esa-sceva/satcom-chunk-collection huggingface dataset:
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Key Encapsulation Mechanism
#### 3.2.2 Secret Keys  
Two secret keys, \(\mathtt{sk}_{2}\) and \(\mathtt{sk}_{3}\), are used to compute \(2^{\mathtt{e_{2}}}\)-isogenies and \(3^{\mathtt{e_{3}}}\)-isogenies, respectively. On the one hand, the secret key \(\mathtt{sk}_{2}\) is an integer in the set \(\{0,1,\ldots,2^{\mathtt{e_{2}}}-1\}\) and is stored as a byte array of length \(\mathtt{N}_{\mathtt{sk}_{2}}=\left\lceil\frac{\mathtt{e_{2}}}{8}\right\rceil\). The corresponding keyspace is denoted \(\mathcal{K}_{2}\). On the other hand, the secret key \(\mathtt{sk}_{3}\) is an integer in the set \(\left\{0,1,\ldots,2^{\lfloor\log_{2}3^{\mathtt{e_{3}}}\rfloor}-1\right\}\) and is stored as a byte array of length \(\mathtt{N}_{\mathtt{sk}_{3}}=\left\lceil\frac{\lfloor\log_{2}3^{\mathtt{e_{3}} }\rfloor}{8}\right\rceil\). The corresponding keyspace is denoted \(\mathcal{K}_{3}\).
# Novel Q-Carbon Anodes for Sodium-Ion Batteries
###### Abstract  
The lack of a standard anode for sodium-ion batteries (SIBs) has greatly hindered their applications. Herein, we show that a novel phase of carbon, namely Q-carbon, is an effective anode material for sodium-ion batteries. The Q-carbon, which is a metastable phase of carbon consisting of about 80% sp\({}^{3}\)- and 20% sp\({}^{2}\)-bonded carbon, is synthesized by nonequilibrium pulsed laser annealing and arc-discharge methods. Two types of Q-carbons, Q1 and Q2, were evaluated as anode material for SIBs. Q1 had a slow quench and was used as the control, whereas Q2 was Q-carbon with a rapid quenching. Q1 exhibits a high initial columbile efficiency of 81% and a low-capacity retention of less than 60%, whereas Q2 has a low initial columbile efficiency of 58% and a high-capacity retention of 81%. Q2 exhibits a stable capacity of 168 mAh\(\cdot\)g\({}^{-1}\) at a cycling rate of C/3 (124 mA\(\cdot\)g\({}^{-1}\)), which is comparable to other hard carbon anodes reported in the literature. This unique synthesis method opens a pathway for the further tuning of Q-carbon with higher trapping/charging of Na\({}^{+}\) ions in improved SIBs.  
Q-carbon anodes; sodium-ion batteries; high initial columbile efficiency; high-capacity retention; high cycling rate +
Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Thin Film Encapsulation for RF MEMS in 5G and Modern Telecommunication Systems
## 7 Conclusions  
A fabrication process of thin film encapsulating caps on CPWs was developed. The tensile stress of deposited SiN\({}_{\text{x}}\)/a-Si/SiN\({}_{\text{x}}\) trilayers gave a concave shape to the caps and made them robust and flexible without breakage under the pressure of a profiler stylus tip, even with a percentage of the perforated cap area as high as 74%. Two etching processes were performed in the same barrel chamber both to create a matrix of holes with a diameter of 7 \(\upmu\)m and 8 \(\upmu\)m through the caps, and to remove the sacrificial layer under them. The removal of the sacrificial layer was investigated as a function of the percentage of the cap perforated area for etching times and powers in the ranges of 8-80 min and 100-600 W, respectively. The widening of etch holes, due to the isotropic nature of the barrel etching, will be taken into account in the design of the next caps, in order to achieve holes with a size favorable for cap sealing. Future work will be focused on the sealing process; the mechanical robustness of the fabricated encapsulating thin films under the external pressure load as well as their reliability in dusty or high humidity environments will be evaluated, as required in some real applications. For a gold CPW on a high resistivity silicon substrate, the fabricated caps were predicted to reduce the reflection loss by a factor up to 1.8 and not degrade the insertion loss.  
The oxygen plasma-based release of caps and the reasonably low temperatures (\(<\)200 \({}^{\circ}\)C) of all involved processes make the fabricated thin film encapsulation caps compatible with a wide range of microsystem technologies, from surface to high aspect ratio microstructures, also integrated with CMOS devices. In particular, the low impact of the fabricated caps on RF performance of gold CPWs is a good premise for transferring this packaging solution to RF MEMS switches, switching units, and other complex devices, such as reconfigurable phase shifters, tunable filters, and digital step attenuators, for 5G and modern telecommunication systems applications.  
Conceptualization, A.P. and F.Q.; methodology, A.P., F.Q. and J.I.; software, J.I.; validation, A.P., F.Q. and J.I.; formal analysis, A.P., F.Q. and J.I.; investigation, A.P., F.Q., A.T. and J.I.; resources, F.Q. and P.A.S.; data curation, A.P., A.T. and J.I.; writing--original draft preparation, A.P. and J.I.; writing--review and editing, A.P., F.Q., A.T. and J.I.; visualization, A.P., A.T. and J.I.; supervision, F.Q. and P.A.S.; project administration, A.P. and P.A.S.; funding acquisition, A.P., F.Q. and P.A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by Fondazione Bruno Kessler (FBK).  
The authors thank [PERSON] and [PERSON] for the technical support in the fabrication process of thin film encapsulating caps.  
The authors declare no conflict of interest.
# An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition
## 3 Problems and Improvement of KIII Model
### Feature Extraction
#### 3.2.3 Feature Fusion Based on Discrete Cosine Transform  
Feature extraction plays a crucial role in image processing and pattern recognition [19; 20], enhancing model efficiency and performance by reducing data dimensions and preserving key information. In Table 3, we conducted a comparative analysis of various feature extraction methods. Due to the energy concentration property of the Discrete Cosine Transform (DCT), the representation of features becomes more compact, enabling better capture of essential characteristics in signals or images.  
The Discrete Cosine Transform (DCT) decomposes an image into a weighted sum of a series of cosine functions, representing the image in the frequency domain. By separating high-frequency and low-frequency information, it provides a more compact representation of the image. Low-frequency coefficients reflect the overall structure, while high-frequency coefficients represent detailed information. Selecting appropriate frequency domain coefficients allows for the extraction of key features. DCT is a global feature extraction method, and its performance in extracting local features is relatively limited. To address this, we perform feature extraction in three steps:  
1. Directly compute the DCT coefficients for the entire image as a representation of the whole image;  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline Median Filtering & Effective for salt-and-pepper impulse noise, capable of removing extreme outliers; does not introduce additional sharpening effects. & It is not suitable for continuous noise; like Gaussian noise; excessive denoising may lead to image blurring. \\ \hline Bilateral Filtering & Preserves image edge information; has parameters to control the degree of smoothing, allowing for a balance between smoothing and denoising. & The computation is relatively slow; parameters need to be adjusted for optimal results. \\ \hline Gaussian Filtering & Fast; effectively reduces Gaussian noise and smooths the image while preserving edge features of the image. & It may reduce the sharpness of the image and is not suitable for removing non-Gaussian noise. \\ \hline Wiener Filtering & Capable of reducing noise while preserving image details; can adaptively filter based on the characteristics of noise. & It is sensitive to parameters, requiring accurate estimation of the statistical properties of the image and noise; the computation is complex. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of denoising modes.  
Divide the image into a grid and apply the Discrete Cosine Transform to each grid. Finally, aggregate the DCT coefficients from each grid to represent the entire image;
3. Concatenate the features obtained from steps 1 and 2, combining local features with global features to represent the entire image with richer features.  
The overall process is illustrated in Figure 3.
# Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell
## 3 Materials and Methods
### Measurement Uncertainty  
The extended measurement uncertainty of the measurement system according to the ISO standard 15530-3 is generally written using the (5), where \(k\) represent the factor of extended uncertainty, \(u_{cal}\) contribution to the uncertainty due to uncertainties of the standard, \(u_{p}\) contribution to the uncertainty due to the measurement procedure, and \(u_{w}\) contribution to the uncertainty due to the measured object itself.  
\[U=k\sqrt{u_{cal}^{2}+u_{p}^{2}+u_{w}^{2}}. \tag{5}\]  
Due to the comparative method used, the term for calculating the measurement uncertainty is simplified in  
\[U=ku(x)+|\bar{x}-x_{cal}|, \tag{6}\]  
where  
\[u(x)=\frac{\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}{(x_{i}-\bar{x})^{2}}}}{\sqrt{n}}, \tag{7}\]  
and \(\bar{x}\) is the average value of measured values, \(x_{i}\) i-th is measurement, and \(x_{cal}\) measured value of master part.  
The Equator has specified measurement uncertainty of \(\pm 2\) um, provided that the measured object is inserted within \(\pm 1\) mm relative to the inserted master object in the re-master procedure. In combination with the UR5e robot, which has declared positional repeatability of \(\pm 0.03\) mm, it would mean that the robot error, due to the repeatability error, does not affect the measurement uncertainty of dimensional measurements so that the measurement system uncertainty would be equal to the measurement uncertainty of the Equator.  
However, we must be aware that this is not the case in our case. The robot's contribution to the measurement uncertainty is not only due to the error due to positional repeatability but also due to the orientational repeatability and the uncertainty in gripping with the gripper. Last but not least, we must also consider the compliance of the robotic mechanism. Therefore, the general measurement uncertainty based on the specified uncertainties or repeatabilities of the Equator gauge and the UR5e robot is not valid in our case or is not credible data.
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 4 Experiments
### Ablation Study  
Table 4 shows the actual impact of the two modules that we proposed on the detection results. The AP is evaluated at three levels in the cyclist category.  
Methods (a) and (b) are the baselines for the outcome evaluation. In method (a), only the feature extraction branch based on the pseudo image is retained, and the whole network degenerates into a single-stage object-detection network. Method (b) only retains the voxel-based feature-extraction branch.  
In method (c), we introduce our multi-BEV feature fusion module to adaptively fuse BEV feature maps from two branches. Compared with method (b), method (c) has significantly improved performance in pedestrian detection tasks, with \(AP_{3D}\) improving 2.23%, 2.03% and 3.03% respectively, verifying the practical role of our multi-BEV feature  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Car—\(AP_{3D}\)(\%)**} & \multicolumn{3}{c}{**Cyclist—\(AP_{3D}\)(\%)**} & \multicolumn{3}{c}{**Pedestrian—\(AP_{3D}\)(\%)**} \\  & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline SECOND [12] & 90.55 & 81.61 & 78.61 & 82.96 & 66.74 & 62.78 & 55.94 & 51.14 & 46.17 \\ PV-RCNN [11] & 91.89 & 84.52 & **82.43** & 91.57 & 72.64 & 69.23 & **68.35** & **60.52** & **55.11** \\ Voxel R-CNN [10] & **93.09** & **85.04** & 82.31 & 92.10 & 73.28 & 70.13 & 63.27 & 56.94 & 51.56 \\ \hline ours & 91.65 & 84.31 & 82.20 & **93.58** & **74.57** & **71.10** & 65.86 & 59.39 & 54.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison on the KITTI val set with AP calculated by 40 recall positions for car class.  
fusion module. However, method (c) performs poorly in the detection tasks of the car and the cyclist.  
Method (d) is based on method (c), and we have added the multi-view feature fusion module. Compared to method (c), on all detection tasks, \(AP_{3D}\) have a certain improvement effect. Especially for the cyclist detection task, \(AP_{3D}\) has improved 1.11%, 1.28% and 2.39%, respectively. The results show that our multi-view feature fusion module can effectively fuse RV and SV features with BEV features, supplement some missing feature information in BEV feature maps, and obtain better 3D target-detection results. However, for the overall network, as we discussed in Section 4.3, although our method has learned more features related to pedestrian and cyclist, it has somewhat reduced the detection performance of car categories.
## 4 Numerical Experiments
### Ablation Study  
The effectiveness of hard fix and branching priority settings is validated, as shown in Figure 6. \"M4\" represents the complete fixing strategy proposed in Section 3, while \"M5\" denotes the comprehensive online strategy introduced in the same section, which includes both the fixing strategy and a customized branching rule. By comparing M3 and M4, it can be concluded that our proposed learning-guided fixing strategy leads to a faster initial decline in the objective function compared to using only a soft fix, especially on the 100-unit and 1080-unit systems. A comparison between M4 and M5 further demonstrates the effectiveness of the branching priority strategy on the 1080-unit system, where M5 consistently achieves an earlier reduction in the gap. However, for smaller-scale systems, adjusting the branching strategy did not have a significant impact.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.6 Evaluation Techniques for RRH Clustering Methods
All the algorithms were executed 50 times with 50 configurations for both the problem scenarios. The average of the results obtained was considered for Monte Carlo analysis. Moreover, the CDI-based CRAN concept was also compared with a fixed-CRAN scenario through simulation. In [55], a real-world mobile network traffic dataset was collected for evaluating the performance of the proposed framework. Furthermore, a set of 61 daily traffic snapshots was generated based on that data. The snapshots of the first 70% were used for generating the training data. The remaining30% of snapshots were used for the test data. Then, the MuLSTM model with two stacked LSTM layers was constructed and trained using TensorFlow, thereby ensuring that the network learns the potential temporal and spatial structure. Then, the proposed method was compared with the traditional approach as well as the ARIMA-DCCA, WANN-DCCA, and MuLSTM-DC methods, in terms of the traffic forecast error, average capacity utility, and overall deployment cost. In [62], for simulation, the network scenario was developed considering 19 RRHs grouped into six sectors with two BBUs. Then, the algorithm was executed to determine the optimal BBU-RRH mapping by predicting the number of users at that time. In addition, the performance of the GA is compared with that of ES in terms of the QoS with varying numbers of iterations. Here, the number of blocked connections in the network is minimized. The authors in [56] considered a network with densely deployed RRUs at distances of 100 m, and for BBU, they considered CPU, memory, and disk as the computational resources for simulation. The performance of the iterative resource allocating (IRA) algorithm in solving the multi-resource allocation problem was evaluated by comparing it with main resource packing, no UE aggregation, two-stage optimization, and exhaustive search, in terms of the number of active BBUs, with different numbers of RRHs.
## 6 Empirical Study
### Medicare Domain  
The predicate domain serves as a challenging planning competitions problem for testing and developing planning algorithms that can handle real-world complexities, especially those involving partial observability. The predicate domain takes into consideration a patient who may have a possible infection or may be healthy, but we do not know which infection the patient has. The planning goal is to diagnose the infection accurately and administer the correct medication to cure the patient. If the wrong medication is given, it can be fatal. But it does not explicitly consider planning quality. The primary focus remains on ensuring the patient's safety and effective treatment rather than on optimizing secondary preferences like plan cost. Thus, the actions in this domain are not evaluated by cost. The treatment for the patient involves diagnosing the infection and taking proper medication to cure it. Inaccurate remedy, however, will kill the patient. In the planning domain, there is one sensing operator that diagnoses the disease and one actuation operator that mediates the patient using the corresponding remedy. One of the challenges is that the patient's condition is not fully observable, requiring the planner to handle uncertainty. Another challenge is the explosion of complexity. The plan generated by the planner diagnoses each disease and cures it with the right medication action conditionally. The complexity of the planning problem and the size of the search space increase significantly with the number of diseases. As the number of possible diseases increases, so does the uncertainty and the need for more actions and considerations, leading to a dramatic increase in plan length.  
Figure 1 shows the runtime of the two planners. Every experiment is run ten times, and the average CPU time is recorded. Figure 1 indicates that the HQCP is faster than the contrastive planner. With the difficulty of the planning problem increasing and the scale of the search space expanding, the efficiency of the HQCP does not decline dramatically, while the contrastive planner costs much time. The experiment results also show that the HQCP is capable of coping with real problems with high complexity. In our opinion, the better performance demonstrated by Figure 1 comes from the more succinct algorithm structure and the more efficient heuristic rules.  
Figure 1: Average runtime for the predicate domain with n diseases.
## 3 Results and Discussion
### Long-Term AOD Analyses  
The first approach involved a global study, i.e., AOD for each station over the years. The first group of data is presented in Figure 3 below.  
The data presented from 2000 through 2011 show a very slight increase during this period. This is evident for all three stations (Gotland, Gustav Dalen Tower, and Helsinki stations). There is a clear increase trend around the years 2004 and 2007 for the Swedish stations and in the years 2008-2010 for the Finland station.  
Subsequently, the Helgoland station AODs were investigated (Figure 4). These analyses provide insight into the southwest region of the Baltic. As shown in Figure 4, it is evident that between 2000 and 2014, there is a clear decrease in the AOD_500 values in Helgoland.  
Two stations, Gustav Dalen Tower and Helsinki Lighthouse, were also analyzed at wavelengths \(\lambda=510\) and \(551\) nm for the period between 2012 and 2022 (Figure 5). An AOD decline is noticeable for both stations; however, it is noteworthy that for both stations, there are values in 2013 and 2014 that do not follow this trend, as they are both slightly higher.  
Figure 4: AODs at 500 nm (level 2.0) from the Helgoland station between 2000 and 2014.  
Figure 3: AODs at 500 and 510 nm (level 2.0) reported for three stations until 2022.  
The next step was to analyze the AOD data from the stations with more recent data, until 2022. These analyses refer to the stations in Peterhof, Toravere, and Helsinki (Figure 6).  
It is clear that the behavior of the Toravere station (Estonia) and Peterhof station (Russia) is very similar and consistent with what was previously observed. That is a clear decline with values that closely follow this downward trend. As for the Helsinki station, there is also a decline, but it is slightly less pronounced, with values that deviate a bit more from the trend curve. Table 1 presents the AOD changes and their character for particular stations.  
Figure 5: AODs at 510/551 nm (level 2.0) from the Gustav Dalen and Helsinki stations between 2012 and 2022.  
Figure 6: AODs at 500 nm (level 2.0) reported for three stations until 2022.  
What is visible for the three northerly stations (especially for Toravere and Peterhof) is the year 2019 stands out, as it deviates from the observed decline. This suggests that there might have been an event or factor influencing this deviation during that year.