esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system's approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.  
day-ahead market; energy arbitrage; electric vehicle second-life batteries; price prediction with deep learning; time-series prediction; neural hierarchical interpolation for time series +
Footnote †: journal: Applied sciences
# Performance of Payement Temperature Prediction Models
## 2 Materials and Methods
### Literature Review
#### 2.1.3 Saudi Arabia Model  
[PERSON] et al. (2015) investigated trends in pavement temperature variation in an arid environment in Saudi Arabia. Pavement temperature variation with pavement depth was monitored for two years, and a statistically reliable correlation developed between air temperature and pavement temperature at any depth if the surface or air temperature is known.  
Measurements were carried out at two test sites, in Riyadh and Dharan. The Riyadh test site used asphalt concrete slabs of 15, 20 and 30 cm thickness. The Dharan test site used a 25 cm asphalt-bound layer on top of a compacted aggregate subphase. The asphalt slabs comprised a 5 cm dense graded wearing course mix and a dense graded base course mix with variable thickness. Temperature measurements were taken at 2, 4, 8 and 16 cm and at the bottom of the pavement. The air temperature was measured at a height of 1.5 m above the pavement surface. A good correlation (\(R^{2}\) = 93%) was established between recorded air temperature and pavement surface and depth temperature. This model, however, was developed for Saudi Arabia, which has a desert climate.
## 3 Results and Discussion
### Results of Test under S3 and S4 Conditions  
#### 3.2.1 Rainfall Test of S3  
At the initial stage of rainfall, the rainfall intensity was less than the infiltration capacity of the soil. Most of the rainwater infiltrated into the dam body, and there was no obvious change on the dam-slope surface.  
After 2 h 5 min, due to the influence of rainfall infiltration, settlement cracks occurred in the dam-slope surface. Transverse crack 1 appeared on the left side of the dam-slope surface and transverse crack 2 appeared near the dam-slope crest, as shown in Figure 9a.  
After 2 h 13 min, because the soil layer at the dam-slope toe was relatively thin, this part was saturated first, then the gullies began to appear. The gullies were short and thin, as shown in Figure 9b. After 2 h 40 min, the gullies on the dam-slope surface became longer and wider, and developed upstream. With the progress of rainfall, the cracks on the dam-slope surface accelerated the infiltration of rainwater, and crack 1 was soaked by rainwater and gradually closed. After 2 h 55 min, the gullies on the dam-slope surface developed upstream, but the erosion on the dam-slope surface was not serious for the whole. At this time, transverse cracks appeared near the dam-slope crest, the dam-slope toe was raised, and obvious settlement occurred in the middle and upper part of the dam slope, indicating that the internal sliding surface of the dam body was forming, as shown in Figure 9c.  
Figure 9: Overall-sliding failure. (**a**) Cracks appeared after 2 h 5 min. (**b**) Gullies appeared after 2 h 13 min. (**c**) Gullies’ development and sliding deformation appeared after 2 h 55 min. (**d**) Overall sliding occurs after 3 h 28 min (test termination). (**e**) Final sliding failure (from side view).  
After 3 h 28 min, the dam body suddenly suffered from an overall sliding failure, as shown in Figure 9d. From the deformation and disconnection of the horizontal white line marked in the dam body, it can be seen that the failure was an overall sliding failure, and the shape of the sliding surface was similar to an arc (as shown as Figure 9e). Therefore, it can be judged that the failure was an overall shear-sliding failure. It can be seen from the test that the overall sliding failure had the characteristics of a sudden and short duration, which is often harmful in reality.
## 4 Discussion
### Example Quality vs. Quantity  
Our experiments with different shot settings reveal an interesting relationship between example quality and quantity. While traditional approaches often focus on maximizing the number of examples within context length constraints (often at the expense of example relevance), our findings suggest that the quality of examples--measured by semantic similarity to the query--plays a more crucial role than quantity.  
This is particularly evident in our comparison of one-shot versus two-shot settings. While adding a second example generally improves performance, the gains are most substantial when both examples are highly relevant to the query. This suggests that practitioners should prioritize example relevance over quantity, especially when working with limited context windows.
## 2 Results and Discussion  
### Meteorological Data  
The vineyard site has a continental monsoon type climate. The climate conditions, including temperature, sunshine duration, and rainfall, are summarized in Figure 1. The average temperature during April to September in 2016 was a little higher than that in 2015. The sunshine duration and rainfall in July displayed great differences between the two seasons. To be specific, rainfall in July in 2016 was three-fold higher than that in 2015; consequently, sunshine duration in July in 2016 was distinctly lower than that in 2015. Notably, veraison commences in late July; therefore, climatic differences in July between two seasons could have a great influence on berry ripening and grape compositions.
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 2 Instrument Description
### 183 GHz Receiver  
The 183 GHz receiver is used to measure the water vapor absorption band. As compared with the oxygen band, the water vapor band has a continuum spectra with fewer features. For this reason, and to reduce the instrument complexity, it was decided to use a modified version of the MTP receiver backend. The detailed block diagram of the 183 GHz channel is shown in Figure 5. A first-stage LNA is followed by a down-converter that uses the 2 nd harmonic of a 95.1 GHz clock (i.e., the central frequency at 190.2 GHz). The lower sideband of the down-converted signal is sampled by the analog backend. Four channels are measured: 179 GHz, 182.6 GHz, 182.8 GHz, and 183 GHz with a bandwidth of 250 MHz each, which are 11.2 GHz, 7.6 GHz, 7.4 GHz, and 7.2 GHz from Figure 5, respectively. The integration time in this case is also 10 ms. Hence, the angular resolution for the water vapor channel is the same as the one in the oxygen channel.
## 4 Conclusions  
In this study, virgin (Steel A) and creep exhaust steel (Steel B) were subjected to various deformation conditions (temperature 900-1050 \({}^{\circ}\)C and strain rates of 0.01-10 s\({}^{-1}\)), and the following conclusions were drawn:  
1. The flow stress-strain curves show that the flow stress increased with an increase in strain rate (0.01 s\({}^{-1}\) to 10 s\({}^{-1}\)) and decreased with an increase in temperature (900 \({}^{\circ}\)C to 1050 \({}^{\circ}\)C) for the two steels. The flow stress-strain curves exhibited a DRV+WH as the deformation mechanism.
2. The apparent activation energy of steel A was 473.08 kJ/mol, and for steel B it was 564.48 kJ/mol. These Q-values were much higher compared to the self-diffusion energy of iron in austenite (270 kJmol\({}^{-1}\))
3. The mathematical constitutive models for steel A and steel B to a total strain of 0.6 are as given in Equations (22) and (23). \[\dot{\epsilon}=9.97885\times 10^{18}[sinh(0.006916\sigma_{ss})]^{5.755175} \left[\frac{-473.0843}{RT}\right]\] (22) \[\dot{\epsilon}=9.58432\times 10^{22}[sinh(0.00647\sigma_{ss})]^{6.66945} \left[\frac{-564.4791}{RT}\right]\] (23)
4. The constitutive models were validated using Pearson's correlation coefficient, \(R\) and the average absolute relative error, AARE. For steel A, \(R\) was 0.97, and AARE was 7.62%. For Steel B, \(R\) was 0.98, and AARE was 6.54%. The developed models were used interchangeably with acceptable accuracy. Using the model for steel A on steel B, \(R\) was 0.96, and AARE was 7.19%. Similarly, using the steel B model on steel A, the \(R\) was 0.95, and AARE was 8.36%.  
Conceptualization, S.M., J.O., J.V.d.M., F.M., M.B. and D.K.; methodology, M.B., J.O., F.M. and M.B.; investigation, S.M., J.O., J.V.d.M., M.B. and D.K.; writing--original draft preparation, S.M.; writing--review and editing, S.M., J.O., M.B. and D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: (**a**) Steel A model used to predict flow stresses of steel B. (**b**) Steel B model to predict flow stresses in Steel A.
## 2 Results and Discussion
### Binding Constant  
The above analyses indicated that the spectral change could be ascribed to the formation of 1:1 host-guest complexation. The obtained binding constants were calculated using the method of non-linear least squares according to the UV-vis data and listed in Table 1[29, 30]. According to Table 1, the binding constants of compounds **4**, **5**, **7**, **8**, **10** and **12** with various anions cannot be calculated due to the very weak spectral changes and the binding ability can be ignored. The binding trend of six compounds (**1**, **2**, **3**, **6**, **9** and **11**) to anions was as follows: AcO\({}^{-}>\) H\({}_{2}\)PO\({}_{4}\)\({}^{-}>\) F\({}^{-}>>\) Cl\({}^{-}\sim\) Br\({}^{-}\sim\) I\({}^{-}\). It was apparent that the sensitivity for multiple hydrogen-bond interactions could be rationalized on the basis of the interaction between the host and the anionic guest. As expected from their basicity, AcO\({}^{-}\), H\({}_{2}\)PO\({}_{4}\)\({}^{-}\) and F\({}^{-}\) bound more strongly than the other anions tested. In general, the AcO\({}^{-}\) ion showed the strongest binding ability with the above compounds. For the acetate ion, the binding ability followed the order of: \(\textbf{6}>\textbf{3}>\textbf{2}>\textbf{1}>\textbf{11}>\textbf{9}\). That is, 1,10-phenanthroline-5,6-dione derivatives containing dinitro groups showed the strongest binding ability among the synthesized compounds. The reason might be that the electron-withdrawing group could strengthen the acidity of the compound and the binding ability increased correspondingly. As for anthracene-9-carbaldehyde derivatives (**10**, **11** and **12**), compound **11** containing one nitro group showed high sensitivity, while compound **12** involving dinitro groups showed almost no binding ability for the acetate anion. The reason may be related to steric hindrance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Anion** & **AcO\({}^{-}\)** & **HzPO\({}^{-}\)** & **F\({}^{-}\)** \\ \hline \(K_{s}\) (**1**) & \((5.69\pm 0.56)\times 10^{5}\) & \((2.14\pm 0.17)\times 10^{4}\) & \((1.02\pm 0.11)\times 10^{4}\) \\ \(K_{s}\) (**2**) & \((4.35\pm 0.41)\times 10^{6}\) & \((1.39\pm 0.89)\times 10^{6}\) & \((1.49\pm 0.38)\times 10^{6}\) \\ \(K_{s}\) (**3**) & \((8.40\pm 0.78)\times 10^{6}\) & \((2.73\pm 0.21)\times 10^{4}\) & \((7.32\pm 0.03)\times 10^{3}\) \\ \(K_{s}\) (**4**) & ND & ND & ND \\ \(K_{s}\) (**5**) & ND & ND & ND \\ \hline \hline \end{tabular}
\end{table}
Table 1: Binding constants of compounds with various anions.  
Figure 3: Fluorescence response of compounds (**1**, **2**, **3**, **6**, **9** and **11**) (\(4.0\times 10^{-5}\) mol\(\cdot\) L\({}^{-1}\)) upon the addition of an acetate anion (\(-2.0\times 10^{-3}\) mol\(\cdot\) L\({}^{-1}\)); arrows indicate the increasing direction of the acetate anion concentration.
# Nanomaterials for Healthcare Biosensing Applications
## 3 Applications of Inorganic Nanomaterials in Healthcare Biosensing
### Quantum Dots
\begin{tabular}{c c c c c c} \hline \hline
**Sensor Platform/Label** & **Analyte** &
\begin{tabular}{c} **Detection** \\ **Mechanism** \\ \end{tabular} & **Detection Range** & **LOD** & **Reference** \\ \hline Au-carboxyIMNP & PSA & SPR & \(10^{-4}\)–\(1\) ug mL\({}^{-1}\) & 100 pg mL\({}^{-1}\) & [166] \\ \hline Au-MUC-1/MNP-FA & MCF-7 & SPR & \(5\times 10^{2}\)–\(10^{4}\) cells mL\({}^{-1}\) & 500 cells mL\({}^{-1}\) & [167] \\ \hline AgNP-DTNB-McAb-BSA/Fe\({}_{3}\)O\({}_{4}\)-APTES-PcAb-BSA & MMP-9 & SERS & \(1\) pg mL\({}^{-1}\)-\(100\) ng mL\({}^{-1}\) & 1 pg mL\({}^{-1}\) & [168] \\ \hline Au-MPA-PA-PAb-EA/carboxylIMNP-pAb-EA & \(S.\)_entreittials_ & SPR & \(1.4\times 10^{1}\)–\(1.4\times 10^{9}\) cfu mL\({}^{-1}\) & 14 cfu mL\({}^{-1}\) & [169] \\ \hline CdS NanoCrys-thiol anti-p53-BSA/biotin anti-p53-AuNP-GO & p53 & ECL & \(20\)–\(1000\) g mL\({}^{-1}\) & 4 fg mL\({}^{-1}\) & [170] \\ \hline GCE-chit-Aubell-GOscore-GOx & Glucose & ECL & \(1.0\) uM–\(4.3\) mM & 0.3 uM & [138] \\ \hline MEA-Au-film-GO-anti-IgM/AuBPs & IgM & LSPR & \(0.03\)-\(32\) ug mL\({}^{-1}\) & 0.03 ug mL\({}^{-1}\) & [141] \\ \hline probeDNA/Exo-III-Ag NPC & Coralyne & FS & \(5\)–\(1000\) nM & \(1.83\) nM & [171] \\ \hline AuNP-HYA-PTA/AuAg NCPs & HAase & FS & \(0.5\)–\(37.5\) U mL\({}^{-1}\) & 0.3 U mL\({}^{-1}\) & [172] \\ \hline Ag\({}_{\text{diel}}\)-AuNR\({}_{\text{core}}\) & p-AP & VE, SPR & \(1\)–\(70\) uM & 0.64 uM & [173] \\ \hline carboxylFe\({}_{3}\)O\({}_{4}\)-m/Ab & Ricin & TMR & \(1\times 10^{-3}\)–\(200\) ug mL\({}^{-1}\) & 1 ng mL\({}^{-1}\) & [174] \\ \hline APTES@silica Fe\({}_{3}\)O\({}_{4}\)-aptamer & AFM1 & HPLC & \(0.3\)–\(50\) ngL\({}^{-1}\) & 0.2 ngL\({}^{-1}\) & [175] \\ \hline Ag\({}_{3}\)O\({}_{4}\)NFC-strotin & dAb/cAb & _Salmonella_ & NMR & \(10^{5}\)–\(10^{6}\) cfu mL\({}^{-1}\) & \(10^{5}\) cfu mL\({}^{-1}\) & [176] \\ \hline AB\({}_{3}\)/AB\({}_{2}\)-Glut-AuAutism-M! & CD-10 & QCM & \(10\)–\(100\) pM & 2.4 pM & [139] \\ \hline \hline \end{tabular}
# Wireless-Channel Key Distribution Based on Laser Synchronization
###### Abstract  
We propose and experimentally demonstrate a wireless-channel key distribution scheme based on laser synchronization induced by a common wireless random signal. Two semiconductor lasers are synchronized under injection of the drive signal after electrical-optical conversion and emit irregular outputs that are used to generate shared keys. Our proof-of-concept experiment using a complex drive signal achieved a secure key generation rate of up to 150 Mbit/s with a bit error rate below \(3.8\times 10^{-3}\). Numerical simulation results show that the proposed scheme has the potential to achieve a distribution distance of several hundred meters. It is believed that common-signal-induced laser synchronization paves the way for high-speed wireless physical-layer key distribution.  
wireless security; physical layer security; key distribution; nonlinear systems; laser synchronization +
Footnote †: journal:  
Article
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
## 4 Results
### Virtual Reality Sickness  
We measured the motion sickness of the experiments performed in this study using the VSRQ. The oculomotor score was 38.54, and the disorientation score was 20.5. The total score was 29.52. For further analysis, ANOVA analysis of the VRSQ scores, per independent variables, was performed (\(\alpha\) = 0.05). Thus, the difference according to the target size was analyzed as significant (oculomotor: \(p\) < 0.01; disorientation: \(p\) < 0.05; total: \(p\) < 0.01). Among the components of the VRSQ, the oculomotor was 33.75 points for the large target and 43.33 points for the small target. Disorientation was 18.33 points and 22.67 points, respectively. The total was 26.04 points and 33 points (Figure 10).  
However, the difference in scores per the input method was not statistically significant (oculomotor: \(p\) = 1.000; disorientation: \(p\) = 0.468; total: \(p\) = 0.689) (Figure 11). Oculomotor was 38.54 points for both methods, the disorientation was 21.17 points for gaze selection, and 19.83 points for manual selection. The gaze-selection showed a score equal to or slightly higher than the manual selection, but no significant difference.  
Figure 10: VRSQ scores for target size.  
Figure 9: The distribution of mean error rate for each condition: (**a**) large target size; (**b**) small target size; (**c**) manual-selection method; (**d**) gaze-selection method.
## 3 Communication and Channel Modeling
### Channel Modeling
#### 3.1.3 MIMO Beamforming
After applying the directional beam steering method for BS-beamforming (i.e., \(\Gamma^{BS}(\varphi^{BSRI},\ \zeta^{BSRI})={\rm a}^{BS*}(\varphi^{BSRI},\ \zeta^{BSRI})\), and RIS phase shift design in (15), the channel gain can be approximated as follows:  
\[\mathcal{G}^{BSRISIV}_{CLoS}\approx\underbrace{\left(\frac{\lambda}{4\pi} \right)^{2}\sqrt{G_{BS}}\sqrt{G_{IV}}}_{Constant}F_{Finc}\Big{(}\varphi^{BSRI},\ \zeta^{BSRI}\Big{)}F_{ref}\Big{(}\varphi^{BSIV},\ \zeta^{BSIV}\Big{)}\underbrace{\frac{e^{-( \varphi^{BSRI}+\varphi^{RIUV})}}{\left\|p^{RI}-p^{BS}\right\|_{2}}\Big{\|}_{d^{ BSRU}}}_{\frac{1}{d^{BSRU}}} \tag{20}\]  
where \(F(\varphi,\ \zeta)\) denotes the cosine shape scan loss function [45] that is embedded in the channel modeling for modeling the mismatches between actual positions and estimated positions of mobile elements such as UAV or IVs:  
\[F(\varphi,\ \zeta)=\left\{ \begin{array}{cc}\left(\begin{array}{cc}\cos( \varphi_{LoS}-\varphi_{beam})\\ \times\cos(\zeta_{LoS}-\zeta_{beam})\end{array}\right),&\zeta\in\left[0,\frac{ \pi}{2}\right],\varphi\in\left[-\frac{\pi}{2},\frac{\pi}{2}\right]\\ 0&,&\zeta\in\left(\begin{array}{c}\pi,\pi\end{array}\right],\varphi\in\left[ -\frac{\pi}{2},\frac{\pi}{2}\right]\end{array} ;\right. \tag{21}\]  
with subscript \"\(LoS\)\" denoting the actual LoS path and the subscript \"_beam_\" denoting the propagated beam based on estimated positions. **Remark 7**. : _By utilizing mMIMO beamforming technology and implementing directional beamsteering with narrow beams, higher angular resolution can be achieved, which enhances the accuracy of localization. This is a significant advantage of mmWave communication [46]. The channel model in (20) can be leveraged to enable cost-effective and computationally efficient localization. Localization is achieved by adjusting the beam-steering based on the estimated position and measuring the variation in signal strength. When the estimated position of the RISeLAV and IV approaches the actual position, the channel gains, and consequently the signal strength, are maximized. The signal strength can be used as a measure for accurate localization._  
Figure 5: Schematic of UAV-enabled, RIS-assisted wireless communication for intelligent vehicles (IVs) in IoVs with mMIMO BSs. Notice that, for the sake of illustration, the sizes of the mMIMO BS and RISeUAV are exaggerated compared with the distances.
# Secure Remote Storage of Logs with Search Capabilities
## 2 Secure Remote Storage and Searchable Encryption
Public key Encryption with Keyword Search, or PEKS, enable the creation of ciphertexts with a public key, and only the private key owner can perform the encrypted search. PEKS has the particularity of supporting multiple user scenarios due to the fact that anyone can encrypt data with the right public key. Examples of PEKS can be found in Reference [6, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]. More recent example works can be found in Reference [59, 60]. In Reference [59], the authors devise a Lattice-based key-aggregate encryption mechanism. Their work, while being quantum-resistant, only supports the encryption of one bit messages and uses a direct index, where each one bit message will have a associated trapdoor, whereas the work proposed in Reference [60] solves the forward secrecy problem with public key encryption techniques that ultimately result slower operation rates, but also in requiring larger storage space, when compared to symmetric encryption techniques.