esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
### Harmonic Response Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
For the three-degree-of-freedom low-frequency resonant mixer operating near its natural frequency, the frequency of the sine excitation force has a significant impact on the response of the equipment's assemblies. Excessive response may adversely affect the normal operation of the equipment or even cause structural damage to the assemblies; conversely, an insufficient response may prevent the equipment from meeting operational requirements. It is therefore necessary to conduct resonance response detection, analyzing the equipment's response to the sine excitation force at the natural frequency, and obtain the relationship between the response value and frequency variation. Since the equipment is in a steady-state response during normal operation, the research focus is primarily on this aspect, and transient response is not considered in this simulation analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Modal**} & **Frequency from the** & **Frequency from the** & **Relative Deviation** \\  & **Workbench Simulation (Hz)** & **Matlab Calculations (Hz)** & \\ \hline
1 & 14.47 & 15.30 & 5.42\% \\
2 & 38.42 & 38.10 & 0.84\% \\
3 & 58.95 & 59.12 & 0.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of two calculation methods for various modal solutions.  
Figure 3: The core part first three vibration patterns: (**a**) first-order vibration pattern; (**b**) second-order vibration pattern; (**c**) third-order vibration pattern.  
To study the dynamic performance characteristics and vibration isolation mechanism of the three-degree-of-freedom low-frequency resonant mixer, this research employs the modal superposition method for harmonic response analysis. The external excitation is set as a sinusoidal excitation force, defined as a vector force with a magnitude of 5800 N and a phase angle of 0, acting vertically downward on the excitation component. Considering the range of the first three natural frequencies, the frequency range is analyzed to be 0-70 Hz. Taking into account the upper limit of frequency adjustment accuracy in actual operation, excessively high simulation precision has no practical value. Additionally, to reduce computational load, the solution interval is set to 100.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### FC Based on Logic Flow (LF)  
Typically, a tree-like logic flow with multi-criteria is used if no AI or ML-based algorithms are devised. [PERSON] et al. compared the extracted features for ground and three phases to pre-set thresholds [98]. Any value exceeding the pre-set threshold results in fault on phases or ground. Comparisons are conducted between thresholds and feature values at each node within the logic flow. In [26; 99], Shanon entropy and WT used to yield features and logic flows are implemented. [PERSON] et al. employed Clark transformation to create fault detection guides for each phase and then compared with the threshold to complete classification [41]. [PERSON]'s transformation with wavelet transform is used in [45] and modulus maxima of WT employed in logic flow to choose the type of fault.
## 1 Introduction
These enhancements collectively serve to optimize network communication and efficiency. * To enhance routing efficiency and bolster routing reliability, a clustering approach is implemented. This clustering technique is designed to improve the overall performance of the routing process within the network. * To minimize transmission delay and reduce the occurrence of high packet loss, we implemented optimal routing strategies and employed a careful selection process to identify the most suitable forwarder nodes. All of these steps improved the network's data transmission reliability and efficiency. * To enhance message transmission, we adopted a strategy that involved selecting decision areas and classifying emergency messages, allowing them to be transmitted effectively in multiple directions and thereby increasing their reach and impact. This article is organized as follows: Section 1 presents the introduction, research problem, and the contributions of the article. A literature review of prior work is presented in Section 2. The proposed method is presented in Section 3. Section 4 presents the details of the experimental results. The conclusion of this study is presented in Section 5, which also provides plans for this research's future work.
# PHEFT: Pessimistic Image Processing Workflow Scheduling for DSP Clusters
## 1 Introduction  
In this paper, we address the multi-criteria analysis of image processing with communication workflow scheduling algorithms and study the applicability of Digital Signal Processor (DSP) cluster architectures.  
The problem of scheduling jobs with precedence constraints is a fundamental problem in scheduling theory [1, 2]. It arises in many industrial and scientific applications, particularly, in image and signal processing, and has been extensively studied. It has been shown to be NP-hard and includes solving a complex task allocation problem that depends not only on workflow properties and constraints, but also on the nature of the infrastructure.  
In this paper, we consider a DSP compatible with TigerSHARC TS2015 [3, 4]. This processor was designed in response to the growing demands of industrial signal processing systems for real-time processing of real-world data, performing the high-speed numeric calculations necessary to enable a broad range of applications. It is optimized for both floating point and fixed point operations. It provides ultra-high performance; static superscalar processing optimized for memory-intensive digital signal processing algorithms from fully implemented 5G stations; three-dimensional ultrasound scanners and other medical imaging systems; radio and sonar; industrial measurement; and control systems.  
It supports low overhead DMA transfers between internal memory, external memory, memory-mapped peripherals, link ports, host processors, and other DSPs, providing high performance for I/O algorithms.  
Flexible instruction sets and high-level language-friendly DSP support the ease of implementation of digital signal processing with low communications overhead in scalable multiprocessing systems. With software that is programmable for maximum flexibility and supported by easy-to-use, low-cost development tools, DSPs enable designers to build innovative features with high efficiency.  
The DSP combines very wide memory widths with execution six floating-point and 24 64-bit fixed-point operations for digital signal processing. It maintains a system-on-chip scalable computing design, including 24 M bit of on-chip DRAM, six 4 K word caches, integrated I/O peripherals, a host processor interface, DMA controllers, LVDS link ports, and shared bus connectivity for Glueless Multiprocessing without special bridges and chipsets.  
It typically uses two methods to communicate between processor nodes. The first one is dedicated point-to-point communication through link ports. Other method uses a single shared global memory to communicate through a parallel bus.  
For full performance of such a combined architecture, sophisticated resource management is necessary. Specifically, multiple instructions must be dispatched to processing units simultaneously, and functional parallelism must be calculated before runtime.  
In this paper, we describe an approach for scheduling image processing workflows using the networks of a DSP-cluster (Figure 1).
## 3 Results and Discussion
### CLA Content in Milk and Yogurts  
Probiotic bacteria used in dairy production are able to synthesize conjugated linoleic acid _cis-9_, _trans_-11 C18: 2 (CLA). According to literature data [62, 63], the content of CLA in milk fat ranges from 2.3 to 6.0 mg/g of fat. Such variety is a result of different factors, including animal feeding, breed, age, and lactation period [64, 65, 66, 67, 68, 69]. According to many authors [36, 41, 42, 62, 70], the content of CLA in fermented dairy products may be different than that of the milk they were made of. The production process can also be a factor that has an impact on its content in dairy products. The content of CLA in dairy products may be influenced by industrial technological treatments and additives used [71, 72, 73, 74, 75]. According to literature data [76, 77, 78, 79], selected bacterial strains are capable of synthesizing CLA during fermentation; however, the process can be impacted by many factors, such as the number of cells, appropriate concentration of the substrate and incubation conditions.  
The conducted research has shown that the content of CLA in raw milk was 2.26 mg/g of fat. In pasteurized milk its content was slightly lower, reaching 2.17 mg/g of fat. In yogurts produced using FD-DVS YC-180 Yo-Flex with BB-12 containing Lactobacillus delbrueckii subsp. bulgaricus, Lactobacillus delbrueckii subsp. lactis, Streptococcus thermophiles, and Bifidobacterium bifidum, it was at a similar level to the one found in raw milk. A significantly (\(p<0.05\)) higher content of this acid (2.67 mg/g fat) was found in yogurts produced using the FD-DVS YC-X16 Yo-Flex starter culture with BB-12, containing Lactobacillus delbrueckii subsp. bulgaricus, Streptococcus thermophiles, and Bifidobacterium bifidum (Figure 1). A study by [PERSON] et al. [32] indicated that raw milk had 14.91 mg/g of fat of cis9 trans11 C18:2 acid, while pasteurized milk and yogurts were characterized by a significantly lower content of this acid, reaching 6.22 mg/g of fat and 5.41 mg/g of fat, respectively. According to [PERSON] et.al. [80], the content of CLA in fermented milk drinks varied from 3.82 mg/g of fat in yogurts to 4.66 mg/g of fat in buttermilk. In non-fermented milk drinks, the content of CLA ranged from 3.38 mg/g of fat in condensed milk to 4.49 mg/g of fat in whole milk. [PERSON] et al. [75] stated that the mean CLA content in Italian fermented milk drinks produced with varied starter cultures ranged from 4.42 mg/g of fat in probiotic yogurts to 6.15 mg/g of fat in fermented milk from mountain pasture cows.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.7 Human Element  
One of the most frequently used attack vectors in smart grid setups remains the human element. Even the most secure architectures can be compromised by social engineering, incorrect setups, and inadequate credential hygiene [128]. Role-based audits, stringent access control, and ongoing security training are, therefore, crucial countermeasures [129].
## 3 Fault Feature Extraction
### Calculation of Fault Characteristic Matrix  
In this paper, the maximum modulus matrix is obtained by db6 decomposition of the zero-sequence current obtained through fault simulation. Since the simulation model selected in this paper has three lines, \(i=3\), and 6 times wavelet decomposition \(j=6\), the maximum matrix of high resistance grounding fault mode is \(A_{36}\).  
\[A_{36}=\begin{bmatrix}0.0033\ -0.0213\ -0.0601\ 0.2109\ -0.1955\ 0.2451\\ 0.00334\ 0.0026\ -0.0732\ 0.1324\ 0.1465\ 0.2652\\ 0.0092\ -0.0597\ 0.0668\ -0.4361\ 0.1872\ -0.5461\end{bmatrix}\]  
According to matrix \(A_{36}\), the largest elements of high-resistance grounding fault mode are all in the sixth column, namely db6 scale, and the corresponding system decomposition scales of the three lines are consistent. According to Equation (12), energy values are constructed to realize feature extraction of high-resistance grounding fault.  
\[B_{36}=\begin{bmatrix}0.71\ \ 0.62\ -0.75\ -0.76\ 0.81\ 0.01\\ 0.64\ \ 0.57\ -0.83\ -0.84\ 0.78\ 0.01\\ -1.23\ 1.87\ -2.04\ -2.56\ 2.85\ 0.01\end{bmatrix}\]  
It can be seen from the characteristic matrix \(B_{63}\) that the characteristic quantity of the fault line is greater than that of the non-fault line, and the characteristic quantity of each scale gradually increases. The first column element of the faulty line is also different from that of the non-faulty line, which conforms to the characteristics of high-resistance earthd fault proposed above.  
Maximum matrix of arc ground fault matrix \(A_{36}\) is:  
\[A_{36}=\begin{bmatrix}-0.05\ 0.23\ -0.52\ 1.64\ \ \ 0.56\ \ \ 0.47\\ 0.04\ -0.24\ 1.55\ \ 5.37\ -2.54\ \ 1.59\\ 0.17\ \ 0.68\ \ 3.56\ -14.66\ -6.23\ -9.53\end{bmatrix}\]  
It can be seen from matrix \(A_{36}\) that the largest elements of the high-resistance grounding fault mode are all in the fourth column, namely db4 scale. The characteristic matrix of the corresponding decomposition scale is constructed as follows:  
\[B_{34}=\begin{bmatrix}1.38\ -1.02\ \ 0.67\ \ 0.37\ 0.53\ -0.51\\ 1.42\ -0.98\ \ 0.72\ \ 0.31\ 0.46\ -0.56\\ -3.56\ 1.39\ -2.13\ 3.24\ 1.53\ -1.43\end{bmatrix}\]  
According to the characteristic matrix, the characteristic quantity of fault line is larger than that of the other lines and the polarity is opposite, which conforms to the characteristics of an arc ground fault proposed above.
## 4 Experimental Results
### Qualitative System Validation Using Experts (News and Media Professionals)  
While the implementation of the major components of the system was completed, the presented framework is a work in progress concerning its overall integration. Data gathering, processing, normalization, propagation to the pipeline, and storage are already in place. Moreover, similarity results are being tested end-to-end, while the algorithm to extract breaking news is under evaluation using a full load of input events. Geo-tag extraction is underway along with a flight plan and control system based on a web user interface. The aforementioned modalities and the system as a whole were validated through formative evaluation sessions, interviewing experts in the field, working in newsrooms, and manually performing most of the underlying tasks. The results are presented in Table 3 and Figure 6, in which the mean and standard deviation values of all answers are calculated. Based on these preliminary results, the usefulness of a system that automatically compares articles for the purpose of breaking news detection can be evaluated and conclusions can be extracted, utilities that most of the sample evaluators/experts would use. Lower results in the trust column are considered expected since the system is under construction and has to prove its applicability and accuracy in practice, even though there is high confidence regarding its performance in the field. It is expected that the gradual elaboration of the systems and the associated datasets would further increase the robustness and efficiency of the service in different real-life scenarios.  
Figure 5: Graph statistics for the respondents’ group (N = 100), concerning some of the important questions (namely, Q1 and Q5–Q9). It is noteworthy that although DJ is not that widespread, the participants showed a high interest in the proposed application. This can be further supported by the fact that the answers’ MEAN values to the questions Q6–Q8 is near 4 (“Agree”), with S.D. values kept below 1.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Road Segmentation
#### 3.3.1 Segmentation Algorithm  
The'shapely' module was used for the road segmentation process. The road path and parcel geometry were loaded from GeoJSON files. The road path was trimmed to match the parcel's boundaries by intersecting them. Then, cut points were calculated every 100 m along the road, starting from one end of the path. A cut line perpendicular to the road trajectory was calculated for each cut point. Parcels were split using these lines, facilitating the'shapely.ops.split' operation. Complex parcel geometries, such as long curves or irregular boundaries, required careful handling, ensuring cuts were made only within the parcel boundary closest to the road centerline. This prevented unintended divisions that could occur in cases like a 180-degree road curve or irregular parcel shape.
## 2 System Description
### Multi-Channel Receiver  
The multi-channel receiver contains six channels adopting digital the intermediate frequency (IF) architecture. It is mainly composed of the analog leading-ends and the Digital Down Conversion (DDC) modules integrated in FPGA. In the analog leading-ends, after being filtered and amplified, the signals are mixed to IF of 71.4 MHz. Then, the sampled signals are extracted in DDC, down converted to zero IF and reduced the data rate to baseband through downsampling. The calibration reference signal source ensures the beamforming accuracy by feeding the reference signal into every receiving channel to calculate the amplitude and phase errors. The synchronization module ensures the time-frequency synchronization with the transmitting subsystem.  
The overall structure of the receiver, the boards of the analog leading-ends, Analog-to-Digital Converter (ADC) and DDC are shown in Figure 3. Each module is packaged in a separate aluminum mold, which effectively reduces external interference and ensures isolation between the channels. The whole receiver is packaged in a 2-Unit case, which is very compact and easy to transfer.
# Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
## 5 Conclusions  
ANN can effectively represent regression prediction in a single model. However, as the formation proceeds deep into the ground, ANN cannot express the matching of different formation patterns adequately. Although LSTM can hierarchically characterize data by associating it with adjacent data, it may cause data redundancy and diffusion. To further improve the accuracy of shear wave velocity prediction, a new LSTM network combined with the attention mechanism is proposed. This network adds a self-attention mechanism and alleviates data distraction caused by much input. In experiment 1, the determinability coefficient and average absolute error of Attention-LSTM reached 0.942 and 51.85, respectively. Comparing this with the results predicted by six different methods (Carroll, Castagna, Multiple regression, SVR, ANN, and LSTM) in Table 3, the proposal method obtained the lowest error in both wells. In experiment 2, the data input length of Attention-LSTM was discussed. With the increase in the input length, the prediction accuracy of the Attention-LSTM network first improved and then gradually decreased. When the sample input length reached seven, the prediction accuracy of the network reached its highest. Overall, the experimental results show that the newly proposed Attention-LSTM network performs better in data prediction than other networks. In addition, the Attention-LSTM network can notice more useful information depending on the quality of the data themselves.  
Conceptualization, X.F.; Methodology, X.F.; Software, X.F.; Validation, X.F.; Formal analysis, Y.S.; Resources, Y.W.; Data curation, H.H.; Writing--original draft, X.F.; Writing--review & editing, Y.W. and Y.S.; Visualization, H.H.; Supervision, Y.W. and H.H.; Project administration, Y.S.; Funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Creative Research Groups of the Natural Science Foundation of Sichuan (Grants No. 2023 NSFSC1984).  
Not applicable.  
Figure 15: Comparison of six models of R2 and MAE based on Attention-LSTM.  
Data Availability Statement: The datasets presented in this article are not readily available because the data are part of an ongoing study. Logging data used in this article had been shown with the form of graphs.  
The authors declared no potential conflicts of interest with respect to the research. Abbreviations
## 1 Introduction
The optimization of a rotor's mass attributes should be especially highlighted to prevent the vibrations of a multi-stage rotor from exceeding its upper limits. [PERSON] et al. [19] proposed a novel turbine balancing method using an Ant Colony algorithm. To optimize rotor unbalance, an assembly method was put forward by [PERSON] et al. [20]. However, the center-of-mass coordinate in this study was not obtained by actual measurement but estimated by assuming a conversion ratio and based on the centroid coordinates. To overcome this defect, [PERSON] et al. [21] made use of a vertical dynamic balancing machine to measure the mass attributes of rotors at different stages; on this basis, an assembly approach was proposed for the purpose of optimizing both the coaxiality and unbalance of rotors. It can be seen from the current studies that an accurate error propagation model is very important for the prediction of assembly cumulative error and the guidance of the assembly process. The existing error propagation models were derived based on the axisymmetric characteristics of the rotating body and combined with coordinate propagation theories; however, the alignment processes and distribution of the screw holes around the rotors at different stages were ignored, and the selection of assembly orientations should be discrete. The above problems are not clearly specified in the existing assembly optimization methods. When optimizing the rotor unbalance, the selection of the assembly datum should not be the same as that of the coaxiality optimization but should be consistent with the actual rotating axis of the dynamic balance test. Targeted at the above problems, an unbalance optimization method of a multi-stage rotor is proposed in this study. In Section 2.1, an assembly error propagation model is developed, and the alignment processes and distribution of screw holes of adjacent rotors are introduced into the model. In Section 2.2, a new assembly datum for unbalance optimization of multi-stage rotor is calculated to ensure that the optimization datum and the measurement datum is truly realized. In Section 2.3, the unbalance optimization of a multi-stage rotor is achieved using a genetic algorithm (GA). In Section 3.1, the calculated accuracy of the assembly error propagation model is validated, and the optimization performance of GA is presented in Section 3.2. Finally, in Section 4, the effect of the unbalance optimization is proven in experiments.