esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Classifier Selection  
To evaluate the efficacy of various feature extraction methods in capturing the characteristics of FRPs, we assessed the performance of each feature representation using six distinct classifiers: SVM, XGBoost, RF, NB, 1D-CNN, and BiLSTM. Figure 4 presents the ROC curves for each feature set, with the corresponding AUC values provided in the legend.  
Analysis of the ROC curves and AUC values (Figure 4) revealed that SVM and XGBoost consistently delivered the highest performance across the various single-feature representations. This suggests their superior capability in discerning the complex patterns associated with FRPs. Furthermore, the DDE and ESM2 features consistently yielded high AUC values across multiple classifiers, demonstrating their strong discriminative power for distinguishing between FRPs and non-FRPs.  
Based on these findings, we selected SVM and XGBoost for subsequent comparative analysis. Figure 5 provides a detailed performance comparison, based on classification accuracy, between the SVM and XGBoost classifiers across the different single-feature sets.  
As illustrated in Figure 5, the XGBoost classifier consistently outperformed SVM across most individual feature representations, achieving higher accuracy in 8 out of 10 cases. This superior performance indicates XGBoost's enhanced capacity to effectively capture complex patterns and interactions within the feature spaces derived from these methods. The gradient boosting framework of XGBoost constructs an ensemble of weak predictive models--typically decision trees--in a stage-wise fashion while optimizing a differentiable loss function. This process enhances its ability to model non-linear relationships and feature interactions, making it particularly well-suited for the high-dimensional and complex nature of FRP prediction. Moreover, XGBoost's built-in regularization techniques help prevent overfitting, further contributing to its robust performance. These findings validate our selection of XGBoost as the primary classifier for subsequent analyses and model development.  
Figure 4: ROC curves for various single-feature methods using six classifiers, with AUC values indicated in the legend. (**a**) AAC, (**b**) CKSAAP; (**c**) DDE; (**d**) GTPC; (**e**) AADP-PSSM; (**f**) S-FPSSM; (**g**) KSB-PSSM; (**h**) ESM-1b; (**i**) ESM2; (**j**) ProtBERT.  
Figure 5: Performance comparison between XGBoost and SVM.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 3 Method
### OOK Modulation  
The input digital signal was a 10 bit binary stream (1110010110) at a data rate of 500 bps. The ultrasonic transducer frequency was 40 kHz for OOK and BPSK modulation. The amplitude was set to 5 V. The baseband digital and carrier signals are shown in Figure 4.  
Figure 3: (**a**) Experimental setup; (**b**) Transducer for frequency shift keying (FSK) modulation; (**c**) Transducer for amplitude shift keying (ASK), and phase shift keying (PSK) modulation.  
The simulated, transmitted, and received OOK signals are shown in Figure 5. The carrier frequency was set to 40 kHz. The simulated OOK signal was theoretically calculated in MATLAB, as shown in Figure 5a. Figure 5b shows the oscilloscope trace of the transmitted OOK signals generated without connecting the ultrasonic transducer. Figure 5c shows that the amplitude of the received OOK signals is sharply reduced; however, the symbol sequences are still distinguishable. The transition from 1 to 0 or 0 to 1 has a delay, which is caused by the impulse response of the transmitting and receiving transducers. At high data rates, the waveform was highly distorted.
## 6 Simulations and Discussion
### Energy Consumption  
This set of experiments considers the energy consumption of cluster formation (Phase I) in the SAMCA scheme. Assume that the communication channel is error-free and the mean node mobility is \(v=1\) m/sec. Given appropriate transmission ranges for various network densities, Figure 16 shows the average number of transmissions and receptions of random networks for selecting clusterheads and gateways, respectively. As derived in Section 5., the number of receptions tends to increase as the ratio \(R_{\mathcal{M}}/\ell\) increases, which implies that energy consumption is higher for the network with larger transmission power. This can be attributed to the fact that larger transmission power allows sensors to detect more neighbors, which increases the number of receptions when assigning cluster ID, updating cluster ID information, or broadcasting gateway information.  
Figure 16: The energy consumption for cluster formation: the clusterhead selection and distributed gateway selection.  
Figure 15: The comparison of a typical network topology applying the proposed scheme (left) and that using the MOBIC [32] algorithm (right) over a time period of 200 seconds.  
Referring to the analysis in Section 5.1. and the experimental result, the proposed self-organization scheme \((\mathcal{O}(4),\mathcal{O}(n))\) for cluster formation is demonstrated to be comparable to Lowest-ID algorithm \((\mathcal{O}(1),\mathcal{O}(n))\)[21], the Max-Min heuristic \((\mathcal{O}(D),\mathcal{O}(n))\)[16], and the tree-partitioning technique for grouping sensors \((\mathcal{O}(S),\mathcal{O}(n))\)[51] in terms of the time complexity and the communication complexity, where \(n\) is the number of sensors in the network, \(D\) is the maximum number of hops that an ordinary node can be away from its clusterhead, and \(S\) is the predefined cluster size.
## 4 Assessment of the Developed Approach
### Assessment of Segmentations and Point Classification
#### 4.2.1 Segmentation into Sub-Spaces  
Table 2 presents the results of floor segmentation for the two datasets and the results of room segmentation for one floor of each building. For both datasets floors are correctly identified. Results of room segmentation are also very satisfying. Indeed, 3D regions describing rooms are distinct from each other. Considering all the rooms of the two buildings, only two of them were falsely fused. This fusion is due to the fact that one of the room was not complete in the initial point cloud and a part of the wall between the rooms was missing.  
The analysis of remaining point clouds of segmentations into sub-spaces is also instructive. For the individual house, all rooms were extracted, whereas for the office building, two rooms out of forty one were not extracted and are in the remaining point cloud. It deals with small rooms partially scanned and for which the minimal density of points required by the approach is not reached. One can also notice that small parts of three rooms are present in the remaining point cloud also because of a too small density of points in the input point clouds.  
Figure 13: Repartition of the processing time regarding the main steps of the developed approach.
## 3 Proposed Work
### Image Restoration Phase
This implies that 01 are the hidden data when the recovered block is \(V_{D}^{1}\), 10 are the hidden data when the recovered block is \(V_{D}^{2}\), and 11 are the hidden data when the recovered block is \(V_{D}^{3}\). Likewise, all the marked image blocks are processed to obtain the original image without compromising the original quality of the cover image and recovering the hidden secret information parallelly. Algorithm 2 returns the restored image \(F\) and the extracted message \(D\). An example of the proposed \(MPSA\) scheme is given in the next section of this manuscript for better comprehension. ```
Input: An image block \(B\) with \(H\) rows and \(H\) columns Output: Smoothness measure \(SM\) Let \(r=1\); \(s=1\); \(SM=0\); while\(((r\leq(H-1))\)do \(s\leq(H-1))\)do \(T_{11}=B_{r,s}\); \(T_{12}=B_{r,s+1}\); \(T_{21}=B_{r+1,s}\); \(T_{22}=B_{r+1,s+1}\); \(D_{1}\)= \(|T_{11}-T_{12}|\); \(D_{2}\)= \(|T_{21}-T_{22}|\); \(D_{3}\)= \(|T_{11}-T_{21}|\); \(D_{4}\)= \(|T_{12}-T_{22}|\); \(D_{5}\)= \(|T_{11}-T_{22}|\); \(D_{6}\)= \(|T_{12}-T_{21}|\); \(SM=SM+D_{1}+D_{2}+D_{3}+D_{4}+D_{5}+D_{6}\) \(s=s+1\); \(r=r+1\); Return the smoothness value \(SM\). ```  
**Algorithm 3**Computing multi-directional smoothness measure.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 4 Simulation Results
### Scenario 1:  
#### 4.1.1 Simulation Parameters  
In this subsection, we summarize the general parameters we used in our simulation experiments. In the first scenario, the simulation parameters were initialized as in [15]. All nodes are homogeneous in terms of initial energy and transmission range. The power consumed by nodes is 2 W, 0.1 W and 0.01 mW in transmitting, receiving and idling, respectively; they are similar to those on a commercial acoustic modem, LinkQuest UWM1000 [34]. For DBR, we used a depth threshold of zero, as in [20]. Other simulation parameters are summarized in Table 2 below. In this scenario, the void avoidance protocol EEDOR-VA is compared with EEDOR proposed in our previous work [15], where EEDOR was shown to be superior to various other algorithms. We also consider the well-known DBR in our comparison results because it is the first depth-based protocol. Statistical and comparison results between the three protocols presented in this section were obtained using 100 runs of our simulation.
# Climate Perspectives in the Intra-Americas Seas
## 3 Regional Climate Features
### Low-Level Circulation  
The IAS circulation is dominated by the descending branch of the Hadley cell, and the low-level flow is controlled by trade winds. In the regional context, low-level jets (LLJs) are fundamental mechanisms to modulate the regional climate and inter-hemispheric interactions. Globally, LLJs are known to be connecting to background circulations, modulating water vapour supply during extreme events, and inducing variations in the cyclone development potential through wind shear control. LLJs may be classified based on wind speed and height as boundary layer (height < 850 hPa) and free atmosphere (600 < height < 850). The free atmosphere LLJs influence synoptic systems and boundary layer LLJs modulate the synoptic and high-low-pressure transitioning systems [94]. In the region, the main low-level structures are the CLLJ and the Choco jet, whereas the development of smaller-scale features results from interactions between the easterly flow and regional topography. This wind-topography interaction allows the seasonal development of gap winds over Tehuantepec, Papagayo, and Panama (as observed from the blue shaded regions on the Pacific coast shown for January and February in Figure 2). These gap winds are important to enhance offshore upwelling in the easternmost tropical Pacific and have a relevant biological role. Here, we focus on the CLLJ and the Choco jet as the two most prominent features of low-level circulation in the region.
## 2 Materials and Methods
### Lipid Oxidation Analysis
#### 2.8.3 Hexanal Content  
Hexanal concentration was determined according to previously described method [22] with minor changes [13]. For isolation of volatiles CAR/PDMS/DVB SPME (solid phase microextraction) fiber has been used. Sample preparation required placement of 5 g of chicken meet sample in 20 mL headspace vials with addition of 5 mL of saturated NaCl solution. For quantitation reasons [2H12]-hexanal (Sigma-Aldrich; Poznan, Poland) as internal standard has been added in the amount to reach 1 mg/kg concentration. Extraction of volatiles was performed automatically using CTC combipal autosampler (Agilent Technologies) at 45 \({}^{\circ}\)C during 30 min. Separation and identification of volatiles was performed using gas chromatography and mass spectrometry (GC/MS) by Agilent Technologies 7890A GC coupled to a 5975C MSD with a Supelcowax-10 column (30 m \(\times\) 0.25 mm \(\times\) 0.5 \(\upmu\)m). Operating conditions for GC/MS were as follows: Helium as a carrier gas with 32.2 cm/s flow; oven temperature program were as follows: 40 \({}^{\circ}\)C (1 min), 9 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C (3 min). Mass spectra were collected in a scan range of m/z 33-350 with ions sourced at 220 \({}^{\circ}\)C and the transfer line heated up to 260 \({}^{\circ}\)C. Desorption of volatiles was with splitless injection at 260 \({}^{\circ}\)C. The identification of hexanal was performed by acomparison of mass spectra and retention indices (RI) with the 05 NIST library and respective standard (Sigma-Aldrich; Poznan, Poland). The concentration in the sample was calculated from the ratio of the peak area of the hexanal and its corresponding internal labeled standard [2H12]-hexanal obtained for selected ions: 56 and 64 respectively and corrected with the response factor (Rf = 1.2). The Rf was calculated in the standard mixture of labeled and unlabeled compound in known concentration of 1 mg/kg.
# Intelligent Space Object Detection Driven by Data from Space Objects
## 3 Space Object Detection Algorithm Model  
### Introduction to YOLOv7  
As one of the most advanced single-stage object detection algorithms, the YOLO series of algorithms is applied to perform various computer vision tasks. YOLOv7, a recent iteration, introduces two new efficient network architectures, namely ELAN (Efficient layer aggregation networks) and E-ELAN (Extended efficient layer aggregation networks), with model reparameterization incorporated into the network architecture. Given the same model size, YOLOv7 outperforms YOLOv3, YOLOv5, and YOLOX in terms of accuracy and speed. The architecture diagram of YOLOv7's feature extraction network [36] shown in Figure 8b.  
Figure 7: Partial dataset labeling results.  
In this paper, YOLOv7-R is built upon YOLOv7, with a convolution module introduced into ELAN to obtain the ELAN-R module. As a result, the accuracy is improved significantly. Combining CSP (Ross Stage Partial) and SPP (Spatial Pyramid Pooling) structures, the spatial pyramid pooling with the context-sensitive context (SPPCSPC) module is retained, as shown in Figure 8d. Through the CSP structure, the input features are divided into two branches, of which one is for feature extraction through convolution layers and the other is for SPP processing. This structure reduces computational workload while accelerating processing. In the SPP structure, four different max-pooling operations are conducted to generate various receptive fields, making the algorithm adaptable to different image resolutions. In the final detection layer, the network is used to predict the objects of different sizes on the corresponding feature maps and non-maximum suppression is performed to remove the highly redundant prediction boxes, which leads to the final prediction results.
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 5 Simulation Results  
### Simulation Initialization  
In this simulation, wireless sensors are uniformly distributed in a \(200\times 200\) square area. Suppose that the square area is in the first quadrant of the plane coordinates. We suppose that the test event area is a circle with a radius of 50 and the centre of the region is located at (100, 100). Assume that normal node readings satisfy the normal distribution \(N(\mu_{1},\sigma^{2})\) while sensor readings are depicted form \(N(\mu_{2},\sigma^{2})\) in event area. Then the threshold \(\mu_{th}=\frac{\mu_{1}+\mu_{2}}{2}\). Mean and variance can be selected according to the actual situation, as long as the standard deviation is small compared with \(\left|\mu_{1}-\mu_{2}\right|\). In this simulation, we set \(\mu_{1}\)= 10, \(\mu_{2}\)= 30, \(\sigma\)= 1.  
The communication radius is \(R=10m\). The boundary width \(r=R/2\). The area \(\delta\) of a grid is computed by dividing the area of a circle disk \(N(S_{i})\), by the network node density. The degree of fitting (\(DF\)) is applied for estimation of accuracy as a performance measure.  
\[DF=\frac{\left|A(<r)\cap A(d)\right|}{\left|A(<r)\right|} \tag{15}\]  
where \(A(<r)\) is the node group signifying that the distance to the boundary is no more than \(r\), while \(A(d)\) is the set of boundary nodes which are detected by proposed method.  
False detection rate (\(FR\)) is used to measure detection error.  
\[FR=\frac{\left|A(>r)\cap A(d)\right|}{\left|A(>r)\right|} \tag{16}\]  
where \(A(>r)\) is the set of nodes that the distance to the boundary is beyond \(r\). The higher \(DF\) and the lower \(FR\) mean the better performance an algorithm does.
# Removal of Nitrate Ions Using Thermally and Chemically Modified Bioadsorbents
## 4 Conclusions  
The present investigation concluded that: (i) evaluated biosorbents are potentially good for use in NO\({}_{3}\) adsorption, obtaining the following performance in terms of adsorption capacity: CYP \(>\) CC \(>\) MYP. (ii) The SEM-EDS analysis showed that the adsorbents have a heterogeneous surface with pores, and the adsorption process is given by the interaction between the anion and cationic adsorption sites in the structure of the bioadsorbents. (iii) It was found that the pH has the most significant effect on the adsorption capacity of NO\({}_{3}\) when using MYP, obtaining as best conditions pH 12 and 0.5 mm of particle size, achieving an adsorption capacity of 25.75 mg/g; when using CYP and CC, the best adsorption capacity was observed at pH 2 with values at 36 and 33.34 mg/g, respectively. (iv) The isotherm of NO\({}_{3}\) adsorption on MYP and CYP was adjusted by [PERSON]'s model, which indicates that the process occurred in multilayers through the pores of the adsorbent, while the equilibrium over CC adsorption was adjusted to [PERSON]'s model, indicating that the process occurred by means of the formation of a monolayer on the surface of the adsorbent. (v) The adsorption kinetics of NO\({}_{3}\) was fast in the initial minutes, achieving equilibrium in the first 30 min for all the evaluated adsorbents, with good adjustment to the pseudo-first-order, pseudo-second-order and Elovich models with an R\({}^{2}\) \(>\) 0.95, suggesting that the adsorption mechanism is chemisorption by means of the exchange of species between the aqueous phase and the surface of the material, with the intervention of an intraparticle diffusion stage.  
[PERSON], [PERSON] and [PERSON] conceived and designed the paper, and wrote the Introduction and Materials and Methods. [PERSON] and [PERSON] wrote the results and prepared figures and tables. The discussions and Conclusions were the collective work of all authors. The writing-review & editing was performed by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that supports the findings of this study are available in the open literature.  
The authors express their gratitude to Universidad de Cartagena for providing equipment and reagents to conduct this research.  
The authors declare no conflict of interest.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 7 Fungicides and Plant Growth Stimulants Based on Fungi of the Genus _Trichoderma_  
### Market of Available Fungicides and Plant Growth Stimulants Based on Fungi of the Genus Trichoderma  
Biopesticides, that is, preparations based on microorganisms or plant extracts used to control undesirable organisms, account for only 1.3% of all plant protection products produced. Current reports indicate that the global market for bioproducts is worth around EUR 1.6 billion, while the European market is worth around EUR 550 million [209]. According to the International Association of Producers of Biocontrols, global biopesticide production will increase by 14.7% by 2050, of which biofungicide production will increase by 16.1% [210].  
According to [PERSON] et al. [211], 41% of the bioproducts available on the market are made from microorganisms, of which 74% are bacteria and 10% are fungi. Due to their antagonistic properties, _Trichoderma_ fungi are currently the most common and widely used microorganisms in biological plant protection. It is estimated that about 50-60% of commercial biofungicides are produced from these microorganisms. Analyses carried out by [PERSON] et al. [32] show that currently there are about 77 biopesticides on the world market, produced from single or several _Trichoderma_ strains, which eliminate more than 100 different fungal plant pathogens [212].  
Recent data from the United States Environmental Protection Agency (EPA) [213] show that biofungicides based on strains of _T. aspecellum_, _T. atrovidride_, _T. gamsii_, _T. hamatum_, _T. harzianum_, _T. polysporum_, _T. virens_, and _T. viride_ are produced in the USA. Bioproducts available in Brazil include _T. afroharzianum_, _T. aspecellum_, and _T. harzianum_[32]. In Australia, authorized biopesticides are produced from _T. atrovidride_ and _T. harzianum_[214, 215], and in India, they are produced from _T. harzianum_ and _T. viride_[216]. Current data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China (MARA) [217] show that China produces biofungicides based on _T. harzianum_ and a group of fungi generally known as _Trichoderma_ sp. According to the recent regulations of the European Commission, bioproducts based on _T. afroharzianum_, _T. aspecellum_, _T. atroviruleum_, _T. gamsii_, and _T. harzianum_ are currently used in the European Union (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} _Fusarium_ spp. \\ _Pythium_ spp. \\ _Rhizoctonia_ spp. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 5: Examples of _Trichoderma_ sp. biofungicides authorized for use in the European Union [218-220].