esa-sceva/satcom-chunk-collection huggingface dataset:
# Electroencephalogram Emotion Recognition via AUC Maximization
## 5 Numerical Experiments
### Results and Analysis  
Figure 4 illustrates the training procedures of three numerical algorithms (ExtraGradient, Alt-GDA, and MGSR1) on feature set 3. From an iteration complexity perspective, the second-order algorithm MGSR1 outperformed the first-order algorithms, achieving faster reduction in the gradient norm and a quicker increase in the AUC score. However, with feature set 4, the second-order algorithm became nearly impractical to train due to the computational burden of the large Hessian matrix. Consequently, the numerical optimization results presented in Table 5 were obtained using first-order algorithms. Figure 5 presents the confusion matrices from three methods trained on feature set 4 (the ultimate feature set), while Table 5 provides various performance metrics for these models across four different feature sets, including both training and test sets. The 'liking' label in the DEAP dataset is notably imbalanced, as highlighted in Figure 1. Due to this imbalance, metrics like accuracy and precision tend to be high across all models, but these metrics do not adequately reflect the models' effectiveness in detecting the minority class. The logistic regression and linear SVM models achieved relatively high accuracy and precision, primarily because these metrics are heavily influenced by the overwhelming presence of the majority class (label = 0). However, these traditional metrics failed to capture the models' ability to correctly identify the minority class, leading to a misleading interpretation of model performance. Figure 4: A comparison of numerical optimization algorithms (EG, Alt-GDA, MGSR1) on feature set 3. The y-axis in (**a**) represents the gradient norm \(\|\
abla f\|_{2}\), measuring convergence. (**b**) and (**c**) display the train/test AUC versus number of iterations. In contrast, more informative metrics in this imbalanced scenario are recall, F1-score, and AUC. As shown in Table 5, our proposed numerical method, which directly optimized for AUC, consistently outperformed logistic regression and the SVM in these areas. For instance, with feature set 4, the numerical method achieved a recall of 81.87% on the training set and 79.69% on the test set, compared with significantly lower recall scores for logistic regression and the SVM. The F1-score and AUC metrics similarly indicate superior performance by the numerical method, with the highest AUC of 0.812 on the training set and 0.791 on the test set. These results demonstrate that the numerical method is particularly effective at identifying the minority class, which is crucial in fields such as neuroscience and medical diagnostics. In these domains, the ability to accurately detect rare but significant events or conditions (represented by the minority class) can lead to more reliable and robust predictive models, enhancing the overall utility of the system.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 3 Instrumentation, Results and Discussion
### Field Tests
#### 3.3.2 Tide Data  
Figure 6 compares the hourly tide data obtained from the conventional tide gauge with those of the GNSS buoy from 20 August to 22 August 2013. The tide data obtained from the GNSS buoy and the Suao tide station are denoted by the red dashed line with squares and black solid line with circles, respectively. The percentages of good altitude data are plotted as a green dashed line with diamonds. The averaged tide of the GNSS buoy was adjusted to be equivalent to that of the Suao tide station.  
Figure 6 shows that the real-time tide data from the GNSS buoy fit well with those of the Suao tide station. The RMSE was 9.5 cm. The RMSE is defined as:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}{(y_{t}-\hat{y}_{t})^{2}}}, \tag{1}\]  
where \(y_{t}\) and \(\hat{y}_{t}\) represent the tides obtained from the Suao tide station and the GNSS buoy, respectively, and \(N\) is the number of tide data.  
Figure 5: Locations of the GNSS buoy and nearby Suao tide station.  
Certain tide data from the GNSS buoy are missing because of the low signal strength of the GPRS at that hour. Notably, four high tides measured by the GNSS buoy were lower than those measured by the Suao tide station. A possible reason is the inclination of the GNSS buoy. Another possible reason is that the GNSS buoy and tide station were at different locations and the tidal waves were affected by the local bathymetry.
## 8 Applications of UWSN Technology  
### Health Care Surveillance  
Wearable and implantable medical applications are the two main categories. Wearable technology is employed on a human's body surface or near the user. Medical gadgets implanted inside a person's body are called implantable devices. Other uses include tracking a person's location and body position and general patient monitoring in hospitals and at home. Body-area networks can gather data about a person's fitness, well-being, and energy usage [72].
###### Abstract  
In terms of indoor and outdoor positioning, in recent years, researchers at home and abroad have proposed some multisource integrated navigation and positioning methods, but these methods are navigation and positioning methods for a single scene. When it comes to the switching of indoor and outdoor complex scenes, these methods will cause the results of position with a marked jump and affect the accuracy of navigation and positioning. Aiming at the navigation and positioning problem in the case of indoor and outdoor complex scene switching, this paper proposes a multisource elastic navigation and positioning method based on particle filters, which makes full use of the redundant information of multisource sensors, constructs an elastic multisource fusion navigation and positioning model after eliminating abnormal data, elastically gives different particle weights to multisource sensors according to environmental changes and realizes the elastic fusion and positioning of multisource sensors through filtering. The test results show that this method has high navigation and positioning accuracy, the dynamic positioning accuracy is better than 0.7 m and there will be no jumping of positioning results in the process of scene switching, which improves the navigation and positioning accuracy and stability in complex and changeable indoor and outdoor environments.  
indoor and outdoor positioning; elastic PNT; multisource fusion; particle filter +
Footnote †: journal: Future Internet  
and
## 4 Experiments and Numerical Results
### Ablation Studies  
We conducted ablation experiments on all four datasets to assess the contribution of each module in RiQ-KGC. Apart from the complete model, we designed four additional ablation models. \"RiQ-KGC w/o allN\" removes all neighboring inputs in each module. \"RiQ-KGC-w/o-234N\" excludes multi-hop neighbor inputs in the instantiation for the relation module. \"RiQ-KGC-w/o-Q\" eliminates the quaternion rotation step in the quaternion space rotation module and replaces the corresponding input position with the source entity. \"RiQ-KGC-w/o-R\" discards the instantiation for the relation module and utilizes original relation to rotate the source entity.  
Figure 9 presents the performance of each ablation model on the four datasets using the MRR metric, which can show the overall recall rate of samples. The most significant conclusion is that \"RiQ-KGC\" achieved the best performance, while \"RiQ-KGC w/o all N\" exhibited the worst performance, indicating the crucial role of neighboring nodes. The efficacy of \"RiQ-KGC-w/o-R\" was generally poor, highlighting the importance of relation instantiation methods. \"RiQ-KGC-w/o-234N\" outperformed \"RiQ-KGC-w/o-R\" in all datasets, demonstrating that multi-hop neighbor information enhances the relation instantiation method. The impact of \"RiQ-KGC-w/o-Q\" is highly uncertain because the proportion of complex relations in the four datasets varied and quaternion rotation patterns have a greater advantage when applied to datasets with a higher ratio of one-to-one relations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Second-Hop** & **Third-Hop** & **Fourth-Hop** \\ \hline Setting-1 & 10 & 25 & 60 \\ Setting-2 & 30 & 100 & 250 \\ Setting-3 & 50 & 150 & 500 \\ Setting-4 & 80 & 200 & 750 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Settings of multi-hop neighbors.  
Figure 8: Impact of multi-hop neighbors on four datasets.
## 5 Applications of THz-FSO Communication
### Space Communications  
The THz-FSO system works well with space applications because effect of the high frequency is reduced by cosmic noise. These systems meet today's increasing bandwidth requirements for Intersat and satellite-earth links [97, 98]. The high data rates offered by the THz-FSO system are of paramount importance to real-time data transmission, especially for space research, including space exploration, earth observation, and deep space communication [40].  
In THz-FSO communication systems in space, electromagnetic interference would not be a factor because THz waves do not interfere with instruments on board a spacecraft, making them a reliable means of transferring sensitive data. THz devices are compact and can achieve data rates of up to gigabits; therefore, THz payload designs in satellites are well-optimized for this application. Moreover, the modern trends in narrow-bandgap material diversification have increased THz-transmitters' effectiveness in terms of their power consumption and the effective transmission distance between space vehicles [41, 75]. The THz-FSO systems offer reliable communication links in atmospheric re-entry situations, as plasma-induced sudden signal losses frequently occur in radio-frequency systems. These features give significant importance to THz-FSO communication in the differentiation of modern space technology.
## 4 Interpolation of Experimental Data by NSF Method  
### Accuracy Evaluation of NSF Method  
Before applying NSF method to interpolate the strain data, the interpolation accuracy of this method should be evaluated. Here, the leave-one-out (LOO) cross-validation method was used [33]. Each of the four measured strains at the cross-section under a certain load level can be considered as a dataset of four samples. LOO-based validation was performed with four iterations, such that in each iteration, the NSF method was trained with three samples to obtain the predictive value and tested on the remaining one sample. The error \(\delta\) between the predictive value \(\varepsilon_{i}^{\text{p}}\) (\(i\) = 1, 2, 3, 4) with the real experimental sample \(\varepsilon_{i}^{\text{e}}\) was calculated by Equation (5):  
\[\delta=\left|\frac{\varepsilon_{i}^{\text{p}}-\varepsilon_{i}^{\text{e}}}{ \varepsilon_{i}^{\text{e}}}\right|\times 100\% \tag{5}\]  
Therefore, smaller \(\delta\) equated to the better fitting degree, indicating the practicality of the method.  
As an illustration, the contrast curves of strains on cross-section 4 in arch-A are given in Figure 4a. It shows that the two curves of one specific section had very high fitting degrees and even overlapped with each other before 180 kN. Thereafter, the intervals between the two curves were also very small. Figure 4b lists the validation errors of all 11 sections with different load levels. Because of the small values of strains, the little difference value between \(\varepsilon_{i}^{\text{p}}\) and \(\varepsilon_{i}^{\text{e}}\) would have caused great error, so the errors were relatively large in the earlier loading stage. Closer to the later part of load application, the error presented an increasing trend. This was mainly caused by random factors which started to gradually affect the structural working behavior, as well as damages in the interior of the arch, which seriously affected the interpolation accuracy of NSF method.  
For all the measured strains validated by LOO, an average error of 4.79% was obtained. The error results were relatively small and greatly met the application requirement. Consequently, the NSF method can be applied with sufficient accuracy to interpolate experimental data for further investigation of the structural stressing state.
###### Abstract  
Accurate prediction of the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and efficiency of industrial systems. This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. The model showed better performance than traditional approaches, with an RMSE of 0.989. (2) A Deep Neural Network (DNN) was designed to predict the extended RUL of bearings after laser shock peening (LSP) remanufacturing. The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension.  
bearing RUL prediction; laser shock peening; deep learning; remanufacturing; data pre-processing +
Footnote †: journal: applied sciences  
0
Footnote †: journal: applied sciences  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 7 Pattern Instance Acquisition Based on Deep Learning Model
### Merging of Judgment Results  
For a pattern instance in the system, there are often multiple classes associated with the same pattern role. For example, in the State pattern, the role ConcreteState is often associated with multiple classes representing specific states. Therefore, sometimes multiple second-level subsystems judged as pattern instances actually correspond to the same pattern instance and need to be merged. The algorithm for merging the second-level subsystems of the subsystem \(s\) for the pattern \(p\) is as follows:  
**Step 1:** If only one of all second-level subsystems is judged as the instance of the pattern \(p\), it means that the subsystem \(s\) contains only one instance of this design pattern, and each pattern role is associated with one class in the subsystem. Otherwise, go to step 2.  
**Step 2:** If \(l\) (\(l\geq 2\)) second-level subsystems judged as instances of the pattern \(p\) have no public class, it indicates that the subsystem \(s\) contains \(l\) instances of this design pattern. Otherwise, go to step 3.  
**Step 3:** Suppose the set of the \(l\) (\(l\geq 2\)) second-level subsystems judged as instances of the pattern \(p\) is \(I=\{I_{1},I_{2},\cdots,I_{l}\}\). For any two second-level subsystems \(s^{\prime}_{p}\) and \(s^{\prime}_{q}\) with public classes in the set \(I\), they are processed as follows:  
(1) If these two second-level subsystems have (a) common class(es), and there is a subclass of a certain class in the non-public classes, the two instances are merged into one instance. Then the two instances before merging in the set \(I\) are deleted, and the new instance obtained by merging is added to \(I\).  
(2) If all non-public classes are not subclasses, merging is not required.  
The above process is repeated until there are no two instances in the set \(I\) that can be merged. At this time, the instances in the set \(I\) are the final instances.  
A schematic diagram of the judgment and merging of the four second-level subsystems for the State pattern shown in Figure 3 is shown in Figure 9.
## 3 Proposed Method
### Steps of the Proposed Method
#### 3.2.3 Sentence Significance Factor Assessment  
The second input to the proposed method is a list of sentence significance factors denoted by \(<\mathit{Factor}_{1},\ldots,\mathit{Factor}_{m}>\) when \(m\) is the number of sentence significance factors considered. To use these factors, metric \(M_{k}\) should be defined for each \(\mathit{Factor}_{k}\), \(1\leq k\leq m\).  
In Step 3, for each sentence vector \(S_{i}\), each sentence significance factor \(\mathit{Factor}_{k}\), \(1\leq k\leq m\), is evaluated using \(M_{k}\). \(M_{k}\) (\(\mathit{Factor}_{k}\) (\(S_{i}\))) represents the score measured with \(M_{k}\) for Factor\({}_{k}\), \(1\leq k\leq m\), for the sentence vector \(S_{i}\), \(1\leq i\leq n\). For example, we could define the metric \(M_{k}\) for \(\mathit{Factor}_{k}\) as:  
\[M_{k}(\mathit{Factor}_{k}(S_{i}))=\begin{cases}1+\sum\limits_{j\in R(S_{i})} f(S_{i},S_{j})&\text{if }|R(S_{i})|>0\\ 1,&\text{if }|R(S_{i})|=0\end{cases} \tag{1}\]  
Figure 3: Structure of autoencoder.  
where \(R(S_{i})\) is the set of sentences in the input bug report that are related to sentence \(R(S_{i})\) in a certain way \(R\) and \(f(S_{i},S_{j})\) is the value for the way \(S_{i}\) is related to sentence \(S_{j}\).
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DTNs & Delay Tolerant Networks \\ BP & Bundle Protocol \\ PKI & Public Key Infrastructure \\ IBC & Identity Based Cryptography \\ PKC & Public-Key Cryptography \\ PKG & Private Key Generator \\ CA & Certificate Authority \\ IPN & InterPlanetary Networking \\ BSP & Bundle Security Protocol \\ BAB & Bundle Authentication Block \\ PIB & Payload Integrity Block \\ PCB & Payload Confidentiality Block \\ GCM & Galois Counter Mode \\ HIBC & Hierarchical Identity Based Cryptography \\ OppNets & Opportunistic Networks \\ DKE & Distributed Key Establishment \\ CRL & Certificate Revocation List \\ SKC & Secret Key Cryptography \\ WoT & Web of Trust \\ DVD & Dynamic Virtual Digraph \\ BSP & Bundle Security Protocol \\ BP & Bundle Protocol \\ SOK & Sakai-Ohgishi-Kasahara \\ LCF & Leverage of Common Friends \\ CPK & Combined Public Key \\ ECC & Elliptic Curves Cryptography \\ PGP & Pretty Good Privacy \\ HMP & Horsters-Michels-Petersen \\ ESKTS & Efficient and Scalable Key Transport Scheme \\ DSC-KM & Digital Signature Chains Key Management Scheme \\ FS & Forward secrecy \\ BS & Backward secrecy \\ CF & Collusion freedom \\ KI & Key independence \\ LKH & Logical Key Hierarchy \\ CRT & Chinese Remainder Theorem \\ AGKM & Autonomic Group Key Management \\ OMPK & One-encryption-Key Multi-decryption-Key \\ ONE & Opportunistic Networking Environment \\ DS & Distributed Signing \\ ESB & Extension Security Block \\ ASB & Abstract Security Block \\ CCSDS & Consultative Committee for Space Data Systems \\ SBSP & Streamlined Bundle Security Protocol \\ BIBE & Bundle-in-Bundle encapsulation \\ BPSec & Bundle Protocol Security Specification \\ TTP & Trusted Third Party \\ KDC & Key Distribution Center \\ \end{tabular}
## 5 Evaluation and Results  
### Wireless Monitoring Platform  
The SWIFTNET algorithm has been implemented on WISPES W24 TH nodes, shown in Figure 6. It is based on the Jennic microprocessor [60], one of the few 32-bit architectures available in the market with a power consumption comparable to a TelosB [61]. It is equipped with 128 KB RAM, 512 KB FLASH with 2.4 GHz radio transceiver (IEEE802.15.4 compliant) and Zigbee Pro compliant [62]. It utilizes an aggressive power management method using 8 \(\upmu\)A in sleep mode, 17 mA in transmit mode, and 23 mA in receive mode; this guarantees a longer battery lifetime. The details of the hardware design and the stack are presented in [63]. A dual client control platform is used that consists of MATLAB for the high-end reconstruction algorithm and the CACTI [64] a powerful network graphic tool for visualization, online data management, and storage.
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 3 Experimental Results
### All-in-Focused Image Combination
#### 3.2.1 On the Images of the 'Bag' Dataset  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Criteria**} \\ \cline{2-3}  & **FMI** & **QAB/F** \\ \hline Light Field Software & 0.9612 & 0.7965 \\ SML & 0.9666 & 0.8015 \\ DCT-STD & 0.9619 & 0.8171 \\ DCT-VAR-CV & 0.9673 & 0.8498 \\ SML-WHV & 0.9650 & 0.7994 \\ Agarwala’s Method & 0.9679 & 0.8542 \\ DCT-Sharp-CV & 0.9687 & 0.8626 \\ DCT-CORR-CV & 0.9684 & 0.8637 \\ DCT-SVD-CV & 0.9687 & 0.8635 \\ The Proposed Method & 0.9785 & 0.8729 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective evaluation of the image results (non-reference fusion metrics) for ’Bag’ image.  
Figure 12: Comparison of ‘Bag’ image results: (**a**) Light Field Software, (**b**) SML, (**c**) DCT-STD, (**d**) DCT-VAR-CV, (**e**) SML-WHV, (**f**) Agarwala’s method, (**g**) DCT-Sharp-CV, (**h**) DCT-CORR-CV, (**i**) DCT-SVD-CV, (**j**) The Proposed Method.
# Modeling of Integrated Nanoneedle-Microfluidic System for Single Cell Temperature Measurement
## 4 Conclusions  
Single cell analysis has become an important field in the medical research and diagnoses. The temperature of a cell in particular is able to reveal vital information on cells' activities, mechanics and development. In this paper, a nanoneedle-microfluidic system to measure single cell temperature was proposed. The design was divided into three main stages, starting with nanoneedle design optimization and mechanical and electrical characterization; then integration with a microfluidic channel; and finally obtaining temperature measurement curve. Tungsten was chosen as the sensing material and the results showed that a rectangular shaped sensor with a gap of 10.8 \(\upmu\)m gave an equally distributed current density within the sensor, and a 90 nm\({}^{2}\) cross-sectional area caused minimal damage to the cell. The mechanical characterization study and the nanoneedle showed to be able to resist ramp force applied on it up to 22.5 \(\upmu\)N before went into failure. Electrical measurement of the cell showed that the current only moved within the nanoneedle and the nanoneedle was independent of the cell conductivity. The thermal characterization within the cell showed that a current difference of 50 nA could be observed, giving a resolution of 0.02 \({}^{\circ}\)C in the range of 24-60 \({}^{\circ}\)C. The nanoneedle reached equilibrium temperature in 10 ms. A Y microchannel design was optimized to have of 63 \(\upmu\)m\({}^{2}\), considering the effect of the nanoneedle to the flow of water. A flow rate of 24.6 pL/min allowed for cell penetration causing minimal damage to the cell. This system is able to measure the intracellular temperature of single cells and has the potential to be used in biomedical application such as drug delivery.  
The research was supported by the Ministry of Higher Education of Malaysia (grant No. 4L640 and 4F351), and Universiti Teknologi Malaysia (grant No. 03E11, 03G47, 02G46, 03H82, and 03H80); we thank them for funding this project and for their endless support. All authors contributed equally in the presented research work.  
The authors declare no conflict of interest.