esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design and Analysis
### Melting Experiment  
To verify the effectiveness of the main modules proposed in this paper, a set of comparative experiments is conducted in this section. Based on the IDCNN-BiLSTM-CRF model, two groups are designed by replacing IDCNN with CNN and removing the IDCNN module as controls to validate the role of using the IDCNN module. The results of ablation experiments are shown in Table 5 below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **F1 (\%)** & **R (\%)** \\ \hline IDCNN-BiLSTM-CRF & 72.37 & 76.42 & 80.33 \\ CNN replaces IDCNN & 71.39 & 75.42 & 79.31 \\ Remove IDCNN & 69.72 & 74.29 & 80.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment evaluation index value.  
Figure 11: The number of each entity and its evaluation indicators.  
The performance of the three models in the table decreases sequentially on the network security dataset. This indicates that both CNN and IDCNN can extract local information from text, but IDCNN has a larger receptive field, allowing it to handle long text information and further improve the performance of network security entity recognition models.
## 3 Security Evaluation Methodology for ICT Systems
### Security Testing  
The _security testing_ phase is essential for creating a comprehensive test description to evaluate the security claims identified in the previous phase. The first step in this phase is the _test design_ in which claims relevant to the TOE are detailed and transformed into structured test outlines, prioritizing each test based on its associated risk levels. This prioritization ensures that tests targeting higher-risk components or properties are implemented first, optimizing security assessment efforts.  
Once the skeleton of the tests is obtained, the _test implementation_ phase translates them into specific low-level steps that the TOE could understand and process. Based on the nature of each test, the most suitable testing technique and tool should be chosen. Although the MBT approach is highly recommended due to the automation of the testing process, this technique can be complemented with others, such as fuzzing for testing inputs and random behaviors. An example of combining multiple techniques can be found in Section 4. The tests can be implemented using a multipurpose programming language such as Python, C, or Java with testing support (e.g., the JUnit framework) or a dedicated language such as Testing and Test Control Notation version 3 (TTCN3) [31], which is supported by TITAN ([[https://projects.eclipse.org/projects/tools.titan](https://projects.eclipse.org/projects/tools.titan)]([https://projects.eclipse.org/projects/tools.titan](https://projects.eclipse.org/projects/tools.titan)) accessed on 3 February 2025) during the execution.  
The _environment setup_ phase ensures that the TOE and its testing environment are fully configured. This environment may be local, using internal devices and resources, or remote, using platforms such as FIT-IoT ([[https://www.iot-lab.info](https://www.iot-lab.info)]([https://www.iot-lab.info](https://www.iot-lab.info)) accessed on 3 February 2025) for broader, distributed testing. Local environments allow for precise control, while remote setups facilitate tests that require multiple entities such as distributed denial-of-service simulations.  
With the environment established, tests are executed in the _test execution_ phase. Automation is encouraged, for example, using the previously mentioned platforms (JUnit or TITAN) to streamline test execution. At the end of the test execution phase, a test report is generated to collect the results of the tests:  
* PASS if the test result meets the conditions of the test specification.
* FAIL if the test result does not meet the conditions for passing the test.  
Specific metrics that are not exclusive to one of the other values (PASS/FAIL). These metrics provide more refined information than a binary result, e.g., the encryption percentage, the algorithm used, or the length of the key, helping to improve the estimation of risk.
# Forecasting the Impacts of Prescribed Fires
## 2 Methodology
### Forecast Evaluation  
Forecasts are evaluated as binary occurrences. The divide between a positive occurrence and a negative occurrence is determined based on certain criteria. For example, recall that we defined a burn day as a day with total burn area of 30 ha or more in a county. In this example, a true positive is a correctly predicted burn day, a false positive is an incorrectly predicted burn day and a false negative is an incorrectly predicted no-burn day. True positives (_tp_), false positives (_fp_), and false negatives (_fn_) are counted and the following metrics are used in evaluating the forecast performance. Precision, \(p\), is the measure of true positives from the total predicted positives:  
\[p=\frac{tp}{tp+fp}. \tag{1}\]  
Recall, \(r\), is the measure of true positives from the total observed positives:  
\[r=\frac{tp}{tp+fn}. \tag{2}\]  
\(F1\) score is the harmonic mean of precision and recall:  
\[F1=\frac{2\times p\times r}{p+r}. \tag{3}\]  
Figure 1: Illustration of HiRes2 daily forecast products: (**a**) 24-h average PM\({}_{2.5}\) (particulate matter with an aerodynamic diameter smaller than 2.5 \(\upmu\)m) concentrations (\(\upmu\)g m\({}^{-3}\)) on 9 March 2016 and; (**b**) on a different scale, the portion of PM\({}_{2.5}\) associated with prescribed burns in Georgia USA.  
True negatives (_tn_) or correctly predicted no-burn days are also important from an accuracy point of view. Accuracy, \(a\), measures the overall correctness of the model:  
\[a=\frac{tp+tn}{tp+fp+tn+fn}. \tag{4}\]  
The number of no-burn days exceeds the number of burn days by a large margin, and since we are more interested in correctly predicting the burn days, accuracy can be a deceiving performance measure. A burn day forecast model can yield high accuracy even if it misses the majority of the burn days. On the other hand, the \(F1\) score places more importance on the burn days, whether they are predicted correctly or missed, or whether the model predicts false burn days; therefore, it is a better performance measure than the accuracy. However, the \(F1\) score treats _fp_ and _fn_ equally while, in reality, those occurrences may have different consequences. A metric that weighs _fp_ and _fn_ according to the economic value of their consequences might be better suited for forecast evaluation and should be considered in the future.
## 5 Simulation Results and Discussion  
Since the testing of the system in real environments are limited and the system features have military value, simulation results and the implications from these results will be presented in this chapter. Map samples taken from Google [48] have been used in the software and simulation studies.  
Figure 9 shows a screenshot including simulation results applied to the HPD method. The system clearly points out the latest spot according to the predetermined simulation results. However, since the distance values are given as \"kilometer\" for the data obtained in the simulation, there is 1-50 meter diversion, and therefore the hidden place has been calculated as a circular area of 50 meters in diameter rather than a specific point.  
Figure 10 shows the graphic including simulation data applied for the HVD method. This graphic, also displays the spectrometer of the recorded voice signals by the animal. The values for all noise levels (pessimistic, moderate, and optimistic) are shown separately. Voice levels for the pessimistic, moderate and optimistic levels are shown by brown, orange and yellow areas respectively. Additionally, blue regions in the figure show the areas of probable human voice detection. It is possible to listen and understand voice strengths of optimistic and above. There are, however, some problems in this sampling, the most important of these is limiting data flow interval. On the other hand, if there is a delay of (even) 2-3 minutes between the recording of the voice data and the decomposition and definition, there is a risk of being unable to pin point the exact location because the detection individuals may move in this time. Consequently data flow intervals should be minimized.  
Figure 9: Sample shot from simulation result for HPD.  
There are advantages and disadvantages of these methods used to detect existence of human activity and the probable hidden places in the targeted region by processing the data acquired from the animals.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 6 Conclusions  
We combined Federated Long Short-Term Memory (F-LSTM) and our proposed dynamic handover algorithm to reduce unnecessary handovers. The F-LSTM model predicts future Reference Signal Received Power (RSRP) and nearest Reference Signal Received Power (NR-RSRP) values. The predicted NR-RSRP and the Mean Absolute Error (MAE) of the predicted RSRP are utilized as reference values and thresholds for the dynamic handover algorithm, respectively. Compared to the 3 GPP A3 Event and Greedy-based handover algorithms, our approach reduced the Handover Ping-Pong Rate (HOPP) by 2.73% and 10.45%, respectively, effectively mitigating the occurrence of the ping-pong effect.  
In terms of future research, we suggest exploring the integration of our model with other advanced machine learning techniques, such as reinforcement learning, to further enhance the decision-making process in dynamic network environments. Testing the model in different and more diverse network environments, such as rural or highly congested urban areas, could provide additional insights and validate the robustness of our approach. Finally, optimizing the computational efficiency of the FL framework, possibly through  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Time (s)** & **Execution Time (s)** & **Prediction Time (s)** \\ \hline Hete\_FL1 & 917.630 & 0.583 & 0.575 \\ Homo\_FL2 & 134.114 & 0.336 & 0.322 \\ Cen\_LSTM3 & 97.572 & 0.357 & 0.338 \\ \hline \hline \end{tabular}  
* Heterogeneous federated learning.2 Homogeneous federated learning.3 Centralized LSTM Model.  
\end{table}
Table 4: Comparison of Model Time Costs.  
the use of lightweight models or distributed computing techniques, would be a valuable direction for improving scalability and applicability in real-world settings.  
Methodology, W.-C.C.; Software, Y.H.; Validation, W.-C.C. and W.-Y.H.; Formal analysis, B.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Council of the R.O.C. under grants 112-2221-E-259-004-MY2 and 113-2622-E-259-002-.  
Not applicable.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Hydrological Model  
The Hydrologic Engineering Centre's Hydrologic Modeling System (HEC-HMS) is designed to simulate the rainfall-runoff processes of dendritic watershed systems. It is a deterministic, semi-distributed, event-based/continuous, mathematically-based (conceptual) model. It is able to model in a wide variety of geographical regions and different climatic contexts, such as arid and semi-arid mountainous climates, several studies having been carried out in the Tensift region. The HEC-HMS model was chosen mainly because it was previously validated in the same study area, and because it provided good and realistic results [36; 42; 43] in order to assess the applicability of the model in such an environment, using different approaches (event-based and continuous modeling).  
The HEC-HMS model consists of four components: the basin model, the meteorological model, the control specifications, and the input data. The basin model provides information about the physical properties of the model, such as basin areas and stream reach connectivity. Similarly, the meteorological model includes information related to precipitation data. The control specification section contains information related to the timing of the model, the timing of a storm event, and the type of time interval (second, minute, hour, or day) that is required to be used in the model. Finally, the input data component contains the parameters or boundary conditions for the basin and weather models. The main input data used for this study are satellite and in situ precipitation and observed flow, as well as the different basin characteristics (number of curves, soil, LULC) resulting from the HEC-GeoHMS process. The method used in this paper includes the SCS-CN (Soil Conservation Service) curve number, the Clark unit hydrograph, and the base flow recession, which are necessary to determine the hydrological loss rate, runoff transformation, and base flow. The model was used in a lumped way (as we only have one measuring station at the downstream of the basin, which does not allow to do semi-distributed modeling). This approach aims to calibrate three rainfall events for each product, and then three more for observed rainfall. A total of 15 events were calibrated, 12 of which were satellite products, and 3 of which were observed data. The validation used a 3H time step of precipitation by implementing the model with different sources of precipitation data, such as observed and satellite precipitation with observed runoff, to evaluate the ability of SPPs to reproduce rainfall events from 2014 to 2017.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic  
### Microbial Degradation of Plastics  
Both synthetic and bio-plastics can be degraded by the action of microbes, including bacteria, fungi, and actinomycetes [42; 43]. Biodegradation of polymers in the environment starts with the process of biodeterioration, where the mechanical, chemical, and physical properties of plastic surfaces change due to thermal and photo-oxidation. These structural changes occur under the influence of environmental conditions (i.e., temperature, moisture, and sunlight). Microorganisms augment this biodeterioration through the formation of biofilms on plastic surfaces [44; 45]. After biofilm formation, the constituent monomers are broken down [46; 47], and these smaller monomers are taken up into the microbial cell, where they are broken down to produce energy [48].
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 3 Method
### Model Introduction
#### 3.2.1 The Principle of the Transformer  
For the transformer model, the traditional convolutional neural network (CNN) and recurrent neural network (RNN) are abandoned, and the whole network is composed of a new mechanism named attention. The transformer block is composed of attention and a feedforward neural network. A neural network based on a transformer can be built by stacking transformer blocks. The transformer model utilizes parallel computing logic whose input and output for each point are independent. The calculation process at any time is based on all time inputs and the time coding of the corresponding inputs. A CNN can only select local inputs for calculation purposes. When using a CNN to make time series predictions, a serious issue is encountered because it cannot smoothly connect the relationship between the two convolution boxes. The prediction curve jitters from the perspective of global time series prediction. RNNs exhibit serial computing logic. The limitation of the RNN calculation process is that the calculation step for the current time \(t\) is based on the calculation results from time \(t-1\), which greatly limits the calculation speed of the model. At the same time, an RNN loses the information contained in early calculation results when calculating long time series.  
A transformer conducts model training and prediction through its encoder-decoder system. For each encoder, the data first pass through the self-attention module to obtain an attention matrix containing three inputs (query, key, value), and then the output probability value is obtained by activating the softmax function:  
\[A(q_{i},k_{i},v_{i})=Soft\text{max}\bigg{(}\frac{QK^{T}}{\sqrt{d}}\bigg{)}V \tag{1}\]  
The training logic is as follows: \(Q\) is the query matrix, \(K\) is the key matrix, \(V\) is the value matrix, \(I\) is the matrix of inputs \(X_{i}\), \(R\) is the normalized matrix, \(K^{T}\) is the transposed matrix of \(K\), \(A^{\prime}\) is the matrix obtained after we normalize matrix \(A\), \(O\) is the matrix of outputs \(B_{i}\), and \(W^{q}W^{k}W^{v}\) are the parameters matrix that can be learned by the model.  
\[\begin{array}{l}Q=W^{q}*I\\ K=W^{k}*I\\ V=W^{v}*I\\ A=K^{T}*Q\\ A^{\prime}=R*A\\ O=V*A^{\prime}\end{array} \tag{2}\]  
For the attention value corresponding to the \(i\)th query, the following probabilities can be used to substitute Equation (2) into Equation (1):  
\[A(q_{i},k_{i},v_{i})=\sum_{j}\frac{k(q_{i},k_{j})}{\sum_{l}k(q_{i},k_{l})}v_{j }=E_{p}\big{(}k_{j}|q_{i}\big{)}\big{[}v_{j}\big{]} \tag{3}\]  
where \(k(q_{i},k_{l})\) is the asymmetry index core in the attention mechanism, which is utilized as \(e^{(\frac{q_{i},k_{l}^{T}}{\sqrt{d}})}\). The attention mechanism describes the similarity between queries and keys and aims to select important q-k pairs.
## 5 Conclusions  
In this study, high-throughput RNA-seq was applied to characterize the transcriptomes of _M. canadensis_ treated with MeJA. A total of 81,843 unigenes were obtained and 64.55% of which could be functionally annotated in at least one database. Additionally, 4430 DEGs with 2383 up-regulated and 2047 down-regulated transcripts were identified under MeJA treatment. A lot of unigenes associated with JA signal transduction were up-regulated, which suggested that application of exogenous MeJA may regulate the JA signaling pathway in _M. canadensis_. KEGG enrichment indicated that \"Monoterpenoid biosynthesis\" was one of the most significantly enriched pathways in metabolism. 9 orthologous genes involved in menthol biosynthesis were identified in _M. canadensis_ and the response mechanism of menthol biosynthetic genes to JA signaling needs further study, especially the possible DEGs including _GPPS-s_, _iPD_, and _MFS_. 157 differentially expressed TFs belonged to 32 families were identified and the WRKY family was the largest differentially expressed family. The number of up-regulated WRKY TFs was much higher than that of down-regulated ones (19 vs. 2). The large number of up-regulated WRKY TFs in _M. canadensis_ suggests that they may play important roles in JA-responsive transcriptional regulation of secondary metabolism. However, the regulation mechanism of JA signaling on development and metabolism of _M. canadensis_ still requires further characterization and putative DEGs identified in this study might be important targets. This is the first reported transcriptome study of _M. canadensis_ and will provide useful information for further study of this species.  
Supplementary materials can be found at [[http://www.mdpi.com/1422-0067/19/8/2364/s1](http://www.mdpi.com/1422-0067/19/8/2364/s1)]([http://www.mdpi.com/1422-0067/19/8/2364/s1](http://www.mdpi.com/1422-0067/19/8/2364/s1)).  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the paper.  
The work was supported by the National Natural Science Foundation of China (30600051) and Fund of Jiangsu Key Laboratory for the Research and Utilization of Plant Resources (JSPKLB201811).  
The authors declare no conflict of interest.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 3 Results
### Normalized Data with MinMaxScaler  
The second data normalization algorithm that was evaluated on the same accelerometry data was MinMaxScaler. Table 3 presents the confusion matrix related to the experiments performed with the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-6}  & & **Walking** & **Walking** & **Walking** & **Standing** & **Walking** \\ \cline{2-6}  & **walking upstairs** & 75 & 116 & 5 & 5 \\ \cline{2-6}
**Actual Class** & **walking downstairs** & 53 & 141 & 2 & 6 \\ \cline{2-6}  & **standing** & 8 & 11 & 7 & 12 \\ \cline{2-6}  & **walking** & 0 & 4 & 3 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix for normalized accelerometer data with MaxAbsScaler.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-6}  & & **Walking** & **Walking** & **Walking** & **Standing** & **Walking** \\ \cline{2-6}  & **walking upstairs** & 95 & 46 & 0 & 3 \\ \cline{2-6}
**Actual Class** & **walking downstairs** & 52 & 81 & 0 & 11 \\ \cline{2-6}  & **standing** & 5 & 1 & 7 & 15 \\ \cline{2-6}  & **walking** & 2 & 4 & 0 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for normalized gyroscope data with MaxAbsScaler.  
accelerometer sensor included in the dataset used. It was verified that the most correctly identified activities are walking downstairs and standing.  
Next, the classification results of the data acquired from the accelerometer sensor after the application of MinMaxScaler were analyzed, verifying that the DNN method implemented reported an accuracy of 46.25%, a precision of 49.20%, a recall value of 49.08%, and an F1-Score of 49.14%.  
MinMaxScaler was also used to normalize the data acquired from the gyroscope data related to the different human activities. Table 4 presents the confusion matrix related to the experiments performed with the gyroscope sensor included in the dataset used. It was also verified that the most correctly identified activities are walking upstairs and walking.  
Further, the classification results of the data acquired from the gyroscope sensor after the application of MinMaxScaler were analyzed, verifying that the DNN method implemented reported an accuracy of 52.75%, a precision of 49.93%, a recall value of 51.06%, and an F1-Score of 50.49%.