esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model
### Mathematical Model
#### 3.3.1 Objective Functions
\[z_{1}=\Sigma_{e\in E}d^{e}\cdot\Sigma_{p\in P_{e}}s^{p}\cdot\Sigma_{t\in T^{p}_{e }}\Sigma_{r\in R^{r}_{e}},f^{p}_{e,t}\cdot x^{p,r}_{e,t} \tag{1}\]  
2 Transportation cost  
\[z_{2}=\Sigma_{e\in E}d^{e}\cdot\Sigma_{p\in P_{e}}\ \Sigma_{t\in T^{p}_{e}}\ \Sigma_{r\in R^{r}_{e}}c^{e,r}_{tr}\cdot f^{p}_{e,t}\cdot x^{p,r}_{e,t} \tag{2}\]  
3 Loading and unloading cost  
The loading and unloading of express cargo takes place at the stations at both ends of the OD and at the stations where transit connections are made. \[z_{3}=\ c_{l}\cdot\Sigma_{e\in E}\Sigma_{p\in P_{e}}\ \Sigma_{t\in T^{p}_{e}}\ \Sigma_{r\in R^{r}_{e}}\ \ I^{e,r}_{e,t}\cdot f^{p}_{e,t}\cdot x^{p,r}_{e,t} \tag{3}\]  
4 Transit costs  
The transit cost incurred by transit of express cargo in the transportation process, including the storage and handling costs incurred by transit. Where the total number of transits on the path \(r\) is less than or equal to the transit limit \(L\). \[z_{4}=c_{sto}\cdot\Sigma_{e\in E}\Sigma_{p\in P_{e}}\ \Sigma_{t\in T^{p}_{e}} \Sigma_{r\in R^{r}_{e}}\ st^{e,r}\cdot f^{p}_{e,t}\cdot x^{p,r}_{e,t} \tag{4}\]  
5 Delay penalty  
High-speed rail courier products are often expected to arrive early and timely. The shipping price and the time requirement of different categories are different, so the penalty cost of delay should be different. At the same time, considering the effect of different delay lengths, a stage function is set to express the different degrees of delay penalties, as shown in Equation (6). \[z_{5}=\ \Sigma_{e\in E}\Sigma_{p\in P_{e}}\ \Sigma_{t\in T^{p}_{e}}\ \Sigma_{r\in R^{r}_{e}}\ \tau_{e,t}^{p,r}\cdot\gamma^{p,r}_{e,t}\cdot f^{p}_{e,t}\cdot x^{p,r}_{e,t} \tag{5}\]  
\[\pi^{p,r}_{e,t}=\left\{ \begin{array}{c}\frac{(f^{p,r}_{e,t}-a^{p}_{e,t})}{f ^{p}_{e,t}}\ \cdot\theta^{p}\cdot s^{p}\beta^{p,r}_{e,t}-\alpha^{p}_{e,t}\leq t^{\pi}_{p}, \forall r\in R^{r}_{e}\\ \theta^{p}\cdot s^{p}\beta^{p,r}_{e,t}-\alpha^{p}_{e,t}>t^{\pi}_{p},\forall r \in R^{r}_{e}\end{array} \right. \tag{6}\]  
Therefore, the final objective function is shown in Equation (7). The objective is to maximize the operating revenue,  
\[\text{Max}Z=z_{1}-(z_{2}+z_{3}+z_{4}+z_{5}) \tag{7}\]
## 3 Metabolic Responses to Different Antibiotic Classes
### Oxidative Stress and Redox Defenses: ROS-NADPH Balance  
Many bactericidal antibiotics induce, in addition to their primary effect, an intracellular accumulation of reactive oxygen species (ROS), such as superoxide (O\({}_{2}\)\({}^{-}\)), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and hydroxyl radicals (OH\(\bullet\)) [86].  
This secondary oxidative effect arises from perturbations in central metabolism, particularly stimulation of cellular respiration and futile metabolic cycling, which elevate ROS production [7; 86].  
Intracellular accumulation of ROS in response to antibiotic treatment causes extensive oxidative damage to essential macromolecules, including DNA, proteins, and membrane lipids [6].  
These molecular events compromise the structural and functional integrity of the bacterial cell, significantly contributing to the lethality of bactericidal antibiotics [6; 7].  
The contribution of ROS to antibiotic lethality is not uniform but depends on the antibiotic's mechanism of action, the bacterial metabolic state, and the intrinsic capacity for redox detoxification [50].  
Therefore, the secondary oxidative effect may amplify the cytotoxicity of certain antibiotics but does not constitute a universal mechanism across all pathophysiological contexts.  
To mitigate antibiotic-induced oxidative stress, bacteria activate redox defense systems that include:  
* the biosynthesis and regeneration of antioxidant molecules such as glutathione and thioredoxins, processes dependent on NADPH availability from the pentose phosphate pathway [87];
* the expression of antioxidant enzymes, including superoxide dismutase, catalase, and peroxideroxins, which neutralize ROS [88]; and
* The activation of DNA repair systems, such as those mediated by RecA and polymerases IV and V, which correct ROS-induced genetic damage and support cell survival [89].  
Redox responses activated under oxidative stress protect genomic integrity and modulate the cell cycle, promoting the formation of persister cells [90].  
Maintaining the balance between NAD\({}^{+}\) and NADPH is crucial to avoiding redox toxicity and sustaining the function of efflux pumps [90; 91].  
The energetic burden imposed by efflux pump activity, regulated by quorum sensing and sustained by the proton motive force, exemplifies the trade-off between resistance mechanisms and bacterial fitness under oxidative and antibiotic stress (Figure 5). This integration of metabolic and regulatory inputs underscores the cost-benefit balance of non-genetic resistance strategies.  
NADPH provides the reducing power necessary for antioxidant regeneration, while NAD\({}^{+}\) is essential for catabolic reactions and the operation of efflux systems, which contribute to antibiotic tolerance and bacterial persistence [86; 90; 91].
## 2 Biomedical Image Segmentation  
### Traditional Approaches to Biomedical Image Segmentation  
#### 2.1.1 Thresholding Methods  
Thresholding is a fundamental technique in traditional biomedical image segmentation, offering a simple yet effective approach to separate regions of interest based on pixel intensity values. This method operates on the premise that pixel intensities in an image can be classified into distinct regions by defining a threshold value. Pixels with intensities above the threshold are assigned to one class, while those below are assigned to another.  
The simplicity and computational efficiency of thresholding make it widely used, especially when the imaging conditions and the characteristics of the regions of interest exhibit clear intensity differences. This method finds application in various biomedical imaging modalities such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and microscopy.  
The classic textbook on digital image processing provides a comprehensive introduction, including a detailed discussion of thresholding methods and their applications in biomedical image analysis [14]. [PERSON]'s method is a widely cited reference for automatic threshold selection; this method introduces an algorithm for minimizing intra-class vari ance in pixel intensities to determine an optimal threshold [15]. [PERSON] and [PERSON] propose an iterative thresholding algorithm based on the minimum cross entropy criterion, demonstrating its effectiveness in various image segmentation tasks [16]. [PERSON] et al. propose an entropy-based thresholding method, outlining a criterion for selecting the threshold that maximizes the information gained from the image histogram [17]. [PERSON] and [PERSON] provide a comprehensive overview of various thresholding techniques, including quantitative evaluation measures, and discuss their applications in biomedical image segmentation [18].
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Proposed Framework for Natural Scene Classification
#### 3.2.3 Fine-Tuning Pre-Trained Model  
The suggested technique for the natural scene classification of remote-sensing images involves using the pre-trained ResNet-50 network for feature extraction, followed by fine-tuning a new fully connected layer for the classification purpose. ResNet-50 is a widely used deep-learning architecture that has demonstrated cutting-edge performance on a variety of image classification datasets including ImageNet.  
To put the suggested strategy into action, we first obtained the NaSC-TG2 dataset, which covers ten different types of natural landscapes, such as mountain, forest, beach, circular farmland, and residential, etc. We normalized the pixel values of the images to obtain zero mean and unit variance. The pre-trained ResNet-50 model was then loaded and its top layer was removed. We added new global average pooling, dropout, and a fully connected layer with 10 nodes for the classification of images from the NaSC-TG2 dataset. For the target task, we froze all the convolutional layers in ResNet-50 and only trained the fully connected layers added after the convolutional layers structure. The revised ResNet-50 model was then fine-tuned on the NaSC-TG2 dataset with the Adam optimizer at a 0.0001 learning rate and 32 batch size. To compute the difference between the anticipated and actual class labels, we employed a \"categorical cross-entropy\" loss function.  
We conducted a number of experiments to evaluate the performance of the deployed models. Finally, we compared the proposed method's performance to that of existing cutting-edge techniques for natural scene classification, such as traditional machine-learning and deep-learning techniques for remote-sensing image classification. Overall, the proposed method combined the advantages of transfer learning and residual learning to achieve a high accuracy on the natural scene classification task, which has significant uses in applications such as environment monitoring, disaster management, urban planning, and protection of natural habitats. Finally, the performance of the fine-tuned model was evaluated using a set of testing images. This is typically performed by comparing the classified image to a reference image that has been collected from the same area.  
Figure 8: ResNet-50 Architecture.
# Calculation of the Insertion Loss of Barriers on Rigid Ground in the Time Domain
## 2 Theory  
### The Secondary Edge Source Model  
Figure 1 shows the geometry of a rigid wedge and cylindrical coordinate system. The source signal is defined as \(q(t)=\rho A(t)/4\pi\) so that the free-field impulse response of the source is \(h_{S}(t)=\delta(t-d/c)/d\), where \(\rho\) is the density of air, \(A(t)\) is the volume acceleration of the point source, \(c\) is the speed of sound in air, and \(d\) is the source-to-receiver distance. Following [PERSON] et al. [11; 12], the sound field diffracted by a wedge can be described as  
\[p_{D}^{SR}(t)=-\frac{\
u}{4\pi}\int_{z_{1}}^{z_{2}}q(t-\frac{m+l}{c})\frac{ \beta}{ml}\mathrm{d}z, \tag{1}\]  
where \(z_{1}\) and \(z_{2}\) are the two endpoints of the finite or infinite wedge; \(\
u=\pi/\theta_{W}\) is the wedge index; \(\theta_{W}\) is the exterior wedge angle; and \(m\) and \(l\) are distances from the edge point \(z\) to the source \(S(r_{S},\theta_{S},z_{S})\) and receiver \(R(r_{R},\theta_{R},z_{R})\), respectively. The function \(\beta\) is described as  
\[\beta(t)=\beta_{++}(t)+\beta_{+-}(t)+\beta_{-+}(t)+\beta_{--}(t), \tag{2}\]  
Figure 1: Wedge geometry and cylindrical coordinate system with the \(z\)-axis along the edge of the wedge.  
According to the sound field components, the space on the right side of the barrier is divided into three regions, as follows:  
Region I:  
\[p_{T}(t)=p_{d}(t)+p_{r}(t)+p_{D}(t), \tag{10}\]  
Region II:  
\[p_{T}(t)=p_{d}(t)+p_{D}(t), \tag{11}\]  
Region III:  
\[p_{T}(t)=p_{D}(t), \tag{12}\]  
where \(p_{T}(t)\), \(p_{d}(t)\), and \(p_{r}(t)\) represent the total sound field, the direct wave, and the reflected wave, respectively. Thus,  
\[p_{d}(t)=\frac{\rho}{4\pi d^{SR}}A(t-d^{SR}/c), \tag{13}\]  
\[p_{r}(t)=\frac{\rho}{4\pi d^{SR}}A(t-d^{S^{\prime}R}/c), \tag{14}\]  
where \(d^{SR}\) and \(d^{S^{\prime}R}\) are the distances from the source \(S\) and image source \(S^{\prime}\) to the receiver \(R\).  
The insertion loss of a barrier describes the difference in the sound level with and without the barrier in the presence of the ground in both cases. Thus,  
\[IL=20\log|\frac{p_{T}(\omega)}{p_{g}(\omega)}|, \tag{15}\]  
where \(p_{T}(\omega)\) and \(p_{g}(\omega)\) are the frequency spectra of \(p_{T}(t)\) and \(p_{g}(t)\), respectively. The total sound field \(p_{g}(t)\) without the barrier can be expressed as  
\[p_{g}(t)=p_{g}d(t)+p_{r}(t). \tag{16}\]  
Figure 2: Schematic diagram showing the sound propagation over a barrier on a rigid ground and coordinate systems.
## 2 Materials and Methods
### Proposed PUTSS Algorithm
For a given station \(i\) at time \(t\), the congestion level is classified into one of six categories as follows: light, moderate, heavier-than-usual, heavy, peak hours, or standstill;
3. The RTCVC component processes the visual data to count passengers at a given station \(i\). The \(RTCVCF\) is calculated based on the number of passengers detected and the bus capacity \(k\). If the number of detected passengers exceeds the capacity, the system triggers an alert for additional resources;
4. The decision to add, cancel, or modify a bus departure is based on the combined factors from the \(SDCF\) and \(RTCVCF\). The dynamic nature of this decision-making process allows the system to respond flexibly to fluctuations in real-time demand;
5. The algorithm's core optimization goal is to prove the possibility of minimizing passenger waiting time and transportation service operational costs;
6. The final schedule adjustments are made based on a pre-defined maximum waiting time of 15 min. If the calculated decision output indicates that passenger demand exceeds the bus capacity at any station, the system adjusts the schedule dynamically, adding or canceling trips accordingly. In the Result Section, these adjustments showcase the algorithm's decision-making for a sample route. This PUTSS algorithm was implemented in C#. A simulator was also developed in C# to facilitate multiple testing scenarios, enabling it to retrieve various pictures from a folder of images. Through numerous runs, the simulator fetched different sets of photographs and buses with varying capacities, generating diverse situations that map onto different scenarios of the algorithm. This approach ensured stable testing and validation across a spectrum of conditions, with high adaptability in real-world applications. The PUTSS algorithm prevents congestion in urban transportation systems by leveraging historical data and real-time contextual information. The goal is to provide accurate congestion predictions and actionable insights to improve the management of public transit systems, optimize schedules, and enhance the overall commuter experience.
## 2 Materials and Methods
### Testing Methods  
During the GBPT, two systems were used to calculate EE [kJ/kg/min] for the entire test as well as for every single heat. For comparability with previously published reference values, EE was additionally presented as kJ/min.  
Oxygen uptake (VO\({}_{2}\)) was measured via a portable respiratory gas exchange analysis system in breath-by-breath mode (K5, Cosmed, Rome, Italy) [29; 30]. EE\({}_{\text{spiro}}\) was calculated using the VO\({}_{2}\) and the respiratory quotient following the standards of indirect calorimetry [31]. REE was adopted from existing literature [32] and normalized on individual body mass. For consideration of EE in a pre-activated, motionless state, EE was determined during breaks of 40 s between all heats, in which the participants were standing still and waiting for starting the next heat. Only those breaks were used, where the total distance obtained via LPM was less than 10 m within the 40 s.  
LPM system (Catapult ClearSky, Catapult Sports, Melbourne, Australia) tracked the position during the GBPT [33; 34]. A transponder (Catapult ClearSkyT6, firmware ver. 5.6), worn in a supplied harness on the upper back, transmitted data to a base station at 10 Hz sample frequency. Horizontal positions, velocity, and acceleration were calculated via the manufacturer's computer software (Catapult OpenField ver. 1.17.2). Subsequently, the metabolic power model provided by [PERSON] et al. [9] was applied to estimate EE\({}_{\text{LPM}}\) based on acceleration data. For this method, acceleration and deceleration during flat running are translated into equivalent slopes during constant uphill and downhill running. For the latter, EE at various inclines is known so EE can be estimated based on incline [10]. This allows for the estimation of EE based on equivalent slopes, thus acceleration. Detailed descriptions of this method were provided in previous publications [9; 35; 36].
# Effect of Asparaginase Treatment on Biscuit Volatile Compounds
## 2 Materials and Methods  
### Experimental Biscuits  
Biscuits were prepared in our experimental baking laboratory using the following formulation: refined flour (cv Providence, 900 g), butter (270 g), sugar (225 g), whole milk (165 g), and baking powder (21 g), with or without the addition of asparaginease. The baking powder was a commercial preparation containing sodium diphosphate (E450i), sodium bicarbonate (E450 ii), corn starch, and stabiliser sodium, potassium, and calcium salts of fatty acids (E470a) but no added flavourings. Asparaginase was added at levels of 500, 750, or 1000 ASNU per kilogram of flour (where ASNU is defined as the amount of enzyme that produces 1 \(\mu\)mol of ammonia per minute under assay conditions), with a control batch prepared without enzyme. The asparaginease used was Acrylaway(r) L (Novozymes, Bagsvaerd, Denmark). The concentrations of added enzyme were chosen based on the manufacturer's recommendations and previously published studies [9; 11]. For biscuits, the optimal concentration range of asparaginease is between 500 and 1000 ASNU/kg of flour and may vary depending on the formulation and processing parameters. In the present study, at each level of asparaginease addition, the following conditions for the enzyme's action were experimented: (A) no resting after dough mixing; (B) the dough was allowed to rest 15 min at ambient temperature (T = 20 \(\pm\) 1 \({}^{\circ}\)C) before making the biscuits; (C) the dough was allowed to rest 15 min at 50 \(\pm\) 1 \({}^{\circ}\)C before making the biscuits (Table 1). A planetary mixer equipped with a 3 L bowl and C-shaped hook (KitchenAid, Whirlpool, Benton Harbor, MI, USA) was used to make the dough. First, the butter was placed in the mixer bowl and mixed for one minute to make it soft and smooth. Then, the sugar was added, and mixing was continued for another minute. Finally, the flour, the baking powder and the milk were added, and the mixture was mixed for six additional minutes. The enzyme, when used, was added to the milk just a moment before pouring the milk into the mixer bowl. After mixing, the dough was removed from the bowl and divided into 3 equal parts (A, B and C), which were processed as follows. Part A was immediately rolled out at 5 mm thickness with a rolling pin equipped with guides, then biscuits were cut by a round steel cookie-cutter, 5 cm in diameter, and baked in a steam-convection oven (Angelo Po, Carpi, Italia) at a temperature of 180 \({}^{\circ}\)C without steam for 16 min. At the same time, parts B and C were shaped into a loaf, wrapped in cling film and allowed to rest for 15 min, respectively, at 20 \({}^{\circ}\)C (B) and at 50 \({}^{\circ}\)C (C). As soon as the resting time was over, parts B and C were rolled out and biscuits were cut and cooked in the same way as it was conducted with A. The following characteristics were measured on all biscuit samples: length (5.19 \(\pm\) 0.25 cm), width (4.39 \(\pm\) 0.23 cm), height (1.18 \(\pm\) 0.08 cm) and weight (10.0 \(\pm\) 3.0 g).
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 3 Methodology
### Uniform Quantization  
The uniform quantization function is defined as follows:  
\[Q(r)=[\frac{r}{S}]-Z \tag{5}\]  
In this equation, \(Q\) represents the quantization function, \(r\) represents the input (activation or weight), \(S\) represents the real-valued scaling factor, and \(Z\) represents the integer zero point. The choice of the integer zero point in quantization can determine whether the quantization is symmetric or asymmetric. Since symmetric quantization is one of the most-straightforward quantization strategies, we implemented symmetric quantization to contribute to making our approach simpler. In full-range symmetric quantization, the integer zero point \(Z\) is set to 0 to reduce the computational cost during inference, and the real-valued scaling factor \(S\) is given by:  
\[S=\frac{\max(|r|)}{2^{b-1}-1} \tag{6}\]  
where \(b\) is the quantization bitwidth.  
In asymmetric quantization, the real-valued scaling factor \(S\) is defined as:  
\[S=\frac{\beta-\alpha}{2^{b}-1} \tag{7}\]  
where \(\alpha\) and \(\beta\) denote the minimum and maximum values for the clipping range (i.e., \(\alpha=r_{\min}\) and \(\beta=r_{\max}\)).
# A New Method of Time-Series Event Prediction Based on Sequence Labeling
###### Abstract
Using the LSTM model for event labeling prediction, and then using conditional random field (CRF) to smooth the prediction results, is expected to further improve the accuracy of event prediction to help solve various application problems in this field. There are shape similarities and change similarities between different subsequences, so the method of pattern recognition can be used to extract representative patterns in the sequence and then express the subsequences as a combination of patterns. Studies have shown [10] that the clustering method (cluster) is superior to other pattern recognition methods such as SAX-VSM [14] and Fast shapelets [15] when extracting sequence patterns for relationship modeling. The cluster method is easy to realize and has other advantages, such as high classification accuracy [16], so this paper mainly uses the cluster method for pattern recognition to obtain the transitional relationship between the patterns. However, after expressing the subsequence as a combination of patterns, problems such as excessive data dimensions and redundant information will occur. The XGBoost algorithm [17] has the advantages of parallelization, high scalability, and fast speed, so this article chooses this algorithm for feature selection and obtains the soft classification probability value of the subsequence sets to assist LSTM-CRF in event prediction. Thus, a novel prediction model based on Cluster-XGBoost-LSTM-CRF is constructed. Compared with previous models, this model can capture the dependency between subsequence sets well and take the interaction of different events into account in the model. In order to verify the effectiveness of CX-LC, this article conducted experiments on five real data sets. The prediction results show that CX-LC is superior to several other models in event prediction. The main contributions of this research are:  
* This paper transformed the problem of event prediction into a problem of sequence labeling and captures the dependency between subsequence sets;
* The CX-LC model developed in this paper can well extract the features in the original data set and smoothly optimize the prediction results;* This paper conducted experiments on five data sets to prove that the CX-LC model has more accurate predictions. The main content of this paper is structured as follows:
* Section 2 introduces the relevant definitions of time series event prediction, as well as the model derivation and theoretical research of CX-LC;
* Section 3 presents the experimental part of this paper;
* Section 4 presents the conclusion and prospect of this paper.