esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Biological Properties
Chitosan membrane biomedical materials are widely used, such as preventing postoperative abdominal adhesions, purifying drugs and serum antibodies, manufacturing artificial renal membrane, artificial skin, contact lens membrane, drug sustained-release, dental surgery and nerve repair materials, etc. [81]. * 5. Regulatory. Chitosan can activate lymphocytes with the immune function, distinguish normal cells from cancer cells and kill cancer cells [82]. Besides, it also has the function of regulating the endocrine system, regulating the pH value of the body to weak alkaline, improving the utilization rate of insulin, making the insulin secretion normal, inhibiting the rise of blood sugar, reducing blood lipid and helping to prevent and treat diabetes [83]. Many researchers have found that chitin, chitosan and chitosan oligosaccharide have immunomodulatory effects. The molecular weight of chitin and chitosan is more than 1 million, so its immunogenicity is very weak or almost negligible [84]. The immune response mediated by chitosan and its derivatives is closely related to its chemical structure. After deacetylation, chitin has -NH2 on the chitosan molecule, which can combine with H\({}^{+}\), enhance affinity, chemotactic leukocyte and induce a local macrophage. Macrophages play an important role in the immune response and regulation [85]. There are receptors of bacterial polysaccharides on the surface of macrophages. Chitosan, as a bacterial polysaccharide like substance, can stimulate the activation of macrophages, thus promoting the phagocytic ability and enhancing the activity of hydrolase secreted by macrophages [86]. As a natural high molecular material, chitosan with unique structure presents natural physiological activity in vivo, which can stimulate local tissues, promote cell proliferation and then evolve into macrophages, produce inflammatory mediators and improve the body's resistance to inflammation. * 6. Biodegradability. Chitosan is a kind of natural medical polymer material with excellent biodegradability, which is determined by its chemical structure. Chitosan has obvious degradation under the action of a lysozyme in vitro or in body fluid [87]. Degradation products are methyl sugar and oligosaccharidescharide, which are safe for the body and can be decomposed, absorbed and metabolized. \(N\)-acetylglucosamine, one of the degradation products, is very important for scar repair of tissues and is toxic to some malignant tumors in vivo, so it can be utilized as cancer chemotherapy drugs [88]. Its degradation products are generally non-toxic to the human body, no accumulation in the body, no immunogenicity, so it can be used to manufacture artificial skin, surgical suture, bone repair materials, contact glasses, artificial dialysis membrane, anticoagulant materials, etc., carried a very broad application prospect in the medical field.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 5 Experimental Results  
This section presents a thorough evaluation of the experimentation environments. Two main aspects were considered: validation, meaning that the environment correctly implements the fat-tree routing protocols, and scale, looking for the limits of each environment over the same underlying hardware.  
Furthermore, the applicability of each environment is discussed, while a use case that goes beyond routing infrastructure is also presented: implementing a Layer 2 switching overlay over the routed fabric, needed for Virtual Machine migration, which is one of the most important use cases in data center management.
# The Application Status and Trends of Machine Vision in Tea Production
## 3 Intelligent Tea Picking
### Tea Production Estimation  
In agriculture, crop yield is the standard measure and direct indicator of agricultural yield per unit area of land, and it is also the ultimate goal of agricultural production. Estimating crop yield is one of the important tasks in modern agriculture. Currently, commonly used yield estimation methods include field sampling surveys, agricultural meteorological model yield estimation [52], spectral-index-based crop yield estimation [53], and image-based crop yield estimation [54]. [PERSON] [55] found through comparative research that the overall performance of faster RCNN based on the principle of object detection and counting is more successful in the recognition and counting of tea buds, which can provide application value for tea garden yield estimation in natural environments. However, the model still has the problem of missing the detection of tea leaf buds under a large shooting area. [PERSON] et al. [56] studied the yield estimation problem of small-scale tea gardens in hilly areas based on [PERSON]'s target detection algorithm and field sampling survey method. By combining the sampling area, the average growth density of tender buds can be calculated, and the relative error between the estimated tender tea bud yield and the actual harvest yield is 29.56%. This study can, conveniently, estimate the yield of tea shoots in tea gardens, provide yield-related data support for farmers during the tea growth period, and facilitate the early management of tea production.
# Assessment of Milk Quality in Skopelos Goats Under Low- and High-Input Farming Systems
## 2 Materials and Methods
### Milk Sample Collection  
Milk samples were collected during the morning milking (am_MY) from the same individual goats across two consecutive lactation periods. In each lactation period, sampling began in February, approximately 80 days postpartum, and continued at 45-day intervals until the end of the lactation in August, following ICAR recommendations [32].  
Each sampling corresponded to a specific lactation stage (LS), which was divided into four stages as follows: the first stage (days in milk, (DIM), approximately (approx.) 80 days), the second stage (DIM approx. 125 days), the third stage (DIM approx. 170 days), and the fourth stage (DIM approx. 215 days). To ensure accuracy and consistency, this process was carried out by trained personnel, including animal scientists and veterinarians.  
Milk samples were collected in 50 mL screw-capped flasks, cooled to 4-6 \({}^{\circ}\)C with ice packs, and transported to the laboratory in isothermal containers for analysis within 24 h. Over the two lactation periods, a total of 1828 milk samples from the two farms were collected and analyzed for chemical composition (fat, protein, lactose, and total solids (TSs) and casein (Cas) content), physical characteristics (pH, electrical conductivity (EC), refractive index (RI), and Brix value), SCC, and total bacterial count (TBC). Milk samples were analyzed within 24 h after their collection. To preserve the milk samples for TBC analysis, sodium azide (0.01 g/100 mL; Merck KGaA, Darmstadt, Germany) was added as a preservative.
# A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus
## 3 Formulation for the dBFT Consensus
### Model Constraints and Blockchain Operational Requirements
#### 3.4.3 Messages/Payloads Constraints  
Equation (8) ensures that a PrepareResponse message is only send if PrepareRequest was received in an instant before. A similar behavior is ensured for sending a Commit messages, but with a stricter need of \(M\) previous PrepareResponse messages to have been received, as in Equation (9). Analogously, Equation (10) limits this by demanding that, at least, \(M\) Commit messages should had been received before relaying a block.  
\[PrepResp_{t,i,v}^{-}\leq\sum_{t2\in\mathcal{T}:t2\leq t}\sum_{j\in\mathcal{R}} Prepeq_{t2,i,j,v}^{+}\qquad\qquad\forall t\in\mathcal{T}:t>1,i\in\mathcal{R},v\in\mathcal{V} \tag{8}\]  
\[Commit_{t,i,v}^{-}\leq\frac{1}{M}\sum_{t2\in\mathcal{T}:t2\leq t}\sum_{j\in \mathcal{R}}PrepResp_{t2,i,j,v}^{+}\qquad\qquad\forall t\in\mathcal{T}:t>1,i \in\mathcal{R},v\in\mathcal{V} \tag{9}\]  
\[BlockRelay_{t,i,v}^{-}\leq\frac{1}{M}\sum_{t2\in\mathcal{T}:t2\leq t}\sum_{j\in \mathcal{R}}Commit_{t2,i,j,v}^{+}\qquad\qquad\forall t\in\mathcal{T}:t>1,i\in \mathcal{R},v\in\mathcal{V} \tag{10}\]  
Equation (11) limits that a node can send messages only once per view; constraints are generated for the following variables \(Send^{-}\) equals \(PrepResp^{-},Commit^{-},ChView^{-}\) and \(BlockRelay^{-}\).  
\[\sum_{t\in\mathcal{T}:t>1}Send^{-}_{t,i,v}\leq 1\qquad\qquad\forall i\in\mathcal{R},v \in\mathcal{V} \tag{11}\]  
Following this reasoning, Equation (12) automatically forces a node to receive a self-sent message. They are created for the following pairs \((Recv^{+},Send^{-})\) equal to \((PrepReq^{+},PrepReq^{-})\), \((PrepResp^{+},PrepResp^{-})\), \((Commit^{+},Commit^{-})\) and \((ChView^{+},ChView^{-})\). The same pairs are applied for Equation (13), which enables messages to be received by a node \(i\) only when they were actually already sent by another node \(j\).  
\[\sum_{t\in\mathcal{T}:t>1}Recv^{+}_{t,i,j,v}=Send^{-}_{t,i,v}\qquad\qquad\forall t \in\mathcal{T}:t>1,i\in\mathcal{R},v\in\mathcal{V} \tag{12}\]  
\[Recv^{+}_{t,i,j,v}\leq\sum_{t2\in\mathcal{T}:1<t2\leq t}Send^{-}_{t2,j,v}\qquad \qquad\forall t\in\mathcal{T}:t>1,i\in\mathcal{R},j\in\mathcal{R},v\in\mathcal{V} \tag{13}\]Since PrepareRequest carries Primary's preparation hash, it also acts as a PrepareResponse for the Primary, as ensured at Equation (14).  
\[PrepResp^{+}_{t,i,j,v}\geq PrepReq^{+}_{t,i,j,v}\qquad\qquad\forall t\in\mathcal{T} :t>1,i\in\mathcal{R},j\in\mathcal{R},v\in\mathcal{V} \tag{14}\]  
Payloads can be received only once per view (\(Recv^{+}\) is assigned to \(PrepReq^{+}\), \(PrepResp^{+}\), \(Commit^{+}\) and \(ChView^{+}\)), which is defined with constraints created following the logic of Equation (15).  
\[\sum_{t\in\mathcal{T}:t>1}Recv^{+}_{t,i,j,v}\leq 1\qquad\qquad\forall i\in \mathcal{R},j\in\mathcal{R},v\in\mathcal{V} \tag{15}\]
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Qualitative Evaluation
#### 3.1.3 Qualitative Evaluation Analysis  
Qualitative evaluation of diagnosability can only determine whether a fault can be detected and isolated. It is a clear guidance for the optimal design of system structure and function based on diagnosability. In contrast, the quantitative evaluation of diagnosability is a more precise guide for designing diagnostic strategies and methods.
## 2 Materials and Methods  
### Materials  
Human keratinocytes (HaCaT) were purchased from ATCC (Manassas, VA, USA). The fetal bovine serum (FBS), DMEM, antibiotic/antifungal solution, and ECL reagents were provided by Hyclone (Waltham, MA, USA). Additionally, 0.25% trypsin EDTA, 4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolzolium bromide (MTT), radioimmunoprecipitation assay (RIPA) buffer, 1,1-diphenyl-2-picrylhydrazyl (DPPH), and 2\({}^{\prime}\)7-dichlororescein diacetate (DCFH-DA) were bought from Gibco (Grand Island, NY, USA) and Sigma Chemical (St. Louis, MO, USA). TRIzol reagent, PCR premix, and nucleic acid staining were obtained from Life Technologies Corporation (Carlsbad, CA, USA), Bioneer (Daejeon, Republic of Korea), and NobleBio (Gyeonggi, Republic of Korea), respectively. The primary and secondary antibodies were secured from Santa Cruz Biotechnology (Santa Cruz, CA, USA), Cell Signaling Technology (Beverly, MA, USA), Bio-Rad Laboratories, Inc. (Hercules, CA, USA), and Proteintech Group, Inc. (Rosemont, IL, USA). All other materials used were purchased from commercial sources in the highest grades available and were used as received without any further purification.
## 3 Proposed ML Method
### Classification
#### 3.4.2 \(k\)-Nearest Neighbour  
The \(k\)-Nearest Neighbour (\(k\)-NN) classification algorithm is one of the most widely used supervised ML methods for categorising unknown signals into a discrete set of classes [49; 50; 51]. The \(k\)-NN method is used to classify target instances based on their similarities to predictor features (training data). The most similar predictor cases are referred to as the \"neighbours\", hence, the title associated with the classification method.  
Classification is achieved by first defining a value of \(k\). The optimal \(k\) value is dependant on the input data. Choosing a low \(k\) value often produces an over-fitted prediction model, which produces inaccurate predictions on out-of-sample target instances. A higher \(k\) value makes the prediction model too generalised as the classes with more predictor instances become prioritised as the target instance. The optimal \(k\) value may only be determined through trial and error, using multiple values to compare accuracy results. The next step to achieve a RUL prediction using the \(k\)-NN framework involves calculating the distance from the features of the target instance from all other predictor instance features. This distance metric can be calculated in a number of ways, including Euclidean, Mahalanobis, City block and Minkowski distances.  
Figure 6: Experimental procedure for selecting the training and testing signals for the different ML approaches.  
As we are dealing with distance metrics to determine classification, it is important that each of the features used are standardised before training the prediction model. Min-max normalisation is applied to each feature to put all training data into the 0-1 scale. The target instance is then normalised using the same min-max values as the training data.  
The same min-max value is used for the normalization process to eliminate the occurrence of data dredging, the statistical inference performed after looking at the complete dataset. The \(k\) nearest observations in the training data that are nearest to the unknown target data point are selected as the \"neighbour\" points. A case is classified by determining the mode class value of its neighbours, with the case being assigned to the class most common amongst its \(k\) nearest neighbours measured by a distance function.  
Six methods of \(k\)-NN classification were used to obtain RUL predictions for the bearings, including Fine, Medium, Coarse, Cosine, Cubic and Weighted \(k\)-NN. The specified parameters varied in each method are presented in Table 3.
## 3 Results and Discussions
### Outcomes of Scheme III  
We had validated that the performances of the scheme I and scheme II were good. Both of the MAE and SD inside trials and across trials were low. The objective of the scheme III was to evaluate the TBLSTM model's generalization ability among different subjects. The MAE and SD were given in column \"Scheme III Bi-LSTM\" in Table 2. The \"corrections\" way showed corrective factors were added after training the TBLSTM model. The final results of the scheme III were calculated as the formula in (8)-(9). The results of the \"corrections\" way was showed in column \"scheme III corrections\" in Table 2. Figure 8 was the five subjects' estimated result. The blue solid curve was the measured RVSBP. The red solid curve was the TBLSTM model's estimated RVSBP. The pink solid curve was the outcomes of adding the corrective factors.  
Figure 7: Performance of scheme II.
### Contributions  
The main contributions of this paper are summarized as follows.  
* We raise the idea of sub-coordinate systems, which make it possible to map information from received signals and transmission channels into a \(K\)-Polygon diagram. The differences in types of \(K\)-Polygon diagrams can be ultimately attributed to the differences in the corresponding transmission symbol combinations, enabling the replacement of the MPA in SCMA decoding with image classification technology.
* We propose the concept of a dynamic dataset with automatic labeling, which can generate the enormous \(K\)-Polygon diagrams needed for training. This method avoids the necessity for offline dataset hand labeling, which, to some extent, reduces the training time of the model, facilitating its faster deployment in new channel environments.
* Compared to other SCMA decoders, our decoder has better BER performance and lower computational complexity, offering a novel perspective for achieving signal detection tasks by directly classifying the generated \(K\)-Polygon diagrams.
# An Integrated Decision Support Model for Evaluating Public Transport Quality
## 1 Introduction
Reference [23] applied the AHP approach in a fuzzy environment to mitigate the uncertainty of the citizens' evaluations for the supply quality of the urban bus transport system. Reference [24] presented how citizens' demand affect public transport improvement decision sustainability by using Multicriteria Decision Making (MCDM) methodologies, such as Fuzzy AHP and Interval AHP. In this study, we shed broad light on the most significant points for future strategic improvement plans. The study presents an integrated MCDM model to evaluate public transport supply quality with participating users, public transport experts, and local government representatives in the questionnaire survey. Reference [25] conducted a review paper to support the decision maker in the design and operation of urban passenger transport systems. The MAMCA approach [26] was introduced to evaluate the transport projects in this research, with the author taking into consideration the quantitative and qualitative factors and their relative importance. In [27], multicriteria decision analysis was introduced to compare a bus rapid transit system (BRT) and a light rail transit system (LRT). Reference [28] improved the traditional service evaluation approaches by using AHP and the Fuzzy Sets Theory by considering the Italian public transit service sector. Reference [29] assisted the service quality in healthcare system by developing a new decision-making model that relies on the SERQUAL-fuzzy AHP approach. The AHP-BWM model introduced here can be recommended in all decision cases in which the attributes are hierarchically structured and pairwise comparisons are necessary among a high number of decision criteria. Connecting BWM unburdens the evaluators, generates more consistent results, and makes the survey process easier and faster. This combination of two MCDM approaches can be considered pioneering, since prior related literature dominantly handled AHP and BWM as competitors. In our case study, the ranking of transport system attributes can demonstrate the applicability of the new innovative model very well. The study questions addressed in this paper are: What are the main criteria affecting the supply quality of public transport? What are the stakeholder perspectives towards developing the public transport plan? To investigate a solution for these questions, pairwise comparisons surveys were conducted in the capital of Jordan, Amman.
# MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification
## 4 Experiments
### Bias Setup  
**Class Imbalance:** To create a class-imbalanced dataset, we gradually reduced the number of samples for each class using an exponential function \(n=n_{i}\mu^{i}\), where i is the index of the class, \(n_{i}\) is the original number of samples for each class, and \(\mu\in(0,1)\). The imbalance level of the dataset is defined by an imbalance factor, which is the ratio of the number of training samples in the largest class to the number of training samples in the smallest class. For example, when the imbalance factor is 100, assuming the class with the most samples has 1200 samples, the class with the fewest samples contains only 12 samples.  
**Label Noise:** We used two commonly used methods to introduce label noise: uniform noise and flipping noise. Both methods change the label of a sample to another label with a probability of noise level \(p\).  
**UNIFORM:** Suppose the dataset contains \(C\) classes. In the uniform noise setting, the sample's label is preserved with a probability of \(1-p\). The label is randomly corrupted to other labels with equal probability \(\frac{p}{C}\) for each class. The UNIFORM noise matrix is definedas follows: for each element \(a_{ij}\) in the matrix, it represents the probability that the label with index \(i\) is corrupted to the label with index \(j\).  
\[corrupted\ matrix=\begin{bmatrix}1-p+\frac{p}{\mathcal{C}}&\frac{p}{\mathcal{C} }&\dots&\dots&\frac{p}{\mathcal{C}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \frac{p}{\mathcal{C}}&\dots&1-p+\frac{p}{\mathcal{C}}&\dots&\frac{p}{\mathcal{ C}}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \frac{p}{\mathcal{C}}&\dots&\dots&\frac{p}{\mathcal{C}}&1-p+\frac{p}{ \mathcal{C}}\\ \end{bmatrix}\]  
**FLIP:** In the FLIP noise setting, we also assume a dataset with \(\mathcal{C}\) classes. Similar to the uniform noise, the label is preserved with a probability of \(1-p\). However, with a probability of \(p\), the label is corrupted to one of the remaining \(\mathcal{C}-1\) classes specified randomly. For example, for the label with index \(0\), the FLIP noise matrix may look like \([1-p\quad 0\quad p\quad\dots\quad 0]\). Due to its randomness, the complete FLIP noise matrix is not provided here.
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 4 SPN Models
### Base Model  
The SPN performance model described in this section is designed to replicate the behavior of the architecture presented in the previous section. Figure 5 shows the Petri Net model divided into five parts: Admission, Front-End, Delay, Back-End, and End_Process. Admission is responsible for generating the requests that will be processed in the Back-End. The Front-End subnet represents the processing of requests before they are sent to the Back-End. The Delay is the link between the Front-End and the Back-End. The Back-End is responsible for the processing of all requests and it will be the focus of the performance investigation. End_Process is the subnet where requests are discarded after processing. Table 1 provides a description of all the components in the system, and Table 2 lists the model's input parameters. Based on the model's overview, it is possible to provide further detail about its components and their functions. The Admission subnet consists of two places: P_Arrival and P_InputQueue. P_Arrival represents the system's state when it is waiting for new requests to be generated. P_InputQueue represents the system's state when requests are generated and are waiting to be distributed by the model. Requests are represented by tokens in P_Arrival. The AD transition (or Arrival Delay) represents the time between arrivals, or the time required for each message to be generated and transmitted to P_InputQueue. Figure 4: Basic SPN template example. Figure 5: Proposed model to evaluate a video-on-demand architecture using Petri nets. The Front-End subnet consists of two places: P_Process1 and P_ProcessCap1. P_Process1 represents the state in which the request is being executed. P_ProcessCap1 represents the Front-End component's processing capability through markup. When T_Process1 is triggered, a token is taken from P_Process1 and sent to the next subnet. Then, another token in P_InputQueue begins to be executed in the Front-End. The Delay subnet consists of two locations and a transition that describes the time it takes for the message to be transferred from the Front-End to the Back-End. When the token arrives at P_Transmit2, it has already been processed by the Back-End. The Front-End and Back-End subnets can be enabled or disabled to analyze the system's availability from different perspectives. A component with two positions and two timed transitions was introduced to perform this control. The Front_ON and Front_OFF places indicate whether the Front-End is on or off. Front_OFF is connected to T1 through an inhibitor arc. When there is a token in Front_OFF, transition T1 cannot be activated, causing the system to shut down completely. The Front_MTTR and Front_MTTF transitions represent the repair and failure times, respectively. The values for these transitions were