esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Campaign and Data Selection  
The experiment was carried out for over a year, starting on 17 February 2020, and finishing on 28 February 2021. Typical office working hours in Chile (9:00 to 18:00 h) were considered in the analysis. Full days with valid data were selected from the complete dataset for thermal comfort, visual comfort, and energy consumption measurements to carry out the performance analysis. A day with valid data was defined as a day with complete measured data for unoccupied offices within working hours. Table 2 shows the number of days with valid data for each season.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Season**} & **Energy** & **Thermal** & **Visual** \\  & **Consumption** & **Comfort** & **Comfort** \\ \hline Summer & 73 & 62 & 55 \\ Autumn & 32 & 37 & 33 \\ Spring & 63 & 71 & 68 \\ Winter & 6 & 34 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantity of days with valid data.  
Figure 1: Office dimensions (mm). (**a**) Room with PCM glazing (main); (**b**) Room with a double-glazed window (reference).  
Figure 2: (**a**) window dimensions (mm); (**b**) partial enthalpy of paraffin RT25.  
Then, three representative days for each season were selected from the entire dataset. For mid-seasons, two groups of three days were selected based on warm and cold outdoor conditions. Sunny, partly cloudy and cloudy days were chosen for the three days in mid-season warm, mid-season cold and winter. In summer, the typical days were sunny or mostly sunny. Table 3 shows the representative days selected.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 1 Introduction
A comprehensive approach combining theoretical analysis, numerical simulation, and industrial test is employed to investigate the stability mechanism and control technology of GER with paste backfill. This study first introduces the engineering background, including the location and geological characteristics of Chahasu Coal Mine, providing essential data for the subsequent study. Next, theoretical analysis is conducted to explore the overburden movement characteristics of GER with paste backfill and the bearing properties of the filling material. Following this, numerical simulations are carried out to examine the deformation patterns of surrounding rock and distribution patterns of abutment stress. Finally, this study proposes a comprehensive control technology system for surrounding rock stability in GER with paste backfill, based on the use of anchor-net-cable as the primary support structure and locally enhanced support with a portal frame. This system addresses the issue of insufficient initial strength and poor bearing capacity of the slurry filling material. The findings provide a solid theoretical foundation for the study of surrounding rock stability mechanisms and control technologies in GER with paste backfill.
# An IoT-Based Solution for Intelligent Farming +
## 2 Related Work
### IoT/M2M Protocols and Communication Technologies
#### Application Layer  
The Application Layer creates the final level of abstraction that allows the development of different kinds of user applications. In addition, the strict computational and energy constraints of IoT devices prompted the emergence of several lightweight application layer protocols, such as CoAP, AMQP, MQTT and HTTP, to cite just a few.  
COAP [12] is based on a request/response architecture and runs over UDP. As it is based on HTTP methods, it is interoperable with HTTP. For security, it uses the Datagram Transport Layer Security, similar to TLS in TCP. AMQP [35] is based on an asynchronous publish/subscribe architecture, runs over TCP and uses TLS/SSL to ensure security. It provides Quality-Of-Service (QoS) guarantees, specifically: _at most once, at least once and exactly once._ As AMQP, MQTT [36] also runs over TCP, it is also based on an asynchronous publish/subscribe architecture; it also uses TLS/SSL to ensure security and it supports three types of QoS, in this case: _fire and forget, delivered at least once and delivered exactly once._ HTTP is a popular web messaging protocol based on a request/response architecture [37]. It runs over TCP, uses TLS/SSL for security and does not define any QoS. Contrary to the aforementioned protocols, HTTP does not define the header and payload sizes, being dependent on the web server implementation. The choice of the protocol to be used is a daunting task and depends greatly on the kind of application and devices used. Thus, normally, there is not just one possible correct choice [9].
# An Empirical Study on Ensemble of Segmentation Approaches
## 3 Results
### Datasets and Testing Protocols
#### 3.1.3 Leukocyte Segmentation (LEUKO)  
Leukocyte recognition is the task of segmenting the white blood cells from the background, with the aim of diagnosing many diseases such as leukemia, and infections. In our experiment, we use the freely available ([[http://users.cecs.anu.edu.au/~hrezatofighi/Data/Leukocyte%20](http://users.cecs.anu.edu.au/~hrezatofighi/Data/Leukocyte%20) Data.htm]([http://users.cecs.anu.edu.au/~hrezatofighi/Data/Leukocyte%20](http://users.cecs.anu.edu.au/~hrezatofighi/Data/Leukocyte%20) Data.htm), (accessed on 20 April 2022)) LASC database [16], a collection of 250 hematological images extracted from the peripheral blood of eight healthy people. Images have been acquired at high resolution (720 \(\times\) 576 pixels) and manually labelled to segment 10 different type of leukocytes. In this work, we do not perform classification; therefore, we consider only the segmentation performance. The testing protocol, as suggested by the authors of the dataset, is a 10-fold cross validation: dice results are calculated at the image level and averaged for each fold and then on the 10 folds. In this dataset, we use resized images of size 513 \(\times\) 513.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Short Name** & **Name** & **\#Samples** & **Ref.** \\ \hline Prat & Pratheepan & 78 & [43] \\ MCG & MCG-skin & 1000 & [44] \\ UC & UChile DB-skin & 103 & [45] \\ CMQ & Compaq & 4675 & [46] \\ SFA & & 1118 & [47] \\ HGR & Hand gesture recognition & 1558 & [48] \\ Sch & Schmuge dataset & 845 & [49] \\ VMD & Human activity recognition & 285 & [50] \\ ECU & ECU face and skin detection & 2000 & [42] \\ VT & VT-AAST & 66 & [51] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test skin datasets. ECU is split: 2000 images for training and 2000 for tests.  
Figure 1: Samples from polyp segmentation, skin segmentation, leukocyte identification, butterfly, and microorganism identification (images and masks).
## 3 Factsage in the Environmental Sector
### Application in the Field of Solid Waste
For instance, [PERSON] et al. [129] used the Equilib module to optimize the process conditions for the reduction and smelting of red mud to obtain the ferrous phase, used the Viscosity module to determine the liquid phase line temperature of the slag during the reduction process is mainly related to the sodium content therein, and validated these simulations with a laboratory scale electric arc furnace. Similarly, [PERSON] et al. [130] applied Factsage to simulate the carbothermal chlorination of aluminum in fly ash, analyzing the feasibility of the process and specific phase composition using dominant zone diagrams, phase equilibrium calculations, and multivariate phase diagrams, providing a thermodynamic basis for the carbothermal chlorination of aluminum in fly ash. Additionally, Factsage can guide the recovery of rare earth elements such as gold and yttrium and optimize their processes, which could contribute to the recycling of metal resources [143; 144]. In conclusion, Factsage offers a diverse array of applications within the realm of solid waste management. It proves instrumental in the thermal treatment of various solid waste materials, allowing for the determination of optimal treatment temperatures and raw material ratios. Additionally, the software facilitates the efficient recovery of valuable metals by optimizing recycling processes, thereby mitigating the generation of secondary pollution. Furthermore, Factsage aids in characterizing the physicochemical properties of solid waste materials, providing a theoretical foundation for the treatment and disposal of solid waste, thereby contributing to the achievement of resource recovery, waste reduction, and harmlessness.
# A Visual User Interfaces for Constant Checking of Non-Invasive Physiological Parameters
## 5 Conclusions and Recommendations  
### Conclusions  
There are an infinite number of microcontrollers and sensors to create electronic prototypes, particularly for the medical industry that measures physiological parameters. To avoid giving the patient inaccurate results, we must carefully select the instruments, so they were assessed to ensure a 95% effectiveness level. All of the free software tools used in this research, such as Cayenne myDevices (cloud storage), Blynk App (graphical user interface and real-time visualization), and Arduino (coding), allow for greater integration of changes and processes and reduce the need for expensive applications and IoT platforms that perform the same function. Patients with COVID-19, cardiovascular patients, diabetics, and others can use an electronic prototype for monitoring physiological parameters such as temperature, heart rate, and oxygen saturation while exercising. A successful prototype was produced using the Max30100, Mk90614 sensors, and Esp8266 microcontroller to enable data transmission and visualization. Additionally, the temperature sensor's technical capabilities were insufficient to accurately measure a human's body temperature, necessitating a calibration algorithm. The Cayenne myDevices platform offers a web dashboard that continuously tracks physiological parameters and stores this data, making it simple for the patient to access and browse a daily log. Data from the server and the electronic prototype are visualized and tracked in real-time by Blynk, an IoT-based application with a graphical user interface. Designing widgets and configurations was simple with Blynk's interface. Surveys revealed that the usability and data quality of the electronic prototype was very high. Response times were quick, but during the final testing, problems with the internet connection prevented the platform and app from communicating with the electronic prototype. Due to excessive finger movement and posture, some measurements were taken at the upper limit of the acceptable range.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 3 System Model and Problem Formulation  
### System Model and Proposed BSs Switching On/Off Mechanism  
This section describes a cooperation management algorithm involving the LTE and 5G networks, motivated by a responsive dual radio access technology (RAT) BSs switching off/on approach that considers the cell coverage and energy savings. The cellular network topology model considered in this study is shown in Figure 3, wherein the 5G small cells are integrated with the existing LTE macrocell. Every seven 5G small cells are covered by one LTE macrocell. In addition, all the cells in the network are connected via a switching off/on server that is located in the central office. The questions that may arise in the proposed approach are: (i) How do we identify the cells that should be switched off, and those that should remain active? (ii) What key performance indicators (KPIs) for BSs switching scheme should be adopted? (iii) What is the mechanism for the switching scheme of BSs? (iv) What KPIs should be considered during the switch-off session? (v) During a switch-off session, is there enough radio coverage for guaranteed QoS considering a given cell?  
Basically, the idea behind the proposed algorithm is to exploit the coexistence between LTE and 5G and achieve an equilibrium between network performance and EE through a dynamic BS switching off/on approach based to the traffic demand conditions. Figure 4 depicts a real-world wireless CN traffic profile, as reported in [33]. The traffic profile during the day is higher than that of the night. The high traffic load ranges between 0.4 and 1, and occurs between 10 a.m. and 11 p.m. (13 h). The low traffic load ranges between 0 and 0.4, and occurs between 11 p.m. and 10 a.m. (11 h).  
Figure 3: Cellular network layout (\(R_{5G}\) = 200 m; \(R_{LTE}\) = 500 m).  
During the off-peak period, a 5G BS is expected to be switched off while the remaining active LTE BSs will provide coverage for users, as well as support the basic operations of the network. However, the BS switching off/on paradigm is expected to adhere to certain guidelines. The guidelines are expected to consider execution, evaluation, and energy utilization. The operational BS off/on decision is subject to three parameters: (i) Maintaining the high-speed packet requirement for mobile users during peak periods; (ii) switching on the smallest possible number of neighboring cells during low traffic, which guarantees an appreciable reduction in the energy used; and (iii) ensure adequate wireless coverage to neighboring cells, while maintaining coverage during switch-off sessions. The details will be given in the following subsections.
## 4 Experiment and Result
### Evaluation Baseline  
The proposed model was compared with different baseline deep learning-based models, Convolutional Neural Network (CNN), Long Short Term Memory Network (LSTM) Bidirectional LSTM (Bi-LSTM), ensemble model CNN-LSTM, pre-trained Multilingual BERT (M-BERT) Model, and machine-learning classifiers such as Decision Tree (DT), Logistic regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), all of which were trained on the augmented dataset of Bengali news. The following sections provide an overview of these models' performance.
## 3 Experiments
### Evaluation Metric  
The goal of the network is making sure the model can accurately segment the LV from cardiac MR images. For objective evaluation of the proposed model, the region-based dice metric (DM) and Jaccard coefficient (JC) are employed as metrics. DM and JC are explained in detail as follows.  
**Dice Metric**--DM calculates the overlap between the manual segmentation area and the automatic segmentation contour area obtained using the proposed method. DM lies in the [0, 1] range. The better the match between manual and predicted segmentation, the larger the DM value is. DM is defined as:  
\[DM(A,B)=\frac{2|A\cap B|}{|A|+|B|}, \tag{8}\]  
where \(A\) and \(B\) represent the area of manual and automatic contour, respectively.  
**Jaccard Coefficient**--The Jaccard coefficient, also named the Intersection over Union (IoU), is used to calculate the degree of dissimilarity between the manual segmentation area and the automatic segmentation contour area using the proposed method. Similar to DM, the JC is between [0, 1]. The larger the JC, the lower the similarity. The formula for JC is as follows:  
\[J(A,B)=\frac{|A\cap B|}{|A\cup B|}=\frac{|A\cap B|}{|A|+|B|-|A\cap B|}, \tag{9}\]  
where \(A\) and \(B\) represent the area of manual and automatic contour, respectively.  
In addition, we also use accuracy (ACC) and positive predictive value (PPV) to evaluate the results of pixel classification. They are defined by:  
\[ACC=\frac{TP+TN}{TP+FN+FP+TN}, \tag{10}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) represent true positive, true negative, false positive, and false negative.  
\[PPV=\frac{TP}{TP+FP}, \tag{11}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) represent true positive, true negative, false positive, and false negative.
## 2 Materials and Methods  
### Framework of the Online Refractive Error Screening System  
Figure 3 shows the overall framework of the proposed online refractive error screening system. It is divided into three main stages as follows:  
First, after logging into the personal profile via facial recognition technology, eccentric photorefraction infrared images of the individuals being screened are collected by the refractive error screening terminal. The collected images are then transmitted to a data server via the cloud or stored locally to provide more data usage options. For instance, some data can be manually labeled and used for local model training with CFGN, but this requires a large amount of data. In the framework we propose, it is best to use cloud computing services to perform refractive error calculations on the uploaded image data to reduce the burden of file storage and computational resource usage. At the same time, the model-eye pretrained model deployed on the cloud can increase screening accuracy in small-sample scenarios. Fine-tuning the pretrained model with a small amount of locally collected data reduces data requirements, and uploading data from different regions simultaneously can improve data diversity and enhance model generalization. Finally, the screening results are transmitted to the individual or the parent's computer or mobile phone to view the results.  
Overall, doctors, schools, and healthcare institutions can access the vision screening platform through devices such as computers, mobile phones, and laptops. The platform is divided into a screening interface and a statistical analysis interface. In the screening interface, experts can choose between training or prediction options. In the training option, experts can train their personalized prediction model with data they have collected, and they can choose whether or not to use the model-eye pretrained model and transfer learning methods. In the prediction option, experts can upload their personal data to use existing models for refractive error screening tasks.  
Doctors and schools can access the statistical analysis interface to understand the vision status of local children and adolescents and take timely countermeasures. Parents and students can access their vision health records through a personal vision health app, which connects to the electronic health record database. This allows them to check screening results, track vision status and trends, and receive early intervention and treatment if necessary.  
Figure 3: Routine online vision screening network system.
# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 2 Materials and Method
### Prediction Confidence Interval  
Besides providing a prediction, it would be useful to quantify the correspondent uncertainty. This kind of information proves important as we are dealing with values collected over large and not homogeneous areas.  
Let us consider, for this task, the so-called _confidence interval_[26], a quantity widely used in the statistic field, which quantifies the uncertainty of a prediction.  
Neural network ensembles, usually referred to as _deep ensembles_, in addition to being a widely used and successful technique for the improvement of predictive performance, is also a practical and, most importantly, scalable method for predictive uncertainty estimation [27]. See [28] for a more in-depth introduction.  
The uncertainty of the model prediction is calculated by providing ten outputs for a given input by changes in the network topologies, such as the number of neurons in each layer. The confidence interval is given by the difference between the third quartile and the first quartile computed over the set containing the 10 different predictions. In fact, the range comprised between these two quantities has been demonstrated to be a solid indicator of the reliability of ensemble deep learning predictions [28].
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results  
Figure 5 displays the boxplots for the SQIs analyzed across normalized CF variations, with 1 representing the maximum CF value for each data record. This format visually highlights the differences among the metrics of the SQIs analyzed. The descriptive statistics for each analysis will be detailed further below. These interpretations showcase the collective characteristics of the dataset rather than individual signal properties.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### LSTM-Based Methods
Methods in this category combine LSTM with existing classical static KGC methods to learn time-aware relations or entities for TKGC. They have clear motivations, simple implementations, and are efficient, but they are unable to model the graph structure inherent in TKGs.
## 3 UniChain Architecture
### Software Components
#### 3.3.3 Cipher/Decipher Manager  
In the UniChain system, the Cipher/Decipher Manager functions as the encryption and decryption schemas. Generally, there are two encryption schemas used in the proposal. First, the symmetric key encryption schema is employed to encrypt the EARs. The Cipher/Decipher Manager first creates a symmetric key to encrypt the academic record, and then re-encrypts that key with the public keys of the: university node and student node. Second, the public key encryption schema is adopted to securely distribute information among parties over HTTPS and to encrypt the record that will temporarily be kept in the Deposit-Box Contract (DBC) to facilitate the access by a third party.
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 2 Materials and Methods
### Related Concepts and Symbol Definition
#### 2.1.3 Skill Mastery Patterns and Observed Response Patterns  
For a given Q-matrix, such as \(\mathbf{Q}\) in Equation (1), there are \(2^{K}\) candidate skill mastery patterns, usually named the attribute mastery patterns (AMPs; [19]) and referred to as skill mastery patterns (SMPs) in this paper. This is defined as shown in Equation (6).  
\[\mathbf{A}=\left(\alpha_{\ell k}\right)_{\ell=1,2,\ldots,L,k=1,2,\ldots,K} \tag{6}\]  
where \(\alpha_{\ell k}\in\{0,1\}\) and \(L=2^{K}\), and the values \(\alpha_{\ell k}=1\) indicate that skill \(K\) has been mastered by the student, whereas \(\alpha_{\ell k}=0\) implies it has not been mastered. Each row \(\mathbf{\alpha}_{\ell}\) of the \(\mathbf{A}\) matrix represents a skill mastery pattern, e.g., \(\mathbf{\alpha}_{\ell}=\left(\alpha_{\ell 1},\alpha_{\ell 2},\ldots,\alpha_{LK}\right)\), which reflects the mastery status of skills in an attribute skill mastery pattern \(\ell\). The student's response to all the test items in a specific test is commonly referred to as the observed response pattern (ORP; [19]), e.g., \(\mathbf{x}_{i}=\left(x_{i1},x_{i2},\ldots,x_{ij}\right),i=1,2,\ldots,N\), where \(\mathbf{x}_{i}\) denotes the answers of the student \(i\) for items 1 to \(J\). \(x_{ij}\in\{0,1\}\), \(x_{ij}=1\) indicates that student \(i\) answered item \(j\) correctly, and vice versa. \(N\) and \(J\) represent the number of examinees and items, respectively. The observed response pattern of all students can be expressed in the form of a matrix \(\mathbf{X}\), as shown in Equation (7).  
\[\mathbf{X}=\left(x\right)_{i=1,2,\ldots,N,j=1,2,\ldots,J} \tag{7}\]  
The task of cognitive diagnosis is to assess students' mastery of each skill based on their response patterns, that is, to predict skill mastery patterns (SMPs) from observed response patterns (ORPs).
## 3 Monitoring System Architecture and Software Architecture
### Data Block Design  
To store the fault characteristic parameters and monitoring data of a DTPPF, it is necessary to design tables for the monitoring parameter data table, including the running state parameter table, core device fault monitoring data table, fault characteristic data table, and fault type table. Among these, the core device fault monitoring data table has too many fields, which can affect query efficiency. Therefore, it needs to be split into data tables for the power supply current, filtering branch current, power supply voltage, and rated current and nominal voltage of the filter capacitor according to different fault identification methods. In this paper, we designed a total of eleven main tables in the database, as shown in Table 5.