esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Electrochemical Performance of MoS\({}_{2}\)-NG/PP Separator in Lithium-Sulfur Batteries
Figure 5d shows the constant current charge/discharge curves of the cell with the MoS2-NG/PP separator at different rates. As the current density increases, only minimal polarization is observed, and even at 3 C, the two distinct charge/discharge plateaus are well maintained, indicating that MoS2-NG plays a crucial role in optimizing the reaction kinetics of the battery. The voltage drop across the resistance, as expected from [PERSON]'s law (the product of current and resistance), was minimized, which may be attributed to the N-doping effect that improves conductivity and reduces charge transfer resistance. This further supports the role of heteroatom doping in enhancing the overall capacity performance by minimizing internal resistance [39]. In addition to rate performance, long-cycle stability is also a critical indicator of the electrochemical performance of lithium-sulfur batteries. Therefore, we conducted long-cycle charge/discharge tests at 0.5, 1, and 2 C. As shown in Figure 5e, the initial specific capacities at 0.5 C for cells with MoS2-NG/PP, MoS2-G/PP, and MoS2/PP separators were 1255, 1006, and 1161 mAh g\({}^{-1}\), respectively. After 500 cycles, the specific capacities were maintained at 722, 482, and 494 mAh g\({}^{-1}\), with capacity decay rates of 0.085%, 0.104%, and 0.115% per cycle, respectively. Similarly, at 1 C (Figure S5) and 2 C (Figure 5f), cells with the MoS2-NG/PP separator exhibited higher specific capacities and lower capacity decay, indicating that MoS2-NG effectively catalyzes polysulfides and promotes the formation of Li2S, thereby suppressing the shuttle effect and enhancing the specific capacity and cycling stability of the battery. Notably, the Coulombic efficiency of the cell with the MoS2/PP separator sharply declined with continued cycling, likely due to the diminished ability of  
Figure 5: CV curves at a scan rate of 0.1 mV s\({}^{-1}\) (**a**) and galvanostatic discharge/charge curves at a current density of 0.2 C (**b**) of cells assembled based on MoS2/PP, MoS2-G/PP, and MoS2-NG/PP separators. (**c**) Rate performance of cells with different separators. (**d**) Galvanostatic discharge/charge curves of the MoS2-NG/PP separator at various rates. Long-term cycling performance of cells with MoS2/PP, MoS2-G/PP, and MoS2-NG/PP separators at 0.5 C (**e**) and 2 C (**f**). MoS\({}_{2}\) to adsorb and catalyze polysulfides after prolonged cycling, leading to an exacerbated shuttle effect and reduced Coulombic efficiency.
## 3 Multi-Channel Membrane CDI (MC-MCDI)  
### Cell Configuration and Key Advantages  
To overcome the limitations of static- and flow-electrode CDI, a unique cell architecture, known as MC-MCDI, has recently been designed (Figure 5). The MC-MCDI cell consists of the middle and the side channels as the feed and electrode channels, respectively. The two channels were separated by a pair of anion and cation exchange membranes (AEM and CEM) (Figure 5A). In the side channels, porous carbon electrodes are attached to current collectors made of titanium mesh. The side of the current collector is coated with rubber to press on the ion exchange membrane and seal the middle and the side channels (Figure 5B,C). As such, both the feed stream and electrolyte can be independently controlled, providing a favorable environment for the formation of the EDL. The cell design of MC-MCDI involves several advantages and has distinctive features compared to the conventional CDI architectures as follows:  
* **Enhanced desalination performance** MC-MCDI involves an advantageous configuration of the membrane CDI (Figure 5A). For example, the major benefits of membrane CDI are the enhancement of charge efficiency and salt removal capacity because the added membranes block co-ion repulsion, which causes the undesirable current during the charging step. As shown in Table 3, MC-MCDI results in enhanced SAC compared with MCDI and CDI, showing a two to five times greater ion removal capacity. Moreover, the system maintains high charge efficiency (>90%), indicating that the system is operated efficiently with a small amount of side reactions. Additionally, by using concentrated solutions in the side channels, the porous electrodes could form the EDL even in nanopores, therefore, enhancing the ion removal capacities [15].
* **Semi-continuous desalination** Two stream channels in MC-MCDI allow for semi-continuous production of clean water, thereby overcoming one of the major limitations for the static-electrode CDI. As mentioned, for the static-electrode CDI, the repeated cycling between adsorption and desorption causes discontinuous production of fresh water [45]. In the case of MC-MCDI, however, separated middle and side channels could be alternately desalinated and regenerated. In other words, one channel is desalinated, while the other channel is regenerated. Therefore, the cell can produce clean water regardless of the charging and discharging processes, resolving the limitation of intermittent CDI operation [32].  
* **Multifunctional system**  
MC-MCDI could be utilized not only in the desalination of brackish water but also in cases such as purification of highly concentrated feed water, up-concentration of certain ions, and possibly selective ion removal. For instance, the MC-MCDI system can be used for treating the concentrated wastewater from the reverse osmosis process. It could also be used for up-concentration of valuable ionic contents such as Li+ and Co2+, as well as for desalting KOH and H2 SO4 or NaCl and KCl by changing operating conditions without using any additional industrial processes.
## 3 Methodology  
### Sound Acquisition System  
The sound signal emitted by the motor was acquired with an embedded system substantially composed of an Arduino DUE development platform and a CMA-4544 PF-W analog microphone electret condenser, as shown in Figure 3. The Arduino DUE platform is formed by a 32-bit ARM microcontroller with an adjustable acquisition frequency, where a sampling frequency of 44.1 kHz was established for this experiment. The CMA-4544 PF-W microphone is an omnidirectional microphone with a 44 dB sensitivity and an operational frequency range from 20 Hz to 20 kHz. This dispositive is highly sensitive to noises close to the source and avoids the ones that are far from it. In the tests that were carried out, positioning the dispositive close to the place where the motor is set up, the surrounding noises did not interfere in the system's accuracy, since spurious emissions of high intensity were not found in the signal spectrum.  
Figure 2: Relationship of torque versus shaft speed of the motor.  
It should be emphasized that the acquisition system was developed because the commercially available smartphones do not have the sensitivity for low-frequency sounds that is required for this type of application.
## 3 Results
### Tests for Surface Disinfection by EN 13697:2015
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Phase II, Stage II**} \\ \hline
**Birch** & **Contact Time** & _Staphylococcus aureus_ & _Pseudomonas aeruginosa_ \\
**CTC 6538** & **ATCC 15442** \\ \hline \multirow{4}{*}{\(\mathrm{C_{P}=20\,g/100\,mL}\)} & \multirow{4}{*}{**\(\mathrm{10^{-0}}\): \(>\)330**} & \(10^{-0}\): \(>\)330 ; \(>\)330 \\  & & \(10^{-1}\): 161; 153 & \(10^{-1}\): 211; 193 \\ \cline{1-1}  & & \(10^{-2}\): \(10\): 9 & \(10^{-2}\): \(11,13\) \\ \cline{1-1}  & & Nd: 4.20 & Nd: 4.31 \\ \cline{1-1}  & & Nts: \(>\)100 & Nts: \(>\)100 \\ \cline{1-1}  & & R: 2.94 & R: 2.82 \\ \hline \multirow{4}{*}{\(\mathrm{C_{P}=30\,g/100\,mL}\)} & \multirow{4}{*}{\(\mathrm{10^{-0}}\): \(245,237\)} & \(10^{-0}\): \(254,269\) \\ \cline{1-1}  & & \(10^{-1}\): 123; 122 & \(10^{-1}\): \(172,181\) \\ \cline{1-1}  & & \(10^{-2}\): \(84,91\) & \(10^{-2}\): \(94,61\) \\ \cline{1-1}  & & Nd: 4.09 & Nd: 4.25 \\ \cline{1-1}  & & Nts: 100 & Nts: 90 \\ \cline{1-1}  & & R: 3.05 & R: 2.88 \\ \hline \multirow{4}{*}{\(\mathrm{C_{P}=40\,g/100\,mL}\)} & \multirow{4}{*}{\(\mathrm{60\pm 10\,s}\)} & \(10^{-0}\): \(138,146\) \\ \cline{1-1}  & & \(10^{-1}\): \(49,58\) & \(10^{-1}\): \(13,11\) \\ \cline{1-1}  & & \(10^{-2}\): \(5,7\) & \(10^{-2}\): \(0,0\) \\ \cline{1-1}  & & Nd: 3.03 & Nd: 3.15 \\ \cline{1-1}  & & Nts: 90 & Nts: 0 \\ \cline{1-1}  & & R: 4.11 & R: 3.98 \\ \hline \multirow{4}{*}{\(\mathrm{C_{P}=50\,g/100\,mL}\)} & \(10^{-0}\): \(74,59\) & \(10^{-0}\): \(72,83\) \\ \cline{1-1}  & & \(10^{-1}\): \(18,23\) & \(10^{-1}\): \(4,6\) \\ \cline{1-1}  & & \(10^{-2}\): \(0,0\) & \(10^{-2}\): \(0,0\) \\ \cline{1-1}  & & Nd: 2.82 & Nd: 2.89 \\ \cline{1-1}  & & Nts: 0 & Nts: 0 \\ \cline{1-1}  & & \(10^{-2}\): \(0,0\) & \(10^{-2}\): \(0,0\) \\ \cline{1-1}  & & \(10^{-2}\): \(0,0\) & \(10^{-2}\): \(0,0\) \\ \cline{1-1}  & & Nd: \(<\)0.10 & Nd: \(<\)0.10 \\ \cline{1-1}  & & Nts: 0 & Nts: 0 \\ \cline{1-1}  & & R: \(>\)7.04 & R: \(>\)7.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._The birch preparation showed significant efficacy on surfaces against Staphylococcusococcus aureus ATCC 6538 already at a concentration of 30 g/100 mL, while against the Pseudomonas aeruginosa strain ATCC 15442, efficacy >99.9% was observed at a concentration of 40 g/100 mL (Tables 10 and 11).
# Natural Rubber/Styrene-Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
###### Abstract
Although current studies have indeed prepared some high-performance NR composites, some of them are difficult to put into mass production because of the high cost of the modification or the complexity of the process. Some other NR composites show superior properties, but there is a lack of research on the properties valued by composites for vibration damping bearings. There is also a lack of basic research on the effects of combining NR with other rubbers on the properties of composites for vibration damping bearings. In general, the factors affecting the compression set of rubber composites are the elasticity of the rubber matrix, the filler (type, particle size and morphology) and the vulcanization system [23; 24]. The compression fatigue temperature rising of rubber composites is also influenced by the rubber matrix (type and number of unsaturated bonds and their configuration), filler (type, particle size and morphology) and the vulcanization system [25; 26]. Natural rubber, with its excellent mechanical properties and elasticity, is the most commonly used material for manufacturing rubber vibration damping bearings. With good processing properties and fatigue resistance, SBR is expected to compensate for the shortcomings of natural rubber. It is noted that there are few studies on the blending proportions of NR/SBR on the physical properties of rubber composites for vibration damping bearings. Therefore, in this study, NR/SBR composites with different blending proportions (100/0, 90/10, 80/20, 70/30, 60/40 and 50/50) were prepared by the mechanical blending method, which is low cost and convenient in the preparation process, and the tensile strength, tear strength, thermal oxidative aging performance, compression set, compression fatigue temperature rising and dynamic viscoelasticity of NR/SBR composites were investigated. This study has certain significance for the development of NR composites for highly durable vibration damping bearings.
## 2 Method
### Improvements Based on Shield Tunneling Characteristics
#### 2.3.2 Moving Prediction Window  
Surface settlement induced by shield tunneling has a time lag, as illustrated in Figure 4. The maximum settlement typically lags the shield face by 5-10 rings. To address the impact of this lag on surface settlement, a moving prediction window approach was adopted (see Figure 5). A series-to-series module [35] was introduced for encoding and decoding, and offset parameters were used to appropriately adjust the order of the input time series, addressing temporal lag differences. The use of variable offset parameters allows the algorithm to more accurately capture dependencies within the time series, increasing the prediction accuracy.  
Regarding Figure 5:  
1. Known input features (orange blocks in Figure 5): These include shield tunneling parameters (e.g., thrust, cutterhead torque, and advance rate) and geological static parameters (e.g., soil layer classification, density, and compression modulus) monitored before tunneling to the target ring. Their temporal regularity can be captured through historical data.
2. Unknown input features (blue blocks in Figure 5): These are lagged variables introduced via the moving prediction window, such as settlement values from the past 5-10 rings and displacement trends of shield posture (gray blocks in Figure 5). These features address time-lag effects in settlement prediction.
3. Supplementary input features (gray blocks in Figure 5): These represent binary indicators (0/1) for abrupt special construction events (e.g., abnormal stoppages and secondary grouting). Their timing and intensity are unpredictable, as detailed in Section 2.3.3.  
Figure 4: Schematic of surface settlement induced by shield tunneling.  
Figure 5: Schematic of supplementary input features obtained using the moving prediction window.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Evaluation Methodology
#### 5.1.3 Modelling PoiFord's Contact Information Overhead  
The contact information overhead incurred by PoiFord is based on the size of the coordinates for POI and centroid information as well as the amount of encounter-based information maintained on a node. A pair of GPS coordinates occupies \(2X_{D}\) bits (i.e., \(X_{D}\) for longitude and \(X_{D}\) for latitude). POI information, which is made up of home and office location, requires \(4X_{D}\) bits, while centroid information requires \(2X_{D}\) bits. This gives a total of \(6X_{D}\) bits.  
For encounter-based routing information, the circular area represents the geographical span of the user's home or office. Hence, the radius of the circle, \(r\), is chosen to reflect this. The choice of \(r\) therefore determines the amount of encounter history maintained by a node. A larger value includes more neighboring POIs and users such as a neighboring house or office, and vice versa. In real-world implementation, a user may be allowed to select this value or a mechanism could be introduced to automatically select this value based on the amount of resources the user is willing to allocate to the technology.  
For analytical purposes, we take the average number of nodes for which encounter history needs to be maintained as \((n-1)/v\) for home location and \((n-1)/v\) for office location, where \(n\) and \(v\) represent the total number of nodes in the network and the number of POIs in the network, respectively. This gives a total number of \(2(n-1)/v\) nodes for which encounter history needs to be maintained. With \(X_{S}\) bits and \(X_{D}\) bits for holding node ID and the corresponding forwarding utility, respectively, a storage space of \(2(n-1)(X_{S}+X_{D})/v\) is required for maintaining contact information. The size of contact information, \(S\), maintained by PoiFord is given by Equation (18).  
\[S=2\bigg{[}3X_{D}+\frac{(n-1)(X_{S}+X_{D})}{v}\bigg{]}\ \text{bits} \tag{19}\]  
For a worst-case scenario of 100,000 nodes, 500 POIs, 80 bit string (i.e., 5 characters) for node ID and a 64 bit double for longitude, latitude, and delivery predictability, PoiFord generates a contactinformation of only about 7.2 KB on each node, as compared with 1800 KB for PRoPHET. This value remains stable if the ratio of node population to POIs remains roughly the same. Otherwise if the ratio increases, contact information size only increases by a small fraction. Contact information size can be reduced either by reducing \(u\) or the size of \(r\). This reduces the number of GPS locations to be maintained in the location table, and the number of encounters for which to maintain history, respectively.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 2 Theoretical Background  
### The Concept of Hypernetwork  
[PERSON] [30,31] proposed the concept of hypergraphs in 1970. The definition of a hypergraph is given below. Let \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) be a finite set. If \(E_{i}\
eq\varnothing\) and \(\bigcup_{i=1}^{r}E_{i}=V\), the pair \(H=(V,E)\) is called a hypergraph. Here, the elements \(v_{1},v_{2},\ldots,v_{n}\) of \(V\) are called the nodes or vertices of the hypergraph. \(E=\{E_{1},E_{2},\ldots,E_{e}\}\) is the set of edges of the hypergraph. The elements of set \(E_{i}\) of set \(E\)\((i=1,2,\ldots,e)\) are called the hyperedges of the hypergraph, and \(|E_{i}|\) denotes the cardinality of the set \(E_{i}\). Moreover, \(H=(V,E)\) is a \(k\)-uniform hypergraph if \(|E_{i}|=k\).  
The definition of a hypernetwork [28] is as follows. Suppose that \(\Omega=\{(V,E^{h})|(V,E^{h})\}\) is a finite hypergraph and \(G\) is a map from \(T=[0,+\infty)\) into \(\Omega\). Then, for a given \(t\geq 0\), \(G(t)=(V(t),E^{h}(t))\) is a finite hypergraph, where t denotes the time step. A collection of hypergraphs is a hypernetwork \(\{G(t),t\in T\}\). The number of hyperedges of the contained node \(v_{i}\) is the hyperdegree of \(v_{i}\). The hyperdegree distribution is the probability distribution of the hyperdegree of all nodes in the hypernetwork.
# Pre-Composting and Vermicomposting of Pineapple (_Ananas Comosus_) and Vegetable Waste
## 1 Introduction  
Pineapple is a tropical fruit rich in nutrients; it is highly valued in international markets because of the fabrication of agro-industrial products and because it can be planted all year long with good irrigation systems. This tropical fruit was second place in worldwide production at 28% in 2017, along with mango, avocado and papaya, it had an average annual growth of 3.6%; Costa Rica is its main exporter [1].  
The use of this fruit leads to different products such as dried pineapple, juice, nectar, pulp, frozen pineapple, gelatin, marmalades, candy and vinegar [2]. The fruit is made up of pulp, its most useful part, and a rind, crown and core, which are waste. This waste makes up 65% to 67% of the total weight of each pineapple produced [2, 3].  
Therefore, direct dumping of this organic waste leads to multiple different negative environmental impacts such as soil and water pollution through the filtration of leachates it generates [4]. To avoid this, waste may be reused or submitted to physical, chemical, biological, thermal or mixed treatments, among which we can find feeding animals, crushing, composting, anaerobic digestion and vermicomposting.  
Of the treatments mentioned, vermicomposting is presented as a viable alternative to degradation and stabilization of organic matter, as it results in excellent quality humus that provides immediate results due to the fact that plants absorb its nutrients faster [5]. Because this is an affordable and sustainable option for the use of the aforementioned organic waste and a practice that seeks betterorganic matter content, it is one of the best options as a substitute for conventional fertilizers that can be harmful to soil [6].  
The literature consulted shows few studies on treatment of pineapple waste, especially through the vermicomposting process [7]; what is referred to as waste in these cases consists of pineapple rinds, but not crowns and cores. Not enough information has been reported on the pre-composing process for this type of waste and even though there are some studies on vermicomposting that include all pineapple waste, it is necessary to study the different mixtures available along with other types of waste so as to enrich and balance nutrients, thus verifying the feasibility of the degradation process for this fruit.  
Taking all this into account, the main objective of this research is to determine operational conditions for the degradation of different mixtures of pineapple waste along with other materials through pre-composing and vermicomposting at laboratory level.
## Abbreviations  
\begin{tabular}{l l} ATR & Attenuated total reflection \\ BTEX & Benzene, toluene and xylene \\ CB & Chlorobenzene \\ DEHP & Disooctylphthalate \\ DLC & Diamond like carbon \\ DLP & Digital light processing \\ DMD & Digital mirror devices \\ ESI & Electrospray ionisation \\ ESI & Electrospray ionisation \\ FPA & Focal plane array \\ FT-IR & Fourier-transform infrared \\ GC & Gas chromatography \\ GMA-DVB & Poly(glycidyl) methacrylate-co-divinylbenzene) \\ HC & Hierarchical cluster \\ HDL & High density lipoprotein \\ HPLC & High-performance liquid chromatography \\ KM & K-means \\ LDL & Low density lipoprotein \\ LLE & Liquid-liquid extraction \\ LOD & Limit of detection \\ LVF & Linear variable filter \\ MALDI & Matrix assisted laser desorption ionisation \\ MCT & Mercury cadmium telluride \\ MEMS & Micro-electro-mechanical system \\ MIA & Multivariate imaging analysis \\ MIP & Molecularly imprinted polymers \\ MLR & Multiple linear regression \\ MS & Mass spectrometry \\ MVA & Multivariate analysis \\ PCA & Principal component analysis \\ PDMS & Poly(dimethylsiloxane) \\ PES & Polyethersulfone \\ PIB & Polyisobutylene \\ PLSR & Partial least square regression \\ PS-DVB & Poly(styrene-co-divinylbenzene) \\ PTFE & Polyettersulfoneethylene \\ PVC & Poly(vinylchloride) \\ RE & Relative error \\ SD & Standard deviation \\ SEE & Standard error of estimation \\ SEIRA & Surface enhanced infrared analysis \\ SEP & Standard error of prediction \\ SERS & Surface enhanced Raman spectroscopy \\ SNR & Signal to noise ratio \\ SPE & Solid-phase extraction \\ SPME & Solid-phase micro extraction \\ TEC & Thermoelectric cooling \\ Vis & Visible \\ \end{tabular}
## 2 Materials and Methods
### Data Analysis  
Several pre-processing steps were applied to the HSV data before proceeding with the proposed spatial segmentation approach. Temporal segmentation [15] and motion compensation [18] were first applied sequentially to automatically extract the vocalized segments of the HSV recordings and capture the location of the moving vocal folds across the frames. HSV kymograms were extracted next at different intersections of the vocal folds. After applying the pre-processing steps, the clustering technique was implemented on each kymogram to initialize a contour line based on the captured glottal area. The ACM method was applied to the kymograms to complete the segmentation task. The segmented glottal edges in the kymograms were registered back to the HSV frames to detect the glottal edges in each individual HSV frame. The algorithms were implemented using the 64-bit MATLAB R2019a (MathWorks Inc., Natick, MA, USA).