esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Electric Field Distribution around an Instrumented Total Hip Stem
## 2 Materials and Methods
### Experimental Setup  
To prove the numerical simulation's accuracy, a validation experiment was set up according to the clinical application. Therefore, the instrumented total hip stem was inserted into a porcine femur. For this, the femoral head was cut and a borehole (\(\O\) 12 mm) was drilled along the axis of the femur. The bone stock was carefully prepared with rasps. Using a hammer, the total hip stem was inserted into a press-fit fixation in order to allow close contact with the surrounding bone tissue. The wire that connected the first electrode was led through the porcine femur to the power supply. The total hip stem was also connected to the power supply (Agilent 33220A, Agilent Technologies Inc., Colorado Springs, CO, USA).  
A three-dimensional (3D) coordinate measuring arm (MicroScribe G2x, Solution Technologies, Oella, MD, USA) was used to determine the coordinates of the readout locations, which were maintained as consistent in the numerical simulations. The function generator supplied an alternating field (20 Hz, 6 V). Before the total hip stem was implanted into the bone, the root mean square (RMS) voltage on the surface of the hip stem was measured using a multimeter. This value was used as a boundary value on the hip stem in the numerical simulation in order to compare the data with the experiment. The potential on the surface of the cross section of the bone was measured at different points along two lines perpendicular to the electrode and to the interface between the insulator and the stem. The points measured were at distances of 2, 3, 5, and 7 mm (Figure 1) along these two lines.  
A raster of 10 different points (readout locations) at a distance of 10 mm was marked at the surface of the frontal femur (Figure 2). The electric potential was measured for all points of the raster at approximately 5 mm and 8 mm depth into the bone. This measurement was performed in triplicate. During measurements, the bone tissue was rehydrated with an NaCl solution (conductivity of about 1.5 S/m) to minimize the influence of dehydration of the specimen.
## 4 Case Studies
### An Experimental Case Study on Complex-Shaped Metallic Turbine Blades  
A case study of first-stage turbine blades with complex geometry and internal cooling channels, consisting of 193 healthy and 39 defective blades (see Figure 3), is presented in this section. The blades have been labeled as good/bad using alternative inspection procedures (e.g., X-ray Computed Tomography, ultrasonic, penetrant testing, microscopic sectioning, operational performance, and operator experience). For more details on the turbine blades, readers are referred to [16].  
The frequency response functions (FRFs) of these blades are measured by Vibrant Corporation (link: www.vibrantndt.com) using the process-compensated resonant test (PCRT) technique in the frequency range of 3-38 kHz. PCRT is a nondestructive testing technology for the vibrational testing of parts. A schematic of the PCRT procedure is displayed in Figure 4. The test part (a turbine blade in this case) is supported by three piezoelectric transducers that are positioned in a fixture. One transducer acts as an actuator and excites a broadband sweep sine signal. The remaining two transducers are used to monitor the vibrational response signal. In this regard, PCRT is implemented as a single input multiple output (SIMO) test. Based on the measured FRFs, important features such as resonance frequencies and their associated damping ratios and amplitudes can then be estimated. These features are used in a classification algorithm, which is based on a learning process, in order to discriminate between good and bad parts.  
Figure 4: A schematic of the measurement procedure (reproduced with permission from Elsevier) [16].  
Figure 3: (**a**) Generic model of the turbine blade; (**b**) sectional drawing of the turbine blade.  
Figure 5 displays FRFs from two healthy samples and one defective sample. A significant difference can be observed between them. Based on the measured FRFs, 15 stable resonance frequencies have been extracted, which serve as the feature set for the \(\mathbf{IMCS^{BPSO}_{PSO}}\) algorithm.  
A training pool comprises 60% of the original dataset. The training pool itself consists of a 60% training dataset and a 40% validation dataset, randomly selected from healthy and defective samples in equal proportions. The remaining 40% of the original dataset is the testing dataset (blind parts) and is used to evaluate the classification performance of the trained algorithm. Thus, the numbers of the training dataset, the validation dataset, and the testing dataset are 87, 52, and 93, respectively. For BPSO, the maximum iteration number is T = 100, and the number of particles in the BPSO is set to N = 10. A 5-folder cross-validation is considered.  
All programs are implemented in the platform MATLAB2018b(r) (laptop with Intel(r) Core(tm) i7-8650U processor (4 cores) up to 4.20 GHz and RAM 32 GB).
## 4 Discussion
### The Effect of Pore Size and Water-to-Cement Ratio on Moisture Transfer
At the same time, the contribution of pores with a pore size of 10-100 nm to the overall moisture diffusion coefficient of cement paste is dominant, and with the increase in the water-cement ratio, water molecules are more likely to migrate through small pores, that is, the greater the water-cement ratio, the greater the influence of Knudsen diffusion. This is mainly due to the fact that the volume ratio of 10-100 nm pores in cement paste is the largest, and as the water-cement ratio increases, the volume ratio of small pores increases.
# Hypergraph Analysis of Functional Brain Connectivity During Figurative Attention
## 5 Graph Construction
### Connectivity Graphs Based on Mutual Information  
The third connectivity measure explored in this study, mutual information, was calculated using Equation (11). The differences in mutual information induced by attention were computed using Equation (14). The resulting graphs depicting the attention-induced changes in mutual information are shown in Figures 9 and 10. Additionally, the corresponding mutual information matrices are provided in Appendix B, specifically in Figures A5 and A6, offering a detailed numerical representation of these connectivity changes.  
Table 3 summarizes the brain connectivity analysis results based on mutual information. It highlights the two largest positive and negative mutual information differences (link weights) along with their corresponding brain regions, providing insight into the most significant connectivity changes induced by attention shifts.  
A strong correspondence can be observed among the connectivity graphs based on coherence, cross-correlation, and mutual information. Although some variations exist in the links with the highest weights, the nodes with the maximum and minimum labels largely overlap across all connectivity measures. This consistency further validates the robustness of our approach in capturing attention-related connectivity changes in the brain.  
The shared features across different connectivity measures can be explored using hypergraphs. In the following section, we describe the process of constructing attention hypergraphs.  
Figure 9: Connectivity graphs based on mutual information in the (**upper raw**) \(f_{1}\) and (**lower raw**) \(f_{2}\) frequency regions, associated with (**left column**) left-cube attention (\(\Delta Inf_{L}>0\)) and (**right column**) right-cube attention (\(\Delta Inf_{R}>0\)). Bold lines highlight the links with the largest mutual information differences (Max \(\Delta Inf\)), which are displayed next to the corresponding links. Orange dots denote the nodes with the highest sums of their link weights, indicated next to these nodes.
# Anti-Bacterial Action of Plasma Multi-Jets in the Context of Chronic Wound Healing
## 2 Materials and Methods
### Biological Samples
#### 2.2.2 Bacterial Strains  
Two classes of bacterial strains have been used in this work. First, the freeze-dried strains, _Staphylococcus aureus subsp. aureus_ ATCC(r)9144 TM and _Pseudomonas aeruginosa_ ATCC(r)9027 TM were respectively cultured on ATCC(r) Medium 18: Trypticase Soy Agar (TSA) and ATCC(r) Medium 3: Nutrient agar (supplied by Institut Pasteur). These two non-resistant bacteria were inoculated on agar substrates from solutions having a 103 CFU/mL concentration. Second, three resistant strains were isolated from wound samples collected from patients suffering from chronic wounds, namely _S. aureus_, _P. aeruginosa_ and _E. coli_. The individual strain selection was achieved by culturing these patient's samples on various selective agar. These resistant bacteria were then inoculated on TSA substrates from solutions having a 104 CFU/mL concentration. The experiment with the combination of these three resistant bacteria, documented in Section 3.4, was performed from mixing the three bacterial solutions prepared with a 104 CFU/mL concentration of each bacteria. The resistance of each resistant bacteria strain was qualified through antibiogram analysis with the inclusion of nine different antibiotic drugs. The most resistant strain was measured to be the _P. aeruginosa_ being only partly sensitive to one aminoglycoside drug. This strain was selected to illustrate the potent bactericidal action of multi jets on one resistant pathogen, as documented in Section 3.3.  
All the decontamination experiments were performed in the microbiology hygiene laboratory of the Orleans Regional Hospital under the supervision of Dr [PERSON].
# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 3 Our Method
### Post-Processing  
**Proposal score.** In Section 3.4, we generate a set of temporal proposals \(P\) through our designed proposal generators. To identify the most fitting video segments from \(P\), it is essential to compute and rank each proposal's confidence score. Intuitively, a straightforward approach could be averaging the similarity scores for each frame within a proposal, or only considering frames exceeding dynamic threshold \(\theta\). However, these methods overlook the impact of proposal length on their scoring. In our experiments, we observe that within certain ground-truth segments containing scene transitions, the similarity of some frames significantly exceeded that of adjacent frames. This led to an excessively high dynamic threshold, resulting in the predicted segments being truncated or fragmented. To address this issue, we develop a length-aware scoring mechanism for proposals, encouraging the model to generate longer segments. Specifically, the evaluation of each proposal considers both its duration and the query-frame similarity, and the final score of each proposal \(S_{f}\in\mathbb{R}^{N_{p}}\) is calculated as follows:  
\[S_{f}=\alpha\times S_{l}+(1-\alpha)\times S_{s}, \tag{3}\]  
where \(S_{l}=L_{p}/L_{n}\). Here, \(L_{p}\) represents the count of frames within a proposal exceeding \(\theta\), and \(L_{n}\) denotes the total number of frames exceeding \(\theta\) across the entire video. The balancing coefficient \(\alpha\) is adjustable to optimize for the influence of length and similarity in the final score calculation.  
**NMS.** In the final stage, considering that multiple debiased queries will produce numerous overlapping proposals, we employ non-maximum suppression (NMS) to reduce redundant overlaps and derive the final predicted video segments \(Seg\in\mathbb{R}^{N_{b}\times 2}\):  
\[Seg=\text{NMS}(P,S_{f},\mu), \tag{4}\]  
where segments exceeding the intersection over union (IoU) threshold \(\mu\) are selectively eliminated. This method ensures that only the most representative and distinct video segments are retained, enhancing the accuracy and relevance of our VTG-GPT output.
## 2 Types of EOR Polymers  
Polyacrylamide (PAM) was applied to polymer flooding, but its usage for enhanced oil recovery has significantly decreased. PAM tends to adsorb strongly on the mineral surfaces of reservoir rock and induces high polymer retention in the reservoir. High polymer adsorption increases the required amount of polymer and decreases injectivity; therefore, the usage of PAM has been avoided for polymer flooding. To reduce polymer adsorption, hydrolyzed polyacrylamide (HPAM) has been applied in the fields. According to [PERSON] et al. [29], HPAM is the most widely used polymer in EOR applications; it is used in 92% of EOR cases worldwide [30]. HPAM is a copolymer of acrylamide and sodium acrylate and a synthetic, water-soluble straight chain polymer of acrylamide monomers (Figure 1a). It has negative charges along its chain, and the repulsion between the negative charges contributes to the chain extension, thereby resulting in high viscosity. In addition, the availability and low cost of HPAM promotes its wide use in the oil recovery business [31]. HPAM cannot be applied in all reservoir conditions mainly due to its thermal degradation at high temperatures and its high sensitivity to cation concentrations. New polymers that are stable in harsh environments (high temperatures and high cation concentrations) attract attention because of the need for polymer flooding in oil reservoirs with these conditions [32; 33; 34; 35; 36; 37]. 2-Acrylamido-2-methylpropane-sulfonate copolymer (AMPS) can be effectively applied to high-temperature or high-cation concentration conditions. AMPS has water-soluble anionic sulfonate, shielding acrylamide and unsaturated double bonds (Figure 1b). Sulfonate has high resistance to cations. Rigid side chains, large chains or chains of the ring structure provide high resistance to thermal degradation [2]. [PERSON] and [PERSON] [38] reported that the AMPS moiety itself is susceptible to thermal degradation at temperatures above roughly 100 \({}^{\circ}\)C. [PERSON] et al. [39] reported that the viscosity of AMPS/AM was about twice than that of HPAM at the same conditions when salinity was increased. After aging at 90 \({}^{\circ}\)C for 16 days, the viscosity of AMPS/AM was almost unchanged [2].  
The polymers investigated in this study consist of two HPAMs with different molecular weights and one copolymer of AMPS and acrylamide, referred to as poly(AM-co-AMPS), which are the most widely-used EOR polymers [40], as shown in Table 1.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### CO\({}_{2}\) Observation Database  
#### 2.3.1 Xco\({}_{2}\) Products  
A global team of researchers uses meteorological observation stations, satellite remote sensing, and ground monitoring to create a high-resolution global dataset of near-surface greenhouse gas (GHG) observations. They compare this data with the Total Carbon Column Observing Network (TCCON) [25] and develop inversion algorithms for accuracy. Current XCO\({}_{2}\) products from satellites (GOSAT-2, TanSat, OCO-2) are processed using the RemoTeC full-physics algorithm, UoL FP algorithm, FOCAL algorithm, and IAPCAS algorithm. These datasets and algorithms are publicly available, with updates and data range extensions made annually [26].  
The latest XCO\({}_{2}\) product information for specific remote sensing satellites is shown in Table 3. Certain data products and releases are still undergoing updates.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Dataset Selection
#### 3.1.1 Legitimate Dataset  
* _DMOZ dataset_  
The DMOZ dataset derived from the Open Directory Project is a sizable collection of URLs arranged according to predetermined categories. It was initially designed as a human-curated directory of websites, with volunteers evaluating and classifying pages according to their content. Because the DMOZ initiative sought to incorporate reliable and high-quality resources, the dataset mostly consists of legitimate links. A hierarchical structure is reflected in the categories, but many versions simplify this by flattening it into single-level classes. About 205,627 entries in 15 categories make up the particular version of the DMOZ dataset that is accessible on Kaggle, which makes it appropriate for tasks like web categorization, URL classification, and natural language processing. Even if dangerous links are intended to be included, researchers who work with URL data should nonetheless verify links and use caution, particularly when working with external or out-of-date URLs. Overall, the DMOZ dataset provides a varied and properly labeled set of data for study and real-world uses [17].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Method Used** & **Accuracy** & **Challenges** \\ \hline
[8] & HDP-CNN & 98.03\% & Lack of detailed information about their dataset \\ \hline
[9] & LogBERT-BiLSTM & 95.50\% &
\begin{tabular}{c} Used too old of a dataset; lack \\ of comparison \\ \end{tabular} \\ \hline
[10] & Char-BiGRU-Attention & 99.55\% & Overfitting \\ \hline
[11] & Naive Bayes with Lexical, Content-based, and Network-based Features & 96.00\% &
\begin{tabular}{c} Does not adapt to evolving URL \\ patterns; lacks keyword \\ feature extraction \\ \end{tabular} \\ \hline
[12] & \
\begin{tabular}{c} Bi-IndRNN + Capsule Network + \\ Attention Mechanism \\ \end{tabular} & 99.89\% &
\begin{tabular}{c} Ignores integration of dynamic \\ URL features \\ \end{tabular} \\ \hline
[13] & Survey on ML-based URL Analysis & - &
\begin{tabular}{c} No experimental evaluation; general \\ overview without implementation \\ \end{tabular} \\ \hline
[14] & \
\begin{tabular}{c} CatBoost, LightGBM, AdaBoost, \\ XGBost for \\ Multiclass Classification \\ \end{tabular} & 95\% &
\begin{tabular}{c} Suggests future improvements via \\ using deep learning techniques \\ \end{tabular} \\ \hline
[15] & BERT: Fine-tuning & 96.71\% &
\begin{tabular}{c} Typo-squatting attack; char \\ substitution attack \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related work.
## 1 Introduction  
The family of Asteraceae contains between 25,000 and 30,000 species belonging to over 1000 genera. _Pallenis spinosa_ (L) Cass., commonly known as the spiny starwort, is a member of this family, belonging to the tribe of Inuleae, a subtribe of Inulineae, and a group of _Inula_[1]. The genus _Pallenis_ is monotypic and is widespread in southern Europe, northern Africa, the Canary Islands, the Middle East, and the Mediterranean region, especially in the desert and in the coastal habitats [2]. It is an annual herbaceous plant characterized by large yellow/orange flowers surrounded by long spiny involucre bracts [3]. Traditionally, the flowers and the spiny leaves of the plant have been used as medicine to treat inflammation, gastalgia, mouth infections, skin injuries, and inflammatory contusions [4; 5]. Usually, the flowers and the leaves are taken orally or applied topically in the form of infusion or decoction [6].  
In the western part of Algeria, _P. spinosa_ is used for the management of skin conditions such as eczema. Furthermore, its flowers hold significant medicinal value in addressing sensitivity issues, as well as aiding in the healing of bruises and wounds. Across the Mediterranean basin, the plant is used in its entirety as an insecticide [7; 8].  
Some studies have investigated the secondary metabolic products of _P. spinosa_ in order to improve traditional knowledge and find novel medicinal applications. Regarding volatile constituents, the plant is rich in sesquiterpenes and oxygenated sesquiterpenoids, including a ketone with a previously unreported carbon skeleton (dihydroxypallenone) [9; 10]. Other compounds such as \(\alpha\)-cadinol, shiroomol, germacrene D derivatives [11], and germacrene A-type epoxides [12] have been reported. Concerning non-volatile secondary metabolites, _P. spinosa_ tends to accumulate 5-O-glycoslavones. Among these, several tricin and patuletin derivatives have been detected in their aerial parts [13].  
Natural products have provided many drugs and drug leads and still remain the richest sources of bioactive compounds. In this work, we aimed at moving a step forward in the characterization of secondary metabolites from _P. spinosa_. Specifically, we investigated volatile compounds and secondary metabolites extracted from the aerial parts of _P. spinosa_ from Northeastern Algeria (Bourj Ghedir). Although the phytochemical content of this plant has already been studied, limited data exists on the species growing in Algeria. Headspace solid-phase micro-extraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), nuclear magnetic resonance (NMR), and high-performance liquid chromatography-mass spectrometry (HPLC-MS\({}^{\text{n}}\)) were used for the analyses, respectively. Overall, the results are intended to contribute to the valorization of understudied Algerian medicinal and edible plants and the rationalization of their traditional uses.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
###### Abstract  
This study concentrates on physical layer security (PLS) in UAV-aided Internet of Things (IoT) networks and proposes an innovative approach to enhance security by optimizing the trajectory of unmanned aerial vehicles (UAVs). In an IoT system with multiple eavesdroppers, formulating the optimal UAV trajectory poses a non-convex and non-differentiable optimization challenge. The paper utilizes the successive convex approximation (SCA) method in conjunction with hypograph theory to address this challenge. First, a set of trajectory increment variables is introduced to replace the original UAV trajectory coordinates, thereby converting the original non-convex problem into a sequence of convex subproblems. Subsequently, hypograph theory is employed to convert these non-differentiable subproblems into standard convex forms, which can be solved using the CVX toolbox. Simulation results demonstrate the UAV's trajectory fluctuations under different parameters, affirming that trajectory optimization significantly improves PLS performance in IoT systems.  
Aticle