esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Few studies have already been performed to assess oral bacteria during steroid therapy for oral lichen planus (OLP). Thus, the aim of our study was to analyze the effect of dexamethasone mouthrinse treatment on the oral bacteria of OLP patients. This prospective study was conducted on patients who were diagnosed with OLP and treated with 0.05% dexamethasone mouthrinse twice per day for 4 weeks. Using unstimulated saliva of the patients before and after treatment, the qualitative and quantitative changes in oral bacteria were analyzed using quantitative real-time polymerase chain reaction (qPCR). The qPCR results were analyzed using Wilcoxon signed-rank test to the quantitative changes with dexamethasone mouthrinse. The statistical significance was considered at a level of 0.05. In total, 20 patients were enrolled in this study, wherein all were noted to show improved symptoms of OLP. Fifteen patients (75%) had a qualitative change in the oral microbial species and an improved relative periodontitis risk score (from 26.1 \(\pm\) 10.7 to 20.9 \(\pm\) 9.2; \(p=0.008\)). However, quantitative changes in all species were determined to be not statistically different before and after the treatment. Most OLP patients had a changed microbial community composition after 0.05% dexamethasone mouthrinse for 4 weeks. In particular, the composition of the periodontal-pathic bacteria was improved after the treatment.  
bacteria; culture technique; dentistry; oral lichen planus; real-time PCR; saliva; periodontology
## 2 Methodology
### DT and Optimization System
#### 2.2.2 Optimization System  
For the optimal operation of the system, two main subsystems are identified. The first includes the PV generator, SESS, grid connection, and industrial loads, resembling the microgrid concept [22]. The second comprises the EV fleet and the associated charging infrastructure, with one charger per vehicle. Both subsystems are fairly complex and nonlinear, making simultaneous optimization computationally burdensome, which could limit the scalability and robustness of the solution. Mixed integer linear programming (MILP) is a common approach to overcoming this issue in energy systems optimization. By using simpler models (piecewise linear models, etc.), MILP can reduce the complexity of nonlinear problems and still achieve good accuracy and higher computational efficiency compared to nonlinear models [23].  
Optimizing EV fleet charging is typically a binary-heavy problem due to the On/Off charging decisions and piecewise linearization used to account for the nonlinear charging behavior. Even if MILP is more efficient than nonlinear approaches, binary-based linearization could limit the solution scalability, especially when microgrid models are added to the same optimization problem [24]. To mitigate this issue, this work proposes a two-layer optimization framework to increase the robustness and scalability of the solution.  
The first layer corresponds to the electric vehicle optimizer (EVO), which optimally schedules EV charging by selecting timing and power delivered to each vehicle. The second layer, represented by the industry asset optimizer (IAO), receives the charging schedule generated by the EVO and other inputs to coordinate the power dispatch from the microgrid energy assets, focusing on the optimal usage of the SESS.  
The decomposition of the problem enables each optimization layer to include detailed models of its main subsystem and simpler models for the rest of the system. This allows for prioritizing modeling detail to achieve more accurate EV charging while acknowledging the impact of EV charging demand on the microgrid. Hence, this approach is scalable for larger EV fleets and microgrid assets, avoiding premature problem relaxations and suboptimal solutions needed with a single optimization approach [24; 25]. Additionally, in the case of IAO failure, the EVO outputs can serve as a suboptimal EMS for the microgrid assets, providing redundancy and robustness to the solution.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
###### Abstract  
Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder for Tibetan speech synthesis. Specifically, we introduce the mixture alignment FastSpeech2 method to correct errors caused by hard alignment in the original FastSpeech2 method. This new method employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby improving alignment accuracy between text and speech and enhancing the naturalness and intelligibility of the synthesized speech. Additionally, we integrate pitch and energy information into the model, further enhancing overall synthesis quality. Furthermore, Tibetan has relatively smaller text-to-audio datasets compared to widely studied languages. To address these limited resources, we employ a transfer learning approach to pre-train the model with data from resource-rich languages. Subsequently, this pre-trained mixture alignment FastSpeech2 model is fine-tuned for Tibetan speech synthesis. Experimental results demonstrate that the mixture alignment FastSpeech2 model produces higher-quality speech compared to the original FastSpeech2 model, particularly when pre-trained on an English dataset, resulting in further improvements in clarity and naturalness.  
FastSpeech2; mixture alignment; pre-trained model; Tibetan speech synthesis +
Footnote †: journal: _Applied_  
_sciences_
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 1 Introduction
[PERSON] [17] proposed a synthetical method to assess the forecasting quality of time series methods and time series models with econometric independent explanatory variables on Miami International Airport and Frankfurt Airport, which indicated that simple models with few independent variables performed as well as more complicated and costly models and that external factors had a pronounced effect on air-travel demand. [PERSON] [18] and [PERSON] [19] evaluated the 5-year, 10-year and 15-year predicting results of 22 airports distributed in the USA, and the evaluations showed that the standard deviation of the predicting results was distributed with very large errors from 30% to 69%. According to the studies on ex-post evaluation of APT prediction, a reasonable framework should be established priorly to evaluating the performances of different forecasting methods [20; 21; 22]. The performances of different forecasting methods are mainly affected by the models and applicable scenes. Influenced by stochastic factors, the models for APT prediction are intuitively inaccurate with plentiful assumptions. The assumptions determined that different models are suitable for certain prediction scenes. A hybrid evaluating method is then proposed in this paper, adjusting to existing prediction methods, and considering multiple prediction scenes. Aiming at evaluating the performance of various methods for APT prediction, the APT of 203 airports in China are studied in this paper. A proposed evaluation method for five common prediction models is introduced. The hypothesis and the evaluating scenes of the proposed evaluation method are put forward. The evaluating method is conducted on 203 airports in China, and the evaluating results of different forecasting methods are obtained. An accurately predicting result of passenger throughput for an airport is one of the most important factors for its development decision on construction and expansion. This paper analyzed the airport passenger throughput (APT) of 203 airports in China, evaluating the performance of various methods for APT prediction and introducing a proposed evaluation method for five common prediction models. It studied the applicability of the five models to different airports with various developing conditions.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
###### Abstract  
In this paper, cooperative simultaneous wireless information and power transfer (SWIPT) terahertz (THz) multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) are considered. The aim is to improve wireless connectivity, resource management, scalability, and user fairness, as well as to enhance the overall performance of wireless communications and reliability. We optimized the current wireless communication systems by utilizing MIMO-NOMA technology and THz frequencies, exploring the performance and gains obtained. Hence, we developed a path-selection mechanism for the far user to enhance the system performance. The EH SWIPT approach used to improve THz communications performance was investigated. Moreover, we proposed a reliable transmission mechanism with a non-LoS (NLoS) line of THz communications for open areas or any location where the intelligent reflecting surface (IRS) cannot be deployed, in addition to using the cheap decode-forward (DF) relaying instead of IRS. The performance and scalability of the upgradeable system were examined, using adjustable parameters and the simplest modulation scheme. The system presents a noticeable improvement in energy efficiency (EE) and spectral efficiency (SE), in addition to reliability. Accordingly, the outcome showed an improvement in the overall reliability, SE, EE, and outage probability as compared to the conventional cooperative networks of the recent related work (e.g., cooperative MIMO-NOMA with THz) by multiple times with a simpler design, whereas it outperformed our previous work, i.e., cooperative SWIPT SISO-NOMA with THz, by more than 50%, with a doubled individual user gain. This system reduces the transceiver hardware and improves reliability with increasing transmission rates.  
cooperative networks; energy efficiency; energy harvesting; MIMO-NOMA path selection; outage probability; reliable THz communications; resource management; spectral efficiency Article
# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 3 Proposed sEMG Spectrogram-Based User Identification
### Noise Removal Including Resting State Signals  
In Ninapro DB2, raw signals are acquired by repeating 40 hand and wrist motions six times each. To partition the sEMG signals into one motion cycle signals for each motion, the signals are partitioned using the label assigned to each motion in the data, as shown in Figure 5.  
After partitioning them into one motion cycles, noise contained in the sEMG signals is removed. The types of noises contained in the sEMG signals include power line noise caused by the measuring device, noise occurring from differences in performance and functions of disposable electrodes, and noise caused by physiological interference. In this study, a band-pass filter was used to pass the 10-500 Hz frequency band containing activity information without attenuating it, whereas the remainder of the frequency band was attenuated and removed. The power line noise, which was generated by the poor grounding of the measuring device or by high power cables around the device, was generally observed at 60 Hz. To remove such power line noise, a band-stop filter was employed to remove the 60 Hz frequency band.  
Subsequently, the resting state signals in the sEMG signals were removed. The resting state signal defined in this study refers to a resting signal included before and after motion is performed during the motion execution time set as the signal acquisition condition. To remove the resting state signal, the sEMG signals of one motion cycle are partitioned into non-overlapping frames and the mean energy for each frame is calculated and set as the threshold. Based on the configured threshold, if the signal is greater than the threshold value, then it is regarded as a motion signal containing activity information and is extracted; otherwise, it is regarded as a resting state signal and is removed. Because the sEMG signals extracted in this process involve different durations, the size of all signals is adjusted to be the same through resampling. Figure 6 illustrates the result of removing the resting state signals from the sEMG signals partitioned into one motion cycle signals.
###### Abstract  
Spatio-temporal change characteristic in leaf area index (LAI), gross primary productivity (GPP), total leaf area and total carbon sequestration of terrestrial vegetation at different geographic regions, provinces and land cover change types over China during 2000-2018 were clarified, respectively. The net increase in total leaf area and carbon sequestration over China from 2000 to 2018 is 1.7491 \(\times\) 10\({}^{6}\) km\({}^{2}\) and 1957.36 TgC, and Southwestern part contributes the most with a contribution of 25.3% for leaf area and 20.1% for carbon sequestration, respectively. The overall contribution of northern provinces to enhanced carbon sequestration capacity is greater than that of southern provinces, with a maximum for Inner Mongolia, even though southern provinces have a much higher leaf area increase. The annual increase rates of LAI and GPP in returning cropland to forest-grassland and artificial restoration of forest-grassland are higher than that of self-rehabilitation of rapid growth stage for planted young vegetation. However, due to self-rehabilitation of forest-grassland has the largest area percentage of 59%, it contributes the most to the increases of leaf area and carbon sequestration among different land cover change types, except for northeastern China, where improved farmland productivity is a dominant factor. It should be noted that the leaf area and carbon sequestration exhibit upward trends in urbanized areas over China. whereas slight increasing and even negative trends are also observed in southwestern, southern, and eastern China (e.g., Shanghai), where artificial surface construction occupied more cropland during urbanization processing. Compared with climatic factors, LAI is the dominant factor influencing GPP increases over China since 2000, and gradually weakened coupling relationship between LAI and GPP is observed from north to south. For climatic factors, GPP is dominantly affected by precipitation in northern parts and by solar radiation in southern parts.  
spatiotemporal change; leaf area; carbon sequestration; land cover change; China 2022
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 3 System Model
### The Proposed Protocol  
The proposed protocol is divided into three phases and each phase relies on the previous phase as shown in Figure 3.  
Phase 1:  
Is called the registration phase, this step aims to preserve the patient's privacy and for them to gain full access control over her/his data. The input of this step is the real identity of a patient, and the output is producing the patient's blockchain address which will be used asa visit card to avoid the patient's identity being disclosed. In this step, the system manager produces the pair of patient's and doctor's private and public keys. The system manager also generates the keyword from the key server as the output of this phase.  
Phase 2:  
With data storage, the aim is to encrypt EHRs with the patient's identity and send it to a private blockchain and encrypt the index of a doctor's public key using Public Key encryption with a conjunctive Keyword Searchable (PKES) scheme and send it to a smart contract in the consortium blockchain. The inputs of this step are the patient's address and his/her EHR, and the outputs are storing the encrypted EHRs and the secure encrypted index.  
Phase 3:  
With data sharing, the aim is to provide a secure EHR sharing based blockchain for the data users under the patient's control; this step receives the trapdoor from the third party, in which is contained the private key of a patient, and the keyword. The outputs sent ordered EHRs to the third party and implements proxy re-encryption (PRE) by the system manager to re-encrypt the index by the patient's private key after each decryption and to renew the keyword set after an epoch similar to [9].
## 3 Results
### Spatial Patterns in SMAP/CYGNSS \(\beta\) Values
#### 3.3.5 Comparison with SMAP Active-Passive \(\beta\) Values  
Figure 6b,c compare SMAP/CYGNSS \(\beta\) values and SMAP active-passive \(\beta\) values. While some spatial disagreements exist, mainly in Australia, the United States, and the Middle East, general spatial agreement can be seen across most of the globe. Complete spatial agreement between \(\beta\) values is not expected, as NSSM variability affects \(\beta\) (Section 3.3.1). Since there is no temporal overlap in SMAP active-passive data and CYGNSS data, and the calculation methods are not the same, a direct comparison of \(\beta\) values is not possible. The SMAP active-passive \(\beta\) values were calculated from 15 April-7 July 2015, and our SMAP/CYGNSS \(\beta\) values were calculated using data from April 2017-December 2020. The \(\sim\)2.5 months of time-series data used to calculate SMAP active-passive \(\beta\) values is not representative of annual precipitation and NSSM variability, which may have caused incomplete \(\beta\) estimations, especially in arid regions [2].
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment
#### 4.1.2 Based on Data Fusion Technique  
Data-level fusion is the integration by ensemble learning models of unprocessed data from numerous IoV nodes prior to analysis. Data-level fusion was carried out in [69], combining network traffic data from several vehicle nodes into a single repository. To examine large volumes and expose complex, scattered attack patterns across IoV networks, this aggregation helped assemble models including RF and Gradient Boosting. Using data-level fusion inside a federated learning system, [PERSON] et al. [61] combined data from many IoV nodes and recognized different risk behaviors while maintaining individual privacy.  
Feature-level fusion combines elements from many data sources into a single feature set so that ensemble models may operate with improved accuracy and relevant data representation. Geographical and temporal data from IoV traffic were used in [75] to accomplish feature-level fusion. Using ensemble techniques such as RF and AdaBoost, the generated feature set was examined to improve anomaly detection precision and lower repetition. By feature-level fusion, the researchers in [77] combined contextual data, including road and weather conditions, with vehicle-specific data. This method enabled the ensemble system to fit changing IoV parameters, therefore addressing dynamic and contextually sensitive threats. [PERSON] et al. [40] proposed a novel ensemble approach integrating feature augmentation and deep learning for robust intrusion detection and achieved 97% accuracy on NSL-KDD and 95% on CICIDS2017 datasets, outperforming existing methods.  
Decision-level fusion combines the outputs of several classifiers inside an ensemble model to get a final decision. Using decision-level fusion, the authors combined classifier predictions, including those of Support Vector Machines (SVM) and Neural Networks [57]. By adding weighted averaging and majority voting, the ensemble framework reduced false positives in IoV IDS and improved durability. Using decision-level fusion in centralized IDS settings, [PERSON] and [PERSON] [74] generated outputs for the conclusion by means of ensemble models trained on many datasets. By reducing the consequences of individual model errors, this fusion approach improved the general dependability of the system.
## 4 Experiments
### Implementation Details  
In this work, the proposed framework was implemented using PyTorch [52], and all experiments were performed on a workstation equipped with an NVIDIA 3090 GPU. Following ref. [15], all the networks were trained from scratch, and the SGD algorithm was used to optimize them with a momentum of 0.9 and a weight decay of 0.0001. For all the experiments, the initial learning rate of all the models was set to 0.1. The total number of epochs was set to 200, and the learning rate was divided by 10 after 100 and 150 epochs,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Classes** & **Training** & **Test** \\ \hline CIFAR-100 & 100 & 50,000 & 10,000 \\ TinyImageNet & 200 & 100,000 & 10,000 \\ CUB-200-2011 & 200 & 5997 & 5794 \\ Stanford Dogs & 120 & 12,000 & 8580 \\ MIT67 & 67 & 5360 & 1340 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of benchmark datasets.  
Figure 3: Visualization of features and their update directions within the CSD framework.  
respectively. We set the batch size to 64 for the regular dataset and 16 for the fine-grained dataset. Standard data augmentation techniques, i.e., flipping and random cropping, were applied to all data. In the proposed method, the temperature \(\tau\) is chosen from \(\{1,2,3,4\}\), \(\lambda\) and \(\beta\) are both set to 0.9, and \(\alpha\) is chosen from \(\{0.1,0.3,0.5,0.7,0.9\}\). The aim of choosing the parameters is to minimize the top-1 error rate in the validation set. Each experiment was run three times. The mean and standard deviation of the results obtained from the three experiments are then reported.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 5 System Performance Evaluation
### Update Frequency
#### 5.1.1 Theoretical Calculation  
The theoretical update frequency is calculated ignoring any delays caused by SPI bus, instruction delay and radio transition times (\(t_{radio}=t_{instructions}=t_{margin}=0\)). The corresponding update frequencies are shown in Table 6. For the basic protocol, the update rate varies between 125 and 790 Hz depending on the PHY settings. After applying the discussed optimizations from Section 3, the update rate varies between 343 and 2892 Hz depending on the PHY settings.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \(T_{poll}\) (UWB) & \(T_{reply}\) (UWB) & \(T_{final}\) (UWB) & \(T_{sync}\) (sub-GHz) & \(T_{report}\) (sub-GHz) \\ \hline Slowest setting & 2930 \(\mu\)s & 2560 \(\mu\)s & 3310 \(\mu\)s & 3840 \(\mu\)s & 1180 \(\mu\)s \\ Fastest setting & 310 \(\mu\)s & 300 \(\mu\)s & 320 \(\mu\)s & 900 \(\mu\)s & 600 \(\mu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Packet duration for different MCS and message types.
## 2 UAV Three-Dimensional Path Planning  
With the rapid advancement of unmanned aerial vehicle (UAV) technology, these systems have found widespread applications in military operations, agriculture, logistics, and environmental monitoring. To ensure that UAVs can safely and effectively execute tasks in complex environments, path planning has emerged as a critical issue. The objective of UAV path planning is to identify an optimal route in three-dimensional space that enables the UAV to travel from a starting point to a destination while avoiding obstacles and adhering to safety and energy consumption requirements. The UAV three-dimensional path planning problem aims to find an optimal trajectory that satisfies the following objectives:  
1. The Shortest Path: The total flight distance from the starting point to the endpoint should be minimized.
2. Obstacle Avoidance: The flight path must navigate around all obstacles to ensure safety.
3. Flight Altitude: The flight path must comply with the specified altitude restrictions.
4. Path Smoothness: The trajectory should be as smooth as possible, avoiding sharp turns and steep inclines.  
Therefore, this problem primarily considers four key factors. The specific cost function and objective function for UAV path planning are defined in the following subsection.