esa-sceva/satcom-chunk-collection huggingface dataset:
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 2 Solution-Based Approaches for Synthesis of Metal Chalcogenides  
Up to now, several strategies for the preparation of bulk or micro/nanostructured metal chalcogenides have been developed. The traditional solid-state synthesis through reactions among molten states with a specific ratio of solid reactants can generate bulk crystalline metal chalcogenides with high density and symmetry [18; 19; 20]. However, high temperature (\(>\)600 \({}^{\circ}\)C) is usually required to overcome the diffusion barriers among the solids in order to achieve an appropriate reaction rate. Kinetic control and mechanism study of these reaction systems are thus rather difficult and also  
Figure 1: Reported figure of merit (ZT) values of the inorganic bulk thermoelectric materials as a function of publication year. Reprinted with permission from Elsevier [4].  
have limited control over the structures coming out of these reactions. The mechanical exfoliation process from high-quality bulk materials can generate pristine single layers of metal chalcogenides with ideal electronic properties [21; 22; 23]. Nevertheless, the low production yield and small lateral size (typically less than 10 \(\upmu\)m) of the exfoliated specimens hamper the practical application of this method. Synthetic approaches based on gas-phase processes such as chemical vapor deposition (CVD) [24; 25; 26; 27] and molecular beam epitaxy growth [28; 29] have been successfully applied to produce defectless, uniform thin films of metal chalcogenide materials. However, high-temperature and high-vacuum reaction conditions as well as specific substrates are required for the material growth. The scale-up application of these processes still remains a great challenge due to the low production rate, limited domain size, and difficulty in controlling thickness over the entire substrates.  
Recent extensive research suggests that solution-based synthesis is the most feasible method for large-scale production of various metal chalcogenide materials. In a solution-based system, the conditions, such as solvents or surfactants employed, are logically chosen depending on the target metal chalcogenides to be synthesized. The kinetic and thermodynamic parameters can also be easily tuned to alter the speed of nucleation and the formation of the products. In addition, the solution-based synthetic approach does not require extreme conditions such as high temperatures or high pressures, yet it is able to generate products with high yields. In this section, we mainly summarize the solution-based approaches for the controlled synthesis of nanoscaled or thin-film metal chalcogenide materials, including the 'top-down' liquid exfoliation method, the 'bottom-up' hydrothermal/solvothermal method, and the wet chemical process for construction of nanostructured metal chalcogenides, as well as the electrodeposition method for the formation of both nanoscaled and thin-film materials. The composition, morphology, and properties of these thermoelectric materials are also briefly discussed.
## 4 Discussion
However, in this study, not all aromas were enhanced by the non-volatile compounds. In fact, the intensity of the pit and alcoholic aromas was more pronounced in Sample Rec A than in the original. This indicates that non-volatile compounds may mask these two specific aromas in baijiu. It is also possible that the heightened fruity notes suppressed the perception of the most concentrated compounds, which contribute to the pit aroma (mainly from ethyl hexanoate) and the alcoholic aroma (mainly from ethanol). The results of the omission test confirmed the contribution of non-volatile acids to the aroma of baijiu. The results indicated that among the non-volatile organic acids, monobasic acids primarily contributed to the aroma profile of baijiu, providing more sour and fruity notes. In this study, lactic acid is the most abundant non-volatile acid in strong-aroma baijiu, directly contributing to the acidic taste. Therefore, a deficiency in monobasic acids would directly result in a decrease in the overall acidity of the baijiu [28]. It should be noted that some of the monobasic acids were semi-volatile, especially at higher temperatures, so it is not surprising that monobasic acids could enhance the sour aroma of baijiu. The enhancement of fruity aroma can likely be attributed to the change in gas-liquid partitioning of esters in the presence of monobasic acids [29]. Meanwhile, organic acids are important precursors for esterification to produce esters, and esters are the key compounds to shape the flavor of baijiu. They provide aroma characteristics such as fruit and flower. For instance, ethyl laurate is believed to impart fruit and flower aroma to baijiu, making a substantial contribution to its flavor [30,31]. Dodecanoic acid, palmitic acid, and oleic acid have also been confirmed as precursors to some fatty acid esters [32]. Additionally, the enhancement of fruity notes might also result from the synergistic effect between the sour aroma and the fruity aroma. During the omission test, we also observed that omitting both monobasic and dibasic acids (OM1) allowed more panelists to perceive the difference compared to omitting only monobasic acids (OM5). Although not statistically significant, some panelists perceived an increase in alcoholic aroma in OM1 compared to OM5, indicating that the dibasic acids could mask the alcoholic notes but had little effect on other aroma characteristics. [PERSON] et al. [33] discovered that long-chain fatty acids, such as oleic acid, influenced the interactions between volatile compounds and ethanol/water molecules in baijiu. Specifically, these long-chain fatty acids inhibited the volatilization of ethanol, thereby helping to reduce the pungent odor of baijiu. [PERSON] et al. found that organic acids can inhibit the volatility of n-butanol due to intermolecular van der Waals forces and electrostatic interactions [34]. These findings align with the results of the current study.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing  
Barring an exception to some of the ultramodern molecular machines, most of the organic nanomaterials are polymeric in nature. Interest in polymeric nanomaterials for biomedical applications such as drug delivery and medical diagnostics has escalated. This can be attributed to their biocompatibility, inherent inertness, and flexibility in design. Such nanomaterials are thermally stable and relatively inexpensive to produce. Nanostructured films involve facile preparation procedures, are easy to handle, and may even be recovered after use [227]. Molecularly imprinted polymeric nanoparticles (nanoMIPs), which are cross-linked polymers that can selectively conjugate to a desired biomolecule, hold promise as biomimetic substitutes to antibody receptors [228]. Dendrimers constitute another class that has captivated researchers in the area of clinical diagnostics. The star-shaped hyperbranched structures allow scientists to tune the properties of various biomolecules. The functionality, size, and shape of dendrimers can be manipulated by making changes to their exterior surface, interior dendritic structure, or the central core [3]. Nanostructured hydrogels, which are 3D polymeric networks made up of cross-linked polymer chains, are able to alter their structure and volume in response to changes in pH, chemical environment, temperature, light, or magnetic or electric field, thereby being termed as stimuli-responsive smart nanomaterials [2]. Hyperbranched polymeric nanoparticles are structurally different from dendrimers, since they possess linear units in addition to dendritic and terminal units. They possess advanced optical, electrical, and magnetic properties, making them supreme elements in various kinds of biosensing systems [229]. Polymeric nanocomposites are ideal transducers because of their morphological variations, elegant responsibility, and simple synthesis procedures. They are low in cost and offer high signal-to-noise ratios and can be employed in sensors based on DNA, aptamer, or antibodies [230].  
Molecular machines are miniaturised devices that are propelled in the liquid medium either by themselves or by external acoustic, electric/magnetic, or catalytic energy sources [231; 232]. Some such nanomotors are Janus particles, which exhibit different properties on different parts of the material. They can be functionalised with molecularly imprinted polymers, as well as biomolecules such as oligodeoxynucleotides (ODN), to function as receptors [231].
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 5 Publicly Available Datasets
### LastFM Dataset  
The LastFM Dataset is a collection of user-generated music-listening data from the Last.fm online music service. The dataset is well suited for FRSs, as it contains large amounts of user-specific music-listening data from various users. These data can create personalized recommendations for users, as they are more likely to be interested in music similar to what they have already listened to. The dataset includes user IDs, the artist name, the release name, the song title, the album name, the number of plays, and the timestamp when the song was played. These data can be used to create more detailed recommendations for each user. By analyzing the number of plays and the timestamp of each song, the system can identify which songs are most-popular among users and make recommendations accordingly.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 4 Discussion and Conclusions
### Challenges and Limitations  
We used an excimer laser to fabricate the side-fire fiber prototypes because it is a versatile and programmable micromachining tool available in our lab [47]. This allowed us to investigate the side-fire fiber concept. This laser was able to ablate the core materials and generate a roughened surface. However, the cylindrical shape of the optical fiber makes it challenging to focus the machining laser at the surface and causes insufficient and non-uniform ablation at the sides of the fiber. This challenge was overcome by dicing the optical fiber surface prior to laser machining. The dicing saw creates a planar surface with ridges, as shown in Figure 1b, so that the ArF radiation could be focused on the intended region of the fiber.  
The focus of the excimer laser to remove the successive layers of glass was set using the inspection camera. This has limited accuracy because of the roughened surface of the ablated window, so the six characterized fibers have variable ablation depths with a mean and standard deviation of \(645\pm 95\) um, consistent with an imprecise laser focal position relative to the surface height and the effect of it is reflected in a variation in the fiber side-fire efficiency (Table 1).  
The nanosecond PA imaging laser pulses have the potential to effectively continue altering the fiber structurally. The long-duration ns pulse allows for the heat to diffuse in the fiber material, which may result in melting and fracturing [48] in the fibers. As an example, a diagonal crack (black arrow, Figure 4g) near the center of the window was observed following the 6000 pulses of the PA imaging laser. This may have been a result of the elongation of an existing crack or the formation of a new crack as the ns laser pulses interact with the modified fiber structure. The beam profile was also altered, increasing by 12% and 18% in the longitudinal and transverse directions, respectively, and the peak intensity shifted proximally (Figure 4g-i). This can be expected as the proximal region of the window interacts with the highest power density, redirecting a portion of the energy out of the fiber, while more distal regions experience lower power densities. The azimuth emission profile was also affected most at the proximal window position (Figure 4i), increasing the beam spread by 10\({}^{\circ}\). Overall, the light emitted from the window side decreased by 37% following the initial sequence of imaging laser pulses, and a drop in the energy was observed after each phantom and in vivo imaging session. Repeated use of the fibers may change the beam characteristics and reduce side-fire efficiency significantly, which may limit their use to single use only.
## 3 Results and Discussion
### Water Uptake, Ion Exchange Capacity, and Conductivity
Water uptake, IEC, and ion conductivity were in the decreasing order of DABCO \(>\) TMA \(>\) TPA \(>\) TEA when the composition of GMA was 75% by weight. The performance of GDV75-TEA was reduced when GMA weight concentration increased from 45% to 75%. The IEC of GDV75-TEA was low, even though GDV75 base membrane had dominant epoxy rings. In other words, TEA does not have affinity with epoxy ring. Interestingly, IEC for the membranes quaternized by DABCO were in the increasing order of G75 \(>\) G45 \(>\) G15, indicating that DABCO can form C-N bond with epoxy ring from GMA more easily compared to bonding with VBC. Understandably, certain functional groups exhibit good performance depending on composition of base membrane. TPA, TMA, and DABCO presented high IEC and conductivity when base membrane contains 15%, 45%, and 75% GMA by weight, respectively. Hydroxide conductivity must have high value (0.01-0.1 S\(\cdot\)cm\({}^{-1}\)) for potential application in AEMFCs [28]. Moreover, ion exchange capacity must be above 0.9 meq g\({}^{-1}\). Six of the membranes with acceptable IEC and conductivity in Table 3 are selected for Table 4. It is shown in Figure 6 that the conductivity of those membranes increased when temperatures increased. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **IEC (meq\(\cdot\)g\({}^{-1}\))** & **\(\sigma\) at 60\({}^{\circ}\)C (S\(\cdot\)cm\({}^{-1}\))** & **E\({}_{a}\) (kJ\(\cdot\)mol\({}^{-1}\))** \\ \hline GDV15-TMA & 1.31 & 0.020 & 19.10 \\ GDV15-TPA & 1.34 & 0.022 & 10.40 \\ GDV45-TMA & 0.98 & 0.019 & 11.55 \\ GDV45-TEA & 0.91 & 0.017 & 15.64 \\ GDV75-TMA & 0.93 & 0.018 & 13.44 \\ GDV75-DABCO & 1.34 & 0.021 & 15.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ion exchange capacity (IEC), conductivity and activation energy of select quaternized membranes. Figure 6: Arrehenius plots for GDV15-TMA, GDV15-TPA, GDV45-TMA, GDV45-TEA, GDV75-TMA, and GDV75-DABCO. Activation energy of ion transport (\(E_{a}\)) can be computed as \(E_{a}=-b\times\mathrm{R}\), where \(b\) is the slope of the linear regression (Figure 5) of \(\mathrm{ln}\sigma\)_versus_ 1000/T, and R is the gas constant. In other words, the Arrhenius plots of the ion conductivity of each membrane as function of temperature can be used to compute the activation energy of ion transport (\(E_{a}\)). Activation energy accords with energy barrier of anion migration from one point to another based on the Grotthuss mechanism [29; 30]. Hydroxide transport is facilitated when the activation energy is low. The \(E_{a}\) of promising membranes are listed in Table 4, and varies from 10.40 to 19.10 \(\mathrm{kJ}\cdot\mathrm{mol}^{-1}\). \(E_{a}\) of GDV15-TPA and GDV45-TMA are smaller than other membranes listed in Table 4. In other words, membranes with high VBC composition bonding with TMA and TPA makes anion transport easier and provides more desirable cation sites than others. Therefore, GDV15-TPA and GDV45-TMA membranes are the best candidates from the perspective of water uptake, IEC, hydroxide conductivity, and activation energy.
## 6 Strategies for Improving Protein Digestibility
### Fermentation  
Fermentation is a natural process that uses microorganisms to modify the structure and properties of proteins [108]. Microbial enzymes break down complex proteins and reduce the levels of anti-nutritional factors, improving the digestibility of pulses by lowering the concentration of non-nutritive compounds that interfere with digestive enzymes, such as trypsin and chymotrypsin inhibitors [149]. Additionally, it reduces the presence of compounds like phenolics and tannins, which contribute to protein crosslinking. Furthermore, fermentation promotes the production of microbial proteases, which partially break down proteins and release them from the matrix [150]. For instance, a study on the fermentation of soybeans showed that this process significantly enhances protein digestibility by reducing antinutritional factors and breaking down structural barriers that hinder enzymatic activity [151]. As digestion progressed, fermented soybeans exhibited the highest protein digestibility, suggesting that microbial activity facilitated protein breakdown and improved bioaccessibility. The biochemical changes induced by fermentation likely promoted better interaction between digestive enzymes and protein substrates, leading to a more efficient digestion process. In addition, the fermentation of pea flour showed that this process significantly enhances protein digestibility while also improving the bioaccessibility of essential minerals such as manganese (Mn) and iron (Fe) [152].
## 2 Categorization and Classification of Quality of Service Routing Protocols
### Deterministic Routing Protons (Soft Real Time)
In second phase, QEMH performs the route discovery process by using multiple conditions such as buffer size, residual energy, distance to sink, and signal-to-noise ratio. Once a node detects an event, it then sends the data to the cluster head node. The responsibility of the cluster head node is to further forward it to the sink station along the paths. QEMH uses the weighted traffic allocation approach to distribute the network traffic amongst the existing paths to increase the throughput and end-to-end delay. In this approach, the cluster head node distributes the traffic between the paths based on the end-to-end delay of each path. The QEMH measures the end-to-end delay during the paths discovery process. QEMH aims to prolong the network lifetime with load-balancing that helps to balance the energy consumption uniformly throughout the network. Furthermore, QEMH deploys a queuing model to handle real-time and non-real-time traffic. The Multi-objective QoS Routing (MQoSR) protocol was introduced [49] and is based on a geographic routing mechanism. The protocol uses a heuristic neighbor selection procedure that combines the geographic characteristics with the QoS requirements to obtain QoS improvements for several applications. The QoS provision issue for routing is articulated as path and link-based parameters. The link-based parameters are divided into delay, reliability, distance to sink, and energy consumption. The path-based parameters are presented in form of reliable data transmission, end-to-end delay, and network lifetime. MQoSR applies a different selection policy for each QoS requirement. The node selects the next hop node based on the requested requirements and the link conditions for improving the QoS provisioning. MQoSR is purely based on the on-demand routing mechanism, which makes the multiple node-disjoint paths. The MQoSR decides the cost of the each link based on link cost function, and total link cost function. The Pheromone Termite (PT) model was introduced in [50,51] and is based on a shortest path mechanism. The protocol uses a termite-based concept to establish the routes. The protocol particularly focuses on finding the shortest path by maintaining the QoS provisions. The PT introduces two new features; pheromone sensitivity and packet generation rate. The pheromone sensitivity helps determining the link capacity prior to sending the packets over the link to avoid congestion. The packet generation rate helps inform the node regarding the number of generated packets. Both features improve the QoS provisioning, avoiding the congestion and extending the network lifetime. Another one of the best features of PT routing is to select an alternate path in case of congestion observed on the network. The PT also has support for fault-tolerance if a link is broken that is easy to maintain. The performance of PT is verified and confirmed using mathematical models and simulation.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 3 Results
### Pre-Processing  
The complexity of the RGB-D (Kinect) datasets required two additional pre-processing steps. The first step was the determination of an alpha value required for volume calculation, using the _alphashape3d_ package [32]. The second step was volume correction for all Kinect datasets, due to volume estimation errors in the datasets. The segmentation accuracy for the RGB pre-processing has also been included in this section.
# Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument
## 3 Results
### Detection Rate of the Distal End of the Surgical Instrument  
Table 2 summarizes the detection rate of the distal end of the surgical instrument. The detection rates, including the calculated coordinates of the center point of the 8 \(\times\) 8 and 16 \(\times\) 16 pixels, are \(0.6100\pm 0.1014\) and \(0.9653\pm 0.0177\), respectively. Figure 7 illustrates representative examples of results for the detection of the distal end of the surgical instrument.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Without Data Augmentation** & \multicolumn{4}{c}{**With Data Augmentation**} \\ \hline  & **AP** & **LAMR** & **FPS [fps]** & **AP** & **LAMR** & **FPS [fps]** \\ \hline Subset A & 0.3895 & 0.7275 & 43.2 & 0.8258 & 0.4234 & 41.9 \\ Subset B & 0.4649 & 0.6361 & 43.5 & 0.6929 & 0.4382 & 28.0 \\ Subset C & 0.4958 & 0.6216 & 44.0 & 0.6866 & 0.4206 & 28.3 \\ Subset D & 0.2960 & 0.7263 & 44.1 & 0.6689 & 0.4124 & 28.0 \\ Subset E & 0.4150 & 0.6515 & 43.9 & 0.8639 & 0.2006 & 28.1 \\ Subset F & 0.3104 & 0.7367 & 43.8 & 0.8533 & 0.4117 & 28.7 \\ Subset G & 0.6216 & 0.5288 & 43.4 & 0.8603 & 0.1821 & 28.3 \\ Subset H & 0.3336 & 0.7159 & 41.5 & 0.8055 & 0.3841 & 28.7 \\ Subset I & 0.5179 & 0.6124 & 42.7 & 0.9083 & 0.2585 & 28.7 \\ mean \(\pm\) SD & \(0.4272\pm 0.108\) & \(0.6619\pm 0.0703\) & \(43.3\pm 0.8\) & \(0.7962\pm 0.0897\) & \(0.3488\pm 0.1036\) & \(29.9\pm 4.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average precision (AP), log-average miss rate (LAMR), and frame per second (FPS) for \(32\times 32\)-pixel ROIs.  
Figure 6: Process to calculate the detection rate of the distal end of the surgical instrument.