Atmel ATMega AVR architecture[69] and Texas Instruments MSP430 families. The former is used in Mica-family motes, while the latter is the core of the TelosB platform, which has been widely used recently. _Design rule 12_: Support for Atmel AVR and Texas Instruments MSP430 MCU architectures is essential for sensor network operating systems. Sensor network motes use eight-bit or 16-bit architectures, with a few 32-bit ARM-family exceptions. Typical CPU frequencies are around 8 MHz; RAM amount: 4-10 KB; program memory: 48-128 KB. It must be noted that program memory size is always larger than RAM, sometimes even by a factor of
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 4 Multipath Propagation  
The multipath phenomenon is caused by atmospheric ducting, reflective surfaces (including water, buildings, and mountains), and ionospheric refraction [4]. This phenomenon results in the receiver antenna receiving two or more copies of the same signal via different routes (see Figure 2). The various path lengths cause each signal to have a different propagation delay. The first received signal is known as the line-of-sight (LoS), and the other signals are non-line-of-sight (NLoS).  
Since all paths differ in their transmission length and propagation delay, the receiver could probably separate some of them and obtain two or more copies from the transmitted signal, considering the generalised assumption that the electromagnetic signals propagate through a vacuum at the speed of light, if the following condition is satisfied [17; 18]:  
\[\frac{\text{speed of light}}{\text{chip rate}}\quad\rightarrow\quad\frac{3.0 \times 10^{8}\text{m}\cdot\text{s}^{-1}}{\text{chip rate}}\leq TL, \tag{4}\]  
_TL_ represents the minimum transmission length. In this case, the delay time (\(\tau\)) needed to receive an uncorrelated path is:  
\[\tau=\frac{TL}{\text{speed of light}}=\frac{TL}{3.0\times 10^{8}\text{m}\cdot \text{s}^{-1}} \tag{5}\]  
Figure 2: Multipath propagation.  
For example, in the wideband code division multiple access (WCDMA) receiver, if the chip rate is 5 Mcps then \(L\) will be at less than 60 m, and the chip duration or multipath delay \(\tau\) is equal to 0.2 us, which will be enough to separate the multipath components to obtain more than one copy. Table 1 shows different chip rates and the delay time required. In order to provide a wireless communication system with a high data transmission rate, these copies are employed to increase the error correction capability of RS and BCH codes without adding more redundant bits.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 2 The Transmitting and Receiving System for the Composite Signal
### Reception of the composite signal  
The reception of the composite signal is divided into two parts: demodulation of the OFDM signal and reception of the BOC signal, as shown in Figure 5. The reception of the BOC signal is further divided into two stages: acquisition and tracking. The main focus of the acquisition stage is to roughly estimate the Doppler frequency offset and code phase offset. After acquisition, high-precision estimation of the BOC signal parameters is realized through tracking. Additionally, the high-precision parameters tracked by the BOC signal can assist in the demodulation of the OFDM signal, decreasing the bit error rate (BER) and improving the communication performance.  
Figure 4: Spectrum of the composite signal (the carrier frequency is 10.23 MHz; the square-wave subcarrier frequency is 4.096 MHz; the code frequency is 1.023 MHz).  
Figure 3: Spectrum diagram of the composite signal (The main lobe of the BOC signal only exists in the blue subcarriers, and the OFDM signal and the side lobe of the BOC signal are superposed in the red subcarriers through power control).  
Since the BOC signal and the OFDM signal share the same frequency spectrum, the two signals have the same frequency. Moreover, the BOC signal adopts a pseudorandom code spread spectrum, which has high anti-interference performance. Therefore, the parameters of the BOC signal are used to assist in the demodulation of the OFDM signal, as described in Section 3.
## Materials and Methods
### QA/QC and Statistical Analysis  
All the chemicals used in the experiment are of analytical grade. The experiments were performed in triplicate (i.e., each of the 7 treatments in the experiment was independently repeated three times). Repeatability analysis shows that the mean RSD for pH, Eh, EC, Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn was <2.80%, <0.75%, <2.82%, <10.6%, <12.1%, <15.3%, <10.5%, <8.00%, <10.2%, <10.3%, <8.39%, <12.8%, and <4.96%, respectively. Statistical analysis of the experimental data was performed using one-way analysis of variance (ANOVA) and the means compared using significant difference (Duncan) method at 5% level (IBM SPSS software Version 17.0). \"Descriptive statistics\" followed by \"explore\" were used to test the data for normality. The \"sig\" value of Shapiro-Wilk is greater than 0.05. The data in this study is consistent with the test of variance homogeneity using Levene  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & **0.01 M Citric Acid** & **0.01 M Oxalic Acid** & **0.01 M Malic Acid** \\ \hline T1 & Yes & & \\ T2 & & Yes & \\ T3 & & & Yes \\ T4 & Yes & Yes & Yes \\ T5 & Yes & & Yes \\ T6 & & Yes & Yes \\ T7 & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of extracting solutions for seven treatments.  
statistic. All experimental data were presented as mean \(\pm\) standard error (\(n\) = 3). Pearson coefficient of correlation was used to determine the relationship between Al, Mn, or Fe and each of the trace elements investigated in this study.
###### Abstract
Based on physical models and reasonable assumptions about technological developments, we could derive predictions for the future up to 2050, which is important as many of today's decisions with regard to new drive technologies will only show their effects in the long term. Building on the results of this prior work, the aim of the current study was to extend the findings to medium-duty road freight transport. Due to the complexity of large trucks, however, it was no longer appropriate to work with standardized vehicle models as before. For both small and large passenger vehicles, we were able to draw on a large amount of data, which enabled us to derive realistic \"typical\" models for our simulation. We were also able to do the same for light trucks [31]. However, the lack of statistical data for heavy trucks did not allow this in the present work without risking an inadequate representation of reality in the model-based simulation. The close cooperation with a nationally active freight forwarder enabled us to develop this model. In order to avoid excessive complexity and ensure the relevance of our model, we concentrated on freight forwarding tours that are carried out within one working day. In this way, we avoided the issue of intermediate charging of the battery in the BET, which would have been associated with many assumptions and unresolved issues. In order to be able to draw on a statistically reliable database within the forwarding company, two tours were selected that are driven almost daily in a very similar way (always the same vehicle, always the same route, similar daily rhythm, and similar payload up to a total weight of ca. 25 t). The shorter tour was 330 km long, with a total duration of about 8 h (including freight loading times), while the long tour covered 630 km and took around 12 h. The commercial vehicles used in each case were trucks from the Dutch manufacturer DAF (two-axle tractor units with three-axle trailers, each with a tare weight of ca. 20 t). A detailed description of vehicles, routes, profiles, and payload will be provided in Section 2. The objective of our work was to determine realistic consumption and GHG emissions of BETs and FCETs in the typical daily forwarding business. In addition, predictions for consumption and GHG emissions up to 2050 were to be derived on the basis of foreseeable technological developments and the assumed development of electricity and hydrogen production. To this end, we first compared the model with reality using the data from the DAF ICEV trucks and their well-documented tours. After this validation, we were able to derive reliable predictions for the new drive technologies on a medium- to long-term time scale in the context of the results of our earlier work.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 1 Introduction
Diamond and silicon carbide detectors are extensively used in a Time-Of-Flight (TOF) configuration [11,12] to rapidly retrieve the beam energy spectra at different positions along the beamline. Furthermore, accurate shot-to-shot measurements of the dose released at the end of the beamline (where the user samples are placed) can be performed. ELIMED's absolute dosimetry systems are independent of the ultra-high dose rate (up to 10\({}^{9}\) Gy/s) and allow to perform online absolute dose determination with an accuracy better than 5%, thus satisfying the internationally established clinical requirements [13,14, 15]. The ELIMED dosimetry system is based on three main devices: (i) a Secondary Electron Monitoring (SEM), (ii) a Multi-Gap Ionization Chamber (MGIC), and (iii) a Faraday Cup (FC) for absolute dosimetry. Passive detectors, such as CR39 and radiochromic films (RCF), are also used to benchmark active ion diagnostic and dosimetry devices. The entire ELIMED beamline (considering the initial ion source as an input) can be fully simulated using the developed ELIMED application of the Geant4 Monte Carlo toolkit [16,17,18]. \begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{X}\mathbf{\varphi}_{\mathbf{x}}\) & \(\mathbf{Y}\mathbf{\theta}_{\mathbf{y}}\) & \(\mathbf{XY}\) \\ \hline \(\alpha\) & 0.8401 & 0.3556 & 0.0002 \\ \(\beta\) (mm/\(\pi\)-mrad) & 2.7094 & 2.4484 & 0.9112 \\ Emit. Norm (rms) \(\pi\) mm\(\cdot\)mrad & 2.9506 & 3.9324 & 24.15 mm\({}^{2}\) \\ \hline \(\mathbf{X_{max}}\) & \(\mathbf{Y_{max}}\) & \(\mathbf{\theta_{x,\,max}}\) & \(\mathbf{\theta_{y,\,max}}\) \\ \hline
14.97 mm & 14.99 mm & 8.632 mrad & 7.162 mrad \\ \hline \end{tabular}
\end{table} 
Table 1: ESS acceptance parameters. The parameters are defined in [8] and summarized here. In this study, the capability of the G4-ELIMED application was exploited to optimize the transmission efficiency along the beamline and the dosimetric characteristics of the final beam. The beamline configuration was optimized to obtain a final beam suitable for pilot radiobiology experiments by using laser-generated proton beams centered around 20 MeV. A Spread-Out Bragg Peak (SOBP) was generated using the ESS as an active energy modulator, which was the main aim of this study and at the same time producing a depth-dose profile similar to the ones required to carry out radiobiological experiments. Additionally, the removal of unwanted ion species accelerated in the laser-generated plasma, such as carbon ions, was studied to assure the capability of the beamline to filter out ion beam contaminants that can be detrimental for accurate dosimetric studies.
## 2 Methods and Data
### Data Source and Pre-Processing
#### 2.2.6 Climatic Factors  
In this study, six climatic factors, i.e., \(\mathrm{CO_{2}}\), SW, VPD, P, T, and SRAD, were selected to input into the PLS regression model. SW at different soil layers was obtained from the ERA5-land dataset, ranging from 1979 to the present, which has a spatial resolution of 0.1 degrees and a monthly interval. Different soil depths include four layers: 0-7, 7-28, 28-100, and 100-289 cm. The dataset can be freely downloaded at the official website of the European Centre for Medium-range Weather Forecasts (ECMWF, [[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/)), (accessedon 25 August 2023)). A global daily maximum VPD from 1979 to 2020 has a spatial resolution of 0.25 degrees and a monthly interval. The dataset is freely available on the website of the multi-discipline research database ([[https://zenodo.org/](https://zenodo.org/)]([https://zenodo.org/](https://zenodo.org/)), (accessed on 25 August 2023)). Annual total P and T were obtained from the CRU ([[https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)]([https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)), (accessed on 25 August 2023)) with the same spatial resolution of 0.5 degrees and temporal interval of monthly. SRAD was obtained from the ERA5 dataset with a spatial resolution of 0.1 degrees and a monthly temporal interval. The dataset was openly obtained from the official website of the ECMWF ([[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/)), (accessed on 25 August 2023)).
## 2 Nonlinear Satellite Channel Model  
Due to the non-linear characteristic of the HPA, which is usually operated near the saturation because of the power limitation of the satellite, the non-linear distortion is introduced to the transmitted signals [31]. Therefore, the non-linear satellite channel model can be established according to the non-linear characteristics of HPAs.  
Figure 1 shows the block diagram of the non-linear satellite channel model, which is composed of the non-linear conversion module and the AWGN module. Assume that the signals transmitted to the non-linear channel model are narrowband relative to the bandwidth of the satellite transponder and the coherent detection is perfect. Let the input signal of the non-linear channel model be  
\[x(t)=r(t)\exp(j\phi(t)), \tag{1}\]  
and then the output signal can be expressed as  
\[y\left(t\right)=A\left[r\left(t\right)\right]\exp\left(j\left(\phi\left(t \right)+P\left[r\left(t\right)\right]\right)\right)+n\left(t\right), \tag{2}\]  
where \(y(t)\) is the received signal, \(n(t)\) is the Gaussian white noise with mean 0 and variance \(\sigma^{2}=N_{0}/2\), \(r(t)\) and \(\phi(t)\) are the amplitude and phase of the modulated signal, respectively, \(A[r(t)]\) and \(P[r(t)]\) are the non-linear distortions in amplitude (usually called AM/AM conversion) and phase (usually calledAM/PM conversion). Moreover, the AM/AM conversion and the AM/PM conversion are described using the Saleh model, as shown in Equations (3) and (4), respectively [32].  
\[A[r(t)]=\frac{\alpha_{\mathrm{a}}r(t)}{1+\beta_{\mathrm{a}}r(t)^{2}}, \tag{3}\]  
\[P[r(t)]=\frac{\alpha_{\mathrm{p}}r(t)^{2}}{1+\beta_{\mathrm{p}}r(t)^{2}}, \tag{4}\]  
where \(\alpha_{\mathrm{a}}\), \(\beta_{\mathrm{a}}\), \(\alpha_{\mathrm{p}}\), \(\beta_{\mathrm{p}}\) are parameters determined by characteristics of the HPA.
# An Improved Fuzzy Vector Signature with Reusability
## 4 Fuzzy Vector Signature
### Security
* Set \(\sigma_{k}=\big{(}\{\sigma_{1,i}\}_{i\in[1,n]^{i}},g^{s_{k}},g_{1}^{s_{k}},h_ {k},c_{k}\big{)}\) and give \(\sigma_{k}\) to \(\mathcal{A}\). * **Output. **\(\mathcal{A}\) outputs \((m^{*},\sigma^{*})=\big{(}m^{*},\big{(}\{\sigma_{1,i}^{*}\}_{i\in[1,n]^{i}}, \sigma_{2}^{*},\sigma_{3}^{*},\sigma_{4}^{*},\sigma_{5}^{*}\big{)}\big{)}\). \(\mathcal{B}\) checks if \((m^{*},\sigma^{*})\
eq(m_{k},\sigma_{k})\) for any \(k\in[1,q_{s}]\), and finds \(k^{*}\) such that \(\sigma_{2}^{*}=g^{s_{k^{*}}},\sigma_{3}^{*}=g_{1}^{s_{k^{*}}}\). After finding the index \(j\) corresponding with the \(k^{*}\)-th query, \(\mathcal{B}\) checks if \(\mathbf{Verify}(\mathsf{VK}_{w_{j}},\,\sigma^{*},\,m^{*})\) outputs \(1\). If it does, \(\mathcal{B}\) outputs a forgery of the one-time multi-user Schnorr signature scheme as follows:
* Set \(M^{*}=\big{(}\sigma_{2}^{*},\sigma_{3}^{*},\big{\{}\sigma_{1,i}^{*}\}_{i\in[1, n]^{i}},m^{*}\big{)}\). * Output \(\big{(}k^{*},M^{*},(\sigma_{4}^{*},\sigma_{5}^{*})\big{)}\). It follows that, as longs as \(\mathcal{A}\) breaks the reusability of the FVS scheme, then \(\mathcal{B}\) can break the MU-SUF-CMA security of the Schnorr signature scheme. Indeed, if \(\mathbf{Verify}(\mathsf{VK}_{w_{j}},\,\sigma^{*},\,m^{*})=1\), this means that \((\sigma_{4}^{*},\sigma_{5}^{*})\) is the valid signature of the message \(\big{(}\sigma_{2}^{*},\sigma_{3}^{*},\{\sigma_{1,i}^{*}\}_{i\in[1,n]},m^{*}\big{)}\) under the \(k^{*}\)-th public key \((\sigma_{2}^{*},\sigma_{3}^{*})\). Since it is clearly proven that the variant of the one-time multi-user Schnorr signature scheme is MU-SUF-CMA secure under the DL assumption in the random oracle model [29], the reusability of our FVS scheme can also be proven in the random oracle model under the DL problem.
## 4 Experimental Test  
### Experimental Setup  
As shown in Figure 11, an experimental prototype was established to test the isolation performance of the SGC vibration isolator. This prototype includes seven main subsystems: a signal generator, a power amplifier, a shaker, the SGC isolator, laser sensors, a controller, and a computer. The shaker generates the forced vibration of the SGC system, which is applied to imitate a real vibration situation. The laser sensor measures the displacement of the base and bearing platform, which is used to evaluate the vibration isolation effect in terms of displacement transmissibility. The length, Young's modulus, and section diameter of the SGC prototype in this experiment are 0.11 m, 3.5 \(\times\) 10\({}^{4}\) Pa, and 0.035 m, respectively.  
As shown in Figure 11, the SGC isolator is mounted on a vibration shaker, and the mass is placed on the bearing platform. A sine signal outputted by the signal generator is amplified to the vibration shaker by a power amplifier. A sine exciting force is applied to the base by a vibration shaker, which causes the base to vibrate. The vibration displacements of the base and bearing platform are measured by two laser sensors. The data collection's signal of the laser sensor is sampled 500 times each second, and the data collected by the laser sensor is transmitted to the computer for analysis.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
###### Abstract  
Automatic Speech Recognition (ASR) provides a new way of human-computer interaction. However, it is vulnerable to adversarial examples, which are obtained by deliberately adding perturbations to the original audios. Thorough studies on the universal feature of adversarial examples are essential to prevent potential attacks. Previous research has shown classic adversarial examples have different logits distribution compared to normal speech. This paper proposes a logit-traction attack to eliminate this difference at the statistical level. Experiments on the LibriSpeech dataset show that the proposed attack reduces the accuracy of the LOGITS NOISE detection to 52.1%. To further verify the effectiveness of this approach in attacking detection based on logits, three different features quantifying the dispersion of logits are constructed in this paper. Furthermore, a richer target sentence is adopted for experiments. The results indicate that these features can detect baseline adversarial examples with an accuracy of about 90% but cannot effectively detect Logits-Traction adversarial examples, proving that Logits-Traction attack can evade the logits-based detection method.  
automatic speech recognition; speech processing; logits; audio adversarial attack; adversarial example detection
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 3 Results  
We now provide the simulation results in terms of the following aspects: learning rate, path selection, E2E loss rate and bandwidth utilization.