esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Total Phenolic Content and Antioxidant Activity  
The results on the effects of the different enrichment levels of FHP, PHP, FGS, and GSP on the total phenolic content and antioxidant activity of the bread samples are shown in Figure 1d and 1e, respectively. Significant increases in the total phenolic content and antioxidant activity of the breads were observed with increasing PHP levels. The total phenolic content and antioxidant activity of the breads initially increased from 0.89 to 14.66 mg GAE/g dry weight and from 5.90 to 16.19 mg Trolox/g dry weight, respectively, when the PHP level increased from 0.5% to 4.0%. When the total phenolic content and antioxidant activity of the control bread were compared, it was found that the addition of FHP, FGS, and GSP also increased them. There was no difference in the total phenolic content between the control bread and the breads with the 0.5% addition of FGS and GSP (\(p>0.05\)), while the total phenolic content and antioxidant activity of all other enriched breads were different from those of the control bread (\(p<0.05\)). Moreover, the total phenolic content and antioxidant activity of breads with powdered additives were higher than those of the breads enriched with fresh additives. This was due to the fact that the total phenolic content and antioxidant activity of powders are intrinsically higher due to the higher extraction yield resulting from the porous surfaces caused by freeze-drying (Table 1). In addition, according to the results from ANOVA, the type and level of additives had a significant effect on the total phenolic content and antioxidant activity, and it was clear that the total phenolic content and antioxidant activity increased with the increasing additive level in all the enriched breads (Figure 1d,e). In agreement with our results, the total phenolic contents and antioxidant activity of wheat bread samples enriched with Moldavian dragonhead [34] and parsley leaf powder [29] increased with increasing additive levels. [PERSON] et al. [29] found that the total phenolic content of bread to which parsley leaf powder was added at a concentration of 5% increased from 0.23 mg GAE/g (wheat flour) to 0.84 (wheat flour + 5% parsley leaf powder). Moreover, the total phenolic content of enriched bread increased linearly with the percentage (0 to 5%) increase in the addition of dragonhead leaves from 4.8 to 10.1 mg GAE/g dry matter, and the highest radical scavenging activity was obtained in bread with the highest percentage of dragonhead leaves [34]. Therefore, it was concluded in this study that the fortification of wheat flour with PHP significantly improved the functional quality of bread, which could have a positive impact on health.
###### Abstract  
Most studies determining data rate or power conversion efficiency (PCE) of simultaneous wireless information and power transfer (SWIPT) focus on ideal models for the non-linear energy harvester, or focus on simplified waveforms that carry no information. In this paper, we study SWIPT using realistic waveforms and a measurement-based energy harvesting model. For a special class of multisine waveforms carrying only information in the phase, we analyze PCE as a function of waveform design, including the impact of pre-equalization to mitigate wireless channel distortion. A balanced pre-equalizer that trades off between the peak-to-average power ratio (PAPR) and signal to noise ratio, maximizing the total PCE is proposed. The impact on the information rate of the analyzed waveforms is also presented. The results show that balanced pre-equalizers can improve the total PCE more than three times within 5% rate loss compared to the pre-equalizer that solely maximizes the signal PAPR or the capacity using the same transmit power. We also show that the maximum normalized PCE is increased by a factor of two by only allowing phase modulation to ensure the PAPR of one symbol, compared to traditional modulation schemes that carry information in both phase and amplitude to maximize spectral efficiency.  
simultaneous wireless information and power transfer; non-linear rectifier; multi-tone signal; transmission strategy +
Footnote †: journal: Electronics
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 4 Experiment and Simulation Analysis
### Working Mode Selection
In order to achieve the minimum energy consumption, each terminal node must choose the shortest communication path. Therefore, the networking mode of the monitoring mode is LoRa point by point transmission. When \(\omega=0.9\), the system gives priority to the delay constraint. In order to minimize the delay, the sink node adds the cellular mobile transmission function, and each terminal node carries out point-to-point transmission with the maximum transmission distance. Therefore, the networking mode of the system in the maintenance mode is a hybrid transmission mode of the cellular mobile network and LoRa point-to-point transmission. When \(\omega=0.5\), the system considers both delay and energy consumption constraints. In order to achieve the highest system revenue, each terminal node sends data to the sink node with the cellular mobile transmission function by the LoRa multi hop transmission mode. Therefore, the network mode of the system in early warning mode is a hybrid transmission mode of mobile cellular network and LoRa multi hop transmission. In wireless communication, packet loss rate represents the ratio between the number of packets lost and the number of packets sent. Packet loss rate is related to packet length, data transmission  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Weight \(\omega\)** & **SF** & **BW (kHz)** & **Transmission Distance (km)** & **\(D\)(x\({}_{k}\))(s)** & **E(\(\alpha_{k}\))(m)** & **Q(\(\alpha_{k}\))** \\ \hline
0.1 & 12 & 125 & 1 & 9.21 & 11 & 10.83 \\
0.2 & 11 & 125 & 1.5 & 5.11 & 15 & 1  
frequency, and communication distance. It is an important indicator to detect the data transmission performance of wireless networks. When 1000 packets are sent at the same time, the test results are shown in Table 4.  
The experimental results show that the packet loss rate of monitoring mode was the highest, reaching 6%, while that of the maintenance mode was the lowest, only 1%. The monitoring system proposed in this paper can change the working mode according to the monitoring status. Although the packet loss rate of the monitoring mode is high, the communication quality is low, and the data can be successfully transmitted within the specified delay; the communication quality of the maintenance mode is high, but the system achieves the lowest packet loss rate, which can meet the communication requirements of the system.
## 2 Expansion Strategies
### Ex Vivo Expansion of V\(\delta\)2 \(\gamma\delta\) T Cells
This binding stirs a change in BTN3A1 that is detected by V82 TCR at the cell surface, leading to cell proliferation and induction of effector functions [92]. Studies have shown that all the three isoforms of BTN3A molecule, BTN3A1, BTN3A2, and BTN3A3, can stimulate \(\gamma\gamma\)9V82 T cells [93], and activate them through mechanisms involving multimerization of BTN3A molecules [94]. Other BTN molecules were discovered to play a role in V82 T cell response to pAgs. In a recent study, butylrophilin 2A1 (BTN2A1) was identified as a key ligand binding TCR V\(\gamma\)9 chain and acting together with BTN3A1 to initiate responses to pAgs. Detection of this heteromeric butylrophilin complexes by V82 T cells represents a distinct class of immune recognition [95]. IPP metabolites can be converted into an ATP analog (ApppI), which could be presented at the cell surface and recognized by \(\gamma\delta\) TCR [96]; however the exact molecular mechanism is unknown. The proposed mechanisms of \(\gamma\delta\) T cells activation by pAgs and cellular stress are illustrated in Figure 1.  
V\(\gamma\gamma\)9V82 T cells can be simply stimulated and selectively expanded to high cell numbers (up to 800-fold) using pAg or BP plus the appropriate cytokine [61; 97]. In clinical studies, the synthetic pAg BrHPP (3 \(\mu\)M) [77] and 2M3B1-PP (100 \(\mu\)M) have been used for ex vivo expansion of V\(\gamma\)9V82 T cells before adoptive transfer [76]. For expanding V\(\gamma\)9V82 T cells, ZOL have been used ranging from 1 to 5 \(\mu\)M [98]. A different protocol was tested in a recent study, wherein a much higher concentrations of ZOL (100 \(\mu\)M) was used to pulse PBMC for only a short time (4 h). This protocol resulted in greater expansion compared to those using continuous low concentrations of ZOL (10 \(\mu\)M) [99]. Some of the in vitro studies using pAgs and BPs for \(\gamma\delta\) T cells expansion are listed in Table 1.
###### Abstract  
The inspirational paper by [PERSON], [PERSON], and [PERSON] in 1978, proposing to utilize in situ resources on Mars (ISRU) rather than bringing them from Earth, originated the field of Mars ISRU that has been the subject of research ever since. In this paper, we reviewed significant research reported on Mars ISRU since 1978 and reported briefly on accomplishments. We found that prior to 2014, progress on small tasks was sporadic and intermittent, always at low Technology Readiness Level (TRL). In 2014, the National Aeronautics and Space Administration (NASA) took a bold, imaginative, unprecedented step to fund a major project in Mars ISRU: the so-called \"MOXIE\" (Mars Oxygen In Situ Experiment), in which an oxygen production plant based on solid oxide electrolysis (SOEC) was developed, and finally demonstrated on Mars in 2022 and 2023. While MOXIE leaves behind it a wealth of accomplishments, there remains the need to close remaining gaps with additional laboratory and field work. Solid-oxide electrochemical cell (SOEC) technology has become a major area of worldwide investment for terrestrial energy and CO\({}_{2}\) control. There is a very strong overlap between this terrestrial technology and Mars ISRU. NASA has already leveraged the terrestrial development work via MOXIE. NASA can leverage further advances with a comparatively small investment beyond 2023. Because NASA is engaged in a major program to return humans to the Moon, NASA's focus is on lunar ISRU. Unfortunately, the mission impact and return on investment for lunar ISRU does not compare to that for Mars ISRU. NASA's concept for Mars ISRU is futuristic, involving autonomous mining, transporting, and processing large amounts of Mars regolith. This might well occur long after initial human landings which could better profit in the near-term from MOXIE technology. By continuing further development of SOEC technology beyond MOXIE, while leveraging large investments in terrestrial applications, NASA can develop the Mars ISRU appropriate to nearer term human missions at modest investment. The goal of this paper is to place the relatively mature MOXIE technology advance and solid oxide electrolysis in general in perspective to the historical evolution of low TRL Mars ISRU technology.  
Mars; ISRU; solid oxide electrolysis; MOXIE; NASA 2024
## 4 Evaluation of Prototypes with Edge Damping
### Acoustic Measurements  
All acoustic measurements were performed in the anechoic chamber at the Technical University of Dresden, as shown in Figure 12, by using a Klippel DA2 analyzer. The microphone (Gras 40 HL) was placed at a distance of 2 m, focusing on the panel's center, and the measurements were performed with a 4 Vrms input signal in the form of a sweeping sine wave averaged four times. The speakers were placed on a rotating turntable (LinearX LT360 EX) to measure the radiation with a resolution of 2\({}^{\circ}\).  
The evaluation process is based on the ANSI-CEA-2034 standard [22], but two significant changes were made. Flat panel loudspeakers tend to have a much more complex directivity pattern at higher frequencies than conventional loudspeakers [23]. Therefore, the spatial resolution is increased from an angular resolution of 10\({}^{\circ}\) to 2\({}^{\circ}\) to provide sufficient data points to evaluate sharp directivity beams. Furthermore, the DI only considers the radiation in the horizontal hemisphere in the range of \(\pm\)90\({}^{\circ}\). The DI is calculated from the spatial average of the LW (\(\pm\)30\({}^{\circ}\)) and the front hemisphere (\(\pm\)90\({}^{\circ}\)) response, as shown in Figure 12.  
Compared to an on-axis response (single point), the LW represents a wider listening zone, which is more realistic for the use case of a flat panel loudspeaker, and small fluctuations caused by acoustic interference are attenuated. The LW is a spatial average of the amplitude responses in the \(\pm\)30\({}^{\circ}\) horizontal angular range. The results are calculated as averages over squared sound pressure values measured at equidistant angle positions \(k\) with a resolution of 2\({}^{\circ}\) with the following common expression:  
\[\bar{p}_{LW,FH}(f)=\sqrt{\frac{1}{N}\sum_{k=1}^{N}\bar{p}^{2}(\phi_{k},f)}. \tag{6}\]  
Figure 11: Front and cross-sectional view of all three variants: Panel-Raw, Panel-BIF and Panel-BOF.  
Figure 12: (**a**) Measurement in the anechoic chamber at the Technical University of Dresden; and (**b**) evaluation of the sound radiation by calculating the LW and FH radiation.  
where \(N\) represents the total number of measured angles (91 positions for FH and 31 positions for LW). The mean SPL for each evaluation window is calculated from the root mean square sound pressure by  
\[SPL_{LW,FH}(f)=20\cdot log_{10}\bigg{(}\frac{\tilde{p}_{LW,FH}(f)}{p_{0}}\bigg{)}. \tag{7}\]  
The equation of the DI is defined as the difference between the LW \(SPL_{LW}(f)\) and the FH curve \(SPL_{FH}(f)\):  
\[DI(f)=SPL_{LW}(f)-SPL_{FH}(f). \tag{8}\]
## 4 The Model of Packet Size Adaptation Using Bounded Multi-Armed Bandits
### Packet Size Adaptation
The CM only sends for \(l<\text{L}\) slots on channel j. P\({}_{j}(\text{l})\) can be expressed as:  
\[P_{j}(\text{l})=(\text{l}-p_{j})^{l-1}p_{j}\ \ 1\leq l\leq L \tag{21}\]  
where p\({}_{j}\) is the transition probability from idle to busy. The successful transmission probability of a package with the length of L in the slots is \(P_{j}^{success}=(\text{l}-p_{j})^{L}\). According to the above equations, the anticipated energy consumption of the CM i transmitting data on the channel j is derived as follows:\[\vec{E}_{\bar{y}}^{data}=\sum_{l=1}^{L}E_{i}^{\
u}(l)P_{j}^{l}+E_{i}^{\
u}(L)P_{j}^ {success} \tag{22}\]  
\(\vec{E}_{\bar{y}}^{data}\) is not related to i because CM and CH is the same distance, so \(\vec{E}_{\bar{y}}^{data}=\vec{E}_{j}^{data}\). The probability of successful transmission only depends on the PU behavior, so the expected amount of successful transmissions in channel j is \(\vec{E}_{\bar{y}}^{data}=\vec{E}_{j}^{data}\). If the number of available channels is more than the number of active CMs, then less channels are selected to reduce the probability of collisions with other PUs during the data transmission, This is because when \(\mathrm{p}_{\mathrm{j}}\) is less, these channels can easily remain idle if they feel idle in the first slot. \(\mathrm{N}_{\mathrm{active}}\) is on behalf of selected the available channels. The optimal packet size in terms of number of slots is obtained by, \(L_{opt}=\underset{L\in\{1_{\mathrm{max}},\,1_{\mathrm{max}}\,\,1_{\mathrm{max} }\,\,1\}}{\text{arg min}}\)_EPB_, \(L_{\mathrm{min}}\) (the minimum packet size) depends on the MAC frame format of a specific network. \(L_{\mathrm{max}}\) (the maximum packet size) is generally selected as the maximum transmission unit (MTU) which is allowed by the network to avoid packet fragmentation. Because an active CM and the available channel change over time, packet size should be adaptive to change, to minimize the EPB used in the current frame of the network. The CH keeps tracking the changes of channel states by interval time between ACK and join by the residual energy balance from CMs to make a decision on the packet size at the beginning of each frame [46; 47; 48].
## 4 Proposed Algorithm
### Clustering Phase  
The proposed algorithm uses a popular unsupervised vector quantisation k-means algorithm to divide the randomly distributed sensors into clusters and performs iterative computations to optimise the centroids. The algorithm tries to minimise the distance between the sensor nodes within the clusters with the respective centroids. FEHCA implements a centralised approach where the BS performs the task of clustering. The BS elects the sensor node nearest to the centroid with energy greater than or equal to the average cluster energy, i.e., \(\text{E}_{\text{res}}>=\text{Cluster}\_\text{avg}\). Here, the current CH retains its role until its energy falls below the average energy of the cluster. Moreover, the number of clusters to be formed is not changed unless the ideal number of clusters required changes. In essence, the clustering phase is divided into two phases, namely centralised clustering and cluster head selection.
# Solar Potential in Saudi Arabia for Flat-Plate Surfaces of Varying Tilt Tracking the Sun
## 4 Comparison of the Three Configuration Modes  
This section is devoted to a comparison of static and dynamic solar systems in terms of cost-benefit. To achieve this goal, we searched for works published in the international literature.  
A study for the USA by [PERSON] et al. (2019) showed that 1-axis tracking systems can increase power generation by 12-25% in relation to fixed-tilt ones, and 2-axis tracking systems by 30-45%. These researchers estimated the installation cost at 0.25 \(\mathrm{\SIUnitSymbolMicro W}^{-1}\), 0.82 \(\mathrm{\SIUnitSymbolMicro W}^{-1}\), and 1.23 \(\mathrm{\SIUnitSymbolMicro W}^{-1}\) for fixed-tilt, 1-axis, and 2-axis systems, respectively. In the same way, their operation and maintenance costs were estimated at 25 \(\mathrm{\SIUnitSymbolMicro W}^{-1}\)year\({}^{-1}\), 32 \(\mathrm{\SIUnitSymbolMicro kW}^{-1}\)year\({}^{-1}\), and 37.5 \(\mathrm{\SIUnitSymbolMicro kW}^{-1}\)year\({}^{-1}\), respectively.  
Another study in Spain by [PERSON] and [PERSON] (2019) concluded that a double-axis solar system may result in an increase in electricity by 30.7% compared to a fixed-tilt system.  
[PERSON] et al. (2019) compared the performance and cost between fixed-tilt (static) and double-axis (dynamic) systems in Jordan. They found 31.29% more energy produced by the 2-axis system in comparison with the static one. Further, they estimated the payback period to be 27.6 and 34.9 months for the dynamic and static systems, respectively, with corresponding electricity costs of 0.08 \(\mathrm{\SIUnitSymbolMicro W}^{-1}\) and 0.10 \(\mathrm{\SIUnitSymbolMicro S}\)kWh\({}^{-1}\).  
[PERSON] et al. (2019) found a 12-20% increase in the energy produced by a dual-axis solar system in comparison to a fixed-tilt one.  
Figure 12: The correction factor, CF, as function of the ratio of the annual solar energy sum differences, \(\mathrm{R}\mathrm{A}\mathrm{H}_{\mathrm{g}}\) (**a**), and the ground-albedo ratio, \(\rho_{\mathrm{r}}\), as function of \(\mathrm{R}\mathrm{A}\mathrm{H}_{\mathrm{g}}\) (**b**) for the 82 sites in Saudi Arabia. The data points in the plots are averages over each site and over the period 2005–2016. The green dashed lines represent the values \(\mathrm{CF}=\rho_{\mathrm{r}}=1\); the vertical black lines are the means of \(\mathrm{R}\mathrm{A}\mathrm{H}_{\mathrm{g}}\); and the red and blue ones are the limits of the (mean + 1\(\sigma\), mean \(-\) 1\(\sigma\)) band, respectively.  
[PERSON] et al. [43] studied the performance of solar boilers for Athens, Greece, and Nicosia, Cyprus, by considering 1-axis, seasonal-tilt, and fixed-tilt systems. They found that the solar fractions (the normalised difference between the hot water energy provided by the sun and the auxiliary one supplied by electricity) were 81.4%, 76.2%, and 74.4% for Athens, and 87.6%, 81.6%, and 79.7% for Nicosia in the case of a single-axis, a seasonal-tilt, and a fixed-tilt solar system, respectively.  
As far as Saudi Arabia is concerned, the results of the present study (see Figure 3) lead to increases shown in Table 4.
## 6 Testing and Measuring Performance: Analysis of PSO, GWO, and Hybrid PSO-GWO Methods
### Results
#### 6.4.3 Delay Reduction  
The chart below shows the total reduction in delay that was achieved with each optimization method.  
The average reduction in delay is 0.25148 for PSO, 0.820957 for GWO, and 0.994543 for hybrid PSO-GWO. The combined method is demonstrably the most effective in reducing traffic delays. Figure 5 shows the reduction in delay when utilizing the three proposed methods.  
Figure 4: Precision of traffic movement for the three proposed methods.  
Figure 5: Reduction in delay when using the three proposed methods.
## 5 Numerical Evaluation Results
### Evaluation Setup
#### 5.1.2 Optimization Parameters  
The iteration parameters are set to \(K_{1}=10\), \(K_{2}=5\), \(K_{3}=10\), and \(K_{4}=1\). Following the lower bounds imposed by the \(K\) values in Equations (18) and (19), we set \(PL=2500\) and \(L=150\). By default, we set the mean drift-plus-penalty parameter to \(V=1000\). For all the updates, \(\alpha=0.4\) and for numerical stability, the computation of \(\lambda_{yg}^{*}\) considers the queue normalization \(\frac{O_{n}}{\sum_{\alpha\in N_{g}}Q_{n}}\frac{|N_{g}|}{2}\), which does not alter the solution.
## 2 Governing Equations
### Analyses of the Velocity  
The solution of Equation (17) can be simplified as follows via trigonometric function theory:  
\[C\sqrt{J_{1}^{2}(s_{r})+Y_{1}^{2}(s_{r})}\sin(\omega t+\varphi+\varphi)=D \cdot\sin(\omega t) \tag{18}\]  
where \(s_{r}=\frac{\omega}{c}r_{r}\), \(\tan(\phi)=\frac{J_{1}(s_{r})}{Y_{1}(s_{r})}\). So, it can be concluded that by solving Equation (18) above:  
\[C=\frac{D}{\sqrt{J_{1}^{2}(s_{r})+Y_{1}^{2}(s_{r})}},\varphi=-\phi=-\arctan( \frac{J_{1}(s_{r})}{Y_{1}(s_{r})}) \tag{19}\]  
Moreover, the Bessel functions have the following asymptotic forms for large arguments, \(s\):  
\[\left\{\begin{array}{l}J_{1}(s)=\sqrt{\frac{2}{\pi}}\frac{\cos(s-\frac{3}{ 3}\pi)}{\sqrt{s}}\\ Y_{1}(s)=\sqrt{\frac{2}{\pi}}\frac{\sin(s-\frac{3}{3}\pi)}{\sqrt{s}}\end{array}\right. \tag{20}\]At last, the analytical solution of the velocity can be concluded using the previous relationship:  
\[u=\frac{D}{\sqrt{f_{1}^{2}(s_{r})+Y_{1}^{2}(s_{r})}}\sqrt{\frac{c}{\omega}\frac{2 }{\pi}}\frac{\cos(\omega(t-\frac{r}{\varepsilon})+\varphi+\frac{3\pi}{4}\pi)}{ \sqrt{r}} \tag{21}\]  
According to Equation (21), the vibration of the boundary at time \(t_{0}\) will be propagated to the location, \(r=ct\), over time \(t\) and the amplitude will decrease by the decreasing rate of \(\
icefrac{{1}}{{\sqrt{r}}}\).
# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
###### Abstract  
Both Nickel and Cobalt have been extensively used in cutting-edge technologies, such as electric vehicle battery manufacturing, stainless steel, and special alloys production. As governments focus on greener solutions for areas such as transportation and energy generation, both metals are increasingly used for energy storage purposes. However, their price uncertainty makes for an interesting case in the modern economy. This study focuses on the price volatility forecasting of Nickel and Cobalt using ANN (Artificial Neural Network) built on a special class of Transformer models used for multi-step ahead forecasts. Our results suggest that the given model is only slightly better in predictive accuracy compared to traditional sequential deep learning models such as BiLSTM (Bidirectional Long Short-Term Memory) and GRUs (gated recurrent units). Moreover, our findings also show that, like conventional approaches, in-sample behavior does not guarantee out-of-sample behavior. The given study could be utilized by industry participants for an inquiry into new and efficient ways to forecast and identify temporal-based structural patterns in commodity-based time series.  
commodity prices; self attention; volatility forecasting; deep learning approaches; transformer models +
Footnote †: journal: Applied