esa-sceva/satcom-chunk-collection huggingface dataset:
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 6 Strategies for Eradication of Biofilms
### Nanoparticles  
The technology of nanoparticles has gained much attention recently, with different applications in various fields. The minute size of the nanoscale offers the ability for high penetration capacity through various barriers, in addition to the ability to apply ultra-thin coats to specific target surfaces. The nanoscale size can modify the properties that ensure high capacity for targeting nanoparticles [220]. Cationic dendrimeric peptides' surfaces are positively charged, that can be electrostatically adsorbed on the negatively charged surface of bacterial cells [168; 221]. The hyperbranched nature of dendrimers enhances their chemical reactivity with the interacting surfaces, conferring therapeutic properties [168; 222]. In addition to small particle size and high penetrating power in biofilm matrix, dendrimers can target DNA [223], and inhibit protein synthesis, exerting an efficient anti-biofilm activity [224; 225]. The combination of nanoparticle technology and anti-biofilm agents (such as repurposed drugs, phytochemicals and anti-befouling agents) offers combined advantages from both methods, which increases penetration and the targeted selectivity of the used method. Some examples of the employed nanoparticles as anti-biofilms are listed in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Agent** & **Target Microbe** &
\begin{tabular}{c} **Expected Mode of** \\ **Anti-Biofilm Action** \\ \end{tabular} & **Reference** \\ \hline \multicolumn{4}{l}{**Repurposed drugs**} \\ \hline \multicolumn{4}{l}{\(\alpha\)**-Adrenoreceptor blockers**} \\ \hline Terazosin & _P. aeruginosa_ & \multirow{2}{*}{Antagonize QS system} & [131] \\  & _S. enterica_ & & [136] \\ \hline Prazosin & _P. aeruginosa, P. mirabilis,_ & \multirow{2}{*}{Antagonize QS system} & [153; 226; 227] \\  & _S. marcescenscens_, Salmonella enterica_ & & \\ \hline \multicolumn{4}{l}{\(\beta\)-Adrenoreceptor blockers} \\ \hline Metoprolol & _P. aeruginosa, S. enterica_ & \multirow{2}{*}{Antagonize QS system} & [132; 228] \\  & _P. aeruginosa, P. mirabilis, S. marcescens_ & & [62; 144; 229] \\ \hline \multicolumn{4}{l}{Antidiabetics} \\ \hline Sitagliptin & _P. aeruginosa_ & \multirow{2}{*}{Antagonize QS system} & [62; 144; 229] \\  & _S. aureus_ & & [62] \\  & _S. marcescens_ & & [146] \\ \hline Saxagliptin & _S. mutans_ & Enzymatical quenching & [230] \\ \hline Metformin & _P. aeruginosa_ & \multirow{2}{*}{Antagonize QS system} & [145] \\  & _S. marcescens_ & & [146] \\ \hline Diclofenac (analgesic) & _P. mirabilis_ & \multirow{2}{*}{Antagonize QS system} & [135] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of antibiofilm agents.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Digital Modulation Based Systems
#### 2.3.1 FD-DSIC Systems  
Due to the high complexity and weak SI cancelation efficiency of the previous FD-UWAC systems, the proposed sparse adaptive algorithms built for estimation of the SI channel and PA nonlinearity in UWAC systems perform poorly [24]. As a result, FD-digital SI-cancellation based UWAC systems have been developed, which can suppress SI more effectively than previously proposed UWAC systems and terrestrial communication systems.  
* **RLS-Adaptive-filter**. A less-complex recursive least-square (RLS) adaptive filter with dichotomous co-ordinate descent (DSD) is proposed [25; 26; 27], which is based on a digital SI cancellation scheme. In this proposed scheme the PA output is used to recreate the non-linear PA-SI signal, which improves the efficiency of the advanced SI cancellation scheme. Addition of Fast convergence, high numerical stability, and low computational complexity are all advantages of the recursive least-square dichotomous coordinate decent phenomenon. With the PA adaptive filter, the RLS-DSD effectively suppresses SI at 69 dB. Additionally, this proposed scheme cancels the near-field interference signal. These systems also have issues with far-field interference cancellation and transmission rage.
* **ANE**. A novel ANE-based digital cancellation scheme is proposed in [60], in which the PA's output signal serves as a guide for digital cancellation to resolve non-linear distortion in the transmitter and receiver chains. The equalization has been performed by using the basis expansion model (BEM), which can model the inverse of the pre-amplifier response. To achieve a better equalization performance, the proposed scheme uses two cost functions, the mean squared error (MSE) and the other one is derived from the power spectrum of the received signal. These systems have been tested on two water tanks. In the first water tank, artificial non-linear restoration along with the Rapp model has been installed to receive the signal from the hydrophone, but without using the pre-amplifier, and in the second tank hydrophone receives the signal with a pre-amplifier by utilizing the non-linear equalization technique. The second tank shows a better SI cancellation performance of 4 dB by applying the ANE technique.  
Figure 3: Self-multipath interference of IBFD acoustic system in shallow water.
## 4 Material and Methods
### HPLC-DAD Analysis  
Polyphenols were analysed using a Shimadzu \"Prominence\" system equipped with an SPD-M20A diode array detector. The separation column was a C18 Hyperchrom Bischoff 250 \(\times\) 4.0 mm i.d., with a 5 \(\upmu\)m particle size, thermostated at a temperature of 35 \({}^{\circ}\)C. The mobile phase consisted of 2% (\(\
icefrac{{v}}{{v}}\)) acetic acid in water (eluent A) and 0.5% acetic acid in water and acetonitrile (50:50, \(\
icefrac{{v}}{{v}}\); eluent B) in the following gradient system (total running time 90 min): initial 10% B, first linear gradient to 18% B in 20 min, second linear gradient to 24% B in 10 min, third linear gradient to 30% B in 15 min, hold at 30% B for 20 min, fourth linear gradient to 55% B in 5 min, final linear gradient to 100% B in 5 min, hold at 100% B for 8 min. The injection volume for all samples was 20.0 \(\upmu\)L. Chromatograms were recorded at 320 nm for hydroxy cinnamic acids, at 330 nm for apigenin derivatives, at 350 nm for luteolin derivatives and at 232 nm for cyanropicrin at a flow rate of 0.8 mL/min. The identification of compounds was obtained using retention time and UV spectra comparison of commercial standards. Quantitative analysis was performed according to standard calibration curves of chlorogenic acid for mono-O-caffeoylquinic acids and cyanrin for di-O-caffeoylquinic acids, respectively. Apigenin and luteolin derivatives were calculated as apigenin 7-O-glucose and luteolin 7-O-glucose, respectively. Cynaropicrin was quantified by the external standard of the commercial cyanropicrin calibration curve. The shown data are the mean of three replicates.
# Study on the Causes and Control Measures of Mg-Al Spinel Inclusions in U75V Heavy Rail Steel
## 1 Introduction
(2) In the direct reaction model [15; 16], the Al2O3 inclusions in the molten steel react directly when they contact the refractorics involved in refining slag. (3) In the aluminum replacement model [17], MgO enters the steel slag after the refractory is eroded, and the dissolved aluminum in the molten steel displaces the MgO in the steel slag and then forms spinel inclusions. (4) In the crystallography model [18; 19], when the temperature of the molten steel decreases during continuous casting, the magnesium-aluminum spinel inclusion phase will crystallize inside CaO-SiO2-Al2O3-MgO composite oxide inclusions with a low melting point. Such inclusions are more common in aluminum deoxidized steel [20; 21]. The formation and control of spinel inclusions in aluminum deoxidized steel have been extensively reported. [PERSON] et al. [22] and [PERSON] et al. [18; 23] pointed out that the formation of spinel inclusions was related to slag basicity and \(\mathrm{w_{CaO}/w_{Al2O3}}\). The formation of spinel inclusions could be inhibited by appropriately reducing the slag basicity and \(\mathrm{w_{CaO}/w_{Al2O3}}\). [PERSON] et al. [24] showed that a reduction in MgO content in the slag by a VD refining process could inhibit the formation of spinel inclusions. [PERSON] et al. [25] studied the influence of different refractories on steel cleanliness and pointed out that MgO and enamel refractories promote the formation of spinel inclusions in steel. In addition, [PERSON] et al. [26] also studied the influence of the CaF2 content in slag on the formation behavior of spinel inclusions, and pointed out that a \(\mathrm{w_{CaF2}}\) of less than 10% in the slag is conducive for spinel control. In this paper, the formation, development and change of spinel inclusions in the actual production process of heavy rail steel are studied. In actual production, the formation of inclusions involves thermodynamics, dynamics, heat transfer and mass transfer. With the progress of metallurgical technologies and innovations in equipment, the formation conditions and process of inclusions are complicated and changeable. In this paper, the causes of spinel inclusions which affect the metallurgical quality of heavy rail steel are described from both thermodynamic and experimental aspects, and measures for avoiding spinel inclusions are proposed.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 7 Numerical Results
### Broadcast Channel  
Our second experiment considers a single broadcast VoWiFi channel. This is a one-way communication service could be used for announcements to ground users. To compare both VoWiFi communication services, we set up the same range of users and terrain sizes as in our previous experiment (and continue using the settings listed in Table 3). As a particularity, broadcast channels are transmitted using the lowest available bit-rate modulation (i.e., MCS 0 in IEEE 802.11n) to maximize coverage.  
Figure 8 shows the results obtained for \(f_{\text{obj}}\), \(P_{\text{WiFi}}\) and the total power consumption. The average drone turnover rate (\(f_{\text{obj}}\)) shown in Figure 7(a) depends on the size of the terrain, but the number of served users does not affect negatively since the problem constraint related to QoS is no longer an obstacle due to the scarce traffic load (i.e., there is only one traffic flow from the AP to all its associated users).  
Figure 7(b) shows that the power spent in radio communications is close to the power consumption of the radio idle state (\(P_{\text{Radio}}\approx 2\cdot\rho_{\text{idle}}\)). This shows that the channel occupation is very low. Indeed, the codec G.711 requires a throughput of 64 Kbps (around 80 Kbps at the physical layer), and the available data bit-rate is 6.5 Mbps. As a result, \(P_{\text{Radio}}\) represents a 14% of the total power (see Figure 7(c)).  
Figure 7: Results for the individual VoIP channels experiment.  
Finally, let us compare the total required power in both experiments (see Figures 6(c) and 7(c)). Since the VoWiFi broadcast service require fewer drones than the individual, the total power consumption is also improved. Energy savings are reduced up to 20% on the largest scenario and up to a 75% on the small one. As expected, energy savings increase with user density and thus, QoS-constrained scenarios take advantage of replacing individual VoIP channels with a unique broadcast channel.
# Permittivity of Undoped Silicon in the Millimeter Wave Range
## 1 Introduction
Plane wave is very desirable in free space measurement. The retrieval of the permittivity incorporates the theories of wave propagation and scattering parameters. Temperature dependent characteristics are examined at 20, 25, and 30 \({}^{\circ}\)C.  
The remaining parts are organized as follows: Section 2 is devoted to a general description on the permittivity and measurement techniques; Section 3 describes the method generating a quasi-plane wave; Section 4 is the retrieval method and Section 5 is the measurement results; the last part Section 6 concludes this work.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Performance Evaluation of Feature Extraction and Cancer Detection on Mammography Datasets
#### 4.2.2 Evaluation of the EfficientNetV2 Variants on the CSAW-M Dataset  
This experiment was carried out using only the CSAW-M dataset, as it provides a large and diverse set of annotated mammography images suitable for robust and clinically relevant evaluation. The results are presented in Table 7. For the voting KNN classifier, the best performance was obtained with the EfficientNetV2M model, achieving an accuracy of 93.45%, a precision of 93.27%, a recall of 93.45%, and an F1-score of 90.29%. The best results for the stacking classifier were achieved with the EfficientNetV2 XL model, reaching an accuracy of 93.47%, a precision of 87.61%, a recall of 93.19%, and an F1-score of 90.32%. The bagging classifier obtained its best performance with the EfficientNetV2S model, obtaining an accuracy of 93.30%, a precision of 87.76%, a recall of 93.30%, and an F1-score of 90.21%. The boosting classifier, when evaluated with the EfficientNetV2S model, produced an accuracy of 93.43%, a precision of 90.51%, a recall of 93.43%, and an F1-score of 90.29%. Considering all four evaluation metrics, the highest overall classification performance was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Classifier** & _Acc (\%)_ & _Pre (\%)_ & _Rec (\%)_ & **F1 (\%)** \\ \hline EfficientNetV2S & Voting KNN & 86.17 & 77.58 & 86.17 & 79.86 \\  & Stacking & 86.28 & 76.24 & 86.24 & 79.87 \\  & Bagging & 86.24 & 76.54 & 85.24 & 79.80 \\  & Boosting & 86.24 & 77.23 & 86.24 & 79.85 \\ \hline EfficientNetV2M & Voting KNN & 86.13 & 78.75 & 86.14 & 80.09 \\  & Stacking & 86.28 & 78.75 & 86.14 & 80.09 \\  & Bagging & 84.88 & 77.70 & 84.88 & 80.11 \\  & Boosting & 86.25 & 84.12 & 85.20 & 84.65 \\ \hline EfficientNetV2L & Voting KNN & 86.10 & 78.50 & 86.10 & 79.95 \\  & Stacking & 86.24 & 86.24 & 86.24 & 79.87 \\  & Bagging & 84.87 & 76.99 & 84.87 & 79.82 \\  & Boosting & 86.25 & 84.00 & 85.05 & 84.50 \\ \hline EfficientNetV2 XL & Voting KNN & 85.95 & 78.60 & 85.95 & 80.20 \\  & Stacking & 86.21 & 85.97 & 86.21 & 79.83 \\  & Bagging & 84.88 & 78.08 & 84.88 & 80.28 \\  & Boosting & 86.18 & 78.90 & 86.18 & 79.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification results of EfficientNetV2S, V2M, V2L, and V2 XL on the combined DDSM, MIAS, and CSAW-M datasets in terms of accuracy (_Acc_), precision (_Pre_), recall (_Rec_), and F1-score.  
obtained with the EfficientNetV2 XL model using the stacking classifier, as illustrated in Figure 5b.
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods
### Feature Extraction and Fusion
#### 3.1.1 RGB Branch  
U-Net [43] has demonstrated outstanding performance in numerous tasks, such as semantic segmentation [43] and image enhancement [45]. Its superior capability in image feature extraction has greatly inspired us. Consequently, we designed a U-shape two-layer encoder-decoder architecture based on convolutional neural networks in the RGB branch. The encoder progressively doubles the number of feature channels during the downsampling process, while the decoder utilizes transposed convolutions to upsample the feature maps and simultaneously reduce the channel count. Furthermore, unlike pointMBF [24], we integrated the features from the encoder and decoder before fusing them with point cloud information. This strategy effectively combines the texture information extracted by the shallow network layers with the semantic information captured by the deeper layers, facilitating comprehensive interaction with the features from the point cloud branch. As a result, the overall performance is significantly improved.  
In the backbone network, we incorporated information from the point cloud branch and performed feature fusion using FusionBlockR, with the specific architecture illustrated in Figure 2a (left). We drew inspiration from the core concepts of PointMBF [24], PointNet++ [38], and KPConv [44], which emphasize that semantic information and local geometric structures are dependent on the neighborhood centered on the target point. Therefore, fusing the features of neighboring points within the corresponding neighborhood of the point cloud into RGB features is both logical and effective. However, the focus on global information in point cloud registration tasks can, to some extent, limit the upper bound of registration accuracy.  
Building on this consideration, we incorporated a single-layer Transformer-based [25] self-attention layer during the feature fusion stage, enabling the RGB features, enhanced with information from the point cloud branch, to perceive partial global context. This  
Figure 2: The detail of our methods. (**a**) Detailed illustration of bidirectional feature fusion. (**b**) Self-attention layer. (**c**) Cross-attention layer. (**d**) Transformer encoder layer. (**e**) Linear attention layer with \(O(N)\) complexity.  
approach generates fused features that balance generality with uniqueness. A detailed description of the self-attention layer can be found in Section 3.3. This design represents a key distinction between our fusion module and that of PointMBF [24].
## 3 Results and Discussion
### Images Analysis  
The floc size distribution can tell us the percentages of different floc sizes (shown in Figure 4), and the removal efficiency is commonly used to estimate the coagulation performance. In order to gain further insight into the floc and sediment properties, image analysis was used to predict floc characteristics by IX71 digital photomicrography. In Figure 5, the average size of flocs in different reactors is clearly indicated. The floc formation and growth processes were carried out in the rapid mixer and no more aggregation occurred due to the strong repulsion between positively charged particles of magnesium hydroxide in the flocculation basin. In the sedimentation tank, flocs aggregated to form relatively large particles. This is consistent with the findings of FSD value which are shown in Figure 4. As for Figure 5a,d, they clearly show that increasing flow caused a decrease of the average size in the rapid mixer. The average floc size of magnesium hydroxide-reactive orange is 8.06 and 7.25 \(\upmu\)m. This is also consistent with the findings of a previous study [14].
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.6 Data Synchronization Module  
The synchronization module performs operations when no available receiving memory blocks on the sender end. The specific process is as follows: the sender uses an RDMA Read request to obtain the status bytes of the receiving memory block from the receiver end and saves the retrieved status bytes in the second status byte memory block.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 2 Materials and Methods
### Experiments Setup  
In this study, the MLDAS system was used to test the impact of data assimilation on land surface variable prediction. Using the same atmospheric forcing data, the MLDAS predictions (red arrows in Figure 1) were compared with simulations of Noah-MP OL (without assimilation, blue arrow in Figure 1) to evaluate the influence of assimilation on predictions. Four assimilation-prediction scenarios (assimilating for 15 days, 45 days, 75 days, and 105 days from 1 May, and then predicting until 1 October) were adapted during the growing season (1 May to 30 September) in 2003, 2004, and 2005, respectively (as shown in Figure 1). Furthermore, considering that the differences between OL and MLDAS are significant in the first month of prediction, the first 30 days of the prediction period of each scenario (15 May-15 June, 15 June-15 July, 15 July-15 August, and 15 August-15 September for scenario I, II, III, and IV, respectively) were used for statistical analysis. This part corresponds to the red-dashed lines in Figure 1. Notably, the main goal of this study was to assess to what extent data assimilation could influence the prediction of land surface variables, so we took the output of assimilation for the whole growing season (1 May to 30 September) as the \"benchmarks\" that contain information from both models and observations considering the uncertainties in raw observations. The \"benchmarks\"corresponds to the green arrow in Figure 1 and will be further compared with the prediction of MLDAS and OL.  
Here, normalized information contribution (\(NIC_{RMSD}\)) was adapted to quantify the differences between MLDAS and OL, which was defined by Equation (1) [24]. \(RMSD_{Analysis}\) (\(RMSD_{Mdel}\)) refers to RMSD between \"benchmarks\" and MLDAS (OL). A positive \(NIC_{RMSD}\) indicates the prediction of MLDAS is improved compared with OL. A negative \(NIC_{RMSD}\) means degradation. Besides, the relative difference (\(RD\)) between OL and MLDAS defined by Equation (2) was used to show how long the influence of assimilation exists during the prediction period. \(RD\) in the equation is relative difference, \(LSV_{Mdel}\) and \(LSV_{Analysis}\) are simulations of land surface variables (LSV) from OL and MLDAS, respectively.  
\[NIC_{RMSD}=\frac{RMSD_{Mdel}-RMSD_{Analysis}}{RMSD_{Mdel}}\times 100 \tag{1}\]  
\[RD=\frac{\left|LSV_{Mdel}-LSV_{Analysis}\right|}{LSV_{Mdel}}\times 100 \tag{2}\]
## 2 Autonomous Decision Support Framework
### Component Overview  
As shown in Figure 3, this framework is divided into three major components:  
Figure 3: Archimate global architecture of the Automated Decision-Support module.  
**Early warning:**: The role of this component is to monitor and forecast different risk levels and indicators. Those risks are relative to the main road key components (obstacles, road attributes, ego-vehicle, environment, driver). Each calculated risk depends not only on the current state of the component itself but also on a forecast of its evolution. Thus, each risk has two thresholds (warning and emergency) and three driving states (regular, cautious/degraded, and emergency/critical). When a risk is higher than the warning threshold, the associated warning is sent to the driver via the HMI, whereas the emergency threshold is used directly by the Mode manager. This component is presented in details in [68].
**Mode manager:**: This component decides which driving mode should be adopted, depending on the current and short time predicted situations, the driver's wish (input from HMI), and the combination of the different risk levels (input from Early warning). The ODD establishes conditions under which the vehicle may operate in autonomous mode. The Mode manager is also responsible for starting the emergency mode and issuing a request to intervene to the human driver.
**Trajectory planning:**: This module generates the trajectories, which the vehicle tracks in autonomous and emergency modes. It uses inputs from the perception module to predict the trajectories of the different objects/obstacles in its environment. It then generates, evaluates and selects the best trajectory for the ego-vehicle. This trajectory must respect a set of constraints (traffic, system, driver rules) relative to safety. Depending on the mode, it uses either the classical Co-Pilot algorithm (see Appendix A) or the new emergency algorithm (see Section 3).