esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Identification of Candidate Genes for Stable Loci

Table 1: Summary of candidate genes predicted for agronomic traits. and _BnaC07s36960D_ (Hypothetical protein) were located in the interval of C07_38735522 and C07_38598079. For the two SNPs only significantly associated with NSS, five genes were predicted as candidate genes. _BnaC06g20430D_ (_GSTU20_, Glutathione S-transferase U20) and _BnaC06g20510D_ (_QUA2_, Pectin methyltransferase QUA2) were identified in the intervals of C06_22559430 and C06_22570315. The other three, viz. _ABH_ (alpha/beta-Hydrorolasesse superfamily), _PPR596_ (Pentatricopeptide repeat 596), and _KTNA1_ (Katanin p60 ATPase-containing subunit A1), were located in the regions of A07_random_1623623. For BAI, two putative candidate genes were identified on A10 (A10_13564961 and A10_13678989), including _BnaA10g18690D_ (Hypothetical protein) and _BnaA10g18940D_ (_BNQ2_, Banquo 2). For the significant SNPs of BN (A05_19368584 and A05_19764389), three promising genes were identified including _BnaA05g26410D_ (_DCN1L_, Defective in cull neddylation protein), _BnaA05g26840D_ (_FTM4.1_, Flora  
Figure 6: Heatmap of candidate gene expressions among different tissues. Each row indicates a tissue, and different colors represent different development stages and traits. More detailed information displayed in Table S4. BnaA05g26860D (FTM4.2)_. For PH, 13 candidate genes were identified on three chromosomes, viz. A01, A03, C04, C06, C09, and A02_random for the significant SNPs. Among them, six genes, _BnaA02g35610D (BCP_, Blue copper protein), _BnaA03g49330D (CFP_, Cotton fiber protein), _BnaC04g49260D (JAL24.1_, Jacalin-related lectin 24), _BnaC04g49270D (JAL24.2)_, _BnaA02g35620D (ATL54_, RING-H2 finger protein ATL54), and _BnaC09g33520D (RTN21, Reticulon protein 21)_, were predicted for controlling plant height. Two genes, _BnaC06g25100D (PK_, Protein kinase superfamily) and _BnaC06g25110D (ACL_, Actin cross-linking protein), were identified in the region of C06_26638717, which were associated with PH and NSS. Five genes were located in the interval of A01_1783685 co-detected by PH and SYP, including _KUP5_ (K+ uptake permease 5), _MYB69_ (MYB domain protein 69), _CDC20_ (Cell division cycle 20), _TIF3K1_ (Eukaryotic translation initiation factor), and _SLOMO_ (Slow motion). These genes were highly expressed in the stem during bolting and flowering stages. For MINS, _BnaC05g10890D (HRGP_, Hydroxproline-rich glycoprotein family protein), _BnaC03g09950D (CBR2L_, NADH-cytochrome b5 reductase-like protein), and _BnaC03g09960D (MPC1_, Mitochondrial pyruvate carrier 1) were the promising candidate genes located in C05_6251826 or C03_4731660. For C03_4731660 (MINS and MIL), the genes _BnaC03g09950D (CBR2L, NADH-cytochrome b5 reductase-like protein) and _BnaC03g09960D (MPC1_, Mitochondrial pyruvate carrier 1) were located in the region of C03_4731660.
## 2 Materials and Methods  
### Reagents and Standards  
The target PhACs were chosen considering the Portuguese data consumption provided by Infarmed (Portuguese Authority of Medicines and Health) and the compounds proposed in the Water Framework Directive [21; 22]. Specifically, a group of 26 PhACs belonging to ten common therapeutic classes was chosen, namely, analgesics (acetaminophen--APAP), antibiotics (erythromycin--ERT, sulfadiazine--SDZ, sulfamethoxazole--SMX, sulfaryridine--SPD, azithromycin--AZM, and clarithromycin--CLR), non-steroidal anti-inflammatories (NSAIDs) (diclofenac--DCF, ibuprofen--IBUP, and naproxen--NPX), beta-blockers (atenolol--ATN, metoprolol--MTPL, and propranolol--PPNL), anti-dyslipidemics (clofibric acid--CFA and bezafibrate--BZF), sexual hormones (17-actinylestradiol--EE2, \(\beta\)-estradiol--E2, estrone--E1, estriol--E3, diethylestlbestrol--DES, gestodene--GTD, and testosterone--TTE), antidepressants (fluoxetine--FLX), anticonvulsants (carbamazepine--CBZ), psychostimulants (caffeine--CAF), and corticosteroids (cortisone--CTS).  
All standards of PhACs are of analytical grade (purity \(\geq\) 95%), suitable for chromatographic analysis. The standards of acetaminophen, clofibric acid, atenol, bezafibrate, carbamazepine, cortisone, diclofenac, erythromycinycin, estrone, fluoxetine, ibuprofen, naproxen, propranolol, sulfadiazine, sulfamethoxazole, sulfaryridine, clarithromycin, and azithromycin were provided by Sigma-Aldrich (Steinheim, Germany), caffeine, and testosterone by Fluka (Buchs, Switzerland), and gestodene and metoprolol by LGC (Teddington, UK). Diethylslitbestrol, estradiol, estriol, and 17-\(\alpha\)-ethinylestradioliol were purchased from Dr. Ehrenstorfer, GmbH(Augsburg, Germany). Individual stock solutions of PhACs were prepared in methanol and stored at \(-\)20 \(\pm\) 3 \({}^{\circ}\)C in the absence of light. A standard mixture solution was prepared by diluting each standard solution in methanol to a concentration of 1 mg/L, except for ibuprofen with 2 mg/L and 6 mg/L for hormones. Methanol was LC/MS purity grade ([PERSON], Deventer, the Netherlands). Formic acid (liquid chromatography grade, \(\geq\)98%) and ammonium acetate (98%, p.a.) were provided by Merck (Darmstadt, Germany). Reagent water was obtained using a Milli-Q device, Academic A-10 model, from Millipore (Molsheim, France).
## 4 Experiments
### Experiments in the Real World  
A D435 depth camera was used, featuring an RGB frame rate of 30 fps, a minimum depth range of 28 cm, and a field of view (FOV) of \(87^{\circ}\times 58^{\circ}\). To thoroughly evaluate system performance, additional dynamic obstacles with varied sizes and speeds were introduced in the real test environment, with more complex obstacle placements. For safety considerations, the maximum speed of dynamic obstacles was limited to 0.4 m/s.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Time(s)**} & \multirow{2}{*}{**Success Rate (\%)**} \\ \cline{3-3} \cline{5-6}  & & **min** & **avg** & **max** & \\ \hline \multirow{3}{*}{Scenario (a)} & Algorithm 1 & 172.3 & 186.7 & 222.5 & 90 \\  & Algorithm 2 & 169.4 & 180.2 & 204.3 & 70 \\  & Ours & 166.7 & 184.3 & 210.2 & 90 \\ \hline \multirow{3}{*}{Scenario (b)} & Algorithm 1 & 197.4 & 208.8 & 230.2 & 60 \\  & Algorithm 2 & 186.4 & 210.4 & 226.9 & 40 \\  & Ours & 192.6 & 202.3 & 220.4 & 80 \\ \hline \multirow{3}{*}{Scenario (c)} & Algorithm 1 & 203.1 & 226.7 & 233.2 & 40 \\  & Algorithm 2 & 207.2 & 233.8 & 235.6 & 20 \\ \cline{1-1}  & Ours & 208.4 & 221.3 & 238.9 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of ablation experiments in different environments.  
As demonstrated in Figure 5, experimental results and analyses conducted in real-world scenarios confirm that the proposed system enables accurate perception and identification of both static and dynamic obstacles in complex, dynamic environments. The system robustly detects 3D bounding boxes of dynamic obstacles and predicts their motion states, including velocity variations. Leveraging this environmental awareness and predictive capability, the robot effectively navigates toward its designated target. Furthermore, comprehensive field tests in diverse, unstructured environments validate the robustness and generalization of the proposed perception and planning framework. These results demonstrate successful perception, recognition, and motion planning performance in complex scenes containing both static and dynamic elements.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Literature** & **Generation Method** & \
\begin{tabular}{c} **Power Supply** \\ **Parameters** \\ \end{tabular} &
\begin{tabular}{c} **Operating** \\ **Pressure** \\ \end{tabular} & **Year** & **Plasma Density** \\ \hline \multirow{3}{*}{This paper} & \multirow{3}{*}{Glow Discharge} & 950 W, & \multirow{3}{*}{200 mA} & \multirow{3}{*}{0.5 torr} & \multirow{3}{*}{2024} & \multirow{3}{*}{\(1.15\times 10^{12}\) cm\({}^{-3}\)} \\  & & 200 mA & & & & \\  [17] & Inductive Coupling & 1500 W & 0.1 torr & & & \\  [18] & Microwave Discharge & 500–700 W & 100 Pa (0.75 torr) & & & \\  [19] & Capacitive Coupling & 50–200 W & 0.15–0.38 torr & & & \\  [20] & Glow Discharge & 300–900 W & 2.13 \(\times\) 10\({}^{-4}\) torr & & & \\  [21] & Glow Discharge & Not Mentioned & 140 Pa (1.05 torr) & & & \\  [22] & Simulation Analysis, & & & & & Theoretical analysis can reach 6.5 \(\times\) 10\({}^{11}\) cm\({}^{-3}\) \\  & Not Mentioned & & & & & \\  [23] & Simulation Analysis, & & & & & \\  & Not Mentioned & & & & & \\  [24] & Capacitive Coupling & 252.5 W & 0.5–0.9 torr & & & \\  [25] & Microwave Discharge & 1000 W & Not Mentioned & & & \\  [26] & Glow Discharge & 150–350 mA & 1.5 torr & & & \\  [27] & Inductive Coupling & 800 W & 100 Pa (0.75 torr) & & & \\  [28] & Glow Discharge & Not Mentioned & 9 Pa (0.07 torr) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent density comparison of low-pressure cold plasma studies. study is that the generated plasma volume can be controlled by the number of component arrays so that the demand for input power can be adjusted accordingly. Regarding the operating pressure parameters, plasma density generally increases with pressure, showing a trend of first rising and then falling. Section 3.2 analyzes the reasons for this phenomenon from a theoretical perspective. For the plasma prepared in this study, before 0.5 torr, the increase in operating pressure allows electrons to gain higher energy and collision ionization, increasing plasma density. After 0.5 torr, the increase in pressure reduces the mean free path of electrons, thereby reducing their average kinetic energy, and thus the plasma density decreases. Given the tungsten material's high melting point and low work function, it is more conducive to achieving the goal of stealth and can effectively avoid the disadvantages seen in similar studies [14; 15; 16]. Building upon these findings, this paper further investigated the influence of the working gas pressure inside the element on the plasma density, and optimizes the pressure parameters of the plasma element. The final plasma elements developed in this paper can be arranged in arrays to form a large-area, ultra-high-density, low-pressure cold plasma, which is suitable for radar stealth applications.
## 5 Discussion
### Comparison between the United States and China  
The results show that Chinese practitioners have a higher average technology utilization level in industrialized construction than those in the United States. This reflects that, currently, the practitioners in China pay more attention to the application of emerging technologies into the industrialized construction than the United States. Recent years have witnessed a prosperous development in the Chinese construction industry. Before 2008, the U.S. had the most top 225 international construction companies in the world. However, China has now surpassed the United States. This booming development brings the increase in the level of construction technologies [33; 34]. Moreover, in order to comply with increasing requirements of urbanization and housing demand, the Chinese government is proactively promoting the industrialized construction strategies [34]. It has also been confirmed by previous researchers [35; 36] that China is gradually leading in the off-site related research around the world. These could potentially explain why Chinese practitioners have a higher average technology utilization level. Moreover, the compositions of the models of the United States and China are different. These differences in the model compositions can reflect some insights about the practitioners' technology utilization conditions in the United States and China. Specifically, working experience acts as the influencing factors in all of the models in the United States. The United States practitioners with different working experience expressed significant differences in their attitudes towards applying emerging technologies in industrialized construction. By comparison, company type and company size are the predictors in all four models in China. Working position is not included in any of the four models in China. It reflects that the practitioners from the same Chinese company may have similar mastery of the applied technologies. The size and types of companies where Chinese practitioners work can significantly affect their technology utilization level in the industrialized construction projects. Overall, it is found that the variable company size exists in six out of the seven models. It indicates that company size can significantly affect practitioners' utilization levels of different technology levels in both countries. As presented in Table 5, the regression coefficients of CS2 and CS3 of different models are almost negative. It means the practitioners from medium-sized and large-sized companies are more likely to have a higher technology utilization level than those from small-sized companies. This is reasonable since technology utilization in small-sized companies is often hampered by high
## 4 Performance Evaluation
### Simulation Results  
Influence of Number of Nodes  
Figure 6 shows the average and standard deviation of the backbone nodes' residual energy as the number of nodes changes. As shown in Figure 7(a), regardless of the number of nodes composing the network, MCBT (\(\omega\) = 0.7) maintains the highest residual energy. The backbone nodes' average residual energy of MCBT, MMM, and HCDD are 2.43 J, 1.89 J, and 1.79 J, respectively. MCBT exhibits 22% and 26% improvement respectively, compared with MMM and HCDD. Considering that the backbone nodes have greater demands in terms of packet transmission than those of the ordinary nodes, we can form a stable backbone by selecting nodes with higher residual energy as cluster heads. The standard deviation describes the difference in residual energy among backbone nodes as shown in Figure 7(b). MCBT selects nodes with higher residual energy as cluster heads in advance, so it has a low standard deviation, averaging around 0.01 \(\sim\) 0.03 J.
## 6 Results and Discussion
### Frame Transmission Efficiency  
Assuming an Eb/N0 of 3 dB, according to the above simulation, in order to achieve a reliable decision, the synchronization overhead of the spread spectrum code synchronization is 128 symbols (after coding) [1,2]. Valid data comprise 128 symbols. Frame length is (synchronization overhead + valid data = ) 256 symbols. The data transfer efficiency with synchronization overhead is (valid data length/frame length=) 128/256 = 50%. After the synchronization cost is removed, the data transmission efficiency can be 100%, and the data transmission efficiency can be increased by up to 50%. The total signal length and transmission time are reduced by half, and the concealment of communication is improved.
## 3 In Situ Test with Vibration Exciter  
### Test Device  
Appropriate test equipment is essential to ensure the seamless execution of the experiment [32]. The excitation system comprised a shaker, frequency converter, counterweight block, and water-cooling system, as depicted in Figure 3. This instrument encompasses the functionalities of variable frequency, variable torque, and variable direction. Tongji University independently developed this system, enabling the simulation of dynamic loads generated by trains at various axle loads and speeds. The counterweight is constructed from cement concrete and serves a dual purpose. Firstly, the contact area at the base of the counterweight block is employed to regulate the dynamic stress imparted by the shaker onto the roadbed surface. Secondly, it acts as a safeguard against equipment displacement caused by the shaker's excessive force, preventing any unwanted upward movement. The counterweight was affixed to the exciter using steel plates and long bolts. The contact areas between the counterweight and the pavement were configured as two rectangular regions (0.32 \(\times\) 0.22 m\({}^{2}\)), positioned at a separation distance of 1.8 m.  
The data-acquisition system comprises a transducer, a dynamic collector, an amplifier, and a test computer. The voltage signal produced by the transducer in response to the vibrational load undergoes amplification through the amplifier; following which, the data are collected by the dynamic collector.  
The sensor calibration information is of paramount importance, as emphasized by previous research [33]. In this investigation, the JMYJ-1410 m dynamic earth pressure cell sensor was employed with a 2.0 MPa range and a calibration coefficient of 0.0009648. Additionally, the CA-YD-117 accelerometer was used, featuring a range of 1500 m\(\cdot\)s\({}^{-2}\), a sensor voltage sensitivity of 0.01319 V\(\cdot\)s\({}^{-2}\)/m, and a charge sensitivity of 61.72 pC/(m/s). The JMDL-5105Y vibration pickup had a range of 5.2 mm, a velocity sensitivity of 34.788 V/m, and a displacement sensitivity of 10,469.34 V/m.  
Figure 3: SBZ30 inverter torque vibrator: (**a**) Exciter and counterweight; (**b**) Inverter.  
Figure 2: Design of the gradient pile–concrete slab composite foundation.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 5 Security Analysis
### Analysis Based on Security Considerations  
* Authentication: The proposed D2D communication system performs primary authentication using 5G-AKA, which is an authentication framework provided by 5G, to perform authentication for a UE before issuing a D2D token. Moreover, the issued token can perform secondary authentication through verification of the process of creating a link of D2D communication through the gNB's public key and SUCI. Finally, in the data transmission step, the token is used as an AD to authenticate the other party for each transmission of data. In this way, authentication of the UE is performed in all processes of data communication before issuing a token for D2D communication so that more secure communication can be performed.
* Data confidentiality and integrity: The proposed D2D communication system generates D2D communication using SUCI, which is the encrypted UE identity, and the secret key of the gNB during the D2D generation process. In this process, there is no case where the identity of the unencrypted UE is transmitted. In addition, in the step of actual data transition after creating a D2D link, encryption is performed using a lightweight AEAD cipher. AEAD cryptography can provide integrity and authentication as well as data confidentiality. Therefore, the proposed D2D communication system can guarantee the confidentiality/integrity of the identity and communication data of the UE.
* Anonymity: In 5G networks, SUCI is an encrypted identity for UE anonymity, which provides anonymity for the UE itself. Moreover, the D2D token used in the proposed D2D communication is a value obtained by signing SUCI with the private key of the gNB, which also provides anonymity by not being able to recognize the identity of the UE directly.  
Figure 6: The energy consumption of AEAD ciphers.  
* Efficiency: Both the authentication process and the data encryption process used in the proposed D2D communication system are based on lightweight cryptography. The lightweight ciphers used in this paper are the ECC-based public key cryptosystem and the lightweight AEAD cipher. The ECC-based public key cryptosystem uses a 256-bit key and operates faster than RSA, which uses a 1024-bit key. Moreover, the lightweight AEAD cipher is designed to be efficiently implemented in a resource-constrained environment such as in IoT and provides data confidentiality/integrity and authentication.
## 4 Plant-Bacteria Interaction Regulates Plant Protein and Metabolites Production
### Bacteria-Mediated Plant Metabolite Biosynthesis for Salt Stress Mitigation (Indirect Mechanism)
#### 4.2.4 Plant Aquaporin  
Aquaporin (AQP) proteins have been demonstrated to transport water and other tiny molecules across biological membranes, which is critical for plants dealing with salt stress (Figure 3). Higher plant aquaporins are members of a large gene family of major intrinsic proteins that are categorized into 5 subfamilies based on amino acid sequence similarity: plasma membrane intrinsic proteins (PIPs), tonoplast intrinsic proteins (TIPs), noduline-26-like intrinsic proteins (NIPs), small basic intrinsic proteins (SIPs), and uncharacterized intrinsic proteins (XIPs). Aquaporin, also known as plasma membrane integral protein (PIP), is the principal channel that mediates water uptake into plant cells. In plants, PIPs are the most prevalent subfamily. PIP1 and PIP2 are the two primary categories of the PIP family. According to [PERSON] et al. [66], salt stress impacted maize plant aquaporin genes (_ZmPIP1s_ and _ZmPIP2s_) were up-regulated after _Bacillus megaterium_ inoculation (Table 1). The PIP2s are all involved in fast water transfer. The _ZmPIP2-1_ gene was found to be strongly up-regulated in maize roots inoculated with _Pantoea agglomerans_ in salt stress condition, followed by _ZmPIP1-1_ and _ZmPIP2-5_[65].
###### Abstract  
Fiber-reinforced concrete (FRC) has become increasingly important in recent decades due to its superior mechanical properties, especially flexural strength and toughness, compared to normal concrete. FRC has also received significant attention because of its superior fire resistance performance compared to non-fiber concrete. In recent years, studies on the mechanical performance, fire design, and post-fire repair of thermally damaged fibrous and non-fibrous concrete have gained importance. In particular, there are very few studies in the literature on the mechanical performance and flexural behavior of steel and basalt hybrid fiber concrets after high temperature and water re-curing. This study aims to determine the mechanical properties and toughness of concrete containing steel fiber (SF) and basalt fiber (BF) after ambient and high temperature (400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 800 \({}^{\circ}\)C). Additionally, this study aimed to examine the changes in fire-damaged FRCs as a result of water re-curing. In this context, high temperature and water re-curing were carried out on non-fibrous concrete (control) and four different fiber compositions: in the first mixture, only steel fibers were used, and in the other two mixtures, basalt fibers were substituted at 25% and 50% rates instead of steel fibers. Furthermore, in the fifth mixture, basalt fibers were replaced by polypropylene fibers (PPFs) to make a comparison with the steel and basalt hybrid fiber-reinforced mixture. This study examined the effects of different fiber compositions on the ultrasonic pulse velocity (UPV) and compressive and flexural strength of the specimens at ambient temperature and after exposure to elevated temperatures and water re-curing. Additionally, the load-deflection curves and toughness of the mixtures were determined. The study results showed that different fiber compositions varied in their healing effect at different stages. The hybrid use of SF and BF can improve the flexural strength before elevated temperature and particularly after 600 \({}^{\circ}\)C. However, it caused a decrease in the recovery rates, especially after re-curing with water in terms of toughness. Water re-curing provided remarkable improvement in terms of mechanical and toughness properties. This improvement was more evident in steel-polypropylene fiber-reinforced concrets.  
steel fiber; basalt fiber; elevated temperature; water re-curing; toughness +
Footnote †: journal: Applied Sciences