esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
This section presents the results obtained from two stages, conducted in three representative environments and using the previously specified Wi-Fi technologies. The structure of this section mirrors the design of the two iterations of the testing campaign. In the first stage, network latency, specifically the Round-Trip Time (RTT), is analysed through the evaluation of test packet propagation. Subsequently, the second stage focuses on determining the operational thresholds for uplink transmission, specifically evaluating TCP and UDP traffic performance to characterise network stability and throughput efficiency.
## 5 Results and Discussion
### Ablation Assay  
In this section, we conducted ablation experiments on the GA dataset to analyze the effect of different modules of MPBCNER. We selected BERT as a default PLM. The experimental results are shown in Table 8.  
The results in Table 8 show that the F1 score of the model decreased after removing different submodules. In this case, the model performance was affected more by the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **K = 10** & **K = 20** & **K = 50** & **K = 100** & **K = 200** & **K = 500** \\ \hline BERT-CRF & 20.1 & 29.5 & 37.8 & 45.6 & 54.8 & 60.1 \\ SpERT & 24.3 & 36.5 & 42.9 & 50.4 & 58.5 & 63.4 \\ BERT-MRC & 30.5 & 38.9 & 44.1 & 51.2 & 60.3 & 67.9 \\ BARTNER & 33.1 & 41.6 & 46.3 & 53.0 & 61.8 & 68.2 \\ TemplateNER & 40.2 & 44.3 & 52.6 & 60.8 & 66.1 & 72.3 \\ PromptNER & 42.8 & 47.5 & 58.3 & 65.1 & 70.2 & 74.8 \\ \hline
**MPBCNER (BERT)** & **45.1** & **52.6** & **61.5** & **67.3** & **72.1** & **78.6** \\ MPBCNER (RoBERTa) & 45.9 & 53.5 & 62.1 & 67.9 & 73.0 & 79.3 \\ MPBCNER (ERNIE) & 46.3 & 55.4 & 64.8 & 69.7 & 75.1 & 81.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results in the GA few-shot dataset (F1%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **K = 10** & **K = 20** & **K = 50** & **K = 100** & **K = 200** & **K = 500** \\ \hline MPBCNER (BERT) & **45.1** & **52.6** & **61.5** & **67.3** & **72.1** & **78.6** \\ \hline -GAT lay & 43.2 & 48.9 & 59.4 & 65.7 & 70.1 & 75.4 \\ -Prompt & 37.5 & 42.5 & 52.6 & 59.7 & 61.8 & 68.6 \\ -ALL & 30.1 & 36.6 & 42.7 & 50.2 & 58.2 & 65.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation experiment.  
removal of the prompt module, which indicates that prompt learning can effectively improve the performance of the model in a few-shot setting. Meanwhile, for Chinese NER, incorporating effective entity boundary features into the model improved the model's entity recognition capability more accurately.  
In order to explore the effect of different prompt templates on model performance, we set up relevant comparison experiments. We denoted the prompt template method that only uses the prompt slot as MPBCNER (Slot), the prompt template method that only uses the hard prompt template as MPBCNER (Hard), and the prompt template method that only uses the soft prompt template as MPBCNER (Soft).  
The results in Table 9 show that different prompt templates had different effects on the models. Of all of these, the soft prompt and prompt slots had the best lift on the model. The effect of hard prompts was the worst. The MPBCNER combining all three cueing features performed the best.
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN
### Service Mapping Models
#### 2.2.1 Basic Models  
Basic models refer to RAN network elements and entities that allow capturing the configuration and the RAN environment information. Figure 1 proposes some basic models, namely _scenario topology_, _gNBs_, _UEs_, _channel_ and _network slices_. Note that the incorporated models correspond to the required ones to represent a 5G RAN deployment in the NDT, but  
Figure 1: RAN NDT architecture and apps. The RAN NDT architecture includes the data repository, service mapping models to represent the RAN elements and operation, and digital twin management to manage the RAN NDT instance.  
more models could be incorporated if required (e.g., an eNB model in case of considering interworking with an LTE network).
* _Scenario topology_ model: covers the topographic information of the RAN area considered for the NDT, such as detailed maps including buildings, streets, parks etc.
* _gNBs_ model: emulates the behaviour of the gNBs (i.e., 5G nodes) including aspects such as their deployment information (e.g., gNB position, gNB height), configuration (e.g., operating frequency, bandwidth configurations, numerologies, beamforming model) and operation (i.e., packet conformance at different levels, etc.).
* _UEs_ model: emulates the behaviour of Users Equipment (UEs) capturing physical aspects such as their position, height, antenna gain or the noise figure but also others such as the UE type (e.g., pedestrian, vehicular, sensor) and/or the UE Quality of Service (QoS) requirements (e.g., minimum bit rate, latency, reliability).
* _Channel_ model: characterizes the links between the UEs and the gNBs, considering the effect of propagation loss, interference or noise in accordance with the parameters of UEs and gNBs in the NDT (e.g., antenna heights, gains, noise figure, etc.).
* _Network slices_ model: for systems with multiple slices, this module embraces all aspects related to their characterization. For each slice, this may include the UEs belonging to the slice, the Service Level Agreement (SLA) to be guaranteed and per-slice parameters to be considered in the scenario.
## 2 Materials and Methods
### Pre-Processing  
Based on the dataset, pre-processing is crucial to normalize the text data, as highlighted in previous research works [22; 23; 24]. Our dataset comprises unstructured data from social media sources, particularly YouTube comments. To enhance the results and structure of this data [25; 26; 27; 28], comprehensive pre-processing steps have been applied. In natural language processing (NLP), text pre-processing involves cleaning and organizing textual data for optimal utilization in various NLP tasks. This includes removing unnecessary characters and common stop-words [29], as well as formatting to facilitate analysis by NLP algorithms. Raw text data is often noisy and difficult for algorithms to analyze, and pre-processing improves the accuracy and efficiency of NLP models [30; 31; 32]. The steps involved in text pre-processing typically include the following:  
1. [noitemsep]
2. Cleaning the text--removing unnecessary or irrelevant characters, such as punctuation marks or special characters [33];
3. Tokenization--splitting the text into smaller units, called tokens, such as words or phrases [34];
4. Removing stop-words--common words that lack significant meaning [35], such as \"the\" or \"and\", were eliminated to reduce the text size and improve the performance of the NLP model;
5. Stemming and lemmatization--leveraging the Sastrawi library, stemming for the Indonesian language (Bahasa) was performed to reduce words to their base form [36], known as the stem or lemma;
6. Part-of-speech tagging--identifying parts of speech, such as nouns or verbs, aids certain NLP tasks [37];  
Figure 2: The proposed automatic annotation process in detecting hate speech using a semi-supervised and self-learning approach.  
Normalization--formatting the text consistently, converting all words to lowercase. This streamlines processing for the NLP model [38].  
Overall, the goal of text pre-processing is to clean and prepare text data for use in NLP tasks to improve the accuracy and efficiency of the NLP model. The specific steps involved in text pre-processing can vary depending on the specific NLP task and the quality of the raw text data.
## 2 Geopolymer Concrete (GPC)
### Mix Design
#### 2.2.1 Hit and Trial Method of Mix Design  
There are different hit and trial mix design studies followed by researchers [75; 76]. Generally, the hit and trial method is dependent on mechanical and rheological properties such as strength and flow parameters. [PERSON] et al. [74] studied the mix design based on the strength of GPC in which alkaline to binder ratio was taken on a trial. The first step was to determine the required target strength, given in Equation (1), and set the slump value. Then after the selection of alkaline/binder and aggregate/binder ratio, the aggregate and alkaline content were determined. This mix design procedure is more clearly presented by flow chart given in Figure 3. Furthermore, some studies of mix design procedures are based on hit and trial method [77].  
\[\mathrm{f_{t}=f_{ck}+1.65\;S_{d}}, \tag{1}\]  
where,  
\(\mathrm{F_{t}=target\;average\;compressive\;strength\;of\;GPC\;at\;28\;days}\),  
\(\mathrm{F_{ck}=characteristic\;compressive\;strength\;at\;28\;days}\),  
\(\mathrm{S_{d}=standard\;deviation}\).  
[PERSON]'s method is also one of the hit and trial methods for mix design of GPC that is feasible to determine the number of factors and their interaction [78]. The factors that need to be considered are the activator to binder ratio, silicates to hydroxide ratio, binder content, activator concentration, and molarity [79]. The main objective of this method is to design a high strength GPC while considering different influential parameters. In this method, the number of trial mixes are prepared based on different values of influential parameters, and the optimum mix is selected by calculating the response index. Here, the response index is calculated by taking the average compressive strength of 7 days of different trial mixes [79]. However, the response index might be calculated at later age strength due to the dense microstructure of GPC.
# Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
## 3 The Architecture of System ISTID
### Proposed Algorithm  
In these works, the intelligent feature selection techniques and intrusion detection (ISTID) is proposed to be the new smart classification algorithm. This algorithm optimizes rule generation during the training stage and stores the optimum rules in a knowledge base that is indexed according to time intervals. In the next step, the input fluctuates and genetic operations are carried out to conduct the classification of intrusion data used for processing. The fused input corresponds to the fused genetic regulations established in the basis of knowledge, and validation is controlled for time constraints. Finally, for each input information, the classification algorithm decides the class.  
Figure 1: Architecture of System ISTID.  
Let 'E' be the 'n' data records database. The database is split into the testing dataset (E1) and the evaluation dataset (E2) in two sections. The genetic time fuzzy rules are produced, and the training datasets are used. This device is checked ten times over. Therefore, 92% of data sets are for preparation, and 8% are for research purposes. The data sets are included. This is done until all records have been tested. This research translates all attributes of the data set into a standard format using the numerical values from zero to one which is simple to compare during classification. For that reason, the minimum and maximum value is known as the limits for each attribute, and the interval between the maximum and minimum values is determined the attribute duration. The standardized cost is calculated using an equation formula:  
\[Normalize\ value\ (J)=\frac{Value\ of\ Given\ (J)}{maximum\ value-minimum\ value} \tag{1}\]  
Equation (1) where the _Jth_data record is indicated, given value is current, normalized value is the computed value between zero and one, the max value is the maximum value for the specific attribute, and the minimum value is the maximum value for the specific attribute.
## 3 Data Preparation and Analysis
### Independence Test  
Considering the possible correlation between the indicator factors, it is necessary to select the indicator factors through multiple covariance analysis. In this study, by obtaining the landslide data and the evaluation factors in the study area, we tested the evaluation factors for mutual independence using the band set statistical tool in ArcGIS software and obtained their correlation coefficients (\(\rho\) = covariance/standard deviation) to ensure the accuracy of the evaluation model of landslide susceptibility. \(\rho\) < 0.3 represents the uncorrelation between the factors, 0.3 \(\leq\)\(\rho\) < 0.5 represents low correlation, 0.5 \(\leq\)\(\rho\) < 0.8 represents medium correlation, and \(\rho\)\(\geq\) 0.8 represents high correlation.  
As depicted in Table 2, the correlation coefficients were all less than 0.3, which indicates that the evaluation factors are independent of each other, and all 13 evaluation factors can be included in the evaluation model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**EF** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **h** & **i** & **j** & **k** & **l** & **m** \\ \hline a & 1.00 & & & & & & & & & & & & & \\ b & 0.04 & 1.00 & & & & & & & & & & & \\ c & 0.00 & 0.02 & 1.00 & & & & & & & & & & \\ d & 0.02 & 0.04 & 0.02 & 1.00 & & & & & & & & & \\ e & 0.03 & \(-\)0.02 & \(-\)0.04 & 0.00 & 1.00 & & & & & &
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 5 Pulsed Electric Fields--PEF  
### General Description of PEF Technology  
Pulsed electric fields (PEF) is an emerging, cost-effective, and environmentally friendly method gaining importance, but further investigation is needed to allow the successful industrial application [115]. PEF is a non-thermal treatment that can be used as a decontamination technique and has minimal effects on the texture and other sensory properties of the food [165; 166]. Additionally, PEF can improve the functional properties of seafood products, often containing high, unhealthy concentrations of additives such as NaCl through the alteration of the protein structure that leads to greater additive diffusion [167]. In PEF treatment, food is placed between two electrodes where a pulsed high-voltage electric field passes through for a short time period, ranging from 1-100 ns to several ms, and the electric field intensities ranging from 0.1 to 80 kV/cm [5]. The PEF process also produces ohmic heating due to the application of high-voltage pulses without causing any effects in the product [167; 168]. The intensity and the treatment parameters of PEF can cause structural changes and reversible or irreversible permeabilization of the microbial cell membrane [169; 170]. This phenomenon is called electroporation and results in altering membrane permeability through the formation of pores allowing the exchange of membrane components with the cell environment [171]. Regarding the sensory qualities of the products, which are equally important with food safety, [PERSON] et al. (2014) [172] suggested that PEF treatment possibly has less effect on meat and fish products than traditional thermal methods, and further investigation is necessary.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 3 Results
### Response of Ecosystem-Scale Phenological Processes to Forest Management  
The SOS in fire years was delayed by 10 d on average compared to non-fire years, and this trend strengthened along the soil moisture gradient with the mesic site being most affected (18 d delay; Appendix B, Table A1). However, no significant differences were indicated by multivariate tests (\(p>0.14\)) (Appendix B, Table A3). Dynamic changes of EOS and LOS showed a weaker relationship with fire; prescribed fire had no discernible effect on the GPP recovery rate of the ecosystem in mid-spring (Appendix B, Table A3; Appendix B, Figure A2), Thus, phenological processes of the ecosystem with higher water availability were more sensitive to prescribed fire, but the responses of EOS and LOS were more subtle than SOS (Appendix B, Table A1).  
Furthermore, in years with prescribed fire, SOS values in our study sites were significantly related to PAR and air temperature in the early spring (January to March) (\(p<0.01\); Table 3); as air temperature and PAR increased in spring after fire, the SOS was significantly advanced and the growing season (LOS) was significantly extended (\(p<0.05\)). In non-fire years, we did not find similar spring phenology trends (\(p>0.05\); Table 3).
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 3 Methodology
### Input Variable Calculation
#### 3.1.3 Calculation of NDVI and GPP Anomalies  
In this study, the AVH13C1 daily scale NDVI from 1990 to 2018 was merged into a monthly NDVI dataset by using the maximum composite method (MVC), and then the residual noise in the time series was removed by S-G filtering to obtain high-quality NDVI data [69]. After that, NDVI anomalies were obtained according to the method proposed by [PERSON] et al. [70].  
First, the overall trend of NDVI values in each pixel over the study years was removed, meaning that the time series was taken as the independent variable \(t\), and the NDVI value of the corresponding pixel in the time series was taken as the dependent variable \(y_{k}\) for linear fitting (Equation (4)). The difference between the NDVI value y and the NDVI fitted value \(y_{k}\) was the detrended NDVI value \(y_{dk}\) (Equation (5)).  
\[y_{k}=\beta+\alpha t \tag{4}\] \[y_{dk}=\gamma-y_{k} \tag{5}\]  
Second, the seasonal component of the NDVI values after detrending was calculated. Assuming that the data were evenly distributed in the time series, the monthly mean value of NDVI in each pixel was taken as the NDVI value in the seasonal period (\(y_{cir}\)).  
Third, the NDVI anomaly \(y_{ab}\) was calculated by taking the detrended NDVI value \(y_{dk}\) minus the NDVI value in the seasonal period (\(y_{cir}\)).  
\[y_{ab}=y_{dk}-y_{cir} \tag{6}\]  
For the monthly NDVI data collected from 1981 to 2018, the corresponding monthly NDVI anomaly (NDVIa) was calculated for each grid using the method mentioned above. Then, for each study year, we calculated the average NDVIa value in the cultivated land area of each municipal administrative region for the whole growing season (from April to September) and for each month in the whole growing season. The average NDVIa from April to September was recorded as NDVIa4, NDVIa5, NDVIa6, NDVIa7, NDVIa8, and NDVIa9, and the average NDVIa of the whole growing season was recorded as NDVIa4-9. Using the same method, we calculated the monthly GPP anomaly (GPPa) for each grid and then further calculated the average value of GPPa in the cultivated land area of each municipal administrative region for the whole growing season (from April to September) and for each month during the whole growing season. The average GPPa from April to September was recorded as GPPa4, GPPa5, GPPa6, GPPa7, GPPa8, and GPPa9, and the average GPPa of the whole growing season was recorded as GPPa4-9.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 3 Background
### IoT Devices
#### 3.2.2 Multi-Channel LoRaWAN GPS Concentrator  
A high-performance multi-channel transmitter/receiver designed to receive multiple LoRa packets simultaneously. It is intended to provide a robust connection between a central wireless data concentrator and a large number of wireless endpoints over a considerable range of distances.
# Estimating the Performance of MIMO SC-FDE Systems Using SISO Measurements
## 6 Conclusions  
The main objective of the work carried out and described in this paper was to estimate the signal coverage of a MIMO system based on the coverage of a SISO system, already optimised with DTs taken for it, in a efficient and practical way.  
Figure 10: Estimated coverage area of the considered scenario for a MIMO system with a BER of \(10^{-4}\) when using the third iteration of IB-DFE receivers, based on coverage estimation with DTs for a SISO system.  
To meet these objectives, the theoretical BER performance achieved by the SISO systems was initially evaluated and then compared with the BER performance of equivalent MIMO systems. From this theoretical analysis it was then possible to estimate the BER performance improvement that occurs, for a given BER and \(R/P\) ratio, when switching from SISO systems to MIMO systems. The expressions obtained in this study allows for the propagation models created and tested for SISO systems to be easily extended to MIMO systems, enabling the use of well-known propagation models in MIMO systems.  
The scenario and simulation data chosen to validate this concept has shown that, for a given BER performance, MIMO systems using IB-DFE receivers can in fact improve the system, as we should expect. It was shown that, although the signal coverage area can be reduced by 3.5%, the existence of more independent and simultaneous data streams in MIMO systems improves the user experience and increases the overall capacity of the system.  
As future work, it would be interesting to experimentally validate the methodology presented and confirm that the coverage for an actual MIMO system corresponds to the predictions.  
Conceptualization and Investigation, D.F., F.C., R.D. and P.S.; Software, D.F.; Supervision and Validation, F.C., R.D. and P.S.; Visualization, D.F.; Writing--original draft, D.F., F.C., R.D. and P.S.; Writing--review and editing, F.C., R.D. and P.S. All authors have read and agreed to the published version of the manuscript.  
This work is part of the OptiNET-5G project, co-funded by Centro2020, Portugal2020, European Union (project 023304). This work is also supported by Instituto de Telecomunicacoes, FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020.  
The authors declare no conflict of interest.
### Internet of Medical Things (IoMT)  
IoT healthcare devices, also referred to as the Internet of Medical Things (IoMT), are IoT devices specifically created for healthcare purposes. Examples of this can be remote patient monitoring, home or hospital patient monitoring, pacemaker implants, wearable devices, diabetes sensors, and others. The IoMT is transforming patient treatment, enhancing the potential for successful patient recovery. The IoMT can improve QoL, prolong life expectancies, provide real-time patient data, directly impact patient health with faster reactions from doctors or nurses, and much more [3]. In recent times, the adoption of IoMT devices has extensively escalated. The utilisation of IoMT is on the rise, with current trends pointing toward increased adoption of these devices. The applications of the IoMT are growing: IoMT revenue in 2017 was USD 28 billion, and it is expected to grow to USD 135 billion by 2025 [4]; a similar report about the growing popularity of IoMT applications is also available in [5]. The IoMT consists of a network system with two primary components: local monitoring and remote monitoring. IoMT devices used for local monitoring can be defined by any IoMT system operating under the same network. For example, local IoMT includes home health monitoring, hospital patient monitoring, and care homes. Remote monitoring IoMT consists of systems and devices that are not within the same network. Examples of remote IoMT can be seen in public health monitoring, remote patient monitoring, and remote healthcare consulting through a General Practitioner.