esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores an integrated control strategy combining Variable Speed Limits (VSL) and Lane Change Control (LCC) to optimize traffic efficiency in ramp merging areas. For scenarios involving multiple ramp merges, a multi-agent reinforcement learning approach is introduced to optimize control strategies in these areas. An integrated control system based on the Factored Multi-Agent Centralized Policy Gradients (FACMAC) algorithm is developed. By transforming the control framework into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), state and action spaces for heterogeneous agents are designed. These agents dynamically adjust control strategies and control area lengths based on real-time traffic conditions, adapting to the changing traffic environment. The proposed Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control in Dynamic Areas (FM-ITC-Darea) control strategy is simulated and tested on a multi-ramp scenario built on a multi-lane Cell Transmission Model (CTM) simulation platform. Comparisons are made with no control and Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control (FM-ITC) strategies, demonstrating the effectiveness of the proposed integrated control strategy in alleviating highway ramp merging bottlenecks.  
variable speed limits; lane change control; multi-agent reinforcement learning; highway merging bottlenecks; integrated control strategy +
Footnote †: journal: Applied sciences  
Article
## 2 Recent Application of Hyperspectral in Food Authentication
### Milk and Milk Products  
Milk and milk products have been also evaluated and analysed using a combination of HSI and MSI. A recent report on the use of HSI to monitor contamination in cheese is described here. The use of HSI (200-1000 nm) to determine starch content as an adulterant of fresh cheese was reported [22]. The adulterated fresh cheese was prepared using varied concentrations of starch of 0.055-12.705 mg g \({}^{-1}\) (0.0055-1.2705%) and PLS regression was used to develop the models [22]. A R\({}^{2}\) of 0.9915 and a root mean square error of cross-validation (RMSECV) of 0.3979 was reported by the authors of this study [22].  
Although NIR HSI has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties [23; 24; 25; 26].
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 4 Current State of Technology
### Spring Boot and Spring WebFlux  
Spring Boot [39] is a popular open-source framework for developing Java applications. It offers a variety of features that streamline the development process, including easy and automatic configuration with minimal requirements of programmatic adjustments, making it a popular choice for developers, especially for fast-paced environments, given its focus on simplicity and the availability of extensive functionalities and a wide array of plugins which ultimately enable the rapid creation and deployment of robust solutions.  
WebFlux [40] is a reactive web framework that became available with the release of Spring 5, specifically designed for building efficient, scalable, and non-blocking web applications. WebFlux provides the required foundation and tools for developing high-performance applications that demand improved concurrency management and robust data flow processing. Compared to conventional blocking I/O, the reactive programming model enables improved utilization of system resources as well as significantly improving overall performance, particularly expected within applications designed to enable increased user concurrency and demanding workloads.  
In addition to reactive programming features, WebFlux also features integration and support for functional programming and is based on an event-driven architecture [41; 42]. This allows for the development of highly responsive applications that can handle large numbers of requests and data flows.  
Spring Boot and WebFlux provide a solid combination for developing modern Java web applications, ensuring a simple and efficient way to build, test, and deploy applications with minimal effort [40].
## 6 Active Control System Performance
It is also noteworthy that disabling eight of 21 actuators did not affect heavily the overall noise reduction by the active casing. In the lower frequency range, where the inertial actuators lack efficiency, the superiority of using more actuators is visible (lesser number of actuators lacks power to control the lowest frequencies). However, for frequencies above approximately 100 Hz, very comparable results have been obtained for all evaluated configurations (with all actuators operating, and some of them disabled). This shows a robustness of the proposed active noise reducing casing.
## 2 Materials and Methods
### Eddy Correlation System  
The study was based on the observation of water flux and the acquisition of radiation in a cabbage agroecosystem using an eddy covariance system (EC system) installed at a height of 2.5 m above the ground. The EC system mainly consists of temperature and humidity sensors (HMP45C, Vaisala, Vantaa, Finland), a Krypton hygrometer (KH20, Campbell Scientific, Inc., Logan, UT, USA), and a three-dimensional sonic anemometer/thermometer (CSAT3, Campbell Scientific, Inc., USA). Soil moisture was obtained using soil moisture sensors (CS616, Campbell Scientific Inc., USA) installed at different depths underground [32]. Based on the EC system, data of sensible heat flux and latent heat flux can be obtained and calculated with formulas as follows:  
\[\lambda ET=\rho_{a}\overline{w^{\prime}q^{\prime}} \tag{1}\]  
\[H=C_{p}\rho_{a}\overline{w^{\prime}\ T^{\prime}} \tag{2}\]  
where \(\lambda ET\) and \(H\) are the latent and sensible heat flux (W m\({}^{-2}\)), \(\overline{w^{\prime}q^{\prime}}\) is the covariance between fluctuations of vertical wind speed \(w^{\prime}\) (m s\({}^{-1}\)) and humidity \(q^{\prime}\) (kg kg\({}^{-1}\)), \(\overline{w^{\prime}\ T^{\prime}}\) is the covariance between fluctuations of \(w^{\prime}\) and sonic temperature \(T^{\prime}\) (K), \(\rho_{a}\) is the air density (kg m\({}^{-3}\)), \(C_{p}\) the specific heat of dry air at constant pressure (J kg\({}^{-1}\) K\({}^{-1}\)), \(\lambda\) the latent heat of water vaporization (J kg\({}^{-1}\)), and \(ET\) is the crop evapotranspiration (kg m\({}^{-2}\) s\({}^{-1}\)) [31].  
The raw data obtained from observations were processed using Eddypro software (version 4.0, LI-Cor, Lincoln, NE, USA) to obtain water flux and latent heat flux. Subsequent data processing was conducted based on [33; 34] to derive datasets used in this study. Missing data were interpolated using the method proposed by [PERSON] [35]. The cabbage agroecosystem area is large enough and the field management measures are consistent enough to ensure the EC system obtains stable and continuous data. The vapor pressure deficit data used here were calculated based on the formula given in [36] using relative humidities and temperatures.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 1 Introduction
Such traditional methods generally have the disadvantages of poor robustness and low detection effect. The intelligent detection method refers to the use of deep learning models to overcome the above shortcomings in an automatic detection method. Therefore, this paper studies the defect detection of blister tablets based on a deep learning model. Challenges in blister tablets defect detection:  
1. Small target characteristics: defects on tablets are usually small and subtle, irregular in shape, and easily masked by information from other areas. This makes it more difficult to extract image features, resulting in lower accuracy of defect recognition. 2. Multi-scale detection problem: the size of the defect image is different, and the size of the defect is also very different, so it is difficult to detect defects of different sizes. 3. Edge detection problems: whether or not the defective edge of a blister tablet can be completely detected is an important indicator of whether or not the defect is completely detected. In order to solve the above problems and accurately detect defects in tablets of different sizes, locations, and shapes, this paper uses the U2 Net [8] basic model and improves on it. In order to improve the detection capability of U2 Net, this paper combines the large kernel attention mechanism with the RSU module of U2 Net to enhance the attention to model sensory fields and local defects, and to cope with small target defects. A new loss function is designed using Gaussian Laplacian operator and cross-entropy function to make the model pay attention to the edge defects of the blister tablets. After strengthening the detection ability of the model as a whole, the local mean and OTSU methods are used to adaptively determine the segmentation threshold, enhance the output effect, and suppress the interference. The contributions are as follows:  
1. The semantic segmentation model is used for the first time to detect the defects of blister tablets, which can not only detect the defects but also detect the size, shape, and position. 2. The U2 Net model is improved, so that the model detection ability is improved and the fine defects can be clearly detected. For the first time, the method of determining segmentation threshold by local mean and OTSU is proposed to determine the accuracy of segmentation.
## 3 Cluster Routing Protocol
### Data Transmission Stage  
The time of the data transmission stage is divided into several frames, and each frame is divided into several time slots based on the number of active nodes. The data transmission by each node occurs in the time slot assigned to it in each frame. If the data are not sent in their own time slot, they will enter into the dormant state and continue sending in the next time slot. The time slot length of a member node (i.e., the time for transmitting data) depends on the number of member nodes in the cluster. To reduce the inter-cluster interference of adjacent clusters and reduce its own energy loss, each node adjusts its own transmission power based on the actual need. Spread spectrum (SPSP) and Carrier Sense Multiple Access (CSMA) are adopted to send data from the CH to the BS. Because mining equipment moves periodically and people walk randomly, a deployed network node can become a hidden terminal; the MAC adopts a four-way handshake scheme (viz., RTS/CTS/DATA/ACK) to address the problem of hidden terminals. When there are data to be sent, the CH should detect whether other clusters are using the spreading code of the BS for data transmission in the channel. If the channel is busy, the CH must wait until the current data transmission is completed. Otherwise, the CH can use the spreading code of the BS for data transmission.  
```
UponTimer BS: broadcast \"Beacon\" frame in network foreachsensor node \(N_{i}\)do  Reset \(C_{i\_request}\) to 0  Upon receiving a beacon frame from BS  Compute \(L_{i\_loss}\)  send \"Probe_request\" frame (contain: \(E_{i\_curr}\), \(C_{i\_request}\), \(L_{i\_loss}\)) end Upon receiving a Probe request frame from \(N_{i}\) BS compute: \(N_{active}\), \(M_{i\_curr}\), \(M_{r\_aver}\), \(M_{r\_max}\), \(E_{r\_max}\), \(E_{r\_aver}\) If\(T_{\text{initialize}}\) < current time \(<T_{\text{initialize}}+T_{\text{CH-election}}\)then  BS broadcast \"\(START\_MSG\)\" (contain: \(N_{active}\), \(M_{i\_curr}\), \(M_{r\_aver}\), \(M_{r\_max}\), \(E_{r\_max}\), \(E_{r\_aver}\)) foreachsensor node \(N_{i}\)do \(W_{i,r}\leftarrow\text{Random}\) (0, \(W_{r}\)) If\(W_{i,r}<T_{i}(t)\)then  Be cluster head \(\leftarrow TRUE\) \(N_{i}\in G_{CH}\) else  Be cluster head \(\leftarrow FALSE\) \(N_{i}\in\overline{G}_{CM}\)  end if end if elseif\(T_{\text{initialize}}+T_{\text{CH-election}}\) < current time \(<T_{CF}\)then foreachCH node do  Broadcast \"\(CLUSTER\_HEAD\_MSG\)\" message  Upon receiving a \"\(REQ\_JOIN\)(intended\_ID,\(CM_{j}\))\" message if\(CH\_ID=\)intended\_IDthen  Record information contained in \"\(REQ\_JOIN\)\"  Add \(CM_{j}\) to CH Member node list else  Discard \"\(REQ\_JOIN\)\"  end if  end foreachCM node do  Upon receiving a \"\(CLUSTER\_HEAD\_MSG\)\" message  Compute: \(L_{i\_CH_{n}}\), \(L_{i\_all}\) If\(L_{i\_CH_{n}}+L_{CH_{n}-BS}<L_{i\_CH_{n}}+L_{CH_{n}-BS}\) and \(L_{i\_CH_{n}}+L_{CH_{n}-BS}<L_{i\_BS}\)then  intended\_ID \(\leftarrow\) CH\_ID  Broadcast \"\(REQ\_JOIN\)(intended\_ID,\(CM_{j}\))\" message  end if  end foreachend
```  
**Algorithm 1** PEAFC-CL
## 3 Results and Discussion
### Effect of MUAE Parameters on the Recovery Yield of Quercetin from DOS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df.** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Significance** \\ \hline Model & Y\({}_{1}\) & 36.97 & 20 & 1.85 & 85.15 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{1}\) & Y\({}_{1}\) & 15.76 & 1 & 15.76 & 726.04 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{2}\) & Y\({}_{1}\) & 6.76 & 1 & 6.76 & 311.41 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{3}\) & Y\({}_{1}\) & 0.13 & 1 & 0.13 & 3.15 & 0.0542 & \\ X\({}_{4}\) & Y\({}_{1}\) & 0.10 & 1 & 0.10 & 4.91 & 0.0614 & \\ X\({}_{5}\) & Y\({}_{1}\) & 2.76 & 1 & 2.76 & 126.94 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{1}\)X\({}_{2}\) & Y\({}_{1}\) & 0.60 & 1 & 0.60 & 27.67 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{1}\)X\({}_{4}\) & Y\({}_{1}\) & 0.09 & 1 & 0.096 & 4.43 & 0.0456 & \(\ast\) \\ X\({}_{1}\)X\({}_{5}\) & Y\({}_{1}\) & 0.11 & 1 & 0.11 & 5.17 & 0.0318 & \(\ast\) \\ X\({}_{2}\)X\({}_{4}\) & Y\({}_{1}\) & 0.25 & 1 & 0.25 & 11.29 & 0.0025 & \(\ast\) \\ X\({}_{2}\)X\({}_{5}\) & Y\({}_{1}\) & 0.23 & 1 & 0.23 & 10.39 & 0.0035 & \(\ast\) \\ X\({}_{1}\)\({}^{2}\) & Y\({}_{1}\) & 0.11 & 1 & 0.11 & 5.11 & 0.0328 & \(\ast\) \\ X\({}_{2}\)\({}^{2}\) & Y\({}_{1}\) & 5.48 & 1 & 5.48 & 252.36 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{3}\)\({}^{2}\) & Y\({}_{1}\) & 0.88 & 1 & 0.88 & 40.37 & \(<\)0.0001 & **\(\ast\)** \\ X\({}_{4}\)\({}^{2}\) & Y\({}_{1}\) & 0.17 & 1 & 0.17 & 7.90 & 0.0095 & \(\ast\) \\ X\({}_{5}\)\({}^{2}\) & Y\({}_{1}\) & 0.86 & 1 & 0.86 & 39.73 & \(<\)0.0001 & **\(\ast\)** \\ Residual & Y\({}_{1}\) & 0.54 & 25 & 0.022 & & & \\ Lack of Fit & Y\({}_{1}\) & 0.51 & 20 & 0.026 & 4.49 & 0.0515 & Not significant \\ Pure Error & Y\({}_{1}\) & 0.029 & 5 & 5.72 \(\times\) 10\({}^{-3}\) & & & \\ Cor Total & Y\({}_{1}\) & 37.51 & 45 & & & \\ Std. Dev. & Y\({}_{1}\) & 0.15 & & & & \\ R\({}^{2}\) & Y\({}_{1}\) & 0.9855 & & & & \\ Adjusted R\({}^{2}\) & Y\({}_{1}\) & 0.9740 & & & & \\ Predicted R\({}^{2}\) & Y\({}_{1}\) & 0.9441 & & & & \\ Mean & Y\({}_{1}\) & 8.44 & & & & \\ C.V. \% & Y\({}_{1}\) & 1.75 & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: ANOVAs for the regression model of Y\({}_{1}\). influence of experimental factors. Also, for Y\({}_{1}\), the predicted R\({}^{2}\) of 94.41% is in reasonable agreement with the adjusted R\({}^{2}\). The quercetin yield for all 46 extraction tests was between 6.51 and 10.32%. The maximum extraction yield (10.32%) was achieved at the conditions of run number 28, with an ultrasound time of 15 min, an ultrasound temperature of 70 \({}^{\circ}\)C, a solvent-to-solid ratio of 30 mL/g, a microwave time of 60 s, and an ethanol concentration of 70% (\(\
icefrac{{p}}{{v}}\), water). In addition, the minimum extraction yield of quercetin from the MUAE method (6.51%) was obtained from run number 17.
# Improved Low Complexity Predictor for Block-Based Lossless Image Compression +
###### Abstract  
Lossless image compression has been studied and widely applied, particularly in medicine, space exploration, aerial photography, and satellite communication. In this study, we proposed a low-complexity lossless compression for image (LOCO-I) predictor based on the joint photographic expert group-lossless standard (JPEG-LS). We analyzed the nature of the LOCO-I predictor and offered possible solutions. The improved LOCO-I outperformed LOCO-I by a reduction of 2.26% in entropy for the full image size and reductions of 2.70, 2.81, and 2.89% for 32 \(\times\) 32, 16 \(\times\) 16, and 8 \(\times\) 8 block-based compression, respectively. In addition, we suggested vertical/horizontal flip for block-based compression, which requires extra bits to record and decreases the entropy. Compared with other state-of-the-art (SOTA) lossless image compression predictors, the proposed method has low computation complexity as it is multiplication- and division-free. The model is also better suited for hardware implementation. As the predictor exploits no inter-block relation, it enables parallel processing and random access if encoded by fix-length coding (FLC).  
Keywords:lossless image compression; adaptive predictor; block-based compression; median edge predictor; data compression
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 3 Methodology
### Software and Tools  
We have employed several software tools and technologies to facilitate the practical implementation of homomorphic encryption techniques:  
* CMake (version 3.27.4) is a key tool for controlling the compilation process, defining project build files, and ensuring flexibility across different platforms. It generates CMakeLists.txt files, which are instrumental in compiling and building the source code. For this work, version 3.27.4 was used, which is the latest stable version available at the time of the project's execution;
* Git is employed for version control, allowing for the maintenance and tracking of different versions of applications and projects. It was specifically used to monitor updates to the Microsoft SEAL repository and clone it locally for implementation;
* Visual Studio 2022 (v.17) is an Integrated Development Environment (IDE) that provides a comprehensive development platform for writing, debugging, and compiling code as well as deploying applications. It supports a wide range of programming languages and project types, including C++ and.NET applications, mobile app development, and web design. In this work, we utilized Visual Studio 2022 for its advanced debugging, compilation, and development tools, along with its stable features up to the current date.  
The applied methodology involves practical application through a simple coding example that demonstrates homomorphic encryption's functionality. Specifically, the example presents a homomorphic sum, in which two integers are encrypted and then added together in encrypted form, after which both the numbers and the result of the sum are decrypted and displayed. The code will be tested in different devices and with different required computational resources, as explained in the next section. The setup encompasses configuring the development environment, integrating necessary libraries and dependencies, and detailing the software used. With this example, we showcase operations on encrypted data using the Microsoft SEAL library, emphasizing the encryption and decryption processes, as well as operations such as addition on encrypted integers.
# An Attention-Based Network for Textured Surface Anomaly Detection
## 2 Materials and Methods
### Attention-Based Fusion Module  
The attention-based fusion module is designed to capture the detailed information, meanwhile strengthen the salient features and weaken irrelevant and noisy responses as well. The detailed procedure is presented in Figure 2.  
In the feature extraction module, the information from encoder and decoder are merged by add operation and ReLU activation function, which ensures that detailed features can be retained, and visual saliency maps are highlighted to some extent. Moreover, the semantic gap between the feature extraction module and following anomaly attention module is narrowed when semantic information is supplemented in this section, which is more conducive to the training process.  
Anomaly attention module provides a more comprehensive perspective to focus on the potential objects and weaken background information. Specifically, on one side, the information of the encoder from the first stage is input into a convolution layer to increase nonlinearity and feature depth. On the other side, the corresponding part from the decoder is exerted by the convolution operation and sigmoid function to obtain an attention coefficient. Then the output of these two sides are integrated via pixel-wise multiplications, which are followed by convolution operation to further increase nonlinearity and feature depth.  
In order to weigh the detailed information and visual saliency extraction, an adaptive fusion module is designed to combine the two information from feature extraction and anomaly attention modules in a learning manner, and the weight parameters can be updated adaptively to maximally meet the demands of the different applications. As shown in Figure 2, the output of the feature extraction and anomaly attention modules above are concatenated firstly, then the result is executed by \(5\times 5\) and  
Figure 1: Segmentation network (AFM: Attention-based fusion module).  
Figure 2: Attention-based fusion module.  
\(1\times 1\) convolution calculations respectively, which can learn the weight parameters from channel inners and inters. The output 1 and output 2 are reserved for the following classification module.  
In addition, for the imbalance issue of samples, the loss of each pixel is formulated as Equation (1) to attach more weight for positive samples.  
\[\ell(X_{i})=-\frac{1}{N}{\sum_{i=1}^{N}{(\alpha log(1-P(y_{i}=0|X_{i}))+\beta logP (y_{i}=1|X_{i}))}} \tag{1}\]  
Here \(X_{i}\), \(y_{i}\) denote feature vector and label at pixel \(i\) respectively. \(P\) represents the sigmoid activation function, and \(\alpha=1\), \(\beta=3\) in this paper.