esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Platform  
### Insulation Defective Cable  
Low-voltage cables are primarily composed of three components: the conductor, the insulation layer, and the outer sheath. To enhance the cable's resistance to pressure, armored cables, which have a metal sheath placed between the insulation layer and the outer sheath, are also commonly used. Depending on the application requirements, cables can be categorized into types such as single-core, double-core, and three-core cables, etc. The cross-sectional structure of a three-core low-voltage XLPE cable is illustrated in Figure 1.  
Independent of the number of conductors in a cable, single-phase incipient faults induced by insulation defects typically occur either between the conductor and the metallic sheath or between the conductor and the surrounding medium. The entire conductive path consists of fault resistance and grounding resistance, as shown in Figure 2. For cables with a metallic sheath or on a metallic tray, the leakage current flows through a low-resistance grounding path, with the resistance typically being less than 10 \(\Omega\). However, if a cable without a metallic sheath is directly laid on or buried in sand, soil, cement, etc., the grounding resistance may reach hundreds of ohms. Considering the actual cable installation situations, the experimental platform was designed as described in Section 2.2.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 2 Ephemeral Rivers
### The Kuiseb River
It was estimated that a flow of 18 days is necessary to fully recharge the aquifer at Gobabeb [18]. The hydrological regime of intermittent flooding of the Kuiseb River influences the growth and distribution of vegetation [22], resulting in lateral and longitudinal patterns of plant species occurrence and zonation due to their physiological characteristics, the availability of nutrients, and the water gradient of the aquifer. For example, the specie _Faidherbia albida_, which is one of the major trees along the course of the lower Kuiseb River, is very reliant on the recharge of aquifers and has the highest mortality rate during extended dry periods. Numbers of this species decline downstream as aquifer recharge becomes less regular [22]. However, the dynamics of the Kuiseb aquifers are still poorly known, but could be monitored using remote sensing data, as is shown in this study.
## 6 Simulation Analyses
### Result Analyses
Figure 8 draws the beam pattern and the specific location position layout in contact with the best optimal ways, according to these peak values of the minimum side-lobe level (\(\Phi_{slp}=-13.06\) dB as well as \(u_{ml}=0.0170\); IGA (a)) and according to the optimal trade-off among the side-lobe level value and those main-lobe widths (\(\Phi_{slp}=-12.32\) dB as well as \(u_{ml}=0.0126\); IGA (b)), achieved via the ways of those IGA-based methods. These specific characters of the array synthesized using the SA-based procedure are given (\(\Phi_{slp}=-12.07\) dB as well as \(u_{ml}=0.0133\); SA). \begin{table}
\begin{tabular}{c c} \hline
**Region** & **BP Amplitude Factors** \\ \hline \(0<\mu\leq 0.0418\) & 0 dB \\ \(0.0419<\mu\leq 0.307\) & \(-13.368\) dB \\ \(0.308<\mu\leq 0.446\) & \(-26.876\) dB \\ \(0.447<\mu\leq 0.797\) & 13.395 dB \\ \(0.798<\mu\leq 1\) & 0 dB \\ \hline \end{tabular}
\end{table} 
Table 1: Beam pattern restrictions. Figure 8: Optimization of the element location positions and power weights—optimal combinations for isophoric arrays. **(a)** Beam patterns; and **(b)** array placement. Due to the random character of the algorithm proposed in this article, the statistical factors that are relevant to the simulations collection were assessed. Table 2 offers all the values over the best, the worst, the average resulting values, and the standard deviation values according to the number of these elements, the minimum peak value of the side-lobe and these values of the main-lobe width. It is beneficial to see that the filling percentage is lower than the value obtained from the paper [7; 8] and the side-lobe peak value at all times. In summary, it could be highlighted that some specific taking-away of the symmetry restrictions significantly improves its performances, indeed (these symmetrical arrays were concerned inside the literatures [7; 8]).
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Experimental Results and Discussion
#### 4.4.4 Computational Efficiency Analysis  
To better evaluate the practicality of ProSAF-Net in real-world applications, we further analyze its computational efficiency. Specifically, we assess the inference speed of our model and compare it with several representative state-of-the-art object detectors on the DIOR dataset. Figure 9 illustrates the trade-off between detection accuracy (measured by mAP) and inference speed (measured in FPS). ProSAF-Net achieves a favorable balance between accuracy and computational cost. Among all compared methods, ProSAF-Net maintains the highest mAP while also significantly outperforming other competitors in terms of inference speed. It is worth noting that although CoF-Net exhibits relatively high detection accuracy, its inference speed is notably lower, which may pose a challenge for deployment in time-sensitive applications. In addition, the lightweight design of SDAM and the progressive feature integration strategy in DSFM contribute to efficient feature reuse and reduced computational overhead. These results confirm that ProSAF-Net is not only accurate but also efficient, making it a practical solution for both offline analysis and real-time detection tasks in the field of remote sensing.
## 2 Materials and Methods
### Confidence Interval Based Fuzzy Numbers
Thus,  
\[p\bigg{\{}Q_{PUh}\leq\Big{(}Q_{PUh}^{*}-1.5\Big{)}\times\bigg{(}\frac{\gamma_ {h}^{*}}{\gamma_{h}}\Big{)}+\frac{Z_{\alpha/2}}{\sqrt{n}}+1.5,\quad\frac{ \gamma_{h}^{*}}{\gamma_{h}}\leq\sqrt{\frac{\chi^{2}_{1-\alpha/2,n-1}}{n-1}} \Bigg{\}}\geq 1-\alpha.\]  
Equivalently,  
\[p\Bigg{\{}Q_{PUh}\leq\Big{(}Q_{PUh}^{*}-1.5\Big{)}\times\sqrt{\frac{\chi^{2}_ {1-\alpha/2,n-1}}{n-1}}+\frac{Z_{\alpha/2}}{\sqrt{n}}+1.5\Bigg{\}}\geq 1-\alpha. \tag{16}\]  
Let \(y_{hi}\) be the observed value of \(Y_{hi}\) and the observed values of \(\delta_{h}^{*}\) and \(\gamma_{h}^{*}\) be  
\[\delta_{h0}^{*}=\frac{1}{n}\sum_{i=1}^{n}y_{hi}\text{ and }\gamma_{h0}^{*}= \sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\big{(}y_{hi}-\delta_{h0}^{*}\big{)}^{2}}.\]  
Similar to [10], the observed value of the \(100(1-\alpha)\%\) upper confidence limit \(UQ_{PUh0}\) can be described as follows:  
\[UQ_{PUh0}=\Big{(}Q_{PUh0}^{*}-1.5\Big{)}\times\sqrt{\frac{\chi^{2}_{1-\alpha/ 2,n-1}}{n-1}}+\frac{Z_{\alpha/2}}{\sqrt{n}}+1.5, \tag{17}\]  
where \(Q_{PUh0}^{*}=\frac{1-\delta_{h0}^{*}}{\gamma_{h0}^{*}}+1.5\). Applying the upper confidence limit \(UQ_{PUh0}\) in accordance with the method from [21], the \(\alpha-\)cuts of the half-triangular fuzzy number \(\widetilde{Q}_{PUh0}\) are derived as follows:  
\[\widetilde{Q}_{PUh0}[\alpha]=\Bigg{\{} \begin{array}{l}[UQ_{PUh0}(1),UQ_{PUh 0}(\alpha)],\text{for }\ 0.01\leq\alpha\leq 1\\ [UQ_{PUh0}(1),UQ_{PUh0}(0.01)],\text{for }\ 0\leq\alpha\leq 0.01\end{array} , \tag{18}\]  
where \(UQ_{PUh0}(\alpha)=\Big{(}Q_{PUh0}^{*}-1.5\Big{)}\times\sqrt{\frac{\chi^{2}_{ 1-\alpha/2,n-1}}{n-1}}+\frac{Z_{\alpha/2}}{\sqrt{n}}+1.5\). Thus, the half-triangular fuzzy number of \(UQ_{PUh0}\) is \(\widetilde{Q}_{PUh0}=\Delta(\ Q_{Mh},\ Q_{Rh})\), where  
\[Q_{Mh}=\Big{(}Q_{PUh0}^{*}-1.5\Big{)}\times\sqrt{\frac{\chi^{2}_{0,5,n-1}}{n-1 }}+1.5; \tag{19}\]  
\[Q_{Rh}=\Big{(}Q_{PUh0}^{*}-1.5\Big{)}\times\sqrt{\frac{\chi^{2}_{0,975,n-1}}{n -1}}+\frac{Z_{0,025}}{\sqrt{n}}+1.5. \tag{20}\]It follows that the membership function of the fuzzy number \(\widetilde{Q}_{PUH0}\) is  
\[\eta_{h}(x)=\left\{ \begin{array}{ll}0&if\ x<Q_{Mh}\\ 1&if\ x=Q_{Mh}\\ \alpha&if\ Q_{Mh}<x\leq Q_{Rh}\\ 0&if\ Q_{Rh}<x\end{array} \right., \tag{21}\]  
where \(\alpha\) is determined by \(UQ_{PUH0}(\alpha)=x\); i.e.,  
\[\left(Q_{PUH0}^{*}-1.5\right)\times\sqrt{\frac{\chi_{1-\alpha/2,n-1}^{2}}{n-1 }}+\frac{Z_{\alpha/2}}{\sqrt{n}}+1.5=x. \tag{22}\]  
The numerical examples of \(\eta_{h}(x)\) are shown in Section 3.
## 4 Discussion
### Relationship between SIF and GPP at Different Scales  
The spatial extent of SIF observations significantly influences its relationship with GPP at flux tower sites, such as SHB1 and SHB2. As the distance from the tower increases, the R\({}^{2}\) between the SIF and the GPP decreases notably. While it would be ideal to match the observed range of the SIF to the flux footprint of the GPP, the practical challenges of mapping the footprint of the GPP flux make this task complex and often infeasible. Figure 10 illustrates that restricting the observation range to specific areas can partially improve the relationship between SIF and GPP. At the SHB1 and SHB2 sites, a diameter of approximately 500 m still demonstrated a strong correlation between the SIF and GPP, corresponding precisely to the distance where the cumulative contribution of flux footprints from both sites reached 80%. [PERSON] et al. [17] delineated the footprint range of 214 AmeriFlux sites, primarily defined as the maximum distance from the tower to the 80% contour of monthly footprint climatologies, ranging mainly between 100 m and 450 m. Hence, to mitigate errors arising from observational scales, it is crucial to refine the observational range using high-resolution SIF data. Furthermore, spatial heterogeneity also constrains the relationship between SIF and GPP. At the SHB1 site, as the distance from the tower increased, the linear relationship between the SIF and GPP consistently declined. However, at the SHB2 site, within 250 m of the observation tower, the relationship between the SIF and GPP actually increased with distance (Figure 10b). This is primarily due to the high spatial heterogeneity of the landscape at the SHB2 site (Figure 1). Research by [PERSON] et al. [53] found that spatial heterogeneity had a lesser impact on SIF measurements with larger measurement footprints, meaning that observations with larger footprints are more representative than those with smaller footprints. Therefore, in this study, when the observational footprint was appropriately expanded, the insufficient representativeness of SIF due to spatial heterogeneity might have been mitigated. In conclusion, emphasizing the representativeness of in situ SIF measurements is essential for elucidating the mechanisms between SIF and GPP and providing reliable observational data for satellite-based SIF validation [53].
## 2 Experimental Details
### Test Methods  
#### 2.3.1 Three-Point Bending Test  
In order to measure the flexural stress using the 3-point bending test, which is widely used to measure the mechanical properties of a plate, a thin rectangular bar-shaped specimen of 76.8 mm \(\times\) 12.7 mm \(\times\) 2.7 mm based on the American Society for Testing and Materials (ASTM) D790 was designed, as shown in Figures 4 and S1 [33]. A micro material tester (E3000 LT, Instron, Norwood, MA, USA) with a maximum static load of 2100 N and an accuracy of 0.15 N was used to measure the flexural stress. A bar specimen manufactured according to a standard test method was placed on two circular supports and a load was applied to the middle part between the supports with a round loading nose. The diameter of the support and the loading nose was 5.0 \(\pm\) 0.1 mm. Since the support spacing should be set between 15 and 17 times the thickness of the specimen, the depths of the beam (\(d\)) and support span (\(L\)) were set to 2.7 mm and 45 mm, respectively. Since a polymer based material was used, it could withstand a relatively large bending deformation; therefore, after setting the rate of straining of the outer fiber (\(Z\)) to 0.1 mm/mm/min, the rate of crosshead motion (R) was set as 12.5 mm/min based on Equation (1). According to ASTM, the test was carried out until the strain reached 5%, and in order to secure reliable results, all specimens were fabricated and tested 5 times each. The flexural stress (\(c_{f}\)) was calculated  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Material A**} & \multicolumn{4}{c}{**Material B**} \\ \cline{2-7}  & \multicolumn{1}{c}{**STD Resin**} & \multicolumn{1}{c}{**C-Red**} & \multicolumn{1}{c}{**Flexible Resin**} & \multicolumn{1}{c}{**C-Yellow**} \\  & \multicolumn{1}{c}{**(Hard Material)**} & \multicolumn{1}{c}{**Pigment**} & \multicolumn{1}{c}{**(Soft Material)**} & \multicolumn{1}{c}{**Pigment**} \\ \hline Weight ratio (\%) & 95 & 5 & 95 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weight ratio of the composite materials.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**UV Exposure Time(s)**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline Material A (\(\upmu\)m) & 60 & 190 & 230 & 320 & 360 & 390 & 420 \\ Material B (\(\upmu\)m) & - & - & 111 & 192 & 271 & 361 & 436 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cured depth according to UV exposure time.  
using Equation (2) based on the obtained load value (\(P\)). At this time, the width of the beam (\(b\)) was 12.7 mm.  
\[\mathrm{R}=\mathrm{Z}L^{2}/6d \tag{1}\]  
\[\sigma_{f}=3 PL/2 bd^{2} \tag{2}\]
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Analysis of Planet Gear Vibration Displacements
#### 4.2.1 Vibration Displacements of the Planet Gears under Rated Condition  
The translational displacements and the torsional displacements of the planet gears are shown in Figures 13 and 14.  
According to Figure 2, in the fixed-axes PGT, the carrier, \(c\), bears a torque of \(T_{c}=-1\), while in the differential PGT, the carrier, \(h\), bears a torque of \(T_{out2}=-\frac{t^{2}}{1+t}\). Thus, in Figures 13 and 14, the radial displacements of the planet gears, \(m\), are less than those of planet gears, \(p\). In the \(x\)-direction, internal and external meshing forces cancel one another out, whereas in the \(y\)-direction, they superimpose. Additionally, in the differential PGT, there is a driving action on the planet gears, \(p\), along the tangential direction of the carrier, resulting in larger displacements in the \(y\)-direction. Along the axial direction, the influence of transmission errors on the meshing forces is relatively small, as the errors primarily affect the radial direction. The primary cause of the axial vibrations is the TMS, with a frequency equal to the meshing frequency. In terms of the torsional direction, the planet gears oscillate around their equilibrium positions with an amplitude on the order of \(10^{-4}\) rad. The frequency of the torsional vibration is a combination of transmission errors and time-varying meshing stiffness. Because of the geometric relationships and meshing  
Figure 12: Frequency-domain curves of the dynamic meshing forces for the transmission system under external excitation: **(a)** fixed-axes PGT; **(b)** differential PGT.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 2 Satellite Signal Acquisition
### System and Local Time Frames  
The acquisition process uses data that starts at time \(t_{0}\), measured by the receiver clock that may be inaccurate. Times measured by the receiver are identified by lower case \(t\). The GNSS system clock times are identified by upper-case time values and are typically accurate to better than 1 \(\upmu\)s [10].  
All GNSS satellites synchronously send their signals at intervals of \(\tau\). The start of each code epoch is synchronous across all satellites in the constellation. The system time that the \(i\)th satellite signal is sent, \(T_{s}^{i}\), is given by \(T_{s}^{i}=T_{s}+M\tau\), where \(T_{s}\) is an arbitrary start time for the GPS constellation of satellites, and \(M\) is the integer code epoch. We choose, quite arbitrarily, that \(T_{s}=0\). Therefore, the time of transmission for the start of the code \(T_{s}^{i}\) becomes  
\[T_{s}^{i}=M^{i}\tau, \tag{1}\]  
where the \(M^{i}\) are integers that are now different for each satellite, and depend on the relative distance between receiver and each satellite.  
We assume that there is a fixed offset, \(t_{u}\), between the local receiver clock and the system time, i.e.,  
\[T=t+t_{u}. \tag{2}\]  
A more sophisticated model of the receiver clock would have both an offset and a different rate, but as the data are sampled for only a few milliseconds, uncertainty in the local clock rate will not significantly affect this algorithm.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 3 Results
### Overview of CRISPR Strategies Used to Target Different Human Herpesviruses
#### 3.2.4 CRISPR Systems Targeting Lytic Human Cytomegalovirusovirus Replication and Reactivation  
Genes encoding the HCMV IE proteins 1 and 2 were targeted using CRISPR-Cas9 in two studies [67; 68]. Targeting the _UL122/123_ gene with a singleplex (one gRNA) or multiplex (three gRNAs) strategy in MRC5 fibroblast cells, followed by HCMV infection, reduced IE1 and IE2 expression but failed to prevent late viral replication events due to unstable Cas9 expression in this cell line following lentiviral transduction [67]. Applying the multiplex strategy in a different cell line (HCMV-permissive astrocytoma cell line U-251 MG) did reduce the number of IE-positive cells by up to 95% and inhibited the genome replication cycle and late viral glycoprotein B production, therefore decreasing the virion release by at least 80% [67]. In the second study [68], IE1 and IE2 expression were targeted in human foreskin fibroblast cells through lentiviral delivery of a more stably expressed CRISPR-Cas9 system targeting a region upstream of the IE-coding gene. A decrease in viral protein production levels was observed, together with a reduction in viral DNA by 25-50% and virion production by 66%. CRISPR-Cas targeting of _IE_ in monocytic THP-1 cells, followed by HCMV infection, reduced viral load and viral protein production upon induction of reactivation [68].  
One study [45] observed impaired HCMV replication in MRC5 cells upon CRISPR-Cas9-mediated editing of different early HCMV genes, but not of nonessential HCMV genes, using lentiviral delivery of the CRISPR-Cas9 system.  
Finally, three studies [45; 69; 70] used the CRISPR-Cas9 system to edit late HCMV genes. In the first study [45], lentiviral transduction of MRC5 cells with Cas9 and gRNAs against _UL86_, encoding the major capsid protein, followed by HCMV infection, impaired viral replication. The second and third study [69; 70] used a different approach, namely viral interference, which implies co-infection of cells with a modified virus carrying Cas9 and gRNA against wild-type (WT) genes, called the gene drive virus, and a WT HCMV strain. Cleavage by Cas9 of a target sequence of the WT virus genome, followed by homologous recombination using the gene drive virus DNA as a template, leads to the conversion of the WT virus into new gene drive viruses, therefore driving the native viral population to extinction [69; 70]. Gene drive viruses targeted against WT _UL23_, encoding an interferon-\(\gamma\)-inhibiting tegument protein, reached up to 95% of the final population and drastically suppressed WT viral infection [70]. Additionally, gene drive viruses against the tegument genes _UL26_ and _UL35_ decreased WT viral replication in fibroblasts [69].
# Dual-Frequency Linear-to-Circular Polarization Converter for Ka-Band Applications
## 3 Simulated Results
### Full Wave Simulations  
The proposed equivalent circuit model was used to obtain a preliminary estimation of the converter geometrical dimensions; however, a final optimization with a full-wave simulator was needed to perform a fine-tuning of the analytic solution. Using the capacitances and inductances obtained with the circuit model, it was possible to estimate the initial physical dimensions of the converter using Equations (5) and (6) of [34]. An HFSS model was created to perform the optimization of the cell. The final optimal values are (see Figure 1): \(W\) = 7.15 mm, \(L\)_lh_/1 = 6.65 mm, _Why_/1 = 0.2 mm, \(L\)_lh_/2 = 1.8 mm, \(Ly\)1 = 2.8 mm, \(Wy\)1 = 0.15 mm, \(Ly\)2 = 0.525 mm, \(L\)_x_1 = 3.66 mm, \(L\)_x_2 = 0.15 mm, _lf foam_1 = 3.5 mm, _lf foam_2 = 1.5 mm, _lhsub_ = 0.13 mm. HFSS simulated results are shown in Figure 7 and compare well with the response predicted by the equivalent circuit model.  
Figure 7c shows the simulated Axial Ratio (AR). \(-\)3 dB bandwidths reach 20.5% (18.2-22.3 GHz) and 12% (28-31.5 GHz) in the lower and upper bands, respectively, which cover a large part of the operation bandwidths for K/Ka-band satellite communication.  
Figure 6: Simulated transmission coefficients of the equivalent circuit of the proposed converter. (**a**) Module of the transmission coefficient: Continuous line, x-polarization; dashed line, y-polarization. (**b**) Transmission coefficient phase difference.  
Figure 7: _Cont._
# Finding Patterns in Signals Using Lossy Text Compression
## 5 The Reverse Engineering System
### After Learning the Protocol  
After learning the desired protocol using our reverse engineering algorithm, we send the signals that are generated by the controller algorithm using a low-cost set of IR LEDs. The amplifier receives the binary commands from the Arduino code and turns them into on/off commands to the LEDsarray. The algorithm that controls the robot runs on a laptop or a mini-computer and generates words according to the learned protocol. These commands are sent to the Arduino through the USB port.  
The whole system works as follows:  
1. Recording analog signals. In the case of IR signals, we use an IR decoder (sensor) that receives the signals from the remote controller. The IR decoder gets its power from the a micro-computer (Arduino), and is connected to a logic-analyzer.
2. Converting Analog signals to binary signals. The logic analyzer converts the analog voltage signal into a digital binary signal that has value \"A\" or \"B\" in each time unit.
3. Transmitting the binary stream to a laptop via a USB port.
4. Running reverse engineering algorithm to learn the protocol.
5. Producing commands to the robot using the mini-computer that is connected to a transmitter or a few IR (Infra-Red) LEDs.  
See Figure 2 for steps 1-3. Note that since the logic analyzer is relatively expensive compared to the other parts of our system, we can use the Arduino board not only as a power provider, but also as a converter from the IR signal to the USB port. An Arduino code that implements this conversion is provided as part of the open source of our system.