esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 General Structure of the Methodology  
The overall architecture of the proposed lightweight rail surface condition identification method for maximum adhesion coefficient estimation is illustrated in Figure 1.  
This method comprises three core modules: (A) a multi-source data input module, (B) a lightweight rail surface condition identification module, and (C) a fuzzy fusion estimation module.  
Module A is responsible for acquiring and pre-processing rail surface condition images using industrial cameras while simultaneously obtaining real-time train operation data, including creep and slide speed and adhesion coefficients, based on the HXD1 locomotive's traction dynamics model. Module B processes the rail surface images acquired by Module A and employs a teacher-student collaborative optimization framework. Specifically, GoogLeNet and MobileNet serve as the backbone networks, where transfer learning is utilized to fine tune the high-level feature pyramid, enabling multi-scale feature extraction. Furthermore, knowledge distillation enhances the performance of the student model, while a mixed-precision training strategy improves computational efficiency. Ultimately, this module achieves real-time and accurate rail surface condition identification. Module C implements a multi-source information fusion mechanism that integrates train operation data from Module A. By designing membership functions and fuzzy logic rules, the system estimates the maximum adhesion coefficient and further refines the estimation using the rail surface condition information derived from Module B. The final estimated adhesion coefficient is then compared against the ideal value for evaluation and validation.  
The key innovations of this approach are as follows. The first is a knowledge distillation-driven lightweight identification model, which enhances both real-time performance and deployability. The second is a fuzzy logic-based multi-source information fusion strategy, improving the estimation accuracy and confidence of the maximum adhesion coefficient. The third is a dual-modal \"vision-dynamics\" data processing mechanism, enhancing the system's adaptability and robustness under complex operational conditions.
## 3 The Proposed Firewall on an FPGA
When all data for packet analysis are available, the signal FW_OUT is asserted. The latency associated with these operations (i.e., the delay between the reception of the last byte of the packet and the assertion of FW_OUT) is 1 clock cycle, thus TRX to FW_OUT = 8 ns. 3. The module 'Packet analysis' calculates the packet fields (Step A1 in Figure 3) and operates concurrently with the 'RX control' module. The module 'Packet analysis' is implemented with a combinational circuit. Thus, when the signal FW_OUT is asserted, all the packet fields are valid and no latency is associated with this operation. 4. When the packet fields are valid, the 'Check firewall rules' module checks the compliance of the packet fields with the firewall rules (Step C1 in Figure 3) and generates the response in the 2-bit register FW_RESULT[1:0] (0 for a packet with checksum error, 1 for a packet that violates the firewall rules, 3 for a packet that can be transmitted) and the 3-bit register FW_PCK_TYPE[2:0] that defines the protocol of the packet (0 for ARP, 1 for TCP, 2 for UDP, 3 for ICMP, 4 for IPv6 and 5 for others). Then, the signal FW_COMPLETED is asserted to inform the 'RX control' module that the results of the packet analysis are available. The latency associated with this operation is 18 clock cycles, thus \(T_{FW\_OUT to FW\_COMPLETED}=144\) ns. 5. When the signal FW_COMPLETED is asserted, the 'RX control' module updates the value of the STATUS BYTE for the Control Word present at address j of the Control memory, according to the value of the register FW_RESULT[1:0] (Step R6 in Figure 3). The latency associated with this operation is of 1 clock cycle, thus \(T_{CW\_UPDATE}=8\) ns. At this time, the 'TX control' module can decide if the packet can be transmitted or must be blocked. The operations of the transmission of the packets that comply with the firewall rules, as presented in Figure 3, can be summarized as follows: 1. The 'TX control' module continuously reads the Control Word stored at address k of the Control memory (Step T1 in Figure 3) and checks if the value of the STATUS BYTE is different from 0 (Step T2 in Figure 3). If the value of the STATUS BYTE is 37h (i.e., the packet complies with the firewall rules), the packet is sent to the 'AXI 1G/2.5G Ethernet Subsystem' module to be transmitted, or it is discarded, otherwise (Step T3 and T4 in Figure 3). Then, the signal TX_COMPLETED is set to inform the 'RX control' module that the packet was transmitted/discarded. The 'RX control' module can update the content of Control memory (Step R9 in Figure 3) and decrease the value of Data memory occupation (Step R10 in Figure 3). Finally, the value of k is incremented by the 'TX control' module (Step T5 in Figure 3). The latency associated with these operations is of 7 clock cycles, thus \(T_{TX\_DECISION}=56\) ns. 2. The Xilinx IP module 'AXI 1G/2.5G Ethernet Subsystem' transmits the Ethernet frame using the fiber optic cable connected to the SFP/SFP+ connector. The latency associated with this operation can be estimated from the module datasheet as \(T_{AXI\_to\_SFP}=211\) ns [45].
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
## 3 Results  
### Bone Contact Surface  
The TC rates for the whole proximal and distal junctions were 45.96% and 15.03%, respectively, and those for the mesh structured segments (P4 and D4) were 82.05% and 16.92%, respectively. Total tissue integration involves osseeous union (bone-to-implant contact, BIC) and fibrous union (P4). The total tissue integration time was 1.02 hours.  
Figure 5: Analyzed segments of the distal region. Micro-computed tomographic images showing (**A**) a longitudinal view and (**B**) axial view, with scout images in the upper right corners. Photomicrograph ((**C**): [PERSON]’s trichrome staining, 1\(\times\) magnification) of the histological specimen of the longitudinal section showing the analyzed segments (D1–D5).  
Figure 6: Analyzed sections of the ulnar implant shaft. Photomicrograph ((**A,B**): [PERSON]’s trichrome staining, 1\(\times\) magnification) of the histological specimens of the longitudinal and axial sections, with scout images in the upper right corners showing soft tissue adhesion without the uncovered area.  
(soft tissue-to-implant contact, SIC). Osseous union was observed in the P1, P2, P4, and D4 segments, whereas only fibrous union was revealed in the other segments. The P1 and P2 segments exhibited rough surfaces without the mesh, however, gaps between the metal implant and the host bone were almost absent. A mesh was utilized to enhance osseointegration to the P4 and D4 segments, and the measured gaps for these segments were 0.2-0.5 and 0.7-1.2 mm, respectively. The BIC rates for the mesh-enhanced P4 and D4 segments were 10.81% and 8.91%, respectively. For rough surfaces without the mesh, segments with accurate contact between the host bone and implant presented osseointegration (21.93% for P1, 26.47% for P2). However, other rough surfaces with a gap between the bone and the implant did not present osseointegration (Figures 4C and 5C and Table 1).
## 4 Results
### The Computational Efficiency of Solutions  
In the final part, we discuss the computational efficiency of the methods for this example. The efficiency is estimated using the following formulation:  
\[Efficiency=\frac{1}{r_{e}\times r_{t}} \tag{14}\]  
where \(r_{e}\) is the strain energy norm error of different methods compared with FEM, and \(r_{t}\) represents CPU time compared with FEM. Tables 8 and 9 list the strain energy norm error and CPU time obtained using four methods with mesh 4 and mesh 5. It is easily found that the solution error of ES-FEM is the smallest even though the CPU time is one of the highest. Taking the two facts into consideration, the computational efficiency objectively describes the performance of different numerical methods. Hence, ES-FEM, whose computational efficiency is about 2.88 times that of FEM, is the most efficient one. Similarly, the computational efficiency of FS-FEM has been improved in comparison with FEM. Thus, the computational efficiency of ES-FEM and FS-FEM can be significantly improved when CPU time is taken into account.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Distance from the Tip** & **FEM** & **ES-FEM** & **FS-FEM** & **NS-FEM** \\ \hline
2 & 0.141 & 0.05 & 0.065 & 0.138 \\
4 & 0.145 & 0.029 & 0.062 & 0.149 \\
5 & 0.11 & 0.038 & 0.037 & 0.14 \\
6 & 0.156 & 0.047 & 0.064 & 0.149 \\
7 & 0.154 & 0.024 & 0.051 & 0.18 \\
8 & 0.1 & 0.05 & 0.052 & 0.19 \\
9 & 0.14 & 0.024 & 0.037 & 0.15 \\
10 & 0.12 & 0.024 & 0.01 & 0.2 \\
11 & 0.15 & 0.01 & 0.02 & 0.15 \\
12 & 0.17 & 0.04 & 0.06 & 0.3 \\
14 & 0.22 & 0.036 & 0.12 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error in the displacement norm for the ES-FEM solution in comparison with those of other methods for the cemented carbide picks using the same distribution of nodes.  
Figure 15: The distribution of displacement v along the tip using different methods for the conical pick.
## 5 Conclusions  
A novel STPAP beamforming algorithm based on the MVDR-CMS criterion, which can achieve a balance between the output C/N\({}_{0}\) performance and computational complexity, has been proposed in this paper. With the assumption that the DOAs of the desired GNSS satellites are known a priori through existing DOA estimation methods, the proposed STPAP algorithm based on the MVDR-CMS criterion is designed to process multiple GNSS satellites with a single adaptive filter, which is beneficial to reduce computational complexity compared with the traditional STPAP algorithm based on the MVDR-CSS criterion in [30]. Meanwhile, the output C/N\({}_{0}\) performance of the proposed STPAP algorithm based on the MVDR-CMS criterion is close to that of the MVDR-CSS criterion when the parameter \(J\) that denotes the number of GNSS satellites processed in one adaptive filter is proper. Concretely speaking, \(J\) is better to be set as two or three, which means that an adaptive filter preferably processes two or three GNSS satellites at once. Besides, an adaptive algorithm based on the CLMS method is derived to calculate weight vector iteratively for the proposed STPAP beamforming algorithm, which can avoid the calculation of matrix inversion. In addition, the proposed STPAP algorithm can be still effective when the polarization discriminator is removed.  
Conceptualization, H.W. and Z.Y.; Methodology, H.W. and J.Y.; Software, H.W. and Z.F.; Validation, H.W. and J.Y.; Investigation, H.W. and Z.Y.; Data curation, H.W.; Writing--original draft preparation, H.W.; Writing--review and editing, H.W. and J.Y.; Supervision, J.Y.  
This research was funded by National Natural Science Foundation of China, grant numbers 61501471 and 61503393.  
The authors declare no conflict of interest.
## 3 Experimental Results
### Spectroscopic Ellipsometer
#### 3.2.3 Non-Encapsulated and Encapsulated MAPbI\({}_{3}\) with ETL Samples  
Absorption curves were determined from non-encapsulated and encapsulated MAPbI\({}_{3}\) with ETL samples before and after being subjected to the degradation process. Additionally,  
Figure 5: **(a)** Absorbance curve of non-encapsulated MAPbI\({}_{3}\) samples after 50 min and 70 min under the solar spectrum and the UV irradiation lamp. **(b)** Absorbance curve of MAPbI\({}_{3}\) + EVA before and after 350 h under solar spectrum.  
an absorption curve was determined by a pristine sample made of ETL deposited on a glass substrate.  
If the absorption curves of the pristine samples are compared with the encapsulated and non-encapsulated samples after subjecting them to 70 min of UV irradiation, it can be seen that the non-encapsulated samples show a reduction in the absorbance curve between 400 and 500 nm (see Figure 6). However, the encapsulated ones keep their absorption curve unaltered. In both cases, the calculated band gap remained the same (1.6 eV).  
Furthermore, considering only the non-encapsulated samples that went through the degradation process, by comparing Figures 5 and 6, it is possible to infer that the addition of the ETL layer resulted in a reduction in the MAPbI\({}_{3}\) degradation, as can be seen at 470 nm (29.68%), at 500 nm (25.35%) and at 750 nm (19.69%). In the same line, non-encapsulated samples without ETL show a similar behavior with a further decrease in the absorption intensity at 470 nm (37.20%), at 500 nm (37.00%) and at 750 nm (28.15%). Therefore, both samples have a similar shift trend, but when the ETL layer is introduced, the absorption intensity peaks show a minor decrease. These results are attributed to the ETL layer effects, which protect the perovskite and EVA layers from degradation by UV irradiation. The ETL layer absorb a fraction of the UV irradiation (from 200 to 350 nm), as shown in Figure 6, therefore reducing the total UV irradiation interacting with the perovskite.
## 5 Simulation and Analysis
### Platform Setting
#### 5.1.4. UCAV Performance Parameters Setting  
For experimental comparison, two different UCAV performance parameters were considered in this study. The variation of the performance capabilities focus on the following parameters: maximum thrust and maximum roll rate, as presented in Table 1. The dominant UCAV performance parameters are called the \"advantage\" parameters, and the non-dominant UCAV performance parameters are called the \"disadvantage\" parameters. The mass of both the UCAVs is same and equal to 10,000 kg.
## 3 Results
### The Joint Effects of Air Pollution Exposures on the Risk of Low Birth Weight
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CO Entire Pregnancy** & **O\({}_{3}\) Entire Pregnancy** & **n/N** & **\% of LBW \(<\)3000 g** &
\begin{tabular}{c} **Adjusted RR** \\ **(95\% CD 1** \\ \end{tabular} & **(95\% CD 1** \\ \hline Low (\(<\)Q\({}_{4}\)) & Low (\(<\)Q\({}_{4}\)) & 102/822 & 12.41 & 1 & 1 \\ High (\(>\)Q\({}_{4}\)) & Low (\(<\)Q\({}_{4}\)) & 53/382 & 13.87 & 1.18 & 0.18 & \\ Low (\(<\)Q\({}_{4}\)) & High (\(>\)Q\({}_{4}\)) & 10/67 & 14.93 & 1.23 & 0.23 & \\  & & & & (0.63-2.36) & (0.37-1.36) & \\ High (\(>\)Q\({}_{4}\)) & High (\(>\)Q\({}_{4}\)) & 8/10 & 80.00 & **5.38** & **4.38** & **3.97** \\  & & & & **(2.50-11.57)** & **(1.50-10.57)** & **(2.17,25.85)** \\ \hline
**PM\({}_{2.5}\) Entire Pregnancy** & **O\({}_{3}\) Entire Pregnancy** & & & & & \\ Low (\(<\)Q\({}_{4}\)) & Low (\(<\)Q\({}_{4}\)) & 104/810 & 12.84 & 1 & 1 \\ High (\(>\)Q\({}_{4}\)) & Low (\(<\)Q\({}_{4}\)) & 51/392 & 13.01 & (0.76-1.49) & (\(-\)0.24-0.49) & \\ Low (\(<\)Q\({}_{4}\)) & High (\(>\)Q\({}_{4}\)) & 13/72 & 18.06 & 1.44 & 0.44 & 0.72 \\  & & & & (0.80-2.58) & (\(-\)0.20-1.58) & \\ High (\(>\)Q\({}_{4}\)) & High (\(>\)Q\({}_{4}\)) & 5/7 & 71.43 & **4.30** & **3.30** & **2.80** \\  & & & & **(0.66-10.16)** & **(1.50-10.57)** & **(1.36-19.89)** \\ \hline \hline \end{tabular} All estimates are from modified Poisson regression models. CI, confidence interval; ERR, Excess Relative Risk; O\({}_{3}\), ozone; RERI, Relative Excess Risk Due to Interaction; RR, Risk Ratio; and SO\({}_{2}\), sulfur dioxide. 1 Adjusted for season of birth, gender, maternal age, family socioeconomic status, maternal smoking during pregnancy, environmental tobacco smoke exposure of pregnant mother, and single parenthood.  
\end{table} 
Table 5: Joint effect of CO and O\({}_{3}\) and of PM\({}_{2.5}\) and O\({}_{3}\) during entire pregnancy on low birth weight less than 3000 g. The Espoo Cohort Study, 1984–1990.
# Structural Methodologies for Distributed Fault Detection and Isolation
## 1 Introduction
[7; 16] proposed a similar robust distributed fault detection and identification approach, but they did not address the problem of determining the minimum number of required shared variables between the subsystems. In this paper, we propose two general approaches for designing a set of distributed diagnosers that together have the same diagnosability performance as centralized approaches: (1) a Minimal Structurally-Over-determined (MSO)-based approach and (2) an equation-based approach. Some of our previous work [17; 18] presented an initial approach and results for our distributed diagnosis approach. In this paper, we present the problem formulations, the proposed algorithms, and the accompanying proofs for the hypotheses on which these algorithms are based in more detail. We compare the computational complexities and the application of these algorithms to a testbed: the spacecraft electrical power distribution system [19]. The advantages and disadvantages of each algorithm are discussed to help the health monitoring engineers select the proper approach for a given application. The first approach uses Minimal Structurally-Over-determined (MSO) set selection [20] and provides globally correct diagnosis results while minimizing the number of measurements shared between different subsystems. Each MSO set used for residual generation represents an analytical redundancy relation in the system [20; 21]; however, the total number of MSO sets is exponential in terms of the system measurements. To avoid the computational complexity of dealing with a large number of MSO sets, we propose a second algorithm for designing distributed diagnosers that is based on system equations. This solution is computationally efficient, and its solution matches the diagnosability capabilities of a centralized diagnoser. Moreover, the equation-based method does not require access to the global model for diagnoser design, which makes it applicable to large, complex systems, where global system models are likely to be unavailable or unknown. The rest of this paper is organized as follows. Section 2 presents basic definitions and the running example we use in the paper. Section 3 presents the MSO-based distributed diagnosis approach, and Section 4 presents the equation-based approach. Section 5 discusses the case study, and Section 6 presents the advantages and disadvantages of each approach along with directions for future work.
## 3 X-ray Photon Beam Transports
One important constraint of X-ray beam optics at an X-ray FEL is the so-called single-pulse damage. Because a large number of photons (corresponding to pulse energies of the order of mJ per pulse) arrive within 10-100 fs, thermal transport does not remove any heat during the pulse, even for excellent heat conductors like copper or diamond. For focused beam conditions (typically smaller than 50 \(\upmu\)m diameter), most materials will vaporize on an ultrafast time scale due to the absorption of energy from a single X-ray FEL pulse within the X-ray penetration depth. More resistant materials  
Figure 3: Optical layout of the three photon beam transports with some of the most important elements in the sequence towards the instruments located in the experiment hall. The most southern beam line is: SASE2 (**a**); then SASE1 (**b**); and SASE3 (**c**). are the ones with low atomic number where the absorption per atom is lower. Most components directly exposed to X-ray FEL radiation, or at least their beam facing surfaces, are therefore made of boron-carbide or diamond, for example slits, beam stops, shutters, collimators and attenuator plates. Exceptions are the cryogenically-cooled silicon monochromators in the hard X-ray beam transports SASE1 and SASE2, but also beam position screens and a few other components. For these components, it is required to carefully monitor the impinging beam size to avoid single-pulse damage effects. Another big challenge is the total power of a train of X-ray pulses which could reach values as high as several kW depending on pulse energy and number of pulses within the train of 600 \(\upmu\)s duration. An automatic protection system will trigger a reduction of the maximal number of pulses if equipment is inserted that does not withstand full pulse trains. In addition, long X-ray pulse trains, when missteered, could easily damage the stainless steel pipes of the vacuum system. To prevent this, photon beam loss monitors have been implemented at strategic places along the beam transport. Up to four diamond plates can be adjusted around the beam trajectory. In case of unwanted beam motions (e.g., due to mechanical drifts of the mirror mounts) the X-ray beam would hit a diamond and produce optical light fluorescence. This light is captured by a photomultiplier and triggers via the machine protection system a rapid interruption of the beam (within the same pulse train).
## 3 Manufacturing of the Device  
Once the viability and improvements of the new dual-mode structure have been confirmed with the simulations in an empty device, the device is manufactured and built over an inverted-microstrip structure (Figure 2). As it was described previously, Taconic TLX-08 (Taconic Europe, Ry, Denmark) is used for the spacers and for the microstrip line support. FR4 (\(\varepsilon_{\mathrm{r}}\) = 4.4) is used as the substrate which supports the ground plane and it is not expected to affect the filter behavior.  
The manufactured device (Figure 4) is filled with the nematic LC Merck MDA-98-1602 (Merck, Darmstadt, Germany), whose properties are, initially unknown at microwave frequencies. It is important to take account that LC are materials employed, specially, for the design of tunable electrooptical devices, so they are not usually tested at microwave frequencies. In the manufacturing process, LC molecules are aligned parallel to the microstrip line by rubbing a thin film of polyimide which acts as an alignment layer. In these conditions, when no voltage is applied, the LC permittivity is minimum, \(\varepsilon_{\mathrm{r}\perp}\). As voltage is applied, the molecules rotate and, at a saturation voltage value, LC molecules are oriented nearly perpendicular to the microstrip line; at this point, LC permittivity reaches its maximum value, \(\varepsilon_{\mathrm{r}\parallel}\). The tunability of the filter central frequency is achieved; the LC permittivity increases, while the filter central frequency decreases, as voltage is applied.
# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 2 Related Work
### Very Deep Convolutional Networks for Image Super-Resolution (VDSR)  
The ultra-deep super-resolution network proposed by [PERSON] et al. [8] extended the SRCNN from a 3-layer shallow network structure to a 20-layer ultra-deep network, and they concluded that the reconstruction effect will be improved as the number of layers increases. Compared to SRCNN, which only depends on the image context in a small area, VDSR, by exploring more contextual information through a larger receptive field, helps to better restore the detailed structure, especially in super-resolution applications with large magnification factors. In addition, in order to solve the problem of slow convergence in SRCNN, VDSR residual learning was introduced, which greatly increased the learning rate.  
VDSR accepts image features of different scales by adjusting the size of the filter to produce a fixed feature output. Although VDSR can achieve specific-scale magnification, it cannot achieve free-scale, multiscale magnification, and its parameter storage and retrieval also have obvious shortcomings.