esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
## 5 Test and Experiment
### Experimental Methods  
An intelligent sensor was used for real-time monitoring of the centrifugal pump, and the water outlet's measuring points were the pump body (measuring point 1) and the water outlet (measuring point 2). The sampling frequency was set to 5120 Hz, the number of sampling points was 4096, and the medium was water. The pump outlet pipe was tapped 30 cm away to simulate the pump water hammer failure. The experimental centrifugal pump speed was 3000 r/min, and the cutoff frequency of the centrifugal pump impeller frequency (300 Hz) was used as the cut-off frequency of the high-pass filter to test the effectiveness and reliability of the centrifugal pump water hammer fault monitoring system and method.  
In this experiment, the ADXL1002 accelerometer produced by ADI Semiconductor Company was selected, with a sensitivity of 100 mV/g. The collector was powered by 220 V AC and had a built-in 24 bit analog-to-digital converter to ensure measurement accuracy requirements. The built-in signal conditioning circuit performed high-pass filtering on vibration signals. The built-in high-performance ARM processor analyzed sensor data and combined specific algorithms to calculate three time domain indicators that reflected the characteristics of water hammer faults, including the kurtosis index, pulse index and margin index.  
The cloud server was responsible for building a diagnostic model for centrifugal pump water hammer, calculating the kurtosis index, pulse index and margin index of centrifugal pump data under normal conditions and using twice the various indicators as the alarm threshold for centrifugal pump water hammer faults. The cloud server was responsible for issuing diagnostic results to the edge collector for early warning. When the evaluation result was normal, the cloud server determined whether the threshold change exceeded 5% of the original threshold and updated the threshold if it exceeded 5%. If it did not exceed it, it was not updated. In practical applications, this percentage can be adjusted according to the actual situation.
## 5 IoT Device Identification Based on Time Series Representations
### Procedure of IoT Device Identification  
First, we describe the creation of a dataset for IoT device identification. Figure 14 shows an example of a representative feature matrix, which can be easily obtained from the time series representations shown in Figures 11 and 12. Similar to these figures, the rows show the time series representations of IoT traffic when the units of time were 30, 60, 120, 300, 600, and 900 s from top to bottom. In addition, the horizontal direction from left to right represents the passage of time, whose width was set at 1800 s as the LCM of the six values of units of time. The width scale of every unit of time was unified to 1800 s. To use time series representations as a representative feature matrix for IoT device identification, the unit widths of the time series representations at every unit of time must be aligned. Therefore, the width of the time element in the feature matrix was set at 30 s, which is the greatest common divisor of the six values of units of time. The first row of the feature matrix consists of 60 continuous unit traffic numbers of independently divided data pieces extracted when the unit of time is 30 s. Similarly, the bottom row has two sets of continuous unit traffic patterns when the unit of time is 900 s; however, each is divided into 30 blocks with the 30-second time element and assigned the same unit traffic number. Thus, the matrix of the time series representations created using the aforementioned approach has \(6\times 60\) elements and is treated as a set of feature values labeled for each IoT device. Zero matrices, which consisted of zero traffic only, were excluded from the dataset for IoT device identification.  
To identify the IoT devices, this paper applied an LSTM network [17], which is a well-known time series analysis method. Adaptive moment estimation (Adam) [25], which is also popular in optimization methods in deep learning, was used as the optimization algorithm. For the processing flow of the device identification, a series of feature matrices created from the time series representations were input into the identification model using  
Figure 13: Occurrence rate of unit traffic patterns, including zero traffic, in time series representations of 21 IoT traffic datasets when the unit time was 900 s. In spite of only two unit traffic patterns being extracted using one-stage clustering, the occurrence rate has an extremely large bias between them. In contrast, the results of the two-stage clustering consist of various unit traffic patterns, each of which has a certain proportion.  
LSTM. Thereafter, the model predicted which IoT device the input dataset belonged to and output one data label corresponding to the device.
## 6 Swine Animal Trials Using the Prototype in Hemorrhagic Shock  
The cart-based SCISCCO prototype system was applied to swine subjected to hemorrhagic shock (HS) and treated with different resuscitation strategies. This preliminary study examines two innovative strategies that have emerged in recent years for mitigating the effects of blood loss. The first strategy is resuscitative endovascular balloon occlusion of the aorta (REBOA). REBOA employs a balloon catheter to obstruct blood flow within the aorta. This maneuver temporarily halts internal bleeding and improves circulation to the heart, lungs, and brain. Blood flow can only be stopped in this way for minutes at a time before deadly ischemic injury develops from a lack of blood flow to vital organs. To mitigate this ischemic burden and extend the safe duration of balloon occlusion, new balloon catheters have been designed. These balloons partially obstruct blood flow to slow blood loss and improve central circulation but still permit enough blood flow to the remaining tissue to avoid ischemic tissue injury. This technique is termed partial REBOA (pREBOA). The second innovative strategy used to enhance survival after major bleeding is the seizure medication valpro acid (VPA). VPA has been found to have properties that alter cell survival and enhance ischemia tolerance. In animal studies, VPA can sustain life despite massive blood loss even without blood transfusion. The swine experiments we describe were conducted to determine if pREBOA and VPA could be used together to achieve synergistic effects. The SCISCCO device was applied to swine undergoing these experiments to test the feasibility of using the new cart-based prototype. Additionally, the measurements obtained in this pilot study validated the instrument's measurements against the physiological and hemodynamic parameters with known outcomes.  
The animal study design is illustrated in Figure 32. In the experiments, the SC probes were secured on the forehead of the swine (Yorkshire swine, weight: 40-60 kg) with surgical tape during the entirety of the surgical procedure to detect the hemodynamic and metabolic responses of the brain. Following induction, intubation, and instrumentation, the swine were subjected to a 40% total blood volume controlled hemorrhage and then an hour of non-resuscitated shock (MAP: 30-35 mmHg) prior to the initiation of pREBOA for 3 h with or without concurrent VPA infusion, followed by whole blood transfusion concurrent with balloon deflation and reperfusion. The surviving animals were provided critical care, employing standard resuscitation and correction of metabolic and electrolyte abnormalities until the end of the study.  
Figure 31: GUI display of data collected by SCISCCO.
## 3 Results
### Spatial Similarity Index  
Figure S4 presents spatial maps illustrating light purse seine catches in the Arabian Sea of the Indian Ocean for 2021 and 2022, with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The spatial distribution of the annual catch data closely mirrors the spatial distribution of the annual fishing intensity. The catch maps reveal a distinct meridional trend characterized by interspersed high and low catches. Notably, the catch data were predominantly concentrated in the peripheral areas of the Omani EEZ, while minimal to no catches were observed near the Indian EEZ line. This spatial distribution underscores a clear trend in light purse seine catches within the specified region.  
Tables 1 and 2 present the spatial similarity index between the fishing effort (FE) information extracted using the three methods and the catch data from actual production operations, considering a spatial accuracy of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) for the periods of January-May and October-December. In 2022, the spatial similarity indices between the FE extracted via the three methods and the catch data consistently exceeded 0.60 for all three methods. Notably, in 2021, the spatial similarity index was highest at 0.76, and this value increased to 0.79 in 2022. For specific months, the spatial similarity index peaked at 0.76 in March 2021 and reached 0.79 in November 2022. In March 2022, the spatial similarity indices calculated using all three methods were consistently greater than 0.71. Upon comparing the three methods, it was observed that the spatial similarity indices between the fishery information extracted using KDHSA and the catch data from actual production operations were the lowest. Additionally, the spatial similarity index between the FE and catch data from actual production operations was the lowest for KDHSA, while it was the highest for the other two methods.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Jan.** & **Feb.** & **Mar.** & **Apr.** & **May** & **Oct.** & **Nov.** & **Dec.** \\ \hline FE & 0.7 & 0.7 & 0.76 & 0.71 & 0.65 & 0.75 & 0.76 & 0.74 \\ KDE & 0.7 & 0.69 & 0.74 & 0.71 & 0.65 & 0.46 & 0.72 & 0.7 \\ KDHSA & 0.59 & 0.55 & 0.59 & 0.56 & 0.52 & 0.69 & 0.54 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indicators of spatial similarity between the extraction results and the actual data by the three methods in 2021.  
Figure 7: Average spatial similarity index under different spatial resolutions.
## 2 Results and Discussion
### Statistical Evaluation of Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & **95 \% Confidence Interval** & & \\ \hline
**Compound** & **(I) Plant** & **(II) Plant** & **Mean Difference** & **Lower** & **Upper** & \(p\)**-Value** & **Significantly Different** \\ \hline \(\alpha\)-pinene & & & & & & & & **Significantly Different** \\ \(\beta\) -pinene & _P. repens_ & _C. dactylon_ & 6.058835 & \(-\)2.923069 & 15.04074 & 0.161362 & No \\  & _P. repens_ & _C. dactylon_ & 12.830001 & 0.4544081 & 25.20559 & 0.0414622 & Yes \\  & _P. repens_ & _C. exaltatus_ & 16.122119 & 3.2082078 & 29.03603 & 0.0134851 & Yes \\  & _C. exaltatus_ & _C. dactylon_ & \(-\)3.292118 & \(-\)16.5954810 & 10.01124 & 0.8048685 & No \\
3-octanone & _P. repens_ & _C. dactylon_ & 3.0250021 & \(-\)4.699573 & 10.749577 & 0.5777641 & No \\  & _P. repens_ & _C. exaltatus_ & 0.2862853 & \(-\)8.946348 & 9.518918 & 0.9964308 & No \\  & _C. exaltatus_ & _C. dactylon_ & 2.7387168 & \(-\)6.020128 & 11.497562 & 0.7014438 & No \\ \(\beta\)-myrcene & _P. repens_ & _C. dactylon_ & 3.447685 & \(-\)3.19232 & 10.08769 & 0.250943 & No \\ citronellol & _P. repens_ & _C. dactylon_ & 3.7893436 & \(-\)3.091271 & 10.66996 & 0.2009792 & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Post-hoc Tukey analysis of five compounds found in the headspace of at least two of the three graminoid plants.
## 3 Tree-Based Relative Support Distance
### Relative Support Distance
The support vectors in \(L_{4}\) are obtained by collecting observations with large values of relative support distance, for example by the rule \(T(r_{\mathbf{x}},h_{\mathbf{x}})>0.9\), from both the pair of \(L_{4}\) and \(L_{1}\) and the pair of \(L_{4}\) and \(L_{2}\). The results reveal that the observations that have a mostly larger distance \(r\) and shorter distance \(h\) are likely to be support vectors. Figure 4: _Cont._  
Figure 3: Distances \(r\) from the center and \(h\) from the virtual hyperplane are shown. The two-star shapes are the centroid of each class. For each region, we calculate pairwise relative support distance with distinct adjacent regions and select a fraction of the observations, denoted by parameter \(\beta\), in the decreasing order by \(T(r_{\mathbf{x}},h_{\mathbf{x}})\) as a candidate set of support vectors. That is to say, for each region, we select the top \(\beta\) fraction of training data based on \(T(r_{\mathbf{x}},h_{\mathbf{x}})\). Parameter \(\beta\) represents the proportion of the selected data points, between 0 and 1. For example, when \(\beta\) is set to 1, all data points are included in the training of SVMs. When \(\beta=0.1\), we exclude 90% of the data points and reduce the training data set to 10%. Finally, we combine relative support distance with random sampling, which means that a half of the training candidates are selected based on the proposed distance and the others are selected by random sampling. Though being quite informative for selecting possible support vectors, the proposed distance is calculated locally with distinct adjacent regions. Therefore, random sampling can compensate this property by providing whole data distribution information.
## 3 Validation
### Case 2  
An experiment from the literature [9], in which free vibration of a suspended cable was investigated, is used to validate the numerical model dynamically. The experimental setup is given in Figure 3 and parameters of model are given in Table 3. After instant removal of additional mass defined at the midpoint of the cable, the displacement of the same point is measured.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Research**} & \multicolumn{2}{c}{**Displacement (m)**} \\ \cline{2-3}  & **Vertical** & **Horizontal** \\ \hline [PERSON] and [PERSON] [1] & \(-\)5.472 & \(-\)0.845 \\ [PERSON] and [PERSON] [45] & \(-\)5.627 & \(-\)0.860 \\ [PERSON] and [PERSON] [44] & \(-\)5.626 & \(-\)0.859 \\ [PERSON] [46] & \(-\)5.626 & \(-\)0.859 \\ [PERSON] et al. [43] & \(-\)5.626 & \(-\)0.860 \\ [PERSON] and [PERSON] [6] & \(-\)5.625 & \(-\)0.859 \\ [PERSON] and [PERSON] [10] & \(-\)5.626 & \(-\)0.859 \\ [PERSON] et al. [11] & \(-\)5.626 & \(-\)0.859 \\ SPH (present study) & \(-\)5.626 & \(-\)0.859 \\ \hline \end{tabular}
\end{table}
Table 2: Final displacement of point L.  
Figure 2: Time history of coordinate of point L in y direction.  
The number of particles used to simulate the experiments is 44, as obtained from a convergence test. The second support coordinate is defined at 43.835 m and 6.43 m initially, and the cable is defined between these supports linearly. The second support is relocated to its original position shown in Figure 3. The cable takes its catenary form under its self-weight and the additional mass. Then, mass is removed, and time history of midpoint displacement is calculated.  
The displacement history of the free vibration at the midpoint of the cable is given in Figure 4. In the figure, the experimental and numerical data in [9] and the present numerical results labeled as SPH are shown. The first peak is slightly overestimated by the SPH method. Other peaks are predicted satisfactorily.
## 1 Introduction
A useful tool named order-frequency spectral correlation (OFSC) [35] was also proposed to transfer the signal into the cyclic order-frequency domain via double Fourier transform, which transfers the angle to cyclic order domain and the time to frequency domain. When calculating the OFSC, the shaft rotating speed is needed and the vibration signal is resampled into the angular domain. Thus, the time-varying FCF in OFSC is transferred into a specific order. By identifying this specific order, the type of fault can be determined. However, like other TFA methods, the specific cyclic order in OFSC will also be interrupted by the strong background noise in real scenarios, where the time-varying fault feature may be difficult to identify. Thus, the problem of how to successfully extract the time-varying rolling bearing fault feature in the heavy interference should be investigated. In this paper, a weak fault feature extraction strategy of rolling bearing with variable speed is proposed. The measured vibration signal is firstly transformed into the cyclic order-frequency domain, which is analyzed by OFSC. Then, the fault feature contained in the OFSC is extracted according to the joint sparsity and low-rankness model via an ADMM based algorithm. Finally, the EEOS is further used to enhance the extracted fault feature, making the fault feature more obvious to identify. The following are the primary contributions of this paper:  
* The OFSC is calculated by converting the measurement of a rolling bearing with variable speed into the cyclic order-frequency domain. The joint sparsity and low-rankness of the fault feature in OFSC is firstly revealed in this paper, which can be utilized to extract weak fault features of rolling bearings under variable speed conditions;
* A joint sparsity and low-rankness constraint is imposed on the OFSC to model the fault feature. To optimize the proposed model, an algorithm named ADMM-SLRJEM is developed to extract the fault feature in OFSC. The rest of this paper is structured as follows. The measurement of a faulty rolling bearing with variable speed is considered, and the corresponding concepts of AT-CS signal are presented in Section 2. Then, the fault feature in OFSC is revealed to possess the joint sparsity and low-rankness and an ADMM based algorithm is derived in Section 3. Finally, in Sections 4 and 5, The feasibility of the proposed method is confirmed by simulations and an experimental case, respectively.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Directed Acyclic Graph (DAG)
#### 5.7.2 Calculating the Longest Path in a DAG  
After topologically sorting the DAG, the dynamic programming algorithm finds the length of the longest path. _Dynamic programing_ (also, known as _dynamic optimization_) is a popular mathematical optimization technique. It breaks down the problems into smaller sub-problems and try to find the optimal solution for the sub-problems to eventually solve the original problem. By applying dynamic programing methods, larger and more complex problems can be solved.  
In DAG, an algorithm based on dynamic programming algorithm is proposed to find the longest path, \(L_{max}\), by calculating the path of all nodes in topological order (Algorithm 3). In Algorithm 3, \(l_{x}\) denotes the length of the longest path from the first node to node \(x\).  
Note that \(T\) in Algorithm 3 is the output of Algorithm 2, and the data structure of \(T\) is predefined as a sorted set. Therefore, in step 2 of Algorithm 3, nodes are removed from \(T\) based on the topological order (the order of set \(T\)).  
```
Output \(l_{n}\) the length of the longest path to node \(n\) \(L_{max}\), the length of the longest path
1. For all nodes \(x\) on the topological sort (\(T\)):
2. Remove \(x\) from \(T\)
3.\(l_{x}=\underset{n_{i}\in\text{pred}(x)}{\max}(l_{n_{i}})+w_{x}\)
4.\(L_{max}=\underset{i\in L}{\max}(l_{i})\)
```  
**Algorithm 3**Finding the length of the longest path using Dynamic Programming Algorithm
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 3 The Proposed Strategy
### Modified Three Sigma Rule-based Fault Diagnosis  
In this section, the modified three sigma rule is introduced first, based on which the corresponding decentralized fault diagnosis is given.  
The data \(x_{i}\) sensed by \(s_{i}\) can be measured by taking both estimated mean \(\hat{\mu}_{i}\) and standard deviation \(\hat{\sigma}_{i}\) of the data collected from neighbors. Let \(t_{i}\) represent the ratio between its deviation to the estimated mean and standard deviation as follows:  
\[t_{i}=\frac{x_{i}-\hat{\mu}_{i}}{\hat{\sigma}_{i}}. \tag{16}\]  
According to the three sigma rule, if \(|t_{i}|>3\), then \(s_{i}\) is considered as faulty; otherwise, \(s_{i}\) is fault-free. However, this rule is unable to detect faulty meters within a lower degree network. The reason is listed as follows. If there exist two suspicious meters \(s_{i}\) and \(s_{i}\) such that \(x_{i}\gg x_{j}\), then \(s_{j}\) may be considered fault-free, which is called the \"masking\" [30]. To solve such a problem, the Median (_Med_) of the data and the Median Absolute Deviation (\(MAD\)) are applied, instead of the \(\mu_{i}\) and \(\sigma_{i}\), respectively. This is because the actual data value deviate from the faulty meter readings such that the _Md_- and \(MAD\)-based fault detecting schemes can achieve better accuracy in results. Then, we give the modified three sigma rule as follows.  
For a data set \(x_{k}=\{x_{1},x_{2},\cdots,x_{n_{k}}\}\) sorted in an ascending order, we have  
\[Md_{i}=Med(x_{i})\left\{\begin{array}{ll}\frac{x_{m}+x_{m+1}}{2}&n_{i}=2m\\ x_{m}&\text{otherwise}\end{array}\right.. \tag{17}\]The \(MAD\) is defined as follows:  
\[MAD(x_{1},\cdots,x_{n_{i}})=Med\{|x_{i}-Md_{i}|\}. \tag{18}\]  
In fact, only the normalized \(MAD\), which is  
\[MADN(x_{i})=\frac{Med\{|x_{i}-Md_{i}|\}}{0.675}, \tag{19}\]  
is suitable to replace Standard Deviation (\(SD\)) [30]. Then, the absolute error for the modified three sigma test is defined as follows:  
\[t_{i}^{r}=\frac{x_{i}-Md_{i}}{MADN(x_{i})}. \tag{20}\]  
It is apparent that the modified three sigma rule defers from the original one only in the choice of the absolute error.  
Next, we give the corresponding decentralized fault diagnosis algorithm as follows:  
Step 1: For each cluster \(C_{i}\) with a fault-free cluster-head \(CH_{i}\), if \(|x_{j}-x_{i}|\leq\eta\), then \(s_{j}\in C_{i}\) is fault-free; otherwise, \(s_{j}\) is faulty.  
Step 2: For each cluster \(C_{j}\) with a faulty cluster-head \(CH_{j}\), the \(MTS\) is applied to \(C_{j}^{{}^{\prime}}=C_{j}\backslash\{CH_{j}\}\) such that the fault state of each cluster-member within \(C_{j}\) is detected.  
By doing so, we can determine the fault states of at least \(|MDS|\ \bigtriangleup(1-p_{r})\) meters, where \(\bigtriangleup\) represents the maximum degree of graph \(G\). The theoretical analysis on the diagnosis accuracy and message complexity is given in the next section.
## 1 Introduction
### Previous BIM-GIS Integration
For example, the environment data, such as the climatic condition of the building, are usually available in the GIS environment; however, the GIS lacks the building interior information for more detailed climatic analysis. Therefore, GIS data are integrated with BIM for more detailed analysis [19]. Moreover, construction supply chain management highly relies on the existing surrounding information that is lacking in the BIM but can be obtained from the GIS. For this purpose, a plugin was developed that integrates BIM and GIS to map the supply chain process in the BIM and optimizes the construction supply chain management [15]. The process of integration can be achieved by connecting both systems with the use of a new platform. A district information model (DIM) was developed by integrating BIM and GIS into a new system. To achieve this objective, researchers utilized the BIM tool Autodesk Revit and GIS tool ArcGIS, which were integrated into a new platform, Autodesk Infrawworks, preserving geometrical and semantic information [28]. The current study provides evidence of the successful application of BIM-GIS in many areas. Despite the wide capabilities of BIM-GIS integration, its application in the construction management field is still in the state of infancy. It needs to explore other purposes in different stages of the construction and actual implementation, such as utility infrastructure, which is the context of this paper.