esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance with Practical Limitations
### Performance with Outdated Channel State Information
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Avg UE Thr.**} & \multicolumn{2}{c}{**5\%-ile UE Thr.**} & \multicolumn{2}{c}{**Avg Cell Thr.**} \\ \cline{2-7}  & **Thr.** & **Gain** & **Thr.** & **Gain** & **Thr.** & **Gain** \\ \cline{2-7}  & **[Gbps]** & **[\%]** & **[Gbps]** & **[\%]** & **[Gbps]** & **[\%]** \\ \hline
**ABF (\(P=1\))** & 0.59 & - & 0.20 & - & 9.8 & - \\
**ABF (\(P=4\))** & 1.52 & 157 & 0.42 & 112 & 25.0 & 155 \\
**ABF (\(P=8\))** & 2.22 & 276 & 0.42 & 111 & 36.4 & 272 \\
**ABF (\(P=16\))** & 2.62 & 344 & 0.41 & 104 & 43.1 & 340 \\
**DP NFS** & 2.86 & 384 & 1.04 & 424 & 47.4 & 384 \\
**DP FS** & 2.96 & 401 & 1.10 & 453 & 49.0 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results with ideal assumptions. signal to interference plus noise ratio (SINR) at the receiver, turns out to be much more robust than DP. For completeness, the average cell throughput for different values of the channel update period including also the DP NFS scheme is depicted in Figure 7. The latter results also confirm the robustness of ABF to the effect of outdated CSI, which presents a maximum penalty in cell throughput of 9.8% for \(f_{D}\Delta T=38.8\). On the other hand, the cell throughput values of DP FS and DP NFS are reduced up to 32% and 34%, respectively. Regarding the comparison between DP NFS and DF FS, both show very similar performance, meaning that the additional complexity of DP FS to adapt the precoder per RB does not pay off with an outdated CSI.
## 2 Materials and Methods
### End User Evaluation  
There was a defined questionnaire based on the Technology Acceptance Model [26] addressing user perception through 14 questions about perceived usefulness (U), ease of use, attitude toward using (AU) and behavioral intention to use (IU). The answers for each dimension were collected through a 5-point Likert Scale (from 1--high level of disagreement to 5--high level of agreement). This questionnaire was applied to collect the perceptions from members of the hospital committee for clinical documentation and healthcare professionals who required the definition of the EHR model or research registries in our hospital.  
* Group of Members of the Clinical Documentation Committee: The system was presented to a group of end users in a 45-min session in order to obtain a functional evaluation of the system by healthcare professionals, experts in clinical documentation and EHR systems. At the end of the session, the professionals filled in the TAM questionnaire on a voluntary basis.
* Group of Principal Investigators: These users received a 45-min training session and used the tool to define the forms to create a research record on the patients they cared for during their daily practice. As described in the piloting section for the definition, several revisions were made to the forms until reaching the consensus version that would finally be implemented in the registry. After finalizing the definition of the forms, users completed the TAM questionnaire on a voluntary basis.
# Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation
## Appendix A  
PSNR (peak signal-to-noise ratio): An indicator for measuring the quality of image or video reconstruction; used to evaluate the error between the reconstructed image and the reference image. The higher the value, the better the quality of the reconstruction.  
LPIPS (learned perceptual image patch similarity): A perceptual similarity indicator based on deep learning. Evaluates the perceptual quality of the image by comparing the differences in the deep feature space. The lower the value, the higher the perceptual quality.  
SSIM (structural similarity index measure): An indicator to measure the perceived quality of an image; used to compare the structural similarity between the generated image and the reference image. The closer the value is to 1, the higher the image quality and the more similar it is to the reference image.  
LMD (landmark distance): Used to measure the error of facial keypoint detection; indicates the average Euclidean distance between the position of the keypoint in the generated image and the position of the real keypoint in the reference image. The lower the value, the higher the geometric accuracy.  
AUE (average unit error): Measures the average error of the unit deformation in the generated image; reflects the modeling accuracy of the target deformation. The lower the value, the better the performance.  
Sync (synchronization score): Used to evaluate the degree of synchronization between audio and video. Calculated based on the accuracy of audio and video time alignment. The higher the value, the better the synchronization effect.  
CPBD (cumulative probability blur detection): A non-reference indicator for evaluating image clarity. Calculates the blur in the image by cumulative probability. The higher the value, the clearer the image.  
NIQE (natural image quality evaluator): A non-reference image quality evaluation indicator. It uses a natural scene statistical model to evaluate image quality. The lower the value, the higher the naturalness and quality of the image.  
BRISKUE (binary robust invariant scalable keypoint underestimation): Measures the stability and accuracy of keypoint detection. It evaluates keypoint extraction performance by detecting error distribution. The lower the value, the better the performance.  
FID(Fr_c_het inception distance): The indicators for measuring the quality and diversity of generated images are evaluated by comparing the differences in feature distribution between generated images and real images. The lower the value, the closer the quality and diversity of the generated image to the real image.
## 3 Results and Discussion
### Myofibrillar Proteins Gels
#### 3.2.3 Proton Density Weighted Pseudo-Color Images  
MRI technology is a non-destructive analytical tool, which can reflect the distribution of H protons in the samples by the formation of an image. The intensity of the color in the pseudo-color image reflects the relaxation signal intensity difference caused by alterations in proton density distribution [42]. Magnetic resonance imaging (MRI) will be brighter and redder if there are more hydrogen protons within the image [43]. In the present study, the protons mainly came from water molecules the in MP gel samples. Therefore, the proton density image can be used to intuitively observe the spatial distribution of water in the MP gel samples. MRI images of the changes to the MP gels are displayed in Figure 8. In the initial stage of frozen storage, all MPs samples had dense, red bright spots. With the extension of freezing time, the bright red spots in the H proton density image gradually disappeared. At 90 days of frozen storage, the control (0%) and the 2% treatment had only sporadic bright spots, while the 4% treatment and 6% treatment samples had more red bright spots, which indicated that addition of an appropriate concentration of OVA/\(\beta\)CD enhanced the water retention capability of the MP gel. The water retention of the MP gels was mainly related to their microstructure, hence we deduced that the structure of the control (0%) group gel was rough, porous and had poor water retention, thus affecting the water distribution of its MP gel. The deteriorated changes of microstructure may have been caused by oxidation in the myosin, accelerating the formation of a poor gel network structure [44]. Meanwhile, the addition of 6% OVA/\(\beta\)CD preserved the MP gels structure and helped set up a homogeneous, dense gel network by preventing protein aggregation. These outcomes were in agreement with the findings in the WHC examination (Figure 6). [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [19] mentioned that cryoprotectants, as protective materials, may bind with the one of functional groups in protein molecules, either by hydrogen or ionic bonds, to prevent the oxidation of MPs during frozen storage and prompt the formation of more ordered and finelt structured gel networks.  
Figure 6: Water-holding capacity (%) of myofibrillar proteins gel treated with different concentrations of OVA/\(\beta\)CD during 90 days of frozen storage at \(-18\)\({}^{\circ}\)C. a–d mean significant differences (\(p<0.05\)) within the similar treatments with different storage times. A–D mean significant differences (\(p<0.05\)) within the same storage period of different concentrations.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 8 Potential Savings of Emissions  
The combination of the time spent in ports differentiated by typology of ships (Figure 2) and the average emission factors (Figure 3) allowed the calculation of the global emissions of auxiliary engines at berths without cold ironing (Figure 4) and the global (CO\({}_{2}\)) and local (NO\({}_{\rm x}\) and SO\({}_{\rm x}\)) emissions savings achievable by using cold ironing according to EU25 electricity production mix and the corresponding average emission factors (Figure 5).  
According to these data, the potential global and local emissions savings at berths in the scenario with full implementation of cold ironing will be the following:  
* CO\({}_{2}\): \(-53\%\);
* NO\({}_{\rm x}\): \(-97\%\);
* SO\({}_{\rm x}\): \(-96\%\);
* VOC: \(-95\%\);
* PM: \(-94\%\).  
The achievable results are very important. The reductions in local emissions (NO\({}_{\rm x}\), SO\({}_{\rm x}\), VOC, and PM) oscillate in the range of 94-97%; meanwhile, the reductions of CO\({}_{2}\) emissions are still over 50% and could be relevantly improved by the planned increase in renewable sources use to produce electricity that was 39.4% for EU27 in 2022, with big fluctuations across coastal countries, ranging between 79% in Denmark and 13% in Malta. Therefore, the achievable reductions would be amplified in ports with a national ratio of renewable sources to produce electricity over the average value, such as Amsterdam, Rotterdam, Piraeus, Barcelona, Hamburg, and Goteborg, and mitigated in Antwerp, Le Havre, London, and Genova.  
Figure 2: Average time spent in the 10 sampling ports by ships’ typologies (2023, weeks 1 and 2).  
Figure 4: Global emissions of auxiliary engines at berths without cold ironing.  
Figure 3: Emissions factors of auxiliary engines using 2.7% sulphur fuel, 0.1% sulphur fuel, and shore connection (cold ironing) based on EU25 power generation mix.
# Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems
## 3 Converter System Description and Analysis
### System Description  
3.1.1. Two-Level Voltage Source Converter  
Figure 7 shows the circuit diagram of the considered VSC. Assuming ideal switches, the converter output voltage \(\underline{u}(t)=\left[\begin{array}{cc}u_{c}(t)&u_{b}(t)&u_{a}(t)\end{array} \right]^{T}\) is given by  
\[\underline{u}(t)=u_{\text{DC}}(t)\cdot\underline{p} \tag{21}\]  
and the corresponding phase voltage vector \(\underline{u}_{\text{P}}(t)=\left[\begin{array}{cc}u_{\text{P},c}(t)&u_{ \text{P},b}(t)&u_{\text{P},a}(t)\end{array}\right]^{T}\) by  
\[\underline{u}_{\text{P}}(t)=\frac{u_{\text{DC}}(t)}{3}\left[\begin{array}{ cccc}2&-1&-1\\ -1&2&-1\\ -1&-1&2\end{array}\right]\underline{p}. \tag{22}\]  
Here, the normalized phase potential vector \(\underline{p}=\left[\begin{array}{cc}p_{c}&p_{b}&p_{a}\end{array}\right]^{T}\), with \(p_{c,b,a}\in[0,1]\), depends on the state of the switches \(S_{c1,b1,a1}\) and \(\overline{S}_{c2,b2,a2}\).  
Both the voltage space vector \(\underline{u}(t)\) and the corresponding current space vector \(\underline{i}(t)=\left[\begin{array}{cc}i_{c}(t)&i_{b}(t)&i_{a}(t)\end{array} \right]^{T}\) can be represented in different coordinate systems, where the most frequently used is the \(120^{\circ}\) phase coordinate system with the three axes \(c,b,a\) and the orthogonal coordinate system with two axes \((\alpha,\beta)\), which is utilized in this paper. The \(\alpha\)-axis of this stationary reference system is aligned with the \(a\)-phase of the VSC, as shown in the converter hexagon in Figure 8. The converter hexagon is formed by the possible phase potentials \(\underline{p}\).  
The unscaled transformation between variables in the \((c,b,a)\) phase coordinate system, i.e., \(x_{c,b,a}(t)\), and variables in the \((\alpha,\beta)\) reference frame, i.e., \(x_{\alpha,\beta}(t)\), is given by [24]  
\[x_{\alpha}(t) =x_{a}(t)-\frac{x_{b}(t)+x_{c}(t)}{2} \tag{23}\] \[x_{\beta}(t) =\frac{\sqrt{3}(x_{b}(t)-x_{c}(t))}{2}. \tag{24}\]  
Figure 7: Circuit diagram of a two-level VSC.  
Considering (21), (23), and (24), the voltage space vector \(\underline{u}_{\text{P}}(t)\) in \((\alpha,\beta)\) reference frame is given by  
\[u_{\alpha}(t) =u_{\text{DC}}(t)\left(p_{a}-\frac{p_{b}+p_{c}}{2}\right) \tag{25}\] \[u_{\beta}(t) =\frac{\sqrt{3}u_{\text{DC}}(t)\left(p_{b}-p_{c}\right)}{2}. \tag{26}\]
# Integrated Adaptive Steering Stability Control for Ground Vehicle with Actuator Saturations
## 4 Simulation Verification  
To verify the effectiveness of the proposed integrated controller, numerical simulations are performed in this section. The parameters of the vehicle model used for the simulations are provided in Table 1, while the parameters of the proposed controller are provided in Table 2. Two different simulation scenarios are performed, with the following state initializations: \(x_{1}(0)=0.1\) m, \(x_{3}(0)=0.1\) rad, \(\dot{\theta_{l}}(0)=\frac{1}{500}\) kg\({}^{-1}\cdot\)m\({}^{-2}\), and the rest set to 0, with a constant vehicle speed of 50 m/s. Using a sampling time of 0.001 s and the aforementioned scenario settings, a detailed elaboration is presented with in following Sections 4.1 and 4.2. To comparatively show the effects of the proposed controller, another controller from [17]  
\[\Psi=\left\{\begin{array}{rcll}M&=&I_{z}\gamma_{r}-k_{\gamma}I_{z}(\gamma- \gamma_{r}),&k_{\gamma}>0\\ u_{\theta}&=&-k_{\theta}I_{x}\dot{\theta},&k_{\theta}>0\\ u_{z}&=&-k_{z}m_{z}\dot{z}_{s},&k_{z}>0\end{array}\right.\]  
Figure 2: Control scheme flowchart.  
is used as a contrast, with the tuning parameters \(k_{\gamma}\), \(k_{\theta}\), and \(k_{z}\) assigned as shown in Table 2. In these comparative simulations, all environmental parameters are set to be the same except for the different controller parameters.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 2 Overview
### Penetration Path Planning
#### 2.2.3 Planning Methods  
Planning methods refer to the specific techniques and algorithms used to generate penetration paths. Common approaches include path planning based on intelligent planning, path planning based on RL, and path planning based on LLMs.  
**Path planning based on intelligent planning:** this method utilizes intelligent planning algorithms or planners, such as the A* algorithm [19] and Graphplan planner [22], to generate optimal penetration paths based on the planning domain model. This approach is suitable for known, static network environments and can quickly generate penetration paths under a predefined planning domain model;  
**Path planning based on RL:** this method involves simulating the attack process to continuously optimize and adjust penetration strategies, making it suitable for unknown and dynamic environmental conditions. RL agents can autonomously learn and improve attack strategies based on environmental feedback, allowing adaptation to continuously changing network environments;  
**Path planning based on LLMs:** this method harnesses the sophisticated natural language processing (NLP) capabilities of LLMs, such as GPT, to analyze and interpret complex textual data within network environments and security intelligence reports. LLMs can assist in simulating attack scenarios by generating plausible sequences of actions that an attacker might take, based on the text description, and can be adjusted in real time based on user feedback.
## 1 Introduction
It was observed that thermal conductivity enhancements are about 8.2% for the temperature range of 25-60 \({}^{\circ}\)C. The electrical efficiency with 3 wt % of SiC nanofluid results in electrical efficiency enhancements of 24.1% and it was observed that the nanofluids were stable for long use. In the study by [PERSON] et al. [22], a new cascade PV/T module was proposed with separate channels. Two nanofluids were used to enhance the electrical and thermal performance of the PV/T module. [PERSON] et al. [23] investigated the effects of silica/water nanofluids on the efficiency of PV/T module. Various sizes of nanoparticles, concentrations and flow velocity were considered. Optimum operational parameters for the economical considerations were also obtained. In the present study, efficiency of a PV/T module with SiO\({}_{2}\)-water nanofluid was numerically investigated for nanoparticle properties (shape, volume fraction) and for different operating conditions. Despite the low conductivity of SiO\({}_{2}\) nanoparticles as compared to other particles, its low cost, favorable physical and chemical properties makes it attractive for usage with water. Artificial neural networks with radial basis functions are used to obtain the correlations for efficiencies of PV-thermal module.
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 3 Semi-Vortices in Spinor SOC BEC with Pure DDI
### Mobility of the SVS and AVS  
Although the SOC breaks the Galilean invariance of the system, analysis of the SVS and AVS reveals that they can be both kicked to move by applying a initial kick to the solution:  
\[\psi_{\pm}(\mathbf{r},t=0)=\phi_{\pm}\exp(i\mathbf{k}\cdot\mathbf{r}) \tag{10}\]  
By tracking the shape change and trajectory of the center of mass (c.m.) of the solitons (mainly determined by the fundamental component) via real-time evolution, three kinds of motion of the SVS can be identified, as shown in Figure 3.  
1. For \(k\) smaller than a depinning threshold \(k_{d}\), the SVS will be pinned to move in a circle trajectory (see Figure 3a) and is isotropic in both directions \(x\) and \(y\).
2. For \(k>k_{d}\), the SVS is depinned and move with a negative effective mass (i.e., opposite the direction of the kick) in a spiral trajectory (see Figure 3b), but still isotropic in both directions.
3. For \(k>k_{upper}\) where \(k_{upper}\) is another threshold larger than \(k_{d}\), the SVS will undergo a more complex \"lacy\" trajectory and become anisotropic (see Figure 3c).  
The AVS can also be kicked to move, but due to the anisotropy of the DDI and the asymmetric shape of the vortex component in that case, the motion pattern is far more complex than that of the SVS. The motion is highly anisotropic to the horizontal and vertical kick, and a kick in one direction will cause not only motion in this direction, but also in another direction. Under different kick parameters, the AVS can either exhibit pinning, spiral or \"lacy\" motions, but no easy pattern, as SVS is yet to be clarified [58]. Typical examples of the motion of AVS are shown in Figure 4. These behaviors of mobility are important in the control and manipulation of solitons both in theory and experiments.  
Figure 3: Motion of the c.m. of kicked symmetric vortex solitons (SVS) with \(N=0.15\). (**a**) The kick \(\mathbf{k}=(k_{x},k_{y})=(0.05,0)\) (blue) and \((0,0.05)\) (red), pinning in a circle. (**b**) \(\mathbf{k}=(0.25,0)\) (blue) and \((0,0.25)\) (red), spiral motion. (**c**) \(\mathbf{k}=(0.5,0)\) (blue) and \((0,0.5)\) (red), “lacy” motion. (**d**) Mean velocity of the spiral motion for \(N=0.15\) and \(0.2\)[58].
###### Abstract
Due to the insufficient consideration of the characteristics of these two structures, the spatiotemporal features extracted using the above methods are still not informative for PV power prediction up to hours. Although various deep neural networks have been applied in PV power prediction, the majority of existing methods consist of conventional deep learning models such as CNNs, RNNs, and fully connected layers. At present, few studies have followed the state-of-the-art transformer [28] architecture, which has achieved great success in natural language processing [29] and computer vision [30]. Compared with convolutional or recurrent neural networks, transformer-based models with the self-attention mechanism are superior in capturing temporal dependencies. [PERSON] et al. [31] combined a graph neural network with a transformer to propose a graph-convolutional transformer for multi-site PV power prediction based on historical weather data. In addition, most existing models based on hybrid neural networks merely concatenate the spatial and temporal features before passing them as a whole input to the following layer. As a result, this rough treatment is incapable of learning and utilizing the correlation between the features of structured local measurements and unstructured satellite images well. Motivated by the aforementioned issues, we renovate the transformer architecture and propose a novel transformer-based PV power prediction model that can process the locally measured sequences and satellite remote-sensing images. The main contributions are summarized as follows:  
1. We propose a novel **dual-encoder** transformer (DualET) for short-term PV power prediction. Distinct from the standard transformer architecture, DualET contains dual encoders, including a local seasonal information (LSI) encoder and a remote-sensing information (RSI) encoder, and a single decoder. The dual encoders are designed to handle sequence and image data, while the decoder is to model the joint meteorological features from the encoders and outputs the short-term prediction. To extract informative features from the satellite images and local sequences, we deploy the two-dimensional wavelet transform block and series decomposition block in the encoding stage. The former in the RSI encoder is capable of the frequency feature extraction of image signals to obtain cloud detailed information, while the latter in the LSI encoder conducts series decomposition to improve the learning capacity of temporal patterns. 3. We propose a cross-domain attention module to learn the correlation between the temporal features in sequence data and the cloud information in image data. Furthermore, we enhance the ability to capture dependencies by modifying the attention modules with the sparse form and Fourier transform. 4. Real-world datasets are applied to evaluate the proposed model. The experiments show that our model achieves state-of-the-art results compared with other models including recent transformers.