esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Present Situation and Development Trend of Rockburst Early Warning
### Development Trend of Rockburst Early Warning  
Conventional microseismic monitoring equipment require cable communication between the sensor and the acquisition instrument and between the acquisition instrument and the data analysis center. In the environment of continuous tunneling, if cables are not arranged appropriately, they can easily undergo damage, posing significant problems to system maintenance and continuous monitoring and early warning. In the future, microseismic sensors will be developed in the direction of telemetry and intelligence, with capabilities such as self-calibration, self-adaptation, self-organization, and self-diagnosis. They can be integrated with other monitoring systems to form a regional automatic monitoring network.  
Until now, the early warning of rockbursts has been limited to predicting the location, level, and risk probability of potential rockbursts, and it remains difficult to predict the occurrence time. On the one hand, it is limited by monitoring means and monitoring technology; on the other hand, the occurrence of a rockburst is the result of the combined effect of many factors. Although the early warning of rockbursts based on microseismic indices can help quantitatively predict the rockburst risk, it considers only the statistical relationship between the microseismicity and rockburst; the complex interaction between the rockburst area and the surrounding rock mass is ignored. Based on the quantitative warning provided through a mechanical analysis, multiple factors can be synthesized to predict the rockburst time. However, rockburst warning based on the mechanical model still lacks a quantitative means. By establishing a fine mechanical model and using numerical simulation, combined with microseismic and other monitoring data, the microfracture evolution, stress field, and damage process can be combined to realize monitoring-based simulation and simulation-based warning, which will become the development trend for rockburst warning.  
In recent years, with the rapid development of artificial intelligence research, deep learning and other methods have been gradually applied to microseismic signal waveform classification, arrival picking, and rockburst early warning. With the help of the relevant methods based on artificial intelligence, early warning methods may overcome discontinuous artificial early warning and realize intelligent continuous monitoring, analysis, and prediction. However, artificial intelligence-based rockburst early warning is still in the exploratory stage, and on-site monitoring is far more complex than the sample set method. How to adapt to the complex engineering environment and realize large-scale applications on the premise of ensuring accuracy is an important research direction.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Additives in Edible Films
#### 3.6.2 Emulsifiers  
Emulsifiers possess both polar and nonpolar components, making them surface-active compounds capable of altering interfacial energy at the interface of immiscible systems, such as a water-lipid interface or a water-air surface. The presence of emulsifiers is necessary for the creation and stabilization of well-dispersed lipid particles in composite emulsion films. Also, emulsifiers contribute to achieving appropriate surface wettability, ensuring proper surface coverage and adhesion to the coated surface. Natural lecithins are important emulsifiers; glycerol monopalmitate, glycerol monostearate, acetylated monoglyceride, sodium lauryl sulfate, polysorbate 60, polysorbate 65, polysorbate 80, sodium stearoyl lactylate, sorbitan monostearate, and sorbitan monooleate are a few other common emulsifiers [37]. Because of their amphiphilic nature, many proteins exhibit emulsifying capabilities [96].
## 3 Results and Discussion
### Intercomparison of Applied Models  
To better elucidate the model prophecy impact further, we applied the Taylor diagram ([PERSON] et al., 2019). The nearer the pentagram was to this line, the nearer the prophecy was to determine the Cu, Co and Pb concentration prediction ([PERSON] et al., 2019). The Taylor diagram is a polar graph in which the cosine of the angle amid the X-axis is the CC in Cu, Co and Pb of the model. The radial direction is the ratio of model to metals standard deviation. The grey arcs signify the RMSE normalized by the standard deviation for the apiece model ([PERSON] et al., 2019). Figure 10 shows the Taylor diagram for the comparison coefficient of correlation and RMSE in the validation stage for predicting Co, Cu and Pb using applied models. This suggests that MLP-established models perform better than BT-built models for predicting the Cu and Pb, whereas BT models perform better than MLP-based models in predicting the Co. Overall, the neural network models based on MLP are closer to the line and fit well, in contrast with other models for predicting the concentration of Cu, Co and Pb.
## 2 AnFIS-PID  
### Theory  
The standard ANFIS controller has a five-layer structure, including a fuzzification layer, rule layer, normalisation layer, defuzzification layer, and summation layer [27]. In the defuzzification layer, the output of each rule is usually defuzzified using T-S-type fuzzy inference (see Equation (1)). In order to solve the problem that the change in the target temperature during the RFA process may lead to the decrease in the control effectiveness of the fixed-parameter PID controller, an improved scheme is proposed in this paper. Part of Equation (1) is replaced by an incremental PID controller to construct a new ANFIS-PID controller, as shown in Figure 1, Equation (2).  
Figure 1: Structure of the ANFIS-PID.  
\[net_{i}=\overline{W_{i}}*(p_{i}*E+q_{i}*EC+r_{i}) \tag{1}\]  
where \(\overline{W_{i}}\) is the output of layer3, \(p_{i}\), \(q_{i}\), and \(r_{i}\) are the consequence parameters, \(i=1\), 2, 3, and 4, and \(E\) and \(EC\) are the inputs.  
\[net_{i}=\overline{W_{i}}*\big{(}K_{p}*EC+K_{i}*E+K_{d}*(E(k)-2*E(k-1)+E(k-2)) \big{)} \tag{2}\]  
Among them, \(E(k)\), \(E(k-1)\), and \(E(k-2)\) represent the temperature error at time \(k\), \(k-1\), and \(k-2\), respectively, while \(K_{p}\), \(K_{i}\), and \(K_{d}\) are the consequence parameters.
## 5 Indirect Cooperation via a Third-Party Entity
### Adjustment of LTE Transmission Based on Wi-Fi Requirements
For the cooperation technique that is described in this section, no synchronization is needed between Wi-Fi and LTE, since there is no need for synchronized access to the medium between the different technologies. The implementation of such a cooperation scheme requires some changes to both LTE and Wi-Fi. Regarding LTE, it needs to be extended in a way that it can receive and use the transmitted messages by the CCE. This extension also includes the introduction of the additional mute period after a transmission in the unlicensed spectrum and variable transmission period. On the other hand, the Wi-Fi STA part has to be extended to be capable of reporting the transmission requirements to the AP. Additionally, the AP must be able to collect, evaluate and report these requirements to the CCE. Finally, a sophisticated CCE needs to be employed. This CCE must be able to collect the requirements from the Wi-Fi AP, process them and inform the eNB about the new configuration parameters. The described cooperation scheme targets serving the Wi-Fi requirements by adjusting the transmission duration of LTE, concurrently maintaining equal channel access opportunities among the participating networks. This technique can be implemented with a relatively small effort, as it requires small modifications for LTE and Wi-Fi as described above. On the other hand, it does not take into account the requirements of LTE. In the next section, a more complex, but enhanced technique is proposed, in which the CCE schedules LTE and Wi-Fi transmissions, considering the requirements of both networks.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 3 Results
### Condition Factor Evolution  
As the change in width appears to be proportionally consistent across different length groupings, this section examines the condition factor, defined as the ratio of the fish's width to its length. Utilising this factor removes the need for length groups, enabling a more distinct separation of the fattening versus growing aspects of the operation.  
In Figure 9, a four-stage evolution of the condition factor can be observed: a significant increase during the initial three and a half months in cages (28 July 2020 to 15 November 2020), followed by a plateau in the subsequent two months (15 November 2020 to 22 January 2021), another increase in the subsequent two months (22 January 2021 to 24 March 2021), and another plateau in the last two months (24 March 2021 to 21 May 2021). When compared to environmental variables and the amount of feed, the increase in the first stage and the plateau in the second stage appear directly correlated with water temperature and feed quantity. However, the increase in the third stage and the plateau in the fourth stage do not exhibit the same direct relationship. In this case, the relationship could be elucidated by considering the dissolved oxygen concentration and the amount of feed. The increase in the factor aligns with an increase in dissolved oxygen concentration, whereas the plateau coincides with a decrease in the amount of feed. Additionally, it is known that  
Figure 8: Average width (\(\overline{\Lambda}\)) for fish grouped according to their straight fork length (SFL) between July 2020 and May 2021.  
Figure 7: (**a**) Width (A) frequency histograms for July 2020 and May 2021. (**b**) Scatter plots of straight fork length (SFL) and width (A) for July 2020 and May 2021.  
from January onwards, when daylight begins to increase, physiological changes associated with the annual reproductive cycle occur. It is important to note that the feeding regime adheres to the company's production policy and was not part of the experimental design.
# Multi-Span Tension Control for Printing Systems in Gravure Printed Electronic Equipment
## 1 Introduction
(2) Utilizing both the extended state observer (ESO) and the tracking differentiator (TD) for the reasonable extraction of differential signals, which can effectively suppress interference signals. (3) Eliminating the integration step and adopting a nonlinear state error feedback (NLSEF) control method, resulting in higher efficiency and better control performance. (4) Real-time estimation of unknown disturbances within the system using an extended state observer (ESO), followed by immediate compensation for the estimated disturbances, thereby enhancing the dynamic performance of the system. A decoupling controller based on ADRC was proposed for the rewinding tension system by [PERSON] [27]. He built an ADRC decoupling controller for the rewinding tension system of the R2R printing equipment [28]. [PERSON] designed a parameter self-tuning decoupling controller based on ADRC for tension systems of roll-to-roll coating machines [29]. [PERSON] proposed a feedforward ADRC parameter self-tuning decoupling control strategy to improve the control precision of the nonlinear register system for flexographic printing [30]. It can be observed that the aforementioned research primarily focuses on both unwinding and rewinding tension systems, whereas relatively little attention has been devoted to printing tension systems. The primary rationale behind this phenomenon stems from the fact that traditional image-text printing requires lower equipment precision, thus ensuring effective control of both the unwinding and rewinding tension systems suffices. However, when it comes to gravure printed electronic equipment, which demands stricter standards for tension-control accuracy, a more profound investigation into the printing tension system becomes imperative. Given its characteristic of multi-span coupling and the substantial influence from the drying system, the printing tension system necessitates the exploration of decoupling control methodologies tailored specifically for it. The control strategy employed ought to possess good decoupling and anti-interference capabilitiesbased on limited tension model information while maintaining ease of implementation in engineering practices. This article is based on previous research on tension models and studies the printing tension-control system. Firstly, based on the characteristics of the printing tension system, a control system structure was constructed. Then, the coupling model was decoupled to obtain a static decoupling model, and the first-order ADRC controllers were designed. Finally, through simulation and experimentation, the performance of the designed control system was analyzed and verified.
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 4 Cluster-Based Channel Allocation Method  
The proposed channel allocation method is composed of two modules. One is called a base module and the other is named an adaptive module. When an interference relationship graph \(G=(N,E,W)\) is given, the base module partitions the graph into a set of clusters so that the modularity of the graph is maximized. Then, it allocates different channels to the WBANs in the same cluster. The adaptive module is devised to adjust the channel allocation process to the gradual changes in the topology of an interference relationship graph. The adaptive module uses the cluster information resulting from the base module. By avoiding the graph partitioning process, the adaptive module can reduce the time complexity for the channel allocation.
# Internet of Things (IoT)-Based Solutions for Uneven Roads and Balanced Vehicle Systems Using YOLOv8
## 3 Methodology
### Dual Detection Methods  
#### 3.2.1 GY-521 Sensor-Based Detection  
The GY-521 sensor is a high-precision device that detects acceleration and angular velocity variations. It plays a vital role in assessing changes in road height and ensuring vehicle stability. Proper calibration of the sensor is essential. Factors such as the type of road surface--whether it is paved, gravel, or uneven--the sensor's precise location on the vehicle, and the vehicle's speed must also be considered. These elements significantly influence sensor readings and, in turn, affect the overall system's accuracy. Effective calibration allows the sensor to adjust to diverse road conditions, providing consistent and reliable data for analysis. The sequential dependence of sensor values is checked to remove bias [42].  
Beyond detecting fluctuations, the GY-521 sensor measures tilt angles, vibrations, and other subtle interactions between the vehicle and the road. It captures multidimensional data across the x, y, and z axes, which is processed using an advanced algorithm.  
Figure 1: System Architecture Featuring Edge–Cloud Collaboration: The Jetson Nano handles real-time anomaly detection and tilt correction, while the cloud layer supports OTA updates, fallback diagnostics, and fleet-level adaptation. This feedback mechanism forms the core of the IoT-enabled learning loop.  
This algorithm analyzes the sensor readings and identifies patterns to detect surface abnormalities such as potholes, bumps, and uneven turns. The capability to extract meaningful insights from these measurements enhances the system's ability to accurately identify road irregularities, improving the overall effectiveness of the detection system.
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Data Analysis
#### 4.1.2 Baselines  
In our experiments, we benchmark our proposed methodology against a diverse set of representative models that span across three major categories in the domain of product demand prediction. This comparative analysis aims to highlight the strengths and potential of our approach in capturing the complex dynamics of demand prediction. Below is an introduction to each baseline model utilized in our study.  
Linear Model:  
* Generalized Linear Model(GLM) [18]: A foundational approach in statistical modeling, GLM extends traditional linear regression to support various types of distribution for the target variable, such as binomial and Poisson distributions. This model is pivotal for understanding the linear relationships between the features and the target demand, serving as a baseline to assess the incremental benefits of more complex models.  
Tree-based Model:  
* XGBoost [19]: A highly efficient and scalable implementation of gradient boosting framework, XGBoost has gained popularity for its performance in various predictive modeling competitions. It leverages an ensemble of decision trees, optimized through gradient boosting, to capture non-linear relationships and interactions among features.
* LightGBM [20]: An advanced gradient boosting model that utilizes a novel tree-growing algorithm to enhance efficiency and scalability. LightGBM is designed to handle large-scale data, offering a faster training process without compromising on model accuracy.
* CatBoost [21]: Another gradient boosting variant, CatBoost is renowned for its handling of categorical features directly, without the need for extensive preprocessing. It provides robust solutions to avoid overfitting, making it highly effective in diverse predictive tasks, including demand prediction.  
Deep Model:  
* Multiple Layer Perceptron (MLP) [22]: MLP is a class of feedforward artificial neural network (ANN) that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. MLP utilizes a backpropagation technique for training, capable of capturing complex non-linear relationships between inputs and outputs.
* LSTM [23]: Long Short-Term Memory networks, a type of recurrent neural network (RNN) architecture, are specifically designed to address the vanishing gradient problem of traditional RNNs. LSTMs are adept at learning long-term dependencies, making them particularly suitable for time-series predicting tasks like demand prediction.
* GRU [24]: Gated Recurrent Units (GRUs) are a variant of RNNs that simplify the LSTM architecture while retaining its capability to capture dependencies over various time spans. GRUs offer a more efficient and equally effective alternative for sequential data modeling.  
CNN [25]: Convolutional Neural Networks, traditionally known for their prowess in image processing, have also been adapted for spatial predicting. By capturing spatial dependencies through their hierarchical structure, CNNs can be utilized to effectively model the geographical information and relationships in demand data.
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 3 BACQ Proposal
### Avoid Inquire-Then-Update by Ticketing  
Before publishing the element, we must ensure the FIFO order. To achieve this, we could use an inquire-then-update approach. CBQ [10] noted that adding an integer at each array position can avoid the inquire-then-update. However, CBQ fails to detect boundary  
Figure 8: BACQ local tail calculation by using virtual cache layer.  
Figure 6: Memory Hierarchy of GPU.  
Figure 7: Shuffle semantics of CUDA.  
conditions. We improved their design by proposing a ticketing mechanism to ensure FIFO order and avoid boundary conditions as much as possible.  
Without properly using the ticketing mechanism, we risk violating FIFO semantics, as illustrated in Figure 9. Consider a group of five concurrent thread requests listed up by their invocation order (T1-T5). Due to the scheduling decisions of the thread scheduler, these requests might execute in a different order T1,T2,T4,T3,T5, deviating from the invocation sequence. On Figure 9, T3,T5 threads execute later than T1,T2,T4 as represented by the bottoms rows. Three requests (enq(x),deq:x,enq(x)) occur on index 0 and the expecting dequeue operation to return \(x\). Without tickets (calculated from epoch of the number of roundtrips to the queue) to manage these requests, the sequence can become jumbled, resulting in dequeue incorrectly returning \(z\) instead of \(x\).  
One effective approach to maintain order is by attaching a ticket to these requests at invocation to establish their order. Figure 10 illustrates how this ticketing scheme resolves the incorrect execution demonstrated in Figure 9.  
For each index in the queue, our strategy attempts to match the ticket associated with the operation before proceeding; if there is no match, we wait until it becomes available. If an enqueue operation arrives early, we attach a ticket calculated as \(2\times epoch\), and the next dequeue operation, the ticket is \(2\times epoch+1\). After employing the ticketing mechanism:  
* Even if T4 initiates before T3, it waits until the ongoing dequeue operation (deq:x) at the same index completes its execution. This wait ensures a FIFO order and prevents the thread scheduler from altering the order.
* Once T3 completes its operation (i.e., publishing the node and issuing a new ticket, which is the old ticket + 1), the subsequent enqueue operation (enq(z)) can match the ticket associated with the zero-th index position. This ticketing mechanism effectively preserves the FIFO semantics of the queue, even when the execution order of threads deviates due to thread scheduling.  
Figure 9: Broken FIFO semantics without tickets (buffer size is 2).
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 4 Results and Discussion
### Generalizability of the Proposed Method  
Conventional methods involve essential elements, e.g., camera equipment and shooting angles, the observer's labor cost, and the analyst's labor cost, and such methods have economic and temporal limitations relative to securing large amounts of data. In contrast, the proposed method, including the sensors and analysis model, can be employed to record and analyze the duration of each task for all on-site equipment when equipped with sensors that can measure the piston displacement of real-world equipment using data loggers. Compared with vision-based methods, the proposed method allows for long-term data collection because the capacity of data storage space is greatly reduced. Compared to a vision-based detection model that uses an image of size 1280 \(\times\) 720 pixels as its basic input data, the model requires storage space for three primary colors (e.g., red, green, blue) for each of the 1280 \(\times\) 720 pixels. Therefore, a total of 2,764,800 pieces of color information are needed. However, in this study, only three pieces of piston length information are needed for the analysis of the same vector. In this study, the training data consist of 69 samples (200 vectors per sample) from six actions performed by five individuals, with a total of 414,000 vectors and a total data volume of only 2.24 megabytes. Assuming that an image of 1280 \(\times\) 720 pixels containing 2,764,800 pieces of data requires 1 byte per piece, each vector would require 2.64 MB of storage space. In order to store data for learning in an experiment under the same conditions, 1,092,960 megabytes (about 1067 gigabytes or 1.04 terabytes) of storage space is required. Thus, it can be used to obtain a large amount of relevant and quantitative data over time at the same investment cost.  
Furthermore, the proposed method can be set up as an independent historical observation device, offering advantages in practical applications that encounter observation blind spots or disruptions in satellite communication. It is particularly beneficial in scenarios such as tunnel and underground construction, nighttime operations, or conditions where distinguishing object boundaries becomes challenging due to extremely bright or dim lighting.