esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Identification and Selection of BF Species  
Based on GenBank sequence match, the three strains were identified as follows. Strain CCFEE 5778 has been reported as _Saccotheciaceae_ sp. due to the ITS (OM390240) best match with _Saccothecium rubi_ MFLUCC 14-1171 (98.16%), and LSU (OM346745) match with _Selenophoma linicola_ CBS 468.88 (98.90%) and _Aurobasidium pullulans_ AFTOL-ID 912 (98.70%). Due to his very poor ITS match (OM568835) with _Diaporthe longispora_ CBS 194.36 (coverage below 40%) and 97.21% LSU (OM346746) identity with _Phaeotrichen benjaminii_ CBS 541.72, CCFEE 5945 was referred as \"Unknown dothideomycete\". Differently, CCFEE 6327 (OM390241) was identified as _Exophilala oligosperma_ due to its 99.83% identity with the species type strain CBS 725.88.  
The selected strains were in total 18, one for each species found, and among them were 4 _Knufia_, 3 _Needevreisia_, and 2 _Exophilala_ species (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline
**Species** & **CCFEE** & **Funerary Monument** \\ \hline _Aureobasidium pullulans_ & 5736 & Giuseppina Ara dei conti Ciarella \\ _Conisporium uncinatum_ & 5737 & Francesca Warzee \\ _Exophilala bonariae_ (T) & 5792 & Zelina Ferra Gastaldi Milleire \\ _Exophilala oligosperma_ & 6327 & Anna Maria Frau Carta \\ _Knufia karalitana_ & 6001 & Unknown tombstone \\ _Knufia marmoricola_ & 6204 & Anna Maria Frau Carta \\ _Knufia mediterranea_ & 5710 & Avv. Giuseppe Todde \\ _Knufia petricola_ & 5776 & Giuseppina Ara dei conti Ciarella \\ _Needevreisia buhillosa_ & 5704 & Ignazio Ruda Roych conte di San Lorenzo \\ _Needevreisia aupensis_ & 6200 & Anna Maria Frau Carta \\ _Needevreisia sardinine_ (T) & 6202 & Anna Maria Frau Carta \\ _Neophaetheca triangularis_ & 5703 & Ignazio Ruda Roych conte di San Lorenzo \\ _Saccotheciaceae_**sp.** & 5778 & Zelina Ferra Gastaldi Milleire \\ _Salinomyces thailandicus_* & 5723 & Antonio Viganigo \\ _Saxophylla tyrrhenica_ (T) & 5935 & Unknown burial \\ **Unknown dothideomycete** & 5945 & Rossino Bolla’s spouses \\ _Vermiconidia calcicola_ (T) & 5770 & Giuseppina Ara dei conti Ciarella \\ _Vermocoladosporium dirime_ & 5707 & Ignazio Ruda Roych conte di San Lorenzo \\ \hline \end{tabular}  
* this new combination has been proposed by Czachura and colleagues as synonym of _Hortana thailandica_[40].  
\end{table}
Table 1: The 18 strains in study, collection number (CCFEE), and monuments of isolation. (T) indicates type strain. In bold the new identified strains. All monuments were built in white Carrarese marble.
# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 3 Results
### Predict Readmission  
One study explored the ability of NLP to predict readmission for patients. [PERSON] et al. used an NLP algorithm on free text discharge summaries, operative notes and multidisciplinary progress notes from the medical record to estimate the risk of readmission within 90 days post lumbar spine fusion [19]. The area under the receiver-operating curve was 0.70, 0.57, 0.57, 0.60, 0.60 and 0.49, respectively. Discharge summaries were most useful at estimating risk, whilst daily progress notes provided little benefit.
## 2 Materials and Methods
### Batch Adsorption Tests  
The batch equilibrium experiments were conducted in a temperature-controlled (30-80 \({}^{\circ}\)C or 303.15-353.15 K) environment, either on an orbital shaker (INNOVA 2100, New Brunswick Scientific, Bordentown, NJ, USA) at a speed of 180 rpm or a shaking thermostatic water bath (GFL1083, GFL, Burgwedel, Germany) as per [PERSON] et al. [13]. The model fermentation broth was prepared from an analytical grade 88% L-(+)lactic acid stock solution (Sigma-aldrich Cheme BV, Zwijndrecht, The Netherlands) and demineralized water as described by [PERSON] et al. [17]. The initial pH (2.0-6.5) of the bulk solution was adjusted by either adding 6.0 M NaOH or 6.0 M HCl. 25.0 mL serum bottles with screw cap containing 5.0 mL of model fermentation broth (4.5 g/L lactic acid) and 0.5 g adsorbent (or 10% \(w/v\)) were used for all the batch adsorption studies unless stated otherwise. The adsorbent GAC and RLX425 were studied at pH 2.0, whereas AMB400 was studied at pH 5.0 based on pre-screening experiments and a literature survey [13]. The model fermentation broth was allowed to equilibrate for 4 h on an orbital shaker and maintained at 303.15 K. All the batch adsorption experiments were performed in duplicate by employing individual serum bottles for each data point.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 3 Problem Analysis  
This section presents a time slice division principle based on the number of satellites per orbit, followed by two fixed-link combinations. Finally, a dynamic non-adjacent orbital plane link (NOPL) selection strategy is proposed, which establishes dynamic links based on the regularity of inter-satellite distances without exceeding four connections per satellite, ensuring that the link length is shorter than the shortest adjacent orbit link length. By calculating the dynamic satellite connectivity in the first two time slots, the dynamic links for the remaining time slices can be determined. A dynamic link selection algorithm is designed with the combined time slices and dynamic link selection strategy, resulting in two dynamic topologies.
## 4 LNSSCL Algorithm
### Time Complexity Analysis  
For a complex network \(G=(V,E)\), assuming the average degree of nodes is \(\overline{d}\), the number of nodes is \(n\), the number of edges is \(m\), and the common neighbors between two nodes is \(c\). The determination of particle starting positions involves calculating node degrees and the Salton similarity index between nodes, with a time complexity of \(O(m)\)Active particles wandering in the network require calculating the probability for each particle to move from the current node to neighboring nodes. The random walk probability for each particle only requires computing node degrees, while the preferential walk probability needs to calculate the similarity index between the current node and its neighbors, with a time complexity of \(O\left(Kc\overline{d}\right)\). When a particle's energy is exhausted, the revival step involves maintaining a hash table to store each particle's control nodes and their corresponding control capability values. Finding the node with the maximum control capability for jumping has a time complexity of \(O(1)\). Updating the particle control matrix has a time complexity of \(O\left(K^{2}\right)\). Since each node in the network is visited at least once by a particle, the total time complexity of particle wandering is \(O\left(Kc\overline{d}n+K^{2}n\right)\). To determine the optimal number of particles, the algorithm needs to gradually change the particle count from 2 to \(K^{\prime}\), where \(K^{\prime}\) is a constant slightly larger than the actual number of communities in the network. Therefore, the entire particle wandering process of the LNSSCL algorithm has a time complexity of \(O\left(K^{2}c\overline{d}n+K^{3}n\right)\). After the particle wandering process concludes, assigning community labels to all nodes based on their control capability values requires a time complexity of \(O(n)\). The node membership selection step involves each node selecting its community label based on the frequency of community labels among its neighboring nodes, with a time complexity of \(O\left(\left(\overline{d}+c\overline{d}^{2}\right)n\right)\). Since complex networks are usually sparse networks, \(\overline{d}\ll n\). In summary, the time complexity of the LNSSCL algorithm is \(O\left(m+K^{2}c\overline{d}n+K^{3}n+\left(\overline{d}+c\overline{d}^{2} \right)n\right)\approx O(M(m+n))\), where \(M\) is a constant. The time complexity of the LNSSCL algorithm is linearly related to the sum of the number of nodes and edges in the network, making the algorithm highly scalable on large-scale networks.
# Reduction of the Cavitation Noise in an Automotive Heater Core
## 2 Measurement and Analysis of Noise and Vibration from the Heater Core  
The engine cooling system in an automobile consists of key components, including the water pump, radiator, heater core, and connecting hoses, as shown in Figure 1. The red arrows in the figure indicate the direction of coolant flow. This system functions to regulate engine temperature by circulating coolant through the engine block, radiator, and associated components, thereby dissipating excess heat to prevent overheating and ensure optimal performance.  
Figure 1: Cooling system of an automobile (the red arrows indicate the flow direction of the coolant).  
Within the heating, ventilation, and air conditioning (HVAC) system, the heater core plays a crucial role as a heat exchanger that utilizes hot engine coolant to warm the air entering the cabin. The HVAC system is responsible for controlling airflow, temperature, and humidity, with the heater core specifically supplying heated air in low-temperature conditions. A blower fan directs air over the heater core, facilitating heat transfer from the coolant to the air, which is subsequently distributed through the cabin vents.  
As illustrated in Figure 2, the heater core consists of a compact heat exchanger composed of upper and lower tanks, thin metal fins, and inlet/outlet pipes. These components are typically constructed from aluminum or copper to enhance heat transfer efficiency. Hot coolant enters the lower tank via the inlet pipe, flows through the fins, and exits into the upper tank before returning to the water pump. Air passing over the fins absorbs heat from the coolant, enabling effective thermal management while maintaining a lightweight and compact design that seamlessly integrates into the HVAC system.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 2 Experiments
### Emulsion Flooding Experiment
#### 2.2.2 Experimental Apparatus  
Figure 1 shows the double-tube core flooding experimental device, consisting of four parts.  
1. **Injection system.** The system consisted of three piston containers and ISCO pumps containing heavy oil samples, formation water, and emulsion. The injection rate was stable.
2. **Pipeline and reservoir simulation system.** The system was composed of two core holders and an oven. The cores of the studied oil field were selected to simulate the actual reservoir. The oven could simulate the actual reservoir temperature, and the specifications of the core holder were 25 mm \(\times\) 300 mm.
3. **Product collection and calculation system.** The system used beakers to collect the produced liquid and used the weighing method to measure.
4. **Temperature and pressure control system.** The system consisted of a gas cylinder, an oven, a back pressure valve, a pressure reducing valve, two pressure sensors, and two pressure gauges.
## 5 Limitations  
This study was conducted under certain limitations, and its findings should be interpreted in a way that takes into account these limitations. First of all, there is the language limitation (included studies only in the English language), which might have led to possibly excluding studies that were written in a language other than English, and in that way, relevant published articles might have been unintentionally excluded from the current review.  
Secondly, it is important to mention that the followed procedure and search strategy, although it used a substantial number of keywords and many of the most known electronic libraries, may have failed to identify any relevant studies that may have not met the search criteria. Moreover, relevant articles published in conferences may have been missed by accident, especially if, by the time of search procedure, they were not available online. However, the search strategy can be considered adequate, since it included seven different electronic libraries, and this should have minimized the danger of missing relevant studies. In addition, we used reference lists from the included studies and any other systematic literature reviews that were also found during the search process, strengthening the search results.  
Third, there was missing information especially in the descriptions of the games (or GEs) which were used in the included studies, and any omitted details regarding the computer-based interventions overall, was another significant limitation. Although there was a supplementary search in every available source online in order to minimize the risk of missing data, sometimes it was impossible to define the included GEs with clarity. Thus, there were cases with subjective judgment in the data extraction if there was any strong supported evidence, and otherwise, the data were considered not available and were documented as such.  
Fourth, the primary objective of this study was to identify the most common GEs that have been used in WM training or assessment, having a measured impact in WM enhancement and/or in the engagement and enjoyment of users. Most of the included studies presented WM outcomes exclusively, and less than half (N = 21) reported engagement and enjoyment outcomes in addition. Four contained only engagement metrics without WM outcomes, which are critical to the current research. Unfortunately, the primary focus for the majority of studies was to measure the effects of gamification in WM outcomes; thus, it is important that future studies focus more on measuring engagement, adherence and enjoyment outcomes.  
Finally, the included studies did not attempt to examine the efficacy of individual GEs or to conduct any comparison among them, as they usually focused on gamification as a whole, combining more than one GE each time and making it difficult to distinguish any effects of individual elements as outcomes.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 5 Numerical Results  
We now test the results of Theorem 2 numerically. Further, we compare the performance of the DWR method and the local error based adaptive methods. The tests were run on an Intel i5-2500K 3.30 GHz CPU with Python 3.6 using FEniCS v. 2018.1.0 [26]. See [27] for the code.  
The following specifications are common to all tests. For local error based methods using controllers, we bound the stepsize ratio by  
\[\Delta t_{n+1}=\Delta t_{n}\,\min(f_{\max},\max(f_{\min},\text{ind})),\quad\text{ ind}=\left(\tau/\tilde{\ell}_{n}\right)^{1/(\hat{p}+1)}. \tag{32}\]  
Here, \(\hat{\ell}=\|\hat{\ell}_{n}\|\) for Equation (10) resp. \(\tilde{\ell}_{n}=|\hat{\ell}_{n}^{\tilde{\mathcal{J}}_{n}}|+|\hat{\ell}_{n}^{ \tilde{\mathcal{J}}_{0}}|\) for Equation (26). This provides more computational stability by preventing too large timestep changes ([11], p.168). We use \(f_{\max}=3\) and \(f_{\min}=0.01\) and do not reject timesteps. Unless specified otherwise, we use \(\Delta t_{0}=\tau^{1/(\hat{p}+1)}\).  
For the DWR method, we use an initial grid with 10 equidistant timesteps. As a refinement strategy, we use fixed-rate refinement [20], refining those 80% with the largest errors. We consider no coarsening. Refinement means to replace a timestep by two steps of half the size. To approximate \(\mathbf{z}\approx\mathbf{z}_{h}^{+}\), we use a finer grid, dividing all time-intervals in half. While this is expensive, it is also highly accurate. We only consider linear problems and thus do not need to compute \(\mathbf{u}_{h}^{+}\) to construct the adjoint problem for the finer resolution. Thus, for this refinement strategy, the costs are significantly higher in the nonlinear case. In the nonlinear case, the dual problem is still linear.  
We refer to the adaptive method from Section 3 as \"Classical\" and denote it by \"Norm\" in figures. For the goal oriented case in Equation (26), we consider the controllers using only quadrature or time-integration estimates, or both; they are denoted by \"Goal Q\", \"Goal T\" and \"Goal T+Q\" in figures. Using the DWR method we get both the error estimate \(\eta(\mathbf{u}_{h})\), which we denote by \"DWR Est\" and the actual error \"DWR Err\", based on a reference solution.  
Unless stated otherwise, we use the trapezoidal rule for quadrature. We verified the correctness of all associated solvers using suitable test problems with exact solutions and visualizations.
## 3 System Model
### VAE Encoder  
To enhance the stability and efficiency of policy learning in complex urban environments, we adopt a VAE-based encoder for the visual perception component of the agent's state vector. In contrast to conventional CNN-based encoders, which tend to produce high-dimensional and semantically opaque latent representations, the VAE compresses high-resolution visual data into a structured low-dimensional latent space. Each latent variable is trained to follow a standard Gaussian distribution, enabling the representation to retain semantically meaningful features such as lane count and object density. This not only facilitates better interpretability of the latent space but also acts as a form of regularization by enforcing smoothness and disentanglement across the latent dimensions. Prior studies [75,76] have empirically demonstrated that under the same reinforcement learning algorithm, agents utilizing VAE-based encoders outperform their CNN counterparts in terms of average rewards and driving distance, while also reducing GPU usage and improving inference speed. These findings provide strong evidence that the VAE serves as an effective representation learner, offering superior capacity, regularization benefits, and interpretability compared to alternative encoder architectures.
## 3 Results and Discussion
### Characterization of Chrysin-Loaded Microemulsion
#### 3.2.2 Physicochemical Parameters and Polarized Light Microscopy  
Physicochemical parameters such as electrical conductivity, pH, refractive index, and surface tension were determined to characterize CS-ME. Since the conductivity of oil-in-water (O/W) NEs, having water as a continuous phase, is much higher than that of water-in-oil (W/O) NEs, having oil as a continuous phase [35], the very high conductivity of CS-ME (24.66 \(\pm\) 0.25 uS/cm) suggests that it was an O/W NE system. The mean pH value (4.48 \(\pm\) 0.10) was within the biologically accepted range for oral administration (2-10), and the refractive index (1.3894 \(\pm\) 0.0001) showed no statistically significant dif  
Figure 1: Ternary phase diagrams formed by a mixture of isopropyl myristate (IPM) as the oil phase, caprylocaproyl polyoxyl-8 glycerides (LAS) as the surfactant, and water as the aqueous phase. The gray area represents the microemulsion system region, and the red dot within this area the selected formulation.  
ference after several repeated measurements. Moreover, the low surface tension value (40.53 \(\pm\) 1.8 dynes/cm), which can help increase chrysin bioavailability by improving its permeation through the gastrointestinal hydration layer [4], could have been mainly due to the high concentration of non-ionic surfactants used to prepare the microemulsion, which are well known to reduce this physicochemical property at the oil-water interface [36; 37]. Finally, when CS-ME was examined with a polarized light microscope, it appeared as a completely dark field (data not shown). These results, together with those of the refractive index, revealed the optically isotropic nature of CS-ME, which is typical of NE systems [34].
## 4 Multi-Objective Area Coverage Optimization Problem
### Coverage Situation Verification  
In order to calculate coverage ratio and redundancy ratio, we conduct grid partition in the monitoring area. As shown in Figure 3a, the area can be regarded as a collection of points when the grid is small enough. Let the monitoring area be a \(m_{1}\times m_{2}\) rectangle area, and \(P\left(x,y\right),0\leq x\leq m_{1},0\leq y\leq m_{2}\) is a point in the area. We define 2-dimensional arrays \(C\) and \(R\) to represent coverage and redundancy situations of each point. We justify their coverage and redundancy situations according to Equations (1) and (2). If the point can be covered by sensor nodes, \(C\left[x\right]\left[y\right]=1\), otherwise \(C\left[x\right]\left[y\right]=0\). If the point can be covered by at least two sensor nodes, \(R\left[x\right]\left[y\right]=1\), otherwise \(R\left[x\right]\left[y\right]=0\).  
Due to the fixed position and limited sensing range of directional sensor node, we only need to justify the coverage and redundancy situations of the grid points in the rectangle area in the Figure 3b in order to reduce calculation complexity, and the four peak points are defined as follows.  
\[x_{sl}=\max\left\{x_{s}-R,0\right\} \tag{6}\]  
\[x_{sl}=\min\left\{m_{1},x_{s}+R\right\} \tag{7}\]  
\[y_{sl}=\max\left\{y_{s}-R,0\right\} \tag{8}\]  
\[y_{sh}=\min\left\{y_{s}+R,m_{2}\right\} \tag{9}\]  
In Algorithm 1, the four peak coordinates are determined according to the coordinate of sensor node \(\left(x_{s},y_{s}\right)\) and sensing radius \(R\). The algorithm justifies the coverage and redundancy situations of each point  
Figure 3: Coverage situations variation of monitoring area. (**a**) Monitoring area gridding; (**b**) Sensing range of sensor node.  
in the rectangle area, and the values of arrays \(C\) and \(R\) are determined. The circle number of the algorithm is \(n\), and the time complexity is only \(o\left(R^{2}\right)\) when the coverage situation of each point in the rectangle area is justified.  
```
1:Sensor nodes group: \(s=\left\{s_{1},s_{2},...,s_{n}\right\}\)
2:Sensing direction and sensing radius of sensor node: \(\overrightarrow{D},R\)
3:The length and width of target area: \(m_{1},m_{2}\)
4:The coverage situations of grid points in target area
5:Calculate the coordinates of four peak points \(x_{sl},x_{sh},y_{sl},y_{sh}\)
6:for\(i=1\) to \(n\)do
7:for\(x=x_{sl}\) to \(x_{sh}\)do
8:for\(y=y_{sl}\) to \(y_{sh}\)do
9:if\(P\left(x,y\right)\) can be covered by \(S\left(x_{s},y_{s}\right)\)then
10:\(C\left[x\right]\left[y\right]=1\);%justify whether \(P\left(x,y\right)\) is covered.
11:endif
12:if\(P\left(x,y\right)\) can be covered by at least two sensor nodes. then
13:\(R\left[x\right]\left[y\right]=1\);%justify whether \(P\left(x,y\right)\) is covered by at least two sensor nodes.
14:endif
15:endfor
16:endfor
17:endfor
```  
**Algorithm 1**Coverage verification algorithm based on the sensing area of sensor node
## 5 Rebars' Detection Methodology and Results
### Binary Classification Model
The window was successively shifted one pixel at a time along the horizontal axis until the right edge of the image was reached, as depicted in Figure 16. This process yielded a continuous probability profile across the image, representing the likelihood of rebar presence at each horizontal location. Notably, variations in this trajectory, and in particular its local maxima, were found to correlate strongly with the actual positions of reinforcement bars, as verified against reference data. In order to eliminate transient noise and improve robustness, the resulting probability signal was then processed using a median filter. Subsequently, peak detection was performed with a defined tolerance margin to identify the most probable rebar locations. Given that each image fragment used for classification spanned 8 pixels in width, a tolerance of 16 pixels was adopted during the evaluation stage to account for possible localization shifts and to assess detection accuracy. Figure 14: Training process visualization: (**a**) loss function curve and (**b**) classification accuracy over epochs. Figure 15: Confusion matrix for the test data.