esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of Coefficient of Restitution (CoR)
### Results and Interpretation  
The trends observed in Figure 14 provide valuable insight into the mechanical behaviour of 'Memory' wheat grains during impact. The coefficient of restitution (CoR, _e_) decreases monotonically with velocity at every moisture level. At ~1.0 m\(\cdot\)s\({}^{-1}\), the CoR remains high (\(\approx\)0.55-0.65), indicating an essentially elastic rebound, but it falls to \(\approx\)0.30-0.40 at 4.5 m\(\cdot\)s\({}^{-1}\) as more energy is dissipated through plastic deformation and micro-fracturing.  
Moisture amplifies this effect. At 7% moisture, the CoR descends gradually with velocity, whereas at 16%, it starts lower (\(\approx\)0.50) and declines sharply to \(\approx\)0.30-0.35 at 4.5 m\(\cdot\)s\({}^{-1}\), reflecting increased energy absorption by wetter kernels. Polynomial regression confirms the nonlinear CoR-velocity-moisture relationship; the coefficient of determination (R\({}^{2}\)) decreases with moisture, implying greater structural variability.  
Comparing these results with the literature shows analogous CoR-velocity-moisture patterns for maize, oilseed rape, soybean, pea and other crop seeds [6; 28; 29; 48; 49; 50]. Al  
Figure 14: Coefficient of restitution (CoR) as a function of collision velocity [7] at different moisture levels: (**a**) 7%, (**b**) 10%, (**c**) 13% and (**d**) 16%.  
though absolute CoR, \(e\) values differ among species, the shared trend of lower CoR at higher velocity and moisture is consistent. Higher collision velocities promote permanent deformation, while moisture acts as a plasticiser that softens seed tissues and increases internal damping [24; 29].  
In wheat, the multilayered husk-aleuorne-endosperm structure likely accentuates these effects, making combined velocity-moisture analyses more challenging than for morphologically simpler seeds such as pea or soybean [48]. These phenomena are universal for plant seeds, albeit modulated by species morphology and genotype [49]. The literature points out that CoR may vary markedly between varieties [22]; variety-specific tests--such as the present study on 'Memory' wheat--are therefore essential for accurate modelling and equipment design.
# Improvement of the Transglycosylation Efficiency of a
## 3 Results and Discussion
### Screening of LnbB Variants
#### 3.1.1 Glycoshrase Variants: D320E, D320T, and Y419N
LNT yield \(-\) LNT yield at 24 h)/[max. LNT yield]). An asterisk (\({}^{\rm\,*}\)) indicates that the LNT concentration after 24 h is not significantly different from the maximum yield (secondary hydrolysis not statistically significant; \(p<0.05\)). Regioselectivity is indicated by the percentage of total transglycosylation products (TG), which is identified as LNT. For each column, the best results are marked in bold, whereas the second best results are underlined. Figure 3: **(Left panel)**: Zoom of the active site in LnbB (light blue) in complex with LNB-thiazoline (yellow; PDB 4]AW [7]) highlighting catalytic residues D320 and E321 (red) and other residues targeted for mutation to improve transglycosylation. The residue coloring matches that of the multiple sequence alignment in Figure 2. **(Right panel)**: Two-dimensional map of the interactions between LnbB and the LNB ligand based on the crystal structure of this complex (PDB 4H04 [7]). Blue line: LNB bonds. Light-green line: residue bonds involved in the formation of hydrogen bonds with the ligand. Dotted green line: hydrogen bonds labelled with their length (Å). Half sun: residues involved in hydrophobic contact with the ligand (pink half sun: Trp residues of subsite \(-1\) belonging to the hydrophobic platform; yellow half-sun: other residues of subsite \(-1\); grey half sun: residues of subsite \(-2\)). Amino acids subjected to mutation in the current work are highlighted in bold. By contrast, the variant Y419N targeting the oxazoline-stabilizing Tyr residue showed the highest LNT yield (1285 uM; obtained after 3 h of reaction) as well as the highest total transglycosylation yield (2388 uM). The secondary hydrolysis of the LNT was negligible, and only 14% of the maximum LNT yield was hydrolyzed after 24 h of reaction (Table 1; Figure 4). However, regioselectivity was remarkably low for Y419N compared to the other best variants (below), as only 54% of the formed product was LNT (Table 1). Contrary to our findings with Y419N, the mutant Y419F previously showed high secondary hydrolysis of the formed LNT in the reaction with LNB-oxazoline and lactose and lower yield than the WT [15]. Again, the choice of amino acid substitution may play an important role, especially for secondary hydrolysis. Mutation to Phe removes the hydrogen bonding capacity but maintains the rest of the residues structure. The Asn amine group has hydrogen bonding capacity, yet Asn is smaller; thus the distance to the substrate increases (approximately 4.5 A to the LNB-thiazoline sulfur atom in PDB 4 JAW), making hydrogen bonding less likely. Possibly, the increased distance destabilizes the transition state sufficiently to impair hydrolysis, while the hydrogen bonding capacity retains enough of the original role of the Tyr residue to foster transglycosylation.
## 5 Result Analysis
### Ranking of mHealth Applications Based on Usability Aspect Using MOORA  
The weighted normalized decision matrix and normalized assessment (\(NA_{i}\)) values were obtained by following Steps 1 to 4 of MOORA, as mentioned in Section 4.4 and displayed in Table 19. The normalized assessment (\(NA_{i}\)) was employed to determine the rank. The option with the highest \(NA_{i}\) value was the best. Figure 7 depicts the ranking given to the alternatives on the basis of the values of \(NA_{i}\). According to the MOORA approach, mySugr was the best application and the Blood Glucose Tracker was the lowest ranked of the five applications available.  
Figure 6: Ranking results produced for various \(\tau\) values based on sensitivity analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Alternatives**} & \multicolumn{14}{c}{**Sub-Criteria**} & \multicolumn{14}{c}{**Normalized Assessment**} \\  & **A1.1** & **A1.2** & **A1.3** & **A2.1** & **A2.2** & **A2.3** & **A2.4** & **A2.5** & \multicolumn{1}{c}{\(\cdots\)} & \multicolumn{1}{c}{**A9.1**} & **A9.2** & **A9.3** & **A10.1** & **A10.2** & **A10.3** & **A10.4** & \\ \hline
**Alt 1** & 0.0076 & 0.002 & 0.0043 & 0.0028 & 0.0063 & 0.0049 & 0.0119 & 0.002 & \(\cdots\) & 0.0082 & 0.008 & 0.0078 & 0.008 & 0.008 & 0.0077 & 0.0076 & 0.05989 \\
**Alt 2** & 0.0079 & 0.0024 & 0.0045 & 0.0028 & 0.0068 & 0.0057 & 0.0139 & 0.0024 & \(\cdots\) & 0.0074 & 0.0071 & 0.0081 & 0.0073 & 0.0083 & 0.0079 & 0.0079 & **0.003905** \\
**Alt 3** & 0.0076 & 0.0024 & 0.004 & 0.0026 & 0.0061 & 0.0052 & 0.0121 & 0.0109 & \(\cdots\) & 0.0077 & 0.0047 & 0.0057 & 0.0078 & 0.0072 & 0.0075 & 0.0079 & 0.05649 \\
**Alt 4** & 0.0075 & 0.0023 & 0.0041 & 0.0028 & 0.0058 & 0.0052 & 0.0113 & 0.0018 & \(\cdots\) & 0.0074 & 0.0076 & 0.0072 & 0.0075 & 0.0068 & 0.0077 & 0.0067 & 0.00807 \\
**Alt 5** & 0.0075 & 0.0024 & 0.004 & 0.0025 & 0.0058 & 0.0045 & 0.0121 & 0.002 & \(\cdots\) & 0.0074 & 0.0079 & 0.0073 & 0.0073 & 0.0077 & 0.0078 & 0.0078 & 0.007588 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Weighted normalized decision matrix and normalized assessment (\(NA_{i}\)) values.  
Figure 7: Rank obtained by MOORA based on normalized assessment value (\(NA_{i}\)).
## 5 Role of sRNA in Quorum Sensing
### AmiL and PqsS  
In addition to PhrS and ReaL discussed above, recent studies have identified AmiL, a QS-regulatory sRNA that modulates virulence in _P. aeruginosa_ by targeting genes involved in key virulence factors and phenotypes such as PYO, elastase, rhamn, biofilm formation, swarming motility, hemolysis, and cytotoxicity [251]. However, unlike AmiL and PqsS, which positively regulate PYO, AmiL acts as a negative regulator. The 100-nt sRNA AmiL is a leader of the _amiEBCRS_ operon involved in biofilm formation [252]. Using transcriptomics, [PERSON] et al. revealed that AmiL expression is negatively regulated by RhlR [251]. As discussed above, PhrS directly targets PhzC (a member of the operon responsible for the synthesis of PYO) and regulates LasI and Rhl expression [249; 253]. This defines a new las/rhl (RhlR)-AmiL-PhzC/Las/Rhl signaling cascade, emphasizing AmiL's central role in PAO1 virulence. While AmiL downregulates last during early growth, it upregulates rhll and C4-HSL production in later stages [251]. It also promotes swarming motility by enhancing rhamn production. This dual role in las and rhl regulation highlights AmiL's function in fine-tuning _P. aeruginosa_'s QS-sRNA network to balance virulence.  
Another Hfq-dependent small RNA PqsS was recently discovered in _P. aeruginosa_ PAO1, which enhances bacterial pathogenicity by regulating the _Pseudomonas_ quinolone signal (PQS) quorum sensing (pqs QS) system [254]. PqsS promotes acute infections by inducing host cell death and stimulating rhamn-mediated swarming motility, while simultaneously reducing chronic infection traits such as biofilm formation and antibiotic resistance. Mechanistically, PqsS represses _pqsL_ transcript, leading to increased PQS levels and reinforcing Pqs QS [254]. Additionally, a PQS-rich environment upregulates PqsS expression, establishing a positive feedback loop. Further analysis revealed that PqsS interacts with PqsL mRNA, recruiting RNase E to facilitate its degradation. These findings provide valuable insights into _P. aeruginosa_ pathogenesis and offer potential avenues for targeted therapeutic strategies
## 5 Discussion  
Design for AM has mainly focused on creating parts with complex geometry for improving functionality, designing parts with considering constraints of AM processes, and consolidating parts for minimizing the number of parts. To consider product recovery including maintenance, part consolidation should be planned to achieve selective disassembly. Therefore, we proposed a design method to guide how to consolidate parts by removing assembly joints that are difficult to disassemble at the EOL stage. The proposed method results in modules based on the SCCI as a modular driver, and functional and physical relationships from a functional diagram and DSM.  
After identifying these modules, it is required to check whether parts can be consolidated regarding material types of the parts. Since the parts in modules 1, 4, and 7 are made of different materials like aluminum, silicon, plastic, and glass, they cannot be consolidated due to limitations of AM processes that mostly support single material. On the other hands, modules 5 and 6 contain parts that have the same material and are closed to each other physically and functionally. Furthermore, since these parts are grouped into modules because they have high SCCI values, modules 5 and 6 are appropriate candidates for part consolidation to reduce the part count of a product, which is a primary goal of part consolidation. Modules 5 and 6 will be fabricated by AM, while other modules will be manufactured by conventional manufacturing. Accordingly, the result of the proposed design method can be used as design strategy to manage which parts will be fabricated by AM selectively to support flexible manufacturing by facilitating both conventional and additive manufacturing.  
However, when designers consider a design feasibility factor as maintenance frequency of the parts instead of the material type between parts in the module, consolidating the filter basket and filter consisting of filter frame, filter net, and filter handle in module 6 may be not acceptable decision because the filter should be frequently cleaned after use. Furthermore, the proposed design method can be applied to generate new candidates for part consolidation, which are parts in modules, by considering other modular drivers related to repairability, reliability, or financial benefit. These modular drivers can be represented by characteristics of parts like SCCI and characteristics between parts. For example, remained useful lifespan (RUL) of each part can be modular drivers, and then parts with the same RUL can be grouped into a module by the proposed design method with using RUL of parts instead of SCCI. Since RUL of the parts is the same, maintenance frequency would be the same. Accordingly, parts with similar lifespan can be consolidated by AM. Furthermore, feasibility analysis for selected candidates for AM should be required to identify AM benefits in terms of redesign cost, manufacturing cost and time, financial benefit, and performance enhancement against subtractive manufacturing.
## 4 Experiment
### Module Validity Experiments
#### 4.4.3 Effects of the SloU  
For the effectiveness analysis of the loss function SIOU, this section compares the performance of SloU with CIoU, Generalized-IoU (GIoU), and Distance-IoU (DIoU) by experimenting on the same benchmark model. The results are shown in Table 6, where the optimal values of the different models in terms of precision metrics are bolded. The table shows that SloU improves mAP\({}_{0.5}\) by 2.1% and mAP\({}_{0.95}\) by 0.8% compared to the baseline model using CIoU. Meanwhile, SloU outperforms GIoU and DIoU in every performance metric listed in the Table 6. Therefore, SloU was selected as the loss function for the model in this paper.
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 5 Data Integrity Algorithms
### Aes-128 Cmac  
CMAC algorithms are MAC generation algorithms that use a sequence of symmetric key block cipher processes with xor operations to output an MAC value that is used as a validation value [45]. The strength of CMAC algorithms lies in the strength of the underlying block cipher algorithm and the appropriate key management mechanisms [46]. Therefore, it is recommended to generate a CMAC key according to the latest specifications and to have a unique CMAC key for each ECU. AES-128 CMAC utilizes the NIST-approved AES block cipher algorithm using a 128-bit key size to output a 128-bit MAC value, which is a secure MAC length against guessing attacks [45].  
Typically, CMAC algorithms and HMAC algorithms can be used interchangeably. However, CMAC algorithms are preferred when strong block cipher algorithms are supported by the underlying hardware and strong hash algorithms are not, or when the hardware offers a faster block cipher accelerator that consumes less time than the provided hash accelerators. Also, CMAC algorithms are used in symmetric key diversification algorithms that are used in access control systems [47].
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 2 Numerical Modelling of H-Section Columns  
Pitting corrosion is a significant concern for structures such as pipelines, ships and offshore structures. It occurs when protective coatings and cathodic protection systems are either absent or no longer effective [21]. Steel structures exposing to marine conditions for a prolonged period often exhibit signs of severe surface corrosion, as illustrated in Figure 1 [9]. A numerical modeling approach was developed to construct finite element models of H-section stub columns, taking into account the presence of random pitting corrosion. These models accounted for both individual and multiple randomly pitted plate elements. The corrosion data were acquired from corrosion inspections conducted on authentic plates from an aged ship structure servicing in an ocean environment [22]. Additionally, the validity of the numerical models was ensured through comparison with existing experimental results. This verification process allowed a series of numerical analyses to be performed, enabling an in-depth exploration of the impact of random pitting corrosion on the ultimate compressive strength of H-section columns under various corrosion scenarios.
Sensors_**2013**, _13_, 3588-3614; doi:10.3390/s130303588  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Utilizing Joint Routing and Capacity Assignment Algorithms to Achieve Inter- and Intra-Group Delay Fairness in Multi-Rate Multicast Wireless Sensor Networks**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] and [PERSON] 2**  
1 Department of Information Management, National Taiwan University, No. 1 Sec. 4, Roosevelt Rd., Taipei City 106, Taiwan; E-Mails: [EMAIL_ADDRESS] (F.Y.S.L.); [EMAIL_ADDRESS] (L.S.C.L.)  
2 Graduate Institute of Information Management, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-2-2799-5768 (ext. 820); Fax: +886-2-2799-2058.  
_Received: 28 January 2013; in revised form: 28 February 2013 / Accepted: 11 March 2013 / Published: 14 March 2013_  
**Abstract:** Recent advance in wireless sensor network (WSN) applications such as the Internet of Things (IoT) have attracted a lot of attention. Sensor nodes have to monitor and cooperatively pass their data, such as temperature, sound, pressure, _etc._ through the network under constrained physical or environmental conditions. The Quality of Service (QoS) is very sensitive to network delays. When resources are constrained and when the number of receivers increases rapidly, how the sensor network can provide good QoS (measured as end-to-end delay) becomes a very critical problem. In this paper, a solution to the wireless sensor network multicasting problem is proposed in which a mathematical model that provides services to accommodate delay fairness for each subscriber is constructed. Granting equal consideration to both network link capacity assignment and routing strategies for each multicast group guarantees the intra-group and inter-group delay fairness of end-to-end delay. Minimizing delay and achieving fairness is ultimately achieved through the Lagrangean Relaxation method and Subgradient Optimization Technique. Test results indicate that the new system runs with greater effectiveness and efficiency.  
**Keywords:** wireless sensor networks; multicast; end-to-end fairness; quality of service; Lagrangean Relaxation method; optimization
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 2 Study Area  
Mongolia is a sovereign state located across east and central Asia, with an area of 1,564,116 km\({}^{2}\), and is the 18 th-largest country in the world. It has a population of over 3.3 million, and 0.43% of the territory is covered by surface water [19]. It is a landlocked country, bordered by Russia to the north and China to the south. It has a diverse geography, being mostly covered with grassy steppes, with mountains to the north and west, and the Gobi Desert to the south (Figure 1). Ulaanbaatar, the capital and the largest city, is home to approximately 45% of the country's population.  
Mongolia has an average annual electricity consumption of 8.5 billion kWh, of which 20% is imported [20], with the Oyu Tolgoi plant receiving most of its imported electricity (approximately 1.4 billion kWh) from China [21]. Approximately 300 million kWh of electricity is imported annually from Russia [22].  
In addition to the huge mineral deposits that contribute significantly to the national economy and increase export earnings, Mongolia is rich in solar energy resources. The entire country is cloud-free for 270-300 days annually, with annual average sunshine of 2250-3300 h, delivering 1200-1600 kW/m\({}^{2}\) at an average radiation intensity of >4.3-4.7 kWh/m\({}^{2}\)/day [23]. Therefore, by exploiting more solar energy, Mongolia could export electricity to other countries rather than importing it.
## 3 Results and Discussion
### The Effect of Using Non-Optimal Carrier Phase Estimation  
The results presented in this study rely on a DSP routine utilizing a feedforward phase noise estimation algorithm [38]. This type of algorithm is particularly effective for the cancellation of free-running laser phase noise, which is the case in the experiment. To better assess its capability in the context of electrically and optically pumped scenarios, we  
Figure 10: Calculated SNR (**a**) and penalty (**b**) for the different mixers, taking as a reference the GaAs device with electrical pumping. The SNR is calculated from the BER measurements with Equation (1).  
alternatively applied different DSP routines. In this instance, the algorithm was replaced by a classic phase-lock-loop (PLL)-based scheme described in [46].  
In Figure 11a, the BER obtained for the modified DSP is shown. The BER was consistently close to or above the SD-FEC limit in every case. The highest BER values were observed at the lowest bit rates, with a slight improvement as the bit rate increased. This behavior is well explained by non-optimal phase noise cancellation. As the rate increases, the relevance of phase noise, linked to the laser linewidth, diminishes as a result of the faster signal.  
The comparison between electrically and optically pumped scenarios reveals that the electrical pumping achieved superior performance, contrasting the results obtained with the feedforward-based algorithm. This reaffirms the non-optimal carrier phase estimation, where the electrical LO introduces less phase noise than the optical LO. In Figure 11b, the constellations derived when using the narrow-band InGaAs mixer under electrical and optical pumping are depicted. It is visually evident how the constellation in the optically pumped case exhibits greater phase noise.  
These results highlight the importance of the phase noise estimation algorithm. The electrical pumping scenario is superior only when the algorithm is not able to correctly estimate the carrier phase; this happens because phase-locked lasers were not used. If phase-locked lasers were employed, the phase noise would have been lower, resulting in significantly improved outcomes even without the use of the DSP. It has been demonstrated that the phase noise can be adequately canceled, and therefore, an optically pumped receiver can achieve equal or better results than with electrical pumping. This is especially remarkable considering that the InGaAs narrow-band mixer performed comparably to the GaAs mixers despite the detriment in conversion loss.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 5 Performance Evaluation
### Synthetic Datasets
#### 5.1.3 Effect of Skyband Size  
We also examined the efficiency of the proposed technique with various skyband sizes. For this evaluation, we used a data dimensionality \(m\) of 6, data cardinality of 100,000, and skyband size \(K\) ranging from 2 to 8. Figure 9(a)-c present the runtime for the different distributions. As indicated by the results, our method was affected by the skyband size. Our experimental results show that the runtime increases dramatically as the skyband size increases. The large cardinality of non-dominating objects causes this.