esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Verification
### Robustness Against Noise  
Robustness against measurement noise is a key factor for assessing the performance of the damage detection methods [56; 57]. The capability of the proposed model to identify damage in noisy conditions is examined by using noisy acceleration responses to simulate actual measurements. The noisy acceleration response is elaborated by adding random Gaussian white noise to the acceleration response obtained from the numerical simulation of the bridge. The noise intensity is quantified by the signal-to-noise ratio (\(SNR\)):  
\[SNR=20\log_{10}\frac{A_{S}}{A_{N}} \tag{16}\]  
where \(A_{S}\) and \(A_{N}\) denote the root mean squares of the numerical acceleration response and the noise, respectively. Three high noise levels of \(SNR\) = 10 dB, 20 dB, and 30 dB are considered to be used in the analysis of damage identification. At each noise level, the hierarchical neural network model is implemented, with the identification results presented in Figure 10a-c for \(SNR\) = 10 dB, 20 dB, and 30 dB, respectively. From the figures, (i) damage location can be discriminated by the model, with errors merely obstructing the judgment in some minor damaged cases; (ii) the results in the noisy conditions show that the model gives an excellent prediction of damage severity, even better than that for the noise-free condition. The effect of noise on damage identification is indicated by the \(MSE\) and maximum error, given in Table 4. From the \(MSE\), we can see that this model still functions in noisy conditions with quite a low error; from the maximum error, it is evident that errors are mostly controlled below the medium level, so that they may only interfere with the identification of minor damage. Therefore, conclusions can be obtained that the proposed hierarchical neural network model can locate and quantify damage in the bridge with great accuracy and strong robustness against noise.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Noise**} & \multicolumn{4}{c}{\(SNR\) (dB)} \\ \cline{2-5}  & 10 & 20 & 30 & \(\infty\) \\ \hline \(MSE\) & 4.892 & 4.94 & 7.138 & 3.309 \\ Maximum error & 13.1 & 7.048 & 10.462 & 7.044 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean square error (\(MSE\)) of damage identification in noisy conditions.  
Figure 10: _Cont._The merit of the robustness against noise of the proposed model is primarily attributed to two factors, i.e., the WPT and the NLPCA: 1 WPT. The WPT is an integral transform that has the function of denoising. Moreover, a WPNE resulting from the WPT is formed with the summation of wavelet packet node coefficients squared, which is an operation of assimilating noise. 2 NLPCA. For some WPNEs of lower magnitude, they are commonly dominated by noise. Such WPNEs can be almost eliminated by the NLPCA due to their insignificance when conducting NLPCA for feature extraction, resulting in the reduction of noise. The two factors endow the proposed model with the merit of robustness against noise.
###### Abstract  
Advancements in information and communication technologies have enhanced our possibilities to communicate worldwide, eliminating borders and making it possible to interact with people coming from other cultures like never happened before. Such powerful tools have brought us to reconsider our concept of privacy and social involvement in order to make them fit into this wider environment. It is possible to claim that the information and communication technologies (ICT) revolution is changing our world and is having a core role as a mediating factor for social movements (e.g., Arab spring) and political decisions (e.g., Brexit), shaping the world in a faster and shared brand new way. It is then interesting to explore how the perception of this brand new environment (in terms of social engagement, privacy perception and sense of belonging to a community) differs even in similar cultures separated by recent historical reasons. Recent historical events may in effect have shaped a different psychological representation of Participation, Privacy and Sense of Community in ICT environments, determining a different perception of affordances and concerns of these complex behaviors. The aim of this research is to examine the relation between the constructs of Sense of Community, Participation and Privacy compared with culture and gender, considering the changes that have occurred in the last few years with the introduction of the web environment. A questionnaire, including ad hoc created scales for Participation and Privacy, have been administered to 180 participants from Turkey and Italy. In order to highlight the cultural differences in the perception of these two constructs, we have provided a semantic differential to both sub-samples showing interesting outcomes. The results are then discussed while taking into account the recent history of both countries in terms of the widespread of new technologies, political actions and protest movements.  
virtual environment; participation; privacy; sense of community; cross-cultural psychology; social psychology +
Footnote †: journal:  
_Future Internet_**2017**, 9, 11; doi:10.3390/fj9020011
## 3 Results
### Thematic Categories
#### 3.2.6 Cost Reduction  
Three studies analyzed the use of CDSSs in reducing healthcare costs [65; 68; 69]. All the studies reported that CDSSs provide real-time diagnostic and therapeutic advice through the continuous monitoring of patients in cardiovascular dynamics, promoting a reduction in health costs. Its use has reduced medical errors and has shown a hospital stay reduction (length of stay (LOS)) and mortality [65; 68; 69].  
[PERSON] et al. implemented a model of readmission in the ICU in real time. The results showed that 11% of the readmissions occurred during hospitalization; there was a greater probability of receiving more interventions and medications, which promoted a higher-quality stay [68]. One of the studies examined the use of CPOE in medical error preventions, resulting in a reduction in hospital stays and improved healthcare [69]. [PERSON] et al. concluded that identifying the most relevant features of patients with cardiogenic shock by CPOE reduces mortality rates and results in improved patient conditions, reduced burden, and reduced hospital care [65].
# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
###### Abstract  
Parkinson's disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and how easily patients adapt to their walking environments. PD is progressive in nature, and if undetected and untreated, the symptoms grow worse over time. Fortunately, PD can be detected early using gait features since the loss of motor control results in gait impairment. In general, techniques for capturing gait can be categorized as computer-vision-based or sensor-based. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, the subjects' consciousness of worn sensors and being actively monitored may further impact their motor function. Recent advances in computer vision have enabled the tracking of body parts in videos in a markerless motion capture scenario via human pose estimation (HPE). Although markerless motion capture has been studied in comparison with gold-standard motion-capture techniques, it is yet to be evaluated in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. First, we perform HPE on the subjects using AlphaPose. Then, we extract and analyse eight features, from which five features are systematically selected, achieving up to 93% accuracy, 96% precision, and 92% recall in arbitrary views.  
gait analysis; markerless motion capture; Parkinson's disease +
Footnote †: journal: _Algorithms_
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.9 Phantomsfc Framework  
PhantomSFC was proposed by [PERSON] et al. [83], which is an SFC framework aimed at decoupling the underlying network from the service plane. The design was implemented to be transport-independent, network agnostic, elastic, and maintains a small footprint by considering end-to-end throughput and latency. The PhantomSFC framework is based on the IETF SFC reference architecture presented by [37], which comprises a classifier, an SFF, SF, and a proxy component. NSH, which was standardised by [87], was used as the encapsulation protocol for SFCs in PhantomSFC.  
Components such as proxies, forwarders, and classifiers are deployed as vNFs, and a centralised SDN controller is employed for the realisation of chain configuration and instantiation. Tasks involving chain configuration, such as creating a new chain and removing and modifying configuration rules in proxies, classifiers, and forwarders are carried out by the logically centralised controller. Using PhantomSFC, resources can be scaled by SPs based on service demands; thus, the PoC evaluation of PhantomSFC achieved improvements in throughput, jitter, and latency, using the DPDK application.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 3 IoT-over-Satellite Performance Modeling
### Communication System Model  
For evaluating the performances of practical IoT technology, we apply the free licensed LoRa chirp spread spectrum (LoRa CSS) modulation. In order to maximize the number of active users, we assume the lowest channel bandwidth of LoRa, which is 125 kHz, is used in the modulation so that we have 64 channels in the ISM band following the LoRa parameters [7]. This \(M_{\text{MCS}}(.)\) function is based on empirical measurements from the literature [23], in which the fitting table can estimate the packet error rate in the following Figure 5. The SINR limit to establish communication is shown in Table 4.
_Applied_  
_sciences_  
_Article_  
**A Multiobjective Perspective to Optimal Sensor Placement by Using a Decomposition-Based Evolutionary Algorithm in Structural Health Monitoring  
**[PERSON] 1**\({}^{\copyright}\), [PERSON] 2,*\({}^{\copyright}\) and [PERSON] 3,*\({}^{\copyright}\)**  
Research Department, CR Classification Society, Taipei 10487, Taiwan; [EMAIL_ADDRESS]  
CCCF Fourth Harbor Engineering Institute Co., Ltd., Guangzhou 510230, China  
Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan  
Correspondence: [EMAIL_ADDRESS] (J.T.); [EMAIL_ADDRESS] (H.-H.H.); Tel.: +86-185-2060-7667 (J.T.); +886-2-3366-5753 (H.-H.H.)  
Received: 9 October 2020; Accepted: 28 October 2020; Published: 30 October 2020  
**Featured Application: A multiobjective approach for optimal sensor placement in structure health monitoring regarding mode shapes, redundancy, and signal strength was proposed. This method can be exploited for various types of structures such as buildings, bridges, and offshore jacket foundations in a preference of weightings on each objective.**  
**Abstract: The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.**  
**Keywords: structural health monitoring; sensor placement; multiobjective optimization; evolutionary algorithm; modal test**
## 3 Possible Relaxation Channels in the Early Stage  
As mentioned before, we are mainly concerned with GaAs. The band structure of GaAs is schematically shown in Figure 2. The conduction bands have a multi-valley structure [15]. The lowest valley is the \(\Gamma\) valley, and the second lowest is L. When a visible light is shone onto GaAs, according to the Franck-Condon principle of the photo-excitation, the electrons are injected only into the \(\Gamma\) valley of the conduction band, at first. After a while, the photo-injected electrons are relaxed toward the energetically lower states. The dominant relaxation trivially occurs within the lowest \(\Gamma\) valley. However, when the photo-injected electrons are distributed in energetically higher region than the minimum of the other valley, such as the L valley in Figure 2, the intervalley scattering from the \(\Gamma\) valley to the other one becomes possible. Then, the major scattering channels of photo-excited electrons in the early stage of the relaxation are the intravalley one and the intervalley one.  
Figure 1: A schematic view of two-photon photo-emission process is shown. \(E_{\rm VBM}\), \(E_{\rm CBM}\) and \(E_{\rm vac}\) represent the energies at the valence band maximum, conduction band minimum and vacuum level, respectively. \(E_{\rm kin}\) is the kinetic energy of the photo-emitted electron. \(k_{||}\) is the wave vector (or momentum) of the photo-emitted electron parallel to the crystal surface. For more details, see the text.
## 4 Results
### Performance of Commercial LLMs  
Figure 5 presents the results achieved by GPT-3.5 in the first and second entries of the Revalida exams from 2017 to 2023. This LLM was the only one that did not exhibit fallback triggers. Its best performance occurred in the second entry of the 2022.2 exam, with an accuracy of 71.95%, while its lowest performance was in the second entry of the 2017 exam (highlighted in red), with 45.56% accuracy. The total number of correctly answered questions was 466 out of 771 in the first entry, and 447 in the second. Considering all exams across both entries, the average accuracy was 60.55%, with a Standard Deviation (SD) of 7.17% and a median of 60.64%. Agreement levels between the two entries ranged from 43.60% to 71.25%. No outliers were observed.  
The results achieved by Gemini are shown in Figure 6. As illustrated in the boxplot, its best performance occurred in the first entry of the 2023.1 exam, with an accuracy of 67.90%, while its worst result was in the first entry of the 2020.1 exam, with 51.08% accuracy. The LLM correctly answered 454 questions in the first entry and 455 in the second. The overall average accuracy across both entries was 59.08% (SD = 5.53%), with a median of  
Figure 4: Bar plots showing the fallback index of the open-source small LLMs.  
Figure 5: Accuracy results obtained by GPT-3.5: (**a**) bar plot and (**b**) box plot.  
58.03%. [PERSON]'s Kappa indicated that agreement levels between the two entries ranged from 42.37% to 85.98%. No outliers were observed.  
GPT-4o's performance is shown in Figure 7. Its best result was in the second entry of the 2017 red exam, with an accuracy of 83.54%, which was the highest score among all LLMs evaluated in this study. As also seen in the boxplot, its lowest performance occurred in the first entry of the 2020.1 exam, with 52.17% accuracy. The total number of correct answers was 465 in the first entry and 499 in the second. The overall average accuracy across both entries was 63.85% (SD = 8.90%), with a median of 61.62%. GPT-4o also showed some of the lowest agreement levels between entries, with concordance dropping to 26.55% in the 2017 red exam. The boxplot also indicates that no outliers were present.  
Figure 6: Accuracy results obtained by Gemini: (**a**) bar plot and (**b**) box plot.  
Figure 7: Accuracy results obtained by GPT-4o: (**a**) bar plot, and (**b**) box plot.  
Figure 8 shows the fallback indexes for the Gemini and GPT-4o. The highest fallback index across all editions was observed with Gemini in the second entry of the 2020.1 exam, where 8.69% of its responses indicated that it was unable to answer the questions. GPT-4o reached its highest fallback rate in the first entry of the 2022.2 exam, at 2.46%, with responses suggesting that the LLM either did not understand the question or was unable to provide an answer.
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 4 Discussion
### Local Experiences and Results in Sand Dune Afforestation Worldwide  
The diversity of approaches in sand dune afforestation shows that successful projects depend on site-specific, multidisciplinary approaches and social conditions.  
The case studies we have identified indicate a series of patterns across different continents. The most essential one is species selection, as successful programs used deep-rooted, fast-growing, and drought-tolerant species such as Robinia, Kheji, Acacia, Casuarina, and Seagrape. Besides their stabilizing purpose, these species also provide economic value (like fodder, timber, resin), creating further incentives for their long-term maintenance.  
The second pattern is using an integrative approach that combines biological, mechanical, and chemical methods. For example, using grass and shrubs improves soil retention and regulates the microclimate. Moreover, water management is essential in supporting both the new plantations and the overall environment [122; 123]. Here we can include Libya's bitumen mulch, China's fertilization protocols, and Saudi Arabia's mechanical barriers. All these examples prove how afforestation can benefit from hybrid strategies.  
The third pattern is represented by water management. This is even more important in arid areas (Pakistan's Cholistan, Libya) where managing water in creative manners (by harvesting, reusing saline water, or dry-season planting) shows higher success margins. On the other hand, the case from Hainan, China, shows how poor survival rates can be caused by not incorporating adaptive water strategies, despite important technical inputs.  
A fourth pattern comes from the engagement of communities and traditional knowledge. Sudan's farmer-based agroforestry, Kenya's collective plots with Azadirachta, and Somalia's refugee-driven afforestation show how important it is to include local communities, impacting both ecology, monitoring, and ownership aspects.  
If we compare these cases, we can also see a series of recurring challenges, namely low post-planting survival rates, species uniformity, livestock damage, and weak monitoring. All these aspects undermine the long-term success of these afforestation efforts. Furthermore, repeating only certain species without diversification (ex. the overuse of Robinia or Casuarina species) can lead to significant ecological imbalances.  
In conclusion, we can say that effective dune afforestation is context-driven. The most successful projects combine resilient species with tailored techniques and the involvement of local communities. In addition, they balance traditional ecological knowledge with innovation techniques. Future success will depend on a long-term monitoring of these projects, as well as exchanging these practices at an international level, especially as climate variability poses more challenges to vulnerable landscapes.
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 4 Near-Crash Event Identification Model
### CNN Layer  
The CNN model is recognized in the field of deep learning for its distinctive capabilities of weight sharing and local connectivity [28]. This architecture excels at performing feature extraction and learning efficient data representations through convolution operations paired with nonlinear activation functions. As shown in Figure 5, the used CNN architecture comprises convolutional layers, pooling layers, and fully connected layers [29]. These components work in concert to diminish the volume of network parameters while preserve the deep features of multi-dimensional input data, thereby enhance the model's efficiency and effectiveness.  
Figure 4: Architecture of the CNN-LSTM-Attention model (* is the multiplication sign).  
Figure 5: CNN structure.  
(1) Convolutional Layer  
The convolutional layer is primarily composed of kernels tasked layer that stands as the cornerstone of a CNN, setting it apart from traditional fully connected neural networks. It executes dot product calculations across the input data according to specified size and dilation rates, facilitating the extraction and mapping of features, effectively condensing the data while retaining essential information. The convolutional layer extracts features from input data through convolution operations and reduces the number of parameters, thus streamlining the network for better performance and efficiency. The output of the convolutional layer [30], \(y_{conv}\), is calculated as follows:  
\[y_{conv}=\sum_{e=1}^{m}\sum_{f=1}^{n}\left(\left(w_{r}^{l}\right)_{ef}d_{ef}\right) \tag{5}\]  
where \(m\) and \(n\) represent the two dimensions of the filter, \(d_{ef}\) is the data value of the input matrix at positions \(e\) and \(f\), and \(\left(w_{r}^{l}\right)_{ef}\) is the coefficient of the convolution kernel at positions \(e\), \(f\).  
(2) Pooling Layer  
The pooling layer, also known as the subsampling layer, is one of the critical components of a convolutional neural network. Pooling refers to extracting a certain attribute (such as the maximum, average, or L2-norm) from the corresponding sampling window as a lower-dimensional output, thus sparsely processing the data to reduce computational load. Moreover, pooling operations enhance the robustness and noise resistance of the extracted features while preserve the main characteristics of the data. The output of max pooling is represented by:  
\[y_{pool}=\max\left(d_{ef}\right),e\in[1\cdots p],f\in[1\cdots q] \tag{6}\]  
where \(p\) and \(q\) represent the two dimensions of the pooling window size, \(d_{ef}\) is the data value of the input matrix at positions \(e\) and \(f\), and \(y_{pool}\) is the pooling output.