esa-sceva/satcom-chunk-collection huggingface dataset:
# Method for Handling Massive IoT Traffic in 5G Networks
## 6 Analytical Model
As discussed earlier, \(r\) is the highest possible number of small packets that the buffer holds until starting the multiplexing process. Upon entering stage \(r\), the sole probable event that can happen then is that the process of packet multiplexing begins immediately, and no more packet arrivals are awaited. At stage \(r\), \(a_{r}=0\) while \(b_{r}=1\). The packet multiplexing process modelling is particularly useful in cases of data traffic where packet arrivals occur in bursts in between relatively silent periods. The generation of traffic at IoT devices is statistically independent. Thus, the traffic arriving at RN would be like bursts followed by periods of comparative silence. The goal of multiplexing is to combine the incoming packets at RN and make large blocks of data rather than sending isolated packets. This arrangement can reduce the number of blocks sent to BS considerably. The number of resource blocks needed for each stage can be determined by checking the TBS value for different resource block allocations and Modulation and Coding Scheme (MCS) combinations. Thus, the resource blocks required for different buffer sizes at the time of multiplexing is derived from the TBS table of 3 GPP. The TBS values for \(M=16\) (as per 3 GPP specifications [33]) and the buffer capacity in terms of data without overhead (352 bits) are given in Table 1 below. At stage 1, the buffer size in bits can reach \(l\) bits, where \(l\) is the fixed size of the arriving packets in bits. In this way, at stage \(x\), the size of buffer would become \(x\times l\) bits. The number of resource blocks needed for each stage \(x\) is denoted as \(N_{x,M}\), which relies on stage \(x\) and MCS \(M\). The values of \(N_{x,M}\) for \(M=16\) and \(l=232\) bits are given as in Table 2 below. To evaluate the performance of the proposed mechanism analytically, a discrete Random Variable (RV) \(X\) is defined, which denotes the number of packets fitted into a single multiplexed packet. The RV \(X\) can have possible values \(x=1,2,3,\ldots,r\). Here, \(r\) can also be defined as the maximum number of IoT data packets inside a single multiplexed large packet. \(P[X=x]\) denotes the probability mass function of RV \(X\). The likelihood of having more than \(x\) packets multiplexed into a large packet is \(a_{x}\). So \(a_{x}\) is now defined in another manner. The probability \(a_{x}\) represents the chance of having a value of \(X\) greater than \(x\).
## 4 Analysis of a Divisible Load Application in a Star Network Cloud
### Computing Tasks Steady States Diagram  
As shown in Table 1, a 3-digit symbol is used to represent the operation status of a computing worker, where digit \"0\" represents the processor in an \"idle\" status and \"1\" represents the processor in a \"busy\" status. For example, symbol \"101\" represents that the process of receiving a task from the master worker is on-going, the computing processor is \"free\" and that the transmitting processor is \"busy\" on sending the results back to master worker. Furthermore, the status \"b\" represents an application task that is blocked due to one application task's arrival into a processor in \"busy\" status. For example, symbol \"b11\" represents that when a completely received application task is sent to a computing processor, this application task is blocked due to the computing processor being in a \"busy\" status, meanwhile, the transmitting processor is also in a \"busy\" on sending results back to master worker. It is clear that a particular scheduling algorithm ([17, 18]) has also an impact on the steady states of a computing worker.  
While a master worker is assigning tasks to a computing worker,  
different states transitions may happen. Figure 2 shows a Markov model for the operating process in the computing worker, where all possible operating states and transitions between these states are presented. When the computing worker is operating in steady-state, its steady state equations (Equations (1)-(12)) can be obtained by the following procedures:  
Considering the state \"000\", which is directly related to the two states \"100\" and \"001\". When the computing worker in state \"000\" is starting to receive a new task from the master worker, it transits to state \"100\" at the rate of \(\lambda\), which is an outbound flow from the state \"000\". On the other hand, when  
\begin{table}
\begin{tabular}{c l} \hline \hline \(n_{1}n_{2}n_{3}\) & Description \\ \hline
000 & System is empty. \\
100 & Application task is in process at Station 1 only. \\
110 & Application tasks are in process at Station 1 and 2 only. \\
111 & Application tasks are in process at Station 1, 2 and 3. \\
101 & Application tasks are in process at Station 1 and 3 only. \\
001 & Application task is in process at Station 3 only. \\
011 & Application tasks are in process at Station 2 and 3 only. \\
010 & Application task is in process at Station 2 only. \\ b10 & Application task is blocked at the output of Station 1 because Station 2 is occupied. \\ b11 & Application task is blocked at the output of Station 1 because both Station 2 and 3 are occupied. \\
0b1 & Application task is blocked at the output of Station 2 because Station 3 is occupied. \\
1b1 & Application task is blocked at the output of Station 2 because both Station 1 and 3 are occupied. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steady State Status during Tasks Processing.  
Figure 2: Steady State Transitions on a Computing Worker.
# Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
## 3 Methodology  
Metaheuristic algorithms are stochastic and non-deterministic. There are various types of metaheuristic methods like local search, simulated annealing, Tabu search, variable neighborhood search, population-based or trajectory-based search, etc. Besides, among all the Metaheuristic search algorithms, one of the most popular algorithms are--gravitational search algorithm (GSA), particle swarm optimization algorithm (PSO), crow search algorithm (CSA), genetic algorithm (GA), differential evaluation (DE), and bat algorithm (BA), etc. Quantum-based optimization techniques were applied in several complex engineering applications using quantum parallelism mechanisms. For multimodal optimization applications, the study of [PERSON] indicated that quantum behaved algorithm is superior to existing metaheuristic algorithms [39; 40]. Another study found that quantum computation can manage highly non-linear multimodal optimization problems naturally [39]. Again, PSO searches linearly. On the other hand, QPSO's next position depends entirely on the probabilistic approach [41]. In QBA, the position of each bat depends on the mean best position. The quantum-behaved bat and mean best position lead local search to jump out of local positions [42]. Hence, QBA can easily avoid local optima. Since these properties are important to tackle multimodal well placement optimization problem, QPSO and QBA are discussed in the following section.
# A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
## 1 Introduction
[16] proposed a novel deep learning-enabled data compression and reconstruction framework based on two different networks: a CNN that extracts features directly from the input signals to detect anomalous data and a data compression and reconstruction method based on an autoencoder, which can recover the data. [PERSON] et al. [17] adopted generative adversarial networks and autoencoders to recognise anomaly conditions. This paper contributes to the literature by proposing a deep-learning-based approach. The proposed algorithm relies on the analysis of the signal's _coherence_: given an acquisition system made of \(N\) sensors, the idea is to train \(N\) models _iteratively_, estimating the readings of one sensor by using all of the other nodes of the acquisition network. The result is an _anomaly detection system_ that can _localise_ and _quantify_ anomalous behaviour. Moreover, being that the model is totally data-driven, it requires no previous knowledge of the structure, can be applied to sensors of different natures and is characterised by a fast deployment on existing time series. The validation of this approach has been carried out by applying it to a masonry arch bridge in Italy, which allowed for the detection and localisation of a flood's effects. The bridge, which flooded in October 2020, has been endowed with a monitoring system to collect data since December 2020 [18]. The proposed algorithm is compared with another one, which analogously performs data fusion on signals from tiltmeters installed on the structure, with the aim of tracking the bridge's structural behaviour. The proposed methodology successfully identified the transient between the flood and the stabilisation on a new equilibrium position. This is a really valuable result be cause it helps ensure the bridge's structural safety after such an extreme event, thus supporting the maintenance of the infrastructural network by providing quantitative and reliable information. The paper is organised as follows: in Section 2, the experimental setup and a description of the available dataset are outlined; in Section 3, a novel methodology is introduced and compared with a state-of-the-art algorithm; in Section 4, a discussion on data preprocessing and algorithm architecture is described; in Section 5, the methodology is applied to a real test case to prove its robustness; and, finally, conclusions are drawn.
# Error Modeling of Fiber Optic Gyroscope Universal Time Measurement
## 2 Analysis Model for FOG Measurements of Earth's Rotation
### Measurement Principle and Modeling of FOG
With increasing resolution, optical gyroscopes enable the detection of an expanding number of regional rotational signals from non-global sources [8]. These signals can be identified both through modeling and by using a large optical gyroscope solidly strapped down to earth. The Allan deviation curves of the FOG and G-ring are presented in Figure 4. It is evident that the outstanding stability of the G-ring laser provides the desired sensor resolution on the order of \(3\times 10^{-13}\) rad/s over at least \(10^{5}\) s. This is enough to capture the effects of the atmospheric angular momentum and ocean angular momentum (AAM/OAM) on the earth spin [10]. However, there remains a gap between FOG and G-ring. Although the giant FOG is still in the early operational phase, with fewer sensitive geophysical effects, there remains considerable potential for improvement.
## 4 Simulation Experiment and Results
### Case 2: Complex Scenario
#### 4.3.1 Simulation Results  
The simulation results of the five algorithms in the complex scenario are shown in Table 4 and Figure 10. The detailed path diagram is shown in Figure 11.  
According to Table 4, it can be seen that similar to the simple scenario, the smallest \(t_{cpu}\) among the five algorithms is the PRM with only 3.843 s. The PH-RPM proposed in this paper is still in the second position, slightly longer than the PRM, with a time of 4.528 s. The longest computation time for the five algorithms is the A* algorithm, which is due to the increased time cost caused by the wider search range. Combining Table 4 and Figure 10, from the comparison of path length, the path length of the RPM is the shortest, only 54.77 m, followed by the A* algorithm with a path length of 93.86 m. The path length of the PH-RPM in this paper is 106.96 m, which is 13.92% longer than the path length of the A* algorithm. The planned paths calculated by the RRT* and PRM methods are longer, 123.73 m and 129.57 m, respectively. As can be seen in Figure 11, the paths planned by the RPM are invalid and do not result in collision-free paths from the start to the endpoint. That is, in complex scenarios, all algorithms except the RPM can obtain valid solutions, and A* calculates the shortest path length among all valid solutions.  
Figure 12 shows the velocity and orientation angle variation curves of the PH-RPM algorithm in the path-planning process of the complex scenario. Since the RPM algorithm is not able to find the effective path in the complex scenario, the velocity and directional angle changes obtained by the RPM algorithm are not discussed. From Figure 12A, it can be seen that the whole moving process is similar to the simple scenario and can still be divided into three stages. Although there is a certain fluctuation of velocity during the maximum velocity motion, the fluctuation range is acceptable and satisfies the boundary range constraint of velocity, which is consistent with the actual motion. From Figure 12B, the overall change of orientation angle is relatively flat, which meets the boundary constraint of angle change, and the angle change at the end stage meets the practical requirements,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(t_{cpu}\)/s & \(t\)/s & \(L\)/m \\ \hline A* & 12.531 & - & 93.89 \\ RRT* & 10.396 & - & 123.73 \\ PRM & 3.843 & - & 129.57 \\ RPM & 10.867 & 136.35 (Invalid) & 54.77 (Invalid) \\ PH-RPM & 4.528 & 239.78 & 106.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the complex scenario planned by different algorithms.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 5 Implementation and Performance Evaluation
### Experiments and Measurements
#### 5.2.1 Experiment 1: Response Time  
The objective of the solution is to reduce the IIoT communication response time, which is one of the most crucial measurement indicators. This comprehends the transmission of the sensor measurement value to the destination broker, along with the transmission of the corresponding notification to the machinery. Two tests are performed inside this experiment, one for scenario A **(test 1.1)** and another one for scenario B **(test 1.2)**.  
For the correct measurement, we have decided to take timestamps of the transmission in the same device, specifically at MQTT publishing and at response arrival. This solves time synchronization issues between distinct devices, because using the same device results in using a single clock as a point of reference. Time measurements have been taken with a precision of microseconds in order to achieve high accuracy.
## 3 Results
### Results  
#### 3.3.1 Comparison with Baseline Models  
We evaluate the effectiveness of PDCNER against widely used NER models on the pig disease corpus. The overall findings of our experiments are presented in Table 3.  
According to Table 3, several inferences can be drawn. Firstly, the F1-scores of pre-trained models are higher than those of models without pre-training by more than 10%, such as the BILSTM_CRF model. This indicates that pre-trained models, with their deeper network structures, have learned more language features and enhanced their ability to recognize entities. Secondly, the pre-trained model incorporating lexicon information demonstrates better performance than models without lexicon integration. This improvement is primarily due to the integration of dictionary information specific to the pig disease domain, which effectively captures entity boundaries and word information. Thirdly, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyper Param** & **Value** & **Hyper Param** & **Value** \\ \hline batch\_size & 16 & dropout & 0.5 \\ learning rate & 0.00001 & optimizer & Adam \\ epoch & 20 & maximum sentence length & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Category** & **Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline baseline model without & BILSTM\_CRF & 71.58 & 67.51 & 69.49 \\ pre-training & & & & \\ \hline \hline \multirow{6}{*}{pre-trained model} & BERT-BiLSTM-CRF & 80.29 & 84.73 & 82.45 \\  & BERT-CRF & 81.62 & 84.39 & 82.98 \\  & BERT-CNN-CRF & 82.44 & 80.55 & 81.48 \\  & BERT-WWM-ext-BiLSTM-CRF & 81.73 & 85.47 & 83.56 \\  & RoBERTa-BiLSTM-CRF & 81.64 & 85.31 & 83.43 \\ \hline \multirow{2}{*}{pre-trained model with lexicon} & BERT-BILSTM-CRF-SoftLexicon & 82.99 & 84.73 & 83.85 \\  & LEBERT & 87.18 & 86.45 & 86.81 \\ \cline{1-1}  & **PDCNER (ours)** & **87.76** & **86.97** & **87.36** \\ \hline \hline \end{tabular}  
* The bold in the last row denotes the results we obtained in our model.  
\end{table}
Table 3: Comparison of experimental results for different NER models.  
recognition performance of our model significantly outperforms other models, achieving a micro-average F1-score of 87.36% in recognizing five major entities. This experimental result indicates that our model can effectively improve entity recognition performance in the domain of pig disease.  
Comparative analysis with the results of BERT-BiLSTM-CRF reveals notable improvement in the precision, recall, and F1-score of PDCNER, with improvements of 7.47 percentage points, 2.24 percentage points, and 4.91 percentage points, respectively.  
Through comparative evaluation utilizing the same datasets and downstream model, PDCNER demonstrates superior accuracy in identifying pig disease entities compared to LEBERT, exhibiting improvements in precision, recall, and F1-score by 0.58 percentage points, 0.52 percentage points, and 0.55 percentage points, respectively.
## 4 Conclusions  
We examined the suitability of three recently developed ionic liquids, namely trihexyltetradoylphosphonium-, methyltrioctylphosphonium- and methyltrioctylammonium 3-hydroxy-2-naphthoate, as heavy-metal-extracting agents by contrasting their extraction capabilities with acute algal toxicity. To elucidate their extraction behavior under environmentally relevant conditions, we spiked water and natural mineral water samples with 20 ug L\({}^{-1}\) of the heavy metals Cu, Ag, Cd, Hg and Pb and extracted them using a liquid-liquid extraction setup. We were able to reproduce the high extraction capabilities we previously reported towards mg L\({}^{-1}\) concentrations. Moreover, the ILs displayed great potential in achieving residual metal concentrations well below hazardous concentrations for algae and fish. The best results were obtained for the extraction of Ag and Hg from a spiked mineral water feed solution using trihexyltetradoylphosphonium 3-hydroxy-2-naphthoate. This yielded final metal concentrations below 0.03 and 0.1 ug L\({}^{-1}\), respectively.  
Acute toxicity assays revealed comparable EC\({}_{50}\) (72 h) values of the ILs on both growth and photosynthetic performance for two freshwater green algae _Raphidocelis subcapitata_ and _Tetradesmus obliquus_. The functional anion 3-hydroxy-2-naphthoate was less toxic than similar compounds; nonetheless, all three compounds must be considered as acute toxicants for algae according to the _Globally Harmonized System of the Classification and Labelling of Chemicals_. The leaching of the ionic liquids into the samples was low but remains a limitation regarding technical applications due to their ecotoxicity. Further research should therefore focus on IL ecotoxicity and biodegradability on one hand and on the development of environmentally friendly extraction setups on the other.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/9/3157/s1](http://www.mdpi.com/2076-3417/10/9/3157/s1)]([http://www.mdpi.com/2076-3417/10/9/3157/s1](http://www.mdpi.com/2076-3417/10/9/3157/s1)), Figure S1: Effect of the three ILs on algal growth (filled symbols) and chlorophyll fluorescence (Rel. \(\varphi\), open symbols) in 72 h acute toxicity assays with _T. obliquus_, n = 3, error bars = \(\pm\) SD.  
Conceptualization, J.A.L.-L. and F.J.; Data curation, M.S. and W.K.; Formal analysis, P.P. and M.S.; Investigation, P.P.; Resources, B.K.K. and C.M.; Supervision, F.J.; Validation, M.S., B.K.K. and C.M.; Writing-original draft, P.P.; Writing-review & editing, J.A.L.-L., W.K. and F.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] for proofreading the English of the manuscript.  
The authors declare no conflict of interest.
# Method of Feature Reduction in Short Text Classification Based on Feature Clustering
## 4 Experimental Validation
### Performance of the TSFC Algorithm
#### 4.2.3 Classification Effectiveness Evaluation  
Table 4 shows the F1 score in classifiers, such as the naive Bayes (NB), the support vector machine (SVM), and logistic regression (LR). The ratio of the training set to the test set is 4:1 in the classification experiment, and a five-fold cross validation is employed. A significant trend is that, if the similarity threshold is too high or too low, the improvement of classification results will be limited. This is because, when the similarity threshold is too high, the size of similar feature clusters is very small and it only affects a few features. When the similarity threshold is too low, the paired features are not close enough in the similarity feature cluster, and the size of the similarity feature cluster is too large, which results in inappropriate feature replacement. When TF-IDF is utilized, the optimal similarity threshold is 50% and the classifier is SVM or NB, while the optimal threshold used by LR is 45%, and the maximum F1 score is increased to 3.04%. However, a high similarity threshold results in an F1 score that is lower than the baseline. It is worth mentioning that the representation of feature reduction is what we expect. The lower the similarity threshold, the stronger the feature reduction, which can reach a maximum of 27.54%. When word embedding is used for vectorization, the maximum F1 score increases by 6.7%, but when the threshold is 40 and 60%, the F1 score is lower than that of baseline.  
Figure 4: Size of similar feature clusters. The legend shows the number of similar feature clusters that are of different sizes.  
In summary, our method achieves a maximum feature reduction of 27% and a maximum F1 score improvement of 3% with TF-IDF.