esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fault Signal and Wavelet Transform
### Fault Signal  
The performance of the proposed classification algorithm was evaluated using simulation software by considering varying parameters to ensure the accuracy of the algorithm. The following case studies with different parameters were analyzed:  
* The fault inception on the voltage waveform in phase A was varied from 0 to 330 degrees with 30 degree intervals;
* The fault type under consideration consists of a single line to ground fault, double line to ground fault, line-to-line fault, and three-phase fault;
* The fault in each phase of the transmission line was located at 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% of the length of the transmission line measured from the sending end to the receiving end.  
The model for simulating and analyzing faults in the transmission line was divided into two parts. First, EMTP/ATPDraw software generated fault signals. The parameters in Figure 2 were input into the software. Then, fault signals were decomposed using WT.  
The signals from the simulation with EMTP/ATPDraw software were obtained with a sampling rate of 100 kHz, and the switch was responsible for the format and the time that the fault occurred.  
Figure 2: Overview structure of the transmission lines between the overhead line and underground cable.  
The fault signals were simulated for five cycles (0-0.1 s) with faults occurring at 0.04 s without clearing the faults out. As an example of the fault signal, a single line to ground fault observed in phase A located 12 km (30%) from the sending side and at an angle of fault of 0\({}^{\circ}\) (voltage sending end reference) is shown in Figure 3.  
Figure 3 shows the current signal of both sides when a fault occurs in the transmission line at 0.04 s. Before the fault occurred, current signals were sinewaves with the same size as that of the load. After the fault occurred, the current on the fault phase significantly increased in both the send and load sides. Then, the current signal from EMTP/ATPDraw was diagnosed using discrete wavelet transform (DWT) by MATLAB program.
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking
### Active Acoustic Signal Detection System of ULAHW
#### 3.1.1 Time-Varying Gain Control (TVG)  
In active acoustic sonar systems, the echo signal from the target is submerged in the reverberation and noise background. Reverberation is a non-stationary stochastic process with time-varying amplitude, frequency, air variation, and color. The target radiation noise and target echo received by ULAWs continuously strengthen as they approach the target. In order to keep the reverberation, echo, and target radiation noise within the effective dynamic range of the receiver and within the voltage range that the A/D converter can operate correctly, the constant false alarm processing of homing signal detection first needs to solve dynamic range compression and background normalization.  
We adopt time-varying gain control (TVG) for dynamic range compression. Its processing flow is shown in Figure 4. The detector detects and integrates the output of the receiver, forming a control code for the controlled amplifier, thereby changing the amplifier gain. TVG can effectively implement dynamic range compression. However, due to  
Figure 3: Signal detection process diagram.  
Figure 2: ULAHW target recognition algorithm flowchart.  
the non-stationary nature of the background, its normalization of the background cannot achieve constant variance. As shown in Figures 5 and 6, the original signal, TVG gain curve, and the signal processed via TVG are presented. It can be seen that after TVG, the reverberation, echo, and target radiation noise are all within the effective dynamic range of the receiver.
## 7 Instrument Testing
### Absolute Accuracy Test  
To test its absolute accuracy, the optically pumped cesium magnetometer probe was placed inside a torque-free coil located within a magnetic shielding cylinder. The schematic diagram of the test setup is shown in Figure 20. The photo of the magnetic shielding cylinder is shown in Figure 21; the shielding cylinder is equipped with a uniform magnetic field generating coil, which can generate a uniform magnetic field inside the shielding cylinder.  
A standard constant magnetic field was generated by the torque-free coil, and the measurement results of the magnetometer were compared with this standard.  
\[\overline{B}=\frac{1}{10}\sum_{i=1}^{10}B_{i} \tag{1}\]  
where \(B_{i}\) denotes the total outputted field strength, and \(n\) indicates the number of test points. The experimental standard deviation of the observed value, calculated using Equation (2), was taken as the absolute accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & **Proposed Optically Pumped** & **CS-3** & **G-824A** \\  & **Cesium Magnetometer** & & \\ \hline Absolute accuracy & \textless{}2.5 nT & \textless{}2.5 nT & \textless{}3 nT \\ Sensitivity & 0.29 pT @ 1 sample/s & 0.6 pT Hz\({}^{-1/2}\) rms & 0.5 pT Hz\({}^{-1/2}\) rms \\ Gradient tolerance & \textgreater{}54,000 nT/m & 40,000 nT/m & 19,685 nT/m \\ Dynamic range & 12,165.10–137,992.85 nT & 15,000–105,000 nT & 20,000–100,000 nT \\ Direction error & \textless{}0.2 nT & \textless{}0.2 nT & \textless{}0.15 nT \\ Weight & 1.49 kg & 1.5 kg & 2.7 kg \\ \hline \hline \end{tabular}
\end{table}
Table 2: A comparison of the test results of the optically pumped cesium magnetometer probe with instruments of the same type.  
Figure 19: The peripheral circuit of the Zynq XC7Z020 processor chip.  
\[\mathrm{S}_{B}=\pm\sqrt{\frac{\sum\limits_{i=1}^{n}\left(B_{i}-\overline{B}\right)^ {2}}{n-1}} \tag{2}\]  
The measurement results (Table 2) indicate the absolute accuracy of the proposed optically pumped cesium magnetometer probe, which is less than 2.5 nT, a value that is equivalent to the leading international standard value for such instruments.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 2 Related Work  
### Semi-Supervised Learning  
Semi-supervised learning is a branch of machine learning which utilizes both labeled and unlabeled data to improve the overall generalization performance of a model. It has earned a tremendous amount of attention in various fields of research such as bioscience, material science, and manufacturing industry where the actual testing of unseen data is time-consuming and labor intensive [4]. Due to its boost in popularity, despite its abstruse nature, several approaches including expectation-maximization, split-learning, graph-based methods, co-training, and self-training have been developed [5]. However, none of these approaches is easily comprehensible to non-experts, especially those practitioners of the deep learning community who do not know the physical meaning of or statistics behind the data they deal with. [PERSON] et al. [6] used a clustering algorithm, Dirichlet process mixture model (DPMM) to provide pseudo-class labels for unlabeled data. It has shown to be effective since DPMM is a non-parametric clustering algorithm and is thus able to figure out the unknown background classes from the unlabeled ones. Even if no labels are given, samples can be grouped based on their similarities, providing them with pseudo labels. To improve the performance, a pre-trained DNN model is first constructed using all the training data with the pseudo-labels. Then, a second DNN model is introduced by taking all the layers except the last one from the pre-trained model. The new model is then fine-tuned using only the true class labels. It gets trickier in a regression problem because the dependent variable is no longer a discrete categorical value and is thus difficult to be categorized into groups. To make the matter worse, most studies deal with semi-supervised classification (SSC) rather than SSR as the former is deemed a more general case [7]. As a result, several SSR algorithms, despite the efficiency, is hard to implement. Similar algorithms such as semi-supervised support vector regression (SS-SVR) [8], COREG [9], CoBCReg [10], semi-supervised kernel regression (SSKR) [11], spectral regression [12] are proposed but only a few of them have been used in real-world applications due to the limited applicability, i.e., highly dependent on data type [7]. Cases of previous literature regarding SSR and its use on real-world applications are tabulated in Table 1. To circumvent the issue, this study utilizes the self-labeling method [13], which is simple and effective to enhance the overall regression performance on data collected from sensors at a production line.
## 4 Simulation Results
### Case Study 1: Conventional Power Systems  
For a loss of generation (G5 at \(t\) = 50 s), the system frequency and voltage magnitude at bus 6, 25, and 28 voltage magnitudes of the IEEE 39-bus test system are shown in Figures 4 and 5, respectively. Figure 4 shows that the system frequency is stable after a fault; however, it fluctuates by more than 1.3 Hz. It is worth mentioning that the frequency is returning to its initial value because of the governor response. In order to compensate for the frequency change, a secondary control is needed, which is not considered in this simulation. In this way, the system is stable from the rotor angle stability point of view, as all generators are staying synchronized with the system; however the system frequency stability may be violated, due to the large frequency deviation. Figure 5 shows the voltage magnitudes of some of the buses. It should be noted that the system security depends on how the fluctuations can be tolerated in the system [15]. On the other hand, to complete the security assessment the of the system, the analyses that are shown in Figures 4 and 5 should be redone for all N-1 contingencies (and if it is necessary, they should be repeated for N-2 contingency analysis as well).
## 5 Model Reconstruction and Secondary Optimization
### Simulation Verification of Subframe Strength  
After the design stage is completed, statics research is carried out to test whether the functional characteristics of the front subframe meet the requirements. When the damping effect and inertial effect are ignored, the structural load can be simplified to static load, and then its displacement and stress distribution can be investigated. This research method is called static analysis. With the help of this method, it is possible to determine whether the material stress exceeds its limit value, thereby evaluating the reliability of the structure. By applying the fourth strength criterion [22] combined with the [PERSON] stress theory [23], we evaluated the mechanical properties of the front subframe. By constructing a finite element analysis model and applying corresponding constraint conditions, the numerical simulation was completed using the OptiStruct (2022) computing platform. After the operation is completed, the operation results are organized. The stress cloud diagram of the subframe after secondary optimization is shown in Figure 18.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode Number** & **Test Frequency (Hz)** & **Simulation Frequency (Hz)** & **Error (\%)** \\ \hline
1 & 102.54 & 104.8 & 2.2 \\
2 & 157.7 & 153.3 & \(-2.7\) \\
3 & 179.3 & 181.2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of simulation and experiment.  
Figure 17: FRF curve graph of the subframe modal test.  
The stress cloud diagrams of the above 10 working conditions can be obtained, and all operating states have reached the expected indicators. In the right-turn scenario, the peak stress endured by the subframe is 262 MPa. This value is much lower than the yield limit of 306 MPa of SAPH440 material, indicating that the structural design fully meets the strength requirements.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 2 Related Work
### Skyline Retrieval  
Skyline retrieval refers to the extraction of image skyline and finding the most similar skyline in the DEM dataset. Since the retrieval of each skyline in the dataset is very time-consuming, many methods to improve retrieval efficiency are applied to skyline location.  
The matching method utilized by the authors of [24] included endpoint matching and shape matching that, respectively, used geometric hash table and k-d tree to improve the matching efficiency. The methods utilized by the authors of [17] and [21] use contour words to represent skyline features. Contour words of all skylines were extracted from DEM dataset, and then sorted by the method term frequency-inverse document frequency (TF-IDF).
# Centrifuge Modelling of the Impact of Excavation with Partition Piles on Adjacent Existing Tunnel
## 2 Design of the Centrifuge Modelling  
### Description of the Test  
This centrifuge modelling test was conducted in the Geotechnical Engineering Laboratory at Tianjin University, as shown in Figure 2. The model box had internal dimensions of 500 mm \(\times\) 200 mm \(\times\) 500 mm.  
Figure 1: Displacement curves of soil in cases with and without isolation piles [35].  
Figure 2: Frontal photo of centrifuge.  
The primary objective of these experiments was to scrutinize how the parameters of partition piles influence the response of a tunnel during excavation under two-dimensional conditions. Referencing Figure 3, the cross-section was depicted. The tests were carried out with a substantial centrifugal acceleration of 50\(\times\)\(g\). Due to the limitations of model box size, the excavation depth (\(H\)) was 8 m. The excavation width, diameter of the tunnel, and the depth of tunnel crown burial (\(S\)) were all designated as 0.5 \(H\), corresponding to 4 m. The distances between the tunnel, retaining wall, and the partition pile were specified as 2 m and 4 m, respectively. The partition pile parameters considered in the test included the top burial depth of the pile (\(h\)) and the length of the pile (\(L\)). The specific parameter design can be found in Table 1. Moreover, the similarity relationships for the parameters used in the tests are presented in Table 2.  
Figure 3: Cross-section of the model box and photo of test 5. (**a**) Cross-section of the model box. (**b**) Photo of test 5.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 4 Experiments
### Dehazing Results  
We conducted dehazing experiments on synthetic datasets (RESIDE/SOTS [41]) and real-world datasets (Dense-Haze [42] and NH-HAZE [43]). Table 4 presents the quantitative results in comparison with previous models. Compared to the most recently proposed OKNet [19], our model obtains significant gains of 0.83 dB and 0.71 dB in PSNR on the SOTS-Indoor and SOTS-Outdoor datasets. For Dense-Haze, FreANet reports PSNR gains of 0.61 dB and 1.58 dB over OKNet [19] and the frequency-based Fourmer [14], respectively. Furthermore, the qualitative results for both indoor and outdoor scenarios are presented in Figure 4. Our model generates images with sharper and more detailed object preservation, even in complex environments, outperforming previous works.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**SOTS-Indoor [41]**} & \multicolumn{2}{c}{**SOTS-Outdoor [41]**} & \multicolumn{2}{c}{**Dense-Haze [42]**} & \multicolumn{2}{c}{**NH-HAZE [43]**} & \multicolumn{2}{c}{**Overheads**} \\ \cline{2-11}  & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **
\begin{tabular}{c} **\#Params** \\ **(M)** \\ \end{tabular} & \multicolumn{2}{c}{**FLOPs**} \\ \hline GridDehazeNet [54] & 32.16 & 0.984 & 30.86 & 0.982 & - & - & 13.80 & 0.54 & 0.956 & 21.49 \\ FFA-Net [6] & 36.39 & 0.989 & 33.57 & 0.984 & 14.39 & 0.45 & 19.87 & 0.69 & 4.45 & 287.8 \\ AECR-Net [20] & 37.17 & 0.990 & - & - & 15.80 & 0.47 & 19.88 & 0.72 & 2.61 & 52.20 \\ MAXIM [23] & 38.11 & 0.991 & 34.19 & 0.985 & - & - & - & 14.1 & - \\ Fourmer [14] & 37.32 & 0.990 & - & - & 15.95 & 0.49 & 19.91 & 0.72 & 1.29 & 20.6 \\ OKNet-S [19] & 37.59 & 0.994 & 35.45 & 0.992 & 16.85 & 0.62 & 20.29 & 0.80 & 2.40 & 17.88 \\ OKNet [19] & 40.79 & 0.996 & 37.68 & **0.995** & 16.92 & 0.64 & 20.48 & 0.80 & 4.72 & 39.71 \\
**FreANet-S** & 39.54 & 0.995 & 36.46 & 0.993 & 17.39 & 0.65 & 20.11 & 0.81 & 1.79 & 19.77 \\
**FreANet** & **41.62** & **0.997** & **38.39** & **0.995** & **17.53** & **0.67** & **20.52** & **0.82** & 5.37 & 40.29 \\ \hline \hline \end{tabular} The best and second-best scores are highlighted in **bold** and underlined, respectively. \(\uparrow\) indicates that a higher score is better.  
\end{table}
Table 4: Image dehazing comparisons on the synthetic and real dehazing test sets.  
Figure 3: Motion deblurring (removal of motion blur) results on the GoPro [39] test set. Our FreANet produces sharper and more distinct results.  
In line with previous work [19], we performed a nighttime dehazing experiment on the NHR [44] dataset, and the results can be found in Table 5. FreANet achieves a PSNR gain of 0.84 dB over the recently proposed OKNet [19].
###### Abstract  
This paper introduces a metasurface-inspired antenna embedded in a circular metallic cavity with a very small diameter (from 0.25 \(\lambda_{0}\) down to 0.12 \(\lambda_{0}\), where \(\lambda_{0}\) is the wavelength in vacuum at the operating frequency). The proposed concept is introduced and explained: It consists of placing a capacitive surface at the cavity aperture. This capacitive effect is obtained using multi-layer capacitively coupled strips. Our numerical results demonstrate that the impedance bandwidth is larger than that of standard patch antennas embedded in the same cavity size. Various prototypes have been manufactured, and measurements are found to be in good agreement with numerical results in all cases. The practical applications of these small metasurface antennas are demonstrated with telemetry applications for flying vehicles.  
Keywords:circular cavities; metasurfaces; microstrip antennas; bandwidth +
Footnote †: journal: Applied Physics Letters
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 4 Gravity Measurements
### Corrections of Gravity Measurements  
After measurements have been completed, all relative gravity values need to be corrected for certain adjustments that affect the measured gravity. These corrections can be divided into two main groups [28]:  
* Corrections related to the measuring instrument;
* Corrections related to the Earth's geophysical processes.
## 3 Results  
### Analysis of Compression Curve  
The stress-strain curves of H13 specimens (as-cast and heat treatment) at room temperature are shown in Figure 4. It could be seen from Figure 4 that the mechanical properties of the H13 steel after heat treatment have been significantly improved. Its maximum compression strength (\(\sigma_{\mathrm{c}}\)) is about four times that of the as-cast steel, which reached 2700 MPa, and shows that the elastic limit has also been greatly improved. However, the ductility of the H13 steel after heat treatment is poor, the reason leading to this phenomenon is the high hardness and high strength of the mechanical properties of tempered martensite, while due to the brittle phase of the tempered martensite, it resulted in the increased strength but decreased the ductility. At the same time, due to the presence of tempered troostite, the elastic limit and yield limit of the material were increased, and a certain degree of toughness also appeared. This meets the requirements of the high strength and high elastic limit of the TBM cutter.  
The stress-strain curves of H13 specimens (heat treatment) at high temperature are shown in Figure 5. It could be seen from Figure 5 that the compression strength, overall, decreased as the temperature increased, and the reduction of the stress between 100 \({}^{\circ}\)C and 500 \({}^{\circ}\)C was relatively small; when the temperature was raised to 600 \({}^{\circ}\)C, the compression strength, the elastic limit and ductility decreased rapidly, and the brittleness appeared. The brittleness of H13 steel resulted in serious deterioration of the mechanical properties of steel, making it difficult to be used in the range of brittle temperatures and would easily get damaged which will affect practical work; when the temperature was raised to 700 \({}^{\circ}\)C, the \(\sigma_{\mathrm{c}700}\) only reached 480 MPa, but ductility of the material was restored.  
Figure 4: Stress-strain curve of H13 steel at room temperature.  
In order to explore the mechanical properties of H13 steel between 600-700 \({}^{\circ}\)C, three groups of compression tests (630 \({}^{\circ}\)C, 650 \({}^{\circ}\)C and 670 \({}^{\circ}\)C) were added. The results of curves are shown in Figure 6.  
As shown in Figure 6, the overall trend of the compression strengths at 630 \({}^{\circ}\)C, 650 \({}^{\circ}\)C and 670 \({}^{\circ}\)C were similar to the one at 600 \({}^{\circ}\)C, which decreased rapidly as the temperature increased, as the ductility also decreased rapidly.  
The reason which might cause the above phenomenon could be the high temperature compression of H13 steel leading to the tempered martensite decomposition, the continuous precipitation of carbide and the decrease of the ductility and strength.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 3 System Model and Problem Formulation
### Service Latency Estimation  
In this section, we present an estimation to determine the service latency of a user. Typically, it is defined as the total time from when the user sends its service request until receiving the result.  
Suppose user \(u\) is scheduled to a computing node \(n\) (\(n\in\mathcal{N}\cup\{l\}\)). Serving user \(u\) at node \(n\) causes communication latency for transferring input/output data between the user and node \(n\), and processing latency at node \(n\). Each latency will be described in the following subsections.
# Securing SDN-Based IoT Group Communication
## 3 Overview
### SDN-Based IoT Group Communication
#### 3.3.1 Resource Advertisement  
IoT devices should advertise their resources by broadcasting their WoT-TD files. As a reminder, we assume a well-known \(Fwd_{ID}\) that can be used for broadcasting. In order to protect advertisements from replay attacks, IoT devices generate a new LDP, similar to the one they have received by the NAP, which, however, includes a _nonce_, and is generated using the private key that corresponds to \(Endpoint_{ID}\). Nonces in our system must not be reused within a specific time frame. This can be easily implemented by maintaining a list of used nonces: each nonce should remain in the list for the duration of the selected time frame, and devices must make sure that a nonce they send or receive is not included in that list.  
Upon receiving an advertisement, clients validate the integrated LDPs. In particular, they validate that the advertisement includes an LDP that can be verified using \(Network_{ID}\), which is \"well known\", and another that can be verified using \(Endpoint_{ID}\) included in the WoT-TD file. Additionally, they verify that the latter proof is adequately fresh, and it includes a unique nonce. If all verifications are successful, each client updates a _lookup table_ that includes mappings from CoAP group URIs to the corresponding \(Endpoint_{ID}\) identifiers.