esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Amidst the escalating need for stable power supplies and high-quality communication services in remote regions globally, due to challenges associated with deploying a conventional power communication infrastructure and its susceptibility to natural disasters, LEO satellite networks present a promising solution for broad geographical coverage and the provision of stable and high-speed communication services in remote regions. Given the necessity for frequent handovers to maintain service continuity, due to the high mobility of LEO satellites, a primary technical challenge confronting LEO satellite networks lies in efficiently managing the handover process between satellites, to guarantee the continuity and quality of communication services, particularly for power services. Thus, there is a critical need to explore satellite handover optimization algorithms. This paper presents a handover optimization scheme that integrates deep reinforcement learning (DRL) and graph neural networks (GNN) to dynamically optimize the satellite handover process and adapt to the time-varying satellite network environment. DRL models can effectively detect changes in the topology of satellite handover graphs across different time periods by leveraging the powerful representational capabilities of GNNs to make optimal handover decisions. Simulation experiments confirm that the handover strategy based on the fusion of message-passing neural network and deep Q-network algorithm (MPNN-DQN) outperforms traditional handover mechanisms and DRL-based strategies in reducing handover frequency, lowering communication latency, and achieving network load balancing. Integrating DRL and GNN into the satellite handover mechanism enhances the communication continuity and reliability of power systems in remote areas, while also offering a new direction for the design and optimization of future power system communication networks. This research contributes to the advancement of sophisticated satellite communication architectures that facilitate high-speed and reliable internet access in remote regions worldwide.  
LEO satellite; handover strategy; power grid scenario; graph reinforcement learning +
Footnote †: journal: _Aerospace_  
11 May 2020  
Revised: 19 June 2020  
Accepted: 24 June 2020  
Published: 24 June 2020
## 3 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Algorithm** & **Advantages** & **Nature of Solution** & **Disadvantages** & **Applications** \\ \hline Genetic Algorithm (GA) ([PERSON] 1992) [8] & Enables us to explore a search space without losing partial solutions, explores various parts of the solution space simultaneously, and effectively combines novel combinations with existing information. & Based on natural selection and generational reproduction of the fitted humans, initialization, fitness, selection, cross-over, and mutation are all phases in the algorithm, follows a constructive greedy heuristic approach for finding excellent pathways. & More computational time, large complexity in network structure, difficulty in showing branching and looping. For decision-making issues, genetic algorithms are inefficient and ineffective. & Data clustering and mining, Traveling Salesman Problem (TSP), neural networks Wireless sensor networks, medical science, Vehicle Routing Problem (VRP). \\ \hline Particle swarm optimization (FSO) ([PERSON] and [PERSON] 1995) [16] & PSO has a few parameters to adjust, it takes a small amount of time to compute, in terms of discovering global optima, PSO has a greater likelihood and efficiency. & The algorithm explores the candidate solution space for the best-known solution and is based on the best location of the entire swarm, as well as the particle’s most known position. & The challenges faced in PSO are low-quality solutions, initial design parameters are quite volatile power management, ideal power flow, neuronal network training. \\ \hline Ant colony optimization (ACO) ([PERSON] et al. 1996) [9] & ACO can be used in dynamic applications, it can adjust to new distances and other changes, and it may also search for a large population at the same time. & Model-based on pheromones that are random and probabilistic, the agent dynamically updates the weight of nodes and edges (ant). & The probability distribution might change with each iteration, which is one of ACO’s problems, ACO’s theoretical analysis is complex, it will take an unknown amount of time to reach a point of convergence. & The probability distribution might change with each iteration, which is one of ACO’s problems, steering problem, retro vehicle
## 1 Introduction
Firstly, unlike previous studies that were mostly limited to a single model type, we systematically compared different types of models, including traditional statistical models (SARIMA)and advanced machine learning models (Prophet and LightGBM). This multi-model comparison approach allows for a comprehensive evaluation of the strengths and weaknesses of each model in the context of PM\({}_{2.5}\) concentration prediction, providing a more robust basis for model selection [66]. Secondly, we employed a variety of visualization methods to deeply analyze the prediction results from multiple dimensions, such as spatial and temporal variations. This approach not only enhances the interpretability of the results but also provides valuable insights into the underlying patterns of PM\({}_{2.5}\) concentration changes [67]. Thirdly, considering the characteristics of PM\({}_{2.5}\) data in Nanning urban area, our study adopted a data-driven strategy, focusing solely on time series data to mine potential laws. This strategy maximizes the use of high-resolution data, thereby improving the reliability and effectiveness of the prediction [68]. Lastly, our research on Nanning urban area has important demonstration value for other regions facing similar situations. By providing a detailed case study, our methods and findings can help promote the widespread application and innovative development of Machine Learning models in the field of environmental science [69]. These innovations not only enhance the scientific rigor of our study but also contribute to practical applications. For instance, the multi-model comparison approach can help policymakers and environmental managers make more informed decisions by selecting the most appropriate model based on specific needs and conditions. The data-driven strategy and high-resolution data utilization can improve the accuracy of PM\({}_{2.5}\) concentration predictions, thereby supporting more effective air quality management and public health protection measures [70].
## 5 Conclusions  
We demonstrated an exhaustive comparison of machine learning (ML), deep learning (DL), and transformer-based approaches in offensive language detection tasks specific to Roman Urdu text here. Utilizing traditional ML classifiers (SVM, Naive Bayes, Logistic Regression, and Random Forest), deep learning architectures (CNN and Bi-LSTM), and transformer-based models (LLaMA 2 and ModernBERT), we perform a comprehensive comparison for text classification, asceraining the most promising approach. We show that fine-tuned transformer models greatly affect the performance (of offensive language detection in Roman Urdu), out of which the fine-tuned version of LLaMA 2 with LoRA showed the best performance with an F1-score of 96.58%, thus making it the optimal solution for offensive language detection in Roman Urdu. The CNN also caught attention as it excelled in learning patterns in the text while underperforming compared to LLaMA 2, owing to its inability to model longer dependencies or capture contextual variations. ModernBERT also performed competitively, demonstrating the relevance of transformer-based models for low-resource and transliterated text processing. These results highlight that when effectively fine-tuned, large language models offer the best-in-class results for offensive language detection in complex linguistic environments.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.8 Hepatoprotective Potential  
Non-alcoholic fatty liver disease is a prevalent liver condition worldwide, characterized by the excessive accumulation of fat in the liver in the absence of secondary causes or other liver diseases [116]. In a randomized, double-blind, and placebo-controlled clinical trial by [PERSON] et al. [117], the administration of 2.5 mL of black cumin oil every 12 h for 3 months in patients diagnosed with NAFLD was associated with a reduction in the degree of fatty liver disease compared to the placebo group. Additionally, significant improvements were observed in the levels of ALT, AST, triglycerides, LDL-C (low-density lipoprotein cholesterol), and HDL-C (high-density lipoprotein cholesterol) [117]. [PERSON] et al. [118] investigated the impact of supplementing black cumin oil in individuals diagnosed with non-alcoholic fatty liver disease on cardiometabolic outcomes such as adiponectin, leptin, and blood pressure. After 8 weeks of supplementing with 1000 mg of oil daily, there were no significant differences in adiponectin, leptin, or blood pressure levels between the experimental and placebo groups [118]. The hepatoprotective properties of black cumin oil have been confirmed in animal models, showing a protective effect in liver damage induced by bisphenol A (BPA) or CCL4, and liver damage caused by carboplatin administration [119; 120; 121]. One of the more recent studies showed that thymquinone exhibits promising hepatoprotective properties by effectively inhibiting thioacetamide-induced liver fibrosis in rats. It inhibits the increase in serum liver enzymes, significantly increases the level of antioxidant markers, and effectively reduces the level of inflammatory markers and fibrosis markers [122].
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 4 End-to-End Synchronization Using TESC
### Synchronization of the Internal Clock of the Small Cell: TSN-Based Synchronization  
The synchronization process from the GM to the SC involves exchanging gPTP messages, such as Sync and Follow_Up messages, between the SC and the gPTP GM clock. These messages carry timing information and are used to calculate the clock offset and clock drift between the SC and the TSN domain. The SC adjusts its local clock based on the received timing information to achieve synchronization with the TSN domain.  
In Figure 5, the UPF receives a Sync message from the GM. Using this message, the UPF implements a first-timing protocol to synchronize the UPF with the SC, whereas NW-TT and SW-TT implement a synchronization protocol following a Precision Time Protocol. For example, NW-TT and SC-TT may implement synchronization using the IEEE 802.1 AS standard [15]. The 5G network may be transparently integrated with the TSN network. The integration between the 5G network and the TSN network may be implemented as a bridge in accordance with IEEE 802.1 standards [16, 17]. Thus, the UPF uses a Sync message to update the local clock at the UPF, and NW-TT updates the UPF timing according to IEEE 802.1 AS while the GM may directly communicate with the UPF. If the network includes multiple UPF facilities, the UPF may indirectly receive a Sync message from the GM through an intermediary UPF.  
As described above, in connection with Figure 5, the first timing protocol, implemented using the NW-TT and SC-TT, may treat the SC as the endpoint of the TSN timing domain. For example, the synchronized time at the SC in the first timing protocol may be represented by the following equation:  
\[\tau_{\text{sc}}=\tau_{0}+\delta_{\text{(link delay) }}+T_{\text{residence}}, \tag{1}\]  
where \(\tau_{\text{sc}}\) is the synchronized time of the SC; \(\tau_{0}\) is the reference time, received from the GM; \(\delta_{\text{Link delay}}\) is the propagation time of the packet, including the sync message, through the cable from the GM to the NW-TT; and \(T_{\text{residence}}\) is the residence time between the UPF and the SC expressed in the GM time base. The link delay may be calculated according to the IEEE 802.1 AS standard. The residence time is measured using ingress timestamps (t\({}_{\text{si}}\)) and egress timestamps (t\({}_{\text{se}}\)). For example, the t\({}_{\text{si}}\) is recorded when a sync message is received at the UPF, and t\({}_{\text{se}}\) is recorded when a sync message is received at the SC. The residence time may be represented by the following equation:  
\[T_{\text{residence}}=t_{\text{se}}-t_{\text{si}}\times\text{RateRatio}, \tag{2}\]  
where the RateRatio is the ratio between the frequency of the GM clock and the SC's local clock. The RateRatio may be determined according to the IEEE 802.1 AS standard. Therefore, the GM, UPF, and SC are synchronized according to 3 GPP 23.501 Release 16 for downlink synchronization and 3 GPP 23.501 Release 17 for uplink synchronization.
# Histopathology and Genetic Biomarkers of Choroidal Melanoma
## 4 Immunohistochemical Features  
CMs are positive for Human Melanoma Black 45 (HMB45) antigen, S-100 protein, Melan-A (also known as melanoma antigen recognized by T cells 1/MART-1), melanocyte inducing transcription factor (MITF), tyrosinase, vimentin, and Sex determining Region Y-Box 10 (SOX10) [15,16]. Differently to the strong labeling of cutaneous melanoma, S100 is frequently weak and variable in CM. HMB45 antigen, tyrosinase, Melan-A, SOX10 and MITF are better immunomarkers for CM considering the strong positivity in all histologic types [17]. Particularly, SOX10 is a nuclear transcription factor with crucial functions in the specification of the neural crest and maintenance of Schwann cells and melanocytes [18,19]. It is usually immunoexpressed at the nuclear level in melanocytes and breast myoepithelial cells and is currently used in the histopathologic diagnosis of many neoplasms, being diffusely positive in breast basal-like (triple-negative) carcinomas, melanomas and soft tissue tumors of neural crest origin, including malignant peripheral nerve sheath tumor [18,19]. SOX10 represents the most specific melanoma cell marker, being also expressed in those histotypes characterized by loss of immunohistochemical expression of other melanoma cell markers, such as cutaneous desmoplastic melanoma [18,19].
# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 1 Introduction
It is being applied in various fields, such as medicine, engineering, and agriculture, owing to the easy generation of information suitable for a new purpose using previously acquired information [11; 12; 13]. In this study, we aimed to diagnose a 5G sub-6-GHz array antenna based on a near field using transfer learning. A trained network based on the near field implemented the array antenna diagnosis technique with high accuracy and a short measurement time. First, the possibility of its diagnosis was reviewed by applying a classification network, and the amplitude and phase of the array antenna excitation signal were accurately predicted by implementing a regression network for the subsequent complete diagnosis. Additionally, to accelerate the measurements, a regression network was trained using the sampled near-field. The measurements verified the validity of the array antenna diagnosis by transfer learning. The remaining paper is organized as follows. The transfer learning and training data formation are introduced in Section 2, and the experimental results for the trained networks are presented in Section 3. Section 4 discusses the effectiveness of the array antenna diagnosis using transfer learning based on measurements on a fabricated array antenna, and finally, Section 5 presents the conclusion.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 5 V2V Aids toward Sustainable Transportation Systems
### V2V DMN Contributions in ITS  
V2V mesh networks are bound to present various advantages to durable ITSs and, as a result, will greatly improve traffic, safety, and sustainability. These networks facilitate real-time V2V communication, which in turn eases traffic and facilitates maintaining order. As a result, fuel consumption and emissions are reduced, and improving air pollution and urban contamination are the main objectives for any eco-city. Safety also receives a massive increase. Vehicle data, including information about road conditions, posted risks, and traffic data, can be quickly received, thereby reducing accident rates [71]. In emergencies, it also speeds up the response. But that is not all. V2V networks will not only allow many advances in ADAS and driverless cars that tune traffic management and decrease brick-and-mortar infrastructure requirements, coupled with the fact that they have a decentralized structure, but they are also swift and resilient [31]. Not only do some cars fall out of the network, but the system works proficiently all the time, ensuring smooth transport. Therefore, V2V mesh networks are the real foundation of ITSs, with extremely efficient communication between vehicles and the integration of technology in smart cities, which, in turn, alters the way people travel and breathe in the cities [72].
# Phase Calibrated Ring Oscillator PUF Design and Application
## 6 Experimental Results
### Authentication Application  
The performance of USR-based authentication is shown in Table 6. Compared to the measured raw response of PCPUFs, USRs had a lower bit error rate. When we set the threshold to 20, all three PCPUFs had a low false positive and false negative rate in theory. We also verified the bound by measuring 300,000 CRPs on an FPGA. The results showed that no false positive or false negative was detected.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PUF Type** & **FPGA Type** & **Bit Length** & **Max BER** & **Min BER** \\ \hline Configurable RO PUF & Spartan 3 & 63 & 1.02\% & 0.71\% \\ Composite RO PUF & Spartan 3 & 4 & 8.46\% & 1.06\% \\ Improved RO PUF & Spartan 3E & 4 & 2.58\% & 1.69\% \\ FST RO PUF & Spartan 2 & NA & 2.68\% & 0.67\% \\ PC RO PUF & Kintex 7 & 128 & 0.91\% & 0.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bit error rate Comparison of different PUF implementations on FPGAs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FPGA1** & **BER** & **FPGA1** & **BER** & **FPGA2** & **BER** \\ \hline RO PUF1 & 7.10\% & PCPUF1 & 0.54\% & PCPUF1 & 0.37\% \\ RO PUF2 & 8.09\% & PCPUF2 & 0.85\% & PCPUF3 & 0.70\% \\ RO PUF3 & 7.13\% & PCPUF3 & 0.62\% & PCPUF2 & 0.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bit Error Rate (BER) of RO PUFs and PCPUFs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FPGA1** & **Bias** & **FPGA1** & **Bias** & **FPGA2** & **Bias** \\ \hline RO PUF1 & 40.85\% & PCPUF1 & 49.21\% & PCPUF1 & 48.72\% \\ RO PUF2 & 47.10\% & PCPUF2 & 49.77\% & PCPUF3 & 51.08\% \\ RO PUF3 & 58.42\% & PCPUF3 & 51.35\% & PCPUF2 & 50.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bias of RO PUFs and PCPUFs.  
Table 7 shows the other authentication solutions with Hamming ECC and Bose-Chaudhuri-Hocquenghem (BCH)ECC. Hamming(255,247) could correct one bit error in 128-bit responses, while BCH(255,128,15) had a correct capability of as many as 15. However, the overhead of BCH ECC was much larger than that of Hamming ECC. We applied both ECCs to the shift register-based RO PUFs and our PCPUFs. Compared to the poor performance on RO PUFs, the lower bit error rate of PCPUFs took better advantage of ECCs. When those 100,000 CRPs were tested with BCH ECC, no false negative was detected. Though it is hard to estimate the accurate false negatives of BCH ECC-based authentication on PCPUFs, we can compare the overhead of these solutions in order to find a better trade-off. The codeword of BCH ECC was almost the same size as the key, while our USR-based solution had no additional overhead at all. Therefore, USR-based authentication is recommended when the memory space becomes a critical bottleneck.
## 3 Smart Sensor Monitoring Approach
### Architecture Design and Traning Setup  
In this research work, three different GRU architectures were used to investigate the influence of different hyperparameters on tool wear estimation performance:  
* The first architecture was characterized by 21 hidden layer neurons, equal to the number of features extracted by the autoencoder from the force, torque, vibration, and acoustic emission RMS signals plus the hole number, as reported in Figure 7. A hyperbolic tangent activation function was selected for the hidden layer, and a rectified linear unit was employed for the output layer. The output of the hidden layer is used as a feedback branch for the GRU network. A memory window of two samples was selected for the input layer. The output layer had only one neuron corresponding to the tool wear value predicted by the GRU network;
* In the second architecture, a deeper structure was realized by introducing an additional hidden layer. In the first hidden layer of this second architecture, 42 neurons were employed, and a hyperbolic tangent activation function was selected. The second hidden layer was characterized by 21 neurons, and the output layer had only 1 neuron;  
Figure 9: Gated Recurrent Unit (GRU) scheme.  
* A third architecture was developed to study the effect of the memory window on the network performance. Since the first results showed that the best performance was provided by the deeper architecture, the input layer of the second architecture was changed from (2, 21) to (4, 21), i.e., using a memory window of four samples instead of two samples.  
The three architectures were trained using the four datasets defined in Table 1, on an industrial AI-based computer with the characteristics reported in Table 5. The datasets were randomly divided into training and validation subsets according to a 75:25 split ratio, meaning 75% of the data were in the training subset and 25% in the validation subset. For all the training sessions, the Adam optimizer was selected [36], and a learning rate of \(1\cdot 10^{-6}\) was used for 500 k epochs to avoid unlearning problems. Finally, a gradient clipping in the range \((-1,1)\) was employed to avoid exploding gradient problems. For the neural network development, training, and inference, the TensorFlow library was used.