esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
###### Abstract  
Power transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, including the Duval triangle method (DTM), the Doernenburg ratio method (DRM), and the Rogers ratio method (RRM), which are suitable for on-line diagnosis of transformers. The imbalanced, insufficient, and overlapping state of gas-decomposed DGA datasets, however, remains a limitation to the deployment of a powerful and accurate diagnosis approach. This study presents a new approach for transformer fault diagnosis based on DGA, one which aims to improve the performance evaluation criteria to predict current faults and to lower the associated costs. We used six optimized machine learning methods (MLMs) for dataset transformation to organize the dataset. The MLMs used for transformer fault diagnosis were random forest (RF), back-propagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). The MLMs were implemented by using 628 dataset samples, which were obtained from laboratories, other studies, and electricity stations in Iraq. Accordingly, 502 dataset samples constituted the training set while the remaining 126 dataset samples served as the testing set. The results were examined according to six important measurements (accuracy ratio, precision, recall, specificity, F1 score, and Matthews correlation coefficient (MCC)). The best results were found for case A with RF (95.2%). In cases B and C, the best results were found for RF and DT (100% and 99.2%, respectively). With respect to the advanced machine learning method, the transformer fault diagnosis based on the MLMs was exceedingly more accurate in its predictions than the conventional and artificial intelligence-based methods.  
fault prediction; transformer fault diagnostic; DGA; machine learning; conventional methods
###### Abstract  
The present study investigated _Staphylococcus aureus_ ATCC25923 surfaceomes (cell surface proteins) during prolonged growth by subjecting planktonic and biofilm cultures (initiated from exponential or stationary cells) to label-free quantitative surfaceomics and phenotypic confirmations. The abundance of adhesion, autolytic, hemolytic, and lipolytic proteins decreased over time in both growth modes, while an opposite trend was detected for many tricarboxylic acid (TCA) cycle, reactive oxygen species (ROS) scavenging, Fe-S repair, and peptidolytic moonlighters. In planktonic cells, these changes were accompanied by decreasing and increasing adherence to hydrophobic surface and fibronectin, respectively. Specific RNA/DNA binding (cold-shock protein CspD and ribosomal proteins) and the immune evasion (SpA, ClfA, and IsaB) proteins were notably more abundant on fully mature biofilms initiated with stationary-phase cells (SDBF) compared to biofilms derived from exponential cells (EDBF) or equivalent planktonic cells. The fully matured SDBF cells demonstrated higher viability in THP-1 monocyte/macrophage cells compared to the EDBF cells. Peptidoglycan strengthening, specific urea-cycle, and detoxification enzymes were more abundant on planktonic than biofilm cells, indicating the activation of growth-mode specific pathways during prolonged cultivation. Thus, we show that _S. aureus_ shapes its surfaceome in a growth mode-dependent manner to reach high levofloxacin tolerance (>200-times the minimum biofilm inhibitory concentration). This study also demonstrates that the phenotypic state of the cells prior to biofilm formation affects the immune-evasion and persistence-related traits of _S. aureus_.  
Keywords:staphylococcus aureus; growth mode; surfaceome; persistence/tolerance; immune evasion +
Footnote †: journal: Microorganisms
## 4 Multi-Physics Simulation Analyses of GMM-FBG Current Sensor
### Ferrite-GMM Magnetostrictive Field Analyses
Accordingly, the stable linear working region and the measurement range of detecting the current by the GMM-FBG current sensor are obtained as the simulated results of magnetostriction (\(\lambda\)-\(H\)) and magnetization (\(B\)-\(H\)) along the longitudinal axis of the GMM rod at the GMM center, as shown in Figure 3b. When the current (in positive or negative direction) magnitude exceeds \(\sim\)600 A, the variation curve of the GMM magnetostrictive coefficient with a magnetic field converts from the linear region of 8-35 kA/m to a high-order function trending to saturation, implying that the nonlinear distortions will arise in current detection so as to limit the detecting range of the ferrite-GMM assemblage into 0\(\rightarrow\)\(\pm\)600 A, which still are qualified for current measurements in a high-voltage power system. As illustrated in Figure 2c, for the longitudinal stretching component of the strain tensor field contoured over the center cross-section and the longitudinal section of the GMM rod, the strain along the longitudinal axis (yy component of the strain tensor) is uniformly distributed only with 1% change along the radial direction in the effective part (out of the ferrite magnetizer) of the GMM rod, except for the narrow areas near the bottom surface and the top terminal at the air gap where the magnetostriction is precipitately reduced due to the end-edge effect. The part of the GMM rod exposed in air with an effective length of 15 mm presents at least a constant magnetostriction region of 13 mm, which is sufficient for a uniform strain modulation workspace of FBG in 10 mm length and two exports, leading out fiber tails.
## 1 Introduction  
In the era of Industry 4.0, with the rapid development of Internet technology, information technology, and manufacturing technology, the traditional large-scale manufacturing mode is gradually being replaced by customized service modes. A series of advanced networked manufacturing modes, such as application service providers, manufacturing grids, agile manufacturing, and global manufacturing have been proposed successively [1]. In this context, [PERSON] et al. [2] introduced the concept of \"cloud manufacturing\"--a new service-oriented networked manufacturing mode that gathers manufacturing resources and capabilities together on the cloud platform, escaping the limitations of space and distance; through service integration, the sharing of manufacturing resources and capabilities is fully realized [3]. Since its inception, cloud manufacturing has attracted widespread attention because of its advanced ideas and technical concepts [4].  
The cloud manufacturing mode extends the manufacturing environment to multiple user subjects, service subjects, and geographical spaces. As such, it faces a high level of uncertainty and disturbance [5]. The cloud manufacturing system (CMS) can reduce the impact of some disturbances (e.g., demand change, order fluctuation, and emergency order insertion) through its own flexible configuration and self-adjustment strategies, but relatively serious disturbances can cause a variety of damage, such as insufficient supervision of the cloud platform, random the withdrawal of cloud service providers from the platform, the failure of cloud resource nodes, and the interruption of cloud paths, among others [6]. Therefore, it is of great practical significance for the implementation and deployment of cloud manufacturing projects to accurately identify the impact of uncertain environments on cloud manufacturing [7; 8; 9], explore the robustness level of the cloud manufacturing process (CMP) under different interference modes, and formulate corresponding robustness recovery strategies to improve the stability and anti-interference of the system [10; 11; 12].
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 3 RS Zero-Shot Classification  
In zero-shot classification, we are given only a test set \(D=\left\{\textit{h}\right\}_{j=1}^{N}\) composed of \(N\) images, where each image is \(\textit{h}_{i}\in\Re^{224\times 224\times 3}\). The aim is to classify each \(\textit{h}_{i}\) into one of the following classes: \(\text{c}=\left\{1,\dots,\text{C}\right\}\). As mentioned previously, we adopted the CLIP family of models for carrying out this task. CLIP is a vision-language model composed of image and text encoders. The text encoders are typically based on natural language processing (NLP) transformers, while the vision backbone is based on CNNs (Convolutional Neural Networks) or vision transformers.  
Figure 1 shows an overall view of the proposed zero-shot classification for RS imagery. Recall that we do not fine-tune these models at all on RS images; instead, they are frozen, and we use them directly for classification only. Typically, the model consists of a parallel vision and language encoders. The vision encoder embeds the test image \(I\) (for simplicity,  
we remove the subscript j) into a visual feature \(f(I)\). For the language encoder, we embed all class names present in the dataset into a set of textual features: \(\{f(T_{c})\}_{c=1}^{C}\). This is done by appending the name of the classes present in the dataset, for example, to a predefined prompt set in the CLIP model. For example, we append the name \"airplane\" to the prompt \"a photo of\", yielding the prompt \"a photo of an airplane\". Next, we feed this prompt to the language encoder to obtain the textual feature representation \(f(T_{c})\) and compute the cosine similarity score between the image representation and all textual representations as follows:  
\[\text{sim}(\text{I},\text{T}_{\text{c}})=\frac{\text{dot\_product}(\text{f}( \text{I}),\text{f}(\text{T}_{\text{c}}))}{\|\text{f}(\text{I})\|_{2}\|\text{f}( \text{T}_{\text{c}})\|_{2}},\text{ c}=1,\ \dots,\text{C} \tag{1}\]  
where \(\|.\|_{2}\) refers to the l2-norm.  
Figure 1: Zero-shot classification with CLIP/Open-CLIP Models.  
Finally, we compute the probability for each class and assign the image to the class yielding the maximum probability:  
\[\mathrm{P_{C}}=\mathrm{Softmax}(\mathrm{sim}(\mathrm{I},\mathrm{T_{c}})),\qquad \mathrm{c}=1,\ \ldots,\mathrm{C} \tag{2}\]  
Since RS images possess unique characteristics and are acquired from top-view satellites, we anticipate that utilizing more appropriate prompts can lead to improved results compared to the predefined prompt 'a photo of'. In this regard, we employ alternative prompts outlined in Table 1, incorporating terms related to RS, scene, and top-view. Through our experiments, we demonstrate that utilizing these prompts can yield superior results when compared to the original prompt defined by CLIP.
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
###### Abstract  
Due to the dynamic mobility feature, the proactive flow rule cache method has become one promising solution in software-defined networking (SDN)-based access networks to reduce the number of flow rule installation procedures between the forwarding nodes and SDN controller. However, since there is a flow rule cache limit for the forwarding node, an efficient flow rule cache strategy is required. To address this challenge, this paper proposes the mobility-aware hybrid flow rule cache scheme. Based on the comparison between the delay requirement of the incoming flow and the response delay of the controller, the proposed scheme decides to install the flow rule either proactively or reactively for the target candidate forwarding nodes. To find the optimal number of proactive flow rules considering the flow rule cache limits, an integer linear programming (ILP) problem is formulated and solved using the heuristic method. Extensive simulation results demonstrate that the proposed scheme outperforms the existing schemes in terms of the flow table utilization ratio, flow rule installation delay, and flow rules hit ratio under various settings.  
flow rule; hybrid rule cache; mobile flow; software-defined networking +
Footnote †: journal: Electronics Electronics
## 4 Experimental Methods and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**0 cm**} & \multicolumn{2}{c}{**5 cm**} & \multicolumn{2}{c}{**10 cm**} & \multicolumn{2}{c}{**0 cm**} & \multicolumn{2}{c}{**0 cm**} \\ \cline{2-13}  & \multicolumn{2}{c}{**0 deg**} & \multicolumn{2}{c}{**0 deg**} & \multicolumn{2}{c}{**0 deg**} & \multicolumn{2}{c}{**30 deg**} & \multicolumn{2}{c}{**45 deg**} & \multicolumn{2}{c}{**60 deg**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} & \multicolumn{2}{c}{**Single**} & \multicolumn{2}{c}{**Multiple**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} & \multicolumn{2}{c}{**Std**} \\ \hline X & 6.227 & 4.592 & 8.122 & 5.695 & 8.122 & 4.579 & 3.446 & 5.018 & 6.823 & 3.804 & 6.908 & 4.739 \\ Y & 3.29 & 2.634 & 7.304 & 5.968 & 7.304 & 3.112 & 6.819 & 4.82 & 7.883 & 4.249 & 8.737 & 4.846 \\ Z & 4.314 & 3.708 & 6.552 & 4.362 & 6.552 & 2.802 & 6.302 & 4.574 & 8.21 & 5.391 & 7.359 & 4.843 \\ d & 4.282 & 2.923 & 4.844 & 3.506 & 3.736 & 2.964 & 5.787 & 3.248 & 4.44 & 3.66 & 5.97 & 4.345 \\ \(\varphi\) & 1.155 & 1.013 & 1.504 & 1.094 & 1.504 & 0.647 & 2.086 & 0.479 & 2.467 & 0.278 & 3.186 & 0.548 \\ \(\theta\) & 3.249 & 1.585 & 3.214 & 2.006 & 3.214 & 1.083 & 0.918 & 0.246 & 0.489 & 0.276 & 0.445 & 0.368 \\ \(\psi\) & 0.855 & 0.329 & 0.816 & 0.491 & 0.816 & 0.254 & 0.487 & 0.343 & 0.296 & 0.119 & 0.466 & 0.399 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standard deviation value of representative marker pose measured with drone-mounted camera. Figure 7: Standard deviation of representative marker pose measured with drone-mounted camera. Red and blue represent experimental results using single marker and multiple marker. (**a**) Position error, (**b**) Orientation error.
## 3 Results  
### Cell Growth of Mono-Culture of Cyanobacterial Strains in Presence of Aminoclay (AC)  
M.S. and A.S. were mono-cultured separately under the conditions specified in Section 2.3 and in the presence of increasing concentrations of MgAC (from 0.01 to 1.0 g/L for 14 days). Two cyanobacterial cell growths in terms of optical density (OD) at A\({}_{680}\), under different AC concentrations, were plotted (Figure 1). The OD values of the M.S. culture increased with the increase in utilized AC concentrations. Cells cultured at low concentrations (at 0.01--0.02 g/L) showed similar or slightly higher numbers relative to the control case. Cell growth in the case of MgAC addition at 0.05 g/L was the highest. Overall, low dosages (0.01-0.1 g/L) of MgAC resulted in higher cell growth rates and OD values than the controlled culture grown without MgAC (Figure 1). However, at the beginning of day 4, microalgal growth was obstructed by the presence of MgAC at high concentrations (0.5-1.0 g/L). The inhibition effects of MgAC on the growth of A.S. were shown after 14 days of exposure at all tested dosages. Growth inhibition was significantly increased with the increase of MgAC concentration. With increasing concentrations of AC (0.01, 0.02, 0.05, 0.1, 0.5, and 1.0 g/L), reduced A.S. cell growth, for all treatments, was observed. High applied concentrations (0.05-1.0 g/L) significantly affected A.S. growth relative to the other concentrations (\(p<0.05\)).  
As shown in Figure 1, the OD values of M.S. and A.S. cells, as compared with the control unit of each mono-culture, increased gradually from the beginning of the experiment (A\({}_{680;\;day\;0}\): 0.0206) and reached the maximum value at the end of the experiment (A\({}_{680;\;day\;14}\): 0.4690). The exposure of both cyanobacterial species to MgAC resulted in apparent differences in cell number between the controls and treated experiments (\(p<0.05\)). The growth inhibition of the A.S. culture, according to the AC concentrations, was higher than that of the M.S. culture. The AC utilization at dosages of 1.0 g/L resulted in M.S.- and A.S.-inhibition efficiencies of 22.59 and 57.38% on day 14, respectively.  
Figure 1: OD values at 680 nm of M.S. culture (**A**) and A.S. culture (**B**) where MgAC concentrations (0, 0.01, 0.02, 0.05, 0.1, 0.5, and 1.0 g/L) are treated in culturing for 0, 2, 4, 6, 8, 10, 12, and 14 days. The standard deviations of the mean (\(n=3\)) are represented by the error bars. A \(p<0.05\) value is considered to represent significant difference.
## 5 Experimental Study
### Comparative Analysis
#### 5.2.4 Impact of False Alarm Rate  
The false alarm rate is the measure of rate of unwanted warnings provided by an approach due to the inefficiency in the classification of driver behavior. The false alarm rate of the proposed approach is compared with the existing approaches with respect to the number of vehicles as shown in Figure 13.  
The false alarm rate increases with an increase in the number of vehicles. The proposed approach possesses a lower false alarm rate than the existing approaches due to precise classification of driver behavior and providing personalized assistance to the driver. This is achieved by implementing TA-SAC in which the three agents are utilized to provide precise assistance and dissemination of warning messages based on the environment. The existing approaches possess low accuracy in detection of driver behavior, thereby providing a high number of false alarms (which affects the regulation of traffic).  
Figure 11: Number of vehicles vs. latency.  
Figure 12: Number of edges vs. latency.  
Figure 14 presents the comparison of the false alarm rate of our proposed approach and the existing approaches with respect to the number of edge nodes. The increase in edge nodes influences the speed of transmission of warning messages to nearby vehicles. The proposed approach possesses less false alarm rate due to the efficacy of both driver behavior detection and generation of personalized assistance. The increase in the number of edge nodes in the existing approaches, which possess an increased false alarm rate, result in frequent dissemination of false alarms, thereby resulting in wastage of bandwidth.
## 4 Numerical Simulation of Slope Stability under Different Vegetation
### Numerical Model Establishment
#### 4.2.2 Numerical Model Working Conditions  
The numerical simulation analysis of slope stability was conducted under different conditions, as shown in Table 3. Four sampling time points of 30 d, 60 d, 90 d, and 120 d were chosen for the experiment. Therefore, the thickness of the root-soil composites varied during different growth periods of the vegetation species. The root depths of C.d and M.m at the four different sampling time points were assumed to be 0.06 m, 0.18 m, 0.24 m, 0.30 m and 0.4 m, 0.6 m, 0.8 m, 1.0 m, respectively. The factor of safety of the different working conditions was calculated using the strength reduction method.  
Figure 4: The geometrical parameters for slope modeling (unit: cm).
## 3 Numerical Results: Performance Evaluation of the Stow Net  
Validation of the numerical stow net is firstly conducted by checking the general trends acquired from experiments with several different models. In addition, the performance of the stow net is evaluated through the stretching ability of the net assembly, i.e., net horizontal length, net vertical deflection, and net-entrance area (also referred to as net mouth area), along with submergence depth of the net assembly in varying wave and current conditions.
## 3 Results and Discussion
### Effects on the AE and Failure Behavior  
Since the F-T damage complicates the fracture processes in the tested samples, understanding the fracture behavior under loading is crucial. The acoustic signal can offer abundant information concerning the fracture processes and the associated stages of deformation in rocks [38]. The dynamic monitoring of the rock deformation and fractured space evolution in the loading process using AE data has become a useful method for damage evaluation. As shown in Figure 14, a possible relationship between AE activity and stress-strain behavior during loading deformation was observed by [PERSON] et al. [39]. Therefore, this study employed AE technology to understand the variations in stress thresholds for fracture processes (cracks germination propagation and damage) that were caused by F-T action. Thus, the changes in AE signals were carefully studied and evaluated with Figure 13 in mind, and these results were then related to the damage that was done to the samples by the F-T cycles.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Fitted Curves** & **Decay Factor** & **Half-Life (H\({}_{\textbf{1/2}}\))** & **R\({}^{\textbf{2}}\)** \\ \hline P-wave \(v\) & \(v=3237.7\cdot\exp(-0.00214\cdot N)\) & 0.00214 & 324 & 0.987 \\ Density \(\rho\) & \(\rho=2.414\cdot\exp(-6.97\times\ 10^{-4}\cdot N)\) & 0.000697 & 994 & 0.912 \\ Strength \(\sigma\) & \(\sigma=27.924\cdot\exp(-0.0217\cdot N)\) & 0.0217 & 32 & 0.991 \\ Modulus \(E\) & \(E=3.092\cdot\exp(-0.0254\cdot N)\) & 0.0254 & 27 & 0.989 \\ \hline \end{tabular}
\end{table}
Table 2: Relationships among the physical and mechanical parameters with F-T cycle.
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 3 Methodology
### Developed Algorithms
#### 3.4.2 BOCEDS-TSHC  
The first added component to BOCEDS is the tempo spatial hyper-cube (TSHC) which is aimed to enable dynamic changing of the cube granularity. The pseudocode of the algorithm is presented in Algorithm 2. It accepts the data stream, initial quantization level, step size, outlier threshold, cell threshold, the ratios of calculating \(R_{max}\), \(R_{min}\). The output of the algorithm is the clustering result as well as the anomaly detection. The algorithm starts with an initialization of the various variables. Next, the data inside the buffer are used for finding the best cube granularity using TSHC. Next, a loop of data processing for clustering is conducted if new points arrive. At each iteration, the algorithm searches for the target micro-cluster using Algorithm 5, update the micro-cluster using Algorithm 6, update the energy of CMC, and assign CMC to macro-clusters using Algorithm 7. Each of these operations is called based on a separate algorithm that will be explained in the subsequent sub-sections.  
```
1:(1) \(B\): Data Buffer.
2:(2) \(q_{0}\): Initial quantization level.
3:(3) Delta: step size.
4:(4) outliersThreshold.
5:(5) cellThreshold.
6:Output:
7:\(Q\) = [\(q_{1}\), \(q_{2}\),... \(q_{m}\)]: the quantization levels where m is the number of dimensions.
8:1- start algorithm
9:2- initialization:
10:notConverged \(\leftarrow\) true
11:\(Q\gets q_{0}\).
12:openDimensions \(\leftarrow\) 1:\(m\).
13:NumPoints \(\leftarrow\) [].
14:while notConverged do
15: use Q to build set of hyper-cubes that contains all data points in \(B\).
16:for each hyper cube do
17: calculate the number of points inside it and add it to NumPoints array.
18:endfor
19: remove outlierscells which its NumPoints < outliersThreshold.
20:if min(NumPoints) > cellThreshold then
21: converged = 1 break
22:endif
23: chose random dimension \(i\) from openDimensions
24:if\(q(i)\) + delta * \(max(B(:,i))\) \(\leftarrow\)\(max(B(:,i)/2\)then
25:\(q(i)=q(i)\) + delta * \(max(B(:i))\).
26:else
27: remove \(i\) from openDimensions.
28:endif
29:endwhile
30:1- return \(Q\)
31:end algorithm
```  
**Algorithm 1**Tempo Spatial Hyper Cube