esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 DRL-Based Online Resource Allocation
### Deployment Mechanism Based on Transfer Learning  
Despite its advantages in learning from the feedback of the environment, the proposed DRL-QoS-RA method still has limitations when deployed in the S-IoT system. The reason can be attributed to the time and calculation expense of the DRL method's training process, which is further illustrated as follows:  
* If the training process is implemented on the satellite, and the system suffers from the agents' decision-making errors during the training process, which will result in the failure of IoT data transmission and a waste of system resources. If the training process is implemented in the simulation environment of the ground control center,the accuracy of the algorithm will suffer from the difference between the simulation environment and the actual onboard environment.
* Each satellite of a large-scale LEO constellation faces a unique environment to learn from. Thus, the computational expense of training can be tremendous if added up in a massive satellite constellation.  
Concerning the shortcomings mentioned above, this section embraces transfer learning and proposes a deployment mechanism for DRL-QoS-RA, whose essential idea is that the filters of deep CONV network are learned to observe the environment feature and can be adapted (or transferred) to another similar environment.  
As present in Algorithm 3, by fixing the first several CONV layers, the knowledge learned by the agent in the simulation environment can be preserved. Then, by fine-tuning the last fully connected layers, the Q network can adapt to the target environment. Compared with direct deployment, merely fine-tuning the fully connected layers can reduce the computation expense and promote system efficiency.
```
Build simulation environment in ground control center based on the historical channel state, terminal location and IoT request distribution. Train the model \(Q(Feature_{k,l}|\theta)\) to convergence in a simulation environment  For satellite = 0, 1, 2...... : Copy the weight \(\theta\) of the DQN \(Q\)  Fix the conv layers in the deep convolutional network and only fine-tune the last few convolutional layers until the agent converges to obtain \(Q(Feature_{k,l}|\theta^{\prime})\)
```  
**Algorithm 3**Deployment process of DRL-QoS-RA
## 3 FTN Signal Processing Based on the UEP Algorithm  
The UEP-based FTN transmission method applies the FTN interference ratio of transmission signals, based on the importance of the encoded bits, obtained using the priority algorithm. This algorithm can determine the importance of each transmitted bit using the HSS-based LDPC decoding algorithm. The HSS-based LDPC decoding algorithm, proposed in [15; 16], is used to accelerate the LDPC decoding speed. However, in this study, it was used to configure the priority algorithm.  
Figure 3 shows the structure of the transceiver for the FTN based on the UEP algorithm.  
Figure 2: The structure of the FTN model.  
As shown in Figure 3, the FTN signal processing-based UEP method first performs the LDPC encoding process using source bits. For the UEP setting, the priority algorithm is used to set up the priority of the codes or signals being transmitted, and bit nodes are classified preferentially using HSS-based LDPC decoding at the receiving section. In this process, the H matrices of the LDPC encoder are utilized. After being prioritized and arranged through priority blocks, codes are arranged in groups through the serial to the parallel (S/P), according to the number of interference ratios to be used. After passing through the interleaver, a transmitting signal is generated by setting the FTN interference rate in each group differently.  
Transmit signals that pass through the additive white Gaussian noise (AWGN) channels go through a demodulation process. Moreover, an amount of FTN interference is removed through the BCJR equalizer. After de-interleaving, reordered signals arranged by the UEP algorithm are inputted into the LDPC decoder. The iterative turbo equalizer consists of inner codes and outer codes, and we the BCJR is employed as an outer code and the LDPC code as an inner code. At the receiver side, powerful turbo equalization algorithms that iteratively exchange probabilistic information between the inner decoder and outer decoder are used, thereby reducing the error rates significantly. The inner coded bits are then subtracted from the input and interleaved. The interleaved output is canceled a posteriori from the proceeding received signal. Thus, the algorithm enhances the performance, while maintaining or improving the transmission efficiency of the existing FTN transmission method.
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### Simulation Scene Construction  
The motion truth file in this article refers to the data provided by the open-source project \"UrbanNavDataset\" [30]. The data set was collected in a typical urban canyon in Tokyo on 19 December 2018, and its trajectory in the northeast geodetic coordinate system is shown in Figure 3.  
The experimental scene in this paper is vehicle navigation at a low speed on the ground (in the ECEF coordinate system, the speed of each axis is less than that of 20 m/s), the site is the urban high-rise canyon area, and the carrier vehicle has multiple right-angle turning maneuvers. At the same time, this paper simulates and constructs different experimental scenes by setting the range of mask angle and azimuth angle.  
The parameter patterns of each simulation scene in this paper are shown in Table 1.  
The schematic diagram of each simulation scene is shown in Figure 4.  
It can be seen from Table 1 and Figure 4 that the simulation scene in this paper includes four modes: Open scene, Semi-occluded scene, Surrounding occluded scene, and Headspace occluded scene. In the Open scene (Figure 4a), there is no restriction on azimuth angle, the mask angle is low so that there are more visible satellite data. The azimuth of the Semi-occluded scene (Figure 4b) ranges from \(-90^{\circ}\) to \(90^{\circ}\), significantly reducing the number of visible satellites. For the Surrounding occluded scene (Figure 4c), the mask angle is raised to \(50^{\circ}\), and the azimuth angle is not restricted to simulate the urban canyon environment. As a result, the number of visible satellites in Surrounding occluded scene is also small. In the Headspace occluded scene (Figure 4d), the mask angle is limited to \(10^{\circ}\) to \(45^{\circ}\), and the azimuth angle is not limited to simulate the environment under the urban viaduct. Naturally, the number of visible satellites in Headspace occluded scene is lower.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Mask Angle (deg)** & **Azimuth Range (deg)** \\ \hline Open scene & 10 & \([-180,180]\) \\ Semi-occluded scene & 10 & \([-90,90]\) \\ Surrounding occluded scene & 45 & \([-180,180]\) \\ Headspace occluded scene & [10; 45] & \([-180,180]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation scene parameters.  
Figure 3: Trajectory diagram of simulation experiment.
###### Abstract  
The AC/DC hybrid microgrid has a large-scale and complex control process. It is of great significance and value to design a reasonable power coordination control strategy to maintain the power balance of the system. Based on hierarchical control, this paper designs a reasonable power coordination control strategy for AC/DC hybrid microgrid. For lower control, this paper designs a variety of control modes for each converter in different application scenarios. For the higher control, this paper analyzes the working mode of the system and designs the power coordination control strategy under the grid-connected and isolated island mode. In grid-connected operation, the DC bus voltage can be stabilized by adjusting the operation mode of the DC energy storage and the on-off of the secondary load. In isolated island operation, the DC sub-microgrid is the main microgrid, and the DC energy storage is the main power regulating equipment. This is based on the principle of \"energy is in short supply in the system, DC energy storage finally discharge, energy supply exceeds demand in the system, DC energy storage gives priority to charging\" of DC energy storage. By adjusting the control strategy of the micro-source, the reference power, and the on-off of the secondary load, the overall power balance is maintained. The Matlab/Simulink simulation software was used to build the AC/DC hybrid microgrid simulation model, which verified the effectiveness and stability of the proposed power coordination control strategy under various operating conditions.  
AC/DC hybrid microgrid; hierarchical control; system working mode; main microgrid +
Footnote †: journal: Applied Sciences
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 2 Background and Related Work  
### Wireless Signal Ranging Technology  
In environments where GPS devices are unavailable or signals are inaccessible, wireless signal ranging technologies offer a critical alternative for enabling location-based services. Devices such as low-power IoT sensors or simplified wearables may lack satellite modules due to hardware constraints or operate in areas with severe signal obstruction. To achieve positioning, current systems primarily employ three wireless methodologies, that is, base station-assisted ranging, Bluetooth, and LoRa. Among them, base station-assisted ranging serves as the primary solution for wide-area coverage, while Bluetooth and LoRa are deployed in specialized scenarios.
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 3 Classification of Lung Ultrasound Data
### Results
#### 3.2.1 Frame-Based Experiments
This complies with findings from Section 2 indicating that patients infected with COVID-19 might not show any abnormalities in LUS (e.g., for asymptomatic patients) or present similar patterns as bacterial pneumonia such as consolidated areas and effusions. For further results including the ROC- and precision-recall curves of all three models see Appendix C.  
Ablation Study with Segmentation Models  
Lung US recordings are noisy and operator-dependent, posing difficulties for the classification of raw data. Hence, we compare VGG and VGG-CAM to approaches from related work where all frames are segmented (i.e., classified on a pixel level into pathological patterns) with the model from [57]; see Appendix B for an example input image. The relevant rows in Table 2 exhibit mixed results, with Segment-Enc marginally outperforming VGG-Segment, but both obtaining substantially lower scores than VGG. The results are thus not encouraging to explore VGG-Segment further, since it comes at the cost of a much larger model, with an ensemble of three models (U-Net, U-Net++, and DeepLabv3+) segmenting the images prior to classification (restricting segmentation to a single model resulted in inferior performance). Possible reasons for the inferior performance of this approach are limited accuracy of the underlying segmentation model ([PERSON] et al. [57] reported a Dice coefficient of 0.75) and the algorithm's pathology-to-color mapping that may dispel a notion of similarity between types of patterns. Future work might explore other architecture along the lines of Segment-Enc, that is, a dense model classifying the encoding produced by a segmentation model. Ablation Study on Other Architectures  
Last, we tested several smaller networks such as MobileNet[74] as an additional ablation study, with NASNetMobile[71] performing best, obtaining 62.5% accuracy, but still suffering from low precision and recall on data of bacterial pneumonia. As most ultrasound devices are portable and real-time inference on the devices is technically feasible, resource-efficient networks are highly relevant and could supersede web-based inference. In an attempt forward real-time on-device inference, our fine-tuned NASNetMobile requires less than a third of the parameters of VGG but does not yet yield satisfactory results (63% accuracy). Figure 3: Performance of the VGG model. Per-class ROC-AUC, sensitivity, and precision are shown on the diagonals of the normalized confusion matrices, highlighting the model’s ability to distinguish COVID-19 from pneumonia and healthy lung images. It further demonstrates similar scores and balanced confusions between each pair of classes. (**a**) ROC-curves, (**b**) Precision-recall-curves, (**c**) Precision-confusion matrix, (**d**) Sensitivity-confusion matrix.
## 2 Monte Carlo Point Probability Model and the Modeling of Light Sources
### Photon Modeling of LED Light Sources  
Part one: We first considered LEDs as the light source. We assumed that an LED is a Lambertian light source [30; 31]. For the sake of simplicity, we assume it as a point light source. Lambertian light sources exist widely in nature, such as the sun, snow, etc. The beam divergence angle \(2\phi_{T}\) of the LED is generally \(120^{\circ}\), which leads its luminous intensity to be very weak. However, it can be \(60^{\circ}\), \(34^{\circ}\), \(30^{\circ}\), or even \(12^{\circ}\) when lens modules are installed. As a Lambertian light source, the irradiance of an LED light source can be expressed by Equation (1).  
\[I(\theta)=I_{0}\cos^{m}\theta \tag{1}\]  
where \(I_{0}\) describes the intensity of light on the luminous axis of the LED light source and \(\theta\) is the off-axis angle. \(m\) is the Lambertian emissivity, and it is defined by Equation (2).  
\[m=-\ln(2)/\ln(\cos(\phi_{T})) \tag{2}\]  
Generally, in industrial contexts, \(\phi_{T}\) refers to the off-axis angle where the light intensity is equal to \(0.5I_{0}\). As shown in Figure 2, the light intensity of LEDs was more densely distributed in the paraxial region and we assumed that it had the same distribution of light intensity at different azimuths.  
Figure 1: The ultraviolet light atmospheric transmission model.  
For the Monte Carlo simulation, we introduced the random number \(\xi_{\theta}\), which satisfies uniform distribution on the interval [0,1], to simulate the initial emission state of a large number of photons. It can be obtained by the following expression.  
\[H(\theta_{\xi})=\left(\int_{0}^{\theta_{\xi}}\frac{I(\theta)}{I_{0}}\mathrm{d} \theta\right)/\left(\int_{0}^{\frac{\pi}{2}}\frac{I(\theta)}{I_{0}}\mathrm{d} \theta\right)=\xi_{\theta} \tag{3}\]  
where \(\theta_{\xi}\) is the off-axis angle uniquely determined by \(\xi_{\theta}\). Since the left side of the equation is a hyper-geometric function, we cannot get the analytical solution of \(\theta_{\xi}\). However, numerical solutions can be obtained by using spline interpolation method. We can obtain a series of numerical values \(\{H(\theta_{1}),H(\theta_{2}),\ldots,H(\theta_{n})\}\) by taking equally spaced samples in \([0,\pi/2]\) and the spline interpolation expression \(H(\theta_{\xi})\) can be obtained using the interp1 function in Matlab2014a.
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 2 Problem Outline
### Biogenic Amine Neurotransmitters
Many psychotic disorders, neurodegenerative disorders, and physical ailments have all been linked to changes in the concentration of NTs in the central nervous system [49; 50]; therefore, both in vitro and in vivo settings are required to comprehend NT levels [51; 52; 53]. The amine NT concentration in biological samples is relatively low, being in the nM range (see Table 1), and it takes susceptible, selective, and reliable sensors to detect them. Many approaches have established transconductance mechanisms for detecting NTs [54], including microdialysis [55], electrochemical electrodes [56; 57; 58; 59], fluorescence [60; 61], chemiluminescence [62; 63], HPLC [64; 65; 66], capillary electrophoresis [67; 68], and FTIR spectroscopy [69]. These methods involve highly trained personnel with time-consuming and complicated setups and thereby are not suitable for point-of-care testing. Thus, an early diagnosis, state monitoring, and a prognosis for associated disorders depend heavily on the precise detection and real-time NT monitoring in human bodily fluids [1; 70; 71]. Traditional detection methods remain restricted due to their lengthy process, need for labelling agents, lack of sensitivity or dynamic range, and high cost [75; 76; 77]. Table 2 summarises the electrochemical sensing that is used in amine NT detection. Therefore, FET-based biosensing has been explored for NT determination because of its straightforward, inexpensive, and quick analysis approach and is reported as one of the most promising available technologies [1], offering real-time monitoring and point-of-care diagnosis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neurotransmitter/Targeted Marker** & **Method** & **Catalyst/Transducer** & **LOD** & **Ref.** \\ \hline \multirow{4}{*}{**DA**} & CV & Graphene/carbon fibre & 1 nM & [78] \\  & CV & Graphene & 2.6 \(\upmu\)M & [79] \\  & CV & rGO & 0.46 \(\upmu\)M & [80] \\ \hline \multirow{3}{*}{**EP**} & CV & Graphene/Au/GCE & 7 nM & [80] \\  & CV, DPV & Au-Pd-rGO/GCE & 1 \(\upmu\)M & [81] \\ \cline{1-1}  & CV & Graphite & 20 nM & [82] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of graphene-based electrochemical biosensing for amine NTs.
## 3 Proposed Model: ABCDE Method and Integration with SSI Concepts
### Some Explicative Diagrams
When the architect decides to share the model with the team of structural engineers, he records the coordination and verification analyses performed on the model (messages 5 and 9) and asks the smart contract to approve the transfer of the model to the shared area: precisely, to area S1/SfC, which is suitable for coordination (message 13). The smart contract verifies that all the data entered by the person in charge satisfy the constraints for passage to the next area, and if the constraints are all satisfied, it stores the new status of the model version, which is now in the shared area, and issues an event to communicate to the CDE that the gate can be passed (message 16). The team of structural engineers receives the notification of the model version uploaded by the architect, performs appropriate clash detection analyses, manages the clashes detected, and creates a BCF file of the analysis results identified by IE01-01, where IE01 stands for a document of type \"information exchange\", and 01 indicates the first version of that file. The team uploads the file created on-chain by associating it with the version of the model to which it refers (message 20), and the smart contract issues an event to signal the successful upload of the file (message 22). Also, the client, who is the appointing party, receives the notification of the upload by the architect, makes a design request IE02-01 related to the BIM model, and uploads the file on-chain (message 24), associating it with the version of the model to which it refers. Also, in this case, the smart contract emits an event to signal that the file has been uploaded (message 26). Once the notifications have been received, the architect checks the files and revises the model. At the end of the revision work, he/she uploads three files on-chain: two response files--respectively, IE01-02 for the structural engineers and IE02-02 for the client--and the DC-0010-INF-M2-D-0002 file relating to the revised BIM model, which is always located in the shared area, and precisely, in S1 suitable for coordination S1/SfC (Note that when the DC-0010-INF-M2-D-0002 file is uploaded, messages 1 to 19 are repeated.). Multiple iterations can be done until a model that satisfies all parties is reached (indicated by the loop in the sequence diagram). Once the iterations are finished, the architect makes a transaction on-chain asking to set the new status of the model version, which is now in the S4/SfA area, to suitable for approval (message 36). The smart contract issues an event to communicate to the CDE the change to the model's state within the shared area of the CDE (message 39). Once the notification has been received, the client approves the BIM model and creates a document, IE000i (message 43), indicating its approval, and, hence, the completion of the model, and makes a transaction on-chain to ask the smart contract to pass the model to the next area D4/SfM (message 47).
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 6 Cryptographic Foundations and Preliminaries  
To support the technical implementation of our system, we provide the core definitions and cryptographic concepts used throughout the design and proof constructions. This section introduces the mathematical notation and definitions used throughout the paper. Also, it offers the important concepts used in the paper. These concepts form the foundation for understanding the cryptographic primitives and zero-knowledge proof systems employed in Post-Quantum VerITAS.  
Let us denote the Set \(\{0,\ldots,n-1\}\) as \([n]\), the \(Set\{a,\ldots,b\}\).  
We denote as \([a,\ b]\). \(F_{q}\) is the finite field of size \(q\).  
As \(r\leftarrow\$S\), we will denote the random \(r\) from the set \(S\), as \(\omega\)--primitive \(n\)-th root of unity in \(F\). The set \(\left\{1,\omega,\ldots,\omega^{(n-1)}\right\}\) of size \(n\) we denote as \(\Omega\).  
\(Z_{\Omega}(X)\) is the vanishing polynomial on \(\Omega\), defined as follows:  
\[Z_{\Omega}\ (X)=X^{n-1} \tag{2}\]  
\(F^{(<\text{d})}[X]\) is the univariate polynomials of degree less than \(d\) over \(F\). The vectors we will write using bold lowercase (e.g., \(v\in F^{m}\)), with elements (\(v_{0}\),...,\(v_{m-1}\)). For concatenation, we will use the following:  
\[v\|w, \tag{3}\]  
which means the concatenations of \(v\) and \(w\).  
All the matrices we will write with old uppercase (e.g., \(A\in F^{n\times m}\)).  
The rows of the matrix: \(a_{0}\),...,\(a_{n-1}\in F^{m}\). The elements: \(a_{i,j}\), where \(a_{0,1}\) is the second element in the top row.  
The hash function we will write as follows:  
\[H:F^{*}\to F, \tag{4}\]  
which takes any finite number of field elements as the input.  
A summary of key mathematical symbols used throughout the paper is provided in Table 2 for reference.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(\mathbb{F}_{p}\) & A finite field of prime order \(p\) \\ \hline \(\omega\) & A primitive \(n\)-th root of unity in \(\mathbb{F}_{p}\) \\ \hline \(\overset{\rightarrow}{\vec{v}}\) & Input image vector (e.g., RGB channel) \\ \hline \(A\) & Random matrix used in lattice hash \\ \hline H & Cryptographic hash function \\ \hline com & Polynomial commitment \\ \hline \(f(x)\) & Polynomial encoding of vector data \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation summary table.