esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing Artificial Intelligence for Wildlife Conservation
## 5 Future Directions
### Technological Advances  
A key area of focus is extending and enhancing the accuracy and robustness of AI models. This will be accomplished by incorporating new data and emerging deep learning models, particularly those with multimodal capabilities, such as vision transformers [30]. Vision transformers will enable more sophisticated and nuanced analysis by integrating diverse data types, improving the platform's ability to detect and classify wildlife and poaching activities. Although audio modelling is already part of the Conservation AI model ecosystem, fully integrating it with the visual components of the platform remains an ongoing goal. This integration will further strengthen the platform's capabilities in providing comprehensive conservation solutions [24].  
Another promising direction for Conservation AI is the development of edge AI solutions. By deploying AI models directly on camera traps and drones, Conservation AI can process data locally, reducing the reliance on constant internet connectivity and enabling real-time decision-making even in remote areas. This approach also conserves bandwidth and energy, making the system more sustainable and efficient.
Conservation AI is committed to expanding its platform to support a broader range of devices and regions. This expansion includes integrating with various types of camera traps, drones, and other data collection tools. Current efforts are focused on abstracting the sensor hardware to create a single interoperable solution. A key feature common to camera traps and other types of edge devices is the presence of an SD card slot for data storage. We intend to leverage this feature by using wireless Wi-Fi SD cards capable of transmitting data to a base station, which can then relay it to our servers via wide area communications (4/5G where available or STARLINK Mini where connectivity is limited) [17]. By extending geographical coverage, Conservation AI aims to support conservation efforts across diverse ecosystems, from tropical rainforests to arid deserts.  
The platform also plans to collaborate more extensively with conservation organisations, research institutions, and governments. As our user base continues to expand, we are committed to promoting widespread adoption in tandem with the platform's growth. These partnerships will be crucial in scaling up the deployment of Conservation AI and ensuring that the technology is customised to meet the specific needs of diverse conservation projects. Furthermore, expanding the user base will contribute to the collection of more data, which in turn will enhance the accuracy and effectiveness of the AI models through ongoing training activities.
## 4 Conclusions  
Volatile compounds are essential components of grape and wine and contribute significantly to the quality and consumer acceptance of the product. Studies have shown the ability of elicitors to enhance grape aroma quality as well as its anti-pathogenic benefits on grapes. Chitosan is involved in many antibacterial and antifungal activities and may positively or negatively affect a grape's volatile compounds and amino acid concentrations depending on its extraction and modification methods. Methyl Jasmonate (MeJ) has the potential to increase the concentrations of volatile compounds, especially terpen and C13-norisoprenoids, with different concentrations among grape varieties. Conversely, benzothiadiazole (BTH) is very efficient in controlling grapevine diseases and improving grape phenolic compounds but has little influence on the concentration of most volatile compounds. Nevertheless, the combined impact of MeJ and BTH enhances grape quality better than BTH only.  
Figure 4: Summarized biosynthesis pathways of major aromas in grapes. Aroma precursors are highlighted in green, common pathways are italicized in red, enzymes are italicized in blue, intermediate substrates and compounds are highlighted in yellow, and volatile compounds are highlighted in gray. **LOX**, lipoxygenase; **HPL**, fatty acid hydroperoxide lyase; **ADH**, alcohol dehydrogenase; **AAT**, alcohol acyltransferase; **PDC**, pyruvate decarboxylase; **CCD**, carotenoid cleavage dioxygenase; **MTS**, monoterpene synthase; **TPS**, terpene synthase; **AT**, aminotransferase; **Acetyl CoA**, acetyl coenzyme A.  
Regardless of the elicitor type applied, its effectiveness is greatly influenced by grape genetics, seasonal variation, and application time. Most studies used elicitors at veraison, which yielded significant increases in grape qualities compared to the controls. However, few studies applied elicitors at the ripening stage and observed better improvement in grape qualities. Therefore, studies on the optimal application time of elicitors to different grape varieties are encouraged. Further studies could also focus on the modifications in grape genes related to the synthesis of volatile compounds after elicitors application.  
Conceptualization, R.S. and Y.J.; investigation, R.S.; writing--original draft preparation, R.S.; writing--review and editing, R.S., Y.J., C.C. and F.E.S.; supervision, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 32060514.  
Not applicable.  
The authors declare no conflict of interest.
## 3 CSCL-WVE-UDI: Method for Identifying Unknown IoT Devices
### WVE-UDI:UDI Algorithm Based on WVE
#### 3.5.5 WVE-UDI Algorithm
Next, Algorithm 4, i.e., the EVDC-CSCL-Siamese algorithm, is applied to train the CSCL-Siamese network based on the positive-negative sample pairs \(Po\) and \(Ne\) as well as obtain the model \(M\), thereby achieving the identification of known IoT devices. Additionally, the algorithm is used to construct the embedded vector database \(ED\) for known IoT devices. Finally, Algorithm 5, i.e., the WVE-UDI algorithm, is employed to integrate three unsupervised clustering algorithms to identify unknown IoT devices. The pseudocode of the CSCL-WVE-UDI method is described in Algorithm 6. The inputs include raw traffic data \(X_{row}\) from known IoT devices and new raw traffic data \(X_{u}\) containing traffic data from unknown IoT devices. The output is the identification results of unknown IoT devices \(\hat{Y}_{u}\). ```
0:\(X_{row};X_{u}\)
0:\(\hat{Y}_{u}\)
1:for each \(x_{i}\) in \(X_{row}\)do
2:\(s_{i}\leftarrow\) call Data Preprocess \((x_{i})\) // Call Algorithm 1 for data preprocessing
3:\(S\gets s_{i}\) // Obtaining payload sample sets
4:endfor
5:\((Po;Ne)\leftarrow\) call PNSP \((S)\) // Call Algorithm 3 (Algorithm 2 is called in Algorithm 3) to generate fully aligned positive and negative pair samples
6:\(ED;M;\hat{Y}\leftarrow\) call EVDC-CSCL-Siamese\((Po;Ne)\) // Calling Algorithm 4
7: Set threshold \(\theta\) // Setting the similarity threshold
8:\(\hat{Y}_{u}\leftarrow\) call WVE-UDI\((X_{u};ED;M;\theta)\) // Calling Algorithm 5 for unknown IoT device identification
9: return \(\hat{Y}_{u}\)
```  
**Algorithm 6** CSCL-WVE-UDI approach  
The computational overhead of the CSCL-WVE-UDI method is primarily derived from Algorithms 1-5. The time complexities of Algorithms 1-5 are \(O\big{(}n^{2}\big{)}\), \(O\big{(}n\big{)}\), \(O\big{(}n^{4}\big{)}\), \(O\big{(}n^{3}\big{)}\), and \(O\big{(}n^{3}\big{)}\), respectively. Thus, the total time complexity of the CSCL-WVE-UDI method mainly results from here. The symbols used in this paper are shown in Table 1. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(x_{i}\) & Raw traffic sample of the \(i\)-th IoT device & \(s_{i}\) & Preprocessed payload data of the \(i\)-th device \\ \hline \(h_{j}\) & 512-bit hexadecimal hash value from improved Nilsimsa & \(g_{i}^{j}\) &
\begin{tabular}{l} Grayscale image fingerprint of the \(j\)-th sample from device \(i\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbol Definitions.
## 5 Simulation and Results
### Simulation
#### 5.3.3 Scenario 3: Peak Traffic with Congestion  
In Scenario 3, heavy traffic congestion occurs during rush hours, or there are major traffic delays. There is a high number of regular vehicles (RVs) at the intersection, resulting in bottlenecks. EVs may still require priority, but the main challenge is to manage increased traffic volumes without creating congestion or excessive delays. This scenario tests the system's ability to efficiently manage high-density traffic, reduce queue lengths, maintain fair resource allocation, and prioritize EVs when necessary.  
Based on Table 3, the hierarchical Stackelberg model maintains an average line length of 25 vehicles, 44% less than the 45 vehicles observed in the base model under high traffic conditions. Hierarchical priority also benefits emergency vehicles, reducing wait times from 200 s to 120 s. For regular vehicles, the hierarchical model has a 150 s waiting time, which is significantly better than the 250 s waiting time of the baseline model, reflecting a 40% improvement in queue management. Standard Stackelberg models perform fairly well but lack the efficiency of hierarchy models, as their average line lengths are 30 vehicles instead of 25. It is demonstrated that the Stackelberg Hierarchical Model provides a better balance between emergency vehicles and regular traffic management, reducing congestion and maintaining fairness during peak traffic hours.  
Based on the analysis, it is clear that the hierarchical Stackelberg model consistently outperforms other models in all scenarios and reduces emergency and regular vehicle travel times significantly. It prioritizes emergency vehicles efficiently, maintains fairness, avoids excessive delays in regular traffic, and manages congestion well, especially during peak hours. In comparison, [PERSON]'s standard model offers improvements over the base model but lacks the hierarchical structure required for optimal congestion management and priority setting. Baseline models consistently underperform, especially in emergency and high-volume situations, with longer travel times, ineffective congestion management, and long queues.
## 2 Data Description and Data Preprocessing
### Data Preprocessing
#### 2.2.2 Features for Classification  
Addressing the issue of anomaly classification caused by data quality requires feature extraction to capture the fundamental distinctions between normal data and data affected by data quality issues. Acceleration data, characterized by high sampling frequencies  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Patterns** & **Description of Data Features** & **Number (Ratio of Total (\%))** \\ \hline Normal & The amplitude of a normal acceleration sequence oscillates around the vicinity of zero, and the distribution of amplitudes exhibits symmetry. & 12,316 (64.50\%) \\ Missing & When data collected or transmitted by sensors contain missing values, & 1761 (9.22\%) \\  & they are directly filled with 9999. Therefore, direct quantification is & 3956 (20.72\%) \\  & feasible without the need for model recognition. & 363 (1.90\%) \\ Outlier & The acceleration time series exhibits significant fluctuations in amplitude. & 123 (0.64\%) \\ Noise & Due to the influence of noise, the acceleration is submerged within & 576 (3.02\%) \\  & high-amplitude noise, with no apparent fluctuations. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about labeled data samples.  
Figure 2: Distribution of labeled data samples.  
and substantial data volumes, present challenges in terms of human-intensive manual assessment for anomaly detection. In this research, a comprehensive analysis of vibration acceleration data from bridge structures reveals a distinctive characteristic of normal acceleration: its amplitude exhibits symmetry around zero and conforms to a normal distribution pattern. This pattern represents the essential disparity between normal and anomalous data.
# Polyacrylonitrile Nanofibers Containing Viroblock as Promising Material for Protective Clothing
## 2 Materials and Methods
### Characterization
#### 2.3.7 Antibacterial Activity  
To investigate the antibacterial properties of PAN nanofibers and VB/PAN nanofibers against _Escherichia coli_ (_E. coli_) and _Staphylococcus aureus_ (_S. aureus_) bacteria, the antibacterial activities were determined using the Kirby-Bauer method. The nanofiber samples of 10 mm diameter were placed for investigation on agar plates seeded with bacterial cells in the log phase and incubated at 37 \({}^{\circ}\)C for 16 h. All the samples were repeated three times using different specimens. The diameter of the inhibition zone was examined using Image J 1.50i software.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 3 Direct Problem  
The direct problem consists of a FE modal analysis of a 2D model in a state of plane stress, representing a damaged free-free beam of span \(L\) with a rectangular cross-section of base \(B\) and a height \(H^{U}\) (see Figure 1). The damage is of notch-type and has a small fixed extension \(b^{D}\), consisting of a localized reduction of the cross-section of the height \(H^{D}\). It is described by two nondimensional parameters, collected in the vector \(\bar{\mathbf{x}}=\{s^{D},h^{D}\}^{T}\), where the overbar indicates the actual damage parameters, which are the location of its middle axis \(s^{D}=S^{D}/L\), and the relative residual height \(h^{D}=H^{D}/H^{U}\). The reduction of bending stiffness is quantified by the parameter \(\beta=(1-EI^{D}/EI^{U})\), where \(E\) is the Young's modulus of the constitutive material, and \(I^{D}\) and \(I^{U}\) are the moments of inertia of the damaged and undamaged cross-sections, respectively. A sketch of the damaged beam is reported in Figure 1, which also lists the main mechanical and geometrical parameters.  
In slender beams, whose cross-section is assumed to remain a plane and rotate about the neutral axis, the longitudinal strains vary linearly about the cross-section. In this case, curvatures \(\chi\) are related to the strains on the top flange \(\varepsilon\) by the law \(\chi=2\varepsilon/H^{U}\). It should be noticed that the results presented could be extended to different shapes of cross-sections and even to laminated beams, considering \(\beta\) instead of \(h^{D}\) as damage parameter. Incidentally, we should note that the coefficient \(\beta\) is not affected by a change in \(E\). In cases when the neutral axis does not coincide with the middle of the beam, the conversion between \(\varepsilon\) and \(\chi\) will have to account for the appropriate distance from the center of mass to the top or bottom flange, depending on where the strain gauges are installed.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### Bit-Flip (X) Mixers
#### a.1.2 Max-\(\ell\)-Sat
###### Acknowledgements.  
**Problem:** The same as Max-\(\ell\)-SAT, except with minimization instead of maximization of the number of satisfied clauses.  
**Approximability:** APX-complete [59]. Unless \(P=NP\), no classical algorithm can do better than 1.3606 for MinSAT [60], or, for Min-\(\ell\)-SAT, better than \(15/14\simeq 1.0714\) for \(k=2\) or \(7/6\simeq 1.1667\) for arbitrary \(\ell\)[61]. On the other hand, constructive algorithms are known which achieve approximation ratios of 1.1037, 1.2136, and \(1-2^{1-\ell}\) for Min-2-SAT, Min-3-SAT, and Min-\(\ell\)-SAT, respectively [61; 62].  
**Mapping:** The same as Max-\(\ell\)-SAT.  
###### Acknowledgements.  
**Problem:** The same as in Max-\(\ell\)-SAT, except that in NAE-\(\ell\)-SAT, each clause is satisfied only if all \(\ell\geq 3\) variables do not all have the same value.  
**Approximability:** APX-complete [51]. A classical 1.38-approximation algorithm is known [63]. For \(\ell=3\) approximable to 1.138 but no better than 1.090 [50; 64].  
**Mapping:** Same as Max-\(\ell\)-SAT but with a slight modification of the phase operator; see, e.g., [5].  
**Variant:** Similar considerations apply to Max-1-in-\(\ell\)-Sat, in which a clause is satisfied when exactly one of its variables is true.  
###### Acknowledgements.  
**Problem:** Given a set \(\mathcal{S}\) and a collection of subsets \(\{S_{j}\}_{j=1}^{m}\), seek a partition \(\mathcal{S}=\mathcal{S}_{1}\cup(\mathcal{S}\setminus\mathcal{S}_{1})\) that maximizes the number of split sets, i.e., subsets \(S_{j}\) with elements in both \(\mathcal{S}_{1}\) and \(\mathcal{S}\setminus\mathcal{S}_{1}\).  
**Approximability:** APX-complete [65]. Can be approximated to 0.7499 [66]. Remains APX-complete if each \(S_{j}\) is restricted to having at most or exactly \(k\geq 2\) elements [67]. For each \(S_{j}\) having exactly \(k\geq 4\) elements, unless \(P=NP\), there is no efficient classical algorithm that does essentially better than a random partition [53; 68]. The generalization MaxHypergraphCut, in which each subset is given a weight and we seek to maximize the total weight of the split sets, can be approximated to 0.7499 [66].  
**Reduction to Not-All-Equal-\(\ell\)-SAT:** This problem is a special case of NAE-\(\ell\)-SAT, where none of the variables are negated.
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 2 Materials and Methods
### Data Processing  
In this work, we used the recordings of the short and the long experiments together. The database was first preprocessed in search of missing data and the subjects with more than 30% missing ECG measurements or without annotations were omitted. The ECG signal was then filtered with a band-pass Butterworth filter between 0.5 and 30 Hz. The self and external annotation scores for the valence dimension range from \(-1\) to 1 and were converted to categorical values (positive or negative) using 0 as the threshold. Similarly, the annotations scores for the arousal dimension range from 0 to 9 and were converted to categorical values (high or low), with a threshold of 5. After the conversion, [PERSON]'s exact test [31] was performed between self and external labels for short experiments, as there is no self-annotation for the long experiment. The test rejected the null hypothesis at the 5% significance level, so there is an association between the external and self-annotations. Based on this result, the higher time resolution of the external annotations, and the availability of labels for both experiments, the classification was performed using the external annotations as targets.  
Since the external annotations were made at 20-s time windows, we divided each ECG signal into segments of that length and assigned them to their respective label. Each class (positive/negative valence and high/low arousal) contains different amounts of data. For example, from the 8610 segments of the long experiment, 29% present positive valence and 71% negative valence, and 20% present high arousal and 80% low arousal. Given this imbalance, classifying all the segments as negative valence and low arousal would give 71% and 80% accuracy in valence and arousal, respectively [32]. There are different methods to avoid the undesirable effects of an imbalanced dataset, namely, data reduction, data augmentation, undersampling, oversampling, resampling, and cross-validation [33]. In this work, a reduction of the number of data from the overrepresented classes was performed; therefore, the number of elements of the smallest group is preserved and random samples are discarded from the classes with more elements until the same amount of data is achieved in all groups.
# Assessing the Functional Response to Streamside Fencing of Pastoral Waikato Streams, New Zealand
## 3 Results  
### Physical Habitat  
The differences in physical stream habitat were not as prominent as expected between fenced and unfenced sites. However, fenced sites had significantly wider vegetated riparian zones and higher levels of stream shading than their paired unfenced sites. Shade estimates were significantly higher for fenced (mean = 58.1%) than unfenced (mean = 25%) stream reaches (\(T(10)\) = 3.30, \(p\) \(<\) 0.05; Figure 2a; Table 2). Whatawhata and Waitomo fenced sites had the highest shading (95%; Figure 2) and the Kakahu fenced site the least shading within fenced sites (10%; Figure 2a). There was no difference in canopy height between the two treatments (\(T(10)\) = 1.34; \(p\) = 0.21) and the level of shading did not depend on time since fencing (Table 3).  
Daily dissolved oxygen (DO) saturation ranged from 76.9% (Matarawa unfenced) to 128.2% (Taupo fenced) and mean DO saturation was not significantly different between fenced and unfenced sites (Table 2). Likewise, maximum daily water temperature was not influenced by treatment nor time since fencing (Tables 2 and 3), and ranged from 10.6 \({}^{\circ}\)C (Taupo fenced) to 21.4 \({}^{\circ}\)C (Tapapakanga unfenced).
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 5 Discussion  
### Robustness and Accuracy from Random and Sequential Experiments  
The EOR method demonstrates strong robustness through both random and sequential experiments, combining information from multiple object types. Lane markings provide strong cues for lateral alignment, while poles enhance longitudinal alignment. The EOR method particularly enhances longitudinal precision and IoU success rates through its integration of multiple feature types.  
From a sequential perspective, EOR effectively handles challenging scenarios like intersections where lane markings may be absent. Frames leaving intersections showed well-aligned registration, suggesting effective use of features, even when lane markings were distant. Additionally, the EKF plays a vital role in minimizing uncertainties, ensuring smoothed trajectories, and reducing the impact of recognition errors across frames. These contributions collectively enhance robustness in complex environments. Additionally, EOR consistently outperformed baselines, demonstrating superior accuracy in all six dimensions.  
Occasional peaks in yaw error, observed in Sequence 2 when approaching intersections, highlight scenarios where the absence of lane markings forces reliance on poles and traffic signs. However, poles and traffic signs are relatively small and prone to imperfect recognition, which can introduce potential errors in the registration process. Despite these peaks, the sequence-average yaw error remained low, with the EOR method achieving an average error of 0.01 radians--comparable to VisionHD and better than MOR at 0.02 radians. This consistency in average error reinforces EOR's robustness and adaptability to diverse scenarios.