esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 2 Problem Formulation and Proposed Algorithm Description
### The TOA Model of the Mixed Pulse Sequences  
Let \(t_{n}\), \(n=1,2,\cdots,N\) be the TOA of pulses, where \(N\) is the number of pulses. The TOA model of \(N\) pulses is denoted by a set \(T\)  
\[T=\{t_{1},t_{2},\cdots,t_{N}\} \tag{1}\]  
The TOA model of the mixed pulse sequences can be expressed by the formula  
\[T=\bigcup_{i=1}^{K}(T_{i}-T_{m_{i}})+T_{j} \tag{2}\]  
where \(K\) denotes the radar radiation source number, \(T_{m_{i}}\) indicates the missing pulses of the \(i\)-th radar, and \(T_{j}\) represents the set of jamming pulses.  
The mixed pulse sequences in this study consider the complex PRI modulation, the pulse missing, the jamming pulses, and the noise. The TOA models of the mixed pulse sequences are shown in Figure 1.  
Represent the TOA model as a discrete signal. It is as follows  
\[\begin{cases}x[n]=\sum_{i=1}^{K}x_{i}[r]\\ x[n]=t_{n},t_{n}\in T,n\in[1,N]\end{cases} \tag{3}\]  
where \(x_{i}[r]\) represents the discrete signal corresponding to the \(i\)-th radar TOA model, \(x[n]\) represents the discrete signal corresponding to the TOA model of mixed pulse sequences, and \(T\) denotes the TOA set of mixed pulse sequences. The signal de-interleaving problem in this study is to obtain \(x_{i}[r]\) according to \(x[n]\).
## 2 Methodology and Materials
### Battery Energy Storage System (BESS)  
Battery systems ensure energy availability during low sunlight periods (e.g., nighttime or cloudy days). Lithium-ion battery storage has been selected due to its high energy density, long lifecycle, and cost-effectiveness. Battery models are mainly classified into two types: electrochemical models and Equivalent Circuit Models (ECM). Whilst electrochemical models analyze internal chemical reactions for high accuracy, they are complex. ECMs, in contrast, focus on external characteristics, avoiding internal reaction details. This study applied the Thevenin ECM (Figure 2), where \(V_{L}\) is the terminal voltage and \(V_{OC}\) is the open-circuit voltage (OCV) in volt, where their relationship is given by [25],  
\[V_{L}=V_{OC}-I_{L}\times R_{O}-\int\frac{I_{L-I_{1}}}{C_{1}}dt-\int\frac{I_{L -I_{2}}}{C_{2}}dt \tag{5}\]  
Where, \(I_{1}=V_{1}/R_{1}\) and \(I_{2}=V_{2}/R_{2}\) in ampere. Additionally, BESS improves Self-Consumption Rate (SCR) and Self-Sufficiency Rate (SSR), which are crucial for Behind-the-Meter (BtM) systems. SCR measures the portion of PV production consumed locally, while SSR represents the fraction of building consumption covered by PV energy [26],  
\[\text{SCR}=\left\{\sum_{n=1}^{t}DPV_{n}+\sum_{n=1}^{t}BCH_{n}\right\}/\sum_{n =1}^{t}P_{n} \tag{6}\]  
\[\text{SSR}=\left\{\sum_{n=1}^{t}DPV_{n}+\sum_{n=1}^{t}BD_{n}\right\}/\sum_{n =1}^{t}C_{n} \tag{7}\]  
where, \(n=\) Year, \(t=\) System lifetime, \(DPV_{n}\) = Annual direct PV consumption (kWh), \(BCH_{n}\) = Annual BESS energy charge (kWh), \(P_{n}\) = Annual PV production (kWh), \(BDS_{n}\) = Annual BESS discharge (kWh) and \(C_{n}\) = Annual energy consumption (kWh).
## 5 Active Coating and its Control Algorithm
### Incident Wave Field  
We will use the following assumptions about the incident wave field. We consider the incident waves appearing, for example, as a result of ray-like (Figure 10) far propagation in the undersea sound channel [39]. We assume that the protected body (area) of characteristic size is fully covered by the cross section of tube of rays (of diameter \(\sim\)\(D_{R}\)), _i.e._\(D_{R}>>D_{V}\). The wave within the tube of rays represents locally a running plane wave. The protected body can be spaced in the intersection of two (or more) tubes of rays and therefore the scattering problem can include two (or more) incident plane waves. We expect wave dispersion effects are very small, and speed of sound propagation is constant within the area occupied by the protected body.  
The source of incident waves can radiate both video (without filling by sinusoidal shapes) pulses and radio-like sound pulses (with filling by sinusoidal shapes). In any case any video pulse will be converted via far propagation into a group of radio pulses, running along ray tubes. In addition we assume that each plane wave has finite duration. In other words each wave has a front border of plane shape moving with the speed of sound (of smooth or step-like shape), behind which the sound field is zero and also nonzero behind the border.  
A step-like front border is more preferable for our approach, but it is impossible due to wave dispersion in long range propagation. The magnitudes of incident waves in the active acoustical detection problem (and in the absence of anti-sonar activity) must ensure the registration of scattered waves by the receiver system spaced on the same large distance between the scatterer (body) and the source of incident waves. Therefore if the distance between the source of incident waves and the  
Figure 10: The protected body in the intersection of two tubes of rays with two incident wave groups (locally plane). This is in the undersea acoustical channel with non-uniform distribution \(c_{w}(z)\) (along the depth \(z\)) of speed of propagation of acoustical waves.  
protected body increases, the incident wave needs to shake the scatterer surface more powerfully for registration by the receiving microphones, without any increase of their sensitivity. This ensures the necessary accuracy of bearing and targeting (see below) of incident waves. Now we'll assume that the incident waves represent a small number \(N_{w}>1\) of powerful, smooth, plane wave groups of finite duration and with amplitudes much greater than natural noise background \(\sigma_{p}\) of sound pressure. In the absence of a scattering body the sound pressure field of incident waves has the form:  
\[p(\mathbf{r},t)=\sum\limits_{i=1}^{N_{w}}\Xi_{i}[t-(\mathbf{r},\mathbf{w}_{i} )\mathcal{c}_{w}^{-1}], \tag{18}\]  
where \(i=1\),\(2\),\(3\),\(\ldots\)\(N_{w}\), (Figure 11b), \(\{\mathbf{w}_{i}\}\)-the normalized vectors of incident waves (\(\left|\mathbf{w}_{i}\right|=1\)), \(c_{w}\)-speed of sound propagation in the fluid, \((\mathbf{r},\mathbf{w}_{i})\) - scalar product of vectors.
However, LFAs are limited in their specificity, sensitivity, or possibility for quantitative measurements. Moreover, the performance and efficacy of the most promising rapid antigen-based tests for actual applications are still questionable. Thus, there is an urgent need for novel alternative methodologies that are rapid, practical, accurate, sensitive, and easy to use for virus diagnostics. Herein, we report MICaFVi (**M**agnetic **I**mmuno-**C**apture **F**low **V**irometry), a novel magnetic nano-based diagnostic tool that combines MNP-based immuno-capture of viruses for enrichment followed by flow-virometry analysis for detection. We sought to develop a highly sensitive, accurate, and practical detection technique that does not require tedious nucleic acid extraction or amplification and could be integrated into a rapid, quantitative, and selective automated workflow. MICaFVi incorporates a 30 min to a few hours incubation step followed by a short readout, achieving fast and sensitive detection. For safety reasons, MICaFVi was developed and optimized using virus-mimicking silica-based particles (VM-SPs). Overall, our MICaFVi technique enabled the accurate, quantitative, specific, and sensitive detection of MERS-CoV/SARS-CoV-2-mimicking particles, as well as MERS-CoV pseudoviral particles (MERSpp). The limit of detection (LOD) was found to be equal to 3.9 \(\upmu\)g/mL (20 pmol/mL). This quick and accurate diagnosis tool will greatly advance surveillance and control strategies for viral diseases, especially for future pandemics.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 4 Results  
### Average Queue Length Results  
Figure 13 presents the average queue lengths across different cycle times (60 to 204 s) for various AV driving behaviors and penetration rates, along with human-driven vehicles. Queue lengths increase with longer cycle times for all scenarios.  
Cautious AVs consistently show the highest queue lengths across all cycle times and penetration rates, reflecting inefficiencies due to their conservative driving behavior, which leads to underutilization of road capacity. Human-driven vehicles also exhibit high queue lengths, though they perform slightly better than cautious AVs.  
Aggressive AVs, across all penetration rates, demonstrate the best overall performance, achieving the lowest queue lengths. While queue lengths increase at higher cycle times, aggressive AVs maintain better performance compared to other behaviors. Their ability to exploit smaller gaps and maintain faster acceleration helps keep queue lengths lower than cautious and normal driving behaviors at all penetration rates.  
Platooning AVs also show highly efficient queue length reduction due to their coordinated driving, with performance nearly identical to aggressive AVs at full penetration. The difference between the two is minimal, with aggressive AVs showing only a slight improvement in queue lengths.  
Normal AVs exhibit moderate increases in queue lengths, striking a balance between cautious and aggressive behaviors. In mixed AV environments, queue lengths are more  
Figure 13: Influence of AV behaviors on average queue lengths at various traffic signal cycle times. Source: processed by authors.  
stable, falling between the extremes of cautious and aggressive behaviors, promoting balanced traffic flow.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 2 System Model  
In this section, we first briefly introduce the model of the SCMA system and then describe the framework of the conventional MPA decoder.
## 3 RIM Formulation Summary, Discussion, and Prospects
### Future Research Topics on RIM  
Having enumerated the advantages and disadvantages of RIM-based optical visualization measurements, the future of RIM-based research depends on the development of materials that overcome current limitations and better serve the needs of experimental fluid dynamics. Here are potential research directions with a focus on non-toxic, environmentally friendly, and cost-effective RIM materials:  
(i) Non-toxic materials. A significant area of research should be devoted to creating RIM materials that do not pose health risks. This includes avoiding toxic organic solvents and developing alternatives that are safe to handle and dispose of, thereby improving laboratory safety and reducing environmental impact.  
(ii) Environmentally friendly materials. There is a need to explore the use of biodegradable or recyclable materials in RIM formulations. This could include the use of materials derived from renewable resources or those that can be degraded by microorganisms after use, thereby reducing the carbon footprint of RIM experiments.  
(iii) Diversification of solid shapes and particles and implementation of 3D printing. The expansion of RIM applications requires the creation of a variety of solid shapes and particles that can be tailored to specific experimental needs. The advent of 3D printing technologies opens up unprecedented opportunities for the precise fabrication of complex geometries, enhancing the versatility and customization of RIM solids.  
(iv) Physical properties that mimic real-world fluids. A critical direction is to develop RIM materials with physical properties such as density, viscosity, and surface tension that closely mimic those of real-world fluids such as water. This would ensure that experimental results are more representative of practical engineering scenarios, thereby increasing the applicability of RIM-based research results.  
(v) Integration with advanced technologies. Future RIM materials should be compatible with state-of-the-art imaging and computing technologies. This includes developing materials that can be effectively visualized using high-speed cameras, 3D scanning, and other advanced imaging techniques, as well as those that can be accurately modeled in computational fluid dynamics simulations.
## 2 Materials and Methods
### LST Derivation  
Due to the uncertain calibration parameters of TIRS11, only the thermal infrared TIRS10 was used to retrieve the LST values from Landsat-8 images [47]. A method of single-channel Algorithm was performed to obtain LST values for 23 July 2016 based on TIRS 10 [48] and for 26 September 1987, 4 October 1996 and 31 July 2007 based on band 6 [49]. Radiation calibration [50] and atmospheric correction [51] were carried out in ENVI. The spectral radiance was then transformed into the brightness temperature throughEquation (1):  
\[T_{sensor}=K_{2}/\ln(1+K_{1}/L_{sat}) \tag{1}\]  
where \(T_{sensor}\) means the brightness temperature (K), \(L_{sat}\) means the radiometric intensity (obtained from radiation calibration), \(K_{1}=607.76\) w/(m\({}^{2}\times\) ster \(\times\) m) and \(K_{2}=1260.56\) K are for band 6 of Landsat-5, while \(K_{1}=774.89\) w/(m\(2\times\) ster \(\times\) m) and \(K_{2}=1321.08\) K are for band 10 of Landsat-8 [47].  
Then emissivity correction is necessary. For band 6 of Landsat-5 and TIRS10 of Landsat-8, the emissivity is 0.9925, 0.9230 and 0.9940 [49] and 0.9908, 0.9212 and 0.9816 [48] for water, built-up area and fully vegetated area, respectively. For partly vegetated areas, the emissivity can be determined as Equation (2):  
\[\varepsilon=1.0094+0.047\,\ln(\text{NDVI}) \tag{2}\]  
For Landsat-5 images, LST values can be calculated as Equation (3):  
\[\text{LST}=T_{sensor}/(1+(\lambda\times T_{sensor}/\rho)\,\ln\,\varepsilon) \tag{3}\]  
where \(\lambda\) represents the wavelength of the band 6 for Landsat-5 with the value of 11.50 \(\upmu\)m, \(\rho=1.438\times 10^{2}\), and \(\varepsilon\) is the emissivity.  
For Landsat-8 images, LST values can be calculated as Equations (4) and (5):  
\[\text{LST}=\gamma(\varphi_{1}\times T_{sensor}+\varphi_{2})/\varepsilon+ \varphi_{3})+\delta \tag{4}\]  
\[\gamma\approx T_{sensor}\,^{2}/(b_{\gamma}\times L_{sat}),\,\delta\approx T_{sensor}-T_{sensor}\,^{2}/b_{\gamma} \tag{5}\]  
where \(b_{\gamma}=1324\)K, \(\varphi_{1}\), \(\varphi_{2}\) and \(\varphi_{3}\) can be determined based on the relationship with the atmospheric moisture content [49].  
The LST values were obtained from multi-temporal Landsat images acquired at different dates in different years, which had a different LST range and lacked direct comparability [14; 15]. To make the thermal characteristics in different observed years comparable, the derived LST values from the images were normalized by Equation (6) [27]:  
\[T=(t-t_{min})/(t_{max}-t_{min}) \tag{6}\]  
where \(T\) is the normalized land surface temperature (NLST), \(t\) refers to the retrieved LST for a certain pixel, and \(t_{max}\) and \(t_{min}\) represent the minimum and maximum LST values in the study area, respectively.
## 3 Results and Discussion  
### Evaluation of Indirect Indexes  
In this analysis, 8 of the 13 kinds of FAs (FA1-5 and FA8-10) were selected to conduct the uncompacted void content test, and the test results were shown in Figure 7. It could be found that the uncompacted void content \(U\) of FA1 (natural sand) is significantly smaller than the other two types of FAs (stone chips and machine-made sand). One-way analysis of variance (ANOVA) was used to determine whether there are any statistically significant differences with respect to uncompacted void content \(U\) between different FA types and the calculated \(F\) value was 0.035. This value is much less than the significance level \(F_{0.05}=5.99\) which means that index uncompacted void content \(U\) cannot effectively distinguish the geometric characteristic difference of different FAs under the significance level of 95%.  
Figure 6: Schematic diagram of the Burgers model.  
Figure 7: Uncompacted void content of different FAs.  
We selected 11 of the 13 kinds of FAs (FA1-5 and FA8-13) to conduct the flow time test, and the test results were shown in Figure 8. It was found that the measured flow time of natural sand FA1 is significantly smaller than that of the other two types of FAs (stone chips and machine-made sand). The calculated \(F\) value from one-way ANOVA analysis was 0.108 and this value was much less than \(F_{0.05}=5.32\). Thus, conclusions can be drawn that the index flow time \(T\) cannot effectively distinguish the geometric characteristic difference of different FAs under the significance level of 95%.  
We selected 5 of the 13 kinds of FAs (FA1-3, FA8 and FA11) to conduct the ASTM D3398 test and the tested particle index \(I\) are shown in Figure 9. It also shows that the measured particle index of natural sand FA1 is significantly smaller than that of the other two kinds of FAs. The calculated \(F\) value from one-way ANOVA analysis was 0.904 and it was also much less than \(F_{0.05}=18.51\). This indicates that the index particle index \(I\) can also not effectively distinguish the geometric characteristic difference of different FAs under the significance level of 95%.
## 3 Results and Discussion
### Speech Recognition Results with Low Bit Rate Coding  
In Figure 1, a large difference in terms of WER can be appreciated between the baseline model and the rest of the models with data augmentation when the three different codecs are applied to test data. The statistical significance of these differences have been analyzed by computing the probability of improvement (_POI_) as defined in [35], showing that all differences between the baseline and the data augmentation models have a \(\textit{POI}>0.99\), which means that the probability of obtaining a result in which the baseline is better is lower  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distortion** & **Test** & **Dev** \\ \hline Original & \(2,27\) & \(2,42\) \\ \hline MP3 8 Kbit/s & \(2,03\) & \(2,19\) \\ MP3 16 Kbit/s & \(1,95\) & \(1,87\) \\ GSM & \(1,85\) & \(1,95\) \\ \hline
5\% Indiv. PL & \(1,27\) & \(1,43\) \\
10\% Indiv. PL & \(1,14\) & \(1,24\) \\
15\% Indiv. PL & \(1,06\) & \(1,11\) \\
20\% Indiv. PL & \(1,03\) & \(1,04\) \\ \hline
5\% Burst PL & \(1,40\) & \(1,61\) \\
10\% Burst PL & \(1,19\) & \(1,33\) \\
15\% Burst PL & \(1,10\) & \(1,19\) \\
20\% Burst PL & \(1,06\) & \(1,10\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean Opinion Score (MOS) for the original data and distorted data in Test and Dev sets of Fisher Spanish database. PL means packet losses.  
than 1%. The performance achieved on distorted data with data augmentation techniques is relatively close to the results obtained with the baseline system on the non-degraded scenario. There is still a small difference of about 1% in all cases, except when coding with MP3 at 8 Kbit/s where there is a difference of about 2% in absolute WER. These small differences between the baseline on the non-degraded scenario and data augmentation results on degraded scenarios are always statistically significant (\(POI>0.99\)). Accordingly, it seems that degradation produced in speech recognition performance by low bit-rate codecs, even several of them, can be almost solved with any of these data augmentation strategies. It must be noted that the five different data augmentation models include data coded with the three speech codecs.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
###### Abstract  
Wake-up receivers are the natural choice for wireless sensor networks because of their ultra-low power consumption and their ability to provide communications on demand. A downside of ultra-low power wake-up receivers is their low sensitivity caused by the passive demodulation of the carrier signal. In this article, we present a novel communication scheme by exploiting purposefully-interfering out-of-tune signals of two or more wireless sensor nodes, which produce the wake-up signal as the beat frequency of superposed carriers. Additionally, we introduce a communication algorithm and a flooding protocol based on this approach. Our experiments show that our approach increases the received signal strength up to 3 dB, improving communication robustness and reliability. Furthermore, we demonstrate the feasibility of our newly-developed protocols by means of an outdoor experiment and an indoor setup consisting of several nodes. The flooding algorithm achieves almost a 100% wake-up rate in less than 20 ms.  
wireless sensor network; wake-up receiver; concurrent transmissions; constructive interference; wake-up flooding; protocol; beat frequency; MAC protocol +
Footnote †: journal: Sensors
# Development of Zirconium-Based Alloys with Low Elastic Modulus for Dental Implant Materials
## 3 Results and Discussion
### Elastic Modulus Measurement  
The metal biomaterials used for implants require high strength and low modulus. The formula of elastic energy can be expressed as follows.  
\[\delta_{\mathrm{e}}=1/2\sigma_{\mathrm{y}}\epsilon_{\mathrm{e}}=\sigma^{2}_{ \mathrm{y}}/2\mathrm{E} \tag{1}\]  
where \(\mathrm{\char 37}\) is the elastic energy, \(\sigma_{\mathrm{y}}\) is the yield strength, \(\epsilon_{\mathrm{e}}\) is the elastic deformation and \(\mathrm{E}\) is an elastic modulus.  
According to Equation (1), the elastic energy of Zr-7 Cu-xSn alloys is possibly calculated, and the results are depicted in Table 2. The elastic energy increased high with Sn addition. Over Zr-7 Cu-10 Sn alloy, it does not increase a little. Most of all, the elastic energy of Zr-7 Cu-xSn alloys is the range of 14.4-28.8 MJ/m\({}^{3}\), which is higher than that of general Ti-based biomaterials [31]. From these results of this study, the developed Zr-7 Cu-xSn alloys may endure the higher deformation than the commercial biomaterials. In addition, the press-fit phenomenon occurring at implant insertion induces biomechanical effects in the bone tissues, which ensure implant primary stability [32]. In this point of view, this Zr-Cu-Sn ternary alloy has the same level of elastic modulus with human bone. Therefore, we can expect that the press-fit phenomenon may be reduced compared with the present biomaterials.