esa-sceva/satcom-chunk-collection huggingface dataset:
# Technology Acceptance in Healthcare: A Systematic Review
## 4 Results
### Main Information Technologies and Their Relationships with Countries and Participants  
Figure 5 presents the distribution of the studied information technologies in the reviewed studies. As suggested by [PERSON] et al. [6], the categorization of information technologies was performed based on the Medical Subject Headings (MeSH) thesaurus [73]. With more than 48% (\(N\) = 69), it is clear that prior research is mainly dominated by five main categories, including telemedicine solutions, HIT systems in general, cloud computing applications, mobile applications, and electronic health records (e.g., health information solutions and electronic medical records). By having a quick look at the analysis in Table 5, it seems that the classification of technologies across the countries is equally distributed, with a slight notable difference in telemedicine and cloud computing. Telemedicine was mainly studied in Taiwan and the USA, while cloud computing was primarily studied in Taiwan.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Frequency** & **Countries** \\ \hline Telemedicine & 19 & Taiwan (4), USA (3), Germany (2), Malaysia (2), South Korea (2), Spain, India, UK, Slovenia, China, Georgia \\ \hline Electronic Health Records & 18 & USA (3), Austria (2), Iran (2), Jordan, India, Turkey, Taiwan, Spain, Saudi Arabia, Singapore, France, Canada, Armenia, Australia \\ \hline HIT Systems in General & 13 & Morocco (2), South Korea (2), UK and UAE (2), Nigeria, Australia, Thailand, Canada, North Macedonia, Turkey, Germany \\ \hline Mobile Applications & 10 & Germany (2), Taiwan (2), China (2), Malawi, Singapore, Spain, UK \\ \hline Cloud Computing & 9 & Taiwan (7), Nigeria, one study conducted in: Malaysia, Pakistan, and Saudi Arabia \\ \hline \hline \end{tabular}
\end{table}
Table 5: Technology types and directions of countries.  
Figure 5: Distribution of studies in terms of technology type.  
Figure 6 presents the distribution of studies according to the participants (user groups). With almost 56% of the total participants, physicians (\(N\) = 30), nurses (\(N\) = 24), and healthcare professionals in general (\(N\) = 26) attracted the attention of scholars to understand their technology acceptance. In terms of technology type and participants, we observed that the focus is scattered with little attention to study the acceptance of electronic health records by the same leading user groups (see Table 6). Additionally, there are efforts to understand the acceptance of patients and the general population as non-healthcare workers for various technologies, including telemedicine, mobile applications, cloud computing, and wearable electronic devices.  
Figure 6: Distribution of studies in terms of participants.
# Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
## 2 LoRaWAN
### LoRa  
Semtech's Long Range (LoRa) [4] is a low-level physical interface (PHY) that uses a special modulation to create long-range communications. LoRa is based on the spread spectrum technique (frequency-shift keying (FSK)) and the extended chirp spread spectrum (CSS) for modulation, allowing it to retain the same low-power characteristics while significantly increasing the range of communication. It also integrates forward error correction (FEC). LoRa uses the entire bandwidth of the channel to transmit a signal, so the signal is more robust against noise, significantly improving the sensitivity of the receiver.  
LoRa is a PHY layer implementation and is independent of higher layer implementations. This allows LoRa to coexist and interoperate with existing network architectures. LoRa modulation addresses all the problems associated with DSSS systems to provide a low-cost, low-power, but above all robust alternative to traditional broad-spectrum communication techniques.  
The data speed is variable, which allows optimization of network performance for a specific bandwidth. For this, LoRa works with orthogonal dispersion factors, called spreading factors (SFs). It operates in industrial, scientific, and medical (ISM) bandwidths, shown in Table 1.  
LoRa is designed for long-distance and low-power communications. This allows the network to have a large coverage area of several kilometers, depending on the characteristics of the environment.
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method
### Comparison Between Numerical and Experimental Results  
The comparison between numerical calculation results and experimental findings is presented in Table 11. It is observed that the calculated values are largely in agreement with the experimental values, with an error margin of less than 10%. This suggests that the thermal analysis model developed in this article effectively captures the principles of heat generation, two-phase flow, heat transfer, and temperature distribution within the bearing chamber-bearing system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature on the Outer Surface of** & **Bearing 1, Bearing 4** & **Bearing 2, Bearing 3** \\ \hline Numerical Results & 142.51 & 157.02 \\ (Under Operating Condition 1) & 142.55 & 157.01 \\ (Under Operating Condition 2) & 142.50 & 157.00 \\ \hline Numerical Results & 135–140 & 135–145 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Numerical and experimental results.  
Figure 7: Oil–air two-phase distribution. (**a**) Chamber 1; (**b**) Chamber 2; (**c**) Chamber 5.
## 3 Watermarking Approaches  
In this section, we analyze the latest research trends in watermarking techniques that operate based on LDMs. LDM watermarking provides a level of flexibility that was previously unattainable by embedding watermarks directly into the image generation process. This high degree of freedom enables more sophisticated integration of watermark signals, ensuring robustness while maintaining generated image quality [23]. Recent studies have introduced effective methods for embedding watermarks at various stages of the LDM generation process, as described below.  
Figure 2 presents a chronological overview of the recent watermarking techniques integrated into LDMs, highlighting key methods for embedding watermarks across different stages of the generation process. It includes advancements such as latent space modulation, initial noise manipulation, and decoder fine-tuning, showcasing the diversity and sophistication of recent approaches aimed at ensuring robust and traceable watermarking in AI-generated content.  
Figure 1: Overview of the structure of the latent diffusion model (source: [1]).
## 4 Discussion  
First of all, the geometrical parameters of the fabricated QFP prisms were evaluated before they were constructed into the optical testing system. The parameters variation is less than 0.1 mm, which proves this structure design and fabrication technology effective. About cost, the price of one QFP prim is about 60 USD when the customized number is no large than 10 in a Chinese optics factory  
\begin{table}
\begin{tabular}{c c c} \hline
**Testing Group** & **Result of \(X_{+1}\)** & **Result of \(X_{-1}\)** \\ \hline
1 & 6829 & 6889 \\
2 & 6843 & 6890 \\
3 & 6877 & 6891 \\
4 & 6874 & 6890 \\ \hline \end{tabular}
\end{table}
Table 1: The result of the displacement solution (\(\upmu\)m).  
Figure 5: **(a)** The interference signal of collected data; (**b**) the detail interference signal of one channel (\(I_{\text{X+1}}\)) during moving.  
and this can greatly reduce to be 15 USD when this product number is larger than 1000. The reduction of cost of this QFP prism will be beneficial for the grating ruler application.  
Second, because the scope of this research is focused on the design, fabrication, construction and evaluation of this proposed QFP prism, the systematical algorithm for motion determination that was illustrated in References [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] was not introduced or not employed to precisely calculate the displacement. Furthermore, because of the slight misalignment of the motion stage and the scale grating, crosstalk will inevitably be involved. The systematical misalignment could be previously calibrated with a compensation factor used [12].  
Lastly, it should be also noted that the proposal in this research requires the grating pitch and the light source wavelength to be time-stable or a small order variation within the designed tolerance. Improvement of the tolerance generally can be achieved by enlarging the beam spot, yet will correspondingly enlarge the size of the QFP prism.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 1 Introduction
After reconstructing continuous data, phenological characteristics are often extracted following different approaches [13], mainly threshold-based methods [11; 33], the moving averages approach [34], the inflection point method [23] and the time of maximum increase [33; 35]. The data reconstruction algorithm and phenology extraction methods chosen would greatly affect the modelled phenology. The simplicity and flexibility of threshold-based methods make them particularly popular. In addition, they were found to predict the start of season (SOS) more accurately than other methods in different forests in Europe [11], North America [36] and China [37]. Phenology modelling studies using remote sensing mostly focused on pure deciduous tree species and showed a good correlation with ground observations. In areas with mixed plant species, the phenology of individual plant species are all grouped under a unique \"pixel ecosystem\" phenology, or land surface phenology, which is often investigated instead of the phenology of the individual tree species in most studies dealing with mixed forests [38; 39; 40]. Land surface phenology is generally found to be more difficult to model in mixed forests compared to pure forests, especially for the end of season (EOS) [41]. The ground observations often used to match remote sensing data for EOS are either the date of leaf colouring or the date of leaf fall depending on plant species [42], which are further difficult to apprehend in mixed forests. To our knowledge, very few (if any) studiesmodelled the phenology of individual tree species in mixed forests using remote sensing. In fact, with multiple plant species coexisting in the extent of a remote sensing pixel, it is not straightforward to match ground observations with remote sensing information in order to model the phenology of different tree species in a mixed forest. In this study, we suggest an empirical approach to model the phenology of different tree species in mixed forests. We take advantage of multi-year observations at multiple phenology stations and satellite data to identify the best thresholds for characterizing the phenology (SOS and EOS) for individual tree species at different stations in mixed tree species forests.
# Person Re-Identification by Low-Dimensional Features and Metric Learning
## 4 Experiments and Discussion
### Comparisons with the State-of-the-Arts  
With the advancement of deep learning [8; 10; 11; 24; 35; 39], person Re-ID has achieved inspiring performance on widely used benchmarks. However, as Table 3 has shown, the parameters of common convolutional neural networks can easily reach millions, such as the GoogleNet used by attention-aware compositional network (ACCN) [10], ResNet50 used by multivariable multi-objective genetic algorithm (MMGA) [11]. Our model has only \(2.4\times 10^{5}\) parameters, but it has also reached the state of the art, as shown in Table 4.  
On the Market-1501 dataset, our model outperforms most of the state-of-the-art algorithms and exceeds the current best models, i.e., it is attention-driven by +0.7% in Rank-1. Additionally, the proposed model is 1.2% lower than UnityStyle+RE in mAP to achieve the second best result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Market-1501**} & \multicolumn{2}{c}{**DukeMTMC-reID**} \\ \cline{2-5}  & **Rank-1** & **mAP** & **Rank-1** & **mAP** \\ \hline ACCN [10] & 85.9 & 66.9 & 76.8 & 59.3 \\ MSCAN [39] & 83.6 & 74.3 & - & - \\ MultiScale [3] & 88.9 & 73.1 & 79.2 & 60.6 \\ HA-CNN [45] & 91.2 & 75.7 & 80.5 & 63.8 \\ AlignedREID [8] & 91.0 & 79.4 & & \\ HPM [24] & 94.2 & 82.7 & 86.4 & 74.6 \\ MMGA [11] & 95.0 & 87.2 & 89.5 & 78.1 \\ UnityStyle+RE [46] & 93.2 & 89.3 & 85.9 & 82.3 \\ \hline ours & 95.7 & 88.1 & 85.3 & 78.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of results on Market-1501 and DukeMTMC-reID.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Rank-1** & **mAP** \\ \hline gBiCov [14] & 8.28 & 2.33 \\ LOMO [4] & 43.79 & 22.22 \\ BoW & 34.38 & 14.10 \\ BoW+KISSME [38] & 44.4 & 20.8 \\ \hline ours & 95.7 & 88.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison results with traditional methods on Market-1501 dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Million Parameters** \\ \hline AlexNet [40] & 60 \\ VGG16 [41] & 138 \\ GoogleNet [42] & 6.8 \\ Inception-v3 [43] & 23.2 \\ ResNet50 [44] & 25.5 \\ \hline ours & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Several classic convolutional neural network model parameters.  
On DukeMTMC-reID, our model achieves 85.3% Rank-1 and 78.4% mAP. In Rank-1, our method significantly exceeds most deep learning methods to achieve the second best result in mAP, which is 0.9% lower than that of UnityStyle+RE.
## 4 Results and Analysis
### Routing Overhead (Bytes)  
Figure 7 shows the results of routing overhead in the network, in which the native AODV and the proposed technique can be compared. It can be seen in the proposed DDBG technique that there is a slight lag at a point, as the number of nodes is increasing; the reason for this is that the IDS node sends packets periodically. When a packet is broadcasted by the IDS node, it takes less time to reach every node in the IDS set instead of the entire network. In spite of sending broadcast status packets periodically, the routing overhead of the proposed technique increases. The overall performance of the proposed method in terms of the overhead is improved more than other methods as shown in Table 7.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 1 Introduction
In the first stage, DGF needs to be able to reach the target site as quickly as possible, and in the second stage, DGF needs to be able to trigger the vulnerability in the target site. Then based on these, we propose a two-stage function sequence-based seed-scheduling strategy that selects \"the best\" seeds for DGF testing at each stage. We implemented the methods above into a tool called SEZZ and conducted experiments on the Magma dataset, demonstrating its applicability to real-world cases. On 35 known vulnerabilities, the experimental results demonstrate that SEZZ achieved average speedups of \(13.7\times\), \(1.50\times\), \(9.78\times\), and \(2.04\times\) on four related state-of-the-art directed fuzzers, namely, AFLGo, Beacon, WindRanger, and SelectFuzz, respectively. SEZZ achieved the best performance by triggering the most evaluated vulnerabilities. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Our main contributions are summarized as follows:  
* We analyze the properties of the instrumented functions' call graph and estimate the complexity of the reaching sequence before the target site. * We propose a two-stage function sequence-based seed-scheduling strategy to guide DGF in selecting the best current seed for fuzzing. * We demonstrate that using SEZZ increases the speed to expose real-world bugs compared with four state-of-the-art directed fuzzers: AFLGo, WindRanger, Beacon, and SelectFuzz. It also triggered three more vulnerabilities than the other compared tools.
changes), cause significant perturbations in chemotypes and levels of PSMs production. For example, plants produce more terpenoids when exposed to high temperatures [2], and UV-B (280-315 nm) radiation induces tree foliage to produce more phenolic acids and flavonoids as protective pigments [3; 4]. Phenolics and flavonoids are well-known for their antioxidative and anti-inflammatory properties [5; 6; 7]. Similarly, the production of antioxidative compounds such as glutathione, g-aminobutyric acid (GABA), terpenoids, and volatile organic compounds (VOCs) increases under elevated O\({}_{3}\)[8].  
PSMs are vital for human health and form many pharmaceutical drugs' backbone. Indeed, more than 25% of the existing drugs belong to PSMs [9]. The most popular PSMs-derived drugs are morphine (isolated from _Papaver somiferum_), digitoxin (isolated from _Digitalis purpurea_), taxol (isolated from _Taxus bacata_), artemisinin (isolated from _Artemisia annua_) and quinine (isolated from _Cinchona officinalis_), vinblastine and vincristine (isolated from _Catharanthus roseus_); and aspirin (first isolated as salicylic acid from _Filipendula ulmaria_). Since plants exposed to various abiotic stress conditions produce many PSMs in higher concentrations as their coping mechanism [10; 11; 12], it presents opportunities for natural product researchers and pharmaceutical companies to explore the biochemical responses of plants to climatic stress for developing many novel therapeutics. However, there is no comprehensive literature review examining the scope of plants affected by abiotic stresses for drug discovery.  
Therefore, this scoping review examines recent advances related to PSMs in plants affected by abiotic stress/or abiotic growth factors, their roles as protective phytochemicals, and their potential for novel drug lead compounds. Although primary metabolites such as carbohydrates [13; 14] and peptides [15; 16] are also known to play roles in the plant's defense response, our review focuses on selected classes of PSMs, including flavonoids, terpenoids, alkaloids, saponins, tannins, and cyanogenic glycosides. We have collected published information on plants affected by abiotic stresses (primarily studied in the simulated growth conditions) and their PSMs (including pharmacological activities) from PubMed, Scopus, MEDLINE Ovid, Google Scholar, Databases, and journal websites using the following keywords: \"stress-affected plants,\" \"plant secondary metabolites,\" \"bioactive compounds,\" \"abiotic stress,\" \"climatic influence,\" \"pharmacological activities,\" \"drug discovery,\" and \"medicinal plants.\" We have retrieved published literature between 1973 to 2021 (only related to PSMs produced under ex situ growth conditions), analysed the content, and presented the information in the form of figures and tables. The chemical structures were drawn by using ChewDraw Professional software, and each structure was cross-checked for their correctness using ChemSpider and HMDB databases. We excluded studies on the effects of biotic stress on PSMs.
## 4 Switching Adaptive Notch Filter Vibration Control Strategy Based on Fuzzy Control
### Bipolar Filter  
In terms of the control strategy of the AMB rigid rotor system, the main controller adopts fuzzy control and adjusts the control parameters according to the real-time monitoring of rotor position information so that the rotor system can respond quickly and operate stably. Secondly, an ANF is added to the input of the fuzzy controller to filter out the signal of the rotor vibration signal measured by the sensor that is at the same frequency as the rotational frequency, which effectively improves the vibration force static dynamic performance of the AMB rigid rotor system but lacks the analysis of the vibration force suppression dynamic performance. However, by switching the symbols of the notch filter gain \(\lambda\) before and after the critical rotational speed, or compensating the phases of the notch filter, the system's stability can be largely improved.  
The equivalent block diagram of the adaptive ANF automatic balancing control is shown in Figure 5.  
Figure 5: Equivalent block diagram based on adaptive ANF.  
When \(\lambda=1\), the transfer function \(N\)(s) of the ANF is expressed as follows:  
\[N(s)=\frac{C(s)}{E(s)}=\frac{s^{2}+\omega^{2}}{s^{2}+\mu s+\omega^{2}}, \tag{6}\]  
where \(\mu\) is the tunable parameter of the ANF, which determines the convergence speed and frequency bandwidth. The closed-loop characteristic equation of the rotor system is shown as follows:  
\[\lambda M(s)G_{c}(s)P(s)=0, \tag{7}\]  
where \(\lambda\) is the switching factor, \(M\)(s) is the concave feedback link, \(G\)c(\(s\)) is the controller, \(G\)p(s) is the power amplifier, \(G\)(s) is the AMB rigid rotor system transfer function, and \(G\)s(s) is the displacement sensor.  
When \(\lambda=-1\), the transfer function \(N_{-}(s)\) is different from \(N\)(s). \(N_{-}(s)\) is expressed as follows:  
\[N_{-}(s)=\frac{s^{2}+\omega^{2}}{s^{2}-\mu s+\omega^{2}}, \tag{8}\]  
In the above analysis, it is evident that the closed-loop system is stable when \(\lambda=1\); this means that the operation speed is higher than the critical speed of the rigid body. When \(\lambda=-1\), the operation speed is lower than the critical speed of the rigid body of the rotor system. Therefore, the automatic switching control of the rotor system should be performed in the full range of rotational speeds of the ANF. The specific switching strategy is to define \(\lambda=-1\) when the rotor operation speed is lower than its rigid critical speed and to define \(\lambda=1\) when the rotor operation speed is higher than its rigid critical speed.