esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### The Effluent pH, HRT and OLR during the AD Process  
The pH of the reactor during the operation is shown in Figure 3. From day 0 to day 27, it varied in the range of 7.8-7.5. However, a sudden drop was seen in the pH from day 28, from where it kept on decreasing and was lowest at 6.9 on day 98. Owing to the sudden drop on day 28, the HRT and OLR were changed due to the risk of the stability of AD getting affected. The HRT and OLR were changed to 50 days and 0.5 g-VS L\({}^{-1}\) day\({}^{-1}\) on operational day 42 to provide sufficient resting time for easy degradation of the AD microorganisms inside the reactor. Consequently, the HRT and OLR were further changed to 40 days and 0.7 g-VS L\({}^{-1}\) day\({}^{-1}\) on operational day 49 until the last operational day.  
Because, the stability of the reactor was getting affected, which could lead to the failure of the AD process, the pH of the liquid fraction of \(L\). _grandiflora_ was neutralised to pH 7 by adding 27.66 g of 1 M NaOH solution from day 67 until the last day of operation (day 98). As NaOH has the potential to buffer the pH for the AD process [54]. This neutralisation of pH was performed to maintain the effectiveness of the AD and keep the methanogenesis bacteria alive inside the reactor. As hydrolysis, acidogenesis, acetogenesis  
Figure 3: Relationship of the pH with HRT and OLR during the AD process.  
and methanogenesis processes are optimum in pH around 7. A similar strategy was applied to improve the pH inside the reactor when using food waste in the CSTR reactor [55] and tofu wastewater in the batch type reactor [56]. The overall pH of the reactor was fluctuating but was in the optimum range of 6.5-8.2 for methanogenesis during the operation. This proved that the effluent had sufficient buffer to keep the pH in the optimum range [57]. However, NaOH addition did not show any increase in the pH of the reactor.
# Beyond Presence: Exploring Empathy within the Metaverse
## 5 Discussion
### Limitations of Empathy in the Metaverse  
While the metaverse holds significant promise for fostering empathy, several limitations must be considered. First, there are technological limitations--such as the current challenges in fully integrating multi-sensory feedback systems, including touch and smell--which may limit the immersive potential needed to evoke deep empathetic responses. Furthermore, the absence of robust longitudinal studies hinders our understanding of how sustained exposure to empathy-eliciting virtual scenarios affects users over time.  
From a psychological perspective, the risk of empathy fatigue or desensitization is an important limitation. Repeated exposure to emotionally charged virtual scenarios might diminish users' ability to maintain empathetic responses, leading to emotional detachment. Additionally, the inherent limitation of virtual environments to fully replicate real-world complexities restricts the depth of empathy that can be fostered. Addressing these challenges will be crucial for the metaverse to fully realize its potential as an empathy-enhancing tool.
## 3 Case Study  
### Necessity and Data Preparation for Optimization  
The landing gear can realize the take-off and landing function of aircraft. It is a typical system to ensure the flight safety of aircraft. The landing gear system of a civil aircraft contains 61 kinds of functional products that are confirmed by option selection. According to statistics, the proportion of unplanned events in the landing gear system is the largest since a civil aircraft has been in operation for 5 years, accounting for 17%, as shown in Figure 3.  
The landing gear product option selection configuration (the MTBF of the 61 products) determined by reliability constraints before optimization is shown in Figure 4. As shown on the right, 28% of the products are inconsistent with the design stage in actual operation, due to the use of environmental and maintenance methods. In such a state of operation, the AFMC of landing gear system has reached 510.5 USD/FH. Unplanned maintenance events have so far been inevitable, so a certain optimization model is reasonably constructed to make the policy on product option selection achieve the purpose of minimizing maintenance measures and reducing the AFMC.  
Figure 4: Product life information before optimization. (**a**) Product life; (**b**) Comparison of design and operating life.  
Figure 3: Proportion of fault maintenance records in typical systems. (ATA: Air transport association). (**a**) Histogram of percentages; (**b**) Pie chart of percentages.  
The different maintenance costs of the landing gear system are further shown in Figure 5. The comparison results clearly show that in the maintenance cost of aircraft fleet, the maintenance tasks of unplanned classes tend to be much higher than planned costs. So, the model also focuses on finding a balance between planned and unplanned costs, serving the AFMC in annual flight hours.  
All the parameters needed to build the model are shown in Table 1. Each product should be analyzed through customer demand, market demand and historical operation data to obtain the reliability index as the constraint condition, which is also the constraint condition of the model selection. In addition, it is necessary to introduce the QPA, work hours, rates, price, MTBF before optimization, MSPT of product and the unit frequency of unplanned repair or maintenance. Because of the limitation of space, this article only shows some data.
## 5 Case Study: An AHP/TOPSIS-Based Optimal Gas Station Site Selection
### Dataset Analysis and Prediction Criteria
#### 5.1.3. FourSquare Check-Ins Dataset and the Area-Popularity Criterion  
The area-popularity criterion can be easily found with a location-aware social networking application. Here, FourSquare is exploited to gather user check-in details at various business attractions to find out their popularity. The FourSquare dataset for this research work consists of check-in information within New York City from 12 April 2012 to 16 February 2013 (approx. 10 months). A total of 227,428 check-ins in New York City are contained in this data set and are shown in Figure 4. There is a time stamp, GPS coordinates, and some semantic meaning (expressed with the fine-grained venue-categories) connected to every check-in [42]. The area _popularity_ factor is computed with the help of this check-in dataset.  
Another significant factor for picking out a suitable location to open a new gas station is area popularity. The potential number of customers can be estimated with this criteria. A distance function, similar to that given in Equation (1), is used for finding the area popularity. Here, \(C\) represents the set of all check-ins in the dataset and \(c\in C\) denotes an individual check-in instance at some geographical location. Hence, with the help of Equation (3), the total number of check-ins of each candidate location \(l\in L\) within the radius \(r_{c}\) are estimated.  
\[|\{c\in C:dist(c,l)<r_{c}\}| \tag{3}\]
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 3 Formation, Survival, and Regrowth  
Persistence, as mentioned, is a physiological state in which small subpopulations of bacteria transiently assume a pronounced non-heritable stress-resilient phenotype. Although a universal definition is lacking, persisters are often described as transiently antibiotic-tolerant phenotypic variants that allow a population to survive antibiotic exposure. Although the physiological mechanisms conferring that increasing fitness can be very complex, one of the main features of persisters is a low metabolic state that may arise due to environmental triggers [13], which does not mean that persisters are completely metabolically inactive. Moreover, persister cells have been identified in almost every bacterial species examined, at levels covering several orders of magnitude, typically between 0.001% and 1% [13]. These have been identified in both Gram-positive and Gram-negative bacteria, as well as in eukaryotes such as yeast, where they exhibit tolerance to antifungal agents [13]. Persistence represents a formidable obstacle to antibiotic treatment since these molecules normally require strongly metabolically active cells to exert their antibacterial effects [14].  
However, there is no scientific consensus on the mechanisms driving the formation of persister cells. To date, persisters are broadly classified into three types: type I, type II and type III based on their formation mechanisms. Type I persisters appear in the stationary phase in response to environmental triggers [1; 2; 3; 4; 5; 6; 7], while type II persisters are stochastically generated throughout the exponential phase [8; 9; 10; 11; 14; 15]. A potential third class of persisters, type III persisters, referred to by [PERSON] et al. [16] as \"specialized persisters,\" has also been observed. Type III persisters are not slow-growing prior to antibiotic exposure and often exhibit persistence mechanisms specific to particular antibiotics, as described by [PERSON] et al. [17], as well as [PERSON] and [PERSON] et al. [18]. Therefore, understanding these mechanisms is essential for devising effective strategies to eliminate persisters and enhance food safety.
## 6 Conclusions  
In this paper, a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm were used to monitor the woodworking tool wear conditions in milling processing. At present, the monitoring level of the woodworking tools wear conditions is still low, and advanced woodworking tool wear monitoring technologies urgently need to be studied to improve the cutting performance of woodworking tools and promote the development of intelligent furniture manufacturing technology. The proposed limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The BP neural network optimized by PSO was used to establish a monitoring model for the woodworking tool wear conditions. Compared with the genetic algorithm (Table 9), the optimization effect of PSO on the BP neural network is obviously better than the genetic algorithm. PSO can quickly optimize the weights and thresholds of the BP neural network and reduce the training time of the BP neural network. The error of the proposed monitoring model established by the PSO-BP neural network algorithm is 9.48%, and the training time is 8.59 s. Therefore, the proposed method can accurately and quickly monitor the woodworking tool wear conditions under different milling parameters.  
The proposed method speeds up the process of intelligent furniture manufacturing and reduces the cost of woodworking tool wear condition monitoring technology. However, the delay effect of the power sensor is not considered, and the hysteresis of the spindle power signal is ignored. In future research, in order to further improve the accuracy of the woodworking tool wear state monitoring model, the question of how to eliminate the hysteresis of the power signal will become the focus.  
Conceptualization, W.D. and X.X.; methodology, W.D. and X.X.; writing--original draft preparation, W.D. and X.Y.; writing--review and editing: W.D. and Y.M.; supervision, X.X.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2018 YFD0600304), Postgraduate Education Reform Project of Jiangsu Province (SJCX20_0267) and Technology Innovation Alliance of Wood/Bamboo Industry (TIAWBI202010).  
Not applicable.  
This research was supported by the College of Furnishings and Industrial Design of Nanjing Forestry University.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & **PSO-BP Neural Network** & **GA-BP Neural Network** \\ \hline Average Error Rate & 9.48\% & 10.81\% \\ Training Time & 8.59 s & 91.53 s \\ Evolutionary Algebra & 8 & NO \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis of algorithm performance.
## 3 Materials and Methods
### Direct Binding Assay
#### 3.2.1 Microscale Thermophoresis (MST)  
The Monolith NT.115 pico (Nano-Temper Technologies GmbH, Munich, Germany) instrument was used for MST studies. His-HA was labeled using the Nanotemper His-Tag Labeling Kit RED-tris-NTA 2 nd Generation as described elsewhere [47]. His-HA protein (100 \(\upmu\)L, 80 nM) in ddH\({}_{2}\)O was added to NT647-NHS fluorophore (100 \(\upmu\)L, 40 nM, NanoTemper Technologies GmbH, Munich, Germany) in labeling buffer and incubated for 30 min at rt. Protein aggregates were removed from NT647-HA by centrifuging it at 15,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. Pretests using standard-treated and premium-coated MST capillaries (NanoTemper Technologies GmbH, Munich, Germany) were performed as reported elsewhere. [46] In assay buffer (PBS), the protein did not adsorb to standard-treated capillary walls. Therefore, the following experiments were carried out on standard-treated capillaries. In order to determine the best state for MST signal repeatability and the suppression of unspecific adsorption to capillary walls, buffer conditions were then assessed. The assay buffer was used to dilute compound stocks (5 mM) in MST buffer to the greatest solubility concentration (50 \(\upmu\)M). In MST tests, 16-fold 1:1 serial dilutions of each compound were added to NT647-HA (20 \(\upmu\)L). The reaction solutions were loaded into standard-treated capillaries after 10 min of incubation at rt, and they were subsequently placed in the chip trap of Monolith NT.115 for thermophoresis analysis and the appraisal of K\({}_{\text{D}}\) values. Signals were captured using 10% LED power and high MST power. K\({}_{\text{D}}\) values were calculated from compound concentration-dependent changes in normalized fluorescence (F\({}_{\text{norm}}\)) of HA after 21 s of thermophoresis. Each compound was tested in triplicate. Data examined using MO Affinity Analysis software (v2.3, NanoTemper Technologies GmbH, Munich, Germany). Confidence values (\(\pm\)) were indicated next to the K\({}_{\text{D}}\) value for each tested compound.
## 4 Dataset
### Network Model Evaluation  
Starting from time cost, accuracy, and classification accuracy, this section compares and evaluates the lightweight network model proposed in this paper with ResNet and Mo  
Figure 16: Continuous frame effect of daytime real car experiment.  
Figure 17: Continuous frame effect of nighttime real car experiment.  
bileNet. The time cost is based on the time required to process a single batch (milliseconds), and the time required to process a single iteration (seconds). The accuracy is the validation set accuracy, and the classification accuracy is the Mean Intersection over Union (MIoU) ratio. The validation set accuracy is calculated as the ratio of the total number of correctly classified pixel points \(p\) in a single data frame to the total number of pixel points \(N\) in the data frame:  
\[ValAcc=\frac{\sum p}{N} \tag{3}\]  
The task of dividing the drivable area in this paper is a binary classification problem, and the target is the drivable area and the background class. Therefore, it is simplified here, and only the mean intersection over union ratio of the drivable area is calculated. The calculation form is as follows:  
\[MIoU=\frac{\sum p}{\sum FN+\sum FP+\sum P} \tag{4}\]  
where \(p\) is the pixel point of the drivable area that is correctly classified, \(FN\) is the pixel point whose true value is the drivable area but is misclassified to the background, and \(FP\) is the pixel point whose true value is the background but is misclassified to the drivable area.  
Model comparison training was performed in the Ubuntu environment, and the results are given in Table 3. The training data include 7000 random data frames, and the verification data frames are 1000. The ResNet50 network model selects a batch of 4, and the remaining two models are batched to 8. The iteration limit is 100 times, and the weight is saved once every 25 times. It is shown that compared with the other two networks, the time cost of the network structure in this paper is lower, and the accuracy and the mean intersection over union ratio have certain advantages. Compared with the typical deep network structure of ResNet50, the network structure of this paper still ensures a certain accuracy while the time cost is greatly reduced, which can better save system resources. Meanwhile, compared to the lightweight network MobileNetV2 [33; 34], the network structure proposed in this paper also improves time consumption and accuracy.
## 4 Performance with Phase Noise
### Result Analysis
This is expected behavior, considering both I and Q channels carry information, and cross-interference of the two channels due to phase noise in the mixers degrades the system's performance. The results for FSK are similar, yet this time the performance of the system is affected when the phase noise is in the positive values, meaning that the noise power level is higher than the carrier's power. The results for the QCPSK communication system also show a change in performance with an increase of the phase noise power in the positive values because, similarly to the 4-QAM communication system, the QCPSK system has I and Q channels which are carrying signals. The results for the FM-CSK communication system show a change in performance with an increase of the phase noise power in the positive values. Still, the system's performance changes rapidly when the phase noise level passes a certain threshold, which is typical behavior for systems based on analog FM.
## 3 Experimental Results
### Analysis and Comparison of Experimental Results  
Table 3 summarizes the yield strength \(V_{y}\), maximum strengths \(V_{peak}\), displacement at the yield strength \(\Delta_{y}\), displacement at the maximum strength \(\Delta_{peak}\), ultimate displacement \(\Delta_{u}\) (displacement at \(0.8V_{peak}\) after the lateral load reaches \(V_{peak}\)), elastic stiffness (the stiffness when the lateral load reaches \(0.4V_{peak}\) in the load-drift ratio relationship) \(K_{e}\) and ductility \(\mu\) (\(=\Delta_{u}/\Delta_{y}\)) of the specimens. The yield strength, \(V_{y}\), was determined using the equation  
\[V_{y}=\left(\Delta_{u}-\sqrt{\Delta_{u}^{2}-\frac{2A}{K_{e}}}\right)K_{e} \tag{3}\]  
as suggested by ASTM E2126-19. For \(\Delta_{u}<2A/K_{e}\), it was assumed that \(V_{y}=0.85V_{peak}\).  
Figure 8: Definition of key parameters according to the American Society for Testing and Materials (ASTM) E2126-19. The energy equivalent elastic–plastic (EEEP) curve is the energy equivalent elastic–plastic curve.  
Figure 7: Failure modes of specimens: (**a**) Screw pull-out of the steel-sheathed panel (SSP) specimen, (**b**) Local buckling and tearing of the integrated panel-inner stud (IP) specimen, (**c**) Local buckling and tearing of the integrated panel-inner stud (IP-IS) specimen, (**d**) Local buckling of the integrated panel-outer stud (IP-OS) specimen.
## 2 Principles
### Synchrosqueezing Generalized S-transform (SSGST)
\[\int_{0}^{\infty}GST_{x}(f,b)\exp(i2\pi f_{0}fb)f^{-1}df=\frac{1}{2}\int_{0}^ {\infty}\overline{\hat{\psi}(\xi)}\xi^{-1}d\xi\cdot\frac{1}{\pi}\int_{0}^{ \infty}\hat{x}(\omega)\exp(ib\omega)d\omega \tag{19}\]  
Let \(C_{\psi}=\frac{1}{2}\int_{0}^{\infty}\overline{\hat{\psi}(\xi)}\xi^{-1}d\xi\), (19) can be rewritten as,  
\[\frac{1}{\pi}\int_{0}^{\infty}\hat{x}(\omega)\exp(ib\omega)d\omega=C_{\psi}^{ -1}\int_{0}^{\infty}GST_{x}(f,b)\exp(i2\pi f_{0}fb)f^{-1}df \tag{20}\]  
Because the signal \(x(t)\) is the real signal, we extract the real component \(x(b)\) from the Equation (20) as the following reconstructed signal:  
\[x(b)=\frac{1}{\pi}\text{Re}\bigg{[}\int_{0}^{\infty}\hat{x}(\omega)\exp(ib \omega)d\omega\bigg{]}=\text{Re}\bigg{[}C_{\psi}^{-1}\int_{0}^{\infty}GST_{x} (f,b)\exp(i2\pi f_{0}fb)f^{-1}df\bigg{]} \tag{21}\]  
Through discretizing the equation on the right-hand side of Equation (21) and combining Equation (18), the inverse transform of SSGST is given as,  
\[x(b)=\text{Re}\bigg{[}C_{\psi}^{-1}\sum_{l}SSGST_{x}(f_{l},b)L_{f}\bigg{]} \tag{22}\]  
Therefore, we can recover the original signal from the SSGST spectrum by using the Equation (22).
## 1 Introduction
(**b**) Analysis extents capturing Bishkek’s designated districts (gray shading) based on the spatial availability of each data source shown by colored polygons (colored lines for ICESat-2). The hashed blue polygon shows the intersecting WorldView-2 and Pleiades extents. In this study, we develop methodologies to quantify urban evolution using satellite data from 1979-2021. Our objectives are to: (1) quantify urban growth and redevelopment 1979-2021 by developing a methodology to classify built-up area in a 1979 declassified KH-9 Hexagon satellite image; (2) use a deep learning methodology and sub-meter resolution satellite imagery to extract and accurately assess building footprints for three time periods (2013, 2019, and 2021); (3) accurately assess stereo and tri-stereo-derived DSMs and use them to assign building heights; and (4) evaluate the methodological capabilities and limitations.
## 3 System Architecture and Methodology
### Implementation Methodology
#### 3.2.1 Custom Refinement Algorithm for Synonym Selection  
The synonym refinement algorithm employs a two-stage process that evaluates and filters potential word substitutions based on semantic compatibility and contextual fit. The algorithm executes the following steps:  
1. Initial synonym generation using GPT-4-omni's semantic understanding: - For each word in the container text, request N synonym candidates (where N = 2 bits_per_word) - Apply temperature control (0.7) to balance creativity and contextual relevance
2. Semantic similarity evaluation: - For each candidate synonym, compute contextual embedding - Calculate cosine similarity between original word and synonym embeddings - Filter candidates using a dynamic threshold based on bits_per_word parameter The algorithm is formalized as follows: