esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LOKA Scenario
### Reference Framework
LOKA is deployedin each sensor more and works independently of the receiver or the sender (coordinator, router or end-device), but in our dependent-persons application the final receiver was the coordinator. It is worth mentioning that LOKA supports the receipt of packets in the end-device motes. Often, an external microcontroller is included into the motes with the aim of performing small data processing since the commercial ZigBee devices only incorporate the necessary resources to implement the protocol. In our approach, we have considered this option, and therefore, we minimize the consumption of both devices (the external microcontroller and the ZigBee device) with the aim of reducing the WSN energy consumption as a whole. This energy consumption minimization is performed by LOKA. Figure 1 illustrates our reference framework composed of N sensor motes sending information to a collector mote that analyses it. The sensor motes are composed of a Cortex M0+ microcontroller (LPC824), a ZigBee module (XB24), a radio, a battery and sensors to obtain the phenomena information. Thus, the microcontroller commands the sensor mote to take samples and then, it activates the radio to send these samples to the collector mote using a communication protocol. The collector mote does not have sensors because it is in charge of receiving and analyzing the information; it only integrates a radio, a microcontroller and a battery. Thus, the microcontroller receives and processes the samples taken by the sensor motes while maintaining the right operation of the sensor network. While it is true that the ability to route packages in duty-cycling state of the art is an issue, the Zigbee protocol precludes to use end-devices as relay nodes. The network coverage area is often  
Figure 1: Reference Framework. It is composed of the collector mote, the router motes and the sensor motes. The sensor motes send the data information to the collector and then the collector processes it. much larger than the radio range of single end-device mote(s). So in order to reach some destination node we can use route device motes as relays in a multi-hop communication. However, we have not focused on how to route packages in the duty-cycling mechanism. Instead, we want to control the end-device in conjunction with the power modes of an external microcontroller implementing two duty-cycling mechanisms in parallel, sleeping and waking up the end-device and the external microcontroller with the aim of reducing the energy consumption of a sensor mote.
## 2 Conventional Channel Estimation Methods
### Pilot-Based Channel Estimation Method
#### 2.1.1 Pilot Arrangements  
According to the different ways of pilot insertion, the pilot arrangements can be roughly categorized into block-type, comb-type, and lattice-type.  
* Block-Type Pilot Arrangement  
Figure 4 shows the block-type pilot arrangement. This pilot arrangement periodically transmits pilot symbols (i.e., OFDM symbols with pilots inserted) along the frequency axis. For each pilot symbol, pilots are inserted into all subcarriers (as pilot subcarriers). Then,  
Figure 3: Flowchart of pilot-based channel estimation.  
Figure 2: Classification of conventional channel estimation methods.  
these pilots are used to interpolate in the time domain and estimate the channel along the time axis. To track the characteristics of the time-selective channel, the period of the pilot symbols must be equal to the channel coherence time. Let \(S_{t}\) denote the period of the pilot symbols, and since the channel coherence time is in inverse proportion to the Doppler frequency deviation \(f_{Doppler}\), \(S_{t}\) must be satisfied:  
\[S_{t}\leq\frac{1}{f_{Doppler}} \tag{1}\]  
Since the pilots are periodically inserted into all subcarriers of the pilot symbols, the block-type pilot arrangement is more suitable for frequency-selective channels compared to time-selective channels.  
* Comb-Type Pilot Arrangement  
Figure 5 shows the comb-type pilot arrangement. The pilots are regularly inserted into the subcarriers within each OFDM symbol, utilized to interpolate in the frequency domain and estimate the channel along the frequency axis. To accurately track the frequency-selective channel's properties, the pilots' period on the frequency axis must be consistent with the coherent broadband. The period of the pilots on the frequency axis is denoted by \(S_{f}\). Since the coherence bandwidth depends on the reciprocal of the maximum delay extension \(\sigma_{max}\), \(S_{f}\) must be satisfied:  
\[S_{f}\leq\frac{1}{\sigma_{max}} \tag{2}\]  
Unlike the block-type pilot arrangement, the comb-type pilot arrangement is more suitable for time-selective channels compared to frequency-selective channels.  
Figure 4: Block-type pilot arrangement.  
Figure 5: Comb-type pilot arrangement.  
* Lattice-Type Pilot Arrangement  
Figure 6 shows the lattice-type pilot arrangement. The pilots are inserted at a specified period and concurrently into both the time and frequency domains. With the regular distribution along the time and frequency axes, they facilitate the interpolation of channel estimation in the two domains. The insertion of the pilots must satisfy Equations (1) and (2) to trace the channel's time-selective and frequency-selective characteristics. This pilot arrangement is a compromise between the block-type and comb-type pilot arrangements. It has a superior performance against time-selective fading compared to the block-type pilot arrangement and performance against frequency-selective fading compared to the comb-type pilot arrangement.
## 5 Simulation Results  
In this section, we study how the parameter settings impact the performance of HCR-1 and then compare the performance of HCR-1 with that of HCR. The network lifetime and residual energy ratio are used to evaluate the load balance of the protocols. In this paper, the network lifetime is defined as the operating rounds when the first dead node (FND) appears [10], and the residual energy ratio is the ratio of residual energy to initial energy at the end of the network lifetime. In addition, the hop count from node to sink is used to evaluate the network topology. The network is assumed to be located in a square area, and the sink resides at the corner coordinate \((0,0)\). Both \(R_{max}\) and \(R_{t}\) are set to \(70\) m. The other parameters used in the simulations are summarized in Table 2.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process
### Feedstock Degradation Pathway
#### 2.2.1 (a) Hydrolysis  
Macromolecules, which include proteins, carbohydrates, and lipids, are the main components of organic substrates, and under anaerobic conditions they are initially broken down to monosaccharides, namely amino acids, long chain fatty acids, and glycerol, respectively. The efficiency of this step is based on the presence and active action of hydrolytic and fermentative microbes (e.g., _Clostridium, Proteus Vulgaris, Bacillus, Bacteroides, Microocccus, Staphylococcusoccus_) to excrete extracellular enzymes. The presence of a variety of extracellular enzymes, including amylase, cellulase, protease, and lipase, which are exerted by bacteria, are mandatory as catalyzers for the decomposition of each macromolecule. For instance, hydrolytic enzymes (e.g., cellulase, \(\beta\)-glucosidase, xylanase) or complex enzyme systems(e.g., cellulosome) attack polysaccharides, protease degrades proteins, and lipase is suitable for breaking down lipids [16; 17]. Hydrolysis is known to be a rate-limiting step, especially for lignocellulosic substrates [18], because of the presence of lignin, which along with cellulose and hemicellulose units forms a rigid three-dimensional complex compound. This physical barrier protects the biomass from the enzymatic attack. Thus, the existence of microbes with augmented enzymatic activity is mandatory for an efficient decomposition, especially in the case of lignocellulosic materials [19].
# Trajectory Planning of Robotic Arm Based on Particle Swarm Optimization Algorithm
## 4 Experimental Simulation
### Discussion  
To validate the effectiveness of the proposed algorithm, we compared the acceleration and operational speed of the robotic arm using the proposed algorithm against other optimization algorithms. We recorded the differences before and after applying the algorithm. In the comparative experiment, we designed a set of rotations and grasping actions for the robotic arm to compare the time taken and acceleration variations.  
According to Table 1, the RTPPSO algorithm demonstrates the best optimization effect on the robotic joint impacts. The average impacts on the drive arm joints before and after optimization are 2.23 \(\times\) 10\({}^{6}\) deg/s\({}^{3}\) and 4.58 \(\times\) 10\({}^{5}\) deg/s\({}^{3}\), respectively, resulting in a reduction in impacts by 79.45%. It can be seen from Table 2 that our algorithm also improves the grasping speed to some extent, which can effectively improve the grasping efficiency of the robotic arm. Compared to other algorithms, the RTPPSO algorithm has better realized trajectory planning work, enhancing the operational efficiency of the robotic arm while effectively reducing vibration issues during operation. Compared to other algorithms, the RTPPSO algorithm better addresses vibration issues during operation. However, this study did not incorporate optimization of computational efficiency, which relates to the operational time of the robotic arm, into the design of the optimization problem. Further improvements are needed in future iterations to address this aspect.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **Average Impact before** \\ **Optimization (deg/s\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Average Impact after** \\ **Optimization (deg/s\({}^{3}\))** \\ \end{tabular} \\ \hline GA & 2.23 \(\times\) 10\({}^{6}\) & 4.89 \(\times\) 10\({}^{5}\) \\ PSO & 2.23 \(\times\) 10\({}^{6}\) & 4.83 \(\times\) 10\({}^{5}\) \\ RTPPSO & 2.23 \(\times\) 10\({}^{6}\) & 4.58 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact optimization comparison.  
Figure 9: Fitness learning curve.
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 4 Case Analysis of Static Wind Instability and Parameter Influence of Xiangshan Harbor Bridge
### Influence of Steel Wire Breakage Rate in Stay Cables on Static Wind Stability  
During the long-term service of long-span cable-stayed bridges, steel wire of stay cables may break due to environmental impact and load action, which will lead to the redistribution of cable stress because of the cross-sectional area decrease of the stay cable  
Figure 11: Variation curve of critical instability wind speed with the initial wind angle of attack.  
Figure 10: Maximum displacement curve of the main beam with wind speed under different initial wind attack angles; (**a**) Lateral displacement (**b**) Vertical displacement; (**c**) Torsion angle.  
and reduce the safety performance of the bridge. [PERSON] [30] pointed out through experimental research that the steel wire breakage rate doesn't exceed 10% otherwise the stay cable should be replaced. For the purpose of exploring the influence of the steel wire breakage rate on the static wind stability of the bridge, 5%, 10%, 15%, and 20% steel wire breakage rates are considered by changing the sectional area of the stay cables. Then the displacement response of Xiangshan Harbor Bridge is calculated and compared under the different steel wire breakage rates. The maximum displacement of the main beam in Xiangshan Harbor Bridge is plotted in Figure 12. Further, the critical aerostatic instability wind speed of the bridge under different steel wire breakage rates can be inferred from the displacement response curves, as shown in Figure 13.  
From the results in the above figure, when the steel wire breakage rate is less than 10%, it has little effect on the static wind instability, but more than 10%, the critical instability wind speed of the bridge decreases rapidly. When the steel wire breakage rate is 20%, the critical instability wind speed is 261 m/s, which is 12% lower than before. Therefore, the stay cables should be inspected regularly, and the broken wire shall be replaced in time during the operation stage of the bridge.  
Figure 12: Maximum displacement curve of main beam with wind speed under different wire breakage rates; (**a**) Lateral displacement (**b**) Vertical displacement; (**c**) Torsion angle.
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 2 General Aspects of Carbon Dot/Polymer Composites
### Carbon Dots and Polymer Hybrid Composite Materials
#### 2.4.2 Photoluminescent Feature of Carbon Dot and Polymer Composites  
The photoluminescence (PL) origin of CDs has been extensively studied; the mechanism generally includes intrinsic state emission (quantum size effect) and surface-defect state emission. The fluorescence origin of the CD/polymer composite structure resembles that of certain fluorescent polymer materials. Standard fluorescent polymer materials include conjugated polymers and polymers that are linked to fluorescent molecules [52]. The fluorescence source may be related to the heteroatom-containing bonds (C-O, C-N, N-O), called sub-fluorophores. Although these sub-fluorophore band gaps are not suitable for visible emission, the configuration of the polymer chains enables interactions among the chemical groups. Therefore, the band gap decreases, resulting in the observed fluorescence emission. Moreover, compared to small-molecule precursors, synthesis conditions such as pH, time, and temperature have a lower impact on the emission wavelength and chemical structure of CD/polymer composite materials [35; 46; 50].  
In addition, the polymer chain can stabilize the CDs. Considering their large surface area, CDs are susceptible to coalesce to form larger aggregates, which enables better fluorescence and sensing behaviors. Polymeric materials act as stabilizers that adsorb onto the surfaces of CDs, resulting from CD/polymer nanocomposites. CD-polymer nanocomposites are currently sufficient to be developed in the field of material science. Thus, CD/polymer composites can be utilized for nanocomposite gel fabrications. Previous studies have demonstrated that carbon dot/polymer composites possess superior physiochemical properties compared to conventional polymeric gels [13; 54]. Furthermore, the hybrid CD/polymer composite film can be used as a highly swellable, rigid, thermally stable, and photo-responsive material.  
Typical polymer passivation agents include polyethylene glycol (PEG), polyethyleneimine (PEI), and similar derivatives. Surface-passivated CDs can simultaneously be modified with different chemical functional groups, including hydroxyl, carbonyl, and carboxyl groups, on the surface defect sites. The doping of organic or polymeric compounds has been used in these methods. Different doping methods, such as N-doping, P-doping, S-doping, and Si-doping, have confirmed the effectiveness of these methods [55; 56; 57]. For example, N-doped CDs demonstrated an average diameter of 2.5 nm in the n high resolution transmission electron microscopy (HRTEM) image analysis and broad and robust PL spectra over bare CDs [56].
# Structural Features Guiding the Design of Liquid-Crystalline Elastomeric Fluorescent Force Sensors
## 2 Discussion
### Structural Factors That Impact the Mechanofluorescent Behavior
#### 2.2.3 Length of the Fluorophore Flexible Spacer  
Another structural feature that impacts the efficiency of LSCE-based optical force sensors is the length of the fluorophore flexible spacer, i.e., the length of the alkyl chain that connects the carbazole moieties to the main polymer backbone [57]. The role of this particular constitutional factor will be illustrated by considering the four different elastomers: EM4_CBZN2_10, EM4_CBZN3_10, EM4_CBZN6_10 and EM4_CBZN11_10 (in Figure 4a), where the length of the flexible spacer of the carbazole monomers increases gradually from sample to sample. These four elastomers also exhibit a monodomain nematic mesophase, which is stable between the glass transition temperature at 276-282 K and the nematic-to-isotropic phase transition temperature at 305-336 K.  
\(\Delta I_{\text{Max}}\) depends significantly on the length of the flexible spacer that links the pendant carbazole fluorophores to the elastomeric network. On the one hand, when the fluorogenic components are attached to the polymer through either the shortest (EM4_CBZN2_10 and EM4_CBZN3_10) or the longest (EM4_CBZN11_10) spacers, the elastomeric material suffers the smallest decrease (\(\Delta I_{\text{Max}}\) of \(-12\%\), \(-11\%\) and \(-22\%\), respectively; in Figure 4b-e) of its fluorescence intensity upon deformation.  
Figure 4: Chemical composition (**a**) and mechanofluorescent response for LSCEs EM4_CBZN2_10 (**b**), EM4_CBZN3_10 (**c**), EM4_CBZN6_10 (**d**) and EM4_CBZN11_10 (**e**) [57]. (Copyright from PCCP Owner Societies 2016).  
On the other hand, LSCE EM4_CBZN6_10, where the carbazole fluorophores have a flexible spacer of a similar length to that of the mesogen M4, exhibits the greatest mechanoluminescent response (\(\Delta I_{\text{Max}}=-34\%\), in Figure 4d) [57]. This latter result has been ascribed to the more favorable overlap between the carbazole units and the surrounding mesogen molecules that occurs when they bear flexible spacers of a comparable length. Indeed, short spacers do not permit an efficient overlap between their rigid cores. In addition, the very high conformational freedom displayed by long spacers enables a moderate interaction between carbazole and M4 molecules, but it does not allow an efficient transmission of the external mechanical input to the fluorogenic components. In both instances, less effective optical force sensors are obtained. Therefore, the length of the fluorophore flexible spacer controls not only the transmission of the external mechanical input from the main polymer backbone to the mesogenic and fluorogenic moieties, but also the interaction between them both, which determines the efficiency of the final optical force sensor.
## 6 Discussion on Gaps, Challenges, and Links to Future Works
### Application of Available Excuation Software to Wildfire Evacuation
#### 6.5.4 Evacuee Behavior Modeling  
Understanding and accurately modeling human behavior during evacuations is a complex task. While some software packages have agent-based capabilities, the accuracy of the simulated evacuee behavior depends on the quality and relevance of the underlying behavioral models and data. Improving the representation of evacuee decision-making, route choices, and destination selection remains a challenge in wildfire evacuation simulations.
## 3 Resultsand Discussions
### Chemical Composition of Peach Pomace
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wavenumber (cm\({}^{-1}\))** & **Functional Grouping** & **Vibration Type** & **Corresponding Compounds** \\ \hline \(\sim\)3400 & -OH (Hydroxyl) & Stretching & Water, alcohols, polyphenols, cellulose, pectins \\ \(\sim\)2920 and \(\sim\)2850 & C–H (aliphatic) & Stretching & Lignin waves, lipids, aliphatic chains \\  & & (symmetrical/asymmetrical) & Carboxyl acids, esters \\ \(\sim\)1740–1700 & C=O (carbonyl) & Stretching & (pectin), aldehydes \\ \(\sim\)1600 & C=C (aromatic) & Stretching & Lignin, polyphenols \\ \(\sim\)1510–1450 & C–H and C=C (aromatic) & Deformation and stretching & Lignin, phenolic compounds \\ \(\sim\)1250–1000 & C–O, C–O–C, & Stretching & Cellulose, hemicellulose, \\  & C-O-C glycosylation bond & Stretching & pectins, sugars \\ \(\sim\)900–600 & C–H (aromatic out-of-plane) & Deformation from the plane & Lignin, phenolic structures \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The correlation between FTIR bands and functional compounds contained in peach pomace. The bands located between \(\sim\)1510 and 1450 cm\({}^{-1}\) are associated with the C-H and C=C stretching vibrations of phenolic compounds [40] and lignin, and the bands at approximately 1250-1000 cm\({}^{-1}\) correspond to the C-O and C-O-C stretching vibrations of cellulose, hemicellulose, pectins, and sugars [41]. The FTIR spectrum, performed for the pomace, confirms the presence of hydroxyl and carbonyl groups--typical of plant fibers, sugars, and aromatic compounds--most likely from lignin and polyphenols, and C-O bonds--common in pectins, cellulose, and hemicellulose. In conclusion, the FTIR analysis of peach pomace revealed the presence of several functional groups and chemical bonds characteristic of polyphenols, polysaccharides, organic acids, esters, simple sugars, and bound water. This information confirms the chemical complexity of pomace and supports its bioactive potential, indicating opportunities for its use in various food and pharmaceutical applications.
## 5 Conclusions and Outlook
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Name** & **Model Type** & \(\mathbf{Nsurplus_{max}}\) & \(\mathbf{Nsurplus_{opt}}\) \\ \hline Meridian 2021 & LP & \(-21.3\) & \(-21.3\) \\ Meridian 2021 & Q & \(-3.5\) & \(-10.2\) \\ Meridian 2021 & QP & \(-3.3\) & \(-10.3\) \\ Meridian 2022 & LP & \(-24.8\) & \(-24.8\) \\ Meridian 2022 & Q & \(-3.0\) & \(-10.6\) \\ Meridian 2022 & QP & \(-2.4\) & \(-10.2\) \\ Meridian 2023 & LP 1 & \(5.7\) & \(5.7\) \\ Meridian 2023 & Q & \(18.0\) & \(14.9\) \\ Meridian 2023 & QP 1 & \(17.9\) & \(14.8\) \\ Sandra 2021 & LP & \(1.8\) & \(14.8\) \\ Sandra 2021 & Q & \(37.7\) & \(29.8\) \\ Sandra 2021 & QP & \(38.0\) & \(30.0\) \\ Sandra 2022 & LP & \(11.7\) & \(11.7\) \\ Sandra 2022 & Q & \(33.4\) & \(24.0\) \\ Sandra 2022 & QP & \(31.7\) & \(22.8\) \\ Sandra 2023 & LP 1 & \(30.0\) & \(30.0\) \\ [PERSON] 2023 & Q & \(47.1\) & \(42.0\) \\ [PERSON] 2023 & QP 1 & \(46.6\) & \(41.6\) \\ \hline \hline \end{tabular}  
* N surplus values were determined using the parameters of the Q function, as the LP and QP functions could not be estimated for these data sets.  
\end{table} 
Table 4: N-balance: evaluation of the model quality of the quadratic, the quadratic-plateau, and the linear plateau function and resulting nitrogen surpluses when fertilizing according to Nmax and Nopt.
## 3 Results and Discussion
### Dependence of Extraction on pH
Since the extraction of Cs(I) or Sr(II) is pH dependent, it appears that the phenolic hydroxyl groups of SC4A have substantial contribution on the observed extraction ability. This can be confirmed by the results of log \(D\) versus equilibrium pH (pH\({}_{\text{e}}\)), as shown in Figure 3b. The plot for Cs(I) and Sr(II) showed that the slopes are equal to 1.09 and 1.74, which indicated that 1 and 2 protons were released upon complexation, respectively.