esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Twin Model Quality Optimization and Control Methods Based on Workflow Management
## 5 Micro-Level Model Quality Optimization Method  
The micro-level model quality optimization process requires effectively retrieving solutions for digital twin model abnormal indexes to optimize model quality. In addition, the solution library must be continuously enriched during the model control process to enhance the ability to address future abnormal indexes of the digital twin model. The cognitive, activation, and effector stages of biological immunity allow for the recognition and elimination of foreign antigenic substances to maintain the balance and stability of the internal environment [23]. Since the biological immunity processes fit well with the micro-level model quality optimization process, a micro-level model quality optimization method is proposed based on such processes. The micro-level model quality optimization model is presented in Figure 5. The solution retrieval methods, which play a key role in the micro-level model quality optimization, are presented in the subsequent sections.  
Figure 5: Micro-level model quality optimization model.  
The steps of the micro-level model quality optimization method are as follows:  
Step 1: Cognitive stage. In the design process, based on the evaluation method in Section 3.1, the evaluation set \(O\) is used for determining whether the index is abnormal.  
Step 2: Activation stage. If there is an abnormal index \(Q\), the appropriate solution is selected using the method in Section 5.2. First, abnormal indexes in the set \(Q\) are input to the solution retrieval method in Section 5.2 to establish the optimized mathematical model of the solution. Then, the mathematical model is solved to retrieve suitable and targeted solutions from the solution library. Finally, the retrieved solutions are fed to the user to correct the abnormal indexes.  
Step 3: Effector stage. The model is processed based on the retrieved solution and rechecked after processing. An abnormal index that has never appeared before is called a new abnormal index, the handling of which requires redesigning; if the model passes recheck after redesigning, the abnormal index and the corresponding solution are recorded in the solution library.
## 4 Method  
In this research, a five-step approach to questionnaire instrument development and data collection is employed:  
1. Initial questionnaire design: a questionnaire with 25 questions was designed for data collection according to the literature. In the questionnaire, the first three questions were designed to exclude invalid data provided by respondents without sufficient expertise.
2. Interview for questionnaire improvement: the content was validated by interviewing five project managers with over five years of experience from three owner organizations and two contractors. They were invited to modify items in the scale that were unclear or incorrectly expressed.
3. Pre-test for questionnaire improvement: a pre-test with 39 valid samples was conducted.
4. Formal questionnaire survey: After the interview for questionnaire improvement and pre-test, two questions about CSCI in the questionnaire were substantially modified and combined into one question. Consequently, in the initial research model, observable variables bi_2 and bi_3 were altered into one observable variable as off-site process monitoring, as shown in Table 4. The modified questionnaire includes 24 questions. A formal questionnaire survey was conducted to collect a larger sample.
5. Interviews were carried out alongside the model evaluation.  
A five-point Likert type scale [63] was adopted to measure variables associated with proposed constructs. Respondents were asked to state their agreement with a given statement on a scale that ranges from'strongly disagree' (score 1) to'strongly agree' (score 5) with its midpoint, i.e., score 3 anchored as 'uncertain, neither agree nor disagree', and score 2 and 4 respectively represent' uncertain, but probably disagree' and 'uncertain, but probably agree'. Respondents were asked to consider the focal construction organization's primary material(s) or material flow(s) while responding to the questions on these constructs.  
The PLS-SEM was undertaken to validate the research model, using the SmartPLS software package [64]. We selected this method because it is able to cope with the small sample size and suitable for explorative studies [21].
## 2 Related Work
### State-of-the-Art FEQS Methods  
#### 2.1.1 Counting FEQS  
Counting FEQS counts the number of loaded trucks to keep track of the amount of material moved. It is applicable to scenarios that have a large quantity of overall transportation, low unit load price, and uniform trucks. Thus, the counting FEQS is suitable for civil engineering projects with a huge earthmoving demand and without precise single truck load weighing requirements, like hydropower, airport, large-scale landscape transformation [5] etc. In these larger projects, the trucks are generally uniform in capacity and truck loading is managed in a standardized manner [2]. Therefore, full-load trucks are assumed to reach a standard loading quantity, and for FEQS, checking whether a truck is full or empty and counting them is required. Moreover, compared to the large overall quantity, the quantity error of a single truck loading is minor, and this error can be shared and fixed by the overall statistics.  
Currently, counting FEQS is mostly dependent on manual recognition of trucks' full/empty state and accounting. Automated solutions have been proposed to aid with keeping count of trucks, such as the use of computer aids to reduce the burden of manual work and human error rate. Tools, like the global positioning system (GPS) and radio frequency identification (RFID), have been applied for vehicle tracking or trip counting [6; 7; 8; 9]. However, these tools can only be used to assist manual or achieve vehicle trip counting, and are unable to solve judgmental problems, like checking if a truck is full or empty. Hence, human labor is still required with these tools, and the related human error, high labor cost, and application limitations cannot be fully overcome. In detail, manual accounting applications are limited under harsh conditions, like high altitude, steep mountains, extreme cold, or hot areas, as labor safety may be threatened [10; 11]; and there is also potential health damages as workers must suffer noise and vibration caused by trucks moving for a long time [12].
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Evaluation Criteria
#### 2.3.2 Classification Type  
The second criterion is the classification type. The single class means that we have many classes, but one document will be classified into only one class. Multi-label means we have many classes, and one document may be classified into one or more than one class.
# Enhanced Specific Mechanism of Separation by Polymeric Membrane Modification--A Short Review
## 5 Adsorptive Membranes
### Selective Properties
Adsorptive membranes (AMs) have demonstrated high effectiveness in heavy metal removal from wastewater, owing to their exclusive structural properties. This article examined the applications of adsorptive membranes such as polymeric membranes (PMs), polymer-ceramic membranes (PCMs), electrospinningning nanofiber membranes (ENMs), and nano-enhanced membranes (NEMs), which demonstrate high selectivity and adsorption capacity for heavy metal ions, as well as both advantages and disadvantages of each one, which are summarised and compared shortly. Moreover, general theories for both adsorption isotherms and adsorption kinetics are described briefly to comprehend the adsorption process. This work will be valuable to readers in understanding the current applications of various AMs and their mechanisms in heavy metal ion adsorption, as well as the recycling methods in the heavy ion desorption process, being summarised and described clearly. Moreover, the influences of the morphological and chemical structures of AMs are presented and described in detail as well [96], which could be explained by the creation of specific complexation between complex conjugates and detected ions (e.g., sulphides in aromatic rings are suitable for capturing Hg\({}^{2+}\)). Hence, the specific transport through the adsorptive membrane could be limited by the specific surface area or the active centre and the effect of electrostatic accumulation (e.g., Langmuir interactions) visible in adsorptive membranes. Moreover, the exclusion of co-ions could play a crucial role in the transportation and receive a highly selective flux [100].
## 1 Introduction
Supervisory Control and Data Acquisition (SCADA) is a well-known and established technology in remote monitoring systems. SCADA is a technology for controlling and monitoring remote devices. The SCADA system consists of remote terminal units (RTUs), a master terminal unit (MTU), and a human-machine interface (HMI). The RTU is a microcontroller system connected to field devices such as sensors and actuators. The RTUs communicate with the MTU via a communication network using a specific communication protocol. The MTU is a central unit of the SCADA system that processes and displays to the HMI for easy monitoring and control by the operator. The human-machine interface (HMI) is a valuable feature of the SCADA system that provides a user or operator with an informative display when monitoring the plant [29]. It is common for the developer to utilize the SCADA software bundled with the HMI designer tools for developing the SCADA user interface, due its easy and fast development. SCADA architecture has evolved from the monolithic, distributed, networked, and IoT-based SCADA system [30; 31]. In the monolithic SCADA, the RTUs connect to the MTU via a wide area network (WAN) with a proprietary network protocol. The system is standalone and cannot be accessed by anyone. In the distributed SCADA, the RTUs connect to a communication server using WAN with a proprietary network protocol. The data are then distributed to the operation stations via a local area network (LAN). In the networked SCADA, open protocols are offered the using the internet protocol (IP). In the system, the SCADA MTU communicates with the networked RTUs using the IP via WAN. The IoT-based SCADA integrates SCADA with the IoT and cloud environment, which offers more flexibility, scalability, and efficiency. There are two approaches to move to the IoT-based SCADA system [32]. The first approach involves developing the system from scratch, where the SCADA is designed based on the IoT or cloud technology. The second approach involves migrating the existing SCADA system to the cloud or other IoT-compatible element. The recent applications of IoT-based SCADA cover many areas, such as plant monitoring [17], electricity [33; 34], electric power plants [35; 36; 37; 38], industrial applications [39], wastewater treatment plant [40], telecommunication system [41], traffic control [42], education [43], oil production [44], and Smarthome [45]. The literature survey of the IoT-based SCADA systems is summarized in Table 2, where the field of application, RTU hardware, protocol, and MTU software and hardware are shown. As listed in Table 1, most IoT-based plant monitoring systems use air and soil parameters as the monitored parameters. There are no IoT systems dealing with leaf temperature monitoring, especially those that employ thermal cameras. Table 2 shows that most IoT-based SCADA systems are developed from scratch using a Raspberry embedded system as the MTU hardware and popular IoT platforms such as ThingsBoard, Node-Red, and
# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 3 Methods
### Metrics and Loss Functions  
In addition to designing robust network models, the evaluation of model performance is critical. Among all evaluation metrics, the ASR and the PARAMs are the key metrics for CAPTCHA recognition. The definition of ASR is the proportion of the number of correctly recognized characters to the total number of characters in a dataset, as shown in the following Equation (1). AASR is another index that is used to measure the overall recognition accuracy of CAPTCHAs regardless of the specific character position.  
\[\text{ASR = the number of characters recognized/the number of all characters} \tag{1}\]  
In the image classification, the CE and BCE are currently mostly used loss functions, and these functions are very suitable for backpropagation to update network weight parameters with the probability distribution input. If \(\text{x}_{\text{i}}\) represents the predicted probability and \(\text{y}_{\text{i}}\) represents the true label, Equation (2) shows the weighted BCE formula for a multi  
Figure 9: The structure of CRNN.  
classification task, and \(\mathrm{w_{i}}\) represents the weight. If \(\mathrm{w_{i}}\) is equal to one, the above formula degenerates into a general BCE function.  
\[\mathrm{L}=-\mathrm{w_{i}}[\mathrm{y_{i}logx_{i}}+(1-\mathrm{y_{i}})\mathrm{ log}(1-\mathrm{x_{i}})] \tag{2}\]  
Another loss function is focal loss, as shown in Equation (3). Among them, \(\alpha\) is used to control the ratio of positive and negative samples, and \(\gamma\) is used to control the weight of difficult and easy samples. By setting different parameter values, different samples were given different weights.  
\[\mathrm{L}=-\mathrm{\alpha y_{i}}(1-\mathrm{x_{i}})^{\gamma}\mathrm{logx_{i}}- (1-\alpha)(1-\mathrm{y_{i}})\mathrm{x_{i}^{\gamma}log}(1-\mathrm{x_{i}}) \tag{3}\]  
Mean Squared Error (MSE) loss is another loss function that calculates the squared difference between predicted probabilities and real labels. Its advantages include smooth gradients, which aid optimization. Different from BCE, MSE is less sensitive to differences between probabilities for true categories due to its quadratic nature. However, BCE provides a stronger gradient signal for low-probability events, which is often desirable in classification tasks where the focus is on the probability of the correct class.
## 6 Predictive Model Experimental Results  
### Data Preprocessing  
The real time temperature performance data were collected from three components of the autoclave equipment while using a developed device that is described in Sections 5.1 and saved on a database for a period of three months. The total of 130,140 timesteps data were collected from each component. The collected data were extracted from database, processed using python, and then used to train and test a predictive model that may fit into the proposed structure.  
The data distribution from mid-November 2019 up to mid-February 2020 is presented in Figure 12. The distribution of collected data shows that the averaged temperature in November is less when compared to other months. From the partial analysis of the data, we noticed that the collected data from inoperative equipment might mislead the prediction and decided to capture and use data only when the equipment operates.  
Among three components, two are similar pumps whose data are shown by Figure 12b,c, which present different changes in physical behaviors. Both data are used in order to train the same model and predict with the aim to check the model performance for different data from similar components. For both data, the performance is same (96%).  
Data preprocessing consists of special values consideration, which involves the dropping of some data that were taken during out serviced equipment to avoid unnecessary long data dispersion. The real timestamps are also converted into datetime format in order to create a suitable time series  
Figure 12: Distribution of collected data. (**a**) represents the data from steam generator, (**b**,**c**) represent the data from two different pumps.  
dataset in Python. We define a function to create a balanced dataset in order to build a relationship between timestep \(X_{i}\) with time dependence \(t\).  
Because LSTM acquires a series of past observations to build a function for new input series, our data are sampled into small subsequences in order to comfort the LSTM learning. Each subsequences is made up by a sample of timesteps from which LSTM learns to predict the next time steps. To fix the length of sample, we iterate the process of model training and prediction using different lengths to obtain their optimal number with minimum error. Our minimum model error obtained a sample size of 70 steps.  
In order to comply with the time steps LSTM array structure, we transformed our data to be in three dimensional array in the form of [sample, time steps, features]. Prior to starting to learn our model, we randomly split our data into the train dataset, which is indexed to 80%, whereas the remaining 20% were reserved for test dataset.
## 4 Backgrounds and Methods
### Handover Procedure of the Dual MRNs  
In this section, we outline the proposed network model with dual MRNs, illustrated in Figure 9. A main MRN (mMRN) and a secondary MRN (sMRN) are located at the front of the vehicle (without distance). The benefit of this model is that mMRN performs the proposed HO scheme and represents it for the sMRN. The dual MRN-based network architecture focuses on broadband wireless communication and is intended for passengers of high-speed vehicles. The passengers connect directly to an access point installed inside the vehicle. This access point connects to the donor eNBs (DeNBs) of the LTE network, which employs dual MRNs on the outside of the vehicle. In addition, the access point collects data that are sent to the DeNBs. The dual MRNs simultaneously execute the HO from the serving DeNB to the target DeNB, which is the same for each user. Two main concepts in the proposed HO scheme enable changes: (i) during the HO procedure, the MME introduces information about the MRNs connecting to the serving DeNB and target DeNB; and (ii) the S-GW utilizes a multipath method and saves the various data transmission routing paths serving the DeNBs and target DeNBs.  
When the serving DeNB sends a measurement control message, only the mMRN replies with a measurement report. The measurement report comprises context information (including the speed and trajectory) associated with the vehicle to the serving DeNB. The UEs are served by the sMRN. Further, the serving DeNB makes the HO decisions based on the measurement report from the mMRN. When the HO is necessary, the serving DeNB calculates the HH and CIO; then, the serving DeNB can start the HO procedure of mMRN.
# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 4 SPME in Food Analysis--Main Fields of Applications
### Limitations of SPME in Food Analysis  
Similarly to SPE, SPME has some limitations. The SPME fibers are not uniformly sensitive to all compounds, thus peak areas for an SPME sample do not properly reflect the true proportion of VOCs in the headspace. Moreover, the adsorption selectivity of the fiber and competition between VOCs in their adsorption of the SPME fiber may affect quantification [131]. A recent commentary underlines the problem of competition on the fiber, including internal standards, which can lead to inappropriate \"semi-quantification\" [115]. The food matrix is in general rich in polar compounds, which have lower affinity to the coatings used for SPME fibers. This can lead to saturation, swelling and displacement effects during the analysis of VOCs in food products [2]. Another limitation of SPME is the high cost of fibers, which additionally are very fragile and easy to break. Some attempts have been made to diminish this limitation by the production of more resistant stable-flex fibers and SPME fibers on metal alloys instead of silica, which improves the mechanical stability of the device [132].
## 2 MAP Detection
### Proposed Model  
Figure 5 illustrates the diagram of the proposed model.  
In a holographic data storage system, data are first converted into a pattern of light and dark areas using a spatial light modulator (SLM). A laser beam is split into two: a reference beam and a data beam. The data beam is modulated by the pattern created by the SLM. The reference beam and modulated data beam intersect at a photosensitive material, such as a crystal or photopolymer, creating an interference pattern that contains the data. This interference pattern is recorded in the material, which changes its refractive index, creating a 3D holographic image of the data. To read the data, a laser beam is shone onto the material at the same angle as the reference beam used to record the data. This creates an identical reference beam that interferes with the holographic image, reconstructing  
Figure 4: Trellis for the horizontal detection.  
Figure 5: Diagram of the proposed model.  
the data. The reconstructed data can then be captured by a detector, such as a camera or photodiode array.  
In the model, an SLM was used to modulate the user data \(u[k]\) into the data page \(a[j,k]\). The signal and reference beams were then combined to create a hologram. Data were stored in the medium as holograms. The readback signal \(y[j,k]\) was affected by 2D interference and noise as below [3; 26]:  
\[y[j,k]=a[j,k]\otimes h[j,k]+w[j,k], \tag{7}\]  
where \(\otimes\) is the 2D convolution operator and \(w[j,k]\) is the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\). We used the range of the discrete point spread function (PSF), \(h[j,k]\) with \(5\times 5\) array pixels. The discrete PSF \(h[j,k]\) can be calculated as follows:  
\[h[j,k]=\int\limits_{j-\sqrt{2}\,k-\sqrt{2}}^{j+\sqrt{2}\,k+\sqrt{2}}h(q,t)dqdt, \tag{8}\]  
where \(\alpha\) denotes the linear fill factor. We choose \(\alpha=1\), same as that selected by [PERSON] and [PERSON] [16]. \(h(q,t)\) is the continuous PSF, calculated as follows:  
\[h(q,t)=\frac{1}{\sigma_{b}^{2}}\sin c^{2}\left(\frac{q-m_{q}}{\sigma_{b}}, \frac{t-m_{t}}{\sigma_{b}}\right), \tag{9}\]  
where \(sinc(q,t)=(sin(\pi q)/\pi q)(sin(\pi t)/\pi t)\), \(\sigma_{b}\) is the blur grade, and \(m_{q}\) and \(m_{t}\) are the misalignments in the horizontal and vertical directions, respectively. We define SNR as follows:  
\[\text{SNR}=10\log_{10}\left(\frac{1}{\sigma^{2}}\right) \tag{10}\]
## 3 Diffuse Reflectance Spectroscopy: Analyzing Results
### Pitfalls and Artifacts
#### 3.3.3 Luminescence  
As excitation spectra require additional filtering to remove secondary signals, similar considerations can be applied on the detection side. During a wavelength-sweep, luminescent materials may emit signals separated by an octave or more to the momentarily investigated wavelength. If the same setup is used on both excitation and detection side, such potentially transmitted higher order diffracted wavelengths entail a similar spectral filtering, especially if luminescence lifetimes become comparable to scan speeds, in particular when samples are cooled [97; 98; 99; 100; 101; 102].  
For short luminescence lifetimes (e.g., \(\tau\approx 65\,\)ns for YAG:Ce [88]), samples may be treated like a non-fluorescent material because the luminescence decay time is significantly shorter than the measurement time of the spectrometer. For long decay times, a reverse wavelength-sweep is preferable, owing to the Stokes shift of the luminescence, i.e., emission can only be triggered by shorter wavelengths (neglecting anti-Stokes emission by upconversion luminescence [59], for instance), thereby circumventing the issue of higher-order diffractions. Alternatively, the forward scan direction (shorter to longer wavelengths) can be used if the investigated spectral range is divided into distinct sections: First, the region is investigated where no luminescence is present; the diffuse reflectance at regions of high absorption that are the prerequisite for luminescence are to be assessed lastly. However, wavelength regions with overlapping emission and absorption bands [89; 102] may pose a hindrance and need to be evaluated on a case by case basis. Nevertheless, the use of color filters is recommended in either circumstance.