esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Characterization of the Root Cause of Fault
#### 3.1.1 Fault Current Duration  
The RMS fault current duration characteristics rising from 10% to 90% are extracted as they provide a picture of the dynamic state of the fault during the initial transient stage. This gives an indication of the rate of change of current relative to the network or load prior to the fault occurrences. Equation (14) below described the equation of \(T_{10/90}\). where represented current duration rise from 10% to 90% of maximum value.  
\[T_{10/90}=T_{0.9 Imax}-T_{0.1 Imax} \tag{14}\]  
Figure 7: Fault current criteria.  
Figure 8: Voltage dip criteria for fault causes.  
Figure 9: DWT process with four decomposition coefficient.  
The fault current duration at 20% of maximum waveform is extracted as it gives an indication of the dynamic state of fault at steady stage. The underlying mechanism by which fault current moves, differs for each of the major fault causes. Tree encroachment and crane encroachment fault current flows via the high impedance medium whilst fault current due to lightning is conducted via air particles. The resistivity of these medium differs significantly, as well as the duration of fault. The equation used to calculate fault current duration at 20% is:  
\[T_{20}=\max(T_{0.2 Imax})-\min(T_{0.2 Imax}) \tag{15}\]  
The fault current duration at 50% from the maximum waveform is extracted as it gives an indication of the dynamic state of fault at the final stage before fault extinction. The equation used to calculate fault current duration at 50% is:  
\[T_{50}=\max(T_{0.5 Imax})-\min(T_{0.5 Imax}) \tag{16}\]  
Figure 7 illustrates the current duration extracted from RMS waveform used in this study.  
Figure 10: Energy wavelet for combined reconstructed coefficient (**a**) and absolute value (**b**).
# X-ray-Induced Heating in the Vicinity of the X-ray Interaction Point
## 3 Results
### Temperature Measurement at Different Distances on Al Plate  
The highest amount of heat was generated at the point where X-rays penetrate, and this heat is transferred to the surrounding material or air. Accordingly, it is expected that the temperature increase would be lower as the RTD moves farther away from the X-ray beam. To prove this, temperature measurements were performed while varying the distance between the X-ray and the RTD. Temperature changes were investigated while exposing the Al plate to X-rays at intervals of 500, 750, 1000, 1250, 1500, and 1750 \(\upmu\)m between the X-ray interaction point and the RTD. The results showed that changes in temperature on the Al plate when the X-ray shutter was opened and closed did not vary significantly. However, the range of temperature change decreased as the distance between the X-rayinteraction point and the RTD increased, as expected. In all experiments, the temperature increased with the highest slope in the initial 10 s, then gradually increased and reached the maximum temperature after approximately 70-100 s.  
When the distance interval between the X-ray interaction point and the RTD was 500, 750, 1000, 1250, 1500, and 1750 \(\upmu\)m, the temperatures at the initial 50 s point were approximately 25.67, 25.62, 25.53, 25.47, 25.47, and 25.41, respectively (Figure 4). When the distances between the X-ray interaction point and the RTD were 500, 750, 1000, 1250, 1500, and 1750 \(\upmu\)m, the maximum temperature increases were 25.82, 25.75, 25.64, 25.59, 25.54, and 25.48 \({}^{\circ}\)C, respectively. Consequently, we experimentally confirmed that temperature decreased as the distance from the point exposed to X-rays increased.  
Figure 4: Difference in temperature change according to the distance between the X-ray interaction point and the RTD.  
Figure 3: Temperature measurement in the vicinity of the X-ray interaction point on Zn, Cu and Al plates.
# Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF
## 2 Materials and Methods
### Time Frequency Separation of Time-Frequency Matrix  
For the original signal \(x(t)\), its STFT coefficient is defined as:  
\[\text{STFT}(t,f)=\sum\limits_{k=0}^{N-1}x(t)w(t-k)e^{-j2\pi fk/N}, \tag{8}\]  
where \(w(t-k)\) is a window function, \(t_{k}\) is the time corresponding to each slip window and \(N\) is the overall number of the computed frames. Considering the frequency band selection and the strong noise in vibration signals, the window function with low side-lobes is applied, such as the hanning window, hamming window, etc.  
From the coefficient STFT(\(t,f\)), the TFD is set to \(\mathbf{V}=\left\lvert\text{STFT}(t,f)\right\rvert,\) and then the NMF algorithm is adopted to decompose \(\mathbf{V}.\) According to the clustering, the sub-matrix \(\mathbf{W}=\left[\begin{array}{cccc}\mathbf{w_{1}}&\mathbf{w_{2}}&\cdots&\mathbf{w_{r}}\\ \end{array}\right]\) will be interpreted as spectral bases; that is, the cluster of the frequency domain. Each \(\mathbf{w_{i}}\) includes some frequency regions. Meanwhile, the magnitude envelope of signals belonging to the frequency region is represented by weight vector \(\mathbf{h_{i}}\). This correspondence can be explained by the bilinear NMF model [34]. In this model, the non-negative data matrix is approximately represented by a sum of the linear combination of rank-one non-negative matrices:  
\[\mathbf{V}=\mathbf{w_{1}}\mathbf{h_{1}}+\mathbf{w_{2}}\mathbf{h_{2}}+\cdots+\mathbf{w_{r}}\mathbf{h_{r}}+ \mathbf{E}, \tag{9}\]  
where \(\mathbf{E}\) represents error or noise.  
Note that the frequency base vector \(\mathbf{w_{i}}\) and the time weight vector \(\mathbf{h_{i}}\) have a one-to-one correspondence. It can be considered that the feature extraction of signals is realized either by spectral basis or by the time weight matrix.  
Figure 2 depicts the correspondence between \(\mathbf{w_{i}}\) and \(\mathbf{h_{i}}\). The original signal shown in Figure 2 is mixed with the two non-continuous sinusoidal components of \(f_{1}=1000\) Hz and \(f_{2}=2300\) Hz, where the component of \(f_{1}\) contains three non-zero segments and the component of \(f_{2}\) contains four segments. Applying NMF with \(r=2\), two spectral bases and two time weight vectors are obtained from the TFD of the original signal. It can be clearly seen that \(\mathbf{w_{1}}\) and \(\mathbf{h_{1}}\) are the descriptions of the sinusoidal signal of frequency \(f_{1}\) in the frequency domain and the time envelope respectively. Likewise, the sinusoidal signal of the frequency \(f_{2}\) is described in both the time domain and the frequency domain by the basis \(\mathbf{w_{2}}\) and the weight vector \(\mathbf{h_{2}}\). Therefore, NMF on a spectrogram is a powerful method to describe each transient oscillation and its position in time with a set of spectral bases and weights.  
Figure 2: Non-negative matrix factorization (NMF) on time–frequency distribution (TFD). The lower left part is the basis matrix \(\mathbf{W}\), and the upper right part is the weight matrix \(\mathbf{H}\).
## 3 Results  
### Reconstructed Absorption and Scattering Coefficients with SP3 Model  
The SP3 model was used to reconstruct the absorption and scattering coefficients of all 219 fingers. The reconstruction parameters used to obtain these results are summarized in Table 1. The absorption and scattering coefficients were rescaled by transforming the absorption range of [0.0, 0.4] cm\({}^{-1}\) and the scattering range of [0.0, 400.0] cm\({}^{-1}\) to [0.0, 1.0] cm\({}^{-1}\). Each reconstruction required between 150 to 200 MB of RAM and took less than 1 min on an Intel Core i9 processor.  
Figure 5 shows some of the reconstruction images of absorption (\(\mu_{a}\)) and scattering (\(\mu_{s}^{\prime}\)) within and around PIP joints--more reconstruction images (32 fingers) are available in Figures 11 and 12 in the Appendix A. The images clearly show distinct differences between subjects with RA and the control group. The region of interest is within and around the PIP joint that corresponds to the synovial cavity of the joint, which we expect has lower absorption and scattering compared to the surrounding tissues (bone, muscle, ligament, tendon). All the joints of healthy subjects are of a lower absorption and scattering in this region of interest. However, subjects with RA exhibit elevated absorption and scattering in that same region when compared to the control group. This suggests that the synovial cavity of these joints has experienced significant changes in physiology due to the onset of symptoms associated with RA.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 6 Simulation Environment and Performance Metrics
### Performance Metrics
#### 6.2.2 Handover Metrics  
For the evaluation of multi-tier handovers, there are eight key metrics, which can be split into two categories: one evaluating the handover performance (QoS metrics) and the other evaluating the throughput performance (QoE metrics). All throughput metrics are measured for uplink and downlink communications.  
Handover performance metrics (QoS metrics):  
1. _Total handovers_: This is the total number of handovers in the whole network, inclusive of failed handovers;
2. _Number of ping-pong handovers_: This is the number of handovers that occur back and forth between two BSs in a short amount of time;
3. _Number of handover failures (HOFs)_: This is the number of handovers that failed due to either desired BS CQI dropping to a value lower than desired or due to a gross handover failure (described in Section 5);
4. _Average handover latency_: This includes the time it takes for a HOF to become successful after retransmissions but excludes gross failures as they are rare occurrences and can significantly skew the latency.  
Throughput performance metrics (QoE metrics):  
1. _Total throughput_: This is the total throughput for the uplinks and downlinks of the whole network in megabytes per second (MBPs);
2. _Average UE throughput_: This is the average throughput per UE in MBPs;
3. _UE satisfaction rate_: This is defined as the percentage of time that the UE data rate requirements are met.
# Global Power Dynamics in the Contemporary Space System
## 6 Systems Structure--New Balance of Power in Space
### The Persistence of Asymmetries  
A persistent characteristic of the balance of power in space systems is the existence of significant asymmetries among nation-states. Historically, space activities have been the domain of a small, select group of states. However, the increasing involvement of countries in space-related endeavors could potentially lead to the establishment of a more democratic system. However, the existing evidence does not provide substantial support for this hypothesis.  
Among the total of 193 countries that are member states of the United Nations, approximately 90 engage in some form of space activity, constituting nearly 45% of the total. Of these 90 countries, only 12 possess full launch capabilities [89]. The top ten countries with the largest space budgets collectively account for 92% of the global governmental investment in space programs, with the top five countries (the United States, China, Russia, Japan, and France) accounting for over 84%. Notably, the United States alone accounts for almost 60% of the global governmental investment in space programs [46].  
In a manner consistent with the dynamics observed in other scientific and technological domains, there are new dispersion and centralization dynamics that are forming what many specialists consider to be a new geography of centrality and marginality of scientific and technological knowledge [90; 91; 92]. This dynamic suggests that scientific knowledge is disseminated in select centers, or \"nodes\", leading to the exacerbation of inequalities between countries and regions and the emergence of new centers and peripheries.  
Currently, the space industry exhibits a pronounced and expanding disparity between a select group of nations that boast advanced space programs and a substantial group of countries that demonstrate minimal or no development in this domain.
## 3 Results
### Regulation of IGF-IR Promoter Activity by c-jun, HMGA1, KLF6 and E2F1  
To evaluate the potential functional implications of our findings we next assessed the ability of selected IGF-IR promoter-binding proteins to activate the promoter. For this purpose cotransfection  
Figure 6: ChIP assays of transcription factor binding to IGF-IR promoter DNA. (**A**). MCF7 cells were cross-linked with formaldehyde, lysed, sonicated, and immunoprecipitated with PARP, c-jun, Sp1, or KLF6 antibodies, or normal mouse or rabbit sera, followed by PCR amplification of precipitated chromatin using primers encompassing the IGF-IR promoter. The position of the 773 bp-amplified fragments is indicated. The input bands represent the amplified PCR product in the absence of antibodies. The insets indicate the endogenous levels of expression of the various proteins as measured by Western blots (WB) with specific antibodies or NRS or NMS as negative controls. IP, immunoprecipitation. (**B**). MCF7 cells were transfected with an expression vector encoding HA-HMGA1 (or empty pcDNA3 vector), after which the cells were cross linked with formaldehyde, lysed, sonicated and immunoprecipitated with an HA antibody, followed by PCR amplification of IGF-IR promoter DNA. HA-HMGA1 immunoprecipitated protein was detected by Western blots using an anti-HA antibody (inset).  
experiments were performed in MCF7 and C4.12.5 cells using expression vectors encoding c-jun, HMGA1, KLF6 and E2F1, along with an IGF-IR promoter-luciferase reporter plasmid, p(-476/+640)LUC, which contains most of the proximal region of the IGF-IR promoter. The results of cotransfection experiments are presented in Figures 7A-D. Results obtained indicate that transcription factors c-jun, KLF6, and E2F1 induced a significant increase in IGF-IR promoter activity in both cells lines. Specifically, c-jun enhanced IGF-IR promoter activity by 445 \(\pm\) 12.4% in MCF7 and by 209 \(\pm\) 3.1% in C4.12.5 cells (_versus_ empty vector), KLF6 enhanced promoter acitiviy by 237 \(\pm\) 9.7% in MCF7 and by 159 \(\pm\) 7.1% in C4.12.5 cells, and E2F1 stimulated promoter activity by 631.5 \(\pm\) 44.5% in MCF7 and by 1127 \(\pm\) 22.2% in C4.12.5 cells. On the other hand, HMGA1 had a minimal effect (131 \(\pm\) 4.3% increase in MCF7 and 80.3 \(\pm\) 1.7% reduction in C4.12.5 cells).  
Figure 7: Regulation of IGF-IR promoter activity by c-jun, HMGA1, KLF6 or E2F1. MCF7 and C4.12.5 cells were cotransfected with the p(-476/+640)LUC reporter construct along with c-jun (A), HMGA1 (B), KLF6 (C) or E2F1(D) expression plasmids (or empty expression vectors). Forty eight hours after transfection the cells were harvested and luciferase activity was measured. Promoter activities are expressed as luciferase values normalized to protein concentration. A value of 100% was given to the promoter activity generated by the reporter plasmid in empty vector-transfected MCF-7 or C4.12.5 cells. The results represent the mean \(\pm\) SEM (N = 3 independent experiments in triplicate wells); *p \(<\) 0.01 versus empty vector-transfected cells and ** p \(<\) 0.01 versus specific vector-transfected MCF7 cells.
## 3 Results and Discussion
### Evaluation of Basic Characteristics of Tight Reservoir
#### 3.2.2 Reservoir Porosity and Permeability Characteristics  
The current study employed the volume measurement method to assess the porosity of rock samples. Initially, measurements were taken for both the total volume of the rock sample and the volume occupied by the solid phase (rock minerals). The disparity between these two measurements yielded the volume of the pores. Subsequently, the pore volume was divided by the total volume to ascertain the porosity percentage. The physical property analysis of 40 sandstones in different regions of the profile indicates that the porosity in the study area is between 3% and 12% (with an average value of 7%). The permeability is between 0 and 1.5 \(\times\) 10\({}^{-3}\) um\({}^{2}\) (with an average value of 0.19 \(\times\) 10\({}^{-3}\) um\({}^{2}\)), indicating that the overall reservoir quality is poor and that the reservoir belongs to the ultra-low permeability category.  
Figure 3 illustrates that 60% of the permeability is distributed at 0.01 \(\times\) 10\({}^{-3}\) um\({}^{2}\), with the porosity of 60% of the samples between 7% and 9%. Samples with permeability greater than 1 \(\times\) 10\({}^{-3}\) um\({}^{2}\) account for a small proportion of the total, indicating that the overall reservoir quality is poor. Conversely, the better reservoir is only developed in local areas. Furthermore, it was observed that the physical properties of sandstone reservoirs in the channel were generally favorable, followed by those in the upper part of the configuration interface. In contrast, the sandstone reservoir in the lower part of the configuration interface exhibited the poorest quality. The physical properties of sand bodies at different positions in the profile were found to be significantly disparate, with notable heterogeneity.  
Figure 4 illustrates the pronounced heterogeneity of the Chang 9 oil layer formation, with a notable distinction between the porosity and permeability of the lipophilic sandstone and hydrophilic sandstone. The average porosity of the lipophilic sandstone is 8%, while the average permeability is 0.33 mD. In contrast, the average porosity of the hydrophilic sandstone is 6.4%, with an average permeability of only 0.03 mD. The permeability of lipophilic sandstone and hydrophilic sandstone is markedly disparate.  
Figure 4: (**a**) Physical properties of oil–hydrophilic sandstone in Chang 9 oil layer formation; (**b**) Extension of centralized data of (**a**).  
Figure 3: Histogram of physical properties of sandstone in a section of the Chang 9 oil layer formation.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
### Related Works  
In SAR images, the locations of ships appear bright because of corner reflections [8,18]. In previous studies, they tried to detect ships based on pixel brightness. However, they often detected other bright objects, such as ship wake and islands, as ships [19, 20, 21]. To reduce this problem, [PERSON] et al. [22] proposed a SAR image preprocessing method. They applied multi-looking twice and used a median filter to reduce speckle noise in SAR images. They generated normalized intensity and texture images as a result of their proposed method [22]. Ref. [23] also applied a similar preprocessing method and demonstrated enhanced detection results. Our proposed preprocess method is mostly inspired by [22,23]. We created two images from the proposed preprocessing method. One is an intensity difference image and the other is a texture image. However, to avoid degrading detection performance due to reduced spatial resolution, we applied multi-looking only once.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 2 Problem Formulation
### System Constraints
#### 2.5.3 Up-Spinning Reserve Constraint  
In order to prevent the power system from insufficient power supply due to unexpected conditions (such as generator tripping or feeder short-circuit), or sudden large changes in load or renewable energy, the reserve should be set. In addition to the power generation required by the load, the additional power generation capacity of the system must be planned for emergency dispatch. Equation (16) shows the up-spinning reserve constraint where \(P_{SRup}\) is the spinning reserve.  
\[\sum_{n=1}^{N}U_{n}(t)(P_{n}^{max}(t)-P_{n}(t))+P_{Bd\_max}(t)-\eta_{Ind}P_{Bd }(t)+\frac{P_{Bc}(t)}{\eta_{Inc}}\geq P_{SRup} \tag{16}\]
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 4 Challenges and Opportunities  
Although a variety of grounding grid corrosion and topological detection methods have been proposed, these methods are still immature, and commercial equipment and technical regulations are lacking. The following are challenges from a technical perspective:  
1. Strong electromagnetic interference  
The electromagnetic environment of the substation is very complicated, and the power frequency interference magnetic field of the substation operating state can reach more than 10 \(\upmu\)T. The high-frequency transient interference caused by high-voltage switching action; large power frequency current from ground power equipment, such as transformers, knife gates, transformers,and gate brackets; power grounding current; harmonic interference; and various secondary and communication equipment will affect the normal operation of the detection instrument by means of spatial and conduction coupling.  
The strong electromagnetic interference poses a serious threat to the electrical network method, surface electromagnetic field method, electrochemical method, and electromagnetic imaging method. By using an active source, the excitation source power is increased, and the signal-to-noise ratio is improved. Changing the frequency of the excitation current signal and using a frequency-selective amplification technique, such as notch, amplification, and filtering, can suppress the power frequency and avoid the main interference frequency.