esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The growth of Internet services, such as streaming video, social networking, and cloud computing has created the fast-growing need for datacenters. Due to their low power consumption [1], high modulation speed [2], and low cost [3], vertical-cavity surface-emitting lasers (VCSELs) based links dominate modern datacenter systems. A lot of development works have been carried out in the fabrication of VCSELs [4; 5; 6]. For these diverse design structures, the number of quantum wells differs. As the demand for greater capacity increases, the VCSEL has incorporated equalization circuitry, embedded in drivers through CMOS technology [7; 8]. To facilitate VCSEL design, fabrication and evaluation, an accurate VCSEL model, accommodating the variable quantum well (QW) number, needs to be built in an electronic-photonic co-simulation platform.  
Previous models were proposed to simulate the performance of multi-quantum-well (MQW) VCSELs [9; 10; 11]. In these models, the number of rate equations are proportional to the number of QWs such that the model cannot be generalized to all VCSEL designs [12; 13], and are not suitable for the system-level simulation of VCSEL-based fiber links. The parameter extraction will also be a significant challenge in view of the extremely large number of parameters. In addition, these models do not account for noise effects, which are significant for assessing the transmission performance.  
In this paper, we propose a versatile compact equivalent circuit model with noise effects for the MQW VCSEL. The proposed model accommodates a flexible number of quantum wells (QW's), which provides the freedom to simulate an arbitrary VCSEL in the system-oriented opto-electronic simulation platform. The model is developed in the commercial Cadence tool suite, a schematic-driven tool for CMOS electronic integrated circuit design. The paper outline is as follows. Section 2 explains the VCSEL parasitic circuit model, which determines the small-signal response. In Section 3, the carrier and photon dynamisms are analyzed for the structure including separate confinement hetero-structure layers, barrier layers, and quantum well layers. In the QW layer, the carriers have two states: the confined state inside the quantum well and the unconfined state above the quantum well. In Section 4, based on the VCSEL structure analysis, we obtain a set of rate equations for the MQW VCSEL. After that, a versatile compact rate equation set is obtained, with a significant reduction in the number of equations. Laser intensity noise is expressed based on the Markovian assumption and added to the compact rate equations. In Section 5, the rate equations with noise terms are translated into the corresponding equivalent circuit equations. The equivalent circuit model for the MQW VCSEL is realized. In Section 6, the accuracy of the proposed VCSEL model is validated using a self-wire-bonded 850 nm VCSEL. In Section 7, we present a summary and then, conclude.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 4 Modelling and Planning
### Optimization  
#### 4.2.1 Only Allocation  
Although few articles optimize the number and size of chargers but do not allocate them spatially [51], a relatively large amount of studies among ones reviewed make use of transport data while solely solving the allocation problem. For example, the authors of [29] take into account the cost of investment for CSs through a multistage approach in which future costs are discounted to the present worth. New O-D pairs are sequentially added to the transportation network, and cities are successively chosen based on a statistical prediction of EV adopters (a logistic regression analysis is used). The added value of this study is in its effort to couple topological dynamics based on user adoption with economic benefits. The authors of [41] aim to allocate CSs for buses or logistics companies, but they allow chargers to have unlimited charging outlets. In contrast, they minimize costs by optimizing the charging operation schedule. However, ref. [39] models en-route charging stations and in-depot charging stations, with the aim of substituting a certain number of conventional buses in the Salt Lake City area. Finally, the authors of [3] distribute a fixed amount of CSs throughout the territories of Hungary (budget constrained problem) without determining their charging power. It is important to analyze the charging outlets' number and power rating in order to assess the coincidence factor, the overall aggregated load for the electrical infrastructure, and, possibly, the flexibility resource.
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 6 Extension of the Maintenance Decision Model  
### Interval Number  
In actual maintenance, due to market price fluctuations, the unit area maintenance cost (\(C_{j}\)) has uncertainty. Uncertain parameters can be described using fuzzy numbers, ambiguous numbers, and interval numbers. Since the range of uncertain numbers is generally determinable, this paper uses interval numbers [33] for description. Specifically, for maintenance cost \(C_{j}\), its uncertain form is \(\widetilde{C}_{j}\). If \(-\infty<\underline{C_{j}}\leq\overline{C_{j}}<+\infty\), then \(\widetilde{C}_{j}=[\underline{C_{j}},\overline{C_{j}}]\) is an interval number. If \(\widetilde{C}_{j}=\underline{C_{j}}=\overline{C_{j}}\), then \(\widetilde{C}_{j}\) degenerates into a real number, which is a deterministic value.
# Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
## 2 Materials and Methods
### Research Tools
#### 2.3.3 Force Platform  
Force Platforms, also known as Force Plates, are sophisticated measurement devices used in sports science, biomechanics, and clinical settings to assess ground reaction forces during various movements, including jumps, gaits, and balance tasks. These devices provide detailed information about the forces exerted by the feet on the ground, which can be used to analyze movement mechanics and performance [45].  
Force Platforms measure three-dimensional ground reaction forces (vertical, anterior-posterior, and medial-lateral) exerted by the feet during different movements. With high sampling rates, they capture rapid force changes during explosive movements, like jumps. Known for their high degrees of accuracy and precision, Force Platforms are considered as the gold standard for biomechanical analyses. They measure forces along multiple axes, providing comprehensive data on force directions and magnitudes [46].  
In this study, a high-quality PJS-4F60S Force Platform with MVV.4.0 software (\"JBA\" Zb. [PERSON], Warsaw, Poland) and a 400 Hz sampling rate will serve as the reference standard for CMJ measurements. The Force Platform connects through an analog-to-digital converter (ADC) to a Dell Inspiron i7 laptop operating the MVV v4.0 software. This setup enables precise measurements of ground reaction forces during CMJs, providing accurate data on jump heights. The platform will be calibrated according to the manufacturer's specifications before data collection to ensure accuracy and reliability. Despite their accuracy, Force Platforms require a controlled environment, limiting their application in everyday training settings.
## 6 Evaluation
### Ciphertext Inference
#### 6.2.2 Analysis of Decryption Error  
For the MNIST and SKIN data sets, we inferred ciphertext for the entire test set with 99.62% and 81.10% accuracy, respectively, which is exactly the same as plaintext accuracy. For the CIFAR-10 dataset, we conducted five random samplings of the test set and inferred 384 images each time, with an accuracy of 92.28 \(\pm\) 2.58% slightly lower than the plaintext accuracy of 92.59% \(\pm\) 2.27%. For this result, we further analyzed the reason. By calculating the relative error between the ciphertext prediction vector and the plaintext prediction vector (called decryption error), we found that the mean decryption error of CNN-6 on the MNIST data set and AlexNet on the SKIN data set were both small, 3 and 4, respectively. However, the ResNet-20 mean relative error on the CIFAR-10 dataset is about 10, which is very large compared to the first two. This is because ResNet-20 uses bootstrapping technology in the ciphertext reasoning process, while bootstrapping in the HEAAN library is approximate, and each bootstrapping will introduce a new error.  
In addition, we also calculated the decryption errors of different categories in the dataset and drew box plots by randomly selecting 20 sampling points in each category, as shown in Figure 5. We tested the differences between decryption errors of different categories. For the MNIST dataset, we first tested the homogeneity of variance of decryption errors for all categories, and the result of the \(p\)-value was 0.596 > 0.05. The initial hypothesis was accepted, indicating that the decryption errors of different categories were considered to satisfy the homogeneity of variance. Then, one-way ANOVA was performed on the decryption errors, and the result of the \(p\)-value was 0.580 > 0.05, indicating that there was no significant difference in decryption errors among different categories in the MNIST dataset. For the SKIN and CIFAR-10 datasets, the \(p\)-values for the homogeneity of variance test were 0.174 and 0.830, respectively, and the \(p\)-values for one-way ANOVA on variance were 0.325 and 0.376, respectively. Therefore, the same conclusion as the MNIST dataset was drawn, indicating that there was no significant difference in decryption errors between different categories.  
Figure 5: Decryption error statistics of different categories.
## 3 Construction  
### Membership  
Our system consists of five parties (as shown in Figure 2). The data owner \((Do)\) is in charge of data encryption and uploading. The data user \((Du)\) is responsible for retrieving and decrypting the data submitted by the data owner from the cloud. The authority \((AT)\) is in charge of giving decryption keys to data users, participating in the ciphertext's signature, and storing the credentials of the data owner. The public auditor \((PA)\) is in charge of publicly auditing the ciphertext and extracting the information of the ciphertext owner from credentials. Finally, the cloud server \((Cloud)\) is responsible for storing the ciphertext uploaded by the data owner.
## 3 Characteristics of FBFET
As the dielectric constant of the insulator layer increases, threshold voltage (V\({}_{\text{TH}}\)) increases, while V\({}_{\text{TH}}\) decreases with increasing the work function of gate material. Depending on the gate voltage, there is a limit to the channel length, and, depending on the gate length, there is a limit on V\({}_{\text{TH}}\). N-/p-type FBFETs are formed based on the gate location adjacent to the source/drain [75]. As the channel length increases over 500 nm, V\({}_{\text{on}}\) increases, I\({}_{\text{on}}\) decreases, and abrupt switching decreases due to the fast regeneration of minority carriers. In addition, hysteresis disappears as the channel length increases [80]. Various simulation studies have found that, the shorter the region of the FBFET channel is, the better the performance is. Shortening the channel length improves the switching characteristics, and the outstanding characteristics of the FBFET are represented with the benefits from scaling a device, unlike the existing MOSFETs [75]. In contrast, as the channel length increases, low SS values and on-/off-current without saturation can be obtained. For channel lengths longer than 100 nm, FBFETs do not exhibit ideal switching characteristics. Nevertheless, all of the SS values were less than 60 mV/decade at 300 K.  
However, recent studies have shown that the positive feedback mechanism does not work, in contrast to the improved characteristics that have been achieved on the \(\sim\)10 nm scale. The cause lies in the role of the potential barriers, which are key in the operation of FBFET. As the channel length increases, the potential barrier becomes wider. The energy and time required to collapse the potential barrier increase when the channel length is longer than 40 nm. At smaller scales, however, the potential barrier becomes too narrow, resulting in short channel effects (SCE), which are expected to block the carrier flow. To solve this limitation, it is necessary to consider ways to overcome the SCE, as in conventional MOSFETs [75]. When the channel length decreases, the threshold voltage decreases and the on-/off current ratio increases. However, there are limitations on V\({}_{\text{DS}}\), depending on the channel length. If the channel length is 40 nm, the V\({}_{\text{DS}}\) value will not be able to cut off the current at 1.1 V. When V\({}_{\text{DS}}\) is 1 V, the channel length of 30 nm cannot cut off the current. As a result, there is a limit on the possible shrinking in FBFET, as shown in Figure 7[75]. In addition, as the drain current increases, a problem arises in that the value of the potential voltage preventing carrier flow in the drain becomes low [63; 64; 65; 75].
## 2 Channel Model
### Channel Noise  
The underwater channel suffers from different noise types, which corrupt the data packets. This makes extraction of information challenging [41]. The underwater ambient noise can be classified into four major types: turbulence, shipping, waves and thermal [42]. The power spectral density (PSD) of each noise sources are represented as \(N_{tr}\), \(N_{sht}\), \(N_{wv}\) and \(N_{th}\) respectively. Figure 3 shows the different noise level in seawater. The PSD of turbulence noise \(N_{tr}(f)\), in dB re \(\mu\) Pa [42] for frequency \(f\) is modeled as:  
\[10 logN_{tr}(f)=17-30 log(f). \tag{8}\]  
Figure 2: Attenuation coefficient in water.  
The PSD of shipping noise \(N_{sh}(f)\), in dB re \(\mu\) Pa for frequency \(f\) and shipping activity factor \(s\) is modeled as:  
\[10 logN_{sh}(f)=40+20(s-0.5)+26 log(f)-60 log(f+0.03). \tag{9}\]  
The PSD of waves noise \(N_{ww}(f)\), in dB re \(\mu\) Pa for frequency \(f\) and wind speed \(w\) is modeled as:  
\[10 logN_{ww}(f)=50+7.5w(1/2)+20 log(f)-40 log(f+0.04), \tag{10}\]  
the PSD of thermal noise \(N_{th}(f)\), in dB re \(\mu\) Pa, frequency \(f\) is modeled as:  
\[10 logN_{th}(f)=-15+20 log(f). \tag{11}\]
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 2 Materials and Methods
### Feature Extraction Networks
#### 2.2.2 MRI Feature Extraction  
The feature extraction from MRI images was performed using a 3D DenseNet network. This network consists of four dense blocks, with each block internally connected through transition layers. The convolutional layers within each dense block facilitate feature reuse by sharing feature maps, which further enhances the network's ability to extract deep-level features from the images. After passing through the four dense blocks, the final extracted features are generated through a Global Average Pooling operation. These features are then used for subsequent multimodal data fusion and classification tasks. Global Average Pooling computes the average across all spatial locations of each channel, producing output features of fixed dimensions. The MRI feature extraction process is shown in Figure 2.  
To increase data diversity and mitigate the risk of overfitting, data augmentation techniques were applied to the MRI images in this study. The augmentation methods included random affine transformations (\(\pm\)5% translation/rotation), Gaussian noise (\(\sigma=0.1\)), and intensity shifts (\(\pm\)10%). These augmentation strategies artificially increase the variability of the data, helping the model generalize better to unseen examples.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 3 SOH Prediction Based on Transformer-LSTM Fusion Model  
Achieving reliable SOH prediction requires effectively dealing with significant inconsistencies and complex nonlinear problems in battery degradation. Battery degradation is not only influenced by various intrinsic mechanisms but is also heavily influenced by external environmental factors and operating conditions, resulting in highly uncertain degradation patterns across different batteries. In addition, as the battery life increases, the degradation rate may vary in stages or even accelerate unexpectedly, which makes the relationship between feature data and SOH complex and nonlinear. These complexities pose great challenges to a prediction model in terms of expressiveness and robustness. To tackle these challenges, this paper proposes a transformer-LSTM-based data-driven prediction method to enhance both the accuracy and robustness of SOH prediction. Figure 7 illustrates the structure of the transformer-LSTM fusion model.
## 7 Experimental Results
### Original BEE
#### 7.1.1 Network Distribution  
Figure 5c shows after 450 rounds performing BEE, the snapshot for the distribution of the sensors still alive in the network. Comparing with the distributions for LEACH and HEED, BEE can provide better coverage at 450 round, with the same number of nodes as LEACH and less number of nodes comparing with HEED. The exact value for coverage is will be discussed in detail later.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Power Consumption Mode** & **Transmission/Receiving (\(E_{elect}\))** & **Transmit Amplifier (\(\epsilon_{amp}\))** \\ \hline Low Power Consumption & 50 nJ/bit & 10 pJ/bit/m\({}^{2}\) (\(d<d_{0}\)) \\ Medium Power Consumption & 50 nJ/bit & 15 pJ/bit/m\({}^{2}\) (\(d<d_{0}\)) \\ High Power Consumption & 50 nJ/bit & 0.0013 pJ/bit/m\({}^{4}\) (\(d>d_{0}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Energy Dissipated For BEEM and Smart-BEEM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Parameters** & **Values** \\ \hline \multirow{4}{*}{Network} & Network area & From (0, 0) to (100, 100) \\  & Sink & At (50, 175) \\  & Number of Sensors & 300 \\  & Initial Energy & 0.5 J \\ \hline \multirow{4}{*}{Application} & Default cluster radius & 25 m \\  & Data packet size & 100 bytes \\ \cline{1-1}  & Broadcast packet size & 25 bytes \\ \cline{1-1}  & Data header size & 25 bytes \\ \cline{1-1}  & Each round & 5 TDMA frames \\ \cline{1-1}  & CH data compress rate & 0.8 \\ \hline \multirow{2}{*}{Simulation} & Duration & 1000 rounds \\ \cline{1-1}  & Default access interface & NAI\({}_{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Simulation Parameters.
## 3 Results and Discussion
### Interface Electronic Properties
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interface Layers** & **TiAl–Mg** & **TiCo–Mg** & **CoCo–Mg** & **TiTi–Mg** & **AlAl–Mg** \\ \hline I-type \(P\) (\%) & 66.21 & 20.16 & 8.22 & \(-\)1.23 & 22.36 \\ I-type \(N_{\uparrow}\) (states/eV) & 5.51 & 6.55 & 3.44 & 6.30 & 4.60 \\ I-type \(N_{\downarrow}\) (states/eV) & 1.12 & 4.35 & 2.92 & 6.46 & 2.92 \\ II-type \(P\) (\%) & 65.72 & 19.85 & 8.13 & \(-\)0.77 & 22.25 \\ II-type \(N_{\uparrow}\) (states/eV) & 5.59 & 6.61 & 3.46 & 6.42 & 4.67 \\ II-type \(N_{\downarrow}\) (states/eV) & 1.16 & 4.42 & 2.94 & 6.52 & 2.97 \\ \hline
**Interface Layers** & **TiAl–Mg** & **TiCo–Mg** & **CoCo–Mg** & **TiTi–Mg** & **AlAl–Mg** \\ \hline I-type \(P\) (\%) & 30.51 & 54.62 & 46.50 & \(-\)27.96 & 68.83 \\ I-type \(N_{\uparrow}\) (states/eV) & 3.58 & 7.02 & 4.42 & 3.38 & 3.48 \\ I-type \(N_{\downarrow}\) (states/eV) & 1.91 & 2.06 & 1.61 & 6.00 & 0.64 \\ II-type \(P\) (\%) & 30.29 & 54.20 & 46.34 & \(-\)27.27 & 65.20 \\ II-type \(N_{\uparrow}\) (states/eV) & 3.72 & 7.51 & 4.58 & 3.47 & 3.61 \\ II-type \(N_{\downarrow}\) (states/eV) & 1.99 & 2.23 & 1.68 & 6.07 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spin polarization \(P\), spin-up state density \(N_{\uparrow}\) at the Fermi level, and spin-down state density \(N_{\downarrow}\) at the Fermi level. I-type includes the interface and subinterface layers in Ti\({}_{2}\)CoAl, while the II-type comprises I-type layers and the interface layer in MgO. Figure 2: The densities of state (DOS) of the two outermost layer atoms in Heusler alloy Ti\({}_{2}\)CoAl and the first interface layer atom in MgO for 10 potential terminations in Ti\({}_{2}\)CoAl/MgO junctions. (**a**) TiAl–Mg termination; (**b**) TiCo–Mg termination; (**c**) TiTi–Mg termination; (**d**) CoCo–Mg termination; (**e**) AlAl–Mg termination; (**f**) TiAl–O termination; (**g**) TiCo–O termination; (**h**) TiTi–O termination; (**i**) CoCo–O termination; and (**j**) AlAl–O termination.
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 2 Materials and Methods
### Semantic Segmentation Model
#### 2.1.2 Self-Calibrating Fusion Architecture  
The depth map contained more contour and position information, which is helpful for RGB image semantic segmentation [16; 17]. Identifying ways that can effectively utilize depth information complementary to RGB images to promote semantic segmentation accuracy is an important research hotspot in the field of RGB-D image semantic segmentation. We proposed a self-calibration module (Figure 4) to learn fusion information from an RGB image and predicted depth maps. The self-calibration module can enhance the diversity of the output features by expanding the size of the receptive field.  
Figure 4: Self–calibrating fusion structure.  
In the figure, _Avg pool_ represents the global average pooling operation; Cov3\(\times\)3 is the 3 \(\times\) 3 convolutional layer; \"+\" and \"\(\times\)\" represent channel-by-channel addition and multiplication, respectively; \(x_{1}^{\prime}\), \(x_{1}^{\prime\prime}\) and \(y_{1}\) are the output features of the intermediate layers; and \(y\) is the final output of the fusion structure.  
When general convolution is used for feature extraction of spatial locations, the size of the receptive field is set to a fixed size, resulting in the inability to extract higher-level semantic information. To extract the effective context information of each spatial location, our proposed self-calibration fusion structure fused the image and depth features to achieve self-calibration of the RGB image features and then added them with a channels-wise deep map. Each branch in the self-calibrated fusion structure nonuniformly divided the convolution filter into multiple parts, and the filters of each part were used in a heterogeneous form. The specific execution process is as follows:  
\[\left\{\begin{array}{l}T_{1}=avg\ pool(x_{1})\\ x_{1}^{\prime}=up(c_{1}(T_{1}))\\ x_{1}^{\prime}=c_{2}(x_{1}).\sigma(x_{1}^{\prime}+x_{1})\\ y_{1}=c_{3}(x_{1}^{\prime\prime})\\ y=c_{4}(x_{2})+y_{1}\end{array}\right.. \tag{1}\]  
where _up_ is the up-sampling operation and \(\sigma(\,)\) is a sigmoid function.  
First, the RGB image was down-sampled using a 3 \(\times\) 3 average pooling layer to obtain \(T_{1}\) (see Equation (1)) and then bilinear interpolation was used to restore \(T_{1}\) to the size of \(x_{1}^{\prime}\), which was added with input \(x_{1}\) to obtain the attention feature map of the spatial domain. After processing the sigmoid activation function, the output features were multiplied channel-by-channel with the feature map after a \(c_{2}\) transformation to obtain feature map \(x_{1}^{\prime\prime}\), which was finally output as \(y_{1}\) using a 3 \(\times\) 3 convolutional layer. The self-calibrating operation described above allows each spatial location to adaptively treat its surrounding information environment as an embedding from the low-resolution latent space and also model the dependence between the channels. Therefore, the receptive field of self-calibrating convolution can be effectively expanded.