esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Intelligent Algorithm and Simulation Modeling
### Analysis of the Simulation Test Results  
It can be seen from the simulation results for the exercise amount (see Figure 8) that the motion inhibition effect of intelligent mooring and conventional mooring was similar, and roll was more obvious in the overall performance of each movement. By comparing the results of the two mooring modes, the active control of the mooring damping force achieved by the intelligent algorithm was able to achieve the purpose of reducing the ship's motion amplitude. Through the setting and judgment of the motion threshold in the intelligent control, the control external force of the damping force set was able to provide the ability to restrain the amplitude of the ship at various frequencies, making the ship's motion amplitude oscillate within the expected range. It can be seen from the change trend of the approximate amount of exercise under the two mooring modes that the intelligent mooring mode was able to achieve a motion inhibition effect similar to the conventional mooring mode in terms of the mooring constraints, and even slightly better than the conventional mooring mode in some exercise performance parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Designation** & **Ship** & **Unit** \\ \hline LOA. & 315 & m \\ LPP. & 290 & m \\ Breath & 50 & m \\ Depth & 27 & m \\ Draft & 12.0 & m \\ Displacement & 146,871.2 & t \\ Roll period & 14.92 & s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the moored ship.  
Figure 7: Comparison calculation model of the two mooring modes. (**a**) Intelligent mooring arrangement; (**b**) Conventional mooring arrangement.  
Under the same working conditions, both the intelligent mooring system and the conventional mooring arrangement were able to achieve the control effect of the wharf mooring ship. From the comparison of the simulation results, the range of motion under intelligent mooring control was reduced, the average amplitude of each operating condition was about 10% and the effect of transverse wave was relatively more obvious. The results showed that the automatic system can indeed play a more advantageous role. Meanwhile, with increases in wave height, the improvement effect gradually decreased. For example, the improvement effect was more significant when H4% = 0.8 m than when H4% = 1.8 m.
## 5 Conclusions and Future Work  
This study proposes the CAQT algorithm to cope with the overestimated number of tags problem in the error-prone channel environment. The CAQT not only estimates current channel condition in order to estimate more accurate number of tags in the rapidly changing channel quality environment, but it also statistically determines the most efficient strategy, which is to ask the tag to retransmit it or to split the collide tags, in order to minimize the number of idle slots. The simulation results show that CAQT significantly outperforms the existing algorithms, which are ERE-ABS, QT, and DFSA. CAQT only used 69.0%, 57.8%, and 87.2% slots consumed by QT, DFSA, and ERE-ABS, respectively, when the  
Figure 12: Estimated number of tags during the tag identification progress.  
PER was 0.5. Therefore, the CAQT provides realistic description and a useful guideline of how things or people can be identified in varying channel quality of IoT environment, which contributes to the development of consumer electronics industry and literature. The simulation results also demonstrate that CAQT provides enhanced performance when the channel quality varies, especially in outdoor environments, for example, ticket checking for railway or subway system. We will extend our work to identify lots of tags in a short period of time duration in RFID systems [36].  
Conceptualization, Y.-H.C.; Data curation, L.-K.C.; Formal analysis, Y.-H.C.; Investigation, Y.-H.C.; Methodology, Y.-H.C.; Project administration, Y.-H.C.; Software, Y.-H.C. and R.-Z.H.; Supervision, Y.-H.C.; Writing--original draft, Y.-H.C. and Y.-R.S.; Writing--review and editing, Y.-H.C. and P.-T.J.  
This work is supported by the Ministry of Science and Technology (MOST) (MOST 106-2218-E-227-001-MY2).  
The authors declare no conflicts of interest.
## 3 Results
### Analysis of the Average Emission Factor
#### 3.1.1 Distribution within the Year  
Figure 6 shows the AEF\({}_{\rm fc}\) for 2015, 2020, 2030, and 2040 in the CAP scenario in a heat map (b) and as a scatterplot (a) with the hourly values' relation to net power consumption and the share of RES. The data from 2015 are shown for comparison, as a backtesting of the model was carried out here with respect to the electricity production volumes and installed capacities. The area with low AEF\({}_{\rm fc}\) values is clearly visible due to the influence of photovoltaics and wind energy during the increased solar radiation at midday and in early summer to autumn. In addition, periods of increased wind power input over several hours can be seen as a vertical strip.
## 2 Background  
### Atmospheric Scattering Formation Model  
In poor weather conditions such as haze, captured images are degraded by the opaque medium (haze), because the atmospheric light \(AL\) is absorbed and scattered whenever the distance from the camera increases. According to [PERSON] and [PERSON] [PERSON] (2018, 2018, 2018), the widely used degradation model (shown in Figure 1) of the hazy image in computer vision and computer graphics can be expressed as follows:  
\[I^{c}(x)=I^{c}(x)t(x)+A(1-t(x)) \tag{1}\]  
\[t(x)=e^{-\beta d(x)} \tag{2}\]  
Note that \(x\) represents image pixel coordinates, \(I\) is the captured hazy image, and \(J\) is the main target of dehazing operation called scene radiance of haze-free image. \(AL\) is the global atmospheric light. \(t\) represents the amount of scene radiance that reaches the camera without scattering and absorbing. \(\beta\) is the attenuation coefficient that can be regarded as a constant in the case of a homogenous atmosphere. \(d\) is the scene depth.  
**Remark 1**.: \(c\) _denotes the color channel in **RGB** color space._  
According to the hazy image attenuation model in Equation (1), designing a high-performance haze removal system requires two main key estimations: transmittance map \(t\) and atmospheric light  
Figure 1: Hazy image degradation mechanism.  
AL._ Hence, the scene radiance \(J\) can be recovered using the degradation model. The inaccuracy of estimated transmission map \(t\) and the atmospheric light \(AL\) leads to unsatisfied dehazing results.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 2 Background and Related Research
### Related Research  
Existing research into and deployment of nanogrid technologies to date have generally not directly focused on the ability of nanogrids to support critical loads from the perspective of energy resilience and especially for military base applications. As far as we are aware, no one has proposed using nanogrids from the perspective of improving resilience of critical loads that support national security. The majority of current nanogrid research focuses on conceptual nanogrid design and defining nanogrid infrastructure. Only a few publications have reported on nanogrids successfully implemented in real-world conditions. For instance, a nanogrid was implemented in a housing community in Okinawa, Japan where it was found that a decentralized DC-DC solution was beneficial [17]. However, the authors noted that additional research is needed to explore limitations of decentralization and the development of higher level intelligent exchange strategies that enhance efficiency [17].  
Enhancing efficiency of nanogrids (an important aspect of energy security for militaries) occupies a significant portion of the existing literature where identified challenges include protocols, demand-side management, security, and the self-control of the overall system [37]. One promising area of research is fully DC-based nanogrids where higher efficiency, better power quality, and better stability is achieved versus other options and may contribute significantly to energy security as a result [18].  
As mentioned previously, a similar concept to DC nanogrids is zonal distribution (sometimes referred to as zonal shipboard power) which is implemented on a growing number of vessels in many Surface Navies. Zonal distribution uses DC power architecture in order to avoid some of the issues associated with AC power architecture such as generator sets working at fixed speed that limit fuel efficiency, reactive power flow and power quality problems, bulky conventional transformers, and challenges associated with supporting pulsed electric loads which will become increasingly common on Navy ships [38].  
In addition, current research in nanogrids fails to support a mission-focused objective for DOD shore installations and instead supports a commercial cost effective mission with an ease of integration to existing infrastructure. Current research suggested the pursuit of future work in resilience of smart load configurations, networks and connections, and fast response to disasters [18; 39]. This segues into the uniqueness of our research where we propose a methodological approach to enhance resilience of mission critical loads to create greater energy security that in turn supports the mission of national security.
## 3 Results
### Mechanical Performance of the Green Composite Pieces
The flexural modulus (\(E_{f}\)) and strength (\(\sigma_{f}\)) of the unfilled PLA piece were 3.17 GPa and nearly 99 MPa, respectively. Similar to the tensile properties, the incorporation of TCHF yielded a progressive  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Piece** & \(E_{t}\) **(GPa)** & \(\sigma_{y}\) **(MPa)** & \(\varepsilon_{b}\) **(\%)** & \(E_{f}\) **(GPa)** & \(\sigma_{f}\) **(MPa)** & **Shore D** & **Impact Strength** \\  & & & & & & & & **(dJ/m? )** \\ \hline PLA & 3.58 \(\pm\) 0.37 & 49.51 \(\pm\) 1.92 & 8.91 \(\pm\) 0.71 & 3.17 \(\pm\) 0.19 & 98.88 \(\pm\) 1.18 & 78.65 \(\pm\) 2.64 & 25.10 \(\pm\) 2.01 \\ PLA/20 TCHF & 3.48 \(\pm\) 0.78 &  
increase in \(E_{f}\) and a reduction in \(\sigma_{f}\). In particular, the green composite pieces filled with 50 wt% of TCHF showed a \(E_{f}\) value of 5.53 GPa, which is approximately 1.7 times higher than that of the unfilled PLA, whereas \(\sigma_{f}\) was reduced to 61.42 MPa. Some previous studies have reported similar mechanical properties of green composites based on coffee waste derived fillers. For instance, [PERSON] et al. [58] showed a worsening of tensile strength and ductility when CSS was incorporated into bio-based high-density polyethylene (bio-HDPE). The described decrease of the mechanical properties was ascribed to the low aspect ratio of the CSS particles as well as their poor adhesion to the biopolymer matrix. In this regard, the use of coupling agents has improved both the tensile strength and ductility of PLA composites prepared with SCG, though the improvements achieved were relatively low and their overall and specific migration limits (OML and SML) should also be carefully determined [59]. One can also observe that, as the TCHF content increased, hardness also increased. The Shore D hardness value increased from 78.65, for the unfilled PLA piece, to 85.05 for the PLA/50 TCH piece. This change in the mechanical behavior corresponds to the typical effect of a relatively stiff lignocellulosic filler on a polymer matrix [60]. In contrast to hardness, the impact absorbed energy of PLA progressively decreased after the TCHF incorporation. The impact energy of the neat PLA piece was 25.10 kJ/m\({}^{2}\) and it was reduced down to 7.19 kJ/m\({}^{2}\) for the green composite piece filled with 50 wt% of TCHF. This notable decrease in toughness can be related to the poor matrix-particle interactions, which could promote microcrack formations and stress concentrations during the impact test [60]. Therefore, the addition of the coffee waste derived lignocellulosic filler led to a more elastic and stiffer material, but the matrix-filler continuity was not achieved, and an early fracture was attained. Thus, in terms of mechanical performance, PLA pieces containing 20 wt% of TCHF were slightly more resistant but less ductile, showing characteristics comparable to those of neat PLA.
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 2 Materials and Methods
### Strategies for \(\epsilon\) Determination
#### 2.3.4 Domain-Specific Methods  
The proposed methods are generally applicable to any problem and search space. The properties of the search space and the implementation of the fitness function can be problem-specific; the general HC idea will work using either the fix or the Brute Force method. However, there may be better \(\epsilon\) determination strategies based on more knowledge about the search space.  
Based on the attributes of the search space, it is possible to change the fixed step size to a dynamic one. The \(\epsilon\) value can be a function of the actual fitness value, the number of iterations, etc. These methods are based on the assumption that the random starting position is far away from the global optimum and has a high fitness value; therefore, it is worth using a large step size at the beginning. As the HC algorithm follows a path of monotonically lowering fitness values, it is worth decreasing the step size to fine-tune the optimal position. This method has several advantages but requires proper mapping from the known information (last \(\epsilon\), current fitness, and iteration count) to the next \(\epsilon\) value.
## 2 Materials and Methods
### Data Collection and Data Set Construction
#### 2.3.2 Dataset Construction  
To make the sensors' measured data consistent with the indoor test sampling depths, a penetration depth of 100-120 mm was taken as the data study area of the smooth curves in Figure 5. From the data in this study area, we extracted the maximum of the load cell and the maximum of the film sensor and converted them into the corresponding force values through Formulas (3) and (4), which are represented by \(R\_max\) and \(P\_max\), respectively. By using \(R\_max\), \(P\_max\) together with the soil water content (\(\omega\)), soil bulk density (\(d\)), soil cohesion (\(c\)), and internal friction angle (\(\varphi\)) measured from indoor experiments, a 6-dimensional soil dataset was constructed. Since there were 83 soil sampling sites, the dimensions of the soil dataset are 83 \(\times\) 6.
###### Abstract  
The signal conditioning treatment to achieve good relation of power with radio-frequency (RF) conversion in conventional transceiver systems require precise baseband models. A developed framework is built to provide a demonstration of the modeling figures of merit with orthogonal frequency division multiplexing (OFDM) support under signal conditioning and transmission restrictions to waveforms with high peak to average power ratio (PAPR) in practical applications. Therefore, peak and average power levels have to be limited to correct high PAPR for a better suited correction power from the amplifier that can lead to compression or clipping in the signal of interest. This work presents an alternative joint crest factor reduction (CFR) algorithm to correct the performance of PAPR. A real-time field-programmable gate array (FPGA) testbed is developed to characterize and measure the behavior of an amplifier using a single-carrier 64-QAM OFDM based on long-term evolution (LTE) downlink at 2.40 GHz as stimulus, across wide modulation bandwidths. The results demonstrate that the CFR accuracy capabilities for the signal conditioning show a reliable clipping reduction to give a smooth version of the clipping signal and provide a factor of correction for the unwanted out-of-band emission validated according to the adjacent channel power ratio (ACPR), PAPR, peak power, complementary cumulative distribution function (CCDF), and error vector magnitude (EVM) figures of merit.  
crest factor reduction; FPGA; LTE; PAPR; power amplifier +
Footnote †: journal: _Sensors_
## 2 Results
### Effects of ABA and NAA on Berry Development and Ripening
#### 2.1.2 Changes of Endogenous ABA and Auxin Biosynthesis and Signaling Pathway
In contrast, NAA application significantly decreased the expression of one VviPYR/PYL (VIT_202s0012g01270), two VviPP2 Cs (VIT_206s0004g05460 and VIT_216s0050g02680), and one VviSnRK2 (VIT_20 2s0236g00130) at E-L 34 stage, as well as one VviPP2C (VIT_213s006g01270), two VviSnRK2s (VIT_207s0031g03210 and VIT_207s0197g00080), and two VviABFs (VIT_204s006g01 150 and VIT_208s0007g03420) at E-L 36 or 38 stage. This seems to indicate that NAA application down-regulated ABA signaling activity. Regarding auxin signaling, we investigated the expression of genes encoding auxin influx carrier (AUX1), transport inhibitor response 1 (TIR1), auxin/indole-3-acetic acid protein (AUX/IAA), auxin response factor (ARF), indole-3-acetic acid-amido synthetase (GH3) and small auxin-up RNA (SAUR) [24]. ABA markedly down-regulated the expression of auxin signaling genes including four VviAUX/IAAs (VIT_205s0020g04670, VIT_215s0046g00290, VIT_211s0016g03540), two VviARFs (VIT_206s0004g03130 and VIT_215s0046g00290), and two VviGH3s (VIT_207s0005g00090 and VIT_207s0129g00660) at the early stage of E-L 34 or E-L 35. On the contrary, NAA treatment up-regulated the expression of most VviAUX/IAAs, two VviARFs (VIT_206s0004g03130 and VIT_210s0003g00420), two VviGH3s ((VIT_203s0091g00310 and VIT_219s0014g04690), and of two VviSAURs (VIT_202s0154g000 10 and VIT_215s0048g00530). One VviSAUR (VIT_216s0098g01150) was up-regulated by both ABA and NAA at E-L 35 stage. In addition, we also found that several genes encoding AUX1, TIR1, AUX/IAA, ARF, and GH3 were down-regulated by NAA at E-L 36 stage. Figure 2: Effects of ABA and NAA on hormone accumulation and related gene expression during berry development and ripening. (**A**) Concentrations of endogenous ABA and indole-3-acetic acid protein (IAA) from E-L 33 to E-L 36 stage. Bars represent \(\pm\) SE and different letters indicate significant differences (\(p\) = 0.05). (**B**) Pathways analysis of genes involved in ABA and norisoprenoid biosynthesis. Purple and red boxes indicate downregulated and upregulated genes, the colors of the boxes represent the intensity of the expression fold changes (log2). Genes with significant expression changes compared with the control groups in each developmental stage are indicated by bold margins. PSY, phytoGene synthase; PDS, 15-cis-phytoene desaturase; ZISO, zeta-carotene isomerase; ZDS, zeta-carotene desaturase; CRTISO, prolycopene isomerase; LECY, lycopene beta-cyclase; LUT5, beta-ring hydroxylase; BCH, beta-carotene 3-hydroxyylase; LUT1, caroten epsilon-monooxygenase; ZEP, zeaxanthin epoxidase; NSY, neoxanthin synthase; NCED, 9-cis-epoxycarotenoid dioxygenase; XDH, xanthoxin dehydrogenase; AAO, abscisic-aldehyde oxidase; CCD, carotenoid cleavage dioxygenase. (**C**) Pathways analysis of genes involved in auxin biosynthesis. TAR, tryptophan aminotransferase related 1; TAA1, tryptophan aminotransferase arabidopsis1; YUC, YUC flavin monooxygenase.
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 3 Results  
### Washing and Removal Methods of the Acid Residues  
The FTIR spectra of various sulfonated PEEKs with varying degrees of sulfonation over time are displayed in Figure 1. According to [18], the carbon-carbon stretching vibrations in the aromatic ring caused peaks in the frequency ranges of 1600-1585 cm\({}^{-1}\) and 1500-1400 cm\({}^{-1}\) in all of the samples. In comparison to the spectra of the untreated PEEK, the novel sulfonate substitution resulted in the appearance of additional peaks between the wavelengths of 1138.00 and 1473.62 cm\({}^{-1}\). The findings demonstrate that functional groups of SO\({}_{3}\)H were incorporated into the polymer chains of PEEK through the process of sulfonation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Samples** & **Immersion Solution** & **Silfonation Time/s** & **Observation** & **SEM Test** \\ \hline A & & & As the control, with a flat and smooth surface (Image 1A) \\ \hline B, C, D, E & Concentrated H\({}_{2}\)SO\({}_{4}\) & 5, 10, 30, 60, and 120 s & The porous structure gradually became more obvious (Image 1B–E) \\ \hline F & Concentrated H\({}_{2}\)SO\({}_{4}\) & More than 120 s & Melting and damage of the whole sample. These sample were neglected \\ \hline \end{tabular}
\end{table}
Table 1: Sulfonation times/s.  
Chemical Characterization and Surface Morphology of the Sulfonated PEEKs with Various Sulfonation Times  
The pictures obtained with SEM and AFM are shown in Figures 2 and 3, respectively. Before it was sulfonated, the PEEK's surface was perfectly flat and smooth (Figure 2A). The sulfonation of PEEK with strong sulfuric acid resulted in the formation of a three-dimensional porous network in all of the sulfonated PEEK samples (Figure 2B-D).  
Figure 1: The FTIR of the sulfonated PEEKs with various times of sulfonation (5, 10, 30, 60, and 120 s) and three post-treatment procedures: (**A**) NaOH immersion, (**B**) acetone washing, and (**C**) H\({}_{2}\)O hydrothermal treatment.  
Figure 2: SEM characterization of the surface morphology of PEEK and sulfonated PEEK samples with different sulfonation times. At different magnifications: 1000\(\times\), 15,000\(\times\), and 30,000\(\times\).  
The location and quantity of sulfur contained in the specimens were further investigated through the use of EDX mapping and analysis. EDX mapping at multiple locations validated the sulfur distribution (Figure 4).  
Figure 3: Surface morphology characterized by AFM for PEEK and sulfonated PEEK samples with various sulfonation times. (**A**) Three-dimensional topographical images; (**B**) mean value of the surface roughness (Sa), with Turkey’s HSD significance; and (**C**) mean value of the interfacial area ratio (Sdr), with Turkey’s HSD significance.