esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 In-Situ Monitoring
### Stress of Bolt
#### 4.2.2 Analysis of Monitoring Results  
The stress on the bolts connecting Tube 9 and Tube 10 were monitored during the construction sequence of the station. The stresses recorded in the eight monitoring sensors at different construction stages are shown in Figure 16. The following observations can be made at different construction stages:  
(1) Pilot tunnels construction: The bolts started going in tension as the excavation started, and the tensile stresses gradually increased along with the soil excavation. When the pilot tunnels were excavated at the distance of \(15\)m, the tensile stress of about \(5\) kN was recorded in the monitoring point \(4\)#. The tensile stress in the bolts began to increase rapidly when the pilot tunnels were excavated further, and the peak value of \(28.9\) kN was recorded when the tunnel excavation reached to its final distance of \(43.5\) m.  
As seen in Figure 16, with the tunnel excavation, the monitoring values increase gradually, and the increment shows up an increasingly large trend. The reasons were as follows: (a) The influence  
Figure 14: Monitoring instrumentation.  
Figure 15: Installation of monitoring sensors.  
of steel pipe jacking in accuracy in the pre-construction stage. According to the construction records, the deviation of the early 25-m is smaller than the rest distance in the steel pipe jacking stage, which caused the quality of flange welding in the last half was inferior to the first half. So the main stress was borne by bolts in the last half of the whole support system; (b) The influence of construction progress. The excavation period of the early 25-m is less slow than the rest distance in excavation soil below STS structure, which causes the ground disturbance to be smaller.  
(2) Beam-column supporting system construction: As the bottom longitudinal beam, cylindrical steel column and top longitudinal beam located inside the pilot tunnel are built, respectively. Tensile stresses in the bolts gradually reduced at this stage. The reason is that part of the overburden load is carried by the column, which releases some of the stress in the bolts.  
(3) Construction of the station hall: The stresses in the bolts decreased during this stage, and even changed from tension to compression. The monitoring location produced negative moment along with soil excavation, and it happened because the stiffness of the STS structure supported by the steel columns was higher than the stiffness of the rest, so the settlement of the structure was larger on the sides compared to the supporting section.  
(4) Construction of the platform layer and the later stage: stresses in the bolts remained almost constant during this stage because the whole supporting system had already reached equilibrium.  
Figure 16: _Cont._
## 2 Materials and Methods
### Model Development
#### 2.3.6 Analysis Pipeline
\begin{table}
\begin{tabular}{c c} \hline \hline
**Learning Algorithms and** & **Hyperparameter Range** \\ \hline Logistic Regression (LR) & C = Float([1,\(\ldots\),10]) \\ \cline{2-3}  & class\_weight = ’balanced’ \\ \hline Decision Tree (TR) & min\_samples\_split = Integer([2,\(\ldots\),30]) \\ \cline{2-3}  & min\_samples\_leaf = Integer([2,\(\ldots\),30]) \\ \cline{2-3}  & criterion = ’gini’ \\ \hline Linear SVM (L. SVM) & C = Float([1,\(\ldots\),10]) \\ \cline{2-3}  & class\_weight = ’balanced’ \\ \hline  & min\_samples\_split = Integer([2,\(\ldots\),30]) \\ \cline{2-3}  & max\_features = [’auto’, ’sqrt’, ’log2’] \\ \cline{2-3} Random Forest (RF) & criterion = ’gini’ \\ \cline{2-3}  & bootstrap = True \\ \hline Gradient Boosting (GB) & loss = [’deviance’, ’exponential’] \\ \cline{2-3}  & learning\_rate = Float([0.001,\(\ldots\), 1], ’logspace’) \\ \hline Recursive Feature Elimination (RFE) & n\_features\_to\_select = Integer([2,\(\ldots\),12]) \\ \hline Imbalance Data Transformer & method\_name = [’RandomUnderSampler’, ’RandomOverSampler’, ’SMOTE’, ’BorderlineSMOTE’] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameter search space for each learning algorithm and other pipeline elements specific to each pipeline. Performance Metrics  
In this study, the balanced accuracy was the chosen primary metric to handle the prediction model's bias towards the more frequent class in our dataset (\"on\" or \"0\"). The balanced accuracy was also chosen so that both a high true positive rate (or sensitivity, Sn) and a high true negative rate (or specificity, Sp) were achieved. The balanced accuracy was defined as the arithmetic mean of the sensitivity and specificity, as defined in Equations (8) and (9) [38; 50]. \[\text{Sn}=\frac{TP}{TP+FN},\;\text{Sp}=\frac{TN}{TN+FP}, \tag{8}\]  
\[\text{Bal. Acc.}=\frac{1}{2}(\text{Sn}+\text{Sp}), \tag{9}\]  
where \(TP\) is the true positive value, \(FN\) is the false negative value, \(FP\) is the false positive value, and \(TN\) is the true negative value, according to the confusion matrix in Table 7.  
In this study, the precision and f1-score were also calculated, as shown in Equations (10) and (11). The precision and F1-score were considered because we wanted to avoid an \"on\" state being predicted as wearing-off. Using the balanced accuracy and F1-score gave further importance to false negative and false positive errors in our prediction model. \[\text{Precision}=\frac{TP}{TP+FP}, \tag{10}\]  
\[\text{F1 Score}=2\cdot\left(\frac{Precision\cdot\text{Sn}}{Precision+\text{ Sn}}\right) \tag{11}\]
## 2 Overseas Seismic Performance Evaluation Criteria  
Concrete with embedded anchors generally exhibits cracks when its durability is significantly reduced in the event of an earthquake. Additionally, a stress distribution occurs along the anchor installation locations, as shown in Figure 2, and the bearing capacity of the anchors is rapidly weakened [7]. In the evaluation of the seismic performance of concrete anchors, these behavioral characteristics are considered, and the effect of the cracks in the fixation unit of the concrete on the adhesive force of the anchors is evaluated. The average maximum loads are calculated by evaluating the static performance of two concrete types (non-cracked and cracked concrete), and a dynamic load protocol is presented based on such calculations. Finally, the seismic safety of the concrete-embedded anchor bolts is evaluated by assessing the performance under these dynamic loads [8].  
In the United States, the seismic performance of post-installed anchor bolts embedded in cracked concrete is evaluated in accordance with the American Concrete Institute (ACI) 355.2 standards [9] because they may cause concrete cracks or be pulled out in the event of an earthquake [10]. According to the ACI 355.2 standards, a simulated seismic load test is conducted by simulating an earthquake using cyclic loading, and it is assumed that post-installed anchor bolts are embedded in concrete with a 0.5-mm-wide crack. The presented cyclic loading is shown in Figure 3. For the cyclic loading pattern, a 50% load with average strength, determined through a static performance test, is applied 10 times; a 37.5% load is applied 30 times, and a 25% load is applied 100 times. Therefore, cyclic loading is applied 140 times until failure. In this instance, the loading rate must be between 0.1 and 2 Hz. The seismic performance is considered verified if the dynamic performance through cycling loading meets 80% of the static performance.  
TR 049, which are the design standards published by the European Organization for Technical Approval (EOTA), include the design standards for mechanical post-installed anchors [11]. For the C1 classification for non-structural elements, it is assumed that they  
Figure 3: Number of cycles in loading pattern according to American Concrete Institute (ACI) 355.2 and TR 049: (**a**) pull-out force; (**b**) shear force. (Unit: mm)  
Figure 2: Condition of concrete under cracking: (**a**) stress concentration around the anchor hole; (**b**) stress in concrete induced by the anchor expansion or by the load acting on the anchor; (**c**) stress distribution in non-cracked concrete; (**d**) stress distribution in cracked concrete.  
are embedded in concrete with a 0.5-mm-wide crack. In addition, a 50% load with average strength, as determined through a static performance test, is applied 10 times; a 37.5% load is applied 30 times, and a 25% load is applied 100 times, in the same way as with the ACI 355.2 standards. The seismic performance is also considered to be verified if 80% of the static performance is met during the static loading test.
## 3 Demand Forecasting Models and Power Theft Detection Strategy
### Recurrent Neural Networks Model  
RNNs are networks that contain loops, which allow information to endure. They are utilized to model data that changes over time [30]. The data is fed into the network one by one, and the network's nodes save their current state at one time step and utilize it to influence the following time step. RNNs exploit the temporal information in the input data, and for this reason, are more suited to manage time series data. The ability of a RNN stands in using recurrent connections between neurons and can generally be described by the following equation [31]:  
\[x_{t}=\left\{\begin{array}{c}0,\text{if}(\text{t}=0)\\ \phi(x_{t-1},a_{t}),\text{otherwise}\end{array}\right. \tag{3}\]
# Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
## 5 Problems and Solutions
### Real-Time Problem  
It is essential to consider real-time problems when performing surface defect detection in real industrial environments. Real-time detection problems involve reducing detection time and improving detection efficiency to maintain roughly the same accuracy. Research has been conducted on real-time problems by some scholars. To detect surface defects on printed circuit boards, literature [153] suggested combining SSIM and MobileNet. Comparing the proposed algorithm with Faster R-CNN, it maintained high accuracy while being at least 12 times quicker than the existing algorithm. Literature [154] developed a novel 11-layer CNN model for detecting welding defects in robotic welding manufacturing. The proposed method was capable of detecting metal additive manufacturing in real time, which meets specific requirements for online detection.
# Soft Contrastive Cross-Modal Retrieval
## 4 Experiment
### Performance Evaluation
#### 4.2.2 Hyperparameter Sensitivity Analysis  
**Temperature \(\tau\).** Equation (4) illustrates the significant impact of the temperature parameter \(\tau\) on the performance of SCCMR in cross-modal retrieval tasks. To corroborate this perspective, we performed a sensitivity analysis of the hyperparameter \(\tau\), utilizing the NUS-WIDE dataset across Image2 Text, Text2 Image, and average performance metrics. Figure 6 demonstrates that retrieval accuracy is subject to variation with adjustments in \(\tau\), with SCCMR realizing its highest mAP scores at a \(\tau\) value of 0.7. These results endorse the premise that judicious selection of \(\tau\) is pivotal for the optimal functioning of the SCCMR methodology.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Method** & **Text2 Image** & **Image2 Text** & **Average** \\ \hline \multirow{8}{*}{Traditional Method} & CCA [13] & 22.70 & 22.50 & 22.60 \\  & SM [59] & 21.12 & 18.74 & 20.14 \\ \cline{1-1}  & MCCA [14] & 68.90 & 66.40 & 67.70 \\ \cline{1-1}  & MvDA [28] & 62.60 & 59.40 & 61.00 \\ \cline{1-1}  & MvDA-VC [28] & 67.30 & 64.80 & 66.10 \\ \cline{1-1}  & JRL [29] & 53.40 & 52.70 & 53.10 \\ \hline \multirow{8}{*}{Deep Learning Method} & Deep-SM [33] & 48.05 & 44.63 & 46.34 \\ \cline{1-1}  & CMDN [34] & 52.60 & 54.40 & 53.50 \\ \cline{1-1}  & DCCA [35] & 67.70 & 67.80 & 67.80 \\ \cline{1-1}  & DCCAE [36] & 67.10 & 68.00 & 67.50 \\ \cline{1-1}  & ACMR [32] & 67.60 & 67.10 & 67.35 \\ \cline{1-1}  & MG-HSF [31] & **71.55** & **69.62** & **70.59** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance comparison (mAP@50 in %) with 12 competitors on Pascal Sentence dataset. The highest performance values are shown in boldface.  
**Hyperparameter \(\varepsilon\).** As shown in Equation (5), the hyperparameter \(\varepsilon\) notably affects the efficacy of SCCMR in cross-modal retrieval tasks. To substantiate this assertion, we examined the responsiveness of the hyperparameter \(\varepsilon\) within the NUS-WIDE datasets, considering Image2 Text, Text2 Image, and average performance metrics. As depicted in Figure 7, the retrieval accuracy exhibits variability with alterations in \(\varepsilon\), with SCCMR reaching peak mAP when \(\varepsilon\) is configured to 0.3. This evidence corroborates the notion that an optimal \(\varepsilon\) selection is crucial for maximizing SCCMR performance.
# Carbon Microstructures Synthesis in Low Temperature Plasma Generated by Microdischarges
## 1 Introduction
The discharge was generated from a tungsten needle supplied by an AC voltage source of 8 kV/25 kHz. [PERSON] and [PERSON] [12] also obtained carbon nanotubes (CNTs) using a tungsten needle as the DC discharge electrode, but without a catalyst. A flat-ended graphite rod was used as a ground electrode and a tungsten wire as the cathode. The feedstock of carbon was ethylene mixed with hydrogen. (C\({}_{2}\)H\({}_{4}\):H\({}_{2}\) (1:100)). The discharge voltage and current were 1.6 kV and 0.3 mA, respectively. The other structures obtained, using a negative corona discharge, were carbon nanowalls. During glow discharge with a rod used as the cathode and a plate as the anode, [PERSON] et al. [13] synthesized carbon nanowalls from the vapors of ethanol or hexane. [PERSON], using cyclohexane as the carbon feedstock in a positive corona discharge, obtained carbon fibers synthesized at the needle electrode tip [14,15]. The carbon micro- and nanostructures could potentially be applied to the synthesis of electrodes for ion batteries [16], anode to electrochemical oxidation [17], light-emitting devices, organic transistors [18], catalyst carriers [19] or for other self-assembled nanostructures. Polymer thin-films synthesized in low-temperature plasma have been found to be a material with a low dielectric constant applied to microelectronics [20], and as thin films with applications in gas sensors for the detection of ethanol and ammonia vapors of concentrations ranging from 100 to 1000 ppm [21] or as a thin hydrophobic layer [22]. These experiments were carried out in a glow discharge in a low-pressure atmosphere. One of the most popular liquid hydrocarbons used as a carbon feedstock is cyclohexane. The products of decomposition of cyclohexane, such as ethylene and methane, have been widely used for the synthesis of CNT or diamond-like structures. [PERSON] et al. [23] showed that the deposition rate of a plasma-polymerized thin layer increases with a decrease in the H/C ratio. Cyclohexane is a hydrocarbon with a relatively low H/C ratio, equal to two, and, for this reason, was used in the experiments presented in this paper as a carbon feedstock for the synthesis of carbon microstructures in micro-discharges. Additionally, the high vapor pressure (13 kPa at 298 K) of cyclohexane, and its high auto-pyrolysis temperature (>1000 K), predestinate this hydrocarbon as a source of carbon, which can be obtained at relatively low temperatures and at atmospheric pressures. The aim of this paper is to investigate the process of synthesizing various carbon structures from cyclohexane vapors in high-voltage, low-current electrical microdischarges at atmospheric pressure between needle and plate electrodes. This configuration is typical for corona discharges, but the distance between the electrodes was shorter in order to obtain at least one linear dimension of the active plasma area in the range of the order of a few millimeters. This configuration allowed the initiation of microplasma in a limited region between the electrodes.
## 3 Proposed Methodology
### Parallel Model Architecture  
The parallel model architecture is designed to leverage the strengths of multiple feature encoders, enabling simultaneous extraction of diverse and complementary features from raw audio signals. This approach creates a comprehensive and robust feature representation by combining various types of features that capture different aspects of the audio signal. The raw audio input signal is processed independently by several feature encoders, each specializing in extracting specific types of features. The distinct feature sets generated by these encoders are concatenated into a single unified feature representation. This combined feature set is subsequently fed into a downstream classifier or regression model, which performs the final prediction tasks.  
The MFCC and STF feature encoders utilize 1 DCNN and BiLSTM networks to process MFCC and STF features, capturing both local patterns and temporal dependencies. The Mel-spectrogram feature encoder is a deep CNN-based architecture that automatically learns relevant features from the spectrogram, capturing both local and global temporal patterns. The raw audio feature encoder uses pre-trained models such as Wav2 Vec 2.0 and HuBERT, which are effective in detecting subtle emotional cues and robust to noise.  
Each feature encoder processes the raw audio signal independently, generating distinct feature sets. The latent features from each encoder are concatenated to form a single unified feature representation, capturing a wide range of information present in the audio signal. This combined feature set is then fed into a downstream classifier or regression model for final predictions.
## 3 Required EMI filter Attenuation in Interleaving Units Using Conventional Phase Shift
Hence, Figure 6 shows the relationship between the input ripple current with the number of the interleaved converter using PLECS simulations. The current ripple decreases by adding the number of interleaved units. From the ripple current perspective, it is not beneficial to increase the number of units above 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameter** & **Value** & **Unit** \\ \hline \(U_{g}\) & Grid phase voltage & 230 & Vrms \\ \(f_{g}\) & Grid frequency & 50 & Hz \\ \(C_{dc}\) & DC-link capacitor & 500 & \(\mu\)F \\ \(U_{dc}\) & Output voltage & 400 & V \\ \(P_{o}\) & Output power & 1 & kW \\ \(\Delta t_{max}\) & Inductor current ripple & 22 & \% \\ \(k_{L1}\) & Inductor size factor & 3 & cm\({}^{3}\)/mH. A\({}^{2}\) \\ \(k_{L2}\) & Inductor size factor & 8 & cm\({}^{3}\)/mH \\ \(k_{L3}\) & Inductor size factor & 1.1 & cm\({}^{3}\)/A \\ \(k_{C1}\) & Capacitor stored energy factor & 62 & cm\({}^{3}\)/F. V\({}^{2}\) \\ \(k_{C2}\) & Capacitor voltage dependent factor & 0.7 & cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Case Study Specification of Single-Phase Boost PFC. Figure 4: Analysis phase shift effects on input ripple current in two units interleaved boost stages. The phase shift between units is considered 180\({}^{\circ}\).
# Single-Stage Step-Down Power Factor Corrector without Full-Bridge Rectifier
## 4 Design Considerations
### Design of Output Inductor  
In the design of an inductor, it is necessary to consider the peak input AC current \(I_{in,pk}\) when the circuit is operated at the lowest input AC voltage \(v_{in,min}\) and under the rated output power \(P_{o,rated}\). By assuming that the input AC voltage and the input AC current are ideal sinusoids, the input AC voltage \(v_{in}\) and the input AC current \(i_{in}\) can be defined as \(V_{in,pk}\sin\omega_{L}t\) and \(I_{in,pk}\sin\omega_{L}t\), respectively, where \(V_{in,pk}\) and \(I_{in,pk}\) are the peak value of the input AC current, respectively, and \(\omega_{L}\) is the line radian frequency. To simplify the analysis, the phase angle \(\omega_{L}t\) is replaced by the phase angle \(\theta\), so the input AC voltage can \(v_{in}\) be expressed as follows:  
\[v_{in}=V_{in,pk}\sin\theta \tag{4}\]  
Similarly, the input AC current \(i_{in}\) can be expressed as  
\[i_{in}=I_{in,pk}\sin\theta \tag{5}\]  
As shown in Figure 9, since there is no input current when the input voltage is lower than the output voltage of the buck-type PFC rectifier, it is necessary to assume that the input AC current \(i_{in}\) in (5) should be rewritten into (6). \[i_{in}=I_{in,pk}\sin\theta=I_{im}\cdot(\sin\theta-\sin\theta_{0}),\theta_{0}< \theta<\pi-\theta_{0} \tag{6}\]  
where  
\[\theta_{0}=\sin^{-1}(\frac{V_{o}}{V_{in,pk}}) \tag{7}\]  
\(I_{in}\) is the amplitude of the ideal input AC current \(i_{in,ideal}(\theta)\). Substituting the system specifications shown in Table 1 and the lowest input AC voltage \(v_{in,min}\) into (7) yields  
\[ \begin{array}{ll}\theta_{0}&=\sin^{-1}(\frac{V_{o}}{V_{in,pk}})=\sin^{-1}(D )\\ &=\sin^{-1}(\frac{80}{90\sqrt{2}})=\sin^{-1}(0.63)=0.68\end{array}  \tag{8}\]  
By multiplying the instantaneous value of the input AC voltage and the instantaneous value of the input AC current by the time integral, the average input power can be obtained as follows:  
\[P_{in}=\frac{1}{T_{L}}\cdot\int_{t_{0}}^{t_{0}+T_{L}}p(\theta)d\theta=\frac{ 1}{\pi/2}\cdot\int_{\theta_{0}}^{\pi/2}v_{in}(\theta)i_{in}(\theta)d\theta \tag{9}\]  
Figure 9: Relationship between input AC voltage and input AC current. Substituting (4) and (6) into (9) yields  
\[ \begin{array}{rl}P_{in}&=\frac{2}{\pi}\cdot\int_{\theta_{0}}^{\pi/2}V_{in,pk} \sin\theta\cdot I_{im}(\sin\theta-\sin\theta_{0})d\theta\\ &=\frac{2V_{in,pk}I_{im}}{\pi}\cdot\int_{\theta_{0}}^{\pi/2}(\sin^{2}\theta- \sin\theta\sin\theta_{0})d\theta\\ &=\frac{2V_{in,pk}I_{im}}{\pi}\cdot(\frac{\pi}{4}-\frac{\cos\theta_{0}\sin \theta_{0}}{2}-\frac{\theta_{0}}{2})\end{array}
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Hop Count and Content Store Size
#### 5.5.1 Hop Count and Content Store Size for QoS Traffic  
QLPCC outperforms the EQPR scheme in the range of 60% to 64%, the PRR scheme in the range of 50% to 64%, the probability cache scheme in the range of 78% to 84%, the LFF scheme in the range of 80% to 84%, and the LFU scheme in the range of 80% to 84%. The QoS-node content store size ranges from 50 MB to 250 MB, as shown in the X axis of the graph below. A graphical presentation of the result is provided in Figure 9.  
Figure 9: Hop count vs. content store size for QoS traffic. (**a**) Hop count vs. content store size (QoS traffic) for \(|M|\) = **500. (b)** Hop count vs. content store size (QoS traffic) for \(|M|\) = **1000.**