esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### RQ9 and RQ10: What Is the Research Focus and Limitations According to the PS?
#### 4.9.2 Recurrent Neural Networks
The limitation of this study is induced by the limitation of static analysis which does not allow to detect malicious JavaScript code that is generated dynamically. In future work, they will explore other types of Neural Networks such as tree and graph structure neural networks. Ref. [59] proposed a modular neural network for the detection of XPath injection attacks. The model comprises two LSTM networks; the first one classifies login attempts as valid or malicious. The second network classifies user input as valid, invalid, or malicious. The final decision about the user request is made based on the classification output of the two LSTM models. If the user request is classified as malicious, fake data are returned in place of real data. If the user request is classified as invalid, a message error is returned; otherwise, the user request is processed by the web server. Ref. [67] proposed a Deep Learning approach for the detection of SQLI vulnerability in PHP codes. They used a tool that captures the opcode of PHP code before it is executed. Then, they converted the opcode slice to an integer vector that they pass to an embedding layer to obtain a matrix that they fed to an LSTM layer followed by two dense layers. The issue in that study is training and testing sets are generated from the same dataset. Thus, it is possible that the selected dataset may not reflect real PHP applications.
###### Abstract  
This paper presents the design and development of a flexible hyper-distributed IoT-Edge-Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT-Edge-Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform's capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform's support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load.  
edge computing; next-generation networking; next-generation IoT; digital twins +
Footnote †: journal: Future Internet
## 1 Introduction
Currently, data-driven techniques have been used in fields such as control, decision-making, scheduling, and fault diagnosis. [PERSON] et al. [16; 17] proposed a data-driven modeling technique for nonlinear systems called compact format dynamic linearization (CFDL). This method only relies on the input and output (I/O) data of the control system, does not need any system model, and implements model-free adaptive control (MFAC) for unknown control systems. Up to now, CFDL data-driven modeling methods have played an important role in many fields [18; 19; 20]. Through theoretical analysis, simulation research, and practical applications, it has been demonstrated that the data-driven CFDL modeling method produced a control system with strong robustness, simplicity, practicality, low computational complexity, and ease of implementation. Unmodeled dynamical problems of unknown nonlinear time-varying systems can be effectively solved with this technique [16; 17]. Meanwhile, sliding mode control (SMC) can force the current state of the system (such as errors and their derivatives) to move according to the predetermined sliding mode state trajectory during dynamic processes. Due to the design of the sliding surface being independent of target parameters and disturbances, SMC has the advantages of fast response, insensitivity to parameter changes and disturbances, and simple physical implementation. Understanding the control system's mathematical model is necessary for designing SMC controllers. \(\backslash\)Therefore, designing SMC based on CFDL data models is of great significance for simultaneously leveraging their advantages [21; 22; 23]. Motivated by the above discussion and aiming at the characteristics of the WWTP, such as the difficulty of accurate modeling and the existence of unknown interference, this paper establishes a CFDL data model for the WWTP and proposes a data-driven model-free adaptive nonsingular fast integral terminal sliding mode control (MFA-NFITSMC) strategy. The advantages of this strategy are as follows:  
1. The proposed method does not rely on the mathematical model or human experience of WWTP and only requires real-time I/O measurement data. It can effectively avoid the uncertainty of the model and the impact of unmodeled dynamics on the closed-loop system. A novel fast integral terminal sliding mode surface (FITSMS) is proposed to ensure that the tracking error can converge quickly when it is far from the equilibrium point. This addresses the issue that the conventional integral sliding mode control (ISMC) cannot ensure that the system state convergences to zero in a finite time and the rate of convergence of the tracking error is slow. 3. The BSM1 was used to validate the suggested approach's control performance and it was contrasted with other control schemes including PID and MPC. The simulation experiment results indicate that the MFA-NFITSMC strategy has a better tracking performance and stronger robustness in the control of WWTP.
###### Abstract  
We present exact and approximate results on the average probability of error (PER) for pulse position modulation (PPM) in pre-amplified optical wireless communication systems with diversity. The approximate results are obtained by combining a new mathematical formula that we derive for binary PPM and an existing formula that associates higher-order PPM PERs with their binary PPM counterpart. The approximate results are compared with the exact in weak, moderate, and strong turbulence, and it is demonstrated that they are in good agreement under all fading conditions. Moreover, the accuracy of the approximation improves with the optical signal-to-noise ratio and the number of diversity branches that are used, which correspond to implementation scenarios that are typically anticipated in practice.  
optical wireless; pulse position modulation; optical amplifiers; diversity reception; error probability; \(\gamma\)-\(\gamma\) fading; negative-exponential fading Article
## 3 Proposed Approach
\[Waiting\ Delay_{t}=\sum_{i=1}^{n}(ArrTime_{lastP}\ -ArrTime_{firstP}) \tag{1}\]  
\[Aggregation\ Delay_{t}=\sum_{i=1}^{n}(AggDelay_{lastP}\ -AggDelay_{firstP}) \tag{2}\]  
Thus, _QDelay_ for each aggregation policy such as FIFO FA (baseline approach), Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA was defined as shown in Equation (3):  
\[QDelay_{t}=\sum(WaitingDelay_{t}+AggregationDelay_{t}) \tag{3}\]  
where:  
* \(n\) is number of stations;
* \(t\) is the \(t\)th simulation time;
* \(ArrTime_{lastP}\) is the arrival time of the last frame;
* \(ArTime_{firstP}\) is the arrival time of the first frame;
* \(AggDelay_{lastP}\) is the aggregation delay of the last frame;
* \(AggDelay_{firstP}\) is the aggregation delay of the first frame. The transmission delay (\(TDelay\)) was defined as the time when a station began to check the channel state (i.e., idle or busy) for transmitting a frame until it received an ACK of the frame [6]. The time required for completing a single MU-MIMO transmission by each of the aggregation policies (FIFO FA (baseline approach), Equal Frame Size FA, EqualMPDUs Agg FA, and Avg Num MPDUs FA) was determined by the transmission time of the longer frame, which was defined as \(max(TData_{i})\)[11], as shown in Equation (4):  
\[TDelay_{t}=TDIFS+BOTime+max(TData_{i})+NumSTA(TSIFS+TBA)+\ldots \tag{4}\]  
Therefore, the average system delay for a single MU-MIMO transmission in different aggregation policies (FIFO FA (baseline approach), Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA) was defined as the ratio of the sum of the queue delay and transmission delay and the number of stations, as illustrated in Equation (5):  
\[AverageSystemDelay_{t}=\frac{(\sum_{i=1}^{n}QDelay_{t}+TDelay_{t})}{n} \tag{5}\]  
where \(n\) is number of stations in the network and \(t\) is the \(t\)th simulation time. Finally, the optimal aggregation policy with the minimum system delay was obtained by comparing the different aggregation algorithms (FIFO FA (baseline approach), Equal Frame Size FA, Equal MPDUs Agg FA, and Avg Num MPDUs FA) using the performance analyzer, as illustrated in Equation (6):  
\[ \begin{array}{l}[SysDelayMin_{t},Index\_Agg\_Policy_{t}]\\ =\min(SysDelayAgg1,SysDelayAgg2,SysDelayAgg3,SysDelayAgg4)\end{array}  \tag{6}\]  
Thus, \(SysDelayMin_{t}\) gave the minimum system delay and the index of the optimal aggregation policy \(Index\_Agg\_Policy_{t}\) at time \(t\). It was assumed that the aggregation policies were arranged in the order of 1 to 4, respectively, for Agg1, Agg2, Agg3, and Agg4. Therefore, if the \(Index\_Agg\_Policy_{t}=1\), the minimum system delay was achieved by Agg1. Otherwise, if the \(Index\_Agg\_Policy_{t}=4\), it was Agg4. The system throughput was the average data rate at which the AP could successfully deliver to all receiving stations [10; 11]. It was computed as the ratio of the sum of all the successful frame sizes of the system over the total channel transmission time.
## 5 Experimental Results and Discussion
### Metrics
#### 5.1.3 High-Level Numerical Spotting Error  
This last metric is specific to our application for spotting numerals in registers. It could be defined as the percentage of mistakenly classified numerals over the count of numerals from the ground truth. The predicted numeral count is the number of numerals predicted by our model. The ground truth of numeral count is the actual number of numerals in the dataset counted by our team. This metric is named as Numeral Spotting Error (NSE).  
\[NSE=||\ \frac{PredictedNumeralCount-GroundTruthNumeralCount}{GroundTruthNumeralCount}\ || \tag{4}\]
# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 6 Conclusions  
In this work, we introduce two publicly available jet segmentation datasets, Library and SegQinhu, designed as benchmarks for training and evaluating fully supervised semantic segmentation methods. Additionally, we propose a novel approach that comprises a backbone network named MobileHDC and a decoder. It avoids common complex designs in previous methods, leading to both high efficiency and high performance. Compared to the original MobileNetV2, the improved model achieved 9.78% and 6.86% accuracy gains while also achieving better performance on the proposed continuity metric, with a slight increase in inference time of 3 ms and 3.6 ms on the Libary and SegQinhu datasets, respectively. The experimental results demonstrate that both of our datasets show a distinct separation in terms of performance between the methods that are specifically designed for jet segmentation and those that are not. Current mainstream semantic segmentation methods are not entirely effective for handling these specific jet scenarios and extensive comparative experiments have shown that our proposed method achieves superior performance on jet datasets, highlighting the necessity for further research in this domain. However, improving computational efficiency without sacrificing segmentation accuracy remains a challenge, especially when dealing with large-scale datasets or high-resolution images. In the future, we plan to explore more lightweight structures and optimize the model further for intelligent jet system applications, such as real-time control systems and embedded devices. We hope that our datasets will serve as a solid foundation for the semantic segmentation of jets, encouraging the computer vision community to develop more data-efficient methods applicable to a wider range of real-world computer vision problems.  
Investigation, Y.C. and Y.L.; methodology, W.W. and Q.Q.; resources, Y.C.; software, Q.Q.; supervision, Y.C. and W.W.; validation, Y.C., W.W. and Y.L.; writing--original draft preparation, Q.Q.; writing--review and editing, W.W.; visualization, Q.Q. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Chinese National Natural Science Foundation under Grant 62202347, Central Science and Technology Development Project led by Hubei Province (2023 EGA001).  
Not applicable.  
Figure 9: Visualization of the jet segmentation results of the different methods on the SegQinhu testing dataset.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications
### WBAN Channel Modeling
#### 4.2.3 HBC Propagation Model  
Apart from the propagation of radio waves in human body, WBANs support HBC, based on data transmission by capacitive and galvanic coupling, as shown in [48; 49; 50; 51]. The HBC propagation characteristics are mainly determined through measurements aiming to study the electrical behavior of the human body. In [48], it has been shown that the transmitted signal level experiences high variations at different locations of the body. For example, short-range links between devices placed on the thorax or on the limbs have shown excellent transmission characteristics, whereas high attenuations that prohibit long-distance transmissions have been measured for extremities and joints.
## 2 Materials and Methods
### DLSS Data Construction
#### 2.2.5 Evaluation Indicators  
In this study, we used precision and recall metrics to determine the performance of DL algorithms. Precision and recall can be calculated by utilizing the values of true positives (TP), false positives (FP), and false negatives (FN), which are the results of correctly detecting a tree as a tree and incorrectly detecting a non-tree as a tree [34]. The f1-score is the harmonic mean of precision and recall and is commonly used for unbalanced data sets. Since there are disproportionately more data from healthy pine trees than from PWDT in this study, we used the f1-score, which is a performance measure for unbalanced data, to verify the optimized performance of DL algorithms [35; 36]. Intersection over union (IOU) is a measurement based on the Jaccard Index, a coefficient of similarity for two sets of data [37; 38]. IOU is a metric that evaluates object detection accuracy and determines whether the detection of an individual object was successful, and has a value between 0 and 1 [39]. In the object detection scope, the IOU is equal to the area of the overlap (intersection) between the predicted bounding box \(B_{p}\) and the ground-truth bounding box \(B_{gt}\) divided by the area of their union [38].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Samples** & **Test Samples** & **Total** \\ \hline Dataset & 795 & 200 & 995 \\ PWDT & 2000 & 350 & 2350 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample data construction.  
Figure 3: Concept of ground truth verification based on TSUI.  
The above-mentioned metrics were calculated as:  
\[\text{Accuracy}\,=\,\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}\,=\,\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}\,=\,\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{f1-score}\,=\,2\,\times\,\frac{\text{Precision}\,\times\,\text{Recall}}{ \text{Precision}\,+\,\text{Recall}} \tag{4}\]  
\[J\big{(}B_{p},B_{gt}\big{)}=\text{IOU}=\frac{area\big{(}B_{p}\cap B_{gt} \big{)}}{area\big{(}B_{p}\cup B_{gt}\big{)}}, \tag{5}\]
# Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving
###### Abstract  
Auto-driving detection usually acquires low-light infrared images, which pose a great challenge to the autopilot function at night due to their low contrast and unclear texture details. As a precursor algorithm in the field of automatic driving, the infrared image contrast enhancement method is of great significance in accelerating the operation speed of automatic driving target recognition algorithms and improving the accuracy of object localization. In this study, a convolutional neural network model including feature extraction and image enhancement modules is proposed to enhance infrared images. Specifically, the feature extraction module consists of three branches, a concatenation layer, and a fusion layer that connect in parallel to extract the feature images. The image enhancement module contains eight convolutional layers, one connectivity layer, and one difference layer for enhancing contrast in infrared images. In order to overcome the problem of the lack of a large amount of training data and to improve the accuracy of the model, the brightness and sharpness of the infrared images are randomly transformed to expand the number of pictures in the training set and form more sample pairs. Unlike traditional enhancement methods, the proposed model directly learns the end-to-end mapping between low- and high-contrast images. Extensive experiments from qualitative and quantitative perspectives demonstrate that our method can achieve better clarity in a shorter time.  
infrared image enhancement; neural networks; pattern recognition; auto-driving Article
## 4 Numerical Example  
In order to verify the proposed automatic calibration method, a numerical example is detailed on a 3-DOF CDPR driven by four cables. The structural diagram of the CDPR is shown in Figure 2.  
As shown in Figure 2, the moving platform was a rectangle whose length and width were respectively 120 and 80 mm. The local coordinate frame \(R_{e}\{O_{e}x_{e}y_{e}\}\) was fixed on the center of the rectangular platform. The origin of the global coordinate frame \(R_{G}\{O_{G}x_{G}y_{G}\}\) coincided with the attachment point \(B_{4}\), which means that the global coordinates of \(B_{4}\) were zero. The nominal values of the other three attachment points \(B_{1}\sim B_{3}\) are listed in Table 1.  
As shown in Equation (20), both \(B_{i}\) and \(A_{i}\) can be calibrated by the proposed method. However, generally, the attachment points on the moving end-effector (\(A_{i}\)) are known after being fabricated. \(A_{i}\) will not change for reconfigurable CDPRs. Thus, the coordinates of the attachment points \(A_{1}\sim A_{4}\) were assumed to be accurate, which did not need calibration. Moreover, the global frame was set on point \(B_{4}\). Therefore, the unknown parameters remaining to be calibrated were the coordinates of \(B_{1}\sim B_{3}\). According to the proposed method, in the calibration procedures, the servo motors for the driving cables \(l_{1}\sim l_{3}\) worked in position mode, while the motor of cable \(l_{4}\) worked in torque mode. Thus, the cable lengths \(l_{1}\sim l_{3}\) were controlled by the servo motors, and the length \(l_{4}\) could be measured by the encoder of the corresponding motor.  
At the beginning of the simulation, a group of random deviations (range: \(\pm 20\) mm) were added to the nominal values of the coordinates of the attachment points \(B_{1}\sim B_{3}\), and the results were taken as the real positions of these attachment points, which are listed in Table 1. Then, 200 poses of the moving platform were randomly generated in the workspace of the CDPR. For each pose, an independent equation can be calculated according to Equation (20). Thus, \(\Delta q\) could be obtained through the 200 linear equations according to Equation (23). After that, the coordinates of the attachment points were revised according to \(\Delta q\) obtained in the previous step. Then, iterations were made to recalculate \(\Delta q\) based on the revised coordinate values, until \(\Delta q\) was small enough, which was \(10^{-6}\) mm in this simulation. Finally, the calibrated coordinates of the attachment points are listed in Table 1, which shows that the difference between the real coordinate values and the calibrated ones was quite small, i.e., less than \(10^{-12}\) mm.  
Figure 2: Diagram of the 3-DOF planar CDPR.
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 3 Results and Discussion
### Corrosion Behavior of Steel in CSA Based Materials  
Currently, research concerned with the corrosion behavior of steel in CSA-based materials is limited in scope. The passivation state of the steel is still in dispute. It has been reported that the steel mesh in a concrete pipe exposed to seawater was uncorroded after 14 years in service. But the chemical composition of the cement used, the mix design of the concrete and the pH of pore solution were unknown [35]. [PERSON] [3] claimed that the passive film on steel in CSA mortar was not stable due to the pore solution of low alkalinity. The pH value of CSA leaching solution was 11.32, lower than the critical value (11.5) for passivation. This justification is not suitable. It can be easily understood that the reported pH value only stands for the leachate, but not the pore solution of mortar. The true pH value of the pore solution should be over 12, which would be able to passivate the steel. Hence, the deterioration of passive film was probably caused by other reasons. According to the research results in this work, the pore solution of CSA always contains a significant amount of sulfate ion, and Figure 5 shows that sulfate ion is able to depassivate steel, even when pH is 12. So, in [PERSON]'s work, not only the relatively low alkalinity, but also the sulfate ion present in pore solution were important factors which contributed to depassivation. [PERSON] [6] noticed that tiny corrosion on steel bars may occur, but the development of corrosion can be controlled, because concrete will become more compactness due to the hydration of CSA. Based on the development of the pore solution chemistry studied in Section 3.1, the tiny corrosion at initial stage was probably caused by sulfate ion in the pore solution of low alkalinity. This kind of corrosion behavior is consistent with the mechanism that how sulfate ion affects passivation. On the other hand, the effect of Al(OH)\({}_{4}\)\({}^{-}\) was not clear in the cases available, and further studies are necessary to verify the function of Al(OH)\({}_{4}\)\({}^{-}\) in CSA mortar or concrete.