esa-sceva/satcom-chunk-collection huggingface dataset:
# Home Automation System Based on Intelligent Transducer Enablers
## 5 HASITE: Self-Configuring Intelligent Transducer System
### Comparison with ISO/IEC/IEEE 21451 Standard  
Finally, in order to clarify the differences between HASITE's architecture and the one proposed by the standard ISO/IEC/IEEE 21451, Figure 7 compares both.  
As Figure 7 reflects (and as explained in Section 3), the ISO/IEC/IEEE 21451 standard defines several elements (i.e., TIM, NCAP, transducers, network) and specifies the communication protocols among them. In such a standard approach, transducers, TIM and NCAP demand processing capabilities higher than the ones actually required. Moreover, the communication protocols between the elements introduce overhead to the data transferred through the network. Therefore, these protocols add complexity to the system and may reduce the number and type of compliant transducers.  
In contrast, HASITE allows for using any COTS transducer, concentrating the intelligence into a single element (into the ITN). The communications between the network interface, the microcontroller and the transducers are not constrained in any way. An ITN only needs to implement the self-configuration and self-registration protocols and to have an ITE to allow access to its transducers.  
With this approach, we remove the dependency on the transducers to be compliant with a standard. This is related to one of the main limitations of ISO/IEC/IEEE 21451: each transducer has to implement the whole standard, which derives higher development costs (i.e., transducers require extra hardware and complexity to implement their TEDS). Moreover, in an ISO/IEC/IEEE 21451 system, TIM and NCAP also have to be implemented and compliant with the specifications. Therefore, the whole system makes a complete implementation not only complex, but expensive, with a cost that increases with the number of transducers.  
Figure 7: Comparison of ISO/IEC/IEEE 21451 and HASITE architecture.
# An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis
## 4 Experiment  
### Synthetic Data and Predefined Templates  
Our experiment would be conducted on the synthetic dataset following the algorithm in [PERSON]'s paper [5] to generate the synthetic data. It consists of three steps to generate the data, time scaling, time warping, and noise adding. The time scaling is used to enlarge the data of the template pattern, we produce different lengths of the scaling time series during this process. Time warping changes the position of the data points extracted by PIP, so the shape of the synthetic data would be different from the template, but the overall picture is similar. The noise adding process we use is different from the original method, a random value \(rnd\) follows the Gaussian distribution \(\mathcal{N}(0,\sigma^{2})\) would be added to each data point if the random value \(r\) is below the threshold (0.7 is used in this paper), we can easily control the noise level by adjusting the value of \(\sigma\). Algorithm 3 describes the process of generating the synthetic data. Later in Section 4.2, the results will show the performance of each model in different noise levels. The predefined template patterns would be shown in Figures 11-13 in Appendix A.  
The accuracy is used to compare the models, which is defined as Equation (11). As described in the last paragraph, each template pattern would be used to generate numbers of corrupted patterns. The correctly recalled patterns would be the final matching pattern.  
\[Accuracy=\frac{correct\ recall\ patterns}{total\ patterns} \tag{11}\]  
Figure 8: The training process and pattern matching process of LAHN.  
```
0: A template pattern \(p\), scaling number \(m\), length of the template \(q_{num}\);
0: Synthetic data of the template pattern; Time Scaling  Compute \(X\leftarrow(m-q_{num})/(n-1)\);
1:for each point \(x_{i}\) in the set \((x_{2},\cdots,x_{end})\)do \(x_{i}\gets x_{i-1}+(X+1)\); endfor
2: Time Warping for each data point \(x_{i}\)do  Randomly change the position of \(x_{i}\) between \(x_{i+1}\) and \(x_{i-1}\);
3:endfor  Enlarge the points between each critical point; Noise Adding
4:for each point \(x_{i}\)do  Generate a random value \(r\) that follows \(\mathcal{U}(0,1)\); if\(r<\) threshold then
5: Generate a random value \(rnd\) that follows \(\mathcal{N}(0,\sigma^{2})\); \(x_{i}\gets x_{i}+rnd\); endif
6:endfor return\(x\);
```  
**Algorithm 3** Generating synthetic data.
# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 3 DMRVR Solution
### Supplier Production Line Delay Treatment  
The _LogisticsService_ application is responsible for receiving and managing the messages from the _VehicleService_ application, which is performed by auxiliary and pickup vehicles. When the _LogisticsService_ gets a notification from the supplier that tardiness occurs, it verifies if the delay is more significant than the \(T^{(up)}\) delay limit defined in the _LogisticsService_. If it happens, the application sends a _NotifyTardiness_ message to the main vehicle, indicating the supplier delay, and a _NotifyAddSupplier_ message to the auxiliary pickup vehicle, so it takes over this pickup.  
Figure 5: Traffic jam messages when the auxiliary vehicle cannot perform the pickup.  
Algorithm 2 shows how _VehicleService_ examines the supplier delay. When a _NotifyTardiness_ message is received by the main pickup vehicle, the _VehicleService_ verifies if the supplier lateness \(t^{(trd)}\) is higher than a \(T^{(up)}\) delay limit. If it is higher, the _VehicleService_ takes out this supplier from the route. When the delay is lower, _VehicleService_ evaluates if the tardiness will damage the total route time. If the new total time is higher than the JIT limit, the _VehicleService_ takes out the supplier from the route and sends the _NotifyWithdrawal_ to the logistics operator.  
```
1 if\(t^{(trd)}>T^{(up)}\)then
2 vehicleRoute.remove(\(E_{i}\));
3 else
4 totalTime\(\gets t^{(trd)}+\)routeTimeLeft() + routeTimeSpent();
5 iftotalTime\(>\)JITlimitthen
6 vehicleRoute.remove(\(E_{i}\));
7 Send(NotifyWithdrawal\(\rightarrow\)LogisticsService);
8 else
9 vehicleRoute[\(E_{i}\)].setTardiness(\(t^{(trd)}\));
10
11 end if
12
13 end for
```  
**Algorithm 2**Supplier tardiness analyses  
When the _LogisticsService_ application receives a _NotifyWithdrawal_ message from _VehicleService_, the handling of the message is the same as explained above. Figure 6 illustrates the messages exchanged between the _LogisticsService_ and _VehicleService_ when a supplier production line delay is identified.
# Towards the Internet of Smart Trains:
## 3 Overview on the Railway Applications Offered by GSM-R
### GSM-R: The Solution Preferred
* ETCS level 2: the communications between trains and the railway infrastructure are continuous and supported by GSM-R technology. The location of the train is estimated by means of fixed beacons. * ETCS level 3: the integrity of the train elements is checked at the train, thus no devices are required in the track. Fixed beacons are used to locate the train. * EURORADIO GSM-R: radio infrastructure. * EUROBALISE: beacons allowing for locating the trains accurately. * EUROCAB: on-board management system that includes European Vital Computer (EVC), Driver-Machine Interface (DMI), and measurement devices such as odometers. The ERTMS/GSM-R project was initiated by the UIC to unite existing and future developers to upgrade the GSM-R specifications. The collaboration continues today as an alliance between ETSI and the GSM-R industry. FRS version 8.0.0 [43] and SRS version 16.0.0 [44] (European Railway Agency (ERA) GSM-R Baseline 1 Release 0) were published in December 2015, representing the latest specifications. The mentioned documents describe the Mandatory (M) requirements regarding the interoperability of railways, according to Directive 2008/57/EC [45], and the requirements towards an IP-based core network architecture [46].
## 2 Sequential Algorithm and Its Software Implementation
### An Example of How the Algorithm Works  
To demonstrate the capabilities of the algorithm proposed in [23], we will use 1) a matrix \(A\) of dimension \(M\times N\) with elements generated as random variables with a uniform distribution in the range \([0,1]\), 2 ) model solution \(x^{model}\)--a vector of dimension \(N\), whose elements correspond to uniformly distributed values of the sine on the interval \([0,2\pi]\):  
\[x_{n}^{model}=\sin\frac{2\pi(n-1)}{N-1},\quad n\in\overline{1,N}.\]  
Given matrix \(A\) and model solution \(x^{model}\), first calculate the right side \(b\): \(b=Ax^{model}\). To solve System (1) with this matrix \(A\) and the right side \(b\), we apply the considered implementation of the conjugate gradient method with an improved criterion for terminating the iterative process. We will perform all calculations with double precision (float64), i.e., \(\Delta\sim 10^{-16}\).  
**Remark 2**: _Note that all subsequent results may slightly differ in details during reproduction, as \(A\) matrix is randomly assigned._  
Figure 1 shows an approximate solution--vector \(x^{res}\) for \(M=1000\), \(N=1000\). The algorithm needed to complete \(s=2476\) iterations, which is significantly more than \(s=1000\) in the case of using the \"classical\" implementation of the conjugate gradient method to  
solve System (1). However, it is perfectly clear that the approximate solution found by the classical method (marked in red on the graph) is quite different from the exact one (sine), even visually. At the same time, the solution found using the improved algorithm does not visually differ from the exact solution.  
If, however, calculations are made for \(M=3000\), \(N=1000\), then the approximate solution, which does not visually differ from the exact model solution, will be found in just \(s=75\) iterations, which is fewer than the \(1000\) iterations that would be required to find a solution using the classical algorithm.  
Thus, this example clearly demonstrates that the algorithm proposed in [23], depending on the situation, allows for both stopping the iteration process ahead of schedule (thus saving computational resources) and continuing the iteration process (thus obtaining a better approximation solution).
# Construction of a Geological Fault Corpus and Named Entity Recognition
## 5 Experimental Evaluation
### Experimental Results
#### 5.3.2 Performance Comparison of Different Models  
Table 4 presents the experimental results of different models; by comparing these results, it can be deduced that the F1-Score of all three models reaches more than 80%, which indicates that all three models have better results in the task of named entity recognition in the fault domain. By comparing the experimental results of the BiLSTM-CRF model and the BERT-BiLSTM-CRF model, the F1 value of the BiLSTM-CRF model is 80.92%, which is lower than that of the BERT-BiLSTM-CRF model because the model is based on the traditional sequence annotation method, which is able to effectively capture the contextual information but does not make use of the pre-trained language model, which results in limited performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline BiLSTM-CRF & 79.62 & 80.08 & 80.92 \\ \hline BERT-BiLSTM-CRF & 82.54 & 84.23 & 84.07 \\ \hline ALBERT-BiLSTM-CRF & 82.85 & 83.62 & 83.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results of different models.  
Figure 9: Recognition effect of different entities. (**a**) The precision of different entities; (**b**) the recall of different entities; (**c**) the F1-Score of different entities.  
In contrast, when the BERT model is incorporated for word vector preprocessing, the F1-Score increases to 84.07%, a 3.15% improvement over the BiLSTM-CRF model. This improvement is due to the BERT's ability to capture richer contextual information via its bidirectional encoder, providing higher-quality feature representations that enhance both precision and recall. It also shows that using the BERT model for word embedding can enrich the features for subsequent learning, and the word vectors generated by the Transformer can better represent the semantic information embedded in the text. Comparing the experimental results of the BERT-BiLSTM-CRF model and the ALBERT-BiLSTM-CRF model, it was found that the precision of the ALBERT-BiLSTM-CRF model is slightly higher than that of BERT, but it is a little bit inferior in the F1-Score, which may be due to the fact that some features are not as good as those of BERT in the process of training. Combined with the results in Table 5, it can be seen that the ALBERT-BiLSTM-CRF model can be used for word embedding to enrich the features for subsequent learning. It can be seen that the ALBERT-BiLSTM-CRF model inherits the strong feature extraction ability of BERT and optimizes the number of parameters, so the training time is lower than that of the BERT model. Overall, the BERT-BiLSTM-CRF model and ALBERT-BiLSTM-CRF model used in this paper are more suitable for the task of named entity recognition in the fault domain, and it also proves that the introduction of the pre-training model significantly improves the performance of the model, which fully reflects the advantages of deep learning in natural language processing.
## 3 A Modified HOSM Controller Using a PID-Sliding Surface for an ABS Laboratory Setup  
The primary function of a controller is to prevent the vehicle wheel from being locked during emergency braking and to maintain the slip ratio at a value of \(\lambda_{\text{ref}}\) corresponding to the maximum \(F_{t,\text{max}}=F_{t}\) of the force \(F_{t}\) (4). Consequently, the slip reference remains fixed to \(\lambda_{\text{ref}}=F_{t}^{-1}(F_{t,\text{max}})\). Hence, considering this reference value, the output signal to be controlled is the wheel slip \(\lambda\), and the _control problem_ is designing a controller such that \(\lambda\) (2) can track the desired reference \(\lambda_{\text{ref}}\) in the presence of uncertainties in the parameters. Thus, the control problem can be solved under the following assumption:  
The angular velocities \(\omega_{1}\) and \(\omega_{2}\) are considered measurable.  
In the following section, the modified HOSM control using a PID-sliding surface is designed to solve the control problem in such a way that the slip-error converges \(e_{\lambda}=\lambda-\lambda_{\text{ref}}\) to zero. Let us propose the slip velocity \(v_{s}=v_{x}-v_{w}=\lambda v_{x}\), the slip-velocity reference \(v_{s,\text{ref}}=\lambda_{\text{ref}}v_{x}\), and the slip-velocity error:  
\[e_{v}=v_{s}-v_{s,\text{ref}}=e_{\lambda}v_{x}=(1-\lambda_{\text{ref}})v_{x}-v _{w}. \tag{6}\]  
Since \(v_{x}\
eq 0\), the control problem is comparable with drive \(e_{v}\) to zero, where the dynamics of the slip velocity error are as follows:  
\[\begin{split}&\dot{e}_{v}=(1-\lambda_{\text{ref}})\dot{v}_{x}- \dot{v}_{w}=(1-\lambda_{\text{ref}})r_{2}\dot{\omega}_{2}-r_{1}\dot{\omega}_{ 1}\\ &=-k(\lambda_{\text{ref}})F_{t}+\frac{r_{1}}{J_{1}}d_{1}\omega_{1 }-(1-\lambda_{\text{ref}})\frac{r_{2}}{J_{2}}d_{2}\omega_{2}+\frac{r_{1}}{J_{ 1}}T_{b}\end{split} \tag{7}\]  
with \(k(\lambda_{\text{ref}})=\frac{r_{1}^{2}}{J_{1}}+(1-\lambda_{\text{ref}})\frac {r_{2}^{2}}{J_{2}}\).  
Then, the second derivative of the slip-velocity error (6) is as follows:  
\[\begin{split}&\ddot{e}_{v}=-k(\lambda_{\text{ref}})\dot{F}_{t}+ \frac{r_{1}}{J_{1}}d_{1}\dot{\omega}_{1}-(1-\lambda_{\text{ref}})\frac{r_{2}}{ J_{2}}d_{2}\dot{\omega}_{2}+\frac{r_{1}}{J_{1}}\dot{T}_{b}\\ &=-k(\lambda_{\text{ref}})\dot{F}_{t}+k_{F}F_{t}-k_{\omega,1} \omega_{1}+k_{\omega,2}\omega_{2}-k_{T}T_{b}+k_{u}b(u)\\ &\ddot{e}_{v}=f_{v}+k_{u}b(u)\end{split} \tag{8}\]  
where  
\[\begin{split}& f_{v}=-k(\lambda_{\text{ref}})\dot{F}_{t}+k_{F}F_{t}-k _{\omega,1}\omega_{1}+k_{\omega,2}\omega_{2}-k_{T}T_{b}\\ & k_{F}=\frac{r_{1}^{2}}{J_{1}^{2}}d_{1}+(1-\lambda_{\text{ref}} )\frac{r_{2}^{2}}{J_{2}^{2}}d_{2};\\ & k_{\omega,1}=\frac{r_{1}}{J_{1}^{2}}d_{1}^{2};\qquad k_{\omega,2 }=(1-\lambda_{\text{ref}})\frac{r_{2}}{J_{2}^{2}}d_{2}^{2};\\ & k_{T}=\frac{r_{1}}{J_{1}^{2}}d_{1}+\frac{r_{1}}{J_{1}}c;\qquad k _{u}=\frac{r_{1}}{J_{1}}c.\end{split} \tag{9}\]
# DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation
## 4 Experiment
### Comparison to the Baseline  
To emphasize the meliority of our framework, we compare it with the traditional intensity estimation method [28; 29; 30], classification method, and our main baseline CNN-TC. Table 1 shows the results.  
The first six rows (1-6) are results using different datasets in related papers. The next five rows (10-14) are some methods that we reproduced on the TCIR dataset and our main baseline CNN-TC. Cross-entropy means we used Resnet-18 as the backbone and cross-entropy loss to supervise the learning. CNN-TC [2] is our baseline. CNN-TC is a regression model which directly estimates intensity by CNN and fully connected layers. Npair and log-ratio are deep metric learning loss. For a comparison with our DC loss, we reproduced them on the same dataset. The last three rows (12-16) are the comparison of experimental results using temporal information. Rows (12-14) are manual intensity estimation methods. CNN-TC(S) is based on CNN-TC and further applies the smoothing procedure. These RMSE results on the TCIR dataset are from [4] and use a smoothing procedure to aggregate temporal information. The smoothing procedure is implemented by a five-point weighted average. In the last row, we applied a transformer to estimate intensity.  
From rows 7-11, our DC + RI loss is superior to other methods by a large margin (10.18 vs. 8.88) when we did not take into account temporal information, as our DC loss can make the feature embedding space consistent with the label space. In this case, even if misclassified, it will only be misclassified into neighboring classes, which leads to a decrease in the RMSE metric. The last seven rows (12-18) are results that make use of temporal information. ADT, AMSU, SATCON, and CNN-TC use a smooth procedure (weighted average) to utilize the historical records. Based on our DR-extractor (16-18), we compared the smooth procedure, LSTM, and transformer to aggregate temporal information. In the transformer and LSTM, we set the length of the sequence to 7. From the table, we see that our method outperforms the other approaches by a large margin, with the second-best model (CNN-TC(S)) having circa 0.63 knots higher in the RMSE metric. The experiment results show that the transformer can make better use of temporal information and can further predict the intensity. Note that the lag period data we use in the temporal model are previously predicted data, that is, a dynamic forecast. When we used real data as the lag period data, the RMSE of the tropical cyclone estimation can decrease from 7.76 to 6.20.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model  
### Beam Shaping Model for Massive MIMO Networks  
We describe how to design a beam with the ULA antenna based on the active antenna array system (AAS) as shown in [29; 30]. We describe an antenna array radiation pattern, and explain the beam pattern based on the obtained antenna array radiation pattern. The antenna array radiation pattern, \(A_{A}(\cdot)\) is a combination of the element radiation pattern, \(A_{E}(\cdot)\) and the array factor, \(A_{F}(\cdot)\). We define the antenna array radiation pattern \(A_{A}(\cdot)\) with a vertical and horizontal angle pair \((\theta,\phi)\) as  
\[A_{A}(\theta_{s},\phi_{s},\theta,\phi)=A_{E}(\theta,\phi)+A_{F}(\theta_{s}, \phi_{s},\theta,\phi), \tag{1}\]  
where \(\theta_{s},\phi_{s}\) are the vertical and horizontal angle pair of main-beam lobes, respectively. \(A_{E}(\cdot)\) is an element radiation pattern and \(A_{F}(\cdot)\) is an array factor with \(n\times n\) elements.
## 4 Hybrid Trust-Based Congestion-Aware Cluster Routing (HTCCR)
### CH Selection Phase
#### 4.1.3 Enhanced GSA (EGSA)  
In the GSA [28], the objects moving out of bounds will be allocated a boundary value. This algorithm is prone to the local optima problem in cases where there are more objects present on the boundary. Specifically, if few local optimal values are present on the boundary, the algorithm stays in the local optima for some time and will be deprived of its population diversity. Hence, [PERSON] et al. [29] presented an improved gravitational search algorithm to resolve this problem in the original GSA. Further, they also presented a hybrid algorithm of KHM and IGSA as a clustering algorithm. We utilize this hybrid algorithm in our proposed study.  
**Hybrid clustering algorithm of KHM and EGSA:**  
* Fix the initial parameters that contain the highest iterative count \(IteCnt\), the size of the population, \(pop\_size\), and \(Ge_{0}\).
* Now, initialize the population size \(pop\_size\).
* Fix the count for iterations \(Gener_{1}=0\).
* Similarly, fix the count for iterations \(Gener_{2}=Gener_{3}=0\).
* Use the IGSA technique as in [PERSON] et al. [29]. 1. Utilize the IGSA operator for updating the \(pop\_size\) objects. 2. \(Gener_{2}=Gener_{2}+1\). If \(Gener_{2}<8\), go to step 5(a).
* Utilize the KHM technique (Equations (14)-(17)). For every object \(i\), 1. Consider the position of object \(i\) as the cluster center for the KHM technique. 2. Recompute every cluster center by employing the KHM technique. 3. \(Gener_{3}=Gener_{3}+1\). If \(Gener_{3}<4\), go to step 6(b).
* \(Gener_{1}=Gener_{1}+1\). If \(Gener_{1}<IteCnt\), go to step 4.
* Allocate the data point \(d_{i}\) to the cluster \(j\) with the highest membership value \(mem(cc_{j}|d_{i})\).
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 3 Generative AI for Threat Detection
### Transformers
For example, in Intrusion Detection Systems, Transformers are used to analyze sequences of network events or system logs to detect anomalies linked to attacks. They are also employed in detecting phishing attempts, analyzing malware behaviors, and inspecting binary code for embedded threats. Furthermore, Transformers' interpretability through attention weights provides valuable insights into which parts of the data contributed to a detection decision, supporting more transparent and explainable cybersecurity solutions.