esa-sceva/satcom-chunk-collection huggingface dataset:
# Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection
## 4 Simulation Results and Discussion  
In [7], it was demonstrated that SC-GFDMA generally outperforms the conventional SC-FDMA in terms of spectral efficiency. Thus, in this section, we demonstrate that the spectral efficiency achieved by SC-GFDMA can be further increased by using the proposed power control algorithm. The near-optimal pattern-aware scheduling algorithm proposed in [23] is applied for all the methods considered in this section for a fair comparison. Consequently, the simulation results obtained without power control in this section corresponds to the results obtained using the pattern-aware scheduling algorithm in [23] after allocating equal powers to users in the same chunk. To the best of our knowledge, [7] is the only study that considered unequal power allocation to different users in the same chunk, although a specific power allocation strategy was not proposed in [7]. Thus, the effectiveness of the proposed method is verified by comparing it with the work in [23] (corresponds to the case without power control in this section) and the work in [7] (which is the best existing SC-GFDMA scheme).
## 3 Results
### LA-Treated PDiff Caco-2 Cells
#### 3.2.2 Gene Ontology  
Among enriched biological processes upon treating PDiff Caco-2 cells with LA were coenzyme metabolic process, regulation of catabolic process, response to insulin, and autophagy. Nucleobase-containing compound transport, ribonucleotide, and nucleotide biosynthetic processes were also enriched. Similar to 13-HPODE-treated cells, we observed enrichment of response to endoplasmic reticulum stress and intrinsic apoptotic signaling pathway in LA-treated cells. Furthermore, alcohol, lipid, and glucose metabolic processes were enriched indicating significant impact of LA on cellular metabolism (Figure 4a; Table S8).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**KEGG Pathway (Upregulated)** & \(p\) **Value** \\ \hline hsa00830 Retinol metabolism & 0.002 \\ hsa00140 Steroid hormone biosynthesis & 0.005 \\ hsa00500 Starch and sucrose metabolism & 0.038 \\ hsa00260 Glycine, serine and threonine metabolism & 0.059 \\ hsa03320 PPAR signaling pathway & 0.059 \\ hsa04146 Peroxisome & 0.063 \\ hsa00980 Metabolism of xenobiotics by cytochrome P450 & 0.068 \\ \hline
**KEGG Pathway (Downregulated)** & \(p\) **Value** \\ \hline hsa03010 Ribosome & 0.0002 \\ hsa03008 Ribosome biogenesis in eukaryotes & 0.0008 \\ hsa03040 Spliceosome & 0.001 \\ hsa00240 Pyrimidine metabolism & 0.004 \\ hsa04145 Phagosome & 0.016 \\ hsa03020 RNA polymerase & 0.024 \\ hsa03060 Protein export & 0.032 \\ hsa00190 Oxidative phosphorylation & 0.036 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pathway enrichment analysis in 13-HPODE-treated PDiff Caco-2 cells. Pathway analysis demonstrates upregulated and downregulated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in 13-HPODE-treated cells relative to untreated (control) cells.  
LA caused enrichment of molecular functions such as cadherin binding, cell adhesion molecule binding, ligase activity, and GTPase binding, which were also enriched upon 13-HPODE treatment. In addition, phosphatase activity, tRNA binding, and phospholipid binding were enriched in LA-treated cells (Figure 4b; Table S9).  
As in 13-HPODE-treated cells, cellular, organelle, and mitochondrial outer membranes, nuclear envelop/speck/pore, focal adhesion, cell-substrate junction, cell leading edge, and spindle were among the enriched cellular components in LA-treated cells. Endoplasmic reticulum lumen, vacuolar membrane--that stores nutrients or waste products--and lysosomal membrane were enriched as well as the peroxisomal part (Figure 4c; Table S10).
## 6 Evaluation
### Security and Privacy
#### 6.2.2 Resilience and Recovery  
The system employs a robust mechanism to ensure data integrity and speedy recovery. This mechanism is built around the strategic use of a unique product identifier, which serves as a mapping key on the blockchain. The integration of IPFS and blockchain technology strengthens our system's ability to protect data. We store data on the IPFS and reference them on the blockchain, creating a tamper-proof decentralised storage system. If the backend systems fail, the unique product identifier is a valuable tool for data recovery. Stakeholders can use it to connect to the blockchain, obtain the relevant IPFS hash, and retrieve detailed MP and other critical data stored on the IPFS. This process ensures that even in the event of an unreachable backend server, each product's integrity and history are preserved and accessible.
## 3 Results and Discussion
### Core Retention
It has been established that this proportion of the core accounts for a major part of the overall core losses [(34; 43; 44; 52; 61)]. Results of PSD analysis (Figure 1, Table 1) indicated that some small among-CIWEs differences in mean particle size were found. However, these differences were very small, and, in all cases, the d\({}_{3,2}\) of the atomized CIWEs was smaller than 0.44 \(\upmu\)m. Potentially, the mean particle size of CIWE can affect core retention during microencapsulation by spray drying [(52; 61)]. However, such an effect was reported for CIWE with a mean particle size larger than 1 \(\upmu\)m and for systems with very significant among-CIWEs differences in PSD properties [(52)]. It can be thus assumed that in light of the overall similar PSD properties and small d\({}_{3,2}\) of all the CIWEs, the effect of these variables on core retention was marginal and significantly smaller than the observed effects of the composition of the CIWEs. Overall, results indicated that maintaining the total solids content of the WS unchanged while increasing the concentration of the WHPI adversely affected core retention. Results thus highlighted the importance of including a relatively high proportion of a low molecular weight carbohydrate, such as lactose, in the CIWE as an effective means to enhance core retention during microencapsulation by spray drying. Results of this study were in agreements with the results of some previous studies where the microencapsulating properties of wall systems consisting of proteins and lactose were investigated [(34; 37; 45)].
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 5 Optimal Dispatching of Micro-Energy-Grid
### Constraint Equation
#### 5.2.3 Energy Storage Element Constraints  
Energy storage elements include batteries and phase-change heat storage devices. SOC and SOH satisfy Equations (3) and (4). Moreover, they should also satisfy:  
\[SOC_{\min}\leq SOC(t)\leq SOC_{\max} \tag{25}\]  
\[SOH_{\min}\leq SOH(t)\leq SOH_{\max} \tag{26}\]  
\[0\leq P_{c}(t)\leq I_{se}(t)P_{\max} \tag{27}\]  
\[0\leq P_{d}(t)\leq[1-I_{se}(t)]P_{d\max} \tag{28}\]  
\[0\leq H_{c}(t)\leq I_{sl}(t)H_{\max} \tag{29}\]  
\[0\leq H_{d}(t)\leq[1-I_{sl}(t)]H_{d\max} \tag{30}\]  
where Equations (25) and (26) are constraints on the energy storage capacity of storage elements. \(SOC_{\min}\) and \(SOC_{\max}\) are the minimum and maximum energy storage capacity of the battery. \(SOH_{\min}\) and \(SOH_{\max}\) are the minimum and maximum heat storage capacity of the phase change heat storage device. Equations (27)-(30) are constraints on charge and discharge heat power at moment \(t\). \(P_{c\max}\) and \(P_{d\max}\) are the maximum allowable charge and discharge power of the battery, and \(H_{\max}\) is the maximum allowable charge or discharge power of the phase change heat storage device. \(I_{se}(t)\) and \(I_{sl}(t)\) are two 0-1 variables, which limit batteries and phase change heat storage devices cannot charge or discharge at the same time.  
Moreover, in a scheduling cycle, Equations (31) and (32) should be satisfied to ensure that the energy storage components are at the same state at the beginning and the end of the scheduling cycle. The consistency in each scheduling cycle should also be ensured.  
\[SOC(t)=SOC(t+24) \tag{31}\]  
\[SOH(t)=SOH(t+24) \tag{32}\]
## 4 Conclusions  
Adsorption rate by almond shell biochar was controlled by chemisorption and worked fast within the first 15 min for Sr\({}^{2+}\) and Mn\({}^{2+}\), removing 92.2% and 95.7% of these metals,  
Figure 5: Schematic diagram of sorption mechanisms of heavy metals.  
whereas it was lower for Pb\({}^{2+}\) and As\({}^{5+}\), removing 37.3% and 4%, respectively. Equilibrium was achieved in 3h for Sr\({}^{2+}\), 2 h for Mn\({}^{2+}\), 7 h for Pb\({}^{2+}\) and 12 h for As\({}^{5+}\).  
In the case of mono-metal adsorption, the maximum uptake of Sr\({}^{2+}\) by the biochar was 12.3 mg/g, of Mn\({}^{2+}\) 23.8 mg/g, of Pb\({}^{2+}\) 24.7 mg/g and of As\({}^{5+}\) 12.4 mg/g. The experimental data were best simulated by the Langmuir model for Sr\({}^{2+}\) and Mn\({}^{2+}\) and by the Freundlich model for Pb\({}^{2+}\) and As\({}^{5+}\).  
In the case of multi-metal adsorption, the uptake of each metal was enhanced as the total ion flux density increased, revealing that there was no competition between these ions for biochar's sorption sites. For an adsorbent dose of 2 g/L, the maximum adsorption capacity of almond shell biochar for Sr\({}^{2+}\), Mn\({}^{2+}\), Pb\({}^{2+}\) and As\({}^{5+}\) could be raised up to 47.6 mg/g, 49.9 mg/g, 25 mg/g and 48.7 mg/g, respectively. The adsorption mechanism of Mn\({}^{2+}\) and As\({}^{5+}\), as described by the isotherm models, was the same as under mono-metal conditions; however, that of Sr\({}^{2+}\) and Pb\({}^{2+}\) changed.  
FTIR and XRD spectra, in conjunction with pH measurements and structural analyses of the adsorbent before and after the simultaneous adsorption of Sr\({}^{2+}\), Mn\({}^{2+}\), Pb\({}^{2+}\) and As\({}^{5+}\), showed that the solid material was rich in oxygen and negatively charged functional groups and formed mineral precipitates with carbonate, phosphate and hydroxyl anions on its surface. Potential adsorption mechanisms were chemical complexation or electron coordination between the metals and the biochar matrix, as well as mineral precipitation on the adsorbent surface.  
Conceptualization, D.V.; methodology, D.V.; software, E.S. and A.S.; validation, D.V. and E.S.; investigation, D.V.; data curation, E.S. and A.S.; writing--original draft preparation, D.V.; writing--review and editing, D.V.; visualization, D.V.; supervision, D.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors kindly thank the laboratories of Hydrocarbons Chemistry and Hydrogeochemical Engineering and Soil Remediation of the Technical University of Crete, for the ultimate, FTIR and ICP analysis, [PERSON] and [PERSON] from the Inst. of Chemical Engineering Sciences in Patra for the BET measurements of the samples.  
The authors declare no conflicts of interest.
Figure A1: Cont.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## Mechanical Properties of the Recycled Aggregates Using Concrete and Durability Characteristics
### Durability Characteristics
#### 5.3.2 Drying Shrinkage  
Freezing and thawing experimental results are as Figure 14.  
After 12 weeks of material aging, the length variation ratio due to the drying shrinkage of the ordinary concrete was \(682\times 10^{-6}\), in the general strength territory, and \(526\times 10^{-6}\), in the high strength territory. For the replacement rates of the recycled coarse aggregate of 30%, 60%, and 100%, the general strength territory exhibited length variations of 619, 652, and \(533\times 10^{-6}\), respectively, which were 4.4%-21.8% smaller than that of ordinary concrete. In the high strength territory, the length variations were 510, 476, and \(508\times 10^{-6}\), respectively, which were 3.0%-9.5% less than that of ordinary concrete. For the replacement rates of the recycled fine aggregate before modification of 30%, 60%, and 100%, the length variations in the general strength territory were 658, 417, and \(454\times 10^{-6}\), respectively, which were 3.5%-38.9% less than that of ordinary concrete, and in the high strength territory were 498, 371, and \(324\times 10^{-6}\), respectively, which were 5.3%-41.4% less than the ordinary concrete. In the replacement of the recycled fine aggregate after modification, for 30%, 60%, and 100% replacement rates, the length variations in the general strength territory were 697, 624, and \(630\times 10^{-6}\), respectively, which were \(-2.2\%\)-8.5% of that of ordinary concrete. The length variations in the high strength territory were 488, 497, and \(424\times 10^{-6}\), respectively, which were 5.5%-19.4% smaller than that of ordinary concrete.  
Figure 13: Relationship between durability and air volume.  
Thus, when the recycled fine aggregate before modification was used, the length variations were similar to ordinary concrete, up to a replacement of 30%, but when the replacement was 60% and 100%, the length variation ratios were significantly reduced. After modification, the use of the recycled fine aggregate produced lower length variation ratios if the volume of the recycled aggregate was increased, but there was no significant decrease up to a difference of approximately 20%. Reducing the water-cement ratio reduced the amount of water absorbed into the air-gap of the mortar attached to the recycled aggregate.
## 3 Results and Discussion
### Preliminary Study to Evaluate the Amount of SSA to Incorporate in the Asphalt Mixtures
#### 3.3.2 Volumetric Characteristics of the Preliminary Asphalt Mixtures  
The volumetric characteristics of the asphalt mixtures were obtained using the maximum density (MD) and the bulk density, which allows the calculation of the air void content (Va) of each specimen. The results of this characterization can be seen in Table 5, which shows the average values of each asphalt mixture.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fraction**} & \multicolumn{2}{c}{**SSA**} & **SSA** & **SSA** & **SSA** & **NA** & **NA** & **NA** & **NA** & **A/10** & **NA 0/4** & **Commercial Filler** \\  & **0/20** & **14/20** & **10/14** & **4/10** & **58A 0/4** & **14/20** & **10/14** & **NA 4/10** & **NA 0/4** & **Commercial Filler** \\ \hline \multirow{2}{*}{AC 14 Surf 35/50} & 50\% SSA & – & – & 36\% & 7\% & 7\% & – & – & 16\% & 30\% & 4\% \\  & 80\% SSA & – & – & 35\% & 20\% & 25\% & – & – & – & 14\% & 6\% \\ \hline \multirow{2}{*}{AC 20 Bin/Base 35/50} & 50\% SSA & 50\% & – & – & – & 16\% & 15\% & – & 15\% & 4\% \\  & 95\% SSA & 78\% & 17\% & – & – & – & – & – & – & 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Composition of the different asphalt mixtures evaluated in the preliminary study.  
Figure 6: Grading curve adjustments for the asphalt mixtures with different SSA incorporation rates: (a) AC 14; (b) AC20.  
As shown in Table 5, the higher density of SSA significantly influences the density values obtained for the asphalt mixtures, making it difficult to comply with the maximum air void content limits, particularly when incorporating higher rates of SSA.
# A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology
## 6 RFID-Based Crack Monitoring Applications for Metal Materials
[51] studied the reliability of a passive RFID sensor system with a reference tag in aluminum alloy structural crack  
Figure 23: Chipless RFID sensor systems for crack characterization of metal structures [46]. Figure 24: Proposed chipless RFID sensor tag [47]. detection under conditions of varying reading distance and ambient changes. As the crack depth increases, the sensor tag impedance shifts to the low-frequency direction, and the gain of the proposed sensor exhibits greater sensitivity to variations in crack depth.
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\omega_{i}\)} & \multicolumn{8}{c}{**Effect of Strain Energy on the Natural Frequency [Hz]**} \\ \cline{2-10}  & & _L/h = 10_ & _L/h = 12_ & _L/h = 14_ & _L/h = 16_ & _L/h = 18_ & _L/h = 20_ & _L/h = 22_ & _L/h = 24_ \\ \hline
1 & TMM & 1310.3 & 927.19 & 690.32 & 533.69 & 424.77 & 345.99 & 287.20 & 242.18 \\  & \(C_{f,a}\) & 0.2977 & 0.3376 & 0.3673 & 0.3899 & 0.4074 & 0.4212 & 0.4322 & 0.4411 \\  & \(C_{f,b}\) & 0.5954 & 0.5732 & 0.5577 & 0.5464 & 0.5381 & 0.5317 & 0.5268 & 0.5229 \\  & \(C_{c}\) & 0.1068 & 0.0892 & 0.0750 & 0.0637 & 0.0545 & 0.0471 & 0.0410 & 0.0360 \\  & Equation (36) & 1310.3 & 927.19 & 690.32 & 533.69 & 424.77 & 345.99 & 287.20 & 242.18 \\ \hline
2 & TMM & 7011.1 & 5007.1 & 3770.3 & 2949.5 & 2374.5 & 1954.9 & 1638.5 & 1393.8 \\  & \(C_{f,a}\) & 0.0867 & 0.1079 & 0.1325 & 0.1582 & 0.1837 & 0.2082 & 0.2313 & 0.2527 \\  & \(C_{f,b}\) & 0.7919 & 0.7553 & 0.7229 & 0.6950 & 0.6712 & 0.6508 & 0.6335 & 0.6187 \\  & \(C_{c}\) & 0.1214 & 0.1368 & 0.1445 & 0.1468 & 0.1451 & 0.1410 & 0.1352 & 0.1286 \\  & Equation (36) & 7011.1 & 5007.1 & 3770.3 & 2949.5 & 2374.5 & 1954.9 & 1638.5 & 1393.8 \\ \hline
3 & TMM & 18,088.8 & 13,139.1 & 9855.2 & 7688.2 & 6182.0 & 5090.0 & 4270.9 & 3639.4 \\  & \(C_{f,a}\) & 0.1203 & 0.0471 & 0.0571 & 0.0716 & 0.0879 & 0.1052 & 0.1228 & 0.1406 \\  & \(C_{f,b}\) & 0.5728 & 0.8471 & 0.8246 & 0.7978 & 0.7722 & 0.7486 & 0.7273 & 0.7080 \\  & \(C_{c}\) & 0.3069 & 0.1058 & 0.1182 & 0.1306 & 0.1399 & 0.1462 & 0.1499 & 0.1514 \\  & Equation (36) & 18,088.8 & 13,139.1 & 9855.2 & 7688.2 & 6182.0 & 5090.0 & 4270.9 & 3639.4 \\ \hline
4 & TMM & 19,682.6 & 18,301.7 & 17,350.3 & 14,312.3 & 11,520.0 & 9470.9 & 7935.7 & 6756.0 \\  & \(C_{f,a}\) & 0.1431 & 0.1705 & 0.1112 & 0.0383 & 0.0418 & 0.0506 & 0.0612 & 0.0729 \\  & \(C_{f,b}\) & 0.3275 & 0.0389 & 0.2809 & 0.8350 & 0.8412 & 0.8240 & 0.8046 & 0.7854 \\  & \(C_{c}\) & 0.5294 & 0.7906 & 0.6079 & 0.1267 & 0.117
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 4 Results of the Micropump Simulation
### Flow Analysis at Reduced Actuation Frequency  
As the volume flow rate available at the biosensor location with the 0.5-mm microneedle extension was 1.6 times higher than the required flow rate, and the actuation frequency was lowered from the first natural frequency. Owing to the direct relation of actuation frequency with pain felt or discomfort, lowering it would further reduce the discomfort due to reduction in suction. Figure 5b shows the variation of the volume flow rate at the biosensor location with actuating frequency. It can be seen from the figure that actuating the FQPB at 22-kHz frequency was able to provide the required blood sample. Hence, further numerical experiments were conducted at actuation frequency of 22 kHz.  
Analyses of results obtained for the 0.5-mm extended microneedle at actuation frequency of 22 kHz are presented here. Only 0.90 MPa pressure inside the pump chamber was observed, as compared to 3 MPa at 35.338 kHz frequency, resulting in one-third reduction. This indicated the reduction in pain during the operation of the device. Accumulation started after 2400 us and average velocity of 4 mm/s was observed, as shown in Figure 5c. From the plot in Figure 6, it can be observed that flow smoothly passed over the biosensor. Based on the same velocity, at the end of 1 s, 1.256 uL of blood was collected inside the pump chamber, which was more than the required volume of 1.1 uL.  
Figure 5: Velocity at biosensor location in the case of the 0.50-mm extended microneedle: (**a**) applied frequency of 35,338 Hz, (**b**) volume flow rate vs. applied frequency, and (**c**) applied frequency of 22,000 Hz.
# Numerical Investigation of Erosion Wear in the Hydraulic Amplifier of the Deflector Jet Servo Valve
## 4 Results and Discussion
### Lifespan Predication Based on Erosion Wear  
Regarding the DJV, there is still no recognized standard to determine the valve failure, different from the jet pipe valve [16]. Here, a failure criterion is proposed according to an analysis of the pressure generating mechanism in the receiver and its structural characteristics. The jet passing through the deflector impacts on the shunt wedge, and then the fluid will flow along gaps on both sides, with the momentum component perpendicular to the impact surface generating the receiver pressure. After this momentum component depleted, the fluid accomplishes its mission and must return to the reservoir smoothly, which is just the function of the guide curve outside the receiver. Therefore, when the impact surface of the shunt wedge, due to wear, is below the point on the guide curve closest to the shunt wedge, as depicted in Figure 24, the flow parallel to the impact surface will be blocked by the guide curve, resulting in internal eddies becoming much more complicated. As a result, the flow stability will materially deteriorate because of eddy-induced cavitation and vibration, and the reliability of the DJV will significantly decline. Consequently, the critical line in Figure 24 is adopted to evaluate the valve condition, and \(E_{c}\) is the failure depth, with the value 0.08 mm in this case. Meanwhile, by reference to the erosion rates distribution (see Figure 16), the average erosion depth per unit time of the shunt wedge can be obtained. It follows that the lifespan of the DJV can be calculated for different contamination levels, as illustrated in Figure 25. Fitting the lifespan calculations for oil of NAS 5, 7, 9 and 11, a prediction formula can be derived as follows.  
\[LS=6.169e^{-0.694l_{p}}\times 10^{5}(h) \tag{17}\]  
Following this exponential function, the lifespan will increase rapidly with the contamination level promoted. Specifically, to gain a lifespan of over 20,000 h one must employ the hydraulic oil with a contamination level superior to NAS 5. However, the excessive cleanliness of the hydraulic oil will lead to a sharp increase of economy and time costs, and it is futile to merely prolong the pilot valve life for the whole valve or valve-controlled systems. Therefore, it matters that we first determine a suitable lifespan for the servo valve to adapt to the systematic lifespan demand, and then a requisite contamination level can be chosen according to the predication formula (17).  
Figure 23: Average erosion depth with contamination level increasing.  
Figure 24: Failure criterion of DJV.  
As already mentioned, the shunt wedge change in shape has been ignored in the erosion depth calculation. In fact, with the erosion wear continuously proceeding, the shunt wedge becomes wider and wider, which means that its capability of sustaining the jet impact will be strengthened. Hence, it may be supposed that the predictions in Figure 25 are relatively safe.