esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabricating Sustainable All-Cellulose Composites
## 3 Dissolution of Cellulose  
### Cellulose Mercerization  
Cellulose is hard to dissolve in most common solvents due to the large quantities of inter- and intra-molecular bonds and van der Waals forces [67]. In addition, hydrophobic interactions that contribute to the stability of cellulose polymers also play a role in dissolution kinetics [43; 68]. The melting temperature of cellulose is above the stage of degradation, meaning that cellulose degrades before it liquifies [8; 45]. Since cellulose cannot melt, as it is not thermoplastic, reshaping the cellulose structure and breaking the strong intra- and intermolecular hydrogen bond network requires the dissolution of cellulose [69]. The amorphous regions of cellulose have higher free energy, and they are more easily accessed by solvents [35]. Hence, prior to dissolution, the crystalline domains of cellulose are often transformed into disordered regions by different activation processes [70].  
Mercerization is widely used alkaline treatment for cellulose modification and was devised already in 1844 by [PERSON] [71]. In the mercerization process, native cellulose fibres are immersed in an aqueous NaOH solution. When the fibres come in contact with strong alkali solutions their properties changes. An initial swelling by the alkali ions occurs in the amorphous regions between the fibrils. The alkali ions will then penetrate further into the crystalline regions which generates a more open and reactive alkaline cellulose structure (Figure 2) [72].  
The merecization process can be used as a pre-treatment to efficiently lower the degree of polymerization, disrupt the crystalline structure of cellulose, and ultimately promote the accessibility of dissolution practices [73]. The merecization process also provides better mechanical properties, shine, smooth surface, dimensional stability and improved dye intake for the cellulose fibres [39]. In addition, on the molecular level the mercerization changes the crystalline structure of cellulose from cellulose I to cellulose II (Figure 3) [41]. There are conflicting views regarding the mechanism on how cellulose I, which have parallel configuration, transforms to antiparallel configuration without undergoing a fully dissolved state [39]. Contrary to this, it is clearly noted that temperature change can affect the mercerization efficiency [74].  
Figure 2: Mercerization (sodium hydroxide treatment) of cellulose in natural fiber cell. Reprinted with permission from [72].
###### Abstract  
This paper demonstrates a method for the development of stepped-metal coating on optical fiber Bragg grating. The paper also analyzes the dual-peak resonance restructured by the nickel/copper stepped-metal coating. According to the coefficients of linear thermal expansion of the coatings, the modeling and experimental analysis is categorized into three types: Type A, Type B, and Type C. We denote the temperature sensitivity difference between the two peaks as \(\Delta K_{T}\) for Type A, \(\Delta K^{\prime}_{T}\) for Type B, and \(\Delta K^{\prime\prime}_{T}\) for Type C. The experimental results show that \(\Delta K_{T}\), \(\Delta K^{\prime}_{T}\), and \(\Delta K^{\prime\prime}_{T}\) are 2.1 pm/\({}^{\circ}\)C, 6.5 pm/\({}^{\circ}\)C, and 0.8 pm/\({}^{\circ}\)C, respectively. The model analysis and the experimental results all show the Type B stepped-metal coating causes the most obvious temperature sensitivity difference between the two resonance peaks. The stepped-metal coating on the same one Bragg grating can restructure the single resonance into dual-peak resonance with different temperature sensing, and Type B can be used to develop a dual-parameter optical fiber Bragg grating sensor at one location which can measure two physical parameters simultaneously.  
Keywords:optical fiber Bragg grating; stepped-metal coating; temperature sensing; restructured dual-peak resonance +
Footnote †: journal: Applied Physics Letters
# Experimental and Numerical Study in Dynamic Compaction of Weakly-Cohesive Soils
## 1 Introduction
The results provided insights into how compaction and density influence the soil's hydraulic properties, helping to improve predictions of water movement in unsaturated soils [11; 12; 13; 14]. Research has shown that water content significantly influences the mechanical properties of filler materials, especially fine-grained fillers, but has little effect on energy absorption [15; 16; 17]. The thickness of the layer to be compacted is inversely proportional to the degree of compaction. For a given compaction energy, the efficiency of the compaction process decreases as the layer thickness increases. This aspect can be explained by the fact that, in thicker layers, the energy consumption per unit weight is reduced [18; 19]. The number of passes by compactor rollers increases the soil's density. It is essential to determine the optimal number of passes for a specific type of equipment and soil at the optimum compaction moisture to ensure maximum compaction efficiency [20]. Contact pressure depends on the weight of the equipment and the contact area. In the case of pneumatic rollers, the tire inflation pressure also contributes to the overall contact pressure. Higher contact pressure increases the soil's dry density while reducing the optimum compaction moisture content [21]. The compaction speed depends on the type of equipment and the chosen compaction method, typically limited to 5 km/h. As the compaction speed increases, the overall duration of the compaction process decreases. However, at higher speeds, there is a risk that deformations may not have sufficient time to develop, requiring an increase in the number of passes for effective compaction. Control of the compaction process is extremely important in construction, especially when it comes to achieving the required degree of compaction, as defined by technical  
Figure 1: Examples of compactors (**a**). Work principle (**b**). standards, for reasons related to structural integrity, bearing capacity, compliance with technical standards (ISO) or local regulations, cost efficiency, durability improvement, etc. The main key factors in controlling the compaction process are moisture content, compaction equipment, layer thickness, and compaction energy (number of passes and applied force). Monitoring and controlling all enumerated parameters is vital to ensure construction quality, improve structural performance, and meet technical standards for durability and safety [15; 22; 23; 24].
## 4 Effectiveness of Data Imputation
### Observations
The model score exhibits substantial variability, with standard errors ranging from 4.8 to 47.6 as the corruption level increases from 0.1 to 0.9. Context filling shows no clear advantage and in fact performs slightly worse in our results. At a data-missing ratio of 0.6, the model score between context filling and no imputation is 22, with a standard error of 15. The \(z\)-score is 1.48 and the \(p\)-value is 0.14. This difference is not substantial, given this high performance variance observed in the Signal-RL experiments.
## 4 Discussion
While the literature provides strong evidence supporting the role of LLMP and CRF in mitigating DKV, further research is needed to determine how training programs should be adapted for individuals with varying physical fitness levels [23; 24]. Adolescent athletes with lower baseline LLMP or CRF may require progressive training strategies that emphasize gradual power and endurance development, while more advanced adolescents might benefit from sport-specific training. Additionally, factors such as age, sex, and training experience should be considered when designing interventions to ensure effectiveness and injury prevention. Although the results of this study agree with many others in the current literature, a correlation between variables does not automatically mean causation [16; 25]. Causation indicates that one event is the result of the occurrence of the other event, i.e., there is a causal relationship between the two events. The literature indicates that DKV, like most sports injuries, is complex and multifactorial, and it results not from the linear combination of isolated and predictive factors, but from the interaction among them [16]. In this sense, we need a broader approach to better understand the complex relationships between risk factors/predictors and injuries. Unmeasured factors such as prior injuries, training load, or limb dominance could influence the results and bring new insights to improve even more injury-prevention programs. In conclusion, the results of this study emphasize the critical role of LLMP and CRF in controlling DKV. Injury-prevention programs that target lower limb power and cardiorespiratory fitness may not only reduce the risk of knee injuries but also improve overall athletic performance. Future studies should consider longitudinal analyses to assess how targeted training interventions influence knee valgus over time and whether individualized training regimens can yield optimal results for injury prevention in adolescent basketball players.
## 1 Introduction  
Fungal endophytes are known to reside within host plants without causing apparent disease symptoms, and display mutualistic interaction. In addition, endophytic fungi have adapted to their respective host plant through the production of metabolites with diverse biactivities such as anticancer, antimicrobial, and antioxidant activities [1]. Of note, fungal endophytes from medicinal plants even mimic the bioactive metabolites similar to their host plant, in which anticancer compounds such as paclitaxel, camptothecin, and vinblastine are outstanding examples [1, 2]. Fungal metabolites extracted from endophytic fungi such as huperazine and resveratrol are reported to be natural and less toxic [3, 4], leading to increasing interest in deciphering their chemical structures using mass spectrometry-based tools. Therefore, the prospection of endophytic colonizing in rare medicinal plants is essential to find new medically important compounds.  
Reactive oxygen species (ROS) such as superoxide, hydrogen peroxide, and hydroxyl radicals are known as either useful by-products or the harmful burden of cellular metabolism [5]. Excessive accumulation of ROS is extremely toxic to cellular macromolecules such as lipids, proteins, carbohydrates and DNA, causing several diseases including cancer, Alzheimer, diabetes, and aging over the human life cycle [6]. Rare medicinal plants such as _Tsuga chinensis_ and _Orosylum indicum_ were previously reported as prolific sources of endophytic fungi with antioxidant activity [7; 8]. Endophytic fungus _Fusarium solani_ CARE-1 was found to produce bostrycoidin, anhydrofusarubin, 4-hydroxybenzaldehyde, and fusarubin, which were highly active against DPPH free radicals [4]. Due to the side effects of synthetic antioxidants used previously, endophytic fungi may serve as an epitome of natural products in preventing ROS-related diseases.  
_A. yunnanensis_ is a threatened conifer listed in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, distributed only in Vietnam, China and Laos [9]. By chemical analysis, the plant was reported to contain sequioialflavone, sotetsuflavone, 7,7\({}^{\prime\prime}\)-dimethoxyaflavone, lutein, beta-sitosterol, sequoyitol, and new amentoflavone biflavonoid, 2,3-dihydro-7,7\({}^{\prime\prime}\)-dimethoxyaflavone in the plant extracts [10]. However, endophytic fungi associated with _A. yunnanensis_ have not been explored yet. Hence, the objective of the present study was to screen for an endophytic fungus from _A. yunnanensis_ collected in Vietnam, which demonstrates substantial antioxidant activity. In addition, the study aimed to identify fungal metabolites, and evaluate their antioxidant potential.
# Stochastic-Based Spin-Programmable Gate Array with Emerging MTJ Device Technology
## 3 Stochastic-Based Logic Block (SLB)
### Detailed Circuit Design of SLB
One rudimentary stochastic subsection unit is presented in Figure 8. More efficient circuit designs are to be explored. Figure 6: (**a**) Simple conventional LB; (**b**) Architecture of stochastic-based logic block (SLB); (c) Programmable random bit generator. Figure 9, our design is largely inspired by the 6T-SRAM cell design except replacing the cross-linked feedback cells with four MTJ devices [27]. We believe this design may be further optimized to use fewer devices. When designing, implementing, and programming a stochastic-based logic block (SLB), four parameters, input number \(m\), output number \(n\), the number of random sample units \(K\), and the number of random samples \(K\) per logic epoch, are essential. We now discuss the tradeoff relationship between them. During technology mapping stage, the granularity of a SLB, defined by both \(m\) and \(n\), can significantly affect the total number of SLB usage for any given target circuit. Intuitively, increasing the output number \(n\) allows each SLB to implement larger logic function. However, when \(n\) increases, as shown in Figure 6b, we need larger muxes and higher ADC resolution, both of which consumes more chip area. Furthermore, to meet the accuracy requirement of stochastic-based logic operations, more random sample are often needed, which prolongs the overall stochastic processing time. Note that adding and evaluating more Taylor power terms can also greatly improve the logic precision. But again, this needs more programmable random sample generators, which almost linearly increases the chip area for each SLB. Apparently, how to optimally tradeoff these competing parameters in the application of SPGA remains to be quite challenging and more systematic investigations are needed.
## 4 Wireless Enabling Technologies
### Hybrid V2X  
Hybrid systems can enhance the overall communication performance by combining and coordinating the capabilities of different access technologies in a flexible way. Since IEEE 802.11p and LTE-V2X might not be able to guarantee the diverse performance requirements under the future autonomous driving scenarios, future V2X communications also foresee the coexistence of both the technologies.  
Some discussions are also ongoing on spectrum sharing and interoperability of IEEE 802.11p and LTE-V2X at 5.9 GHz [43]. In fact, since current V2X technologies are not flexible and reliable enough to serve the different requirements of future connected and autonomous driving, hybrid V2X communications can enable the coordination of multiple communications technologies and efficiently adapt to the requirements of future driving. Especially for safety applications, cooperative principles(providing every road user an equal level of access to safety information such that all have a similar safety change) are under discussion. 5 GAA, supported by Qualcomm (San Diego, CA, USA), proposed to split, in the short-term, the channels and to allocate distinct 10 MHz channels at 5875-5905 MHz to each of the two technologies, while the final configuration in the long-term will apply full sharing of all available channels across the two technologies [44]. Car to Car Consortium answered that the solution proposed by 5 GAA is not possible since the 176 and 180 channels are both in use by ITS-G5 and used for different services. A split as proposed will lead to adjacent band interference and potential incorrect functioning of the ITS-G5 system [45]. The Consortium proposes to extend the currently designated spectrum from 30 MHz to 50 MHz (also incorporating the support for urban rail) and to develop 5G cellular technology (including peer to peer V2x communications) outside the 5855-5925 MHz range at such a distance that non-interference is guaranteed so that both systems can be implemented in the same vehicle.  
The future landscape of vehicular communications will, however, probably see the use of both LTE-V2X and IEEE 802.11p working together, even if the final policies and functionalities for interoperability still have to be clearly defined.
## 7 Routing Protocols proposed by Spanish Universities
### Beacon-less Geographic Routing Protocols  
The geographic routing protocols were initially conceived to operate with the periodic exchange of messages that inform about the position of nodes in the network. These messages or beacons incurs in an additional overhead, which represents the main disadvantage of this kind of protocols. In [29] the suitability of suppressing the beacon messages in the geographic routing protocols [29] is analyzed. The beacon-less algorithms are then supported by the reactive exchange of location information just when the nodes need to route data. The paper analyzes five beacon-less routing protocols: IGF (Implicit Geographic Forwarding), GeRaF (Geographic Random Forwarding), CBF (Contention Based Forwarding), BLR and BOSS, which was proposed by the group.
## 2 Introduction to Optimized Method
### Damped Newton Method for Solving Single-Objective Programming Model
#### 2.4.2 Solving Steps  
Based on the above analysis, the solving steps of the damped Newton method can be expressed as follows:  
1. Provide the initial iterative values \(\Delta\mathbf{N}^{(1)}\), a permissible error \(\varepsilon>0\), and make \(k=1\).
2. Check if the formula \(\Big{\|}\mathbf{H}\Delta\mathbf{N}^{(\mathbf{k})}+\mathbf{c}\Big{\|}\leq\varepsilon\) is valid. If it is, set the optimal solution \(\Delta\mathbf{N}^{*}=\Delta\mathbf{N}^{(\mathbf{k})}\), and the iteration can be terminated; otherwise, go to step (3).
3. Calculate \(\mathbf{P}^{(\mathbf{k})}=-\Delta\mathbf{N}^{(\mathbf{k})}-\mathbf{H}^{-1}\mathbf{c}\) and use \(\mathbf{P}^{(\mathbf{k})}\) as the search direction for the Armijo one-dimensional search. Solve the optimal step size \(\lambda_{\mathbf{k}}\): \(\underset{\lambda\geq 0}{\text{min}}f\Big{(}\Delta\mathbf{N}^{(\mathbf{k})}+ \lambda\mathbf{P}^{(\mathbf{k})}\Big{)}=f\Big{(}\Delta\mathbf{N}^{(\mathbf{k})}+ \lambda_{\mathbf{k}}\mathbf{P}^{(\mathbf{k})}\Big{)}\).
## 2 Materials and Methods
### Experimental Analysis
#### 2.5.2 Thymoquinone (TQ) Quantification  
The identification of TQ and its quantification in the studied samples was carried out using High-Performance Liquid Chromatography. The information on the instrument and the procedures applied is detailed in an earlier study [20]. Briefly, an Agilent 1100 setup complete with a G1313A autosampler, G1322A vacuum degasser, G1311A quaternary pump, and G1365B multi-wavelength detector, alongside an Agilent Eclipse XDB-C18 column (dimensions: 150 \(\times\) 4.6 mm, particle size: 5 \(\upmu\)m), was employed. The column functioned under isocratic harmony with a mobile concoction of water: methanol (40:60, \(v/v\)), maintained at a flow rate of 1 mL/min. A sample volume of 5 \(\upmu\)L underwent scrutiny at ambient conditions over a span of 10 min, with UV absorbance surveilled at 254 nm. The plotted calibration curve using the pure TQ standards (10-200 ppm) demonstrated excellent linearity (R\({}^{2}\) = 0.9997), with the equation y = 17.182x + 9.2269. This equation was used to quantify TQ in the samples, and the results were reported as mg of TQ per 100 g of dry sample weight (mg TQ/100 g DW).
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 2 Materials and Methods
### Experimental Design  
The participants underwent a working memory task twice, before on-call shifts (up to 28 h with little or no sleep--Rested) and after work shift ended (Fatigued). As such, to assess the influence of mental fatigue on the working memory capacity, a visual n-back task was employed under an N = 2 design [14]. During this task, the participants were asked to remember and compare the image displayed in 2 trials prior to the current visual stimulus. In each trial, an image would appear in one of the four corners of the screen, with the participants indicating among 1 of the 4 possible conditions by pressing the corresponding button. Each condition required comparisons with regards to the image content and location as follows: (a) same image and same location; (b) same image (different location); (c) same location (different image); and (d) no similarities (different image, different location). The experiment consisted of 72 tests (4 conditions balanced) and lasted approximately 5 min. The visual stimuli were displayed for 3.5 s, interposing a fixed cross for 1 s. Practice trials were conducted before the EEG recordings to ensure individuals' comprehension regarding the execution of the task.
## 2 Pedestrian Color Image Sequence  
The TUM-GAID Gait Dataset [1] is a pedestrian gait dataset maintained by Kinect that contains raw color image sequences and corresponding depth map sequences; the raw resolution is 640 \(\times\) 480 pixels.  
This dataset initially contained gait sequences of 305 pedestrians. These image sequences comprised two trajectories, namely left to right and right to left. In addition, each pedestrian had his or her own scenario, described as follows:  
1. Normal clothing (regular daytime outfit; marked as \"N\" for normal): Six sequences, namely walking three times from left to right and three times from right to left.
2. Backpack (addition of a backpack weighing approximately 5 kg; marked as \"B\" for backpack): Two sequences, namely walking once from left to right and once from right to left.
3. Shoe covers (addition of white shoe covers to the original shoes; marked as \"S\" for shoe covers): Two sequences, namely walking once from left to right and once from right to left.
4. Seasonal clothing (documented at various times with notably different clothing; marked as \"TN\" for elapsed time--normal): Six sequences, namely walking three times from left to right and three times from right to left.
5. Seasonal clothing + backpack (marked as \"TB\"): Two sequences, namely walking once from left to right and once from right to left.
6. Seasonal clothing + shoe covers (marked as \"TS\"): Two sequences, namely walking once from left to right and once from right to left.  
As shown is Figure 2, each pedestrian had at least six N sequences (N1, N2,... N6), two B sequences (B1 and B2), and two S sequences (S1 and S2) documented. Subsequently, 32 pedestrians participated in documentation in multiple seasons; thus, an additional eight sequences marked as TN, TB, and TS were documented for these participants. In total, the dataset currently contains more than 3000 pedestrian sequences. Each sequence spans 1 to 2 s (approximately 30 frames per second) and contains 60 to 90 frames.  
At the beginning and end of each sequence, where the pedestrian entered and exited the screen, respectively, most of his or her body was cropped. Although the pedestrian-tracking NN was able to detect the pedestrian, the present study eliminated the first and final five frames of each sequence for the integrity of the pedestrian. In addition, misaligned  
Figure 2: Scenarios in the TUM-GAID Gait Dataset [1]. (**a**) people is with seasonal clothing; (**b**) people is with seasonal clothing and backpack; (**c**) people is with seasonal clothing and shoe covers; (**d**) people is with normal clothing; (**e**) people is with normal clothing and backpack; (**f**) people is with normal clothing and shoe covers.  
images shown in Figure 3 caused by the equipment were discarded. Misalignment resulted in a considerable number of unlikely values generated from the captured optical flow value such as sudden displacement of more than 100 pixels. After such misaligned images had been discarded, the horizontal and vertical moving speeds mostly fell within the reasonable range.