esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Phytochemical Investigations  
#### 2.1.1 Determination of Polyphenolic Compounds by HPLC-PDA/ESI-MS  
The polyphenolic characterization of the hydroalcoholic extracts of the aerial parts of the two subspecies of _M. frticulosa_ is reported here for the first time. In Figure 1, the HPLC-PDA chromatograms (\(\lambda\) = 280 nm) of the polyphenolic compounds occurring in the extracts of _M. frticulosa_ subsp. frticulosa_ (A) and subsp. _coronopifolia_ (B) are shown. A total of 51 compounds were detected, 31 in _M. frticulosa_ subsp. frticulosa_ and 22 in subsp. _coronopifolia_ (Table 1). Among them, 20 phenolic compounds were tentatively identified in _M. frticulosa_ subsp. frticulosa_ according to retention times, PDA, MS, and literature data, 11 belonging to a class of flavonoids and 9 to phenolic acids; on the other hand, 11 compounds were characterized in _M. frticulosa_ subsp. coronopifolia_, 6 flavonoids, and 5 phenolic acids. The identified flavonoid compounds belong to a class of flavonoids, namely, quercetin, kaempfer, and isorhamnetin, and of flavones, namely, luteolin and its derivative isoorientin, while the phenolic acids are benzoic acids, hydroxybenzoic acid and syring acid, and cinnamic acids, sinapic acid and ferulic acid. Except for peak n. 6 in _M. frticulosa_ subsp. frticulosa, the rest of the polyphenolic compounds occurred in a glycosylated form. In addition to polyphenols, 4-(methylthio)but-3-enyl glucosinolate (glucoraphenin) and its isomer (peaks n. 3 and n. 4) were detected in traces in the extract of _M. frticulosa_ subsp. frticulosa only. The presence of this glucosinolate compound in a 70% methanol extract of _M. frticulosa_ aerial parts collected in Spain was previously reported by [PERSON] and [PERSON] [12]. Interestingly, from the comparison of the phenolics detected in the two subspecies, a quite different profile was highlighted since no compounds in common were found. Regarding quantitative determination, the total amount of polyphenols identified in the extract of _M. frticulosa_ subsp. frticulosa was found to be approximately three fold higher than that of subsp. coronopifolia (151.6 mg/g extract and 51.8 mg/g extract, respectively), with the flavonoids being 91.7 mg/g extract and 33.5 mg/g extract and the phenolic acids 59.9 mg/g extract and 18.3 mg/g extract. In addition, peak n. 36, feruloylhydroxyferuloyl-dihexoside, was the most abundant polyphenolic compound in _M. frticulosa_ subsp. frticulosa (26.74 mg/g \(\pm\) 2.48), whereas in _M. frticulosa_ subsp. coronopifolia, peak n. 30, kaempfer derivative, turned out to be the main component (11.93 mg/g \(\pm\) 0.82). Figure 1: HPLC–PDA chromatograms of the polyphenolic compounds, extracted at 280 nm wavelength, of the hydroalcoholic extracts obtained from the aerial parts of _M. frticulosa_ subsp. frticulosa_ (**A**) and subsp. coronopifolia (**B**). For peak identification, see Table 1.
## 3 Energy Management Strategies for HEVs
### Rule-Based Control Strategies  
Rule-based EMSs typically rely on predefined operating modes that are formulated either through human expertise, mathematical frameworks, or computational models. These approaches often function without the necessity of prior knowledge about the specific driving cycle [68]. These strategies have simple control methods that are easy to implement and were therefore first used for the control of HEVs. They mainly consist of two forms: one is a deterministic rule-based control method, where the motor or engine operating state is divided into modes according to different torque, speed, and SOC conditions (e.g., accelerator pedal, brake pedal commands) or their efficiency Map diagrams, and rules are formulated for switching control. Another control strategy relies on FL, which is particularly advantageous for HEVs due to their multivariable, nonlinear, and time-varying characteristics. By leveraging the strengths of fuzzy control, this strategy establishes state variables and the affiliation functions of state variable change rates. These affiliation functions are then used to formulate fuzzy control rules that govern energy distribution and SOC.  
[PERSON] et al. [90] (2020) focused on the application of rule-based strategies in commercial HEVs, emphasizing the continued reliance on these strategies despite the evolution of new energy management systems. Conversely, [PERSON] et al. (2020) [91] devised a rule-driven algorithm to assess fuel efficiency, battery SOC, and potential energy savings for series HEVs. Additionally, [PERSON] (2020) [92] employed an ECMS in order to optimize the fuel consumption of an Advanced Hybrid Powertrain-II (AHS-II). The findings revealed that, with regard to the electric motor's efficiency, the discrepancy between the ECMS and the rule-based baseline control strategy was negligible. [PERSON] et al. (2021) [93] discussed how rule-based control strategies can be continuously improved to incorporate more predictive, cognitive, and artificial intelligence tools for real-time applications. [PERSON] (2021) [94] developed a rule-based topology simulation tool for HEVs, emphasizing the continued relevance and applicability of rule-based strategies in HEVs. Together, these studies demonstrate the continued relevance and evolution of rule-based control strategies for HEVs and the continued development and implementation of novel energy management systems.  
Figure 5: Energy management strategies overview for HEV [89].
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction
### Data Processing and Dataset Construction
#### 3.3.3 Feature Optimization  
The Minimum Redundancy Maximum Relevance (mRMR) algorithm was employed to rigorously evaluate feature importance for landslide prediction. mRMR quantifies both the relevance of each feature to landslide occurrence and the redundancy among features. Analysis confirmed that all 16 original features exhibited significant predictive  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**No.** & **Land Use** & **Lithology** & **DEM** & **Slope** & **Curvature** & **Aspect** & **NDVI** & **SPI** & **TWI** \\ \hline
1 & 2 & 3 & 341 & 23.255 & 38.652 & 273.366 & 7062 & 1.517 & 2.374 \\
2 & 3 & 3 & 280 & 24.953 & 51.294 & 192.529 & 6961 & 0.844 & 4.412 \\
3 & 2 & 3 & 127 & 25.893 & 34.655 & 242.103 & 7861 & 2.966 & 2.293 \\
4 & 2 & 2 & 39 & 26.783 & 48.139 & 233.130 & 6815 & 0.926 & 2.454 \\
5 & 2 & 3 & 373 & 38.975 & 35.483 & 266.423 & 6918 & 1.397 & 1.821 \\ \hline
**No.** & **dis2 road** & **dis2 drainage** & **dis2 catchment** & **dis2 faults** & **DV** & **X** & **Y** & **label** \\ \hline
1 & 6.796 & 911.046 & 1629.682 & 1251.960 & 0.00001 & 804.235 & 810.807 & 1 & \\
2 & 102.833 & 172.909 & 4.685 & 1512.070 & \(-\)0.26099664 & 857,547.4 & 831,943.4 & 1 \\
3 & 27.917 & 319.300 & 6903.066 & 2803.783 & 0.34379743 & 819,718 & 825,885 & 0 \\
4 & 131.746 & 306.661 & 5351.267 & 510.760 & 1.061006904 & 833,572.8 & 839,678.7 & 0 \\
5 & 29.458 & 424.368 & 1853.138 & 261.500 & 14.7746563 & 834,785.8 & 827,973.5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A sample of the dataset.  
relevance and acceptably low mutual redundancy. Consequently, the complete feature set was retained for model training.
###### Abstract  
Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A \"ready to use\" approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon.  
scum; hypertrophic ecosystem; Sentinel-1; Sentinel-2; Sentinel-3; cloudiness +
Footnote †: journal: Journal of Marine Science and Engineering
## 2 Background
### Wireless Sensor Networks (WSNs)  
Due to the advances in micro-electromechanical systems (MEMs), it has been possible to create sensor nodes that are small in size, have limited resources (sensing, data processing, transceiver and non-rechargeable batteries), and are able to communicate over short distances [10]. Hundreds or even thousands of sensor nodes usually make up a WSN. Due to their limited battery capacity, WSN nodes must consume little energy in order to keep the WSN lifetime as long as possible. The usual large number of WSN nodes favors the use of multi-hop communication (as opposed to using long-range communication) to save energy in data transmissions inside the WSN [11].  
Routing protocols are responsible for transferring data packets from a source node to a target node through the network [12]. They use routing algorithms, which are responsible for determining the path (next nodes) that a packet should be forwarded along [13]. As illustrated in Figure 1, routing protocols can be single hop, _i.e._, a packet is directly sent from the source to target, or multi hop, _i.e._, a packet is sent to the target through several nodes. Multi hop protocols can be divided in flat, in which all nodes have the same responsibility, or hierarchical, that creates clusters inside the network. A flat routing protocol adopts different strategies to create a path. It can determine a path when: (i) a sensor node dies or is started (Reactive); (ii) when a packet should be sent (Proactive); (iii) when it receives the location of the others sensor nodes (Location); (iv) or any other strategy (Others). In hierarchical protocols, each cluster has a leader (Cluster Head--CH) and several participants. In the case the cluster is purely hierarchical, the participants of a cluster send packets to their CHs, which forwards the packet to the sink node. Otherwise (in a mixed hierarchical), the network is flat inside and/or outside the cluster: participants collaborate to deliver a packet to their CH that forwards it to another CH or to the sink node.  
Whatever the strategy, packets do not arrive to the sink node if the path fails, which may occur in the communication link or in the WSN node. A link failure can happen due to noise, interference, distance (e.g., weak signal), or environmental conditions (e.g., walls). Meanwhile, a WSN node can fail due to software (e.g., application) or hardware (e.g., radio) failures.  
Additionally, routing protocols can use a multipath strategy instead of a single path. In this case, the same packet is sent through different paths (normally called disjoint paths), which increases the probability of being delivered [14]. However, multipath mode uses more sensor nodes and, consequently, requires more power consumption (shorting the network lifetime) than single paths.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 6 Implementation of the FPGA-Based Embedded System
### Efficiency of HW Design  
For the designed FPGA-based system, the HW efficiency has been quantified in terms of  
* area occupancy and or amount of used hardware resources;
* latency, here defined as the time between the reception of the last sample at the input and the availability of the FFT at the output;
* power consumption due to the system processing activity;  
The implemented system not involving the use digital-signal-processor (DSP) devices and occupies less than 1% of the FPGA ALMs resources of the selected chip. Therefore, good efficiency in terms of area consumption is achieved.  
Figure 16: Black box of the multiplier module with input and output signals.  
Figure 15: Schematic design of end_sumBlock.  
Real-time performance is obtained. In fact a latency equal to 1 clock pulse characterizes the implemented system. The yellow line in Figure 17 highlights that all the processing phases start synchronized with a latency of 2 clock pulses.  
For power consumption and thermal power dissipation statistic evaluation, the Power Analyser tool operating in post-fitting mode is adopted. This tool is powered by INTEL in the Quartus environment. Adopting a power supply of 5 V, a total thermal power dissipation equal to 307.27 mW is evaluated, in particular it results in 99.06 mW and 208.21 mW of core static thermal dissipation and I/O thermal power dissipation, respectively.
## 4 Results
### Triangulation of Research Results  
Both the transcripts and the survey results support our interpretation of the research results. Students repeatedly describe the GPT tutor as easy to approach and quick to help. Almost every transcript praises the tutor's instant answers, and roughly 80% of the transcripts praise the practical examples used by the tutor.  
The numbers support the same conclusion. On a five-point scale, students rated the tutor's clarity, response speed, and overall usefulness at M = 4.31 (\(\alpha\) = 0.84), well above  
Figure 1: Mean and standard deviation per survey question.  
the neutral midpoint (t = 20.67, \(p\) < 0.001, d \(\approx\) 2.9). Those who scored the tutor higher also reported larger gains in critical thinking, self-management, and problem-solving (r = 0.571, \(p\) < 0.001).  
The qualitative results show that all analyzed transcripts contain evidence of guided questioning (Teaching Presence). By contrast, 85% of responding students apply key concepts in their own words (Cognitive Presence), and the encouragement of students to continue with their learning (Social Presence) appeared in 75% of sessions. Although the survey did not analyze those constructs in detail, the quantitative data, especially a high score for the perceived pedagogical quality of the GPT tutor, acknowledge them implicitly. A linear regression further shows their importance: perceived tutor quality explained 32.6% of the variance in StudCmp (R\({}^{2}\) = 0.326, \(\beta\) = 0.583, \(p\) < 0.001). Therefore, the tutor's perceived pedagogical quality and the level of student support are both shown in transcripts and reflected by over one-third of students' self-reported improvement in competences.  
Taken together, the two strands of evidence tell a coherent story about why the GPT tutor works. The transcripts show the qualitative themes, describing the student engagement with the AI tool, while the survey results confirm that these processes drive the gains in learning outcomes.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 3 Results and Discussion
### UWB Positioning Accuracy Test  
#### 3.2.1 The Ranging Test  
The ranging experiment tested the error of ranging between a label node and a base station node. The test distance range of this test was 200-300 m. After the base station and the label were placed, the real distance was measured and recorded using a laser ruler, and then 20 measured values were obtained using UWB ranging. The average value of one measured value was calculated, and the distance between the base station and the label was changed after a test. A total of 10 groups of tests were completed, and the results are shown in Table 3.  
Figure 4: Comparison of error curves between the Chan algorithm and Taylor algorithm in four base stations.  
Figure 5: Test site of the rail-mounted robot.  
Figure 6: Interior layout of the control box.  
Figure 7: Application scenario of a rail-mounted logistics robot.  
As calculated from Table 3, the average ranging error is \(\pm\)0.11 m. Therefore, the UWB positioning system in this paper can measure stable distance information and provide a reliable data source for the robot's accurate positioning.
## 2 Algorithm
### Refinement of the DPL Convergent Coordinates by AFM  
When the initial coordinate bias (ICB) reaches the dm-level, the LAMBDA fails to obtain the correct ambiguity resolution (cannot pass the ratio validation) [8]. Consequently, the AFM is applied in this paper, and then the search grid is established based on the convergence epoch of DPL. When using AFM, the size of the search space is generally determined by the coordinate precision of the rover station [8] (DPL is around the decimeter level). The precision of the coordinates after searching can be considered to be dependent on the step length. The precision is relatively high when the step length is small. That is, the coordinates of the AFV [26] can reach the precision level of the step length. For example, if the step length is 2 cm, the initial coordinate precision can reach the centimeter level. The definition of AFM corresponding to the single-frequency carrier phase observations is given as follows:  
\[AFV(X_{c},Y_{c},Z_{c})=\frac{1}{n}\sum_{i=1}^{n}\cos 2\pi(\phi_{real}[E_{base} |E_{rover}]-\phi_{cal}[E_{base}|X_{c},Y_{c},Z_{c}]) \tag{9}\]  
where \(E_{base}\) is the base station. \(\phi_{real}[E_{base}|E_{rover}]\) represents the DD observations of the base and rover stations. (\(X_{c}\), \(Y_{c}\), \(Z_{c}\)) represents the coordinate candidate value in the search scope of the rover station. \(\phi_{cal}[E_{base}|X_{c},Y_{c},Z_{c}]\) represents the DD observations calculated from the candidate value of the rover station. Due to the integer nature of the ambiguity, it remains an integer after DD. If the coordinates of the rover station are found correctly, the cosine value of the difference should be 1. The noise error is always the key factor to the success rate of AFM [27]. Considering the effect of noise and multipath, the highest AFV is close to 1. Afterwards, the coordinate corresponding to the highest AFV is taken as the optimal solution of the initial value for the rover station.  
However, there are two important defects in the traditional AFM method: efficiency and reliability [28]. The efficiency of AFM depends on the search space and the step length. The size of the search space is determined by the convergence precision of DPL in our method, and the step length will be explored in the experiments section. In addition, there is a multi-peak problem in AFM because the value is calculated by a cosine function. The reasonable values of the step length and the search space can also effectively reduce the wrong peaks. Additionally, the unreasonable expansion of the search space will inevitably lead to the emergence of the wrong peaks, which will also be described in the post. The decimeter level precision of the DPL is also a guarantee for the reliability of AFM. In order to further enhance the reliability of AFM, some strategies can also be added, such as a linear constraint which will be introduced later.
## 3 Materials and Methods  
### Research Data Collection and Refinement  
#### 3.1.1 Collecting Research Data  
The data for this study were obtained from Elsevier's SCOPUS database, one of the largest and most comprehensive scientific citation indexing sources. SCOPUS was selected due to its robust bibliometric tools, broad coverage of AI-related research, and structured metadata that facilitate scientometric analysis. While Web of Science was considered, significant overlap with SCOPUS led to its omission to avoid redundant data processing. SCOPUS offers rich metadata, including abstracts and citations, from a vast collection of scientific publications. The database encompasses over 76 million records from more than 39,100 journals, 120,000 conferences, and 206,000 books [33]. Given this extensive coverage, SCOPUS serves as an ideal resource for analyzing the rapidly evolving field of GAI and its security implications. To ensure a comprehensive review of the existing literature, a targeted search query was formulated. The query included key terms such as \"generative AI\", \"ChatGPT\", \"GPT-3\", \"large language model\", and \"LLM\", combined with security-related terms such as \"security\", \"cybersecurity\", \"privacy\", \"threat detection\", and \"adversarial attack\". This query was applied to the SCOPUS database, covering the period from June 2022 to June 2024--an essential timeframe marked by significant advancements and discussions surrounding ChatGPT and related GAI technologies. The search period was set from June 2022 to June 2024, capturing the most recent peer-reviewed research indexed in SCOPUS. While extending the timeframe to December 2024 would provide additional insights, many academic articles from the latter half of 2024 had not yet been indexed at the time of data collection. Given the typical publication and indexing delays in SCOPUS, a later cutoff could introduce incomplete data and inconsistencies in the analysis. Thus, to ensure methodological consistency and stability in the dataset, we maintained the cutoff at June 2024. Future studies may revisit this analysis once more 2024 publications become available.  
The initial search yielded 1241 articles, which were then refined through a systematic literature review process. Articles lacking abstracts or not directly related to generative AI and its security implications were excluded. After refinement, a total of 1047 articles remained, forming the foundation for the subsequent text mining and scientometric analyses. These articles provided the empirical basis for uncovering key insights and trends regarding the security challenges and opportunities posed by ChatGPT and other generative AI technologies across various industrial domains.
## 3 Results and Discussion  
### Analysis of Crystal Pattern of the Photocatalysts  
The phase structure can be analyzed using XRD, which is a non-destructive technique. The XRD pattern of the processed ZnO is displayed in Figure 1. Wurtzite ZnO (red line) has diffraction peaks at 31.78\({}^{\circ}\), 34.32\({}^{\circ}\), 36.23\({}^{\circ}\), 47.48\({}^{\circ}\), 56.46\({}^{\circ}\), 62.69\({}^{\circ}\), 66.32\({}^{\circ}\), 67.88\({}^{\circ}\), 69.09\({}^{\circ}\), 72.55\({}^{\circ}\) and 76.87\({}^{\circ}\) corresponding to the (100), (002), (101), (101), (110), (103), (220), (112), (201), (004) and (202) planes (JCPDS card: 36-1451) [26]. The monoclinic body centered structure of Dy\({}_{2}\)WO\({}_{6}\) is confirmed by JCPDS card: 26-0595 [42; 43], which accounts for the20 values of 22.82\({}^{\circ}\), 23.51\({}^{\circ}\), 24.20\({}^{\circ}\), 28.18\({}^{\circ}\), 30.26\({}^{\circ}\), 38.04\({}^{\circ}\), 41.02\({}^{\circ}\), 53.60\({}^{\circ}\), 64.67\({}^{\circ}\) and 74.87\({}^{\circ}\) and the corresponding planes (011), (211), (130), (321), (231), (320), (132), (741), (441) and (622), respectively. It can be seen in DWZ (pink line) and DWZG patterns (dark yellow line) that loading of Dy\({}_{2}\)WO\({}_{6}\), the 20 angles of the (100), (002) and (101) planes for pure ZnO were displaced to the lower diffraction values (31.51\({}^{\circ}\), 34.15\({}^{\circ}\), 35.98\({}^{\circ}\)). Since there was such a small amount of GO mass loading, the GO peak had a relatively weak intensity [44]. After Dy\({}_{2}\)WO\({}_{6}\) and GO loading, the crystalline structure of the ZnO-containing DWZG remained unmodified, demonstrating that no changes were observed in the final composites.