esa-sceva/satcom-chunk-collection huggingface dataset:
A CMOS-Compatible Carrier-Injection Plasmonic Micro-Ring Modulator (CIPMRM) with Stable Performance as Temperature Varying around 60 K  
[PERSON]  
1 Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; [EMAIL_ADDRESS] (J.S.); wangwen  
Plasmonics breaks the diffraction limit because the electromagnetic wave propagates along the interface between metals and dielectrics [11]. It offers a possibility for compressing the mode and improving the performance of the device. Various physical phenomena have been utilized in plasmonic modulators, such as the free-carrier dispersion effect of transparent oxide films and the Pockels effect in nonlinear polymers [12; 13; 14; 15; 16]. However, most of them require unique active materials that are not fully compatible with the standard CMOS process.  
This article proposes and theoretically investigates a carrier-injection plasmonic microring modulator (CIPMRM). It also proposes a method of tuning resonant wavelength based on the carrier concentration. CIPMRM provides a way to solve the problems of the micro-ring modulator, which is sensitive to variations in the temperature and linewidth. Moreover, CIPMRM meets the requirements of the zero-change CMOS fabrication process. It has a compacted footprint of 43.4 \(\upmu\)m\({}^{2}\), a data rate of 45 Gbps, an insertion loss of \(-8\) dB, a static extinction ratio of 22 dB, and an energy consumption of 4.5 pJ/bit when 2.5 V peak-to-peak voltage applied.  
The remainder of this paper is organized as follows. Section 2 presents the structure and physical mechanism of CIPMRM. Section 3 discusses, in the framework of the optical simulation method, the passive structures, including plasmonic waveguide, silicon waveguide couples, and plasmonic micro-ring resonators. Section 4 analyzes the effect of temperature fluctuation on CIPMRMs. Section 5 introduces the method of tuning resonant wavelength based on the carrier concentration and analyzes the performances of CIPMRM based on electrical stimulation. Finally, Section 6 summarizes the key findings.
## 2 Materials and Methods
### Calculation of Radiomics Image Features  
We calculated 486 image features, including 54 original image features and 432 wavelet-based features, within the ROI in the MRA images for the texture patterns. The original MRA images were requantized to 8 bits based on a look-up table between 0 and 255 and between minimum and maximum values within the aneurysm regions. As shown in Table S1, the 54 original radiomics features consisted of 14 histograms and 40 texture features. The 40 texture features were calculated from the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), and neighborhood gray-tone difference matrix (NGTDM). We derived 432 wavelet-based radiomics features from the same 54 features as the original features in each of the eight wavelet decomposition images. Wavelet decompositions were performed by applying either a low-pass filter (scaling function, L) or a high-pass filter (wavelet function, H) in the x, y, or z directions. The eight wavelet decomposition filters consisted of a combination of three filters using either a low-pass filter or high-pass filter in each direction.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments  
### Training Configuration  
In the crack segmentation task, since pixels can only be assigned a probability of being or not being part of a crack, it can be viewed as a binary classification problem. The binary  
Figure 5: An illustration of the \(3\times 3\) deformable convolution.  
cross-entropy loss (BCELoss) is commonly used for binary segmentation tasks. However, this loss function is not suitable for crack segmentation, because in the crack images, there are far more non-crack pixels than crack pixels, which may cause the network to prefer to segment the background pixels. We use the sum of binary cross-entropy and Dice loss as the loss function, called BCEDiceLoss. The BCELoss can be calculated as:  
\[L_{BCE}=-\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}t_{i}log^{p_{i}}+(1-t_{i})log^{(1-p_ {i})}\Bigr{)} \tag{3}\]  
where \(N\) is the number of pixels in the image, \(t_{i}\) is the truth value of the pixel, and \(p_{i}\) is the predicted value of the pixel. The Dice Loss can be expressed as:  
\[L_{Dice}=1-\frac{2\sum_{i=1}^{N}t_{i}p_{i}+\varepsilon}{\sum_{i=1}^{N}t_{i}+ \sum_{i=1}^{N}p_{i}+\varepsilon} \tag{4}\]  
where \(\varepsilon\) is the smoothing factor for preventing the denominator from being 0. Therefore, the BCEDiceLoss we use can be calculated as:  
\[L_{BCEDice}=1-\frac{2\sum_{i=1}^{N}t_{i}p_{i}+\varepsilon}{\sum_{i=1}^{N}t_{i} +\sum_{i=1}^{N}p_{i}+\varepsilon}-\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}t_{i}log^{ p_{i}}+(1-t_{i})log^{(1-p_{i})}\Bigr{)} \tag{5}\]  
where we set the smoothing factor \(\varepsilon\) to \(1\times 10^{-5}\).  
We use the stochastic gradient descent with momentum as the optimizer to minimize the loss function, and the momentum is set as 0.9. The initial learning rate is \(1\times 10^{-3}\), and the poly learning rate strategy is used to adjust the learning rate while training.  
Our experiments are conducted on a system with an NVIDIA RTX A4000 GPU (Manufactured by Nvidia in Santa Clara, CA, USA) and an Intel Xeon Gold 5320 CPU (Manufactured by Intel in Santa Clara, CA, USA). The software environment of the system is Python 3.6 and Pytorch 1.7.
# A Novel Slicing Strategy to Print Overhangs without Support Material
## 2 A Novel Slicing Strategy with Geometrical Pre- and Post-Transformation
### Geometrical Transformation for Cone Shaped Layers  
As described in Section 1.3, a conventional slicer divides the model in layers with a fixed z-value. On the other side, the cone shaped layers consist of concentric circles each circle having a different (constant) z-value and a different (constant) radius. The conic  
Figure 3: The surface of the geometry is defined with triangles (**a**) in the STL file. The slicer then converts this surface information into a solid geometry and splits the geometry into slices (layers) with an equal layer height and generates the path for the printer according to the settings. The layer consists at least of the outline of the part (so called perimeter) and the infill. Figure (**b**) shows the outer (blue) and the inner (light blue) perimeter and the 10% infill (orange) of the layers. The lowest and the most upper layers are printed solid to close the part (green surface in Figure (**c**)).  
Figure 2: Depending on the direction of the overhangs, a different cone has to be chosen: (**a**) An outward cone for parts with overhangs towards the outside and (**b**) an inward cone for overhangs towards the inside.  
layers of circles are transformed in a plane with a constant z-value to slice the part with a conventional slicer. This is done with the transformation \(T\) shown in Equation (1)  
\[T\colon\mathbb{R}^{3} \to\mathbb{R}^{3}\] \[\begin{pmatrix}x\\ y\\ z\end{pmatrix} \mapsto T(x,y,z)=\begin{pmatrix}\sqrt{2}\cdot x\\ \sqrt{2}\cdot y\\ z+\sqrt{x^{2}+y^{2}}\end{pmatrix}=\sqrt{2}\cdot\begin{pmatrix}x\\ y\\ 0\end{pmatrix}+\begin{pmatrix}0\\ 0\\ z+\sqrt{x^{2}+y^{2}}\end{pmatrix} \tag{1}\]  
The transformation \(T\) is a radial dilation by the factor \(\sqrt{2}\) in the x-y-plane and a radius-dependent translation in the z-direction. Figure 4 shows an example of an original cube and its transformation.  
After slicing the geometry, the reverse transformation \(T^{-1}\) has a very similar form as \(T\) itself and is shown in Equation (2)  
\[T^{-1}\colon\mathbb{R}^{3} \to\mathbb{R}^{3}\] \[\begin{pmatrix}x\\ y\\ z\end{pmatrix} \mapsto T^{-1}(x,y,z) =\begin{pmatrix}x/\sqrt{2}\\ y/\sqrt{2}\\ z-\sqrt{x^{2}/2+y^{2}/2}\end{pmatrix} \tag{2}\] \[=\frac{1}{\sqrt{2}}\cdot\begin{pmatrix}x\\ y\\ 0\end{pmatrix}+\begin{pmatrix}0\\ 0\\ z-\sqrt{x^{2}/2+y^{2}/2}\end{pmatrix}\]  
\(T^{-1}\) is a radial dilatation by \(1/\sqrt{2}\) in the x-y-plane and again a radius-dependent translation in the z-direction.  
Figure 5 illustrates the workflow for the presented slicing strategy. First the STL file is convertedwith the shown transformation \(T\), which is described in more detail in Section 2.2. Then this STL file is sliced with a conventional slicer with optimized settings as described in Section 2.3. After that, the G-code is back-transformed using \(T^{-1}\), which is presented in Section 2.4.  
Figure 4: Visualization of the Transformation \(T\) with a cube. (**a**) shows the original cube, whereas (**b**) shows the transformed geometry in the same scale.
## 2 Test Materials
### Test and Analysis Methods
#### 2.2.2 Analysis Method  
The picosecond laser produces a Gaussian beam with a radius of 3 mm through a laser beam extension mirror and a Gaussian beam through a Bessel lens. In this paper, the basic Angle \(\alpha\) inside the Bezier lens is selected as 5\({}^{\circ}\), the axial cone refractive index n = 1.45, and the main Bezier beam cone Angle \(\theta\) are calculated as follows according to equation (1):  
\[\theta=arcsin(n\times sin\alpha)-\alpha \tag{1}\]  
The primary Bessel beam produces a secondary Bessel beam through a telescope system using a scaling factor \(M\) = 8 (4f system). The conical Angle \(\varphi\), diameter \(D\), and length \(l\) of a secondary Bessel beam are calculated by the following Equations (2)-(4):  
\[\varphi=arctan(tan\theta\times M) \tag{2}\]  
\[D=4.8096/ksin\theta \tag{3}\]  
\[l=w_{0}/Mtan\theta \tag{4}\]  
where \(\omega_{0}\) = 3 mm is the waist radius of the incident Gaussian beam and \(k\) is the dielectric wave number. The intensity distribution of the second-order Bessel beam can be calculated by Equation (5), where is the zero order Bessel function and is the incident laser energy.  
\[I=I_{0}\frac{z}{l}I_{0}^{2}(4.8096r/D)exp\left(-2z^{2}/l^{2}\right) \tag{5}\]  
The zero-order Bessel beam, also referred to as a non-diffracting beam, is characterized by its perpendicular orientation to the transmission direction of the transverse light intensity distribution. This configuration features a central bright spot (main lobe) surrounded  
Figure 2: Sample separation scheme and sample cross-section scheme. (**a**) Sample separation force application scheme, (**b**) sample cross-section diagram, the white box in the figure is the AFM test area.  
by multiple concentric rings (sidelobes). Each sidelobe carries approximately equal energy, resulting in a highly concentrated light intensity at the center of the main lobe. Notably, the transverse light intensity distribution remains invariant over non-diffracting propagation distances. In this study, Zmax (ANSYS Zemax OpticStudio 2023 R1) and MATLAB (MATLAB R2024a) were employed to simulate both the spot and intensity distribution of the Bessel beam. The central spot and surrounding interference rings collectively form what is known as a Bessel spot. Figure 3a illustrates the simulated Bessel spot generated using Zmax, while Figure 3b presents the results of intensity distribution obtained through MATLAB simulation. It can be observed that the intensity of the central main lobe is maximized, whereas the intensities of sidelobes diminish with an increase in beam diameter.
## 3 Results and Discussion  
The Gavina(r) WDF samples were obtained by freeze-drying. Unlike oven drying, this technique allows the nutritional value and the volatile fraction of the matrix to be maintained more unaltered. Therefore, we believe that the WDF_t\({}_{0}\) aroma and bioactive molecules are extremely similar to those of fresh Gavina(r) watermelon. The oven-drying process leads to the loss of volatile VOCs even at low temperatures. Moreover, thermal stress accelerates the degradation of unstable bioactive molecules, thereby reducing the functional properties of the matrix. The low water content (3.5%) after freeze-dryingprevents the proliferation of molds and bacteria. In fact, even after two years of storage, Gavina(r) WDF was free of mold and unpleasant odors, as confirmed using the HS-SPME-GC-MS analysis.  
Figure 1a-c shows the chromatograms obtained using HS-SPME-GC MS from a sample of Gavina(r) watermelon WDF: freshly prepared (t0) (Figure 1a), after 30 days (t1) (Figure 1b), and after 24 months (t2) (Figure 1c).  
The chromatogram profile (a) relating to the WDF_t0 sample was divided into two sections with scale expansion to better visualize the less intense peaks. Comparing the TIC scales of the three chromatograms in Figure 1, an overall decrease in analytical signals was observed with increasing sample storage time.  
As shown in Figure 1a (WDF_t0), 102 compounds were detected, including 34 aldehydes (ALD), 15 alcohols (ALC), 1 ester (EST), 8 ketones (KET), 2 acids (ACD), 7 hydrocarbons (AHA), 25 terpen and derivatives (TER), and 10 other compounds (OTH). After 30 days of aging (Figure 1b, WDF_t1), 88 compounds were identified, including 27 aldehydes, 14 alcohols, 2 esters, 10 ketones, 7 acids, 3 hydrocarbons, 16 terpenenes and derivatives, and 9 other compounds. Finally, in the samples stored for 24 months (Figure 1c, WDF_t2), only 45 analytes were identified: 9 aldehydes, 7 alcohols, 2 esters, 2 ketones, 7 acids, 1 hydrocarbon, 10 terpenenes and derivatives, and 6 other compounds. Figure 2 shows the trend of the eight molecular classes for each sample and provides a better representation of the data.  
The identified analytes are listed in the tables below and divided into their chemical classes.  
Figure 1: _Cont._  
Figure 1: Chromatograms obtained using HS-SPME-GC-MS from samples of Gavina® watermelon-WDF; (**a**) freshly prepared (t\({}_{0}\)); (**b**) after 30 days of storage (t\({}_{1}\)); and (**c**) after 24 months of storage (t\({}_{2}\)).
# 5G Mobile Phone Network Introduction in Colombia
## 4 MIMO Systems  
Modern wireless communication systems must evolve to support a growing number of subscribers, all of whom are requesting services at the same time. This trend encourages the widespread application of multiple input/multiple output (MIMO) systems. In fact, MIMO techniques can increase data rates, service area coverage, and communication reliability without the need for additional radio frequencies [17]. In other words, MIMO technology can transmit more data with the help of multiple antennas on both the transmitter and receiver sides. MIMO makes use of the multipath phenomenon, in which data are received at the receiver several times with a certain time delay. MIMO performance is measured using envelope cross-correlation (ECC) between the different antennas. The goal of MIMO is to maximize channel capacity by reducing ECC [23]. Additionally, through the use of MIMO technology, multiple independent channels can be achieved in the original spectrum by the diversity method, and multipath fading can be reduced to improve the data rate [27]. In summary, MIMO systems with multiple antenna units on both the transmitter and receiver sides can take advantage of multipath components sufficiently to improve the performance of wireless systems.  
It is a difficult task to achieve a desirable isolation of each antenna element of the MIMO system within a compact size at future 5G terminals, with mutual coupling of the adjacent antenna, especially for dual-band arrays, being one of the urgent difficulties to overcome [28,29].
## 3 Results and Discussion
### Single-Factor Experiments
#### 3.1.3 Effect of Extraction Time  
The effect of extraction time on the yields of the polysaccharides and essential oil was investigated in the range of 5-25 min. Based on the results shown in Figure 3B, it is clear that the yields of the polysaccharides and essential oil increased significantly as the extraction time was increased from 5 to 15 min. However, further increasing the extraction time from 15 to 25 min led to a slight decrease in the yields of polysaccharides and essential oil. This decrease in the yields following an extended  
Figure 3: Effects of the different factors on the yields of the polysaccharides and essential oil from _T. chinensis_ var. _mairei_. (**A**) effect of particle size of sample; (**B**) effect of extraction time; (**C**) effect of the microwave power; (**D**) effect of liquid/solid ratio. The error bars represent the standard deviation (\(n=3\)).  
extraction time could be attributed to the decomposition of polysaccharides and essential oil under the high temperatures required of the extraction process, resulting in a decrease in their yield. Based on these results, an extraction time of 15 min was identified as the optimum extraction and used for the subsequent experiments.
## 3 Results and Discussion
### Recovery Time of \"FIX\" State after Its Loss  
Outages from the \"FIX\" state, which had a known period between its boundaries (outage and recovery), were not detected during the static measurements. The detected periods of outages came only from the dynamic measurements, which are presented in Table 4. Although Receiver A had the most outages, in most cases, it was able to recover back to the \"FIX\" mode within 2 s. In the case of two detected periods, however, the outages lasted 98 and 186 s. Receiver B was able to repair most outages within 3.4 s, but nine outages lasted in the range of 10-54 s, and one outage lasted even 228.3 s. The recovery of Receiver C was much worse, as twelve outages lasted between 13.45 and 88.35 s, and the longest outages lasted 118.8, 189.7, 236.1 and 753.15 s. Receiver D did not have as long of outages as Receiver C. Its longest outages lasted 146.4, 152.0, 230.4, 244.2, 286.2 and 472 s. Receiver D was able to repair most of the outages after 21.4 s, and thirty-six outages lasted between 10 and 91.6 s.  
Based on the results above, it was possibly assumed that the RTK receivers with an INS unit (Receiver A and Receiver B) were able to return to the \"FIX\" state earlier than the receivers without an INS unit (Receiver C and Receiver D).
## 3 Results
### Ten-Fold Cross Validation  
The average 10-fold cross-validation accuracies were computed. K-fold cross-validation is one of the methods to validate the results, as it shows how well the classifier performs on unseen data. Hence, its average accuracy holds importance by showing that the trained model performs well on both the seen and unseen data effectively. The average 10-fold cross-validation accuracies are shown in Figure 8A. Upon 10-fold cross-validation, accuracy of 90%, 87%, 71%, 97%, 98%, 98%, 96%, 69%, and 97% was achieved by k-Nearest Neighbor, Linear Discriminant Analysis, Gaussian Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, Multilayer Perceptron, AdaBoost, and Bagging classifiers, respectively, in the classification of imbalance and its intensities. The error bars were used to plot the standard deviation of the classifiers in computing average 10-fold cross-validation accuracy. It shows the variation in the data and measures the uncertainty or error in the results. Hence, it can  
Figure 6: Feature extraction and machine learning classification: Signals of both modalities, i.e., EMG and GRF, are first normalized. Features are extracted from both signals. Features extracted from EMG are the mean, standard deviation, skewness, and kurtosis. Features extracted from GRF are dominant frequency and force envelope. Features of both modalities are concatenated after extraction along with their labels, i.e., high, medium, and low intensities. Features and labels are then fed to the machine learning classifiers which classify the body imbalance intensities into high, medium, and low using the GRF and EMG signals of the subject.  
be seen in Figure 8B that the error in computation is negligible in the case of Decision Tree, Random Forest, Gradient Boosting, and Bagging classifiers.
## Abbreviations  
The following abbreviations are used in this manuscript:  
5G  
Fifth Generation  
VNF  
Virtual Network Function  
SBA  
Service-Based Architecture  
CN  
Core Network  
NF  
Network Function  
SA  
Standalone  
SSN  
Social Security Number  
5G-GUTI  
Globally Unique Temporary ID  
NAS  
Non-Access Stratum  
RNTI  
Radio Network Temporary Identifier  
MAC  
Medium Access Control  
HN  
Home Network  
AKMA  
Authentication and Key Management  
TSN  
Time-Sensitive Network  
AMF  
Access and Mobility Function  
RESTful  
Representational State Transfer Application Programming Interfaces  
PII  
Personally Identifiable Information  
UE  
User Equipment  
SUPI  
Subscription Permanent Identifier  
QoS  
Quality of Service  
PDU  
Protocol Data Unit  
UDM  
Unified Data Management  
UDR  
Unified Data Repository  
IMSI  
International Mobile Subscriber Identity  
LTE  
Long-Term Evolution  
SUCI  
Subscriber Concealed Identity
## 2 Proposed Framework
### Data Preprocessing  
For better performance of the electricity load prediction models, the training data should be analyzed before training. As previously mentioned, the historical data of electricity consumption include abnormalities that affect the model performance. In this study, we used individual household electricity consumption prediction (IHEPC) and appliances load prediction (AEP) datasets, which consist of missing and outlier values. These abnormalities are removed from the data in the preprocessing step of the proposed framework. For missing values filling, NAN interpolation techniques are used, whereas for outlier values, three sigma rules of thumb [39] are applied. After the outlier reduction and filling missing values, the datasets are normalized using the min-max normalization technique to transform the dataset into a particular range which the neural network can learn easily.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Authentication  
Given that the vehicle has previously acquired the ciphertext of the RSUs' authentication key during route planning, the vehicle simply needs to decrypt the ciphertext before entering the coverage of the RSU to obtain the RSU's authentication key. Subsequently, the vehicle and RSU mutually authenticate each other. The interactive implementation process between the vehicle and RSU is shown in Figure 5, and the details are as follows.  
1. When the vehicle drives into the coverage range of RSU, the vehicle computes the temporary session key \(K_{v}=H(Rep(PUF(C_{v}),P_{Fv})||t_{2})\). Next, the vehicle is able to obtain the authentication key of the RSU by computing \(K_{u}^{a}=D_{K_{v}}(E_{K_{v}}(K_{u}^{a}))\). Next, the vehicle generates a timestamp \(t_{3}\) and a random number \(rn\). And the vehicle sends \(M_{3}:\{E_{K_{u}^{a}}(AID_{v}||rn||t_{3}),t_{3},\phi_{3}=H(E_{K_{u}^{a}}(AID_{v }||rn||t_{3})||t_{3})\}\) to the RSU;
2. On receiving \(M_{3}\), the RSU verifies \(t_{3}\) and \(\phi_{3}\). If both are correct, the RSU computes \(K_{u}^{a}=H(Rep(PUF(C_{u}),P_{Fu}))\), then decrypts the \(E_{K_{u}^{a}}(AID_{v}||rn||t_{3})\) to obtain \(AID_{v}\) and \(rn\) using \(K_{u}^{a}\). Last, the RSU sends \(H(rn+1)\) to the vehicle;
3. On receiving the response, the vehicle checks whether \(H(rn+1)\) is correct. If the checking process passes, the authentication is successful. Otherwise, it fails.  
Figure 4: Route planning.