esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Condition
### Routing Density  
In FPGA, connecting two logics separated into several blocks (LABs) may involve wires and switching circuits. Therefore, delays due to the interconnects are more significant than the circuit's logic delays [37]. Quartus assign efficient routing, which allows a design to be realized for minimizing space and delay. However, the problem of routing is architecture-dependent in different FPGA technologies available in the market. The row and column interconnect in the Intel FPGA vary on the technology. For Cyclone V, as shown in Figure 2(a), the row routing uses R3, R6, and R14 interconnects, connecting logics traversing up to 3, 6, and 14 horizontal LABs, respectively [38]. Similarly, column routing is realized using C2, C4, and C12 interconnects. These interconnects (C2, C4, and C12) can connect logics traversing up to 2, 4, and 12 vertical LABs. Those rows and columns may be combined to create a route traversing few LABs of the row to column. In every interconnect type, half of them may be utilized to create a route to one direction only.  
Besides row and column interconnect, a direct-link interconnect exists [38] in Cyclone V, which is dedicated to creating a route of logics of adjacent LAB. This work utilizes \"wire utilization\" and \"routing hotspots\" to estimate Intel FPGA chips' routing density [38; 39]. The former refers to the percentage of wire utilized around a specific LAB, while the latter refers to the maximum percentage of any row or column interconnect of any direction. Routing hotspots are estimated based on the maximum total wire utilized of any direction of C2, C4, C12, R3, R6, or R14. Figure 2(b) views routing density of RO at location 9 (RO-9). The total wire used in this LAB is 5% (wire utilization). There are 28 wires available of R14 interconnect; 14 are to the right, and another 14 are to the left. Hence, the routing hotspots of this LAB are about 36% (5 of 14 wires).  
This work prelocated the ROs and their counters. At the same time, other supporting circuits are not prelocated. As a result, routing around ROs differs significantly. Consequently, the wire utilization and routing hotspots of every ROs are also varied subjects to their location, where the ones in the middle of the chip are higher than others. Figure 4 shows the relationship between average wire utilization and routing hotspots. In general, wire utilization and routing hotspots are strongly related. Most of the higher wires utilized ROs, the routing hotspots were also higher, but this is not always true. For instance, in RO-15, when realized using Pattern-1, 6% of wires are used (wire utilization). The peak wiring (routing hotspots) in this RO is C12 down, where 10 out of 12 wires were utilized, which is equivalent to 83%. Conversely, in RO-14, 25% of wires are used, which is higher than RO-15. However, the routing hotspots are less than RO-15, 71% (10 out of 14 wires of the R14 left interconnect). These phenomena only occur in ROs of more than 5% wire utilization.  
Figure 3: Overview of (**a**) Interconnects in Cyclone V (**b**) Routing density of RO-9 in Chip Planner (available in Quartus).
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Propagation Models
#### 3.1.3 Log-Distance Propagation Model  
This model is suitable for propagation in dense areas. The model estimates the path loss for a more considerable distance and communication range. This model computes an exponential path loss incurred from a distance between a transmitter and a receiver [42]. The model has assumed an exponential path loss over the transmitter's and receiver's distance.  
\[L=L_{0}+10n\log\biggl{(}\frac{d}{d_{0}}\biggr{)} \tag{3}\]  
Here, \(L\) denotes the path loss, \(d\) the distance, the reference distance is denoted by \(d_{0}\), with path loss for reference distance denoted by \(L_{0}\) and \(n\) states the path loss exponent.
## 3 Results and Discussion
### Operational Aspects and Judement of the Plant Managers
#### 3.2.4 Critical Operational Aspects
The weather conditions affect the variability of the treatment performance in terms of energy and reagent consumption (e.g., the lower the water temperature, the higher the water viscosity, the higher the energy consumption for membrane cleaning) but does not involve a reduction in process yields. This aspect, combined with the foaming issue, which is instead related to the biomass conditions and to the input load, underlines the great importance of pre-treatments and equalization. Thus, the possibility to maintain a more constant quality of influent wastewater allows us to increase the lifespan of the membranes. Moreover, the concomitant improvement of the membrane materials has allowed a significant increase in their useful life up to over a decade with guarantees offered by some manufacturers even up to 10 years [43]. A future challenge will be the higher cost effectiveness of MBR, mainly due to the energy cost increasing. Constant efforts will be essential to extend the membrane lifetime which will reduce the depreciation cost. A development of more efficient membranes, bioreactor combination modes, sharpness in designing of membrane scouring, and biological aeration systems are essential to higher cost effectiveness [36]. Regarding the comparison between CAS and MBR, [PERSON] et al. [44] proposed the application of a sustainability index that takes into account economic, technological and managerial factors. The authors showed that the sustainability of full-scale MBR process is lower than CAS process, in agreement with the findings of [PERSON] et al. [7]. However, MBR is extensively an alternative solution to replace CAS process when influent volume is below 50,000 m\({}^{3}\)/day. For a comprehensive comparison, local economic conditions need to be given top priority, and it is necessary to understand the current situation of local water pollution, influent quality and land resources.
## 3 Establishment of Numerical Model for Surrounding Rock of Roadway Excavation  
### Composition of Surrounding Rock Structure in Roadway Excavation  
Generally, rock layers are present above the coal seam within the roadway. These rock layers possess different properties and thicknesses, which significantly impact the stability of the roof formed by these layers. The roadway roof is categorized into three sections: the basic roof, the direct roof, and the false roof [16]. The composition of the surrounding rock structure in the roadway is illustrated in Figure 7.  
(1) The basic roof is generally located above the direct roof, but if there is no direct roof, it directly overlays the roadway and coal seam. This rock layer exhibits good stability due to its thickness and hardness.  
Figure 6: Process aging comparison chart.  
Figure 7: Root of fully mechanized roadway.  
(2) The direct roof is usually positioned above the false roof. In the absence of a false roof, it directly overlays the roadway and coal seam. The thickness of the direct roof is typically greater than 0.8 m and is mainly composed of sandstone, Sandy mudstone, or mudstone. Its stability is generally acceptable. If the thickness of the direct roof is less than 0.8 m, in order to ensure roadway safety, the use of stepped temporary support technology and equipment should be avoided.  
(3) A false roof may exist in certain coal seams, typically constituted of relatively fragmented mudstone. The thickness of the false roof is usually between 0.3-0.8 m, rendering it unstable. In the presence of a false roof, the implementation of stepped temporary support technology and equipment should be avoided.  
(4) The roadway floor can be classified as either a direct floor or basic floor. However, the impact of temporary support on the roadway floor is negligible. Consequently, when establishing a numerical model for the roadway, the roadway floor is simplified to a single lithology.  
According to the stress formation mechanism in roadway excavation, the surrounding rock of the advancing roadway experiences gradual damage from shallow to deep. During the initial period after excavation, the direct roof of the roadway is primarily prone to destruction. Simultaneously, temporary support serves the purpose of preventing roof collapse and excessive settlement of the direct roof, with limited influence on the deeper surrounding rock. Hence, the direct roof plays a crucial role in controlling the stability of the fully mechanized roadway.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 3 Results and Discussion
### Preparation and Characterization of Coated AuNRs
Analyses were conducted for a total duration of 100 s by fixing the power of the diode laser at \(10\times 10^{-3}\) W/mm\({}^{3}\) on samples at gold concentrations of 2.5 and 5 \(\upmu\)g\(\cdot\)mL\({}^{-1}\). These parameters were selected to maximize the hyperthermia efficiency of the selected gold concentrations and, concurrently, minimizing the possible undesired effects due to an excessive rising in temperature. In particular, the AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA samples were placed in 24-well plates and irradiated by laser, monitoring the temperatures of each well through an infrared camera. Temperatures reported (Table 4) and images acquired by a thermal infrared camera (Figure 6) confirm the results of the previous study, but with some differences. AuNRs/LA,GG sample, once again, proves to be the most effective among the nanovehicle tested. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Temperature (\({}^{\circ}\)C) versus Exposure Time (Seconds)**} \\ \cline{2-7}  & **0 s** & **20 s** & **40 s** & **60 s** & **80 s** & **100 s** \\ \hline AuNRs/LA,GG & 25 \({}^{\circ}\)C & 33 \({}^{\circ}\)C & 3  
The comparison between the maximum temperatures reached by AuNRs/LA,GG and AuNRs/LA shows that the former system is far more efficient than the latter (reaching temperatures of 54.5 and 45.6 \({}^{\circ}\)C, respectively). The greater difference recorded in this study compared to the previous one is attributable, in first instance, to the temperature detection method. In the experiment carried out by setting three different powers of the laser treatment, the measurements by optical fiber allowed to obtain the average temperature value in each well. On the contrary, temperatures, acquired through infrared camera and shown in Table 4, represent the maximum temperatures detected. A further explanation of the differences found might be the use of lower concentrations. In fact, in view of the considerable dependence between gold concentration and thermal rise, it is possible that the use of low sample concentrations may allow more detailed observation of the gap between the two hyperthermic behaviors. However, it is not surprising that the AuNRs/GG-LA sample shows the mildest hyperthermic effect, touching the maximum temperature of 41.5 \({}^{\circ}\)C.  
The collected data were used as reference temperatures for the hyperthermia experiment later conducted on HCT116 tumor cell line.
## 1 Introduction  
Probabilistic seismic hazard analysis (PSHA) considers both aleatory variability and epistemic uncertainty. The aleatory variability is apparent randomness in the earthquake process due to the use of simplified models, and the epistemic uncertainty represents the range of alternative models that are technically defensible [1]. In PSHA, the aleatory variability is treated by integrating over the random variables (e.g., magnitude, distance, ground-motion epsilon), whereas the epistemic uncertainty is usually modeled by logic trees with alternative models or model parameters. The hazard is computed for each combination of the logic tree branches from each node of the logic tree.  
Seismic hazard analyses commonly use the ground-motion model (GMM) to describe the median and aleatory variability in the ground motion for a given earthquake scenario (magnitude, distance, site condition). Traditionally, GMMs are based on regression analyses of empirical ground-motion data with constraints from seismology and geotechnical engineering (e.g., [2]). To have enough data to constrain the GMM at large magnitudes and short distances that tend to control the hazard, ground motion data from different regions of the world have been pooled together. The resulting global GMM has large aleatory variability due to ignoring systematic differences in the source, path, and site scaling of the ground motion for different locations.  
The application of a global GMM to a specific site assumes that the distribution of the ground motion from the different sites applies to a single site. Using the variability over space over a short time as a proxy for the variability over time at a single location is called the ergodic assumption. Ref. [3] showed that the ergodic assumption led to seismic hazard curves for sites in California that were inconsistent with the existence of precariously balanced rocks near the major faults. They also showed that by using a smaller aleatory variability, this inconsistency could be resolved, indicating that the GMM should be based on site-specific data, not on global data. The site-specific GMMs are called non-ergodic GMMs.
## 3 Results
### Second-Site AlaRS Mutations Alleviate the Slow-Growth AlaRS C666A Phenotype  
While the sequencing data indicated second-site mutations in the _alaS_ gene were likely responsible for the suppression of the AlaRS C666A slow-growth phenotype, it was possible that other mutations in the suppressor strains may have been missed by our sequencing analysis and the second-site mutations were a coincidence but not responsible for the suppression. To confirm that the three identified mutants were acting as the MG1655 AlaRS C666A suppressors, the three mutations were made in isogenic MG1655 and MG1655 AlaRS C666A strains, creating single and double mutants for the suppressors. One possibility for these mutants was the potential for the second-site mutants acting as compensatory mutations for the AlaRS C666A substitution, while single mutants alone may elicit a separate growth defect.  
Upon generation of the six isogenic strains, growth of all of the strains was monitored in LB liquid media at 37 \({}^{\circ}\)C. This growth condition has previously been used to highlight the AlaRS C666A growth defect [13]. Interestingly, all single and double mutant suppressor strains grew as well as wild-type _E. coli_ (Figure 2A). This observation indicated that suppressor mutations identified from the various sequencing analyses were acting as suppressors. Furthermore, substitutions at D551 or R561 alone do not cause any change in growth at 37 \({}^{\circ}\)C in rich media. As previously eluded to, perturbation to AlaRS proofreading leads to defects in growth in rich media. It was further shown that this defect is serine dependent, presumably through elevated levels of Ala to Ser mistranslation [9; 13]. To determine if the suppressor mutants also exhibit a serine-dependent growth defect that may have been masked in rich media, the suppressor mutants were grown in minimal media supplemented with exogenous serine, and their growth was monitored. Despite the excess non-cognate amino acid stress, the single and double suppressor mutants were able to grow just as well as wild-type _E. coli_ (Figure 2B), which may suggest that the level of serine mistranslation was reduced in the presence of the second-site substitutions.  
Figure 1: AlaRS C666A suppressors map to the AlaRS proofreading domain. (**A**) Suppressor mutations map to the AlaRS proofreading domain. (**B**) The sites for suppressor mutants do not appear to be conserved across all domains of life. (**C**) Suppressor variants were modeled on the AlaRS proofreading domain using Phyre2 (96% of residues modeled at \(>\)90% accuracy).
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### Dynamic Data-Driven RUL Prediction Method  
To decrease the difficulty of obtaining the dynamic data of special power plants, based on the random, non-contact test theory, the incentive transfer models and boundary conditions such as vibration, impact, and pressure are studied. Based on the dynamic data obtained from single-working-condition and multi-working-condition tests, the RUL prediction method of special power plants with small-sample and unbalanced data can be researched, the mapping relationships between different types of trial data and RUL can be analyzed, and the change rule of RUL under different types and levels of random incentives can be explored. Then, the method of dynamic data acquisition and pretreatment can be researched for different combined structures, such as series, parallel, and hybrid structures. The dynamic data characteristics are analyzed, and dynamic data-driven RUL prediction models are constructed for different combinatorial structures. RUL prediction methods for complex institutions can be explored. The key research contents can be summarized according to the following four aspects:  
1. Theoretical research is conducted on the soft measurement method for the follow-up dynamic parameters;
2. Research is conducted on RUL prediction methods using small samples and non-equilibrium data conditions;
3. Research is conducted on the RUL prediction method driven by dynamic data under multiple working conditions;
4. Research is conducted on the RUL prediction method driven by dynamic data under different combination structures.  
The implementation flowchart of RUL technology prediction using the data-driven dynamic method is shown in Figure 6.
## 6 Research Challenges and Future Directions in Unstructured Outdoor Environment Navigation  
Despite the significant progress that has been made in improving the abilities of robots to perceive their environment, localise themselves within it, and plan paths to navigate through it; significant challenges remain with the application of these methods to unstructured outdoor environments. Many limitations exist with respect to perception capability, particularly when sensor and computation costs are a factor, and effective methods for robust scene understanding within such environments have not yet been demonstrated, substantially hindering the ability to deploy fully autonomous robots into such environments. This section of the paper explores the current state of these significant research challenges and provides suggestions for the most promising directions for future research efforts to overcome them.
## 3 Results and Discussions
### Simulation of the Solidification of Radioactive Wastes Using SRC  
A massive amount of solid waste, such as CW, waste soil, and metal waste, originates from the decommissioning process of nuclear power facilities, and that type of waste affects the compressive strength of SRC [25]. For this reason, the changing tendency of SRC's compressive strength was investigated according to the type of waste, and its maximum capacity for packing the waste was determined. As the coarse aggregate of CW occupies the highest proportion of the solid waste occurring from the dismantling process, the maximum capacity of SRC containing CW was first investigated [25]. The results showed that 30 wt% was the maximum proportion of coarse aggregates packed in SRC. Then, the maximum solidifying capacities of SRC for waste soil and metal waste were evaluated using a fixed ratio (30 wt%) of coarse aggregates. Moreover, because experiments were impossible using practical radioactive nuclides, their leachability was indirectly assessed using nonradionuclides, such as Co, Cs, and Sr, which were intentionally introduced into each waste. Before the experiments on the leachability of waste soil, the maximum adsorption capacities of the nonradionuclides were empirically determined to choose their relevant spiking levels [25], and the maximum adsorbed concentrations (MAC) of Co, Cs, and Sr were estimated as 23,622, 9863, and 17,138 mg/kg, respectively. The nonradionuclides were injected at three different levels (<10% MAC, equal to MAC, and >10% MAC) [25]. On the other hand, with regard to metal waste, the MAC of each nuclide could not be determined using an identical experimental procedure, and instead, it was sufficiently soaked for 24 h in a solution containing them at a concentration of 50,000 mg/L before the leachability tests [25]. A standard method (ANS 16.1) was used for the leachability tests, and the concentrations of each nuclide were analyzed whenever the leaching solutions were replaced during the total period of 90 days. Finally, their leachabilities were calculated using Equation (1). Whether each waste type was suitable or not was evaluated by comparing their leaching indices based on the acceptance level of 6. The SRC's compressive strength was also analyzed again following the leaching tests.  
Figure 9: The Raman spectra. (**a**) Without the addition of CSH and ettrin96, (**b**) with the addition of CSH (5 wt%), and (**c**) with the addition of ettrin96.
## 3 Data and Methodology
### Integrated Geophysical Exploration Method
#### 3.3.2. SRT Survey  
SRT involves the deliberate generation of seismic waves on the ground, which will change propagation direction when they encounter a velocity-dividing interface during their downward propagation. If the velocity of the lower medium is greater than that of the upper medium above and the angle of wave incidence equals the critical angle, the wave will be transformed to propagate along the interface, and refracted waves will be generated in the overlying medium. Then, ground-based instruments can collect these seismic data, which are subsequently subjected to computer postprocessing to produce interpreted images [25,26].  
Figure 7: Layout of engineering geophysical exploration and borehole and interpretation results by SRT survey.  
For data collection, the ANT-1C wireless intelligent node acquisition system from Earth Pulse Technology Co., Ltd. based in Beijing, China, was employed with a heavy hammering excitation method. The detection process utilized an encounter observation mode with a fixed receiving arrangement and a 4 m channel distance. Multiple coverage techniques were implemented to mitigate strong interference from heavy mechanical equipment within the pit. Each excitation point underwent 20 hammer strikes, leading to the completion of seven survey lines (Figure 8). In the digital processing phase, the raw seismic data underwent processing using the break-picking program to generate the first arrival file in \".bpk format. Subsequently, the first arrival files from multiple encounter observation systems were imported into the SIPQC program. The first arrival times were then allocated to each layer based on the refracted wave time-distance curve's gradient. This process led to the creation of a measurement point-depth map, facilitating the calculation of wave velocities for each layer and determining the depth of the underground bedrock at each geophone location. Given the considerable dip in the stratigraphy of the study area, superimposed offsets were managed accordingly.
## 2 Security Considerations for Marine Network Protocols
### Availability  
Marine networks must guarantee access of data and services, which under certain conditions they can become critical for the operations and mission of the vessel. The majority of maritime communication protocols support slow transfer speeds [4; 5]. As such, protection for availability depends on the hardware specification and configuration to allow data to be transferred at maximum data rate of the protocol. Use of hardware redundancy and backups are necessary for critical systems that require constant accessibility.  
For instance, communications between ships and stations are critical, so crew members generally have multiple communication channels, such as Voice over IP (VOIP), marine VHF radios, and satellite services, such as AmosConnect 8 [12].