esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
One of the most important factors for successful agricultural production is the irrigation system in place. In this study, a precision irrigation system, which takes advantage of the various phases of plant growth, was developed and implemented using the sensor network technology integrated with IOS/Android. The amount of water in the soil was measured via sensors that were placed on certain points of the area to be irrigated. These sensors were placed near the root of the product. Data from sensors was transmitted via Wi-Fi in real-time to a mobile phone based on IOS/Android. In the light of obtained data, the seasonal precision irrigation system was created depending on the amount of water required by the plants at each stage of their growth stage. The required energy of the system was provided by solar energy. The system can be controlled by smart phones, which increases the usability of the system. When design performance was analyzed, it was observed that some important advantages such as obtaining high efficiency with water, time and energy saving and reducing the workforce were ensured. Five separate laterals were used for the irrigation system. There were valves on each lateral, which realized the opening and closing process depending on the water need. A total of 16 humidity sensors were used in the irrigation system and the data from these sensors was transferred to the IOS/Android server via the programmable controller (PLC). The basic electrical equipment in the irrigation system was monitored and controlled via mobile devices. Control parameters were obtained by comparing the real values and reference values by a closed-loop system and determine the new working status of the irrigation system.  
Mobile Control; irrigation system; closed-loop control Article
## 4 Conclusions  
In order to describe the creep behavior of rocks under the coupling effect of hydro-mechanical properties, based on the basic theory of rock hydro-mechanical property coupling, there are several conclusions as follows:  
1. In accordance with studying the variation characteristics of porosity in rocks under the coupling effect of hydro-mechanical properties, as well as the relationship between permeability and volumetric strain, this paper proposes the concept of hydro-mechanical properties variable.
2. Through introducing the instantaneous damage variable \(d\) as the internal variable parameter of the thermodynamic state equation, the internal variable \(\zeta\) is introduced in order to describe the time-dependent damage of rocks.
3. With introducing the coupling variable, instantaneous damage variable, time-dependent damage variable into the yield surface equation, plastic potential energy equation and stiffness matrix of the elastoplastic creep constitutive model, a hydro-mechanical properties creep damage coupling constitutive model is established, which can describe the creep deformation characteristics of rocks under the coupling effect of hydro-mechanical properties damage, and is consistent with the creep deformation characteristics of rocks under the coupling effect of hydro-mechanical properties.
4. Based on the triaxial creep test results of granite gneiss, the model parameters are determined. By comparing the constitutive simulation results with the experimental results, the numerical simulation results are in good agreement with the corresponding experimental results, which can better interpret the creep behavior of granite gneiss under the coupling effect of hydro-mechanical properties.  
Figure 1: Model validation: (**a**) Confining pressure 4 MPa, seepage pressure 1 MPa; (**b**) Confining pressure 4 MPa, seepage pressure 2 MPa; (**c**) Confining pressure 4 MPa, seepage pressure 3 MPa.  
However, the constitutive model established in this paper only considers the coupling effect of hydro-mechanical properties, while in practical engineering, there are usually factors such as temperature and chemistry that affect the long-term safety and stability of the project. In future research, the constitutive model considering the coupling effect of multiple factors will be further developed to provide a more favorable basis for engineering safety construction.  
Conceptualization, L.L.; methodology, L.L.; software, B.W.; validation, L.L. and B.W.; formal analysis, L.L.; investigation, L.L. and B.W.; resources, L.L.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, B.W.; visualization, L.L.; supervision, B.W.; project administration, L.L. and B.W.; funding acquisition, L.L. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province, grant number BK20191302 and the Natural Science Foundation of Jiangsu Higher Education Institutions of China, grant number 22 KJB560026.  
Not applicable.  
The authors declare no conflict of interest.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 2 Envelope Tracking PA Linearization
### Optimized Envelope Generation for ET PA Linearization
However, having the empirical evidence of the capabilities of the EGMP model to compensate for the RF leakage and linearize the ET PA, it is fair to say that the EGMP model can generate an optimized envelope signal that contributes to the linearization of the RF signal at the ET PA output. In this context, the goal of this paper is to extract the EGMP model coefficients to generate an optimized envelope that contributes to the linearization of the RF signal at the ET PA output. Since the RF output of the ET PA is always available for offline training, it is possible to model/predict the ET PA output given the baseband I-Q input and the supply envelope. Figure 7 shows the block diagram of the proposed optimized envelope gener  
Figure 6: Block diagram of the RF leakage compensation and its coefficient identification. ation scheme. Unlike the ELC method, the proposed envelope optimizer requires only monitoring the ET PA output to build a supply-envelope-dependent PA behavioral model. Then, it uses the ET PA behavioral model to train the coefficients of the EGMP model and generate the optimized envelope in (6). The supply-envelope-dependent ET PA model is presented in the following subsection.
## 8 Analysis of Commonly Occurring Errors and Discussion
As this dataset contains significantly more dropped capitals than our other datasets, it is important to mention that, out of the 102 dropped capitals, 96 were correctly detected, and the width of the detected elements was always correctly predicted (using a threshold of 2%). A review of the results showed that most false positive dropped capital predictions occurred at the beginning of lines (similar to the CamMarg dataset), which led to these lines not being fully detected or at black spots (degradations) in the middle of a text block. As previously stated, the precision of the dropped capital detection could be improved in the future by incorporating the results from the subsequent OCR or by applying feedback from the layout analysis in a postprocessing step for the dropped capital detector. The header category also showed similar issues as in the CamMarg dataset, with headers often missing the page number. For the footers, the errors were also similar to the CamMarg dataset, with most of the footers correctly detected, but sometimes not classified correctly as a footer element. To conclude the analysis of our results, it can be stated that most observed errors only affected individual lines (or two lines in the case of separated marginalia/columns or headers). This can be seen as an advantage of our method, as such errors are easy to correct manually, together with the subsequent OCR step. Furthermore, slightly incorrect line endings must not be corrected at the segmentation level at all, but can be corrected in the OCR step, where it could even be possible to highlight some of these errors, as the resulting OCR contains partial words that are not found in a dictionary.
## 3 Technology, Hardware and Software of Edge Computing
### The Software and Tools of Edge Computing  
#### 3.2.1 Software Platforms  
EC aims to reduce latency and enhance efficiency by processing data closer to the source. Software and tools in this domain play a crucial role in efficiently managing edge infrastructures, optimizing communication between devices, data processing, and resource utilization. There are several programming frameworks that have been specifically designed for EC, with popular choices including MXNet v1.9.1 (Apache Software Foundation, Forest Hill, MD, USA) [128], TensorFlow Lite v.2.17.0 (Google LLC, Mountain View, CA, USA) [129,130], CoreML v.2.3 (Apple Inc., Cupertino, CA, USA) [131], Caffe2 v.0.8.0 (Meta Platforms, Inc., Menlo Park, CA, USA) [132], PyTorch v.2.0 (Meta Platforms, Inc., Menlo Park, CA, USA) [133], and TensorRT v10.9.0.34 (NVIDIA Corporation, Santa Clara, CA, USA) [134]. TensorFlow Lite v.2.17.0 [129], developed by Google (Mountain View, CA, USA), is a version of TensorFlow v.2.15.0 optimized for mobile and edge devices. It uses advanced optimization techniques to perform deep learning model inference with a low latency on edge devices. In contrast, Caffe2 v0.8.0 [132], an updated version of the Caffe v.1.0 framework released by Facebook (Meta Platforms Inc., Menlo Park, CA, USA) in 2017, supports newer computational models such as mobile and distributed computing. It also offers cross-platform support, allowing developers to leverage GPU power on both cloud and edge devices.  
FocusStack [135] is another key platform in EC, offering edge-cloud collaboration. By utilizing a container-based execution environment, FocusStack aligns edge devices with the capabilities of their infrastructure, ensuring scalability and efficiency for IoT applications [136]. This collaboration optimizes the deployment of IoT solutions by leveraging the strengths of both edge and cloud computing.  
In [137], DeepThings, a framework for the adaptive distributed execution of CNN-based inference applications on IoT edge clusters, is proposed. This framework enhances the flexibility of EC systems by enabling dynamic adaptation to varying workloads, thereby improving computational efficiency in edge environments. Another important contribution is the use of Docker container-based analytics services at the edge for data processing, as highlighted in [138]. This approach reduces latency by processing data locally on edge devices before sending it to the cloud for further analysis. Moreover, the feasibility of deploying a deep learning framework for real-time video stream analysis on a Raspberry Pi is explored, with the MXNet v1.9.1 [139] framework selected for this task. Additionally, TensorFlow v.2.15.0 [140] employs a placement algorithm to distribute computational tasks based on estimated execution and communication times, optimizing the processing flow.
## 1 Introduction  
Wireless sensor networks (WSNs) have been developed as a critical component in various monitoring and surveillance applications, including environmental sensing, healthcare, industrial automation, and smart cities. These networks consist of spatially distributed sensor nodes that collect and transmit data wirelessly. However, the energy limits of battery-operated sensor nodes pose a significant challenge, as frequent data transmissions rapidly deplete battery power, reducing network longevity. To address this, extensive research has been conducted on energy-efficient communication techniques to optimize network performance while prolonging sensor node lifetime [1, 2].  
Among the many energy-saving mechanisms, opportunistic routing (OR) has gained considerable attention for its dynamic and adaptive forwarding strategy. Unlike conventional routing, which relies on predefined paths and fixed next-hop nodes, OR allows data packets to be forwarded via any suitable candidate within the sender's communication range, ensuring higher reliability and energy efficiency [1]. Moreover, OR is particularly beneficial in duty-cycled WSNs, where nodes alternate between sleep and active states to conserve energy. By selecting early-waking nodes as forwarders, OR can significantly reduce transmission delays and improve network responsiveness [2; 3].  
Despite its advantages, OR introduces several challenges, particularly in high-density and non-uniformly distributed networks [4]. The fundamental broadcast nature of OR results in multiple receivers capturing the same packet, leading to redundant transmissions, increased collisions, and excessive energy consumption. In dense WSN deployments, this problem becomes more pronounced as more nodes contend for transmission, causing severe network congestion. Additionally, non-uniform node distribution leads to inaccurate routing metric estimations, increasing the likelihood of suboptimal forwarder selection, packet losses, and routing loops. Another key challenge arises when long transmission ranges cause an excessive number of nodes to receive the same packet, further aggravating redundancy and energy inefficiency [5; 6].  
Several OR-based protocols have been proposed to mitigate these issues. ExOR [1], the pioneering OR protocol, introduced the concept of selecting forwarders based on the expected number of transmissions (ETX) [5], demonstrating improvements in throughput and reliability [1; 5]. However, later studies revealed that even minor miscalculations in ETX could lead to routing loops and inefficiencies in multi-hop transmissions [6]. Other protocols, such as ORW [3] and ORR [7], attempted to optimize forwarder selection by limiting the number of active forwarders based on network density. However, these methods rely heavily on global network knowledge, making them less adaptive in dynamic environments.
## 2 Digital Image Processing
### Image Enhancement Processing of Expressway Asphalt Pavement
For the decomposition covariance matrix \(COV=U\Lambda\), \(\Lambda=diag[\lambda_{1},\lambda_{2},\cdots,\lambda_{n}]\) by the \(COV\) characteristic value of \(\lambda_{i}\) diagonal matrix is formed; \(U=[u_{1},u_{2},\cdots,u_{n}]\) is the covariance matrix; \(COV\) is the eigenvector corresponding to the eigenvalue. The formula for calculating the variance contribution rate \(\iota\) of the standardized matrix is shown in Formula (3):  
\[\iota=(\lambda_{1},\lambda_{2},\cdots,\lambda_{d})/trace(COV) \tag{3}\]  
When the value of \(\iota\) reaches more than 70%, the main information of \(d\) contains a feature vector \(x_{i}\). Before taking \(x_{i}\) characteristic values and \(\Lambda_{d}=diag[\lambda_{1},\lambda_{2},\cdots,\lambda_{d}]\) and eigenvectors \(W_{d}=[w_{1},w_{2},\cdots,w_{d}]\) as the base coordinate of the subspace, the principal component \(Y\) in the data matrix can be standardized by \(X^{\prime}\) to keep \(d\) projection on base coordinates, as shown in Formula (4):  
\[Y=W_{d}^{T}X^{\prime} \tag{4}\]  
We apply the first \(d\) principal components to estimate the reconstruction model of \(X\), as shown in Formula (5):  
\[X=WY+X^{\prime} \tag{5}\]The proposed method uses the median \(\hat{X}\) of the absolute values of the standardized data matrix as the threshold \(T\) for pixel classification in the image. The calculation formula is shown in Formula (6):  
\[T=median(\left|\hat{X}\right|) \tag{6}\]  
where _median_ indicates the median value. Based on the above steps, the threshold value in the NSCT image enhancement algorithm can be determined. In the process of image enhancement, two factors directly determine the enhancement effect: the selection of threshold and the threshold function. A reasonable threshold can distinguish the noise from the image information and enhance the weak edge information of the image. The calculation formula for the enhancement function \(g(x)\) used in the proposed method is shown in Formula (7):  
\[g(x)=\left\{ \begin{array}{cl}1&x\in SE\\ \max(T/\left|x\right|_{p})&x\in WE\\ 0&x\in Noise\end{array} \right. \tag{7}\]  
where \(x\) is the pixel value after NSCT decomposition; \(p\) is the adjustment coefficient, which can be taken from 0 to 1; \(SE\) indicates a strong edge; \(WE\) is a weak edge; \(Noise\) indicates noise. This formula preserves the strong edge of the image, enhances the weak edge, and suppresses the noise.
## 3 Results
### Model Performance and Evaluation
#### 3.2.3 Effects of Hyperparameter Optimization  
In this study, hyperparameter optimization was implemented to maximize the model's performance. Various values for the XGBoost model's hyperparameters, such as n_estimators, max_depth, learning_rate, and subsample, were tested. The results of the GridSearchCV analysis determined the best values that maximized the model's prediction accuracy.  
According to the optimization results, the values n_estimators = 100, max_depth = 3, learning_rate = 0.1, and subsample = 1 provided the highest R\({}^{2}\) values and the lowest MSE values for predictions of both protein and fat contents. Selecting these settings was crucial for achieving balanced performance in terms of the model's learning speed and complexity. Keeping the learning_rate low helped prevent overfitting, thereby enhancing the model's generalization capability. Maintaining a low max_depth allowed the model to be trained faster and kept the structure uncomplicated.  
In conclusion, hyperparameter optimization played a significant role in improving the model's predictions of milk components. These optimizations enable the model to be effectively used in decision support systems and quality control processes within the dairy industry.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 3 Cross-Layer CS-MAC Design
### The Cross-Layer CS-MAC Scheme  
In a WSN system, \(N\) sensor nodes are randomly distributed and access the destination. Most of the sensor nodes remain inactive; only \(K\) nodes are active to transmit data. The destination broadcasts the request to all sensor nodes, and the sensor nodes should reply to the request so that the destination can schedule the transmission slots of these active nodes. The proposed cross-layer CS-MAC (CL CS-MAC) scheme is shown in Figure 1.  
The CL CS-MAC scheme begins with the destination initiating a request solicitation. The destination enquires all the users in the system by request solicitation, querying whether the node has data to transmit. Upon receiving the solicitation, sensor nodes reply with a training sequence frame so that the destination can obtain the channel matrix, which is used as the measurement matrix in CS. After that, any active nodes randomly reply with requests, which is combined with wireless transmissions--i.e., compressive complex requests. The destination identifies active nodes and then broadcasts the schedule of active node transmission. Finally, sensor nodes transmit data according to the schedule.
# XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection
###### Abstract  
In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaloT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.  
IoT security; botnet detection; random forest; XGB; feature selection; Mirai Article  
26 November 2021  
28 December 2021  
1 Computer Science and Engineering Discipline (CSE), Khulna University (KU), Khulna 9208, Bangladesh; [EMAIL_ADDRESS] (J.A.F.); [EMAIL_ADDRESS] (A.S.)  
Electromatics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, Bangladesh; [EMAIL_ADDRESS] (S.T.M.); [EMAIL_ADDRESS] (J.S.T.); [EMAIL_ADDRESS] (K.M.M.); [EMAIL_ADDRESS] (M.M.A.)
## 3 Transient Voltage Stability Boundary  
This section may be divided by subheadings. The goal is to provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.  
In order to study the applicability of the practical criteria, the transient voltage stability boundary needs to be derived firstly. To facilitate the theoretical derivation, the following assumptions were made:  
Figure 1: Single-load system: (**a**) structure of the single-load system; (**b**) equivalent circuit of the single-load system.  
1. Simplify the voltage curve of bus 1, as Figure 2 shows. The voltage magnitude was initially \(U_{0}\), it dropped to \(U_{\text{sqg}}\) during the fault, and then was restored to \(U_{\text{re}}\) after fault clearance. The voltage sag lasted for \(t_{\text{sqg}}\).
2. The power system was considered unstable if the induction motor stalled, and the system was stable if the induction motor did not stall.  
Based on the above assumptions, the voltage stability boundary of the induction motor can be derived. Before deducing the voltage stability boundary, we first introduce two crucial parameters, namely, the safe voltage magnitude and the safe sag duration.