esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project Description
### Description of the Shield Machine  
A high construction risk is present because the tunnel needs to pass through many existing buildings in the shield tunneling section. The geology of the section is variable and includes water-rich sandy, argillaceous siltstone, and silty clay strata. Compared with the EPB shield, the slurry shield can better control formation and deformation in water-rich sandy strata. However, the EPB shield has a higher tunneling efficiency in argillaceous siltstone and silty clay strata. Thus, a slurry-earth pressure balance dual-mode shield was selected for tunneling to improve the adaptability of the shield machine. The slurry discharge pipe and the screw conveyor are connected in series. The machine has no bubble chamber. When the EPB mode is adopted for tunneling, a belt conveyor is arranged at the outlet of the screw conveyor to transport the muck directly (Figure 2a). When the slurry mode is adopted, a crusher and slurry box are installed at the outlet of the screw conveyor. The function of the crusher is to crush large stones. The slurry in the shield chamber enters the slurry discharge pipe after passing through the screw conveyor, crusher, and mud box (Figure 2b) and finally flows into the slurry treatment station.  
Figure 2: Schematic diagram of the shield tunneling mode; (**a**) EPB mode; (**b**) Slurry mode.  
The cutterhead adopts a composite structure of 6 spokes and 6 panels, with an excavation diameter of 6700 mm and an opening ratio of 32% (Figure 3). The cutterhead is equipped with 6 foam injection ports (F1-F6) to condition the soil in the central area. Each pipeline is designed with a single pump to avoid pipe clogging. The cutterhead center is also equipped with 6 high-pressure water injection nozzles (W1-W6).
## 6 Abuse Attack Techniques
### Cryptography
#### 6.3.3 Insights  
The use of cryptography techniques plays a crucial role in malware and botnet operations. It is primarily used for two purposes: protecting C&C communications and hiding malicious commands or data within legitimate-looking content. Our review of cryptography in the context of abusing the CPLS as C&C channels presents several important insights:  
* The combined use of cryptography along with the abuse of popular CPLS platforms for C&C channels illustrates the increasing sophistication of botnet strategies. These platforms provide an additional layer of security and reliability, making it more difficult to detect botnet activities due to the reputable nature of these services.
* Botmasters frequently embed encrypted malicious commands into digital content, such as images or social media posts. These commands are later extracted and decrypted by bots for execution as demonstrated in cases like SUbot, ComRAT, and RegDuke.
* Cryptography is often combined with other techniques to enhance security and evasion capabilities. For instance, the ComRAT botnet merges cryptography with COM hijacking to ensure persistent execution.
## 3 Circuit Implementation
### Noise Analysis  
In the absence of electrical noise, reading from a single ID cell would be entirely deterministic. However, when the sense amplifier is activated, the total difference between the ID cell currents is the combination of the inherent offset due to device mismatch and any electrical noise, such that  
\[\Delta I=\Delta I_{m}+\Delta I_{n} \tag{3}\]  
The internal nodes of the sense amplifier, \(V_{+}\) and \(V_{-}\), are pre-charged to VDD prior to sensing, and devices M5 and M6 are off at the start of a read operation. When the sense amplifier is activated, the internal nodes are discharged by the ID cell current. When one of the nodes discharges sufficiently to turn on M5 or M6, the positive feedback is engaged and the amplifier settles to a stable state. A simulation of this is shown in Figure 3, showing the three stages of operation.  
The total difference voltage developed on the internal SA nodes, \(\Delta V=V_{+}-V_{-}\), is the sum of the individual difference voltages due to mismatch and noise.  
\[\Delta V=\Delta V_{m}+\Delta V_{n} \tag{4}\]  
Figure 2: Schematic of the latch-based sense amplifier in Figure 1.  
The effect of the mismatch and noise on the circuit output can be calculated. The probability of \(\Delta V\) being positive at the time the SA feedback is activated is:  
\[0<\Delta V_{m}+\Delta V_{n} \tag{5}\]  
This is given by:  
\[p =P\left(\Delta V_{n}>-\Delta V_{m}\right) \tag{6}\] \[=\int_{-\Delta V_{m}}^{\infty}\Phi_{\Delta V_{n}}(v)dv\] (7) \[=\frac{1}{2}\left[1+\mathrm{erf}\left(\frac{\Delta V_{m}}{\sqrt{2 }\sigma_{\Delta V_{n}}}\right)\right] \tag{8}\]  
where \(\Phi_{\Delta V_{n}}(v)\) is the probability density function of the normally distributed random variable \(\Delta V_{n}\), with variance \(\sigma_{\Delta V_{n}}\).  
Finally, this can be rewritten in terms of the equivalent input noise, \(\sigma_{V_{in}}\).  
\[p=\frac{1}{2}\left[1+\mathrm{erf}\left(\frac{\Delta V_{T}}{\sqrt{2}\sigma_{V_{ in}}}\right)\right] \tag{9}\]
## 5 Results Discussion
### The Results and Analysis of the Fractional Model of ML-ABH Structure
From the overall results, the greater the film thickness \(h_{0}\), the stronger the damping performance of the material, and the greater the attenuation degree of \(\mid R_{01}\mid\). However, in Figure 12b, the dissipation results of \(\alpha\) = 0.6 in the high-frequency range are more significant than those of \(\alpha\) = 0.8 when \(h_{0}\) is 0.7 mm. This is because the right end of the ABH structure is truncated, and its thickness is 8.2554 \(\times\) 10\({}^{-4}\) mm, which is equivalent to the film thickness of 0.7 mm, and there is a scale effect. The research results on the energy transfer characteristics of the ABH section with an infinitely long structure connected to the right side are shown in Figures 13 and 14, where \(T^{+}\) represents the forward energy transmission and \(\mathbf{R}^{+}\) represents the forward energy reflection. When the film thickness \(h_{0}\) is 0.01 mm, only the result of \(\alpha\) = 0.8 is more significant, and the difference in the results of other fractional orders is small, indicating that the difference in the viscoelastic properties of the material is small. When the film thickness \(h_{0}\) is 0.7 mm, there is a significant difference in the results at different fractional orders, indicating a large difference in the viscoelastic properties of the material, especially when the order is high. From the numerical values in the figure, it can be seen that the higher the fractional order, the smaller the forward energy transmission and the smaller the forward energy reflection. Furthermore, based on the values of \(\mid R_{01}\mid\) and \(R^{+}\) matrix elements, it can be inferred that no matter how materials are selected or structures are designed, ABH structures cannot dissipate all energy and there must be energy reflection. According to the values of the  
Figure 12: The relationship curves between the reflection coefficient \(\mid R_{01}\mid\) of the ABH beam and frequency under different fractional-order values for (**a**) a 0.01 mm absorber film; (**b**) a 0.7 mm absorber film. elements of matrix \(\mathbf{T}^{+}\), we can connect a damping device on the right side of the ABH for secondary energy dissipation, further reducing vibration energy.
# Bandwidth Density Analysis of Coded Free-Space Optical Interconnects
## 5 Bandwidth Density of Coded FSOI System
The codes are used at the optimum data rates of each codeword length that is demonstrated in Figure 5. From the figure, the optimum data rates of the codeword lengths 15, 31, 63, 127, and 255 are 0.59, 0.61, 0.62, 0.65, and 0.68, respectively. At the optimum data rates, RS-ECCs achieve the best possible improvement to the BD of optical interconnects. This improvement is due to the ability of coding to reduce the SNR required to obtain a given \(BER\), and in turn, this increases the BD of interconnects. In the simulation results presented in this paper, we used \(SNR_{uncoded}=10\) dB, which means the \(SNR_{uncoded}\) for codeword lengths of 15, 31, 63, 127, and 255 at their designated optimum data rates would be 5.4, 4.6, 4.0, 3.6, and 3.5 dB, respectively. Figure 6b-d demonstrate the effect of using RS-ECC on the higher-order transverse modes TEM\({}_{01}\), TEM\({}_{10}\), and TEM\({}_{20}\), respectively. It is clear from the results that increasing the length of the codewords leads to a direct increase in the BD. In addition,  
Figure 5: Bandwidth density gain for six distinct codeword lengths (n) of RS-ECC. the optimal channel spacing that achieves the maximum BD is the same with and without coding for all investigated modes.
## 4 Discussion
### Other Factors  
While the scope of this review was to analyze how the mechanical properties of clear aligner materials can impact treatment outcomes, it is important to acknowledge that other factors also play a significant role. Clear aligner therapy involves a wide range of variables that may affect the predictability and success of treatment. Intraoral conditions such as periodontal ligament (PDL) compliance, crown morphology, root surface area, and alveolar bone height can influence the accuracy of tooth movement [11; 12].  
Patient compliance is another critical factor. A systematic review by [PERSON] et al. [13] reported that aligner wear time is often below the recommended duration and that patients tend to overestimate their actual wear time. Moreover, the algorithms used in clear aligner treatment planning software attempt to predict tooth movement and force systems without accounting for individual anatomical factors such as root morphology or the position of the tooth's center of resistance. As a result, force predictions for certain movements may be inaccurate and are less adaptable during treatment compared to fixed appliance therapy [45]. While this review focused on standardized flat specimens for flexural testing, it is worth noting that anatomical differences may also affect aligner behavior during thermoforming. For example, [PERSON] et al. [46] reported that aligners formed on taller upper jaw models exhibited more pronounced thinning at specific locations compared to those formed on shorter models. Although such anatomical models are not compatible with standardized mechanical testing, they help illustrate how morphological complexity can influence aligner fit and force distribution in clinical scenarios.  
Certain types of tooth movement, such as anterior extrusion, derotation of round teeth, and torque control, remain particularly challenging and unpredictable with clear aligners. Movements requiring precise root control have been consistently reported to have lower success rates [47]. These limitations highlight the importance of appropriate case selection, with better outcomes generally achieved in cases of mild to moderate malocclusion [9].
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 3 Results
### Distribution of Operating Cost Components
#### 3.1.8. Flotation  
A visual breakdown of all cost components associated with flotation is provided in Figure 10.  
As shown in Figure 10, consumables and reagents contribute the most to flotation cost, representing 79.6% of the total cost. Energy consumption is also significant, at 11.5%, while labor and maintenance and repairs, as well as equipment depreciation, collectively contribute 6.9%.
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 3 Bacteria-Encoded STKs in Phage-Bacteria Interactions  
So far, there are very few studies (Table 1; Figure 1A) reporting the involvement of host bacteria-encoded STKs in mediating interaction with phages.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Kinases** & **Origin and Types** & **Functions** & **Reference** \\ \hline StpK7 (MSMEG\_1200) & _Mycobacterium smegmatis_ BREX; STK & Mediates TM4 phage infection & [30] \\ \hline StpK & _Staphylococcus aureus_; STK & Activated by phage protein PacK to mediate abortive infection & [31] \\ \hline PglW & _Streptomyces coelicolor_; STK & Linked to phage growth limitation (Pgl) and BREX anti-phage systems & [32] \\ \hline IreK & _Enterococcus faecalis_; STK & Mediates stress-responsive activation of the type VIIb secretion system (T7 SS) and contact-dependent antagonism of bystander bacteria & [33] \\ \hline PfkA/B & _Pseudomonas aeruginosa_; STKs (PfkA/B) and phosphatase (PfpC) & Coordinates prophage activation and antiphage defense & [34] \\ \hline Gp0.7 & _E. coli_ T7 phage; STK & Phosphorylates multiple host _E. coli_ proteins to hijack cellular processes & [35; 36; 37; 38; 39] \\ \hline Stk\({}_{933W}\) & lambdoid phage 933W; tyrosine kinase & Exclusion of secondary phage infection & [40; 41] \\ \hline JSS1\_004 & Salmonella phage JSS1; STK & Phosphorylate and dismantle multilayered host defences e.g., CRISPR-Cas & [42] \\ \hline Rcs & Serratia sp. ATCC 39006; HK & Balancing innate and adaptive immunity & [43] \\ \hline BarA/UvrY & _Marinomonas mediterranean_ MMB-1; HK & Regulating the dual CRISPR-Cas systems (PAM- dependent or -independent) to confer phage resistance & [9] \\ \hline Cpx & _Klebsiella pneumoniae_, etc; HK & Dual role in enhancing carbapenem resistance and conjugative plasmid transfer; potential interference with phage infection & [44; 45] \\ \hline \end{tabular}
\end{table}
Table 1: List of kinases discussed in this article. STK, serine/threonine kinase; HK, histidine kinase.
## 2 Terminologies in EMI Shielding  
Firstly, there are two major parameters that need to be quantified for characterizing the efficiency of any electromagnetic shield material. The first is the shielding effectiveness (SE) and the second is the reflectivity (R) of the shield. The former corresponds to the measure of power which is transmitted through the shield material (P\({}_{\rm T}\)) to the incident power (P\({}_{\rm I}\)), and  
Figure 1: (**a**) Year-wise number of publications and (**b**) patents in the area of porous EMI shielding materials (foams and aerogels) over the last decade (2011–2021).  
the latter corresponds to the power reflected back from the shield (P\({}_{\text{R}}\)) to the incident power. In general, the shielding effectiveness is expressed in terms of reductions in the magnitude of the incoming power when power is transmitted through the shield. In general, the total shielding efficiency of a shield is expressed on a logarithmic scale, as shown below [88]:  
\[\text{SE}_{\text{T}}(\text{dB})=\text{SE}_{\text{A}}+\text{SE}_{\text{R}}+\text {SE}_{\text{M}}=10\,\log_{10}\left(\frac{\text{P}_{\text{T}}}{\text{P}_{\text{ I}}}\right)=20\,\log_{10}\left(\frac{\text{E}_{\text{T}}}{\text{E}_{\text{I}}} \right)=20\,\log_{10}\left(\frac{\text{H}_{\text{T}}}{\text{H}_{\text{I}}}\right) \tag{1}\]  
where P\({}_{\text{I}}\) (E\({}_{\text{I}}\) or H\({}_{\text{I}}\)) and P\({}_{\text{T}}\) (E\({}_{\text{T}}\) or H\({}_{\text{T}}\)) are the power (electric/magnetic field intensities) of incoming and transmitted EM waves, respectively. The aforementioned equation clearly suggests that the total electromagnetic shielding efficiency of a material depends on the sum of the three different mechanisms, viz. shielding by absorption (SE\({}_{\text{A}}\)), reflection (SE\({}_{\text{R}}\)) and multiple internal reflections (SE\({}_{\text{M}}\)). Figure 2 depicts a schematic illustration of the different electromagnetic shielding mechanisms.  
The incident EM wave is either reflected back to the surrounding areas or is transmitted through the material, or will be absorbed inside the material. However, it is highly desirable to fabricate materials which have high conductivity and low reflectivity. For instance, metals are used as conventional EMI shielding materials with high SE values, but at the same time, most of the incoming EM wave is reflected back to the adjacent electronic circuits. This leads to further EM interference, and hence there is a strong need to develop microwave-absorbing materials which have high SE values and low reflectivity in the noted frequency region. For such materials, they must exhibit high conductivity (for high SE values) and a low dielectric constant (for low reflectivity).
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 5 Discussion
### Subsidence Map for Evaluation or Prediction  
Most mining subsidence studies have generated subsidence susceptibility maps over specific study areas by analyzing correlational information (values or equations) between maps of past subsidence occurrences and the contributory factors. Subsequently, they tend to present subsidence inventory maps overlain on generated subsidence susceptibility maps and simply analyze whether the locations of past subsidence occurrences matched regions with high subsidence potential. Certainly, such verification assists in the examination of the prediction accuracy and reliability of GIS analysis models used in these studies. However, this type of interpretation is simply 'evaluation' and does not encompass 'prediction,' Perhaps logically, regions of past subsidence are shown to have high subsidence potential in a subsidence susceptibility map irrespective of the method used, because it was used to derive the correlation. Moreover, the danger associated with regions of subsidence identified from the subsidence inventory map is already known. To extend the function of the generated map from 'evaluation' to 'prediction,' interpretation and discussion regarding areas with high subsidence potential, without past subsidence occurrences, should be addressed in future studies. These regions should also be included as locations where detailed site investigations or engineering measures are required to minimize the risks associated with ground instability.
# UAV Formation Trajectory Planning Algorithms: A Review
## 3 Traditional Algorithm
### Fast Marching Method  
The fast marching method (FM) is an efficient numerical algorithm for solving the optical path function equation (Eikonal equation), and the optical path function equation is as follows:  
\[|\
abla T(x,y)|V(x,y)=1 \tag{1}\]  
Figure 6: An LRL trajectory diagram of Dubins curve (black circle: circle curvature; yellow lines: the connecting line between the centers of trajectories; blue line: initial flight direction; green line: final flight direction; pt1-pt2: intersection point between curvatures; C1–C3: curvature name).  
where \((x,y)\) is the coordinate of the calculation point in the pose space, \(T(x,y)\) is the time when the interface function arrives at the calculation point, \(V(x,y)\) is the propagation velocity set by the interface function, and it is a fixed value in trajectory planning. The optical path function solution model is shown in Figure 7.  
The fast marching method first establishes a rasterized space for storing time values, and then, the time cost will be converted into the distance cost during planning. Then, we set reachable points and unreachable points and complete the minimum value search operation by continuously updating the distance cost to obtain the distance matrix. We use it to construct the potential field and then use the gradient descent method to iterate continuously from the starting point along the direction of the fastest gradient descent in the generated potential field, obtaining a smooth trajectory without collision.  
Aiming at the problem that the trajectory obtained by the traditional FM algorithm in the 3D environment will be too close to obstacles and the trajectory is not smooth enough, [PERSON] et al. (20,21) proposed a fast marching square algorithm (FM2), which improves the FM algorithm by changing the propagation speed in space so that the wave will tend to follow the track travel.
## 3 MLP Neural Network and Training
### Training for Fundamental Harmonic Detection
#### 3.2.1 Patterns Generation  
After a huge number of tests with different points per cycle and number of ANN inputs, a slow sampling rate of 2.5 kHz could be chosen, i.e., 50 points per waveform cycle, which means that the neural network must contain 50 inputs. For the strategy of extracting the fundamental harmonic, there was no need to increase the number of samples. It also works with faster sampling, but at the cost of increasing the size and complexity of the ANN, as well as the training times, without achieving higher performance.  
Different harmonic spectrums of nonlinear loads were analyzed to choose suitable magnitudes of the different harmonics. Table 1 shows the intervals of values allowed for the rectangular coefficients of the odd harmonics, up to the 35 th, used in the training patterns. The determination of the suitable harmonics and amplitudes was also the result of a long process, based on the authors experience and the trial-and-error method.  
An important requirement for the control method is to maintain its performance in case of electric frequency deviations from the nominal value of 50 Hz. A robust behavior of the ANN requires the use of different fundamental frequencies in the training patterns. In some previous tests, we could observe that an ANN trained only with 50 Hz does not work properly in practical cases due to the possible frequency deviations of the supply voltage. Therefore, the patterns were generated using five different values of frequency between 49.5 and 50.5 Hz.  
A key feature enabling the required MLP learning has been the use of random combinations of harmonics. Initially, we generated high amounts of patterns in determined manners, without obtaining a low enough _mse_. Then, using the MATLAB function \"unidrnd\" it was possible to randomly generate any number of patterns: \(A_{1}=(unidrnd(5,1,1)-3)^{\prime}0.5\); \(B_{1}=(unidrnd(5,1,1)-3)^{\prime}0.5\); \(A_{3}=(unidrnd(5,1,1)-3)^{\prime}0.3\); and so on, up to \(B_{35}\). This operation gives, for \(A_{1}\), random values from the set {\(-1\), \(-0.5\), \(0\), \(0.5\), \(1\)}. This approach provided a very efficient set of patterns. For every different frequency, an amount of 20,000 combinations of harmonics were generated, providing an input matrix \(P\) of size 50 \(\times\) 100,000. The size of the desired targets matrix \(T\) is 2 \(\times\) 100,000, containing the \(A_{1}\) and \(B_{1}\) values of every waveform.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Atb  
Another promising lncRNA-based biomarker for BC is Activator of TGF-\(\beta\) (ATB). It is upregulated in BC, particularly in the luminal B, Her-2, and TNBC subtypes. It facilitates the EMT through miR-141-3p suppression or by enhancing the miR-200c/Twist1 pathway [77; 78]. [PERSON] et al. proved that the levels of ATB and protein kinase Nek9 in serum were significantly associated with the progression of BC, especially the LNM and TNM stages [79]. Moreover, the study by [PERSON] et al. demonstrates that serum lncRNA-ATB serves as a superior non-invasive biomarker for the early diagnosis of BC compared to the conventional marker CA15-3 (AUC of 0.844 and 0.738, respectively), with better performance in distinguishing early-stage disease. Additionally, another lncRNA, FAM83H-AS1, correlates with advanced tumor characteristics, including the TNM stage and LNM [80]. A similar study revealed that elevated lncRNA levels in BC patients correlated with increased rates of distant metastasis, greater LN involvement, and elevated Ki67 levels, indicating a more aggressive cancer behavior. Additionally, patients with higher lncRNA expression experienced worse clinical outcomes, including reduced DFS and OS [78]. These findings highlight IncATB's potential as a marker for assessing disease severity and predicting patient prognosis in BC.