esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study
#### 4.3.2 Analysis of Feature Fusion Method  
As for feature fusion, we introduce the self-attention mechanism to handle feature maps of different scales, instead of simple average pooling or max pooling. Furthermore, there are two conventional fusion methods: element-wise addition and concatenation. For cSLR, we conducted experiments to explore the effect of different fusion methods.  
In Table 2, first, in the vertical comparison, the fusion methods of concatenation and element-wise addition have almost no significant impact on the performance, indicating that the information contained in both operations is almost consistent. Therefore, it is recommended to choose a more convenient method according to the specific task requirements. The results indicate that our proposed feature map processing method with the self-attention mechanism is significantly better than both pooling methods, with a reduction of 3.5-4 in the WER metric. This is because both pooling methods result in a loss of information [48]. Max pooling only retains the maximum value in a local region, discarding other information that may be valuable [47]. Global average pooling operates on the entire feature map, potentially ignoring detail information and reducing sensitivity to details. Our proposed method flattens the feature maps and uses a Transformer encoder to process them as a sequence, which can perceive global information while dynamically preserving details. In addition, it is more flexible compared to pooling operations. The success of the ViT model can also attest to the effectiveness of this approach to some extent.
## Abbreviations  
SLAMSimultaneous Localization and Mapping  
RGB-DRed-Green-Blue Depth  
GNSSGlobal Navigation Satellite Systems  
LIDARLight Detection and Ranging  
IMUInertial Measurement Unit  
BABBundle Adjustment  
LSDLine Segment Detector  
LBDLine-Based Descriptor  
MADMedian Absolute Deviation  
TSDFTruncated Signed Distance Function  
SFMStructure from Motion  
CNNsConvolutional Neural Networks  
APEAbsolute Pose Error  
RPERelative Pose Error  
RMSERoot Mean Square Error  
OctoMapOctree Map  
PCLPoint Cloud Library  
6-DoFSix Degrees of Freedom  
Log-oddsLogarithmic Odds  
ROSRobot Operating System  
SDFSigned Distance Function  
PGBAPose Graph Bundle Adjustment  
JDSAJJoint Depth and Scale Adjustment  
VIOVisual-Inertial Odometry  
EVOEvaluation of Odometry
## 4 Experimental Methods and Results
### Experimental Methods and Results of News Sentiment Analysis  
FinBERT with Thai news fine-tuning was used in the financial news sentiment analysis model in the feature engineering part of the proposed model. In order to assess the efficacy of Thai news analysis, FinBERT with Thai news fine-tuning was compared to the original FinBERT, which is FinBERT with default, and other popular sentiment analysis models, such as Vader [50] and Text-blob [51].  
This research manually annotated the financial news dataset. The annotated dataset was random from news data to label with three classes of sentiments: positive, negative and neutral, totaling 1500 articles. The annotated dataset was used for training, FinBERT supervised fine-tuning and model performance testing. The top 80% of the data is used as the training dataset for supervised fine-tuning training and the remaining 20% was used as the testing dataset to evaluate the model performance. Moreover, each class has an equal number of examples in the testing set. Other models were used, similar to both training and testing datasets and to FinBERT with Thai news fine-tuning.  
From Table 3, the result shows that the FinBERT with Thai news fine-tuning has the highest average accuracy and average F1-score of the compared models. When considering the F1-score in each class, the FinBERT with Thai news fine-tuning has the highest value. Both FinBERT models perform similarly well when it comes to categorizing news as Class Negative. In classes Positive and Neutral, the FinBERT with Thai news fine-tuning has a moderate F1-score value. However, both Vader and Textblob have very low model performance for this dataset.
## 3 Proposed System
### Weather & Environment Data Agents  
Weather & Environment Data Agents obtain the weather data requested by the Processor Agent, they provide historical weather data for the requested dates and locations which will later be merged with trip or station data. These agents will also provide weather forecasts to Prediction Model Agents; they need this information when making predictions for a specific station at a given time.  
The weather data obtained by these agents is daily data on: minimum, maximum and average temperature, wind speed, rain and minimum and maximum pressure. There is a common base agent to provide this information and specific data agents responsible for implementing that functionality for each weather provider such as Wunderground [42] or Aemet [43].
###### Abstract  
AU-Agenda 2063 was adopted at the 24 th Ordinary Session of the African Heads of State and Government in 2015 as the blueprint for the future development of the continent. Built upon the continent's past experiences, challenges, and successes, AU-Agenda 2063 comprehensively describes the strategic path for Africa's future development in the next 50 years. Thus, the monitoring of its implementation in various African states is critical for ensuring sustainable development and track progress. However, the higher cost of collecting data for accurately and reliably monitoring the implementation of Agenda 2063 may hinder the progress towards achieving these goals. Satellite Earth observation provides ample data, and thus has provided opportunities for the development of novel products and services with the potential to support implementation, monitoring and reporting for AU-Agenda 2063 development imperatives. However, it has been limitedly exploited in Africa, as evidenced by lower research outputs and investments. This calls for increased capacity building in the use of available EO data and products for various users including decision makers to advance national, regional and continental priorities. The use of such data products is often hampered by the capability to understand the products and thus their value for addressing socio-economic challenges. This paper discusses the potential of Earth observation capacity building for supporting the implementation, monitoring of, and reporting towards achieving AU-Agenda 2063 development imperatives. Specifically, this paper identifies existing capacity building resources, including the role of open and free Earth observation data, open-source software, and product dissemination platforms that can be leveraged for supporting national development, service delivery and the achievement of AU-Agenda 2063 targets. Furthermore, the paper recognizes the importance of bilateral and multilateral partnerships in leveraging existing know-how, technology and other resources for advancing strategic goals of African emerging space agencies and promoting sustainable development, with examples from South African National Space Agency (SANASA). Then, the challenges and opportunities for capacity building and the wide adoption of EO in Africa are discussed in the context of AU-Agenda 2063. The paper thus concludes that EO capacity building is essential to address the skills and data gaps and increase the use of EO-based solutions for decision making in various sectors, critical for achieving AU-A2063.  
AU-Agenda 2063; Capacity building; Earth observation; UN-SDGs
## 2 Principle and Theoretical Analysis
### System Principles
If the double-sided power spectral density of white noise is \(N_{f}\), then the SNR at time \(t_{0}\) is given by  
\[SNR=\frac{P_{s}}{P_{n}}=\frac{s_{o}(t_{0})s_{o}^{*}(t_{0})}{E[n_{o}(t)n_{o}^{ *}(t)]}\leq\frac{E_{s}}{N_{f}} \tag{4}\]  
In the equation, \(P_{s}\) represents the power of the filtered output signal \(s_{o}(t)\) at time \(t_{0}\), \(P_{n}\) is the average power of the filtered output noise \(n_{o}(t)\), \(E[\cdot]\) denotes the expectation function, and \(E_{s}\) is the energy of the input signal \(s_{i}(t)\). When matched filtering is employed, the SNR can reach its maximum value. It is evident from this formula that the achievable SNR of the system depends solely on the energy of the input waveform and is independent of other details, such as the modulation type of the input signal. Therefore, for systems with a constant white noise level, increasing the power and duration of the detection waveform is the only way to enhance the SNR. The use of chirp frequency modulation can increase the effective linewidth of laser pulses, thereby reducing the Brillouin gain and raising the threshold for stimulated Brillouin scattering (SBS). Additionally, when the carrier spacing is greater than the Brillouin gain bandwidth, employing multi-carrier pulses distributes pulse energy across optical sideband carriers, and the SBS threshold is determined by the carrier with the highest spectral power, further reducing the Brillouin intensity. Therefore, utilizing chirp-modulated pulses and multi-carrier pulses enables the injection of higher-energy laser pulses into the sensing fiber, enhancing the SNR for long-distance sensing while also expanding the frequency response range.
## 4 Conclusions  
1. According to a mix design procedure, when the target porosity of pervious concrete was designed from 8% to 30%, the measured porosity of the pervious concrete ranged from 7.05% to 29.32%, while the effective porosity ranged from 5.12% to 27.14%. It is relatively accurate to utilize the mix design procedure for pervious concrete using iron tailing as coarse aggregates.
2. The dry density of the pervious concrete, ranging from 2240 to 1798 kg/m\({}^{3}\), was linearly distributed with both the measured porosity and effective porosity. The 28 d compressive strength of the pervious concrete decreased from 42 to 11 MPa as the effective porosity increased from 5.2% to 27.2%. The relationship between the dry density and effective porosity can be expressed as \(\rho_{\text{d}}=15.86\times\text{P}_{\text{e}}+2242\), while the relationship between the compressive strength and effective porosity can be expressed as \(\text{f}_{\text{c}}=72.9-18.4\times\text{ln}(\text{P})\).
3. The limitation and conditions for the compressive strength and permeability coefficient functions can be summarized as following: (1) P.O. 42.5 grade Portland cement is used as cementitious materials, (2) the water to cement mass ratio is about 0.23, (3) the compaction method is a compact effort of five drops of Proctor hammer per layer for two layers in the molds, and (4) the aggregate size is 4.75-9.5 mm.
4. The permeability coefficient of pervious concrete, ranging from 0.67 to 8.2 mm/s can be predicted by as a function of the effective porosity, as displayed \(\text{k}=0.57\times\text{e}^{0.98\text{P}}\). The effective porosity of the equilibrium point of the compressive strength and permeability coefficient of pervious concrete was approximately 16%, where the compressive strength was 21.5 MPa and the water permeability was 3.2 mm/s. The products prepared here can be used in parks, gardens, open parking lots, sidewalks, and some riverbanks.  
[PERSON] is responsible for the idea providing and the paper revision. [PERSON] is responsible for the experimental process and the manuscript writing. [PERSON] is responsible for the experiment directing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Technological Innovation Program of Hubei Province (Grants no. 2018 AAA028), National Natural Science Foundation of China (NSFC) [Grants no.51408203], Green Industry Project of Hubei University of Technology (Grants no.BSQD12060).The authors would like to express their appreciation to these financial assistances.  
The authors would like to appreciate the anonymous reviewers for their constructive suggestions and comments to improve the quality of this paper.  
The authors declare that they have no conflicts of interest.  
Figure 11: Comparisons of the permeability coefficient (**A**) and compressive strength (**B**) of the pervious concrete with different types of aggregates.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 3 An RL-Based Framework of Congestion and Awareness Control
### A Q-Learning Approach
From steps 8 to 13, the vehicle considers each of the possible average transmission rates from the surrounding vehicles and uses these values with the vehicle density as inputs for Equation (7) to determine which inputs give the smallest estimated CBR value that is greater than or equal to the current CBR. From steps 14 to 20, the vehicle selects the best beacon transmission rate, based on the Q-table given as input. Each row in the Q-table represents a state of the environment with vehicle density, the estimated average transmission rate of surrounding vehicles, and the corresponding estimated CBR, and contains the Q-table values for each possible transmission rate that the vehicle can use. The transmission rate with the maximum value in a row is the best action, i.e., the optimal policy for the corresponding state. An example with selected rows of the Q-table (rather than the entire table) is shown in Table 2; the complete table could not be included due to space limitations. In steps 14-20, the vehicle checks each possible transmission rate for its current state and selects the one with the highest corresponding value in the Q-table. In step 15, it accesses the row in the Q-table corresponding to the state, and in steps 16-19, it compares it with the maximum Q-table value encountered so far, updating the maximum value if the new value is higher. After it has checked each value in the row, it selects the beacon rate with the highest value. Once this is selected, the beacon interval is calculated, which determines the time before the next BSM is sent and is equal to the reciprocal of the beacon rate. For example, if the current vehicle density is 15, with vehicles sending 1 BSM/s, and the estimated CBR is 0.1816, then using Table 2, the best transmission rate is 3 BSMs/s, with \(qVal=86\), and the corresponding beacon interval is 0.333 s. Since the iteration number is fixed in Algorithm 2, the time complexity will be just \(O(1)\) which will be very quick for the vehicle to select the best action. Figure 3 lists the flow chart of Algorithm 2. Figure 3: Flow Chart of Policy Application of QBACC.
## 5 Conclusions  
This paper examines the feasibility of a standalone energy system for Crete, relying on hydrogen-based storage infrastructure. The suggested energy system prioritizes consumption coverage through renewable energy production, local fuel cell production using green hydrogen, and conventional production from locally produced Mazut and diesel power plants, supplemented by imports via the mainland grid interconnection. Surplus energy generation is earmarked for storage as green hydrogen is produced through electrolysis, either for future use or potential sale. The system's optimal sizing is determined using financial and environmental metrics, such as NPV and carbon neutrality. Carbon neutrality is pursued by balancing emissions from transportation, residential heating, and energy production with absorption through carbon sequestration in olive groves. This study highlights the significant research idea of enhancing renewable energy integration in islands, particularly focusing on Crete, to reduce carbon emissions and fossil fuel consumption. The research achievement lies in proposing a method to boost the use of renewable energy sources, contributing to the European goal of achieving carbon neutrality by 2050. The methodology fails to introduce a feasible sizing for the proposed installation that meets all objectives. Yet, its high level of replicability justifies the study of the installation under these conditions. This study underscores the pivotal role of islands in pioneering renewable energy systems and emphasizes the importance of transitioning towards sustainable energy solutions to combat climate change effectively. Conceptualization, E.K. and I.M. ; methodology, I.M. and M.N. ; validation, E.K., I.M., M.N., and A.T.; formal analysis, I.M. and M.N. ; investigation, I.M. and M.N. ; resources, I.M. and M.N. ; writing--original draft preparation, I.M. and M.N. ; writing--review and editing, I.M., M.N., and E.K. ; visualization, M.N. ; supervision, E.K. ; project administration, E.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original data regarding Crete's participation in the HEnEx presented in the study are openly available at [[https://www.enexgroup.gr/web/guest/markets-publications-el-day-ahead-market](https://www.enexgroup.gr/web/guest/markets-publications-el-day-ahead-market)]([https://www.enexgroup.gr/web/guest/markets-publications-el-day-ahead-market](https://www.enexgroup.gr/web/guest/markets-publications-el-day-ahead-market)) (accessed on 5 May 2024). The data regarding fuel prices are publicly available at [[http://oil.gge.gov.gr/](http://oil.gge.gov.gr/)]([http://oil.gge.gov.gr/](http://oil.gge.gov.gr/)). The recorded data of photovoltaic and wind park production are not publicly accessible as they were shared in confidence by the relevant stakeholders. **Conflicts of Interest:** The authors declare no conflicts of interest. **Nomenclature**
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
## 2 Materials and Methods
### Speech Communication System Design
#### 2.2.1 Hardware System  
The proposed hardware system architecture is shown in Figure 2. It encompasses a transmitter terminal, an arbitrary function generator (AFG), a power amplifier, a mechanical antenna, a receiving loop antenna, a lock-in amplifier (LIA), and a receiver terminal. The hardware system architecture is shown in Figure 2. The transmitter terminal consists of a  
Figure 1: Mechanism and performance of the PZT-42 mechanical antenna. (**a**) Simplified illustration of the working mechanism. (**b**) The PZT-42 resonator in 3D and from the side. (**c**) Impedance curve of the PZT-42 mechanical antenna in the range of 160–180 kHz. (**d**) Received wireless spectrum using the PZT-42 mechanical antenna in the range of 160–180 kHz.  
computer equipped with a microphone and functions as the input processing device and the AFG control module. The signals are amplified by the power amplifier and fed directly to the PZT-42 mechanical antenna to stimulate it to radiate the signals. At the receiver side, the signals are received by an active loop antenna. The receiving device is an LIA with noise filtering function. Finally, the receiver terminal reads the signals and outputs the messages.
## 5 In Vitro Antimicrobial Susceptibility Pattern of the _B. burgdorferii_ Complex
### \(\beta\)-Lactam Agents  
\(\beta\)-lactams belonging to the penicillin class of antimicrobial agents, such as penicillin, amoxicillin, and III generation cephalosporions such as ceftaxone, have been shown to be highly active against _B. burgdorferi_ s.l. in vitro. They are also clinically efficacious and, thus, regarded as agents of choice when it comes to the treatment of LB (52; 55; 65). The penicillin derivatives mezlocillin, azlocillin and piperacillin seem to be even more active than penicillin and amoxicillin (Table 2), whereas sulbactam and the monobactam aztreonam revealed no significant in vitro activity against _B. burgdorferi_ s.l. (58).  
In terms of the cephalosporions, the group II compound ceftaxine is highly active against borreliae, whereas loracarbef is not (81). Ceftriaxone, cefotaxime, cefdinir andcefixime are effectual, while other group III agents like ceftamet-pivoxil, ceftibuten and cefpodixime-proxetil are inefficacious in vitro. From this, it is clear that the activity of compounds against _B. burgdorferi_ s.l. belonging to a specific class of substances (e.g., cephalosporions) does not seem to correspond to the traditional grouping of these antimicrobial agents with regard to their spectrum of activity against Gram-negative and Gram-positive bacteria (Table 2). Interestingly, these observations resemble the findings for the activity of cephalosporions against _Leptospira_, which also show variable activity independent of the cephalosporion groups [81].  
With respect to the carbapenems, the testing of 11 isolates of _B. burgdorferi_ s.l. against faropenem, etrapenem, imipenem and meropenem demonstrated that etrapenem was the most potent carbapenem on a \(\upmu\)g/mL basis, with in vitro activity against borreliae comparable to that of ceftaxone (Table 2). These findings were supported further by the results of time-kill experiments (Figure 3) in a clinical isolate _B. afzelii_, demonstrating a \(>\)3 log reduction (99.9%) of the initial inoculum after 96 h of exposure to either drug at a concentration of three log\({}_{2}\) unit dilutions above the respective MIC [17; 52; 55; 65; 77].
## 1 Introduction
To overcome these limitations, our proposed method introduces the following:  
* **2E-HRIO** (Elitist Elk Herd Random Immigrants Optimizer) to dynamically optimize device parameters and reduce decoherence effects in quantum memory,
* **ADS-CNN** (Adaptive Depthwise Separable Convolutional Neural Network) for real-time detection and classification of side-channel threats, and
* **CHMAC-EPQH-based authentication to ensure robust verification of both transmitting and receiving devices. Furthermore, we address the issue of _point-to-point connection failures_ by integrating  
**Radial Density-Based Clustering** to predict and avoid unreliable communication links, thereby enhancing overall system resilience. These constraints highlight the pressing necessity for further research, to create effective solutions that improve memory stability, error resilience, device authentication, network dependability, and side-channel resistance, and to guarantee the practical and secure implementation of CV-QKD systems. A series of focused methods have been suggested for the improvement of security and performance, in order to circumvent the major drawbacks of CV-QKD systems. The Two-Stage Enhanced Hybrid Randomized Incremental Optimization (2E-HRIO) method is used to dynamically optimize system parameters such as laser power, temperature regulation, and optical filter settings, which keeps the quantum state coherent for longer durations. More dependable transmission is achieved through strategic insertion of pilot symbols and the performance of channel equalization using the Robust Recursive Least Mean Squares (R2 LMS) algorithm, which mitigates the negative impacts of channel noise and signal loss. Efficient reconciliation of mistakes is achieved with the use of the Von Neumann Entropy (VNE) approach, in conjunction with Improved Rate Low-Density Parity-Check (IR-LDPC) codes for precise calculation and repair of the errors caused by the continuous quadrature measurements. Utilizing the Robust Density-Based Spatial Clustering of Applications with Noise (RDBSCAN) clustering technique, network resilience is enhanced by addressing probable point-to-point connection failures and facilitating safe key exchanges between connected quantum nodes. As a measure to prevent quantum devices from being tampered with or replaced without authorization, authentication procedures are put in place utilizing the CHMAC-EPQH technique, which is based on Extended Priority Quantum Hashing. An Anomaly Detection System based on Convolutional Neural Networks (ADS-CNNs) provides strong defense against covert exploitation of hardware flaws and can identify and neutralize side-channel assaults, a significant danger to CV-QKD systems. The combined effect of these approaches is a thorough plan to improve the future performance of CV-QKD networks in terms of their resiliency, safety, and feasibility.