esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CIA and RT-PCR  
The comparison of chromatographic immunoassay (CIA) and real-time reverse transcription polymerase chain reaction (RT-PCR) for detecting gastrointestinal viruses and agreement metrics are presented in Table 2.  
For norovirus, RT-PCR identified nine positive samples, of which only one was concurrently identified by CIA. CIA missed eight samples, while three CIA-positive samples were negative by RT-PCR. The positive percentage agreement (PPA) for norovirus was 25%, with an overall percentage agreement (OPA) of 82.54% and a negative percentage agreement (NPA) of 86.4%. According to clinical state, one false positive CIA result was from a patient with noninfectious diarrhea and two from patients with infectious diarrhea. The only concomitant positive result (CIA and RT-PCR) was from a patient with infectious diarrhea; however, this specimen was also falsely CIA-positive for rotavirus and adenovirus. The remaining two positive RT-PCR results, missed by CIA, were from patients involved in a school outbreak of diarrhea and vomiting.  
For adenovirus, no samples were identified as positive by RT-PCR, whereas CIA identified 12 false positives. This resulted in an OPA of 80.95%, with an NPA of 100%, although PPA could not be calculated due to the absence of true positives. Four false positive CIA results were from patients with noninfectious gastroenteritis, one from an immunocompromised patient, and the remaining seven results were from patients with infectious diarrhea.  
Rotavirus detection showed one true positive sample identified by both RT-PCR and CIA (from the patient with noninfectious gastroenteritis), while ten samples were false positives by CIA (four from patients with noninfectious gastroenteritis and six from patients with infectious diarrhea). The PPA for rotavirus was 90.9%, with an OPA of 84.13% and an NPA of 100%.  
For sapovirus and astrovirus, RT-PCR detected one sapovirus-positive sample, marking the first reported identification of this virus in human stool samples in Croatia. The testing was repeated twice on the same specimen with consistent results. No astrovirus-positive samples were identified by RT-PCR, and neither virus was tested using CIA due to the unavailability of compatible assays.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
###### Abstract  
Fixed wireless access (FWA) provides a solution to compete with fiber deployment while offering reduced costs by using the mmWave bands, including the unlicensed 60 GHz one. This paper evaluates the deployment of FWA networks in the 60 GHz band in realistic urban and rural environment in Belgium. We developed a network planning tool that includes novel backhaul based on the IEEE 802.11 ay standard with multi-objective capabilities to maximise the user coverage, providing at least 1 Gbps of bit rate while minimising the required network infrastructure. We evaluate diverse serving node locations, called edge nodes (EN), and the impact of environmental factors such as rain and vegetation on the network design. Extensive simulation results show that defining a proper EN's location is essential to achieve viable user coverage higher than 95%, particularly in urban scenarios where street canyons affect propagation. Rural scenarios require nearly 75 ENs per km\({}^{2}\) while urban scenarios require four times (300 ENs per km\({}^{2}\)) this infrastructure. Finally, vegetation can reduce the coverage by 3% or increment infrastructure up to 7%, while heavy rain can reduce coverage by 5% or increment infrastructure by 15%, depending on the node deployment strategy implemented.  
radio access network (RAN); 5G networks; Massive MIMO (MaMIMO); fixed wireless access (FWA); 60 GHz; millimetre-Waves (mmWaves) +
Footnote †: journal: _Sensors_**2023**, 23, 1056. [[https://doi.org/10.3390/s23031056](https://doi.org/10.3390/s23031056)]([https://doi.org/10.3390/s23031056](https://doi.org/10.3390/s23031056))
# Preparation of Piezo-Resistive Materials by Combination of PP, SEBS and Graphene
## 3 Results and Discussion
### Morphology  
The morphology of cryo-fractured samples was analyzed by SEM after extraction of the SEBS component with xylene. The voids observed in the images indicate the sites where the SEBS was initially located. The control samples show a more homogeneous distribution of the SEBS domains throughout the inspected area when prepared via R1 (Figure 2A,B and Figure 3A,B and Figure S1).  
Figure 1: Formulation strategies for the preparation of isotactic polypropylene (iPP)/styrene–ethylene–butylene–styrene (SEBS) nanocomposites with graphene (G) or polypropylene chains to graphene (GPP) filler. (A) solution mixing (SoM) route, R1 and (B) solution/solid state mixing (SoM/SSM) route, R2.  
This was expected since mixing in solution assures a better contact and interfacial interaction between components. At higher magnifications, the images obtained for R1 suggest the presence of more agglomerates in the sample prepared with pristine graphene (Figure 2C,D with respect to the sample with GPP (Figure 2E,F)). Premixing the filler with iPP via R2 reduces to some extent the aggregation of G and confirms an optimum exfoliation and homogeneous integration of the GPP filler within the matrix (Figure 3C-F).  
The analysis of the cavities that correspond to the volume previously occupied by the SEBS phase exhibit average domains sizes of 1.2-1.5 \(\upmu\)m (Figure S1). The incorporation of G and GPP filler into the blend resulted in a significant decrease of the size of the cavities in all nanocomposites by approximately 80%, indicating a better dispersion of the elastomeric phase throughout the PP matrix, driven by the filler (Figure S1). Similar results have been observed previously for iPP/SEBS blends reinforced with montmorillonite [21]. In particular, the mean of cavity diameter is slightly higher when GPP is used as filler. A statistical analysis with Image J software shows cavity mean area of -0.20 \(\upmu\)m\({}^{2}\) and 0.25-0.31 \(\upmu\)m\({}^{2}\) for G and GPP, respectively. In principle, the smaller the voids of SEBS domains, the better the impact resistance of the blends (see discussion below). It is interesting to note that beyond the changes in size, the morphology of the voids was also affected. While in almost all cases the shape of the voids looked circular, in the GPP sample they had an elongated shape and were found along the graphene surface (Figure S1). This can be attributed to the high affinity of graphene to  
Figure 2: SEM images of cryo-fractured samples of iPP/SEBS (**A**,**B**); iPP/SEBS/G (**C**,**D**); iPP/SEBS/GPP (**E**,**F**); prepared via the SoM route. Scale bars in (**A**,**C**,**E**) correspond to 50 \(\upmu\)m), while those in (**B**,**D**,**F**) correspond to 10 \(\upmu\)m.  
the styrene blocks of the SEBS domains through \(\pi\)-\(\pi\) interaction. However, the enlarged morphology can have a negative influence on the mechanical properties, as will be discussed later.
## 1 Introduction
The proposed solution was evaluated based on deep reinforcement learning using the latency metric. Thus, the study of the influence of the number of hops on end-to-end delay in IAB systems, in addition to solving the general problems of route selection and network topology, has its own relevance. However, to date, most of the research has been devoted to meeting the requirements for network reliability and throughput, as can be seen from the detailed review in Section 2. This paper aims at assessing the impact of multi-hop transmission on the end-to-end delay in an IAB radio access network (RAN) by explicitly accounting for the half-duplex constraint. We evaluate the delay due to queuing in IAB-nodes for several IAB network topologies and different time allocation strategies between access and backhaul. The numerical results were obtained via computer simulations by utilizing the open-source software OMNeT++. The main contributions of our work are:  
* The detailed queuing-based model for delay assessment in TDM-based IAB deployments with multiple network configurations and various time allocation strategies between access and backhaul;
* Numerical results showing a linear increase in the total packet delay on the uplink and downlink as the maximum number of network tiers (hops) increases, as well as a noticeable influence of the resource allocation scheme between access and backhaul, which requires further study;
* The impact of increasing the maximum number of IAB-nodes on the end-to-end delay strongly depends on the choice of the topology and requires maintaining the optimal ratio between the maximum number of hops and the average network load. Determining this ratio is subject to further research. The rest of the paper is organized as follows. Section 2 provides an overview of the IAB technology and its main features and also reviews the recent research on the topic. In Section 3, we describe the adopted system model and related assumptions. Further, in Section 4, we parameterize the model and outline the implementation specifics. Numerical results are then provided and discussed in Section 5. Section 6 concludes the paper.
## 4 Experiments and Results
### Model Comparison
The experimental results demonstrate that our method effectively improves the classification accuracy of BiLSTM for semi-structured short text, allowing BiLSTM to effectively capture both local and global features of the text while reducing semantic fusion, thereby providing a distinct advantage in the task of classifying HS codes for import and export goods. To verify the effect of corpus size on the classification performance of the EBLCS model, datasets of varying sizes are selected for further study. The experimental datasets consist of 20%, 50%, and 80% of the original dataset, all using stratified sampling to ensure a balanced distribution of HS code, with a total of 227 categories. Comparison experiments are conducted with three different word embedding approaches, using BLCS as the baseline classifier. The effects of varying dataset sizes on model performance are summarized in Table 4, which shows the performance of the models with different word embedding methods across the different dataset sizes. As shown in Table 4, all metrics of the model improve with an increase in the number of samples in the training set. Among the accuracy metrics, the word2 vec-BLCS model improved by 10.72%, the BERT-BLCS model by 10.15%, and the EBLCS model by 10.9%. The EBLCS model consistently outperforms the comparison models across different dataset sizes, further demonstrating the superior text characterization capability of the ERNIE model. The experimental results indicate that while all models show improvement, the performance gains become more limited as the dataset size increases. Notably, this paper's model maintains robust performance even with just 20% of the training set size. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset Size** & **Weight-P** & **Weight-R** & **Weight-F1** & **Accuracy** \\ \hline \multirow{3}{*}{word2 vec-BLCS} & 20\% & 0.7335 & 0.7122 & 0.7088 & 0.7122 \\  & 50\% & 0.7895 & 0.7772 & 0.7772 & 0.7772 \\ \cline{1-1}  & 80\% & 0.8143 & 0.8037 & 0.8037 & 0.8037 \\ \cline{1-1}  & 100\% & 0.8266 & 0.8194 & 0.8196 & 0.8194 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model performance for datasets of different sizes.
## 2 Impact of Directed-Detected Optical Fast-OFDM Signals over AWGN Using Linear Equalization  
For our numerical simulation, the direct-detected optical Fast-OFDM transceiver design is similar to [6], using 64 sub-carriers and ASK formats for modulation with 4, 8, and 16 amplitude levels [6]. The adopted sampling rate was taken at 6.25 GS/s, resulting in sub-carrier frequency bandwidth of about 98 MHz and a length per symbol of 12.8 ns, out of which 3.2 ns were wasted by the insertion of a cyclic prefix (CP) to avoid interference among symbols during transmission.  
In Figure 1, the optical Fast-OFDM modem performance over an AGWN channel is illustrated in terms of optical signal-to-noise ratio (OSNR) at 0.1 nm without using any form of nonlinear equalization.  
In Figure 1, the DAC/ADC clipping noise (or ratio) and quantization bits were isolated to investigate the true impact of OSNR on the developed optical Fast-OFDM modem. From Figure 1, it is noticed that for a fixed bit-error-rate (BER) value, a higher order modulation format needs higher OSNR. More specifically, the OSNR is required for achieving a BER of \(10^{-3}\) when using 4-ASK is 18.5 dB, 25.5 dB for 8-ASK, and 36 dB for 16-ASK. It should be noted that simulated results agree very well with the analytical results reported in [6], which confirms the validity of the used model.
## 3 System Functional Modules
### Base Station Module Hardware and Software Design
#### 3.3.2 Design for the GPRS Module  
After initialization, the system enters the serial communication state and the power of GPRS module is turned on. The GPRS module boots, waits for its initialization, lands the mobile phone network, and then sets the operating parameters of the GPRS module.  
The TCP/IP connection with the remote server is conducted; if the connection fails a couple of times, it will automatically turn off the module and repeat the above steps; when the connection is fulfilled, the networking maintaining communication is repeated every five minutes (if there is no mission data sent during this period).  
Figure 9: Control configuration page of base station. **(a)** mainboard of base station; **(b)** the base-station system with LCD display, keyboard and wireless transceiver module; **(c)** interface of node configuration; **(d)** interface of system configuration.  
Data is waiting to be uploaded through the GPRS module. If it is text messages, then the message content will be analyzed and whether the message format and password are correct is determined. If correct, the execution results will be sent back to the cell phone in the form of text messages, and the read message will be deleted from the SIM card in case that the SIM card is full and cannot receive new text messages.  
When an alarm phone number is set in the system, and at the same time the data uploaded in the node meet the alarming conditions, the text messages in the specified format will be sent to the designated phone number.  
Once per second, the module signal strength from the GPRS module is read and displayed on the screen (Figure 11).  
Figure 10: The flow chart of the 2.4G (JN5139) program in the base station.
# The Modification of pBFT Algorithm to Increase Network Operations Efficiency in Private Blockchains
## 4 Conclusions  
With the increase in data usage through a contact-free society and the development of communications, blockchain technology applications are becoming more widespread to manage the mass of data accurately and transparently. Since pBFT, where BFT consensus is possible, requires redundant consensus and verification processes by all nodes, the network operation cost for consensus increases as the number of nodes increases. In this paper, the purpose of the request is identified through the status bit of the client's request, and the mpBFT algorithm is applied flexibly. In addition, the mpBFT algorithm selects a leader node group to speed up the consensus process, and when consensus fails, a new leader node group is selected to give an opportunity for consensus once again. The selection algorithm for the new leader node group election and leader node failure was explained through the paper, and the RAFT algorithm was modified and applied.  
The paper presents the mBFT blockchain consensus algorithm for a safer and quicker method of managing internodal data. Through mpBFT, the request process is simplified, and the network communications cost is minimized through the application of reliability-based consensus nodes. While mpBFT requires 33.3% of node sacrifice, mpBFT is shown to only need 16.7%. In terms of network costs, pBFT costs \(2N^{2}\) while mpBFT requires \(\frac{N^{2}}{3}+\gamma\) and \(\frac{2N}{3}+\gamma\). \(\gamma\) is computational cost for frequency of use (\(\alpha\)1) and the institution's reliability (\(\beta\)1). mpBFT shows to also have superior TPS compared to pBFT.  
In order to further solidify the research content proposed in this paper, more in-depth studies on the number of nodes and accurate evaluation of institutional reliability are needed.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
###### Abstract  
Aerial electrostatic spray technology for agriculture is the integration of precision agricultural aviation and electrostatic spray technology. It is one of the research topics that have been paid close attention to by scholars in the field of agricultural aviation. This study summarizes the development of airborne electrostatic spray technology for agricultural use in China, including the early research and exploration of Chinese institutions and researchers in the aspects of nozzle structure design optimization and theoretical simulation. The research progress of UAV-based aerial electrostatic spray technology for agricultural use in China was expounded from the aspects of nozzle modification, technical feasibility study, influencing mechanism of various factors, and field efficiency tests. According to the current development of agricultural UAVs and the characteristics of the farmland environment in China, the UAV-based aerial electrostatic spray technology, which carries the airborne electrostatic spray system on the plant protection UAVs, has a wide potential in the future. At present, the application of UAV-based aerial electrostatic spray technology has yet to be further improved due to several factors, such as the optimization of the test technology for charged droplets, the impact of UAV rotor wind field, comparison study on charging modes, and the lack of technical accumulation in the research of aerial electrostatic spray technology. With the continuous improvement of the research system of agricultural aviation electrostatic spray technology, UAV-based electrostatic spray technology will give play to the advantages in increasing the droplets deposition on the target and reducing environmental pollution from the application of pesticides. This study is capable of providing a reference for the development of the UAV-based agricultural electrostatic spray technology and the spray equipment.  
agricultural aviation; UAV; plant protection; review; electrostatic spray technology; droplet deposition; aerial pesticides application; charge to mess ratio +
Footnote †: journal: Applied Sciences
## 3 LIB SOH Prediction Case Study and Analysis
### Analysis of Prediction Results
In the SOH prediction results for #1, #6, and #26 LIB, RF improved model 1 achieved a reduction of 19.82%, 17.07%, and 17.61% in _RMSE_ compared to the Class RF model, a decrease of 19.94%, 17.39%, and 17.59% in _MAPE_, and an increase of 1.24%, 0.82%, and 1.14% in \(R^{2}\), respectively. RF improved model 2, when compared to RF improved model 1, achieved a decrease of 21.93%, 15.90%, and 24.00% in _RMSE_, a reduction of 23.24%, 18.21%, and 24.25% in _MAPE_, and an increase of 0.92%, 0.61%, and 0.92% in \(R^{2}\), respectively. These results demonstrate that RF improved model 2 has the best evaluation index. By comparing the SOH prediction results of various models across multiple sets of data, as shown in Figures 8 and 9, as well as in Tables 7 and 8, a comprehensive ranking of the prediction results for each model under three different LIB experimental datasets is presented in Table 9. From Table 9, it can be observed that the comprehensive ranking of prediction results varies with different LIB experimental data. However, among the four traditional machine learning models, the Class RF model is consistently better than the DT model, CNN model, and LSTM model, affirming the feasibility of using the RF model for LIB SOH prediction in this study. Additionally, among the three RF models, the RF improved model is consistently better than the Class RF model, with RF improved model 2 consistently being the best. This validates the effectiveness of the proposed RF improved model for LIB SOH prediction in this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**LIB** & **Ranking** & **Model** & _RMSE_ & _MAPE_ & \(R^{2}\) \\ \hline  & 1 & RF improved model 2 & 0.840 & 0.968 & 0.987 \\  & 2 & RF improved model 1 & 1.076 & 1.261 & 0.978 \\ \#1 & 3 & Class RF model & 1.342 & 1.575 & 0.966 \\  & 4 & LSTM model & 1.468 & 1
## 6 Conclusions  
This paper proposed a PFL approach to maximize the available UAV battery power and minimize the sojourn time in UAV-assisted C-V2X communications. The proposed PFL approach utilizes an attention mechanism to model aggregation errors and ensure an adaptive and personalized vehicle selection in an uncertain and time-varying environment, subject to bandwidth, UAV power, and vehicle QoS constraints. Furthermore, using an attention mechanism, our PFL model achieved a reduction in model training time with an increase in the number of vehicles compared to existing FL methods. The PFL mechanism addresses the straggler effect by quantifying the contribution of vehicles participating in the model computation and training. This enables us to achieve a balance between model accuracy and straggler impact by dynamically managing the straggler rate. To address packets of varying size, arrival rate, and service rate, we proposed that PFL automates the selection of vehicles by the UAV through attentive state-space modeling. This is achieved by clustering the vehicles to model the similarities between the datasets in a cluster. The experimental findings reveal a greater than 25% reduction in training time and 20% increase in model accuracy across varying non-i.i.d. data scenarios. We propose using PFL with attention mechanism to collaborate with vehicles with similar data distribution through attention-based grouping. PFL achieves approximately 20% lower latency than conventional FL methods such as FedAvg and FedSGD. In the future, we aim to include the complexity in assessing changes in the channel state when using multi-modal datasets. The proposed approach can be improved to predict outcomes such as recurrence of similar data transmissions and incorporate channel state-based predictive information. In addition, future research includes extending the proposed PFL framework to adaptive optimization methods or group-based training methods. Moreover, in future work, the authors aim to conduct an in-depth computational complexity analysis of the proposed approach. The model training complexity can act as a severe bottleneck for real-world deployment of PFL approaches. Furthermore, introducing a channel state estimator into the system model is expected to contribute to reducing the model convergence-time. A few other challenges in applying PFL and attention mechanism to UAV-assisted vehicular communications are to track the changes in the UAV and vehicle states with variations in the instantaneous channel characteristics. The feasibility of utilizing reward functions based on channel estimation is an avenue for further work.