esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Parking: A Literature Review from the Technological Perspective
## 3 Smart Parking Solutions
### Types of Sensors  
Regarding transportation planning and traffic management, the most important topic is parking [38]. Nowadays, determining the availability of a parking is difficult if there are no elements that allow to identify if it is vacant or not. The intention of the sensors is to solve this problem and sense its availability and communicate to parking system through a network gateway. Although the sensors solve the detection problem, a large number of these are required to exercise adequate control over a given space. There are sensors that do not cover large spaces, and therefore one per spot is required. Therefore, the larger the space, the greater the quantity and therefore a higher cost. Note that for the sensors to be well used, they require technological infrastructure for the transport of the data. This implies the installation of data networks whether these are cellular or gateway based [13; 39]. Also, in cases where wireless communication is not feasible, a structured cable approach to collect information must be in place. Weather conditions are a limitation that need to be considered and supported by sensors. Even though there are alternatives for using other sensors like smartphones on cameras in large spaces, there are security concerns that need to be addressed first [13]. Sensors are the most important component of a smart parking system as they feed system with valuable data. Therefore, they need to provide reliability and require very little or almost no maintenance. Sensors define the network technology and the mechanism for sending data to the backhand (smart parking system). They should not depend on human interaction to provide information about the environment. Energy consumption must be minimal and if possible and have its own self-sustaining source of energy (i.e., solar energy). Indeed, the inclusion of Microelectromechanical System (MEMS) sensors will help reduce size, power consumption, cost, and extend performance and lifetime [40]. This evolution will lead to sensors with multiple capabilities rather than specific types. Information retrieval is one of the most important aspects of smart parking solutions, and for this process, a great variety of sensors offered in the market can be used. Among the different types of sensors for smart parking, the most common ones are ultrasonic, magnetometers, cameras (used for detecting vehicles and free slots), cellular sensors, and radars [41]. A summary of the type of sensors used on the reviewed papers is shown in Table 3.  
This table is structured as follows. First, the reference to the reviewed paper is given. Then, all types of sensors identified during the review are listed as columns. Any paper that uses a type of sensor is marked with a black bullet \"\(\bigcirc\)\" under any column that applies; otherwise, a \"-\" is placed. The column \"Other\" refers to a type of sensor that is not clearly specified in the reviewed paper. The following table only contains papers that detail the use of any sensor.
## 8 Anonymity and Security Analysis  
We need to analyze FAC for both passive and active adversary attacks. The adversary (ADV) model has a global view of the network. ADV could target the source, intermediary and BS nodes. Usually, ADV starts by monitoring transmission somewhere in the network and then attempts to acquire sources (downlink direction) or BS (uplink direction). Passive attack is ordinarily the base for active attack. OnceADV determines the identity and location of a source or the BS, it consequently can launch various active attacks against certain nodes or disrupt the operation of the entire WSN. The main strength of passive ADV is the fact that neither SNs nor the BS will know about their existence. Nonetheless, active attacks can be detected if the framework instruments reasonable IDS. Any comprehensive solution for location privacy should protect against anonymity attacks, temporal attacks and rate discovery attacks. We believe that routing privacy is useful only against local advisory and once the WSN faced with a global or a multi-local adversary, routing privacy is not crucial. Thus, we have chosen short-path routing technique for this work. Any other routing protocols should be utilized to reduce delays and energy consumption.
## 4 Results and Discussion  
Twenty simulations for each loading rate were performed, generating twenty different crack patterns. Only one example of a representative crack pattern by loading rate is presented in this paper. Considering that, at most, one experimental load-displacement curve was provided for each loading rate (except for the loading rate of 3000 mm/s, where two experimental results were given), it can be considered that the proposed model correctly captures the global behavior in dynamics. In the load-displacement curves presented below  
Figure 5: Geometric data.  
Figure 6: Mesh and boundary conditions adopted.  
(see Figures 7-11), the black lines represent the experimental results, the gray lines are the results for each numerical specimen, and the red lines are the mean numerical results.  
For some loading rates (2000 and 2500 mm/s, see [38]), the experimental load-displacement curve has not been recorded. It is probably an indication of the difficulty for the authors of this experimental work to measure the displacement on the specimen. Therefore, the comparison between the numerical and experimental results are not considered for these loading rates. In Figure 6 (quasi-static loading) and Figure 10 (4500 mm/s), the elastic behavior of the specimens is very different when experimental and numerical results are compared. Such a difference is possibly explained by the inappropriate displacement measurement in the experimental test, as displacements were read at the same location where the load was applied. This might have caused material failure in the neighboring region, leading to inaccurate data. Taking this reasoning for granted, it can be stated that the post-peak behavior was correctly reproduced, which is relevant here as the present model is mainly devoted to the propagation of a macroscopic crack.  
Figure 8: Load–displacement curves for 500 mm/s.  
Figure 7: Load–displacement curves for 0.25 mm/s.  
The crack pattern for three loading rates, 0.25 (quasi-static loading), 500, and 2000 mm/s, are presented in Figures 12-14, where missing elements are those completely damaged. These loading rates have been chosen, because they represent significant changes in the crack pattern. It is possible to observe that the crack pattern is highly dependent of the loading rate, both numerically and experimentally (Figures 12-14). In the quasi-static regime (Figure 12), one macroscopic crack propagates horizontally from the corner of the L-shaped specimen. In the intermediate regime (Figure 13), the main macroscopic crack starts at the corner, but it is slightly oblique and a branching attempt appears. For a very fast loading rate (Figure 14), a strong change in the crack pattern is observed. The speed of crack propagation becomes so fast that the inertia at the crack tip forces it to split in two.  
Figure 10: Load–displacement curves for 3000 mm/s.  
Figure 9: Load–displacement curves for 1000 mm/s.  
Figure 11: Load–displacement curve for 4500 mm/s.  
Figure 12: Crack pattern for 0.25 mm/s.  
Figure 13: Crack pattern for 500 mm/s.
# Multi-Source Satellite Imagery and Machine Learning
## 6 Ablation Analysis  
The ablation of our study consists of an analysis to evaluate the individual impact of different sets of parameters or characteristics on the performance of classification models. In this case, the study compares the performance of the algorithms when different datasets are used, including:  
* Data from geographic parameters (DGPs).
* Data from OLI spectrometric parameters (DOLIPs).
* Data from PCA parameters (DPCAPs).
* Combinations of these data sets.  
For each configuration, performance metrics, such as accuracy, MCC, and execution time, are used to determine which data or combinations contribute the most to overall performance.
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 3 Numerical Method
### Edge-Gradient Evaluation  
On the RHS of Equation (23), the edge gradient was evaluated by the Green Theorem, and the values at the vertices are required, which are interpolated from the cell centers.  
\[(\frac{\partial\mathbb{C}}{\partial x})_{0-m}=\frac{1}{\Omega_{0-m}}\oint Cdy \approx\frac{1}{2\Omega_{0-m}}[(C_{m}^{c}-C_{0}^{c})\Delta y_{i-j}-(C_{j}^{v} -C_{i}^{v})\Delta y_{0-m}] \tag{25a}\] \[(\frac{\partial\mathbb{C}}{\partial y})_{0-m}=\frac{-1}{\Omega_{0-m}}\oint Cdx \approx\frac{-1}{2\Omega_{0-m}}[(C_{m}^{c}-C_{0}^{c})\Delta x_{i-j}-(C_{j}^{v} -C_{i}^{v})\Delta x_{0-m}] \tag{25b}\]  
Note that the subscript \"\(K\)\" is omitted here for simplicity.  
If the Taylor series expansion is used, no interpolation is needed, and one can obtain:  
\[(\frac{\partial\mathbb{C}}{\partial x})_{0-m}=\frac{1}{\Omega_{0-m}}\oint Cdy \approx\frac{1}{\Omega_{0-m}}[(C_{m}^{c}-C_{0}^{c}+D_{0-m})\Delta y_{i-j}+(G _{x})_{0-m}]\] (26a) \[(\frac{\partial\mathbb{C}}{\partial y})_{0-m}=\frac{-1}{\Omega_{0-m}}\oint Cdx \approx\frac{-1}{\Omega_{0-m}}[(C_{m}^{c}-C_{0}^{c}+D_{0-m})\Delta x_{i-j}+(G _{y})_{0-m}]\] (26b) \[D_{0-m}=[(C_{x})_{m}-(C_{x})_{0}]\cdot\Delta x_{F-F^{\prime}}+[(C_{y})_{m}-(C_{ y})_{0}]\cdot\Delta y_{F-F^{\prime}}\] (26c) \[(G_{x})_{0-m}=\frac{1}{2}[(C_{x})_{0}\Delta x^{\prime}_{0-i}+(C_{y})_{0}\Delta y ^{\prime}_{0-i}]\cdot\Delta y^{\prime}_{0-i}+\frac{1}{2}[(C_{x})_{0}\Delta x^{ \prime}_{0-j}+(C_{y})_{0}\Delta y^{\prime}_{0-j}]\cdot\Delta y^{\prime}_{j-0}+\] (26d) \[\frac{1}{2}[(C_{x})_{m}\Delta x^{\prime}_{m-i}+(C_{y})_{m}\Delta y ^{\prime}_{m-i}]\cdot\Delta y^{\prime}_{i-m}+\frac{1}{2}[(C_{x})_{m}\Delta x^{ \prime}_{m-j}+(C_{y})_{m}\Delta y^{\prime}_{m-i}]\cdot\Delta y^{\prime}_{j-m}\] (26e) \[\frac{1}{2}[(C_{x})_{m}\Delta x^{\prime}_{m-i}+(C_{y})_{m}\Delta y ^{\prime}_{m-i}]\cdot\Delta x^{\prime}_{i-m}+\frac{1}{2}[(C_{x})_{m}\Delta x ^{\prime}_{m-j}+(C_{y})_{m}\Delta y^{\prime}_{m-i}]\cdot\Delta x^{\prime}_{j-m}\] where \[\Omega_{0-m}\] denotes the areas of the quadrilateral at the edge \[0-m\] ; \[C_{x}\] and \[C_{y}\] denote the first-order derivatives; \[D_{0-m}\] is the correction term for mesh irregularity (will automatically vanish in well-connected mesh), and all the coordinates with the superscript \"\[{}^{\prime}\]\" count in the distance vector \[\
abla\overset{\rightarrow}{r}_{f}\] as well. For example, \[\Delta x^{\prime}_{0-i}=x_{i}^{v}-(x_{0}^{c}+\Delta x_{F-F^{\prime}})\] and \[\Delta y^{\prime}_{m-i}=y_{i}^{v}-(y_{m}^{v}+\Delta y_{F-F})\], and so on.  
This evaluation method for the edge gradient was also applied to the velocity gradient at edges in the momentum equations and the pressure gradient at edges when coupling the continuity equation and the momentum equations. Compared to the conventional method requiring vertex values, this method accounts for mesh irregularities and avoids interpolations for the vertex values.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 5 Mining of Complex Relationship Weights in Dynamic Vehicle Clusters  
This section emphasizes the detection of misbehavior nodes by evaluating the trustworthiness reflected in their interactions and relationships. Existing research reflects that trust among nodes evolves dynamically through repeated collaborations and communications. Positive interactions over time strengthen trust relationships, and analyzing interactions and associations is crucial in trust evaluation. Therefore, our approach integrates the representation and mining of complex associative relationships between vehicles and between vehicles and clusters. Specifically, a hypergraph-based model is employed to represent dynamic vehicle clusters, enabling the effective assessment of relationship weights and their influence on trust evaluation throughout the process.  
Figure 3: Dynamic behavior-homogeneous vehicle clusters.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 8 Conclusions  
This work integrates the NARX model with SCAPA, and such a combination has outperformed the conventional methods for the detection and identification of point and collective anomalies within real-time non-stationary time series. The autoregression approach of the model is capable of handling variance forms of anomalies within an online setting, and the statistical anomaly detection provides the change of mean and variance for each anomalous point. Furthermore, it explores the clustering of collective anomalies within the time series using an ECDF-based fuzzy c-means clustering approach. Such a paradigm can create a foundation for more advanced anomaly analysis and decision-making for online systems. The proposed methods can also be applied to many diverse areas such as sensor monitoring, traffic control, and network optimisation.  
Conceptualization, data curation, funding acquisition, and project administration, D.S.L.; investigation, methodology, software, visualization, and writing--original draft, C.H.; resources, supervision, and writing--review and editing, D.S.L. and J.A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Next-Generation Converged Digital Infrastructure project EP/R004935/1.  
Not applicable.  
The data used in this study includes private data from the UK's national communications network and public data from Kaggle, which the latter can be found at [[https://www.kaggle.com/datasets/yuriykaster/skoltech-anomaly-benchmark-skab](https://www.kaggle.com/datasets/yuriykaster/skoltech-anomaly-benchmark-skab)]([https://www.kaggle.com/datasets/yuriykaster/skoltech-anomaly-benchmark-skab](https://www.kaggle.com/datasets/yuriykaster/skoltech-anomaly-benchmark-skab)) (accessed on 16 January 2023). The collection and use of the data follow the regulation of BT Group plc.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the analyses, or interpretation of data; or in the writing of the manuscript.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ECDF & Empirical Cumulative Distribution Function \\ NARX & Nonlinear Autoregressive with Exogeneous Input \\ SCAPA & Sequential Collective and Point Anomaly \\ SKAB & Skoltech Anomaly Benchmark \\ \end{tabular}
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 8 Challenges
### Challenge 2  
Security and privacy must be considered while implementing quality of service (QoS) optimization in smart healthcare systems. Sensitive patient data should be protected from unauthorized access and hackers using security measures like encryption and authentication. One can limit who has access to patient information by using access restrictions. Privacy is a significant problem in smart healthcare applications due to the sensitivity of personal health information. The de-identification of patient data, safe data storage, and strict access controls are all actions that can be taken to protect privacy. Healthcare organizations must also abide by appropriate rules and regulations, such as HIPAA in the US, to ensure that patient privacy is protected [74]. Owing to the blockchain's decentralized structure, healthcare providers may securely share patient data while also guaranteeing that the data is impregnable and cannot be changed without the network's consent. Additionally, smart contracts can automate several processes and ensure that only those with the right authorization can access data [75]. This has the potential to greatly improve healthcare delivery's effectiveness and precision, which would ultimately benefit patients.
###### Abstract  
Measuring lower extremity joint angles during cycling is interesting to derive joint loading from contact forces at the pedals and to determine the cause of cycling injuries. Traditionally, joint angles are determined in a stationary setup with high-resolution cameras that track marker movement in a lab environment. Alternatively, joint angles can be estimated mathematically in-situ: the lower extremities, bicycle frame and pedal arms are presented as a 5 bar linkage system which is fully defined by the lower extremity segment lengths, seat height and pedal position. As most instrumented pedals for power measurements include pedal orientation measurements, the bar linkage system demands no special equipment to derive joint loadings from pedal loads. The aim of this study is to validate the bar linkage system for joint angle estimation in the sagittal plane during in-situ cycling. Ten subjects cycled on a stationary bike while the hip, knee and ankle angles were measured with a Vicon optoelectronic motion capture system and simultaneously calculated with the bar linkage system. The results were averaged to subject-specific and method-specific ensemble average curves in the function of the phase in the pedal cycle and compared by a correlation analysis, Bland Altman plot, and Spm1D paired \(T\)-test. The hip and knee angle estimation of the bar linkage system showed no statistically significant difference from the optoelectronic system. Moreover, the ankle showed a statistically significant difference in the last half of the recovery phase of the pedal cycle. As the difference was maximally \(3^{\circ}\), it has no clinical significance when deriving joint loading from contact forces at the pedal.  
bar linkage model; optoelectronic motion capture system; validation; kinematics; cycling 10.3390/app10155104; doi:10.3390/app10155104
## 5 Discussion
### Attention Mechanism  
For the attention component in MRAN, we explored three attention mechanisms: Pre-Layer Normalization attention (Pre-LN), Post-Layer Normalization attention (Post-LN), and Residual Bi-directional Layer Normalization attention (ResiDual), as illustrated in Figure 5. The attention layers were stacked three times (N = 3).  
Figure 3: Entity embedding t-SNE two-dimensional visualization on the WN18 RR dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline CAKGC & 0.579 & 0.512 & 0.614 & 0.704 \\ CAKGC (w/o type) & 0.571 & 0.502 & 0.606 & 0.698 \\ CAKGC (w/o attn) & 0.550 & 0.482 & 0.580 & 0.681 \\ CAKGC (w/o type and attn) & 0.545 & 0.467 & 0.587 & 0.692 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments of the CAKGC (BERT-tiny) model on the WN18 RR dataset.  
From Table 6, it can be observed that ResiDual outperforms the traditional Pre-LN and Post-LN attention mechanisms. In Pre-LN, the normalization is performed before the self-attention, which means that the normalization heavily depends on the input. In Post-LN, the input of each sub-layer is the sum of the outputs from all preceding sub-layers. Therefore, any errors in the preceding sub-layers can accumulate and propagate to subsequent sub-layers, potentially leading to error accumulation. ResiDual, with its bidirectional attention mechanism, effectively addresses both of these issues, resulting in further performance improvement.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 2 Literature Review
### Patent Analysis  
Patent analysis analyzes and compares different types of patent data to clarify the innovative activities in the industry [22]. Patent analysis can reveal current technological developments, stimulate new technological solutions, demonstrate the relationship between technologies, and motivate investment policies [23]. Patent analysis can also help researchers understand the current research foci of different inventive organizations and provide a basis for the analysis of technological trends [24]. Patent analysis can involve statistical calculations of the external characteristic indices of patent documents, such as analyzing the number of patents or using the contents of patent documents to perform analysis by grouping technological characteristics [25].  
Past studies related to machine learning in photonics have focused on algorithm technologies [2; 10] or current developments and applications [1; 3; 6] and rarely explored and classified the knowledge or patents of machine learning in photonics. Machine learning in photonics is an interdisciplinary field that combines computer science, communication, and optics, and technological exploration of the different aspects of the industry is essential for its development. Past studies have used patent analysis to perform technological grouping and determine the technological development trend and model of industries involving many fields [26; 27; 28]. Patent analysis was also used to create a knowledge framework for biomedical three-dimensional printing (3D printing) [29]. This study used patent analysis to explore the technology distribution of machine learning in photonics and create a patent map. Few patent maps of machine learning in photonics currently exist, particularly for the technological positioning of technology developers; therefore, this study created a patent map to serve as a reference for government and industry.
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 5 Discussion  
Based on extensive comparative experimental results, our proposed model exhibits significant improvements in two key aspects: (1) Enhanced accuracy under BG and CL conditions on CASIA-B dataset. This improvement is attributed to the utilization of MSFE, FLSFE and MSTFE. MSFE can expand different perceptual fields and observe more detailed gait features in spatial and temporal domain. Besides, both FLSFE and MSTFE in a two-path parallel structure can extract muti-scale spatial and temporal discriminative features, which can enhance the robustness of the proposed method, particularly under unfavorable conditions. (2) Enhanced accuracy on the OUMVLP dataset. The proposed method demonstrates outstanding performance in the large-scale dataset, this is due to the fact that the view embedding module can predict the best view and embed view angle into the multi-scale spatial-temporal features, which can help mitigate the intra-class variations resulting from view differences and enhance the recognition ability of gait recognition.  
In the future, we will further improve the performance of our proposed method in more complex test scenarios and gait in-the-wild datasets, such as the GREW [43] dataset and the Gait3D [44] dataset. Simultaneously, whether our method exists overfitting over CASIA-B and OU-MVLP datasets also needs to be verified in the wild datasets and real-word scenarios. Additionally, as silhouettes are easily disturbed by pedestrians' clothes and objects, it is essential to explore multi-modal gait recognition approaches that combines silhouettes, skeletons [45], and pose heatmaps [46].