esa-sceva/satcom-chunk-collection huggingface dataset:
# Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
## 2 ISP for Fault Detection  
The instantaneous space phasor (ISP), also known as the Park vector, is a tool used in power systems and has applications in fault detection for electrical machines and power systems [16]. The ISP represents three-phase voltages or currents in the complex plane, capturing the information of all three phases in a single phasor. Equation (1) presents the ISP of the current \(\overset{\sim}{I}\).  
\[\overset{\sim}{I}=\frac{2}{3}\left(i_{a}+ai_{b}+a^{2}i_{c}\right), \tag{1}\]  
where \(a\) is the rotation operator \(a=1\angle 120^{\circ}\) and the three-phase currents are \(i_{a},\;\;i_{b}\), and \(i_{c}\), with phase \(\varphi\) and angular frequency \(\omega\) corresponding to a frequency \(f\) as follows:  
\[i_{a}=Icos(\omega t+\varphi);\;i_{b}=Icos(\omega t+\varphi-120);\] \[i_{c}=Icos(\omega t+\varphi+120);\;\omega=2\pi f.\]  
The ISP can also be represented using the positive \(\overset{\sim}{I}^{+}\), negative \(\overset{\sim}{I}^{-}\), and zero-sequence \(\overset{\sim}{I}^{0}\) components, as shown in Equation (2). This representation is widely used in power systems for fault and imbalance analysis.  
\[\overset{\sim}{I}=\left(\overset{\sim}{I}^{+}+\overset{\sim}{I}^{-}+\overset{ \sim}{I}^{0}\right), \tag{2}\]  
where  
\[\overset{\sim}{I}^{+}=\frac{2}{3}\left(i_{a}{}^{+}+ai_{b}{}^{+}+a^{2}i_{c}{}^{ +}\right);\;\overset{\sim}{I}^{-}=\frac{2}{3}\left(i_{a}{}^{-}+ai_{b}{}^{-}+a^{ 2}i_{c}{}^{-}\right);\]  
In a balanced three-phase system, the zero-sequence component is equal to zero. Therefore, under these conditions, the ISP can be expressed as shown in Equation (3):  
\[\overset{\sim}{I}=\overset{\sim}{I}^{+}+\overset{\sim}{I}^{-}. \tag{3}\]  
By squaring the ISP, its magnitude can be separated as shown in Equation (4):  
\[\overset{\sim}{I}^{2}=\left(\overset{\sim}{I}^{+}+\overset{\sim}{I}^{-} \right)^{2}=\left(I^{+}\right)^{2}+\left(I^{-}\right)^{2}+2(I^{+}I^{-})\text{ cos}\big{(}2\omega t+\varphi^{+}+\varphi^{-}\big{)}. \tag{4}\]  
In this way, it is found that the mean value of the ISP is given by Equation (5):  
\[\left\langle\overset{\sim}{I}\right\rangle=\sqrt{\left(I^{+}\right)^{2}+\left( I^{-}\right)^{2}}. \tag{5}\]  
The mean value of the squared ISP is used to detect incipient short-circuit faults in the three-phase windings of rotating machines, but it can also be applied to three-phase power systems and static electrical machines such as transformers. The analysis of the mean value of the squared ISP can be performed using various signal analysis techniques, such as Wavelet and Hilbert-Huang transforms [17], as well as with a simple and practical tool like the FFT [18].
# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
###### Abstract  
With the development of urbanization, building maintenance units (BMUs) have been widely used in super high-rise buildings. As aerial work machinery, condition monitoring plays a vital role in the safety maintenance and management of BMUs. However, BMUs have multi-source heterogeneous data relationships that are difficult for systems to understand. Moreover, at this stage, there is a lack of sufficient samples to support fault diagnosis data. Therefore, this paper proposes a real-time monitoring and fault diagnosis system for BMU operating conditions. This system, based on the Internet of Things (IoT) architecture, acquires and stores data from distributed BMU systems, improving the data collection and sharing rate throughout the entire process. A collaborative fault reasoning chain diagnosis model was established based on heterogeneous knowledge sources and real-time process signals, which increased the accuracy of fault identification to 97%. Finally, through simulation testing and evaluations, the system can stably transmit data within 6-7 days and accurately analyze the operational and fault status of BMU, with an error rate within 5%. It effectively improves the efficiency and accuracy of BMU condition monitoring and fault diagnosis and also provides a new method for the practical application of intelligent BMU operation and maintenance.  
Internet of Things; data monitoring system; knowledge-data dual driven; data fusion; fault diagnosis Article
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 6 Robot Control Module
### Robot Speed Override Computation  
The last step of the developed robot control module is to process the information about the separation distances, \(d_{i}\), and the minimum separation distances, \(S_{i}\), related to each \(i\)-th tracked rigid body according to the regulations. More in detail, these values have been compared to compute the robot speed override, or scaling factor, \(k\), which satisfies the SSM principle. A key aspect of the strategy is also to ensure smooth and stable changes in robot speed.  
The speed override is computed by checking the relationship between the maximum scaling factor (\(S_{max}\)) and the corresponding distance, \(d_{i}\). The speed override is defined as follows:  
\[k=\begin{cases}1.0&\text{if }d_{i}\geq\Delta S\times S_{max}\\ 0.0&\text{if }d_{i}<S_{max}\text{ or if }d_{i}<d_{min}\\ \frac{d_{i}-S_{max}}{\Delta S\times S_{max}-S_{max}}&\text{otherwise}\end{cases} \tag{12}\]  
where \(S_{max}\) is the maximum value in the \(S_{i}\) array, \(\Delta S\) is a constant scaling factor that determines the range of distances for which the speed override is computed (it was chosen as 2) and \(d_{min}\) is a minimum distance threshold that has been introduced to stop the robot if the operator is extremely close to the robot itself (it was chosen as 0.30 m). The first condition represents a totally safe condition for the human operators; thus, the robot can move to its maximum speed while the second condition satisfies the regulation ISO/TS 15066 [4]. For intermediate distances, the proposed strategy uses a linear interpolation to gradually shape the scaling factor between 0.0 and 1.0 based on the distance.
# Transmission Power Control in Wireless Sensor Networks Using Fuzzy Adaptive Data Rate
## 2 System Implementation
### Implementation of TDMA  
The TDMA scheme adopted in this paper is illustrated in Figure 2. Obviously, time-slots are divided into odd and even slots to be compatible with two topologies; the bridge communicates with sensor nodes for obtaining sensor data and power control in odd slots, and it updates topology information to sensor nodes in even slots. Sensor nodes would be assigned two slots (odd and even) when they have built a connection with the bridge. In the odd slot, the required packet is utilized for transmitting the control packet from bridge  
Figure 1: System architecture.  
to sensor node. The control packet includes transmission power control, data rate control, and slot request, and it is also used for time synchronization. Response packet is utilized for response after sensor nodes receive the required packet from the bridges. The even slots are utilized for running low-power control algorithms, updating topology information, or have a handshaking to other nodes in network topology.
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
## 4 Experiments and Discussion  
To test the performance of the new GNSS single-epoch AR method, GPS/GLONASS/BDS real data of two stations (Sta1 and Sta2) for 24 hours on 3 March 2014, were collected. The length of the baseline between the stations was 9.5 m. The observation interval was 30 seconds. Moreover, the position of the two stations is exactly known. In this section, we first fix the ambiguity of the BDS triple-frequency signals and then realize the AR of GPS/GLONASS signals using the method proposed in this paper. Figure 2 shows the number of visible satellites of BDS and GPS/GLONASS. From Figure 2, we can see that there are 8-14 BDS visible satellites in the experimental period. For the GPS and GLONASS combined system, over 14 satellites can be used in the data processing.
## 1 Introduction
A solution, Near Front Distance (NFD) for estimating near-front distance using a monocular camera on public objects based on a deep learning approach is proposed, which consists of camera calibration and distance estimation modeling. A distance estimation process is applied to the images taken by the camera on intrinsic parameters after calibration. The position of detected objects on images are converted from the image coordinates to the actual coordinates based on the position or size information and within assumptions. Our distance estimation model can combine deep-learning-based object detection methods to accurately measure the distance between objects and the camera inside the field of view. Ultimately, our work contributes to bridging the gap between computer vision advancements and practical applications, particularly in scenarios where the accurate measurement of distances to obstacles is crucial. We utilized a published dataset on public objects in the uncontrolled environment, which was specifically designed for navigation-aiding purposes [15]. The primary contributions of this work can be summarized as follows:  
1. Development of a novel integration algorithm that utilizes image data from a monocular camera and the camera's pose to estimate the distance to target objects effectively. The algorithm calculates the object's distance on the front by the pixel on the picture after YOLOv4 detection. 2. Evaluation of the performance of deep learning models when applied to the novel algorithm for distance estimation. The remainder of the work is divided into the following sections: The related works are comprehensively addressed in Section 2. In Section 3, the methodology used in this study is described in detail, including the approach for measuring distance from various positions and points of interest. The results of the suggested approach are shown in Section 4, which also presents the empirical findings. Finally, in Section 5, we offer a comprehensive summary of the study, highlighting the main conclusions, and provide some closing thoughts.
## 3 Results
### Electro-Optical Measurements of the Optical Links
#### 3.3.3 Electro-Optical Measurements of the IR Link  
This section presents the electro-optical characterization of the system's IR link. The measurements included the bandwidth, signal-to-noise ratio (SNR), bit rate, bit error rate (BER), and length of the IR optical link.  
Bandwidth (BW) of the IR Link  
Despite the equalized IR LED bandwidth being more than 20 MHz, the IR PD driver managed to achieve a bandwidth of 12.27 MHz, which constrains the bandwidth of the uplink. In summary, the bandwidth of the uplink is 12.27 MHz. This value exceeds the requirement of 10 MHz, as the system transmits information at 10 MHz. This frequency  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Distance (cm)** & **BER** & **Errors per Packet** & **Number of Packets with Errors** & **PER** \\ \hline
62.5 (1) & \(1.7166\times 10^{-5}\) & 3 & 48 & 0.1875 \\
72.5 (1) & \(3.3259\times 10^{-5}\) & 3 & 93 & 0.3632 \\
82.5 (2) & \(7.3909\times 10^{-5}\) & 5 & 124 & 0.4843 \\
92.5 (2) & \(1.5258\times 10^{-4}\) & 8 & 160 & 0.625 \\
102.5 (2) & \(7.3432\times 10^{-4}\) & 28 & 220 & 0.8593 \\ \hline \end{tabular} (1) Measurements carried out in the set-up in the passenger van depicted in Figure 34. (2) Measurements carried out in the set-up depicted in Figure 27.  
\end{table}
Table 6: BER and PER measured for the VLC link.  
ensures an exact number of samples per symbol, as it is a submultiple of the 40 MHz sampling frequency of the A/D and D/A converters.  
Signal-to-Noise Ratio (SNR) of the IR Link  
Similar to the visible link, the received signal level in the infrared link must be at least 200 mV\({}_{\text{PP}}\), with a noise level of 45 mV\({}_{\text{PP}}\). These signal and noise levels yield a minimum signal-to-noise ratio (SNR\({}_{\text{min}}\)) of 10.74 dB for the infrared link.
###### Abstract
Figure 1: The scope of human resources (HR) management and the impact of employee attrition on these domains. They change how strategic decisions are made in addition to increasing operational efficiency [7, 8]. Today, nearly every industry stands to benefit from the adoption of these cutting-edge technologies. The capabilities for data collection, management, and analysis provide substantial advantages, boosting productivity and strengthening competitive positioning [9]. Machine learning algorithms offer a powerful tool for predicting employee attrition by analyzing factors such as job satisfaction, engagement levels, career progression, and other relevant variables. This predictive capability enables HR managers to implement targeted interventions designed to retain top talent. An essential component of enhancing decision-making processes is machine learning, which stands at the forefront of the data science field. These algorithms are designed to surpass human accuracy, continuously learning and adapting to new data inputs to make more informed predictions [10]. Machine learning in artificial intelligence (AI) is broadly categorized into two primary types: classical machine learning (CML) and deep learning (DL). Both approaches leverage historical data to enable machines to learn and make future predictions, with deep learning models often offering more complex, nuanced insights. In this study, we introduce a deep learning framework for predicting employee attrition. The main contributions of our research are as follows:  
* We propose an ensemble deep learning model that leverages a Bidirectional Temporal Convolutional Network (Bi-TCN) for employee attrition classification. * To enhance model performance and address data imbalances, we incorporate a fully connected GAN-based data augmentation technique, which not only balances the dataset but also increases the volume of training data. * We implement several baseline classical machine learning models, along with a few baseline deep learning models, and compare their results with the proposed model to better illustrate its effectiveness. * We also apply the SHAP method, an explainable AI technique, to the proposed model to identify the key features that have the most significant impact on employee attrition. The paper is structured as follows: Section 2 offers a thorough review of recent advancements in employee attrition prediction, emphasizing key methodologies and their contributions to the field. In Section 3, we explain the proposed deep learning methodology, detailing its architectural components and the rationale behind its design. Section 4 presents experimental results, including performance evaluations and comparative analyses of the proposed model. The work is finally concluded in Section 5, which offers a summary of the main conclusions and possible directions for further study.
# Preliminary Findings on Antibacterialativity of Selected Marine Invertebrates
## 3 Results
### Antibacterial Activity of Marine Organisms  
Methanol and water-methanol extracts did not show any antibacterial effect, and ethyl acetate extract showed an antibacterial effect against all bacterial strains; while only solvents used as controls produced similar results.  
Diameters of the inhibition zone of the combined solvent extracts of selected marine organisms against environmental bacteria are shown in Table 2. Among these bacteria, all marine-derived strains of _Citrobacter_ sp. were very sensitive, being inhibited by all four extracts, as was _Bacillus licheniformis_ from the soil. The soil bacteria _Geobacillus_ sp. was sensitive to two out of four extracts, while _Paenibacillus_ sokii was not inhibited at all.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source** & **Bacteria** & **GenBank** \\  & & **Accession Number** \\ \hline \multirow{3}{*}{Soil origin} & _Paenibacillus cookii_ strain L1.2 & PP549971 \\  & _Bacillus licheniformis_ strain MA-42 & PP549972 \\  & _Geobacillus_ sp. HBB272 & PP549973 \\ \hline \multirow{3}{*}{Marine origin} & _Citrobacter_ sp. strain YB074 & PP549974 \\  & _Citrobacter freundii_ strain EF55 & PP549975 \\ \cline{1-1}  & _Citrobacter_ sp. strain EB-C-4 & PP549976 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of bacterial strains obtained from environmental sources (supplementary materials, [[https://www.ncbi.nlm.nih.gov/genbank/](https://www.ncbi.nlm.nih.gov/genbank/)]([https://www.ncbi.nlm.nih.gov/genbank/](https://www.ncbi.nlm.nih.gov/genbank/)), accessed on 28 March 2024).  
The results of the antibacterial activity of the combined solvent extracts of the selected marine organisms against the reference bacterial strains are shown in Table 3 as diameters of the inhibition zone. Among these bacteria, _Listeria monocytogenes_ ATCC 19111 was the most sensitive, being inhibited by all four extracts. Next, both _Salmonella enterica_ ATCC 13076 and _S. enterica_ NCTC 12023 showed the same result, they were inhibited by three identical extracts. Interestingly, _Staphylococcus aureus_ ATCC 6538 was inhibited by two of the four extracts, while _S. aureus_ NCTC 12981 was not inhibited at all, like _Klebsiella aerogenes_ ATCC 13048 and _Escherichia coli_ NCTC 12241.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 3 Hardware-Based Mitigation Techniques
### Exploiting the NX Bit Mitigation Technique
#### 3.2.1 Return-Oriented Programming (ROP)  
Return-oriented-programming (ROP) is an advanced form of code-reuse attack that permits code execution in the presence of NX bit as well as does not suffer with the mentioned limitations of return-to-libc attacks. It was first presented by [PERSON] in 2007, in which attackers use the chaining of existing code instead of injecting their own to perform the buffer overflow exploitation [30], which become Return-oriented programming (ROP) later on. They inject malicious data as code pointers instead of shellcode injection [31].  
ROP uses pre-existing small instruction sequences named \"gadgets\" instead of the complete function to overcome the limitations of the return-to-libc attack. These gadgets are defined as short instruction sequences that combine to perform different high-level tasks [32]. Using ROP, there is no need to call a function at all; only small instruction sets (two or three) are used that neither have procedure prolog nor epilog. Although ROP attacks have been launched on various architectures, including SPARC, PowerPC, and ARM [33], Intel x86 is the most likely target of ROP attacks due to its variable-length CISC instruction set properties. Because of the x86 instruction set properties, it is quite easy to find random instruction sequences that provide various similar gadgets in the x86 executable code body. The short instruction sequence of the gadget must be the valid instructions sequence with return instruction as the last one, thus causing the CPU to carry on to the next gadget or payload. Generally, when launching an attack, the attacker overwrites the saved return address on the stack with a code pointer that jumps to the first gadget. Figure 8 gives a general idea of an ROP attack that is used to obtain a shell on the victim machine.  
First of all, attacker sends some specific number of A's and then overwrites the function return address with a code pointer that points to the first gadget. When the callee executes the return instruction, the control flow is redirected towards the first gadget, and stack  
Figure 8: Return-oriented Programming.  
pointer is increased by eight (in a 64-bit machine), where it starts pointing to the value residing at the top of the stack, which is in our case is the address of \"/bin/sh\". After that the first instruction of the first gadget will execute, for instance the pop rdi instruction will pop out the data from the top of the stack (/bin/sh/) and place it in rdi register, and increase the stack pointer by eight. The ret instruction will then redirect the control flow to the second gadget by reading the next code pointer that points the system() function. When system() executes, it checks its first argument, i.e., contents of rdi register, which is /bin/sh, thus spawning a new shell. The following section explains the practical implementation of discussed strategy to obtain shell.
# Improving Decodability of Polar Codes by Adding Noise
## 3 Conventional Schemes
### PB-SSC Decoding Algorithm  
According to the perturbation decoding principle in Section 3.1, the PB-SSC decoding algorithm has been proposed to improve the performance of the SSC decoder in [33].  
When the SSC decoding fails, the PB-SSC decoding algorithm is activated. The perturbed noise is added to the received signal \(y^{N}\) to help the received signal to approach the error correction domain. The perturbed received signal \(y^{N}_{1}\) can be expressed by  
\[y^{N}_{1}=y^{N}+Q_{p}\cdot randn(1,N), \tag{1}\]  
where \(Q_{p}\) is the variance of the perturbed noise, and randn generates random numbers from the standard normal distribution. The decoding is an iterative process that continues until a valid codeword is obtained or the prescribed maximum number of iterations, \(T_{1}\), is reached. We refer the readers to [33] for further details on the PB-SSC decoding algorithm.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 4 Discussion
### Extraction of Impending Geomagnetic Anomalies  
Based on our monitoring and data analysis, the one-to-one correspondence between geomagnetic anomalies and earthquakes depends on the strength of the gradient of geomagnetic anomalies. According to the new algorithm proposed in this article, the geomagnetic anomalies before, during, and after earthquakes can be captured, and a prediction sequence can be constructed according to the threshold and extraction rules provided in this article. The one-to-one correspondence between the predicted time sequence and the time sequence of earthquakes reaches about 75-85%.
## 4 Experimental Results
### Estimation of SoH with Neural Network-Based Approaches  
The results obtained according to the models of neural networks and the parameter data in the models are outlined in Table 7. Considering the results obtained from neural network-based models, it might be seen that there were 100 neurons in one hidden layer. As has been highlighted in Table 7, the number of neurons in the single hidden layer was more than the number of neurons for the narrow and medium presets. In the structure containing a bilayered network and a trilayered hidden layer, there were 10 neurons in each layer. It was clarified that lower results were obtained due to the lower number of neurons in the layers.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 3 Data Collection  
This study formulated a data collection scheme that includes four adjustable blasting parameters: spacing (\(S\)), burden (\(B\)), specific charge (\(q\)), and subdrilling (\(H_{0}\)), along with blast fragmentation (P\({}_{50}\)). A total of 37 blasts were recorded at the mining site, primarily targeting magnetite ore in the mining area. Additionally, the rock mass conditions (\(T\)) at the mining site, which include fractured and intact states, were assigned values of 1 and 2, respectively, and incorporated into the blast fragmentation prediction model. Thus, there are a total of 37 sets of data encompassing blasting parameters, rock mass conditions, and blast fragmentation. The data are presented in Table 1. The first 30 data sets in Table 1 are used as the training set, while the remaining 7 data sets are used as the test set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & \(S\) **(m)** & \(B\) **(m)** & \(q\) **(kg/m\({}^{3}\))** & \(H_{0}\) **(m)** & \(T\) & \(\mathbf{P_{50}}\) **(cm)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Blasting parameters and blasting fragmentation data from the mine.  
Figure 1: Rock blasting parameters.