esa-sceva/satcom-chunk-collection huggingface dataset:
Biochar is characterized by pores of different sizes. The most abundant pores are 5-20 \(\upmu\)m in diameter and are thus suitable for microbial habitation, as the sizes of bacteria range from 0.3 to 3 \(\upmu\)m. Mesopores (2-50 nm) are responsible for the capacity to adsorb organic molecules, and micropores (below 2 nm) are generally inaccessible. The percentage of pores that are located on surface areas typically increases the adsorption of microorganisms [8,10]. However, these are not the only characteristics that play a key role in adsorption mechanisms. In fact, on the one hand, the functional groups on the biochar surface (e.g., hydroxyl -OH, carboxyl -COOH, and aldehyde -CHO) interact with microbial cell walls through various mechanisms, such as hydrogen bonding, electrostatic attraction, and hydrophobic interactions [11]. For instance, biochar with a high degree of aromaticity tends to have stronger hydrophobic properties, which can increase the adsorption of hydrophobic microbial species. On the other hand, the surface charge of biochar also plays a crucial role in adsorbing charged microbial cells. For example, negatively charged biochar surfaces may effectively adsorb positively charged bacteria. In addition, microorganisms can be physically trapped within the pores of biochar, especially if the pore size matches the size of the microbes [12]. All these properties have recently led the scientific community to focus on the interactions between biochar and microorganisms. Several studies have demonstrated that biochar interacts with soil microbiota, which improves soil quality, enhances the nutrient content, and indirectly makes plants more resistant to pathogens [13, 14, 15]. The use of biochar has also been explored as a feed ingredient. The results demonstrated a beneficial impact on animal production, and in particular, an improvement in animal health and a reduction in gas emissions. In terms of animal production, macroscopic effects such as zootechnical performance have been evaluated, which may be attributed to its capacity to modulate gut microbiota and influence metabolic processes in animals. [PERSON] et al. (2018) [16] reported that the administration of rice straw biochar to rats improvedgut mucosal structure and modified cecal microbial communities. This was accompanied by an increase in the _Firmicutes_ to _Bacteroidetes_ ratio and an increase in beneficial bacteria, including _Lachospiraceae_ and _Clostridium_. Given the morpho-structural characteristics of biochar and its functional properties, this study evaluates, in vitro, the capacity of biochar derived from vine pruning biomass to adsorb microorganisms such as pathogenic _E. coli_ and probiotic _Lactobacillus_ sp. Simultaneously, the potential bioactive effects of biochar on the same microorganisms were investigated when biochar was used as feed by simulating in vitro digestion. The ultimate aim was to explore and understand the potential beneficial effects of biochar and to provide evidence to support the hypothesis that it can modulate gut microbiota when incorporated into animal diets.
## 1 Introduction
[28] compared the tube-reinforced honeycomb sandwich structure with the traditional honeycomb structure and found that the filling of the metal tube improved the stiffness and peak load of the traditional honeycomb structure, making the stress and deformation of the front and rear skins more uniform. The energy absorption characteristics of the structure are faster than those of the empty honeycomb structure, and the deformation of the front skin is reduced. In addition to the overall design of honeycomb structures, reports on this study of composite honeycomb structures have gradually increased. [PERSON] et al. [29] conducted an experimental study on the impact and tensile properties of honeycombs with different matrix aluminum materials. It was found that the energy absorption effect of the pyramid-type tandem multi-layer honeycomb structure will be relatively improved after crushing. [PERSON] et al. [30] also carried out a numerical simulation and static compression test on the tandem aluminum honeycomb structure and obtained a buffer structure with better energy absorption characteristics. It can be seen that the honeycomb sandwich structure of a new structure and new material is still one of the main research directions. The protection law and energy absorption characteristics of composite honeycomb sandwich structure in the face of other damage elements, especially for the related problems of composite honeycomb sandwich structure in high-speed collisions, need to be further studied. In this paper, in order to study the damage mode and anti-damage performance of composite honeycomb sandwich structure under high-speed fragment impact, four groups of composite honeycomb sandwich structure and two groups of comparative structure were studied by fragment impact experiment. The damage law of the composite honeycomb sandwich structure under different velocities of fragment was analyzed, and the energy absorption characteristics of the composite honeycomb sandwich structure under fragment impact were compared and analyzed according to the experimental results.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 2 Different Types of Electrospun Nanofiber Process and Structures
### Parameters Affecting Electrospinning
Environmental temperature is another parameter that impacts the diameter of the nanofibers by controlling the viscosity of the solution [60]. Problematic beads or flat fibers will arise at low or high temperature. Summarized parameters that influence the nanofibers' characteristics in the electrospinning process are presented in Table 1.  
Based on the discussion above, it is easy for us to conclude that current electrospun fibers are to a great extent affected by complicated parameters throughout the whole production process. Hence, the final products cannot be precisely controlled for targeted characteristics. Significantly, on account of the above properties of being highly susceptible to environmental influences, minor changes could affect the final electrospun fibers and they are still far from industrial production.
## 4 Test Results and Analysis
### Sensibility Analysis of Factors
#### 4.2.3 Compressive Strength  
The range analysis results of compressive strength are shown in Table 7. According to Table 7, the range from large to small is the proportion of Portland cement/gypsum, filling material/bonding material, mica powder/standard sand, and silicone oil. It indicates that Portland cement/gypsum was the main factor controlling the compressive strength. Portland cement/gypsum and filling material/bonding material had a great influence on the compressive strength of the sample, and the influence degree was similar. The mica powder/standard sand and silicone oil ratio had little effect on the compressive strength of the samples, and the influence degree was similar.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level**} & **Mica Powder/Standard** & **Filling Material/Bonding** & **Portland** & **Silcone Oil Ratio** \\  & **Sand** & **Material** & **Cement/Gypsum** & **Silcone Oil Ratio** \\ \hline
1 & 1773.84 & 1794.81 & 1724.40 & 1781.84 \\
2 & 1753.42 & 1780.06 & 1765.39 & 1769.56 \\
3 & 1752.51 & 1768.76 & 1735.54 & 1765.40 \\
4 & 1765.43 & 1735.77 & 1805.29 & 1752.86 \\
5 & 1787.88 & 1753.68 & 1802.46 & 1763.41 \\ \hline Range & 35.37 & 59.03 & 80.89 & 28.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Range analysis of density kg/m\({}^{3}\).
## 4 Numerical Methodology
### Coupling FE Model
Limit grooves were set on the surface of the base slab to achieve mechanical limit with the convex plate of the self-compacting concrete layer, as shown in Figure 8c. Both the self-compacting concrete layer and the base slab layer are simulated by eight-node hexahedron solid element. In addition, the subgrade was also simulated by eight-node hexahedral solid elements, which was divided into three layers: the surface layer of the subgrade, the bottom layer of the subgrade, and the embankment filling. In this study, tie constraints are used to deal with the contacts FEof CRTS III ballastless track structure, such as the contact between track slab and self-compacting concrete, the contact between self-compacting concrete and base slab, the contact between convex platform and groove, and the contact between base slab and roadbed. The material parameters and structural dimensions of the unballasted track and subgrade are shown in Table 3[27]. Considering the influence of the constraints at the end of the rail, the running time, distance, and speed of the high-speed train on the analysis results, the longitudinal length of the track is set at 100 m. The FE model of CRTS III ballastless track structure is shown in Figure 8d. Figure 8: CRTSIII unballasted track division structure and overall structure. (**a**) Track slab structure, (**b**) self-compacting concrete layer structure, (**c**) base structure, (**d**) overall structure. In the process of polymer grouting repair, the repair material is injected into the joint of the base and the subgrade. The material reacts and expands, and the settled track slab is lifted and reset. After the repair is completed, a layer of polymer material is formed between the base and the subgrade. Therefore, the lifted layer of high polymer in the model was set as an area with a longitudinal length of 16.84 m and a transverse length of 3.1 m, which was located in the middle longitudinal position of subgrade just below the base, as shown in Figure 9. It is used to simulate the force characteristics of track slab, self-compacting layer, base, and high polymer material under different lifted heights of polymer and different densities of polymer. A scheme of the section whose results will be reported in each Figures is shown in Figure 9c,d.
## 5 Calculation of Fiber-Sprayed Concrete Stacked Beams with Flexural Stiffness Correction
### Modified Calculation of Fiber-Sprayed Concrete Stacked Beams Considering the Thickness Ratio  
After fiber shotcrete beams are layered, the interface location may have an effect on the flexural performance of the beams. Slips may occur between layers of fiber shotcrete beams, and the interfacial friction can partially inhibit the slip between layers, thus reducing the losses caused by the slip. At the same time, delamination can also lead to changes in the stress distribution pattern of the beam, and crack retardation and plastic deformation sharing can vary with the location of the interface. Therefore, interface effects need to be incorporated into the calculation of flexural stiffness.  
For this reason, the interface influence factor [48] about the location of the cross-section is introduced \(f_{\lambda_{h}}\), where \(\lambda_{h}=\frac{h_{2}}{h_{1}}\). Taking into account that the single-layer lining is a lining structure with sufficient transmission of shear forces between layers and coordinated deformation, the calculation of the flexural stiffness is corrected on the basis of the assumptions (1) (the fiber-sprayed concrete stacked beams are coordinated in deformation, and the neutral axis is located in the location of the center of the form), and the corrected flexural stiffness can be expressed as follows:  
\[EI_{eff}=f_{\lambda_{h}}EI_{0} \tag{10}\]  
In the formula, \(EI_{eff}\) is the effective stiffness of the stacked beam, \(\mathrm{N}\cdot\mathrm{mm}^{4}\), and \(f_{\lambda_{h}}\) is the coefficient of influence on the interface, \(f_{\lambda_{h}}=\frac{EI_{eff}}{EI_{0}}\) and \(\lambda_{h}=\frac{h_{2}}{h_{1}}\).
the younger population is at greater risk for inadequate intake. Moreover, early life dietary habits and dietary intake from the complementary diet and school or family meals may not cover the needs for specific fatty acids for these age groups [1,10].  
Many existing studies have explored fat and fatty acid intakes and imbalances in children [11,12] and several reviews highlight inadequate intake of certain FA (particularly PUFA) among children when compared to the recommendations [13,14,15,16]. However, most of the reviews have grouped and analyzed together the intakes of children of quite different ages, e.g., less than 10 years [14,15] or less than 5 years [16]. Two of these reviews assessed observed intake against WHO 2003 reference nutrient intakes (RNIs) for adults [14,15], one against Institute of Medicine RNIs for children from 2002 [16] and one against the European Food Safety Authority (EFSA) 2010 dietary reference values (DRVs) for children [13]. While two studies are limited to children from Europe [13,14] and one from the Eastern Mediterranean region [16], another included intake data reported globally [15]. Lastly, only two [13,15] reported intake of individual PUFA.  
The aim of this review is to gather the available literature on the intake of total fat, SFA and individual PUFA in children across different age ranges: 1-2 years, 2-3 years, 3-5 years, and 5-7 years worldwide and to compare data obtained against the most recent and age-appropriate fat recommendations. Firstly, an overview of the most recent recommendations for intake of fat according to two international health authorities, the Food and Agriculture Organization/World Health Organization (FAO/WHO) [17] and EFSA [18,19]) within these age ranges is given. Secondly, the available intake data in children aged 1 to 7 years are summarised and reported, and the gaps between reported intake and recommendations according to the FAO/WHO and EFSA are highlighted and discussed for each age group.
## 5 Evolution of Rock Mass Mechanical Parameters Under Freeze-Thaw Cycles  
### Structural Geometry Parameters and Rock Mass Quality Quantification  
The Geological Strength Index (GSI), a critical parameter in the Hoek-Brown criterion, serves both as a rock mass quality classification metric and a determinant of mechanical properties, based on rock structure and structural surface characteristics [21]. Rock structure and structural surfaces are quantified via Structure Rating (SR) and Structural Surface Rating (SCR), respectively. SR correlates with volume joints (\(J_{v}\)), while SCR integrates assessments of surface roughness (R\({}_{r}\)), weathering degree (R\({}_{w}\)), and the filling state (R\({}_{f}\)) of discontinuities. These parameters are derived using the following equations:  
\[SR=-17.5\ln J_{v}+79.8 \tag{3}\]  
\[\text{SCR}=\text{R}_{r}+\text{R}_{w}+\text{R}_{f} \tag{4}\]  
Through field measurements, the geometric parameters of structural planes exposed on the slope surface of the 645 m bench (including trace length, strike, dip direction, dip angle, spacing, and volumetric joint count \(J_{v}\)) were obtained. The measurement results are shown in Figure 7. Based on Equations (3) and (4) and referencing literature [22], the average Geological Strength Index (GSI) for the 645 m bench was determined as 41.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 4 Logistics Warehouse Target Tracking
### DeepSORT  
DeepSORT differs from SORT, mainly defining a new state of target tracks and matching the original detection result with the prediction by judging the state of the tracks [26]. At the same time, tracks with distinct states can be judged by the quantities of matching, which either increase or decrease. The DeepSORT algorithm process is shown in Figure 13[27; 28].  
Figure 12: Test result.  
The DeepSORT algorithm process is introduced as follows.
* The existing track is divided into confirmed and unconfirmed states.
* For the tracks with a confirmed state, they are cascaded to match with the detection result. The priority of tracks depends on the time that the tracks exist with the video stream and the number of successful matching attempts. A track that has just been successfully matched has a higher priority for cascade matching [29].
* The process of cascade matching: 1. The cosine distance and cost matrix between the track feature and the depth feature of the detection are computed. 2. The martingale distance between the prediction bounding boxes and the detection bounding boxes is computed in the cost matrix. 3. The previously obtained input cost matrix is matched in Hungary to obtain the final pairing result.
* The unconfirmed state tracks are formed into a new set with the unconfirmed state tracks that were not paired in the second step. The set is matched with the detected bounding boxes that have not been paired in a Hungarian matching with a 1-IOU cost matrix.
* The paired tracks need to update the Kalman filter information based on the detection bounding boxes' information. 1. If the tracks are paired more than three times, they will change from unconfirmed to confirmed. Tracks lacking pairings will be removed from the set of unconfirmed tracks if the number of consecutive pairing failures exceeds the setting value. 2. Unpaired tracks will also be removed if they have not a successful pairing previously. 3. If there is an unpaired detection bounding box, a new target track will be created for it.
## 2 Problem Description and Design Considerations
In principle gravity can be derived through semantic analysis of an image, which is however not necessarily trivial and not always reliable. Obviously in most images \"north\" is not given. All motions are given in pixels per frame with the relationship between pixels and physical units depending on the camera position and settings. Overall the translation between the image frame of reference and pixel units and the sensor frame of reference and physical units is a non trivial challenge, even more in videos obtained from online repositories where cameras are not calibrated. Figure 2: Short depiction of the motions present in the 10 exercises of the Drill dataset, in which participants performed the exercises of [40]. The first examples are of one of our subjects, while the last are from the original YouTube video each subject tries to replicate. In this dataset, each exercise is repeated once for 30 s.  
In summary the generation of IMU sensor data from videos cannot be accomplished with an exact physical model. This is a fundamental difference to the well understood and largely solved problem of generating IMU values from 6 DOF trajectories. Instead a heuristic approach is needed that minimizes the unavoidable errors with respect to the needs of a given domain. Here a key question is whether we narrowly optimize the system to a specific domain or follow a more general domain agnostic approach. Other core design concerns are how much, at which stage in the process and in what form can physical models be included and what they can accomplish and which part of the task will be delegated to what type of data driven ML approaches. The most obvious choice is between (1) using existing ML methods for estimating 3D poses from 2D videos [23; 24; 25; 26; 27] and then using bio mechanical models [15] to generate the sensor data and (2) training an end-to-end ML model that goes directly from 2D video to sensor data. For the ML approach (this work), questions include how to accomplish the conversion between image frame of reference and pixel coordinates, how to handle the sensor frame of reference and its physical units and whether to explicitly filter, scale and normalize the signal.
# Assessing Table Tennis Technical Proficiency in Individuals with Disabilities: A Scoping Review
## 4 Discussion
### Table Tennis Technical Proficiency Tests
#### 4.1.2 Skill and Control  
The majority of the included studies (\(n=9\), 64%) incorporated test protocols to assess the table tennis-specific skills and ball control. We adopted the term \"skill and control\" proposed by [PERSON] et al. [21] in their study aiming to classify para-players with ID using table tennis-specific tests. Broadly speaking, this category reflects a player's ability to effectively return services and effectively execute table tennis strokes with a good control of ball spin and direction of travel. Participants may play against a human opponent [19; 20] or receive shots delivered by a table tennis robot [17; 18; 22; 23] while their movements were being assessed. Some studies, however, did not specify how the table tennis performance data were collected [21; 30].  
Many different types of table tennis strokes have been included in the skill assessment protocols, of which the forehand topspin and backhand topspin have been examined the most [18; 21; 22; 23]. Other skills such as contra, push, block, flick, smash, serve and attack, and high ball attack have also been tested. Some tests required players to return the ball to the opposite side of the table [18] or diagonally on the lower half of the table within a targeted zone of 0.80 m \(\times\) 0.76 m [22; 23], while others did not specify the landing area [21]. Previous studies using a robot to deliver shots typically used fast speeds (speed \(>\) 30 mph for push, flick, and smash) and high frequencies (34.5 to 43 balls per min) because the tested participants were well-trained competitive para-table tennis athletes [16; 17]. It is important to note that these test protocols and robot settings may not be suitable for players with a lower proficiency level. For beginners and recreational para-table tennis players, it is recommended to use a slower ball speed with fewer balls per minute. The difficulty of the tests should also be adjusted as lower-proficiency players may not be able to execute a wide variety of strokes and counter different types of spin.  
There exist different approaches to assess the effectiveness and/or quality of table tennis strokes. For instance, some studies developed a technical observation protocol to rate each stroke criterion as 1 (the criterion was clearly observable) or 0 (the criterion was not observable or incompletely executed) [17; 18; 20]. Another study classified each stroke as none, poor, reasonable, or good [21]. Others used biomechanical equipment to quantify the joint movement patterns and range of motion [22; 23]. Given that only limited studies have been published in this area, there is currently no consensus or standardised scoring methods to differentiate different levels of skills and control in para-table tennis players. In general, the current practice relies more heavily on expert ratings than quantitative measurements determined from biomechanical instrumentation.