esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Model-Free Coupling Controller Design for Multi-Axis Motion Systems
## 2 Multi-Axis Systems and Recurrent Fuzzy Neural Networks
### Recurrent Fuzzy Neural Network  
In this study, we introduce an adaptive model-free coupling controller while using recurrent fuzzy neural network (RFNN) for multi-axis system with uncertainties to minimize the contour error. The architecture diagram of RFNN is shown in Figure 3[21]. The RFNN is a generalization of fuzzy neural system with learning ability, dynamic capability, temporal information storage, universal approximation, and fuzzy inference system. The RFNN system is composed of input layers, membership layer, rule layer, output layer, and feedback nodes. Layer 1 is the input layer that accepts input variables; the nodes in this layer only transmit input variables to the next layer directly, i.e., \(O_{i}^{(1)}(t_{k})=x_{i}(t_{k})\). Layer 2 is membership layer; it denotes the fuzzification operation via calculating the membership grade  
\[O_{ij}^{(2)}(t_{k})=\exp\left[-\frac{\left(u_{ij}^{(2)}-m_{ij}\right)^{2}}{{ \sigma_{ij}}^{2}}\right] \tag{5}\]  
where \(m_{ij}\) and \(\sigma_{ij}\) are represented as the center and width of the Gaussian functions. In addition, the inputs of this layer for discrete time can be denoted by  
\[u_{ij}^{(2)}(t_{k})=O_{i}^{(1)}(t_{k})+O_{ij}^{(f)}(t_{k}) \tag{6}\]  
where \(O_{ij}^{(f)}(t_{k})=O_{ij}^{(2)}(t_{k-1})\cdot\theta_{ij}\) and \(\theta_{ij}\) denotes the link weight of the feedback unit. It is clear that the input of this layer contains the memory terms \(O_{ij}^{(2)}(t_{k-1})\) that store the past information of the network. The RFNN adopt the \(t\)-norm product operation to perform the fuzzy rule in layer 3. Finally, the output layer is for actual output of this FNN system, which is connected by weighting value \(w_{j}\), the \(p\)th output, is represented as  
\[y_{p}=O^{(4)}(t_{k})=\mathbf{w}^{T}\psi=\frac{\sum\limits_{j=1}^{R}w_{j}O_{j}^ {(3)}(t_{k})}{\sum\limits_{j=1}^{R}O_{j}^{(3)}(t_{k})} \tag{7}\]  
where \(\mathbf{w}=[w_{1}\ w_{2}\ \dots\ w_{R}]^{T}\) is the weighting vector and \(\psi=\left[\psi^{1}\ \psi^{2}\ \dots\ \psi^{R}\right]^{T}\), \(\psi^{j}=O_{j}^{(3)}(t_{k})/\sum\limits_{j}^{R}O_{j}^{(3)}(t_{k})\) represents the normalization, and \(R\) is the chosen rule number. Herein, we propose adaptive model-free controller while using RFNN.  
We control the multi-axis linear motor system using RFNN, according to above description and adaptive model-free coupling control scheme. The RFNN is a generalization of fuzzy neural system with learning ability, dynamic capability, temporal information storage, universal approximation, and fuzzy inference system. Thus, we adopt RFNN to perform the ideal stabilizing controller to overcome nonlinear phenomena.
## 3 Material and Methods
### Data Augmentation  
The original dataset for this research comprises 6400 crash accidents from the Missouri State Highway Patrol, recorded between 2019 and 2020. Each record is classified as \"fatal\", \"property damage\", or \"personal injury\" based on the crash severity. The dataset is notably imbalanced, with only 41 instances labeled as \"property damage\".  
The dataset was initially divided into separate training and testing sets to prevent information leakage. The testing set includes 588 records of \"Personal Injury\", 48 records of \"Fatal\", and 20 records of \"Property Damage\". The remaining data were allocated to the training set, comprising 5295 records of \"Personal Injury\", 428 records of \"Fatal\", and 21 records of \"Property Damage\".  
Addressing the imbalance in the training set, we employed data augmentation techniques exclusively on the training data to upsample the minority classes. Specifically, the \"Fatal\" and \"Property Damage\" records were increased to 1000 each while maintaining the \"Personal Injury\" records at 5295. This augmentation was achieved by utilizing a BERT model, which generates new samples through techniques like insertion and substitution to create synthetically meaningful data. Table 1 details the class distributions of both the original and augmented training datasets alongside the testing set. We incorporated regularization techniques and dropout during model training to mitigate overfitting risks. Additionally, we applied 5-fold cross-validation to validate the model's performance across various data subsets. This rigorous approach ensures the robustness of our models, preventing overfitting by assessing the model's performance on multiple data partitions.  
Figure 2: Research methodology.
# Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE
The PSInSAR and GRACE are independent datasets and at different spatial resolution, hence the two datasets have not been directly correlated. The results are rather verified with observed borehole groundwater data and observed GNSS height values at various stations. In this paper, we comprehensively analysed the relationship between surface deformation and groundwater variation. We also discussed various natural and anthropogenic factors which affect both these phenomena.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 6 Experiments and Results
### Performance Metrics  
The aim of this paper is to detect ICPS anomalies and attacks based on the operation pattern. To measure the performance of the proposed model, we focused on the metrics of precision (Equation (1)), recall (Equation (2)), F1-score (Equation (3)), and false-positive rate (FPR) (Equation (4)).  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
\[Recall=TPR=\frac{TP}{TP+FN} \tag{2}\]  
\[F1\text{-}Score=\frac{2*Precision*Recall}{Precision+Recall} \tag{3}\]  
\[FPR=\frac{FP}{FP+TN} \tag{4}\]  
where \(TP\) is the number of abnormalities detected as abnormal, \(FN\) is the number of abnormalities detected as normal, \(TN\) is the amount of normal data detected as normal, and \(FP\) is the amount of normal data detected as abnormal. F1-score [50] is defined as the harmonic mean of the precision and recall, which is a widely used metric to evaluate the classification performance in imbalanced data.
# Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
## 4 Simulation and Results
### Simulation Results and Analyses  
Figures 6 and 7 illustrate the results for the first and the second simulation cases. From simulation results, the round trip delay is decreased with the increase of the system bandwidth. For the second case, the round trip delay is higher than that of the first case, this is because the core network controller is not involved in the communication process as the Mini-cloud unit manages and performs the communication process. Based on [6,51] the user bit rate for the future 5G cellular system will range from 1 Gbps to 10 Gbps. For the least expected bit rate (1 Gbps), the proposed tactile system affords a round trip delay of 0.95 ms for the first simulation case and 1.22 ms for the second case. Moreover, as the bit rate is raised above 1 Gbps the round trip delay gets below the previous values which make the proposed structure get ride off the challenge of 1 ms round trip latency.  
Based on the 3 GPP release 13, the LTE system achieves round trip latency of 16 ms for the 100 Mbps bit rate and 8 ms for 1 Gbps based on release 15 [52,53]. Thus, the proposed system can improve the latency efficiency and provides a way for Tactile Internet realization. When compared to the traditional Evolved Packet Core network based on the 3 GPP release 15, the proposed system achieves 88% reduction of the round trip latency for the first case and 84% for the second case, which is the worst.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Value** \\ \hline OpenFlow Switch processing delay & 5 μs \\ SDN Controller processing delay & 0.5 μs \\ Arrival rate of the Micro-cloud unit Ai & 15 \\ The communication latency inside the cellular cell & 100 μs \\ Bandwidth & Variable \\ Micro-cloud RAM, Storage & 1024 Mb, 1 Gb \\ Mini-cloud RAM, Storage & 2048 Mb, 5 Gb \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 6: Simulation results for the first case.
# Lightweight Object Detector Based on Images Captured Using Unmanned Aerial Vehicle
## 3 Results  
### Dataset and Experimental Environment  
The publicly available dataset used in this study was Visdrone2019 [29], which consists of 8599 high-quality images captured by UAVs. This dataset was carefully divided into three subsets: the training set contained 6471 images, the validation set contained 548 images, and the test set contained 1580 images.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 2 Materials and Methods
### Introduction to Manual Inspection Method
#### 2.4.2 Verification Methods  
The repeatability and accuracy of manual inspection results are critical indicators of inspection method reliability. Manual inspections may be influenced by the experience, skills, or subjective judgment of the inspector, potentially causing some degree of bias in the measurement results. To improve reliability, multiple measurements and inspectors are used to minimize random or systematic errors. This study uses the Coefficient of Variation (CV) to assess the precision and accuracy of the manual inspection results, comparing the data as follows:  
1. Repeatability: Repeatability assesses the consistency of PCI scores obtained by the same inspector under identical conditions during multiple inspections of the same road segment. To evaluate repeatability, this study arranged for the same inspector to perform four repeated inspections of the same road segment, resulting in four sets of PCI data. First, one outlier (i.e., the value with the largest deviation from the others) was excluded to minimize the impact of abnormal data. The Coefficient of Variation (\(CV\)) was then calculated for the remaining three datasets. The \(CV\) is used to quantify the relative dispersion of the measurement results, and its calculation formula is as follows: \[CV=\frac{\sigma\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(X_{\ell}-Reference\ value_{\ell})^{2}}}{Reference\ value}\times 100\%\] (1)
2. Accuracy: Accuracy measures the consistency of PCI scores among different inspectors. This is evaluated by comparing the measurements to a reference value, which is the average PCI score from multiple inspectors. Each inspector will perform four measurements on the same section, and the average PCI score will be calculated. The  
Figure 3: Interface of the developed pavement inspection system for manual PCI assessment: (**a**) login page where inspectors enter credentials to access the system; (**b**) input section for basic road segment information such as section ID, length, width, direction, and inspection date (highlighted in red); (**c**) historical records page showing previous inspection results, with the highlighted area displaying different inspected road segments and their inspection timestamps; (**d**) input interface for pavement distress types, severity levels, and measured dimensions (highlighted in red), including options to upload photos and edit records.  
consistency of the individual measurements compared to the reference value will determine the accuracy [11,12].
# Evaluation of Link Overstrength Factor for the Seismic Design of Eccentrically Braced Frames
## 1 Introduction
Subsequently, [PERSON] [11] conducted a nonlinear time history analysis of a D-braced EBF and proposed values of 1.25 for columns in the first four stories below the top floor, 1.1 for the next 10 stories (if any), and 1.0 for the following 10 stories (if any). [PERSON] [12] also found from similar nonlinear time history analysis results for an EBF with the chevron braces that the axial forces developed in the columns at the bottom story were only 55-70% of the estimated member force based on the capacity design principle. Previous studies have indicated that the overstrength factor in the current provision tends to overestimate the axial forces in the lower-story columns, while it underestimates the axial forces in the upper-story columns. However, in previous studies, the analysis results and number of design variables considered are not sufficient to improve the current provision. In this study, we investigated the link overstrength factor of EBFs to verify the  
Figure 1: Link overstrength factor (\(\Omega\) factor) values from previous experimental studies [2]. capacity design principle. A large number of example buildings with different design variables, such as building height (number of stories), length-to-depth ratio of the links, and intensity of shaking (seismic design category), were used. To generate a sufficient number of design alternatives, an automated design algorithm based on a multi-objective optimization technique was applied. Nonlinear dynamic analyses were performed to verify compliance with the capacity design principle. Additionally, a link overstrength factor equation for column design in EBF systems was proposed based on analysis results.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 6 Measurement of Fruit Maturity  
Knowledge of the quantity of fruit expected at harvest is important but knowing when that fruit will be harvest-mature is equally important to the supply chain. Harvest date variation is a result of variation in both flowering time and fruit developmental time. Developmental time is primarily a function of temperature. Thus, tools for the assessment of time of flowering and a temperature integral can be used to forward estimate the time of harvest maturity. Alternatively, or additionally, harvest maturity can be gauged using certain commodity-specific fruit attributes, such as internal flesh color or dry matter content.
## 3 Experiment and Results
### Implementation and Evaluation Metrics  
When training the model using the model-eye dataset, we resized the images to 128 \(\times\) 128 pixels, used the Adam optimizer [41] with a learning rate of 0.001, and employed \(MAE\) as the loss function. We adopted a step-wise learning strategy, reducing the learning rate to 0.005 at 120 epochs and to 0.0001 at 240 epochs. In addition, we used image augmentation techniques such as translation. The batch size was set to 32, and the network was initialized using the He initialization method [42]. We trained the model for 300 epochs to ensure convergence and saved the model as the model-eye pretrained model. Subsequently, we loaded the base pretrained model and fine-tuned it using the real-human-eye datasets (Dataset1 and Dataset2) with the same parameter settings as the base model. All models were trained for 300 epochs to ensure convergence. All models were implemented using the Pytorch framework [43] and trained on a single GTX 4090 GPU. Table 2 describes the specific parameters.  
The refractive error measurement within the \(\pm\)1 D range is sufficient to meet the requirements of routine refractive error screening. Therefore, this paper uses the accuracy  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline Diopter & 2 D & 2.5 D & 3 D & 3.5 D & 4 D & 4.5 D & 5 D & 6 D \\ \hline Jinzhou & 17 & 9 & 1 & 4 & 3 & 3 & 2 & 1 \\ Tacheng & 22 & 5 & 4 & 6 & 0 & 3 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of hyperopic high-diopter images.  
metric \(ACC1\) (the error range is within \(\pm 1\) D) as a quantitative indicator to compare the performance of different models. The accuracy metric \(ACC0.5\) (the error range is within \(\pm 0.5\) D) is introduced to verify that the proposed method still performs better even when evaluated at higher precision levels. Additionally, \(MAE\) is used to evaluate the performance of each regression model. For each experiment, both comparative and ablation experiments are repeated four times, and the highest accuracy is selected as the experimental result.  
\(MAE\) is expressed as  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{6}\]  
where \(y_{i}\) is the true value (target value), \(\hat{y}_{n_{i}}\) is the predicted value, \(n\) is the total number of samples, and \(n_{i}\) is the samples within the error range.  
\(ACC1\) is expressed as  
\[ACC1=\frac{\sum{n_{i}}}{n},(|y_{n_{i}}-\hat{y}_{n_{i}}|\leq 1) \tag{7}\]  
\(ACC0.5\) is expressed as  
\[ACC0.5=\frac{\sum{n_{i}}}{n},(|y_{n_{i}}-\hat{y}_{n_{i}}|\leq 0.5) \tag{8}\]
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 8 Conclusions  
We have presented the Multiple-Observations (MO) helper data scheme for binding a secret key to multiple observations of an SRAM-PUF. We have shown that the MO helper data scheme can achieve secret-key capacity corresponding to \(t\) enrollment observations, and therefore the scheme is optimal in information-theoretic sense. Furthermore, we have evaluated performance of the scheme with Monte Carlo simulations for a standard statistical model for SRAM PUFs with average error probability \(\overline{\psi}\approx 0.15\). Secret-key rate \(R=1/6\) is sufficient to achieve \(\text{FER}\leq 10^{-6}\) after \(t=5\) enrollment observations. This is a key rate that is \(11/6\approx 1.8\) times higher (better) than for the single enrollment scheme with comparable FER.  
The MO helper data scheme is very similar to the Soft-Decision (SD) helper data scheme; however, the SD scheme assumes one-probabilities as an input, which in practice are non-observable. Therefore, we proposed a new strategy that considers binary observations instead. We have shown that this new strategy is optimal and achieves the same reconstruction performance as the MO scheme.  
We have introduced a variation on the MO scheme, which we call the Sequential Multiple-Observations helper data scheme. The scheme supports a sequential update of the helper data after each successful reconstruction of the key, resulting in a reduced FER over the lifetime of the device. The SMO scheme enables the usage of less enrollment observations, by accepting a (slightly) worse initial FER that is quickly improved upon.  
Figure 9: Reconstruction error probability FER for consecutive reconstructions for the Sequential MO helper data scheme, with 128 bit key.  
**Author Contributions:** Conceptualization, L.K. and F.M.J.W.; Formal analysis, L.K. and F.M.J.W.; Software, L.K.; Writing--original draft, L.K.; Writing--review & editing, F.M.J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Eurostars-2 joint program with co-funding from the EU Horizon 2020 program under the E! 11897 RESCURE project.  
**Data Availability Statement** The plots in Figures 2, 4 6 and 9 are based on calculations and simulations performed in MATLAB. The scripts can be found at [[https://github.com/TUe-ICTLab/Multiple-Observations-for-Secret-Key-Binding-with-SRAM-PUFs](https://github.com/TUe-ICTLab/Multiple-Observations-for-Secret-Key-Binding-with-SRAM-PUFs)]([https://github.com/TUe-ICTLab/Multiple-Observations-for-Secret-Key-Binding-with-SRAM-PUFs](https://github.com/TUe-ICTLab/Multiple-Observations-for-Secret-Key-Binding-with-SRAM-PUFs)) (accessed on 7 April 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 5 Numerical Results and Discussion
### Spectral Efficiency  
Figure 5a,b plots the achievable data rate versus the received signal power \(P_{\text{R}}\) for a dimming factor \(\gamma=20\%\), background power \(P_{b}=10\) nW and \(P_{\text{e}}^{\text{th}}=3\times 10^{-3}\) BER target for the studied modulation methods. It should be noted that a vector of QAM modulation order [4; 8; 16; 32; 64] is used to attain the O-OFDM data rate results. The BERs for each  
Figure 4: The BER versus the received signal power with background power. \(P_{b}=1\) nW with different dimming levels (\(\gamma\)) for considered systems; (**a**) OOK; (**b**) VOOK; (**c**) 4-QAM DCO-OFDM; (**d**) 16-QAM ACO-OFDM.  
when different modulation schemes are used are computed using (10), (15), (17), (24), and (55), and the maximum spectral efficiency with a BER smaller than \(P_{\text{e}}^{\text{th}}\) is noted.  
Figure 5a shows that OOK caps at 120 Mbps as a result of the available limited 120 MHz bandwidth. It reaches this speed at \(P_{\text{R}}=0.12\)\(\upmu\)W which is a considerably higher data rate than the VOOK digital dimming that reaches 24 Mbps maximum at around 24 nW when both employ VOA. This result can be explained by focusing on the codewords in Table 1, where the VOOK data bits cover only 20% of the whole codeword resulting in less background power collection compared to the OOK scheme where the pulse covers the whole symbol period. Figure 5b demonstrates that for SPAD-based receiver, DCO-OFDM can achieve a peak data rate of 239.34 Mbps when \(P_{\text{R}}=0.33\)\(\upmu\)W, while for ACO-OFDM, the data rate is capped at 179.5 Mbps, which is achieved when \(P_{\text{R}}=73\) nW. For both OOK and OFDM modulations, we can view that for traditional SPAD receivers, with the increase of the received optical power, the data rate first increases and then decreases as a result of non-linear distortion induced by SPAD saturation. However, as depicted in Figure 5a,b, our proposed adaptive receiver can effectively mitigate this saturation effect by monitoring the channel and adaptively changing the VOA transmittance, hence facilitating these systems to reach optimal performance and keep the performance when the received power further increases.  
One can also note that at a low received optical power regime, ACO-OFDM is performing significantly better than its DCO-OFDM counterpart for all considered receivers. For example, for the SPAD receiver, when \(P_{\text{R}}=10\) nW, DCO-OFDM cannot operate at this power regime, whereas the ACO-OFDM can achieve a data rate of 55.4 Mbps. The reason behind this is that ACO-OFDM in general has better power efficiency compared with DCO-OFDM. Hence, it can be concluded that ACO-OFDM is well suited to some practical applications that require a critical level of dimming control.