esa-sceva/satcom-chunk-collection huggingface dataset:
# Selection of Optimal Path Control Algorithms for Probe Heads Used on Five-Axis Measuring Systems
## 3 Experiment and Results
The local coordinate system of the measured object was defined with the measurement of the external cylinder (the axis of the cylinder determines z-axis of the coordinate system), with the measurement of the plane cut into the side of the cylinder (of which the normal vector defines the x-axis of the coordinate system), and with the measurement of the front plane of the cylinder, which was measured in order to define the origin of the coordinate system. Next the results were compared with the actual values of the controlled dimensions taken from the MFC's calibration certificate. Calibration of the multi-feature check was performed on the Leitz PMM machine, which was located at LCM. The machine maximum permissible error Equation (1) was:  
\[E_{L,MPE}=0.8+2.5\times L/1000\ \mathrm{\mu m} \tag{1}\]  
where \(L\) is the measured length given in mm. It was located in an air-conditioned room with a thermal stability of \(\pm\)0.05 \({}^{\circ}\)C. The LCM is laboratory accredited, by the Polish Centre for Accreditation, for calibration of artifacts using the coordinate measuring technique. The following dimensions were checked (the default method for all measured features is guide point and the best method is given in brackets after each task described): The distance between the front planes of the gauge (GR); the angle between the front and side planes (GR); the parallelism deviation between the front planes of the gauge (GP 45\({}^{\circ}\)); the flatness of the front plane (GP 45\({}^{\circ}\)); and the circularity (GC 0) and diameter of the circle (FTAA 30\({}^{\circ}\)/30\({}^{\circ}\)). Figures 8 and 9 show the results obtained during this part of the experiment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Measuring Task** & **Best Inspection Path Planning Method** \\ \hline Flatness & Guide point (stylus orientation 45\({}^{\circ}\)) \\ Distance between planes & Guide rail \\ Parallelism & Guide point (stylus orientation 45\({}^{\circ}\)) \\ Angle between planes & Guide rail \\ Circularity & Guided curve (with offset set to 0 mm) \\ Circle diameter & Fixed tilt and advance (tilt 30\({}^{\circ}\); advance 30\({}^{\circ}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The best inspection path planning method to be used for basic measuring tasks.
## 2 Materials and Methods
### Shadow Detection Method Combining Topography and Spectra  
The image is divided into several homogeneous regions, i.e., image objects, after superpixel segmentation. The CTS method is performed on these homogeneous regions. The proportion of shadow pixels contained within each image object is determined by the DEM rough shadow mask as follows:  
\[p_{\text{dem}}^{\text{obj}}=\frac{n_{\text{dem}}^{\text{obj}}}{n_{\text{ obj}}} \tag{5}\]  
where \(n_{\text{dem}}^{\text{obj}}\) is the total number of shadow pixels in the DEM rough shadow mask contained within the object, and \(n_{\text{obj}}\) is the total number of pixels in the object. After calculating \(p_{\text{dem}}^{\text{obj}}\) for all objects, the SPM can be obtained, which reflects the probability that each object is geographically in shadow.  
The mean value of MC3 is calculated for each image object:  
\[\overline{C_{m3}}^{\text{obj}}=\frac{\sum_{(x,y)\in\text{obj}}C_{m3}(x,y)}{n_ {\text{obj}}} \tag{6}\]  
where \(C_{m3}(x,y)\) is the pixel value of \((x,y)\) in the MC3 component. After assigning all objects their MC3 mean values, the MC3 mean map can be obtained, which reflects the probability that each object belongs to shadow on the spectra.  
The probability that each image object belongs to shadow is shown as follows:  
\[p_{s}^{\text{obj}}=w_{\text{dem}}\cdot p_{\text{dem}}^{\text{obj}}+(1-w_{ \text{dem}})\cdot\overline{C_{m3}}^{\text{obj}} \tag{7}\]  
where \(\overline{C_{m3}}^{\text{obj}}\) is normalized to the \([0,1]\) range, \(w_{\text{dem}}\) is the weight of the DEM rough shadow mask, and \((1-w_{\text{dem}})\) is the weight of the MC3 mean map, which controls the degree of influence of topography and spectra on shadow detection. In that way, the topography can  
Figure 8: Image and its MC1C2C3C4 color model components.  
correct the shadow misclassification due to spectral similarity and the spectra can correct the roughness and inaccurate localization of the terrain in detecting shadows. Both of them can correct each other's shadow misdetection and omission problems so that the shadow detection accuracy can be further improved.  
Figure 9 demonstrates the process of generating the JSM. After fusing the two maps to generate the superpixel JSM of the image, the final shadow mask can be obtained by applying the multi-level Otsu threshold method to the histogram of JSM in terms of superpixels.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
###### Abstract  
Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.  
CNN; GRU; GNSS deformation monitoring; hydraulic engineering; forecast +
Footnote †: journal: Medical Engineering
## 5 Experimental Verification and Analysis
### Effect of Mistuning  
If the working condition of the battery changes abruptly, the SOC measurement error would probably be varied largely with before, so the prior tuning of the measurement variance of the DEKF will not give an optimal estimate of the SOC. But there is no such issue in the proposed VB-ADEKF since the measurement variances are estimated online. This effects of mistuning brought about by inappropriate measurement variance of the traditional DEKF and VB-ADEKF are mainly reflected in the SOC estimation error, as shown in Figures 17-19. Here, the measurement noise variances of DEKF are mistuned to \(\Sigma_{k}^{x}=0.01\) and \(\Sigma_{k}^{\theta}=0.001\) in constant current discharge test, and \(\Sigma_{k}^{x}=0.1\) and \(\Sigma_{k}^{\theta}=0.005\) in the pulse current discharge test and UDDS test. The initial estimates of the measurement variances of VB-ADEKF are also correspondingly mistuned. From the results, we can see that the convergence rate of DEKF slows down and the SOC estimation accuracy also declines in the case of mistuning, while the SOC estimation performance of the proposed VB-ADEKF remains almost unchanged. This suggests that the proposed VB-ADEKF is more robust than the traditional DEKF.
# Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
## 4 Results
### Dynamical Model Mis-Modeling
These results seem to indicate that an anomaly is occurring in the system to which radiometric data is more sensitive than optical data except right near entry. Because the high uncertainty filters generally perform better, we might assume that this means the source of the anomaly is dynamic in nature, but as already mentioned this gating network currently is vulnerable to filters with over-inflated uncertainty metrics. This motivates us to address this vulnerability through future research. The performance of the gating network in the middle of the arc (near measurement index 30,000) is also an area of future research, as this is an unexpected result that does not have an obvious answer. As with the measurement biasing analysis, we computed the average optimal network weightings for each filter in the bank for both dynamic mis-modeling scenarios (Table 4). These results reinforce the following conclusions we have previously made:  
* An anomaly is detectable in both dynamic mis-modeling scenarios due to the gating network's deviation from the ideal weights
* The source of the anomaly in both instances more significantly affects radiometric-based solutions than optical-based solutions. Radiometric and fused filters perform  
Figure 22: Optimal gating network weights with mis-modeled dynamics and varied process noise models. similarly, so this seems to indicate that the radiometric and optical data are not fighting each other
* The higher uncertainty filters are preferred in both scenarios, which seems to indicate that the source of the mis-modeling may be dynamic in nature--specifically, mis-modeling to which optical data is less sensitive than radiometric data. The simulations and results discussed in this section have shown that our optimal gating network has the ability to identify and characterize filter anomalies related to measurement and dynamic mis-modeling. These results show promise and warrant future research into this method with the ultimate goal of being a method that can autonomously navigate a spacecraft while detecting and diagnosing problems as they occur.
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
###### Abstract  
In this article, a dual-loop dual-output frequency synthesizer designed for IEEE802.11 aj (45 GHz) standard is presented. In order to support the super-heterodyne transceiver, the Loop1 output frequency is fixed for easy design of high-performance IF transceiver and filter, and the Loop2 output frequency varies for the channel selection according to the IEEE802.11 aj (45 GHz) standard. The power hungry high-speed prescaler (or multi-modulus-divider) is replaced with a mixer in Loop2, thus the in-band phase noise and DC power consumption can be improved. The dual-loop dual-output synthesizer is fabricated in 0.13 \(\upmu\)m SiGe BiCMOS technology, occupies an area of 2.7 mm \(\times\) 2.4 mm, and consumes 610 mW DC power. Measured results show the phase noise of the frequency synthesizer are \(-\)79.3 dBc/Hz@10 kHz and \(-\)129.1 dBc/Hz@10 MHz at 12.96 GHz for Output1 and \(-\)76.6 dBc/Hz@10 kHz and \(-\)117.2 dBc/Hz@10 MHz at 32.535 GHz for Output2. The low-reference spur of \(-\)69.2 dBc and low-power level spurious tones at the outputs are observed during the measurement. To the best of our knowledge, this work is the first reported dual-loop dual-output synthesizer designed for IEEE802.11 aj (45 GHz) standard.  
phase noise; dual-loop phase locked loop; cap-bank voltage-controlled oscillator Article
# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
###### Abstract  
Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.  
additive manufacturing (AM); metal defects; quality inspection; deep learning; convolutional neural network (CNN); defect classification +
Footnote †: journal: Applied Sciences  
_Article_  
Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
# Neighbor Session Solutions for Integrated Routing Protocols
## 2 Dual-Stack IP Routing Protocols
### Intermediate System to Intermediate System  
Intermediate System to Intermediate System (IS-IS) is, like OSPF, a link state routing protocol suitable for use in wide area networks and provides very fast convergence in case of topological changes. Similar to the OSPF protocol, it uses [PERSON]'s Shortest Path First (SPF) algorithm to calculate the shortest paths. It was originally developed for OSI networks as the ISO-IEC 10589:2002 [38] standard, but it is not heavily dependent on OSI protocols and can, therefore, be used with minor modifications for IP networks. This specification was published as RFC 1195, and the modified protocol is also called Integrated IS-IS [36; 39].  
As an only routing protocol, IS-IS does not transmit messages using any L3 network protocol but embeds messages directly into link-layer frames. Addressing and adjacency information is transmitted in structures consisting of type, structure length, and separate content; therefore, they are usually referred to as Type-Length-Value (TLV) structures. Thanks to TLV structures, the protocol can be easily extended and has more flexibility. Therefore, it supports multiple network protocols (it is also referred to as protocol agnostic) and, unlike other routing protocols, it allows the calculation of the shortest paths for all network protocols together but also separately. Thanks to the design used, support for IPv6, described in RFC 5308, was also added to IS-IS in 2008. Its other functions include support for variable netmasks, arbitrary summarization, the division of the autonomous system into areas, and authentication [36; 39].
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 2 Related Work
### Beam Recovery Schemes Based on User Mobility  
A set of schemes focus on investigating the beam blockage problem due to mobility. For instance, performance analysis is conducted in [25] for beam blockage due to mobile pedestrians in urban mmWave networks. The approach depends on the ergodic capacity in evaluating susceptible links, while considering AP density and height, blocker density and speed, number of antenna elements, array switching time, and degree of multi-connectivity. Results in [25] show that dual connectivity delivers the desired performance, and there is an optimal density of APs that maximizes the capacity of cell-edge users. Here, beam tracking is enabled when active connections are lost in the dual-connectivity mode. In [26], mobility and blockage problems are investigated in massive multiple-input multiple-output (MIMO) transmission form mmWave and terahertz (THz) downlinks. The massive MIMO precoding design at the downlink transmission mode is based on the statistical channel state information (CSI) available at the BSs, which is formulated as an optimization problem to maximize the sum rate of mitigated Doppler and delay dispersion effects. Computational complexity can be intractable here due to the large number of antennas and the per-beam synchronization at each individual BS, despite the use of Karush-Kuhn-Tucker (KKT) conditions to tackle this problem from a nonlinear programming approach. Additionally, the combination of mmWave and THz bands can be irrelevant to the three GPP standards, where each band possesses different propagation characteristics and transceiver designs.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 4 Performance Metrics
### Discussion  
We make a few remarks on the lower bound. First, the receivers do not need to know \(\alpha\). Second, the rate \(R_{\text{a}}\) in (17) is achievable if one assumes stationarity and coding and decoding over many OFDM blocks. Third, as \(S\) grows, the channel estimate of the data-aided detector becomes more accurate and the performance approaches that of a coherent receiver. Related theory for PAT and large \(S\) is developed in [49]. However, the PAT rate is generally smaller than for a data-aided detector because the PAT channel estimate is less accurate and because PAT does not use all symbols for data.  
We remark that blind channel estimation can approach the performance of data-aided receivers for large \(S\). Blind channel estimation algorithms can, e.g., be based on high-order statistics and iterative channel estimation and decoding. For polar codes and low-order constellations, one may use the blind algorithms proposed in [50]. We found that the PAT rates are very close (within 0.1 bpcu) of the pilot-free rates multiplied by the rate loss factor \(1-S_{p}/S\) for pilot fractions as small as \(S_{p}/S=10\%\).  
Depending on the system under consideration, we choose one of \(T_{F}=32\),256,396, one of \(T=35\),270,277,286,410, one of \(S=256\),1584, and \(B=200\). For most simulations we have \(T_{F}=S=256\) and estimate the channel based on individual OFDM symbols, see Section 1.3. For example, for \(T=270\) and a symbol time of 30 ns (symbol rate 33.3 MHz) the coherence time needs to be at least \((30\;\text{ns})\cdot T=8.1\;\text{\mu s}\). Of course, the transmitter needs to know the channel also, e.g., via time-division duplex, which requires the coherence time to be substantially larger. The main point is that channel estimation at the receiver is not a bottleneck when using ZF based on channel inversion. Finally, for the coded simulations we chose \(T_{F}=396\) and \(S=4T_{F}=1548\) because the LDPC code occupies four OFDM symbols.
# Relative Intensity Noise of Hybrid Mode-Locked Bound Soliton Fiber Laser: Theory and Experiment
###### Abstract  
Relative intensity noises (RIN) of mode-locked lasers are properties which are crucial for applications. In the literature, there have been plenty of theoretical/experimental studies on the RIN noises of passive/active single-pulse mode-locked lasers. Since some mode-locked lasers can also be operated under the bound-pulse mode-locking state, it is thus very interesting to further examine the RIN properties under bound-pulse mode-locking, and to verify if there are possibilities for RIN noise reduction as predicted by some previous theoretical works. The conventional analytical formula based on the soliton perturbation theory can no longer be applied due to the pulse shape complexity for the bound-pulse mode-locking cases. New theoretical tools for modelling general mode-locked lasers are eagerly awaited. In the present work, the RIN noises of an environmentally stable 10 GHz hybrid mode-locked Er-doped fiber laser capable of bound-soliton generation are experimentally investigated, and a novel theoretical method based on the linearized backpropagation approach is theoretically developed for calculating the RIN noise spectra of general mode-locked lasers. Both the theoretical and experimental results demonstrate that the RIN noise of the bound-soliton state can be lower than that of the single-soliton state by following the laser power scaling tendency.  
mode-locked fiber laser; bound soliton; relative intensity noise Article
## 3 Results and Discussion
### Considering Uncertainties  
Particularly when considering \(\delta R_{rs}\) retrievals from satellite, it is important and necessary to contextualise the magnitude of the PFT signal with respect to uncertainties on the satellite radiometry. A brief study on model and associated radiometric uncertainty is available in Appendix D. An important observation is that, while the 500 to 600 nm region of the promising PFT signal may be mostly insensitive to the effects of non-algal constituents, it is also where variability in \(R_{rs}\) due to the different approaches to phytoplankton phase functions is important, emphasising the critical role of phytoplankton scatter in this signal.
# Detection of Software Security Weakness Using Cross-Language Source Code Representation (CLaSCoRe)
### Problem Statement and Research Objectives  
Early detection of security vulnerabilities in source code is the most economical way to increase software application resilience. The number and complexity of weaknesses that can be introduced in source code by programmers with insufficient experience in building secure software, or as a result of the pressure to deliver their products in a very short timeframe, are increasing each year. Automated solutions to scan the source code for security vulnerabilities are available to the communities of programmers, as commercial or freeware tools. They are known, in general, as Static Application Security Testing (SAST) technologies. SAST technologies may cover one or multiple programming languages, especially the most popular, like C/C++, Java, JavaScript, or PHP. The most professional tools can identify more than 1000 security weaknesses in source code written in those languages. However, the majority of less popular languages do not benefit from automated scanning solutions, as the market potential is less attractive for developing SAST solutions.  
Based on various sources, there are between 250 and 700 active programming languages in use, and about 8,945 programming languages in history, according to the Online Historical Encyclopedia of Programming Languages [2]. The Tibe Index [3] continuously updates the top 50 popularity scores for the 278 programming languages in the inventory. Wikipedia [4] presents a list of 687 programming languages per name, category, chronologically, or per generation. An important part of the source code written globally remains exposed to security exploitation, as SAST technology coverage remains limited to a subset of less than 50 programming languages. As the access to software security experts able to manually identify flaws in less popular languages is limited, the embedded cyber resilience of the corresponding software applications is questionable.  
As an alternative to SAST technologies, there is an increasing trend in using machine learning-based algorithms to identify security vulnerabilities in source code or within the binary format of software components. Most of them are developed using specially designed datasets, with software modules labeled as containing a \"vulnerable\" or \"not vulnerable\" piece of code, per each supported security weakness listed in public repositories like NVD (National Vulnerability Database) [5] or MITRE CVE (Common Weakness Enumeration) [6]. As labeled source code is available only for a limited subset of programming languages, these solutions complement SAST technologies for the most popular languages, especially C and Java, and are not able to detect security weaknesses in languages not supported with labeled datasets.  
The objective of the current research is to identify the optimal representation of the source code and the zero-shot classification model able to detect security vulnerabilities in source code written in a different programming language than those used for training.