esa-sceva/satcom-chunk-collection huggingface dataset:
## Results
### Functional Annotation and Classification of Unigenes  
The putative functions of the assembled unigenes were annotated by blast searching against several public protein databases, of which 51.08% (46,804 unigenes) of the unigenes were successfully annotated against the Nr database (Table 2). The species distribution analysis revealed that the _E. rituximab_ unigenes showed the highest homology to genes from the Euphoriaceae plant family, with 44.8% of the unigenes related to _Jatropha curcas_, and 30.1% of the unigenes ascribed to _Ricinus communis_ (Figure S4).  
Based on the KOG (EuKaryotic Orthologous Groups) database, 14.50% (13,292 unigenes) of the unigenes were categorized into 25 different functional groups (Figure 2A). Among them, the cluster of \"General function prediction only\" represented the largest group (1864 unigenes, 14.02% of KOG hits), followed by the group of \"Post-translational modification, protein turnover, chaperones\" (1653 unigenes, 12.44%). With respect to the attention of this study, we found that there were 253 unigenes associated with the group of \"Secondary metabolite biosynthesis, transport and catabolism\". To further elucidate the function of these _E. rituximab_ unigenes, GO (Gene Ontology) analysis was also performed by using the Blast2 GO program [30]. A total of 38,652 unigenes (42.18% of the total unigenes) were successfully assigned to GO functional classification and grouped into three main categories (\"Biological process\", \"Cellular component\", and \"Molecular function\"). Among them, \"Biological process\", \"Cellular process\" (22,862 unigenes, 59.14%) and \"Metabolic process\" (21,734 unigenes, 56.22%) represented the two most abundant GO terms. In reference to the KEGG database, 19.04% (17,447 unigenes) of the unigenes were mapped into 130 pathways based on five types of KEGG classifications: \"Cellular processes\", \"Environmental information processing\", \"Genetic information processing\", \"Metabolism\", and \"Organismal systems\" (Figure S5). Most of them were located in the pathway of \"Carbohydrate metabolism\" (1408 unigenes, 8.07% of KEGG hits), followed by \"Translation\" (1360 unigenes, 7.80%) (Table S1). In addition, there were 447 unigenes classified in the group of \"Metabolism of terpenoids and polykeptides\". Among them, 106 unigenes were annotated to play roles in \"Terpenoid backbone biosynthesis\" (ko00900) and the other 90 unigenes were involved in \"Sesquiterpenoid and triterpenoid biosynthesis\" (ko00909).
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
## 2 Materials and Methods  
### Materials and Sample Preparation  
Angiotensin II (Ang \(\geq\) 95%), ubiquitin from bovine erythrocytes (Ubq \(\geq\) 98%), myoglobin from equine heart (Myo \(\geq\) 90%), and carbonic anhydrase isozyme II from bovine erythrocytes (CAII \(\geq\) 3000 W-A units/mg protein) were purchased from Sigma Aldrich (St. Louis, MO, USA). Cytochrome C from equine heart (Cyt C \(\geq\) 90%) was obtained from Alfa Aesar (Ward Hill, MA, USA). Methanol (\(\geq\)99.9%) was obtained from Honeywell Inc. (Charlotte, NC, USA). Acetic acid and chloroform were purchased from Merck Pty Ltd. Deionized water (18 M\(\Omega\)) was obtained using a water purification system (MilliQ, Merck, Darmstadt, Germany). Stock solutions of Ang, Ubq, Cyt C, Myo, and CAII were prepared in 100% deionized water at a concentration of 200 to 500 \(\upmu\)M. The stock solutions of Ang, Ubq, Cyt C, Myo, and CAII were diluted into 47.5:47.5:5 methanol:water:acetic acid to prepare solutions for ESI-MS at a concentration of 1 to 5 \(\upmu\)M. A solution mixture containing 20 \(\upmu\)M of each of the four proteins in 47.5:47.5:5 methanol:water:acetic acid was also prepared.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 3 Direct Mobile Coaching
### Backend Server  
The backend server is responsible for data persistence and feedback generation. This means that all the sensor data sent by mobile clients are parsed and stored in a database. While conceptually, no limit on the type of used database is imposed, a relational database offers the benefit of being a well-established principle, which can thus be used by many practitioners. Having such a data persistence makes recorded activities reproducible. Additionally, collecting all the data on the server provides the opportunity to model and implement inter-client communication during or after activities without the need of an ad-hoc network between the individual client devices.  
An additional function of the backend server is to provide the means for concurrent feedback by using AI modules and terminal feedback using post-processing modules. Each of these modules can be configurable for certain types of activities.
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 2 Theoretical Background and the Related Work
### Basic Concepts of Fuzzy Sets  
Let \(X\) be a universal set. A fuzzy set is formed by a function \(\mu_{\widetilde{A}}(x)\), that corresponds each element \(x\) in \(X\) to a real number in the interval \([0,1]\). The function \(\mu_{\widetilde{A}}(x)\) is designated as a membership function in the fuzzy set \(\widetilde{A}\). An \(\alpha\)-cut of a fuzzy set \(\widetilde{A}\) is a crisp set \(A_{\alpha}\) that covers all the points of \(X\) that have a membership grade in \(\widetilde{A}\) greater than or equal to the prefixed value \(\alpha\). It is defined as \(A_{\alpha}=\left\{x\in X\colon\mu_{\widetilde{A}}(x)\geq\alpha\right\}\), where \(\mu_{\widetilde{A}}(x)\) is the membership function of \(\widetilde{A}\), \(\alpha\in[0,1]\). A fuzzy set \(\widetilde{A}\) is normal if there exists at least oneelement \(x\in X\) such that \(\mu_{\widetilde{A}}(x)=1\). \(\widetilde{A}\) is convex if every \(\alpha\)-cut of \(\widetilde{A}\) is a convex set. A fuzzy set is called fuzzy number when it is convex and normal.  
A fuzzy number \(\widetilde{A}=(a,b,c)\), where \(a\leq b\leq c\) is called a Triangular Fuzzy Number (TFN), and its membership function \(\mu_{\widetilde{A}}(x):X\rightarrow[0,1]\) is as follows:  
\[\mu_{\widetilde{A}}(x)=\left\{\begin{array}{ll}\frac{x-a}{b-a}&\text{if }a \leq x\leq b\\ 1&\text{if }x=b\\ \frac{c-x}{c-b}&\text{if }b\leq x\leq c\end{array}\right.\]  
Let \(\widetilde{A}=(a,b,c)\) be a triangular fuzzy number, then \(\alpha\)-level set of \(\widetilde{A}\) is \(A_{\alpha}=\left\{x\in X:\mu_{\widetilde{A}}(x)\geq\alpha\right\}=[A_{\alpha} ^{-},A_{\alpha}^{+}]\) where \(A_{\alpha}^{-}=a+(b-a)\alpha\) and \(A_{\alpha}^{+}=c-(c-b)\alpha\), \(\alpha\in[0,1]\). Now, we can represent \(\widetilde{A}\) as \(\widetilde{A}=\underset{\alpha\in[0,1]}{\cup}A_{\alpha}\). Here, we can derive the signed distance [47] from \([A_{\alpha}^{-},A_{\alpha}^{+}]\) to \(\widetilde{0}\) as \(D(A_{\alpha},\widetilde{0})=\frac{1}{2}(A_{\alpha}^{-}+A_{\alpha}^{+})\). If \(\widetilde{A}=(a,b,c)\) is the TFN, then we have \(D(\widetilde{A},\widetilde{0})=\frac{1}{2}\underset{0}{\overset{1}{\underset{ 0}{\int}}}(A_{\alpha}^{-}+A_{\alpha}^{+})d\alpha=0.25(a+2b+c)\).
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 6 Conclusions  
This study developed a novel multimodal co-simulation system that enables real-time interaction between drivers, cyclists, and pedestrians in a shared 3D virtual environment. A key innovation is the integration of CARLA with PTV Vissim via real-time data exchange and physiological sensors using Lab Streaming Layer (LSL), allowing synchronized analysis of behavioral and physiological responses under dynamic traffic conditions.  
Three case studies were conducted to demonstrate the capabilities of the system. In the first, bicyclists shifted their lateral position significantly (0.94 m to 1.14 m, \(p<0.00001\)) and showed an increased heart rate (69.45 to 72.75 ppm, \(p<0.00001\)) during overtaking events. In the second, pedestrians exposed to vehicles traveling at 50 mph versus 30 mph accepted longer gaps (3.70 s vs. 3.18 s, \(p<0.00001\)) and exhibited stronger heart rate responses (3.54 bpm vs. 1.91 ppm, \(p<0.00001\)), indicating elevated stress. The third case study demonstrated that the system can realistically reproduce real-world traffic conditions, with strong alignment observed between simulated and real-world mean speeds.  
By presenting the technical details and experimental applications of this multimodal simulation system, this study provides a foundational resource for researchers interested in human-centered traffic simulation. The system's flexibility allows for a wide range of applications, including studying the impact of automated vehicles, evaluating the effectiveness of infrastructure changes, and understanding real-time decision-making processes of different road users. The developed simulation framework offers a scalable and adaptable platform for transportation safety research. By enabling real human interactions in a synchronized virtual environment, the system bridges the gap between traditional driving simulation studies and real-world traffic dynamics, supporting the development of safer and more efficient mobility systems. Future research can expand on this work by exploring additional variables such as environmental conditions, driver intentions, and interactions involving multiple agents. Interdisciplinary collaborations involving medical and psychological experts could also enhance the interpretation of physiological data, enabling deeper insights into stress, cognition, and human behavior in complex traffic scenarios.  
Conceptualization, I.G.E. and A.S.; methodology, I.G.E., A.S. and S.G.; software, I.G.E.; validation, I.G.E.; formal analysis, I.G.E.; investigation, I.G.E. and S.G.; resources, A.S.; data curation, I.G.E. and S.G.; writing--original draft preparation, I.G.E.; writing--review and editing, S.G. and A.S.; visualization, I.G.E. and S.G.; supervision, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research did not require IRB review according to the IRB policies of the University of Pittsburgh.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Trepenoids in Marine Heterobranch Molluscs
## 6 T triterpenonoids  
T triterpenonoids consist of six isoprene units (three terpene units) with the molecular formula C\({}_{30}\)H\({}_{48}\). The linear triterpene squalene is derived from the reductive coupling of two molecules of FPP and is the precursor to all steroids [6], as mentioned above. T triterpenon exist in a great variety of structures.  
Nearly 200 different skeletons have been identified in nature [6]. These skeletons may be broadly divided according to the number of rings present. In general, pentacyclic structures tend to dominate in the organisms. Triterpenes are biosynthesized through the head-to-head condensation of two FPP units to form a squalene. In turn, the squalene serves as a precursor for the formation of triterpenoids, including eukaryotic sterols. The squalene synthase is a preryltransferase that catalyzes a complex series of cationic rearrangements to join the C-1 carbons of two farnesyl residues. Differently from other marine invertebrates, such as echinoderms, heterobranch molluscs do not seem to present saponins (triterpenoid glycosides). As far as we know, no linear triterpenoids are described in heterobranchs, while cyclic triterpenoids are present only in nudibranchs, pleurobranchoids, and anaspideans (Table 5).  
The doridacean nudibranch from the North Sea, _Adalaria loveni_, contains lovenone (83) (Figure 11), a cytotoxic degraded triterpenoid suggested to come from an unidentified bryozoan prey [260], Testudinariols A and B (84) (Figure 11), from the Mediterranean pleurobranchoid _Pleurobranchus testudinarius_ are present in the mantle and mucus and are similar to some sponge compounds [261] although they are thought to be biosynthesized. Testudinariols are chemically related to limatulone of the limpet _Lottia limatulone_[262]. Similarly, an unidentified species of _Pleurobranchus_ from the South China Sea also possesses testudinariol B [263].  
In sea hares, aplysiols A and B are tetracyclic triterpene polyethers found in the mantle of _Aplysia argus_ from China, similar to compounds from the red algae _Laurencia_[264]. On the other hand, _Dolabella auricularia_ modifies its molecules from dietary brown and red algae, while _de novo_ synthesizing polypropionates and peptides [265]. Among the metabolites derived from red algae, the polyether bromotriterpene auril (85) (Figure 11) is cytotoxic to the HeLa tumor cell line [266].  
Figure 11: Structures of selected triterpenoids from Heterobranch molluscs: (**83**) lovenone; (**84**) testudinariol B; (**85**) aurilol.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group (Species)/Compounds** & **Nudibranchia (1)** & **Pleurobranchoidea (2)** & **Anaspidea (2)** \\ \hline Cyclic Triterpenoids & 1 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of triterpenoids in different Heterobranch groups in the reviewed period. In brackets: number of species with triterpenoids.
## 4 Results and Application
### Application  
In the study area, seismic waveform analysis was performed on the target interval corresponding to the No. 8 coal seam in the Benxi Formation. Initially, the UMAP-CNN model was employed to leverage its feature extraction and memory capabilities for dimensionality reduction and global sorting of seismic waveform data, enabling unsupervised learning of seismic facies features. Subsequently, seismic facies feature analysis was conducted by integrating existing geological knowledge and drilling data to enhance the interpretability and geological relevance of the extracted features.  
Figure 9 presents the results of layer flattening and seismic waveform analysis along the top boundary of the Taiyuan Formation. As indicated by the color bar on the right, the top interface of the Taiyuan Formation is located at approximately 10 ms on the time axis. Using this as a reference, the seismic waveform analysis effectively reveals the temporal thickness variation in the top boundary of the Benxi Formation, observed within the 40-60 ms range on the time axis. This variation highlights favorable zones for the development of the No. 8 coal seam, as greater temporal thickness typically correlates with enhanced coal seam development. A comparison with known well thickness data (represented by the black bars in Figures 9 and 10, where longer bars indicate greater coal seam thickness) and manually interpreted seismic stratigraphy (Figure 10) confirms that UMAP-CNN provides a high degree of accuracy in characterizing coal seam thickness and reconstructing the paleogeography of the Benxi Formation. These findings demonstrate the proposed method's excellent performance in capturing seismic waveform features and its capacity for efficient data processing.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Overall Accuracy** & **Average Accuracy** & **Kappa** \\ \hline UMAP-CNN & 92.5\% & 91.8\% & 0.9161 \\ t-SNE & 91.2\% & 89.7\% & 0.9024 \\ Isomap & 85.3\% & 83.1\% & 0.8356 \\ LLE & 68.2\% & 65.9\% & 0.6237 \\ \hline \end{tabular}
\end{table}
Table 1: Classification of different methods.  
Figure 9: UMAP\(-\)CNN dimensionality reduction results.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 2 Method  
### Construction of the Coupled Model  
#### 2.1.1 Overview of the SWMM 1D Model  
The SWMM (Version 5.2.4) is a hydrologic and hydrodynamic model that calculates various hydrological processes, including infiltration, evapotranspiration, runoff, ponding, and sewer conveyance, in response to rainfall events. It comprises two main modules: a hydrologic module for calculating rainfall runoff processes for different surface types in the watershed and a hydraulic module for modeling water movement in drainage facilities, such as pipes and open channels.  
The Horton model is commonly used to describe the infiltration process. The equation is as follows:  
\[i(t)=i_{c}+(i_{0}-i_{c})\text{exp}(-kt) \tag{1}\]  
where \(i(t)\) is the infiltration rate at time t, \(i_{0}\) is the initial infiltration rate, \(i_{c}\) is the constant (or final) infiltration rate, and \(k\) is the decay constant.  
The flow in drainage pipes is typically computed using [PERSON]'s equation, which is given by the following:  
\[Q=\frac{1}{n}AR^{\frac{2}{3}}S^{\frac{1}{2}} \tag{2}\]  
where \(Q\) is the flow rate, \(n\) is Manning's roughness coefficient, \(A\) is the cross-sectional area of the conduit, \(R\) is the hydraulic radius, and \(S\) is the slope of the channel.
# Antivirus Evasion Methods in Modern Operating Systems
## 7 Discussion  
This research has demonstrated that there are readily-available software tools that can be used to mask malicious files, effectively bypassing antivirus security regardless of the level of advancement of the attacker. The effectiveness of these tools depends on several factors, including the popularity of the tool, time, and how the tool works. When a given mechanism is frequently used, identical versions of malware may have been generated before, making the file useless if an antivirus engine detects it. The effectiveness of the software is also influenced by time, as antivirus software often introduces updates related to malware signatures, along with successive patches to fill defense gaps. This forces malware developers to update their programs with newer and newer solutions. The most important factor in the effectiveness of the cloaking mechanism is how it works. Changes in the hex editor or the use of an encoder to obfuscate the code are insufficient in the face of advanced heuristic searches combined with signature-based searches. Among the programs tested, Shellter was found to be the most concerning, as it can create a stable connection of meterpreter sessions on half of the tested antiviruses. The effectiveness of Shellter is also influenced by the injected payload, as the basic payload could bypass most antiviruses, but only on a static level, whereas the payload with the.raw extension could bypass all of them, half of which could be bypassed completely.
## 2 Materials and Methods  
### Synthesis of Metal Titanate Nanorods  
All photocatalysts were synthesized via an ethylene glycol-mediated route at room temperature using metal acetates, magnesium (II) acetate tetrahydrate (Sigma-Aldrich \(>\) = 99%), nickel (II) acetate tetrahydrate (Sigma-Aldrich 98%, Darmstadt, Germany), zinc (II) acetate tetrahydrate (Sigma-Aldrich \(\geq\)99%); cobalt (II) acetate tetrahydrate (Alfa Aesar, 98%, Karlsruhe, Gerrmany), titanium butoxide (TBT, Sigma-Aldrich \(\geq\)97.0%) as metal sources; ethylene glycol (EG, Honeywell \(\geq\) 99.5%, Offenbach, Germany,) as a solvent and reducing agent; and sodium hydroxide (Sigma-Aldrich \(\geq\)98%) to control pH. All chemicals were used as received without further purification.  
In a typical polyol process, the steps followed include the dissolution of the metal salt in a polyol either at room temperature or at a heating step, the possible formation of an intermediate phase, the nucleation step, and finally the growth step that leads to the formation of the metal nanoparticles. Herein, 0.005 mol of the metal acetate was dissolved in 30 mL EG, 1.7 mL TBT was added, and finally 0.22 g sodium hydroxide was added (Figure 1). The solution was stirred for 24 h, then it was centrifuged in order to separate the solid and liquid phase and washed with ethanol two times. Following the centrifugations, the samples were aged for 24 h and finally were annealed at 600 \({}^{\circ}\)C for 2 h in air (heating rate 5 \({}^{\circ}\)C/min) in order to obtain metal titanate nanorods with ilmenite and cubic structure. It has to be mentioned that some of the prepared solutions had different colors in the beginning and the termination of the stirring process, for instance MgTiO\({}_{3}\) and ZnTiO\({}_{3}\) were white during the whole process while NiTiO\({}_{3}\) and CoTiO\({}_{3}\) turned from green and purple to light blue and light pink, respectively. In addition, MgTiO\({}_{3}\) and ZnTiO\({}_{3}\) were white before and after annealing while NiTiO\({}_{3}\) turned from light blue to yellow, CoTiO\({}_{3}\) turned from light pink to dark green.
## 2 Materials and Methods
### Iron- and Copper-Chelating Activities  
The determination of the iron-chelating activity of the saffron samples followed the protocol described in [PERSON] et al. (2013) [14] involving the formation of the Fe\({}^{2+}\)-Ferrozine complex. This method is based on the chelation of this metal ion with ferrozine to yield a red-colored complex. In the presence of phenol-chelating agents, the formation of the complex is disrupted, resulting in a decrease in the red color of the complexes. Samples (0.5 mL) at different concentrations (10-200 mg/mL) were mixed with 2 mL 0.1 M sodium acetate buffer pH 4.9 and 50 \(\upmu\)L 2 mM iron(II) chloride, and incubated for 30 min at room temperature. Then, 0.2 mL of 5 mM ferrozine was added and absorbance was measured at 562 nm. Similarly, copper-chelating activity was determined using pyrocatechol violet (PV) as described in [PERSON] et al. (2013) [14]. The determination of the chelating activity against Cu\({}^{2+}\) is based on the formation of the blue-colored Cu\({}^{2+}\)-PV complex. The blue color turns yellow in the presence of chelating agents that dissociate the complex, and the chelating activity can thus be estimated by measuring the rate of color fading. Samples (0.5 mL) at different concentrations were mixed with 2 mL of 50 mM sodium acetate buffer pH 6.0 and 50 \(\upmu\)L 5 mM CuSO\({}_{4}\) and incubated at room temperature for 30 min. Then 50 \(\upmu\)L 4 mM PV was added, and absorbance at 632 nm was measured. The percentage of inhibition of Ferrozine-Fe\({}^{2+}\) and PV-Cu\({}^{2+}\) complex formation was calculated as [(A\({}_{0}-\) A\({}_{1}\))/A\({}_{0}\)] \(\times\) 100, where A\({}_{0}\) was the absorbance of the negative control, and A\({}_{1}\) was the absorbance of the samples. Distilled water and gallic acid served as negative and positive controls for the assays, respectively.
## 3 Methodology
### Evaluation Metrics  
The model's efficacy is assessed using key performance metrics, including accuracy, Recall, \(F_{1}\) Score, and Intersection Over Union (\(IoU\)). The \(F_{1}\) Score, a harmonized mean of accuracy and Recall, encapsulates the model's accuracy and inclusiveness. Conversely, \(IoU\) offers an extensive evaluation by quantifying the congruence between predicted and actual water body areas. The respective computations for these evaluation metrics are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{10}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{11}\]  
where \(TP\) is a pixel correctly recognized as a water body; \(FP\) is a background pixel incorrectly recognized as a water body; and \(FN\) is a water body pixel incorrectly classified as a background.