esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation Methods  
The improvement in surface roughness due to diamond burnishing is also investigated using so-called improvement factors. Since the surfaces and their roughness characteristics after turning (i.e., before the diamond burnishing) may differ, it is important to know the initial condition to determine the improvement due to burnishing. The improvement factor was calculated using formula (5). This formula is a general expression applicable to various surface roughness parameters, where \"I\" stands for improvement, \"X\" stands for \"R\" in the case of 2D parameters and \"S\" in the case of 3D parameters, and \"y\" generalizes the different types of roughness measures.  
\[IX_{y}=\frac{X_{yturned}-X_{yburnished}}{X_{yturned}}\cdot 100\ [\%], \tag{5}\]  
For visual representation of the data, so-called Kraljic matrices were applied, the layout of which is shown in Figure 4. These matrices are based on a coordinate system, where the horizontal axis represents power consumption, and the vertical axis represents a specific surface roughness parameter or its improvement factor. All previously measured values are plotted as individual data points based on these two parameters. Once all values--in our case, 16 in total--are placed on the diagram, it is transformed into a matrix by drawing lines vertically and horizontally at the median of each axis. It is easy to see that as a result, the two left, two right, two bottom, and two top cells will each contain eight data points in total, although the number of points in individual cells can vary between zero and eight.  
For surface roughness parameters, the bottom-left cell is considered optimal, as it contains machining processes that result in low power demand and good surface quality. In contrast, the top-right cell represents processes to be avoided, since they result in relatively poor surface quality with high power consumption. The remaining two cells can be interpreted as compromises, where surface quality and energy consumption balance each other in a trade-off. The matrices for the improvement factors of the roughness parameters were constructed based on similar considerations, with the logical difference that in this case, the top-left cell is considered optimal, while the bottom-right is the one to be avoided.
## 5 Results and Discussion
### Impacts of the Uncertainties of Controller Parameters  
In this section, the impacts of the uncertainties of controller parameters are investigated. A three-phase fault has been applied at CPL3 when the integral controller parameter (\(K_{i,AL}\)) of the AC current of the CPL has been deviated from its optimal value by 10% from this value. The system dynamic response of the DGs output powers and DC voltages of the CPLs are depicted in Figure 15a,b. There is no variation in both DGs output power and DC voltages of the CPLs as compared with the results shown in Figure 9. It can be concluded that the optimal design has worked fine, since the system goes to stable mode with fast dynamics and accurate power sharing as expected.  
Figure 14: Microgrid response with losing DG1. (**a**) Measured active powers of DGs. (**b**) DC voltages of CPLs.
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 5 Feature Extraction of BRISQUE Algorithm
### Spatial Feature Extraction  
The existence of distortion will destroy the regularity between the adjacent MSCN coefficients. The characteristics of the normalized coefficients include the generalized Gauss distribution features and the correlation of the adjacent coefficients.  
(1) Generalized Gauss distribution characteristics:  
The model formula can be expressed as Formula (14).  
\[f(x;a,\sigma^{2})=\frac{a}{2\beta\Gamma(1/a)}\exp\biggl{(}-\biggl{(}\frac{|x|} {\beta}\biggr{)}^{a}\biggr{)} \tag{14}\]  
where \(\beta=\sigma\sqrt{\frac{\Gamma(1/a)}{\Gamma(3/a)}}\), \(\Gamma(\cdot)\) is gamma function. The shape parameter \(\alpha\) controls the shape of the generalized Gauss model, and \(\sigma^{2}\) is the variance. This article uses the literature [42] fast matching method to estimate (a, \(\sigma^{2}\)) as the features of generalized Gauss distribution.  
(2) Correlation of adjacent coefficients:  
We obtain correlation images from the horizontal, vertical, diagonal and diagonal four directions [43], and use Asymmetric Generalized Gaussian Distribution (AGGD) to fit the images. We use fast matching method [44] to estimate parameters (\(\eta,\
u,\sigma_{l},\sigma_{\tau}\)) for each direction. Thus, we utilize the 16 estimation parameters of four directions as the correlation feature of the adjacent coefficients.  
However, due to multiscale statistical characteristics of natural images, the author found that it is much more reasonable to extract two generalized distribution characteristics and 16 neighboringcoefficients of correlation characteristics at two scales. Therefore, the total extracted features are (2 + 16) \(\times\) 2 = 36.
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 4 Results and Discussion
### SNR Equalization  
The estimated SC SNRs for four kinds of 64-QAM PS-DMT signals are shown in Figure 7, where the ROP and IR for the DMT symbols are \(-9\) dBm and \(4.9\) bits/QAM, respectively. The SC SNR fluctuation is up to \(13\) dB for the original signal. The imperfect frequency response of the MZM and EA are the main reasons for low SNRs on the low-frequency SCs. Additionally, the degraded SNR on the high-frequency SCs is mainly caused by the bandwidth limitations of the optical/electrical devices and fiber dispersion. By using WHT-based FDSP or PDSP schemes, the SNR values on the precoded SCs are well equalized. Unlike the FDSP scheme, only \(64\) data-carrying SCs, with indices of \(1\)st-\(32\)nd and \(65\)th-\(96\)th, are used for the proposed PDSP scheme. Therefore, we can observe that the SNR value of the precoded SCs with the PDSP scheme is slightly lower than that of the FDSP scheme, but higher SNR performance on the middle data-carrying SCs with indices from \(33\)rd to \(64\)th is achieved with the PDSP scheme. Compared with the FDSP scheme, the Preq scheme can also play the role of equalizing the SC SNR.  
When the ROP is set to \(-9\) dBm, the recovered \(64\)-QAM constellations for four kinds of PS-DMT signals are shown in Figure 8a-d. Compared to the conventional PS-DMT technique, the Preq, WHT-based FDSP and WHT-based PDSP schemes make the constellation points more distinct and convergent.
## 3 Methods
### Vision Transformer (ViT)
#### 3.2.5 LeViT  
The LeViT, introduced by [PERSON] et al. [25], was designed to balance speed and accuracy, making it particularly suitable for real-time applications. This model combines convolutional layers with attention mechanisms to achieve efficient GPU inference, ensuring both high performance and computational efficiency. The key features of the LeViT include the following:  
* Hybrid architecture: The LeViT integrates convolutional layers with Transformer-based attention mechanisms. The convolutional layers capture local spatial features efficiently, while the attention mechanisms enable the model to understand the global context and long-range dependencies.
* Efficient GPU inference: By leveraging the strengths of both convolutions and attention mechanisms, the LeViT optimizes the GPU inference, allowing for faster processing times. This efficiency is crucial for real-time applications where a rapid response is essential.
* Balanced speed and accuracy: The LeViT was engineered to provide a good balance between speed and accuracy. This makes it an ideal choice for applications that require both high performance and quick processing, such as autonomous driving, video surveillance, and augmented reality.
## 5 Results and Discussion
### Reliability Analysis
#### 5.3.1 Time of Corrosion Initiation  
The corrosion initiation time \(\mathrm{t}_{\mathrm{ini}}\) is estimated once the chloride concentration at the cover concrete depth is equal to the threshold value \(\mathrm{C}_{\mathrm{th}}\) (see Equation (3)). In the case of cracked concrete, the acceleration factor \(\alpha\) is considered in the assessment of \(\mathrm{t}_{\mathrm{ini}}\) as follows:  
\[\mathrm{t}_{\mathrm{ini}}=\frac{\mathrm{c}^{2}}{4\alpha\mathrm{D}}\mathrm{[ erf}^{-1}\Big{(}1-\frac{\mathrm{C}_{\mathrm{th}}}{\mathrm{C}_{\mathrm{g}}}\Big{)} \Big{]}^{-2} \tag{8}\]  
Figure 11: Distribution of chloride profiles with Equation (2) and Equation (4) percentile for the static load case.  
Figure 12: Distribution of chloride profiles with Equation (2) and Equation (4) percentile for the cyclic load case.  
Monte Carlo simulations are used to evaluate the corrosion initiation time by considering the parameters \(\mathrm{C_{s}}\), \(\mathrm{D}\), \(\alpha\) and \(\mathrm{C_{th}}\) as random variables. \(\mathrm{C_{s}}\), \(\alpha\) and \(\mathrm{D}\) follow log-normal distributions [32; 49; 50] with the mean and standard deviation given in Table 5; and \(\mathrm{C_{th}}\) follows a uniform distribution with mean 0.4% and a coefficient of variation (COV) = 0.19% [51]. The value of the concrete cover considered is \(\mathrm{c}=40\) mm and corresponds to the value considered for the design of the beams [4]. Probability density values of \(\mathrm{t_{ini}}\), obtained from Monte Carlo simulations for the different loading cases are given in Figure 13. It is noted that the static and cyclic loadings decrease the mean of the corrosion initiation time respectively by 1.1 and 1.31 years compared to unloaded tests. Standard deviations of corrosion initiation time are also decreased as a function of loading. These values are close to the experimental results which indicate that the mean corrosion initiation time for static and cyclic loading decreases by 1 and 1.5 years, respectively, compared to the unloaded case. Table 6 shows the mean and standard deviation values obtained with the parameters characterized for the three loading cases.
# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 5 Experimental Results
### Summary of Results
Data received by the gateway will be forwarded to  
Figure 14: General system architecture. a cloud-based server for storage, ensuring scalability, performance, and sensor range across multiple device platforms. Additional sensors like soil moisture sensors may be integrated to enable predictive modeling and facilitate real-time decision-making based on environmental conditions. For example, soil moisture sensors would complement the system by allowing predictive models when the sensors detect a decrease in the humidity percentage, expediting decision-making regarding the real-time environment. The proposed architecture addresses the technical challenges of urban water distribution and supports sustainable resource management by enabling precise control over water networks. Its flexible, modular design suits diverse urban environments, from small towns like Balerma to larger, more complex cities like Lugo. Integrating IoT, satellite communication, and cloud computing forms the foundation for smarter, more responsive water distribution systems capable of adapting to changing demand and environmental conditions. Our results demonstrate significant improvements in water conservation compared to traditional water distribution models, largely due to the integration of predictive AI and real-time IoT data. Unlike prior studies limited to static optimizations, our approach dynamically adjusts to fluctuating demand, achieving up to a 96.62% reduction in wastage. However, our methodology has limitations. Firstly, scalability remains challenging; the model may require adaptation to handle more extensive urban networks or datasets. Secondly, while EPANET suits our simulations, a real-world implementation may encounter data limitations or connectivity constraints. Lastly, model adaptability is affected by urban infrastructure variances, and additional customization may be necessary in cities with older or highly complex water networks. After analyzing the results, we also determined significant challenges in implementing this technology in urban environments with outdated infrastructure. From our experience and expertise, we observed that these challenges often include limitations in energy capacity, deficiencies in network connectivity, and difficulties in adapting legacy systems to modern technologies. Consequently, we identified that achieving successful and sustainable integration requires implementing solutions that minimize additional infrastructure needs and maximize energy efficiency. These findings lead us to propose strategies and approaches that address these challenges through modular technology use, energy feedback options, and close collaboration between the public and private sectors.
## 5 Conclusions  
The present work addressed the limitations of absolute capacitive grating sensors by developing a novel VATG sensor that employs a double-row capacitive structure based on a previously proposed single-row capacitive sensing structure. One capacitor row is composed of \(n\) periods over the total sensor length and serves as a fine measurement scale analogous to the auxiliary scale of vernier calipers. The other row is composed of \(n-1\) periods over the total sensor length as a coarse measurement scale analogous to the main scale of vernier calipers. A prototype VATG sensor with a total length of 600 mm was fabricated using PCB manufacturing technology with a fine measurement capacitor row composed of 150 periods and course measurement capacitor row composed of 149 periods. Based on an analysis of the measurement performance of the prototype VATG sensor, the sensor design was optimized by increasing the distance between the two rows and adding a shielding layer between them to reduce the influence of signal crosstalk, and a differential induction structure was adopted to reduce common mode disturbance, which significantly improved the measurement performance of the sensor. Experimental results demonstrated that the measurement error of the optimized prototype sensor was \(\pm\)1.25 \(\upmu\)m over the full 600 mm range, \(\pm\)0.25 \(\upmu\)m over a single 4 mm period, and the first-order harmonic error was very small.  
Conceptualization, X.L.; Writing and preparation of original draft, H.Z.; Methodology, K.P.; Validation, Q.T. and Z.C.  
This work was funded by the National Natural Science Foundation of China (Grant No. 51435002), by the National Natural Science Foundation of China (Grant No. 51605062), by the University Innovation Team of Chongqing (Grant No. CXTDX201601029), and by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN201801127).  
The authors would like to thank Engineering Research Center of Mechanical Testing Technology and Equipment (Ministry of Education), for equipment support services.  
The authors have no conflicts of interest to declare.
# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 3 The Pipeline
### Radar Recognition Model  
Following the deinterleaving step, the next task in our pipeline is the pattern recognition process. Our radar pattern recognition model is designed to accommodate varying sizes of input data--in this case, the PDW trains produced by the previous deinterleaving step. The lengths of the PDW trains within a single batch can vary, due to the different time interval of radar emitters [35]. To handle these varying-sized inputs, we propose using the RNN encoder introduced in Section 2.2, which converts them into a fixed-length vector.  
The structure of our RNN encoder is multi-layered. The reason for choosing a multi-layered RNN is to allow the model to learn a richer representation of the input data sequence. Each layer in the RNN encoder is capable of capturing different aspects of the input sequence. Therefore, we do not solely rely on the top layer's hidden state as our context vector, like a general RNN encoder. Instead, we concatenate the hidden states from all the layers, to create a composite representation. This enhances the model's ability to understand the data better, incorporating various features at different levels of complexity into the context vector. Finally, this context vector passes through a fully connected (FC) layer, leading to the final classification of frequency and PRI patterns. Figure 3 shows an example of the radar recognition model structure. The RNN encoder in the example is structured in four layers.
## 4 Experiment and Analysis
### Results and Analysis
#### 4.5.1 Comparative Experiment
Experimental results show that even though SR-GNN is based on single session modeling, its performance is almost on par with algorithm CSRM, which considers multiple sessions, proving that it is meaningful to use GNN to model session sequences. FGNN designs a weight map attention layer for learning the item embedding in the session graph and also uses a GRU-based readout function to abstract the session embedding. FGNN uses a variety of deep learning techniques and achieves pretty suitable performance in Yoocoso1/64 and Diginetica. STAMP is an algorithm that uses an attention mechanism to explicitly represent the last item of the session and the average features of the session and items embedding in the session to capture the preference of the session. The recent session recommendation algorithm based on self-attention has shined in many fields. CoSAN uses the strong encoding ability of self-attention for sequence data to learn session sequences from the perspective of similarity and location information between items, incorporates the collaborative information based on the adjacent session identification, and also gets quite suitable results. The experimental results show that the algorithm of the attention mechanism outperforms some existing deep learning algorithms and is a powerful choice for dealing with the session recommendation problem. In contrast, the SE-GNNRM algorithm proposed in this paper uses both a GNN and a self-attention mechanism to encode session sequences and designs a fusion function to fuse the two session vectors to complement each other. Then, in order to reasonably learn the long-term and short-term preferences of the session, the soft attention mechanism fused with reverse position information is used to obtain the session embedding, which not only takes into account the transformation of item information in the session but also strengthens the influence of the sequential position of the items. Finally, a session interaction layer is designed based on the idea that session preferences with high similarity may also be similar, which introduces the influence of multi-session collaborative information into the algorithm. The preference information of neighbor sessions is further aggregated through the graph attention network on the constructed global session graph, which makes it more fully and efficiently process session sequence information. In summary, the experimental results and analysis demonstrate the rationality and effectiveness of the SE-GNNRM proposed in this paper.
## 2 Materials and Methods
### Oxidation Products Analysis
#### 2.3.2 Hexanal Analyses  
The analyses to determine hexanal content were carried out on homogenised samples (1.0 g) put into 10 mL glass cylindrical auto-SPME vials (Supelco(r)) to which a NaCl saturated solution was added to reach 5 mL [30]. The vials, immediately sealed with an assembled aluminium screw cap with a hole and a polytetrafluoroethylene/silicone septum,were stirred for 1 min at room temperature, conditioned at 40 \({}^{\circ}\)C for 30 min and then placed in an autosampler at 50 \({}^{\circ}\)C for 5 min for absorption of the headspace volatiles using SPME. A mixed SPME fibre 50/30 \(\upmu\)m divinylbenzene-carboxen-polidimethylsiloxane (DVB/CAR/PDMS, Supelco(r)) was used to extract the headspace volatiles. The target analyte on the loaded fibre was thermally desorbed for 5 min at 250 \({}^{\circ}\)C directly in the GC injector port, used in splitless mode. Both the absorption and the desorption phases were controlled by the Varian CP-8200 autosampler. The GC analyses were carried out on a Varian 3380 GLC equipped with a fused silica capillary column Equity-5 Supelco (30 m \(\times\) 0.25 mm i.d. and film thickness 0.25 \(\upmu\)m) and a flame ionisation detector (FID). The chromatographic conditions were: H\({}_{2}\) as a carrier gas (20 mL/min); temperature program: 35 \({}^{\circ}\)C was held for 5 min, 8 \({}^{\circ}\)C min\({}^{-1}\) to 75 \({}^{\circ}\)C, 40 \({}^{\circ}\)C min\({}^{-1}\) to 200 \({}^{\circ}\)C and final isotherm at 200 \({}^{\circ}\)C for 5 min; the FID was held at 250 \({}^{\circ}\)C.  
The data were processed using a Varian Star Chromatography Workstation. The identification of hexanal levels was carried out using internal standards. An external calibration procedure was used to quantify the extracted amount of target analyte from the SPME fibres, with a calibration range from 50 to 1000 ng. Linear concentration-response relationships were always obtained with high confidence (\(r^{2}>\) 0.95).
## 3 Results and Discussion
### Wear and Tool Life Analysis
Foreign nitrogen atoms in the WC-Co material create point defects that inhibit the movement of linear defects called dislocations. Therefore, an atmosphere of dopant atoms (in this case nitrogen), referred to as the Cottrell atmosphere, will be formed along the dislocation line [26]. The Cottrell atmosphere inhibits the dislocation movement, which results in strengthening carbide grains. Generally, it can be said that any disturbance of the crystal lattice increases the strength of the material and decreases its plasticity because both properties depend on the free movement of defects in the material [1]. Tungsten nitrides WN were detected at higher fluences of implanted nitrogen ions 5 \(\times\) 10\({}^{17}\) cm\({}^{-2}\) at interstitial phases. It is well known that the hardness of nitrides is lower than the hardness of carbides of the same metals (including tungsten), but at the same time the plasticity of nitrides is higher [27]. Such a change in properties can be beneficial for cutting tools' exploitation. Under impact conditions (during machining), tools that are less hard but preserve brittleness have a longer lifetime [1]. In EDS analysis of the tool's surface after machining, another mechanism was indicated that could contribute to reducing the tool's wear and increasing the tool's life. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Circular Saw Blade** & **Total Cutting Length** & **Tool Wear \(\widetilde{\mathbf{Z}}_{i}\)** \\  & **(m)** & **(mm)** \\ \hline Non-treated 1 & 131.62 & 0.67 \\ Non-treated 2 & 104.19 & 0.45 \\ Non-treated 3 & 161.32 & 0.48 \\ \hline average & 132.38 & 0.53 \\ \hline Ion-implanted 1 & 131.62 & 0.56 \\ Ion-implanted 2 & 104.19 & 0.45 \\ Ion-implanted 3 & 161.32 & 0.47 \\ \hline average & 132.38 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total cutting length and wear of circular saw blades.