esa-sceva/satcom-chunk-collection huggingface dataset:
# Insider Threat Detection Using Machine Learning Approach
## 2 Literature Review
### Deep Learning
#### 2.2.2 Convolutional Neural Networks
There is a general lack of research on feature-engineering-based deep learning techniques for insider threat detection. Future research could address the possibility of using a hybrid ML approach and leveraging data mining for feature extraction as a means of reducing FP rates. The literature review has highlighted that most of the existing methods suffer from inefficiencies, especially when using a large dataset consisting of imbalanced data, which provides an opportunity for further research. In this study, we propose a solution to fill this gap using the CERT Insider Threat dataset. Table 1 summarises the most significant results from the literature review. All apply their methods with different versions of the CERT insider threat dataset using either supervised or unsupervised machine learning algorithms. The anomaly detection method produced the best results, with an accuracy of 99.91% with a very low false positive rate, while the best results achieved by classification methods were an accuracy of 99% and a precision and recall of 99.32%. Anomaly detection studies had no need for balancing techniques, and three classification studies employed balancing techniques with two forgoing balancings. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref** & **Method** & **Learn** & **Data** & **Results** & **Type** & **Balancing** \\ \hline
[2] & Random forest & S & r4.2 & acc: 98\% & Classification & Under/Over sampling \\ \hline
[16] & iForest & U & - & AUC: 82\% & Anomaly detection & - \\ \hline
[22] & constraint network & U & r4.2 & acc: 99.91\% & & & \\  & & & r4.2 & FPR 0.06\% & & \\  & & & r: 99.84\% & & & \\  & & & r: 55.00\% & & & \\ \hline
[39] & CNN & S & r4.2 & acc: 99\% & & & Random \\  & & & r: 99.32\% & & & & under \\  & & & r: 99.32\% & & & & sampling \\ \hline
[19] & LSTM & U & r6.2 & & TPR: 92.46\% & & & \\  & & & r: 6.83\% & & & & \\  & & & r: 95.12\% & & & & \\  & & & & acc: 93.85\% & & & \\ \hline
[23] & attention-LSTM & U & r6.2 & & R: 95.79\% & & & \\  & & & r: 6.67\% & & & & \\  & & & r: 95.47\% & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review summary. Abbreviations: Accuracy (acc), False Positive Rate (FPR), False Alarm Rate (FAR), Precision (P), F1 - Score (F1), Recall (R). The learning method is either supervised (S) or unsupervised (U). The data column shows the version of CERT used in the study.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 5 Prototyping and Performances Exploration
### Performance Evaluation of CERP  
To evaluate the performance of our proposed protocol in terms of energy consumption, we compare our work with the related works, as discussed in Section 3. Table 3 summarizes the main results of the previous works when dealing with cooperative transmission and reveals the importance of this work where the routing protocol is bounded into an Arduino-based sensor node and tested not only by simulations but also by real  
Figure 11: Comparison of the CH power consumption between the proposed protocol and RPL.  
Figure 12: Comparison of the packet loss ratio between different transmission protocols for SNR = 15 dB, \(\alpha=3\) and data rate = 6.67 bps.  
tests. Table 3 shows the comparison of our proposed CERP with the main existing RPL improvement solutions. Some work only improves the reliability, such as [20; 27; 28], whilst others only improve energy [26]. However, our work improves both reliability and energy. Additionally, the existing work only evaluated their work by simulations. However, we evaluate our work by simulation and real test, and we found that real test results converge with the simulations ones. Improving RPL reliability will improve the packet delivery ratio. Moreover, energy saving will increase IoT devices and network lifetime.  
As illustrated in Table 3, this work is the first mapped on an Arduino-based board. Furthermore, real tests demonstrate the effectiveness of the proposed protocol. All performance evaluations of the earlier solutions have been limited to the simulation analysis. Therefore, they ignored several practical aspects, such as more limitations affecting real sensor more implementation.
## 3 Model Construction and Parameter Analysis of Space-Based Information Network
### Model Mapping Based on Complex Network  
#### 3.2.1 Characteristics Analysis of SBIN  
With numerous nodes and complex connection relationships in SBIN, the complex network theory provides tools for the interaction and influence of nodes and their connection relationships. Complex-network theory defines the multi-layer, multi-stage, multi-attribute and multi-objective networks nested in SBIN that make the abstract network more concrete to achieve the purpose of facilitating the theoretical analysis and research. Through the complex network theory, the features of SBIN can be summarized in Table 1.  
Figure 2: Functional-relationship diagram of the space-based information network.
## 5 Experimental Measurements  
The last step consisted of constructing the proposed antenna in order to validate our design. Thus, we checked all obtained simulation results and found the real antenna performance. The proposed double-dielectric linear array of three patch elements is illustrated in Figure 5. The bottom of Figure 5 shows the input SMA connector. This female connector was soldered to a 50 \(\Omega\) microstrip line. The other parts that are also visible in the figure are the \(\lambda\)/4 transformer, the three radiating patches, and the connecting lines between patches.  
The base of substrate FR4 was raised with plastic M3 screws at a distance of 3.2 mm from the ground plane made of aluminum foil with 2.0 mm thickness. Experimental results of reflection coefficient S\({}_{11}\) were obtained by measuring with a vector network analyzer (VNA), as shown in Figure 6. The center antenna frequency was clearly very close to 754 MHz, and at this frequency, S\({}_{11}\) was less than \(-20\) dB. We observed good agreement between the simulations and the measurements. However, from the practical measurements, we obtained a slightly greater bandwidth of about 25 MHz. The simulated and experimental radiation patterns obtained, in the anechoic chamber, of our proposed antenna are presented in Figure 7. The experimental and simulation diagrams were very similar. Nevertheless, there was a small difference of 7\({}^{\circ}\) in the radiation diagrams in the vertical plane. This very small rotation in the radiation diagram was caused by a misalignment between the transmitting and receiving antennas.  
Figure 5: Construction of double-dielectric linear array.  
Figure 6: Measured and simulated S\({}_{11}\) of proposed double-dielectric antenna.  
The simulated and measured values of the gain were very similar, 11.3 and 10.5 dBi, respectively.  
The vertical/elevation plane (\(\phi\) = 0\({}^{\circ}\)) had half-power bandwidth (HPBW) of 30\({}^{\circ}\) and the horizontal/azimuth plane (\(\phi\) = 90\({}^{\circ}\)) had a HPBW of 65\({}^{\circ}\). These results proved that the proposed antenna had fan-beam radiation characteristics.  
We also measured antenna input impedance through its input SMA connector, which was equal to \(Z_{\rm A}\) = 55.9-j10.4 \(\Omega\) at the central frequency, as shown in the Smith chart in Figure 8. The Smith chart was drawn from 700 to 800 MHz. There was clear mismatching at frequencies away from the desired central frequency in the simulation and experimental curves.
## 3 Results and Discussion  
In our preliminary studies we found that, when administering IQP (50 \(\mu\)M) on the isolated smooth gastric muscle of rats, IQP exhibits a relaxation effect [21].  
It is well known that the main prerequisite for SM relaxation is the reduction of intracellular Ca\({}^{2+}\) concentration. This reduction can be obtained by the inhibition of transmembrane Ca\({}^{2+}\) transport, reduced release of Ca\({}^{2+}\) from intracellular Ca\({}^{2+}\) depots, and activation of various types of Ca\({}^{2+}\) pumps, as well as the synchronous (or sequential) activation of several of the above-mentioned processes [22; 23; 24].  
This course of action can be activated or inhibited under the influence of basic neurotransmitters affecting cholinergic transmission [25].  
NO is a chief inhibitory non-adrenergic, non-cholinergic (NANC) neurotransmitter in the gastro-intestinal tract (GI) [26; 27; 28; 29; 30; 31]. NO is released as a response to nervous stimulation and causes SM relaxation in the gastro-intestinal tract [32]. NO is a gaseous messenger molecule functioning mainly in vascular regulation, immunity, and neurotransmission [33]. NO is formed from L-arginine by NOS.  
The lack of this key enzyme, linked to the impaired local release of NO, may be responsible for results in impaired relaxation in the gastro-intestinal tract, which occurs in various diseases such as diabetic neuropathy, diabetes Type 1, etc. [34].  
In this regard, what is interesting is the clarification of the interrelationship between IQP in its role as a new biologically active molecule with the neurotransmitters ACh and 5-HT, as well as with the non-cholinergic neurotransmitter NO.
## 3 Result and Discussion
The ROC-AUC values in Figure 9 are close to 1 for all classes, highlighting the model's strong ability to distinguish between different types of tomato leaf diseases. This comprehensive evaluation using multiple metrics confirms the model's robustness and reliability, making it well-suited for real-world agricultural disease detection applications. Testing on a separate dataset further validates these results, demonstrating the model's strong generalization capabilities to new, unseen data.
# An ELECTRA-Based Model for Power Safety Named Entity Recognition
## 3 Results
### Experimental Results
Additionally, the \"result\" entity type has many nested relationships with other entity types, such as \"equipment\" and \"component\" entities, leading to missed predictions. Compared to the Bert-BiLSTM-CRF model, the model proposed in this paper shows improved accuracy for all entity types. The improvement is particularly significant in recognizing low-frequency entities, which is because of replacing the original GeLU activation function with the SwiGLU activation function. The SwiGLU activation function combines a gating mechanism with two parallel nonlinear functions, enabling it to capture complex patterns in the input data that might be missed by a single nonlinear function, such as GeLU. Additionally, the gating mechanism dynamically adjusts the activation function based on the input data, enhancing the model's feature extraction capability. This allows the model to learn which information is critical and which can be ignored. This feature enables the model to better adapt to different data distributions, thereby improving the accuracy of recognizing various entity types. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Entity**} & \multicolumn{2}{c}{**BiLSTM-CRF**} & \multicolumn{3}{c}{**BERT-BiLSTM-CRF**} & \multicolumn{3}{c}{**RS-ELECTRA-BiLSTM-CRF**} \\ \cline{2-9}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Equipment & 0.89 & 0.83 & 0.87 & 0.94 & 0.89 & 0.92 & 0.96 & 0.97 & 0.96 \\ \hline Component & 0.90 & 0.88 & 0.89 & 0.94  
In the model's error analysis, \"equipment\" and \"component\" types frequently confuse each other. The model often confuses one for the other. For \"operation\" identification, the highest error rate involves mistaking it for the \"reason\" type. This is because \"reason\" often includes operational details, which complicates distinguishing it from \"operation\". The \"status\" type has a clear issue with missing identifications. This primarily stems from the need for a deep understanding of the context to identify a piece of equipment's operational status. The \"condition\" type, often lacking context, is frequently misidentified as the \"other\" type. The \"reason\" and \"result\" types, with a strong logical connection and significant overlap with other types, make it difficult for the model to capture their contextual features, leading to scattered identification errors. The \"purpose\" type is misidentified as the \"result\" type. This is due to the sequential occurrence of the two types, where \"purpose\" often immediately precedes \"result\". In summary, the RS-ELECTRA-BiLSTM-CRF model demonstrates its advantages in entity recognition tasks, particularly for entity categories with abundant electrical engineering terminology. This model aids power maintenance personnel in troubleshooting equipment failures, assessing operational risks, and planning maintenance schedules based on power safety texts. It also provides technical support for constructing knowledge graphs or datasets in the power safety domain.
## 3 Results
### Effect of the Pellet Size  
In order to explore the effect of the pellet size on the redox reaction kinetics, dedicated measurements were carried out on samples S1 (\(d_{\text{sl}}\approx 12.7\) mm, \(m_{\text{s2}}=3658.5\) mg) and S2 (\(d_{\text{s2}}\approx 9.8\) mm, \(m_{\text{s2}}=1388.4\) mg). Figure 7 depicts the obtained reaction kinetic results for both samples at a temperature of 700 \({}^{\circ}\)C, a reactant gas mixture flow rate of 250 mL/min, and the gas concentration according to r0\({}_{3}\) of Table 2. Reducing the sample diameter by 23% results in a decreasing of the reduction time at \(\gamma_{\text{Re}}=0.2\) of 39% (from 72.8 min for S1 (12.7 mm size) down to 44.3 min for S2 (9.8 mm size)). The temporal change of the oxidation fraction shows  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Reduction** & **Oxidation** \\  & \(\mathbf{a_{\text{Re}}}\) (\(-\)), \(\tau_{\text{Re}}\) (min) & \(\mathbf{a_{\text{Ox}}}\) (\(-\)), \(\tau_{\text{Ox}}\) (min) \\ \hline ro\({}_{1}\) & \(a_{\text{Re,ro}_{1}}=0.10\) & \(a_{\text{Ox,ro}_{1}}=1.0\) \\  & \(\tau_{\text{Re,ro}_{1}}=291.0\) & \(\tau_{\text{Ox,ro}_{1}}=133.5\) \\ ro\({}_{2}\) & \(a_{\text{Re,ro}_{2}}=0.15\) & \(a_{\text{Ox,ro}_{2}}=1.0\) \\  & \(\tau_{\text{Re,ro}_{2}}=110.8\) & \(\tau_{\text{Ox,ro}_{2}}=64.3\) \\ ro\({}_{3}\) & \(a_{\text{Re,ro}_{3}}=0.19\) & \(a_{\text{Ox,ro}_{3}}=1.0\) \\  & \(\tau_{\text{Re,ro}_{3}}=59.5\) & \(\tau_{\text{Ox,ro}_{3}}=37.3\) \\ ro\({}_{4}\) & \(a_{\text{Re,ro}_{4}}=0.29\) & \(a_{\text{Re,ro}_{4}}=1.0\) \\  & \(\tau_{\text{Re,ro}_{4}}=28.5\) & \(\tau_{\text{Ox,ro}_{4}}=21.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic time constants \(\tau\) and empirically determined concentration-dependent parameter \(a\) for best fitting the experimental data with Equations (8) and (9) for the sample S1 at 700 \({}^{\circ}\)C, 250 mL/min, and the gas concentration ro\({}_{1}\) to r0\({}_{4}\), as defined in Table 2.  
Figure 6: Experimental (E-) and analytical (A-) reaction behavior of S1 at 700 \({}^{\circ}\)C and 250 mL/min for the gas concentrations ro\({}_{1}\) to r0\({}_{4}\) (cf. Table 2) for reduction (**a**) and oxidation (**b**).  
almost the same trend with varying the pellet size. The time needed to reach an oxidation fraction of \(\gamma_{\text{Ox}}=0.8\) drops from 41.8 min with S1 down to 26.8 min with S2, implying a decrease of the oxidation time of 36%, as illustrated in Figure 7. Indeed, increasing the pellet size results in slowing down the solid state diffusion and, consequently, the reaction kinetics [43; 47].
# Deep Regression Neural Networks for Proportion Judgment
## 2 Materials and Methods
### Methodology and Architectures
#### 2.2.1 Vanilla Deep Regression  
In this set of experiments, we compared the vanilla deep regression model (custom CNN with a regression top layer) with other models that are partly or entirely based on established algorithms. The architecture is based on VGG-16 and VGG-19 architectures [32], where the size of the applied network is the result of numerous experiments, including the grid-search used to find the optimal hyperparameters of a model. The values of the notable hyperparameters are as follows: The number of epochs is 100; mini-batch size is 32; learning rate is 0.001; Adam optimizer [33] and early stopping patience 15. For comparison purposes, we later used the same hyperparameter settings for all proposed models. It should be noted that other optimizers were also tested, where the best performance was achieved by Adam, alternating with RMSprop [34] in the first place for individual datasets and models.  
The layers configuration of the vanilla deep regression model is as follows:  
Conv2D(64) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\) Conv2D(64) \(\rightarrow\) ACT(ReLU) \(\rightarrow\)  
BN \(\rightarrow\) MP(0 \(\rightarrow\) DR(0.1) \(\rightarrow\) Conv2D(128) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\)  
Conv2D(128) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\) MP(0 \(\rightarrow\) DR(0.2) \(\rightarrow\)  
Conv2D(256) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\) Conv2D(256) \(\rightarrow\) ACT(ReLU) \(\rightarrow\)  
BN \(\rightarrow\) MP(0 \(\rightarrow\) DR(0.2) \(\rightarrow\) Conv2D(512) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\) MP(0 \(\rightarrow\) DR(0.2) \(\rightarrow\)  
Conv2D(1024) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\) Conv2D(1024) \(\rightarrow\) ACT(ReLU) \(\rightarrow\) BN \(\rightarrow\) MP() \(\rightarrow\) DR(0.5) \(\rightarrow\) DN(1)  
\(\rightarrow\) ACT(ReLU_100)  
where Conv2D(n) denotes the 2D convolution layer with n filters, ACT(0) denotes the activation function, BN denotes the batch normalization layer, MP denotes the max pooling layer, GAP denotes a 2D global average pooling layer, and FL denotes a flatten layer and DN(n) denotes a dense layer.  
Since a proportion is the comparison of a part to the whole, it can have a value ranging from 0 to 1 (i.e., between 0% and 100%). Therefore, the original ReLU (rectified linear unit) activation function is modified (ReLU_100) as follows:  
\[f(x)=\left\{\begin{array}{rl}0,&if\ x<0\\ x,&if\ x\geq 0\ \text{and}\ x\leq 100\\ 100,&if\ x>100\end{array}\right. \tag{1}\]
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## System Model  
### Notation  
Matrices (vectors) are denoted with upper (lower) case bold-face letters (e.g., **A** or **a**); the operator \(\left(\cdot\right)^{H}\) denotes the Hermitian; the operator \(\left\|\cdot\right\|\) denotes the vector norm; the operator \(\otimes\) denotes the Kronecker product; the set of real, complex and binary numbers are respectively denoted by \(\mathbb{R}\), \(\mathbb{C}\) and \(\mathbb{B}\); the vector \(\mathbf{1}_{k}\) denotes an all-one column vector with size \(k\) and the vector \(\mathbf{0}_{k}\) denotes an all-zero column vector with size \(k\); the operator \(p\left(\mathcal{A}\right)\) denotes the probability of the event \(\mathcal{A}\). the operator \(\mathbb{E}\left(\cdot\right)\) denotes statistical expectation; \(\mathcal{CN}(\mu,\sigma^{2})\) denotes the circularly symmetric complex normal distribution with mean \(\mu\) and variance \(\sigma^{2}\).
## 2 Materials and Methods  
SiO\({}_{2}\)-doped La-Ca-Co:SrM samples were prepared by a conventional solid-state reaction that employed two different procedures, referred to as pre-Si and post-Si. In the pre-Si method, precursor powders of Fe\({}_{2}\)O\({}_{3}\) (99%up, Kojundo Chemical Lab., Co., Ltd., Saitama Pre, Japan), SrCO\({}_{3}\) (99.9%, Kojundo Chemical Lab., Co., Ltd., Saitama Pre, Japan), La\({}_{2}\)O\({}_{3}\) (99.9%, Sigma-Aldrich, St. Louis, MO, USA), CaCO\({}_{3}\) (99.9%, Sigma-Aldrich, St. Louis, Lab., Co., Ltd., Saitama Pre, Japan) were weighed according to the target cation composition of Sr\({}_{0.3}\)Ca\({}_{0.4}\)La\({}_{0.3}\)Fe\({}_{9.8}\)Co\({}_{0.2}\) and mixed together. Additionally, SiO\({}_{2}\) (99.9%) was added to the mixture at a dose of 1 wt% of the total precursor powder. The powder mixture was ball-milled in water for 24 h using a polypropylene jar and yttria-stabilized ZrO\({}_{2}\) balls with 3, 5, and 10 mm diameters. The dried slurry was calcined at 1100 \({}^{\circ}\)C in air for 4 h. The calcined powders were crushed, ball-milled in water for 24 h, dried, and pressed in a mold with a diameter of 15 mm at a pressure of 16.6 MPa to produce a disk-shaped green compact pellet. The pelletized samples were sintered in air at a temperature range of 1200-1250 \({}^{\circ}\)C for 2 h. During the sintering process, the heating rate was 5 \({}^{\circ}\)C/min and the samples were then furnace-cooled to room temperature.  
In the post-Si method, the same amount of SiO\({}_{2}\) powder as used in the pre-Si method (1 wt% of total precursor powder) was added before the second ball-milling process after calcination, as opposed to being added before the first ball-milling process before calcination. The target composition and all the other processes were the same as in the pre-Si method.  
The density of the sintered sample was calculated based on the weight and geometric dimensions of the disk-shaped sample. The phase of the sintered samples was characterized using X-ray diffraction (XRD, D8 Advance, Bruker) with a Cu-K\({}_{\alpha}\) radiation source (\(\lambda\) = 0.154056 nm). The microstructures of the sintered samples were observed via field-emission scanning electron microscopy (FE-SEM, JSM-7610F, JEOL) of the fracture surfaces of the samples. Magnetization (M-H) curves of the sintered samples were measured using a vibrating sample magnetometer (VSM, 7410-S, Lake Shore) at room temperature with a sweeping magnetic field within \(\pm\) 25 kOe.
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
#### 2.1.3 The Micro-Controller and Peripheral  
The MCU is a STM32H743 (STMicroelectronics, Paris, France) which is developed by STMicroelectronics (ST). Integrated with the ARM(r) Cortex(r)-M7 core, the STM32H743 is a compromise between power consumption and computability. When STM32H743 works in standby mode, the current reaches as low as 2.95 \(\upmu\)A. The STM32H743 is integrated with a double-precision floating point unit (FPU) and L1 cache, the frequency is up to 480 MHz, and the computability can reach 2424 CoreMark/1027 dhrystone million instructions per second (DMIPS) [22]. The peripherals are shown in Figure 4. The universal synchronous/asynchronous receiver/transmitter (USART) is used for interacting with the host computer when debugging the program; the Ethernet is used for setting parameters and uploading raw acoustic signals, the purpose of the real-time clock (RTC) is to trace the time, and the function of SD card is used to store the raw acoustic data and log files. In addition, the power management is used to provide different voltages and control the power consumption. Finally, the GPIO is used to control the gain of the pre-amplifier.