esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Scheme
### One-Class Electricity Theft Classifier  
The second phase of our proposal involves transforming the previously explained predictor into a classifier by calculating the errors between the predicted and actual consumption readings and using these errors to classify the input readings. The underlining idea is that if the input power consumption readings are correct, the errors should be within a certain range, and thus, deviations from normal errors can be identified, and the input readings are classified a malicious. Unlike traditional methods that experimentally set a threshold on the obtained errors to differentiate between benign and malicious readings, we propose a dynamic method for learning the threshold using an autoencoder combined with an OC-SVM in a two-stage process. The main advantage of our method compared to traditional methods is that it does not need malicious data to calculate a good threshold, and the assumption of the availability of malicious data is not practical in the case of designing of a one-class detector.  
The first stage involves training an autoencoder on the obtained benign prediction errors, as shown in Figure 7. In particular, the encoder component, denoted as \(g_{\omega}\), progressively compresses the input data, \(x\), through a series of densely connected hidden layers into a latent space representation. These compressed data are subsequently fed into the decoder, denoted as \(f_{\omega}\), which mirrors the encoder's structure, to reconstruct the original input. The discrepancy between the original data and their reconstructed counterparts constitutes the reconstruction error. Autoencoders are trained on normal data to minimize this reconstruction error, represented by the loss function \(\mathcal{L}\), with the mean squared error (MSE) serving as the optimization objective as follows:  
\[\mathcal{L}_{MSE}(x,g_{\omega}(f_{\omega}(x))) \tag{3}\]  
The underlining idea is that since the autoencoder is trained on benign data and it is able to reduce the reconstruction error of these data, the reconstruction errors of the input false data have a different pattern. After training the encoder, the second stage involves training an OC-SVM on the encoder output. This approach is more effective than training the OC-SVM directly on the raw errors, as it leverages the encoder's ability to transform errors into a distribution that the OC-SVM can learn more effectively. Specifically, the OC-SVM is trained to establish a decision boundary for the features of benign errors (i.e., the errors obtained after training the predictor on benign data) within the feature space. The OC-SVM constructs a hyperplane in this high-dimensional space, strategically positioned to maximize the inclusion of normal feature instances while maximizing its distance from the origin.  
Figure 6: Selecting the number of clusters.
## 3 Results and Discussion
### Visible Observation and UV-Visible Spectroscopy of AgNPs Biosynthesis
#### 3.1.1 Impact of Nitrogen Source
The nitrogen contents in the microalgal cultures gradually decreased through the cultivation period (18 days) due to their utilization for algal growth, as represented in Figure 2.  
Visible observations and the UV-Visible spectroscopy results indicated the influence of the nitrogen source on AgNP biosynthesis during the cultivation of _S. obliquus_. Extracellular AgNP biosynthesis was highest for nitrate followed by sulfate, while it was almost negligible in the cases of the urea and ammonium carbonate supernatants. These results might be attributable to the reductase enzymatic activities, which increased due to the utilization and consumption of nitrate and sulfate in the microalgal culture. The role of reductase enzymes in extracellular biosynthesis of AgNPs was reported in a previous study [49]. Figure 1: Spectrophotometer scanning of synthesized AgNPs by _S. obliquus_ cultivated in BBM media containing different nitrogen sources (**a**) NaNO\({}_{3}\); (**b**) urea; (**c**) (NH\({}_{4}\))\({}_{2}\)CO\({}_{3}\); (**d**) KNO\({}_{3}\); (**e**) NH\({}_{4}\)NO\({}_{3}\); (**f**) (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)).
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Detecting Low-Quality and Compressed Deepfakes  
Detecting low-quality and compressed deepfakes remains a significant challenge for many existing video deepfake detection models. These models often struggle when videos are noisy, compressed, or low-resolution, which is common when deepfakes are circulated on social media platforms [34]. Compression artefacts, pixelation, and noise can obscure the visual cues that models like CNNs rely on, reducing their effectiveness in identifying subtle signs of tampering [2]. As a result, video deepfake detection in these real-world scenarios becomes significantly more difficult, allowing manipulated content to evade detection and spread unchecked [87]. To overcome this challenge, researchers should focus on developing techniques that are specifically designed to handle compressed and low-resolution video data. Multi-scale learning methods could be employed, where models analyse video data at different resolutions to detect patterns that may be invisible at a single scale.
## 2 Data and Methods
### Ocean Wave Spectrum
The spectral widths in two coordinate directions are represented by \(\sigma_{Kx}\) and \(\sigma_{Ky}\), and in this paper, \(\sigma_{Kx}=\sigma_{Ky}=0.0025\) m\({}^{-1}\). Figure 2 shows the swell spectrum with different significant wave heights of swell \(H_{s}\) and swell wavelengths \(\lambda_{c}\). Figure 2a,b show that for a certain \(H_{s}\), the wavelength of swell has little effect on the intensity of the swell spectrum. From Figure 2b,c, we can see that \(H_{s}\) affects the spectral intensity, which will become larger as \(H_{s}\) increases. Figure 1: The variation of D, A, E, and H18 omnidirectional spectra \(S(k)\) and curvature spectra \(B(k)\) with wavenumber k at wind speeds of 5 m/s, 10 m/s, and 15 m/s. **(a–c)** Omnidirectional spectra vary with wavenumber k, **(d–f)** omnidirectional spectra that account for Bragg scattering for incidence angles between about 30\({}^{\circ}\) and 60\({}^{\circ}\) in the C band, **(g–i)** curvature spectra vary with wavenumber k. Green curve, D spectrum; blue curve, A spectrum; red curve, E spectrum; magenta curve, H18 spectrum. Figure 2: The swell spectrum with different significant wave heights and dominant wave wavelengths. **(a)** The significant wave height of swell = 2 m, swell wavelengths = 200 m; **(b)** the significant wave height of swell = 2 m, swell wavelengths = 300 m; **(c)** the significant wave height of swell = 4 m, swell wavelengths = 300 m.
## 6 Conclusions  
CACTI can afford the low-cost temporal super-resolution, but there is image degradation in the process of reconstruction. The imaging results are mainly determined by the performance and robustness of the reconstructive algorithms.  
The GAP with transform regularization algorithm and the GAP with the TV regularization algorithm are the state-of-the-art algorithms used in CACTI. However, the GAP algorithm cannot easily select the groups and group weights in the transform domain, and the selection of groups and weights affects the results significantly. The GAP + TV algorithm utilizes TV regularization only imposed on the 2D frame, which may result in information loss or over-smoothness of reconstructed results, although the TV algorithms are almost parameter-free.  
A maximum a posteriori probability and Markov random field (MAP-MRF) super-resolution approach is proposed to enhance the clarity and fidelity of the reconstruction results. It is based on a weighted 3D neighbor system and adopts the coordinate descent method to perform joint estimation on model parameters, including motion vector flow and SR results.  
The key processes of the proposed approach are the joint estimation of the MAP-MRF model using coordinate descent method, and the image reconstruction using multi-regularization. A multi-reconstruction algorithm is proposed in this paper to reconstruct high-speed videos from CACTI, and simulation results demonstrate its robustness and efficiency. The proposed approach renders noise into a total variation minimization problem for compressive sensing and utilizes the redundant information among multi-frames.  
The proposed approach can provide richer texture information and sharper structure. Extensive simulation results verify the robustness and efficacy of the proposed approach. Future work may focus on incorporating this optimization framework into the hardware system.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for helpful comments and valuable remarks. We also thank [PERSON], the implementation and the ground truth have been adopted from his works.  
**Author Contributions:** All authors contributed extensively to the work presented in this paper. [PERSON] designed the algorithm, analyzed the data and wrote the paper. [PERSON] edited and revised the manuscript and provided his valuable suggestions to the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Study
### Simulation Result  
Figure 9 shows the reward profile of each epoch, where the epochs represent the number of iterations. With epochs at 2000, the reward almost converges, which means that the training is almost complete; therefore, the variables of the neural nets are rarely updated. Thus, the policy that returns optimal action when observing the state has been trained.  
After the convergence of the reward, the experiment was conducted on cloudy and sunny days to evaluate the performance of trained inverters. The Figures 9(a) and 9(a) shows the normalized PV generation and load consumption for each day, which are applied o various PV generation and load consumptions per unit. The Figures 9(b) and 9(b) shows the \(VUF(\%)\) at the PCC in modified IEEE 13 bus system for each case during the day. The Figures 9(c) and 9(c) show the \(VUF(\%)\) at the PCC in the modified IEEE 34 bus system for each case during the day. Table 2 shows the average \(VUF(\%)\) at the PCC for each case during the day.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Network** & **Case 1** & **Case 2** & **Case 3** \\ \hline \multirow{3}{*}{\(VUF(\%)\)} & Sunny day & IEEE 13 & 1.23 & 1.38 & 1.37 \\  & IEEE 34 & 0.05 & 0.0514 & 0.52 \\ \cline{1-1}  & Cloudy day & IEEE 13 & 1.29 & 1.40 & 1.42 \\ \cline{1-1}  & IEEE 34 & 0.053 & 0.054 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average \(VUF(\%)\) for each case during both conditions.  
Figure 9: Reward profile.  
Figure 11: PV and load profile and \(VUF(\%)\) at the PCC for a cloudy day.  
Figure 10: PV and load profile and \(VUF(\%)\) at the PCC for a sunny day.  
In Figure 11, in the time period of 6-7 h and 19-20 h, there doesn't seem to be any difference between \(VUF(\%)\) of each case. It is not allowed to provide/absorb reactive power from each PV inverter when the inverter's active power generation is less than \(0.05\times P_{t}^{rated}\). So the \(VUF\)s(\(\%)\) of each case are the same while normalized PV is lower than 0.05. Additionally, the \(VUF(\%)\) of case 1 is lower than others for each day. This means that the proposed strategy mitigates the voltage unbalance at the PCC more than for other comparisons. However, the proposed strategy compensates the reactive power based on its volt-var curve for each inverter and not the mathematically calculated value. Therefore, it is impossible to make the \(VUF(\%)\) zero, but it is a significant advantage to compensate reactive power in real-time according to its volt-var curve. It is expected that the \(VUF(\%)\) of case 1 is lower than that of case 2 for the sunny condition because of the similarity between the actual and forecast profiles of PV and load. Moreover, the outstanding performance of case 1 can be seen in cloudy conditions. This demonstrates that the proposed strategy can also act appropriately even when far from the forecast data owing to cloudiness and other factors. The effect of case 2 was either insignificant or made matters worse than case 3, despite the compensation of reactive power based on the fixed volt-var curve. In other words, inappropriate volt-var control should be discouraged.
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 3 Formation Control System Design  
### Delay Analysis in Control System  
Section 2 analyzed the 5G transmission delay and proposed an estimation method for communication delay. In NCS, the relationship between the communication cycle \(T_{com}\) and the control cycle \(T_{control}\) may also cause delay. Effective control commands require new data. Therefore, \(T_{control}<T_{com}\) will not be considered. When network conditions are stable, \(T_{control}=T_{com}\) is the optimal setting. However, 5G communication has randomness and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **S\(e\)/ms** \\ \hline Kalman Filter & 1.8916 \\ Mean & 2.0482 \\ Median & 2.0329 \\ Max value & 2.0914 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE of methods.  
Figure 5: Estimation error distribution histogram.  
there may be no new data in one communication period. To ensure that new data can be obtained in each control cycle, this paper sets \(T_{control}>T_{com}\).  
Therefore, the 5G NCS total delay \(\tau_{sum}\) of data from generation to validation consists of the following two parts:  
1. Network transmission delay \(\tau_{com}\): This delay describes the time that takes for data packet to AGV2 application layer from AGV1 application layer, which is the E2E delay mentioned above.
2. Execution waiting delay \(\tau_{control}\): This delay describes the time that takes for data packet from being received to being processed.  
Therefore, \(\tau_{sum}=\tau_{com}+\tau_{control}\). Figure 6 shows a schematic diagram of the \(\tau_{sum}\) in the case of \(T_{com}<T_{control}\).  
\(\tau_{control}\) can be calculated by the system clock at the receiving end. The receiving end usually sets the data packet buffer to deal with the out-of-order. Therefore, the receiving end processing flow is as follows:  
Step1: Obtain the decoded data of 5G CPE, record the current time as the arrival time \(t_{a}\) of the data packet, and store the date packet sequence number, content and \(t_{a}\) in the variable of controller.  
Step2: Compare the packet sequence number with the data in the buffer, and the buffer retains the data packet with latest sequence number.  
Step3: When the formation control is processing, the difference between the current system time \(t_{c}\) and the buffer data \(t_{a}\) can be used to obtain \(\tau_{control}\), which can be combined with the delay predicted in Section 2 to obtain the total data delay \(\tau_{sum}=\tau_{com}+\tau_{control}=t_{c}\) - \(t_{a}+\tau/2\).
## 3 Low-Dimensional Reconciliation
### Achieving the Uniform Distribution
#### 3.2.3 Application  
In comparison to the multidimensional approaches, here, one can recognize that these results make no necessary the use of the multidimensional spherical space. The key idea is as follows: do the reconciliation in the scalar space to reduce the problem from \(\Gamma^{d-1}\) of \(\mathbb{R}^{d}\) into \(\mathbb{R}\). The main drawback of the multidimensional reconciliation approaches is the use of spherical space \(\Gamma^{d-1}\) of \(\mathbb{R}^{n}\) to achieve the uniform distribution. As we have found in a CVQKD scenario it is not a required condition, and completely can be eliminated. The uniformly distributed elements of \(\mathbb{R}^{d}\) have to be transmitted over the classical authenticated channel, but it _per se_, does not imply that the reconciliation has to be executed in the spherical space. The spherical correction of the errors of the raw data is a completely undesirable and unwanted event in a practical CVQKD, because it would just cause a further decrease in the very fragile, sensitive, and so strenuously established secret key rates. The use of \(\Gamma^{d-1}\) of \(\mathbb{R}^{d}\) served only one purpose in the multidimensional reconciliation: to guarantee the security requirements of the QKD post-processing phase. From this, it immediately can be concluded that the use of spherical space is, in fact, unnecessary, and a mathematically equivalent and more efficient solution exists in the scalar space of \(\mathbb{R}\).  
One can recognize two improvements in our proposed scheme in comparison to the existing approaches. First, the uniform distribution will be reached by a simple operation, the Gaussian-CDF function applied separately on each unit of the raw data. Second, the approximation of the Gaussian channel will be justified by the CLT, using arbitrary dimensional vectors. As follows, the physical-logical channel conversion can be established with arbitrary high precision, since the \(d\leq 8\) limitation has also been eliminated from the picture. To conclude, the spherical space can be replaced by the CDF transformation on the raw data units, and the Dirac distribution can be replaced by the CLT. It is clear now that the existing reconciliation methods require a revision since its application just leads to further slow-down in a practical CVQKD scenario. By these reasons, we drop away the spherical space, and instead of it, use the CDF-transformed units. These improvements allow for very efficient decoding and error-correction, however, this step does not modify any fine property of the code: in other words, it keeps the desired uniform distribution and guarantees the arbitrary high-precision in the approximation of the logical binary Gaussian channel. Finally, we have to emphasize again that the whole reconciliation procedure is implemented through the logical layer only, without any need of physical-layer tomography.
## 3 Results and Discussion
### Relationship Between RT and Reading Error  
The relationship between collected RTs and the reading errors is shown in Figure 6. The scattering of the results indicates that there was no strong mathematical correlation between the two, although a relatively long RT usually resulted in a correct reading: despite the complexity of the IC, the participant had enough time to correctly read the speed value. Conversely, striving to complete a task as quickly as possible could increase the rate of errors on the same IC, but this rule was not confirmed.  
Most errors tend toward negative values, which means that the participant read a lower value than the value on the speedometer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**IC** & **01** & **02** & **03** & **04** & **05** & **06** & **07** \\ \hline age-RT correlation & 0.454 & 0.340 & 0.579 & 0.492 & 0.439 & 0.254 & 0.119 \\ \hline \end{tabular}
\end{table}
Table 2: Correlation coefficients between participant age and RT averaged for both sessions.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 2 Theoretical Review
### Ad-Hoc Networks Routing Protocols
#### 2.2.1 Better Approach to Mobile Ad-Hoc Networking (BATMAN)  
The BATMAN [51] is a proactive routing protocol for Ad-hoc network. It uses a different approach for sharing the knowledge about the best paths. Basically, each node has information about which jump distance neighbor has the best route to a given destination X, that is, which neighbor must be chosen when it is desired to send a packet to node X.  
In the BATMAN each node in the network sends a message, called OriGinator Messages (OGMs), to all its neighbors to inform of its existence. The OGMs are small messages that contain the address of the originating node, the address of the node that relayed, a Time To Live (TTL) and a sequence number to record the route already taken by the packet. When a node receives an OGM message it updates its routing table, decreases the TTL and increases the sequence field. After that it forwards the message to its neighbors; this procedure is repeated until all nodes in the network receive the message.  
BATMAN uses the exchange of OGMs messages to influence the choice of routes, basically this happens as follows: When an X node in the network receives the same OGM from a Y emitter through two different paths it discards the last message and considers only the first message. The idea is that OGM that arrived first probably traveled the best route.  
The node X then records which neighbor of a jump emitted the OGM that arrived first. This neighbor is defined as the best path for a possible route to the Y transmitter. When OGMs go through bad routes are usually lost or take a longer time to arrive, thus, the node will only consider OGMs from good routes, that is, only the routes considered the best are recorded.  
Another important mechanism of BATMAN is the selective flooding system that works as follows: When a node receives an OGM in addition to relaying the OGM received to neighbors it also responds the source node with another OGM message. However, it does not send the message in broadcast, it first queries in its table which neighbor has the best route to the source node and sends only to this neighbor. In this way, messages are sent selectively. Which decreases the overhead of control messages.  
BATMAN is used as a reference in this work because it is a well-known protocol for ad-hoc networks.
# The Potential Therapeutic Role of Bruton Tyrosine Kinase Inhibition in Neurodegenerative Diseases
###### Abstract  
Bruton Tyrosine Kinase (BTK) has emerged as a critical mediator in the pathophysiology of neuroinflammation associated with neurodegenerative diseases. BTK, a non-receptor tyrosine kinase predominantly expressed in cells of the hematopoietic lineage, modulates B-cell receptor signaling and innate immune responses, including microglial activation. Recent evidence implicates aberrant BTK signaling in the exacerbation of neuroinflammatory cascades contributing to neuronal damage in disorders such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, ischemic stroke, and Huntington's disease. Pharmacological inhibition of BTK has shown promise in attenuating microglial-mediated neurotoxicity, reducing pro-inflammatory cytokine release, and promoting neuroprotection in preclinical models. BTK inhibitors, originally developed for hematological malignancies, demonstrate favorable blood-brain barrier penetration and immunomodulatory effects relevant to central nervous system pathology. This therapeutic approach may counteract detrimental neuroimmune interactions without broadly suppressing systemic immunity, thus preserving host defense. Ongoing clinical trials are evaluating the safety and efficacy of BTK inhibitors in patients with neurodegenerative conditions, with preliminary results indicating potential benefits in slowing disease progression and improving neurological outcomes. This review consolidates current knowledge on BTK signaling in neurodegeneration and highlights the rationale for BTK inhibition as a novel, targeted therapeutic strategy to modulate neuroinflammation and mitigate neurodegenerative processes.  
bruton tyrosine kinase (BTK); BTK inhibitors; neuroinflammation; microglia; Alzheimer's disease; Parkinson's disease; multiple sclerosis; ischemic stroke; Huntington's disease; targeted immunomodolation