esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategies to Combat Multidrug-Resistant and Persistent Infectious Diseases
## 3 Repurposing Treatments against MDR and Persistent Bacteria
### Anti-Helmuthic Drugs  
Salicylanilides are antiseptics that are used worldwide, and they are widely studied. These compounds are thought to act by uncoupling oxidative phosphorylation, thereby impairing the motility of parasites. [PERSON] and colleagues described the efficacy of niclosamide (a bacteriostatic agent included in the salicyanilide family) against methicillin-, vancomycin-, linezolid-, and daptomycin-resistant _S. aureus_ isolates, and they concluded that they probably damage the bacterial membrane [66]. Niclosamide inhibits QS and virulence genes in _P. aeruginosa_[67], such as phospholipase C, LasA protease, pyocyanin, chitinase, and rhamn. It also increases the negative charges on the cell walls of _A. baumannii_ and _K. pneumoniae_, resulting in synergistic interaction with cationic colistin, resensitizing both pathogens to this antibiotic [68]. These studies show consistent results although they were performed with four different bacterial species and conditions.  
In 2016, [PERSON] and [PERSON] reported that other members of the salicyanilide family, such as rafoxanide and closantel, presented greater bactericidal activity against the logarithmic and stationary phases of _C. difficile_ than vancomycin [69]. Avermectins, a group of broad-spectrum anti-helmthics, demonstrated in vitro efficacy against _M. tuberculosis_ and _Mycobacterium ulcerans_, with MIC values ranging from 1 to 8 mg/L and 4 to 8 mg/L, respectively [70]. [PERSON] and colleagues observed the bacteriostatic effect of ivermectin, an avermectin, against clinical isolates of _S. aureus_ in vitro [71]. Ten years previously, [PERSON] and colleagues reported that ivermectin improved the survival of mice challenged by lethal doses of lipopolysaccharideaccharide (LPS), significantly reducing the levels of tumor necrosis factor \(\alpha\) (TNF-\(\alpha\)), IL-1b, and IL-6 [72]. These researchers observed the same findings in vitro. Ivvermectin also blocked the nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-kB) pathway and reduced endotoxin (presence of endotoxins in the blood) and the associated inflammation.
## 3 Results and Discussion
### Simulation Results with the Default Configuration
Within this zone, despite the refrigerant temperature remaining nearly constant, the heat transfer coefficient showed a slow decline in response to the rising x, thereby leading to a gradual increase in the CPU temperature. Additionally, the refrigerant within the second mini-channel evaporator resided in the saturated boiling region, mirroring the trend  
observed in the saturated boiling region of the first evaporator. Within the condenser, the heat transfer coefficient gradually reduced as the x in the two-phase region declined. Notably, the single-phase region within the condenser constituted a minimal proportion. Figure 8: Internal parameter distribution in the PCTCS with default configuration: (**a**) pressure and pressure drop distribution; (**b**) thermodynamic quality and void fraction distribution; (**c**) temperature and heat transfer coefficient distribution. As illustrated in Table 3, even when the pump efficiency was fixed at 0.1, the pump's power output amounted to a mere 27.9 W. Meanwhile, the EERp reached an impressive figure of 115, signifying the considerable efficiency of the PCTCS. Throughout the heat transfer processes in the PCTCS, two instances of subcooling became evident: the first at the entry point of the initial evaporator and the second at the condenser outlet. The subcooling degree of the refrigerant entering the evaporator was 14.44 \({}^{\circ}\)C, markedly higher than the 0.78 \({}^{\circ}\)C at the condenser outlet. The system's maximum temperature differential was 34.5 \({}^{\circ}\)C. Simultaneously, the first and second CPUs exhibit temperatures of 78.72 \({}^{\circ}\)C and 79.5 \({}^{\circ}\)C, respectively. Despite a substantial Tdt, the CPUs' temperatures were significantly lower than typical CPU overheating protection thresholds, indicating the PCTCS could effectively deal with the CPUs' heat under the default simulation conditions. Furthermore, the variances in the temperatures of the first and second CPUs--0.08 and 0.34, respectively--indicated a uniform temperature distribution across both CPUs.
# Assessment of Parent-Child Interaction Quality from Dyadic Dialogue
## 2 Related Work
### Term Frequency-Inverse Document Frequency  
Term Frequency-Inverse Document Frequency (TF-IDF) is a statistical measure employed to determine the relevance of words in a text document or corpus. TF-IDF frequently serves as a weighting factor in information retrieval searches, text mining, and user modeling [16].  
TF-IDF is composed of two metrics: term frequency (TF) and inverse document frequency (IDF). The TF score measures how often words appear in a particular document. In simple words, TF counts the occurrences of words in a document. The weight of a term is directly proportional to its frequency in the document. This implies that words appearing more frequently in a document are assigned a higher weight [11]. In contrast, IDF measuresthe rarity of words in the text, assigning more importance to infrequently used words in the corpus that may carry significant information. By integrating IDF, TF-IDF reduces the significance of frequently occurring terms while amplifying the importance of less common terms [17].  
TF-IDF has been one of the most widely used methods in NLP and machine learning for tasks like document classification, text summarization, sentiment classification, and spam message detection. For example, it can identify the most relevant words in a document and then apply these words as features in a classification model. A survey conducted in 2015 on text-based recommender systems found that 83% of them used TF-IDF [18]. Furthermore, many previous studies have demonstrated the effectiveness of TF-IDF for tasks like automated text classification and sentiment analysis [19; 20; 21; 22; 23]. However, TF-IDF has limitations. TF-IDF does not efficiently capture the semantic meaning of words in a sequence or consider the order in which terms appear. Additionally, TF-IDF can be biased towards longer documents, meaning that longer documents will generally have higher scores than shorter ones.
# Defect Tailoring in HfO2/Si Films upon Post-Deposition Annealing and Ultraviolet Irradiation
## 3 Results
### Structural Characterization
#### 3.1.2 Raman Spectroscopy  
Figure 3 shows the Raman spectra of the four HfO\({}_{2}\) samples and p-Si as the reference sample (P1). The monoclinic HfO\({}_{2}\) structure has 18 optical phonon modes, which are (9 Ag + 9 Bg) for Raman active modes and (8 Au + 8 Bu) for IR-active [8]. Figure 3 depicts optical vibration modes centered at 133 cm\({}^{-1}\) (Ag mode), 301 cm\({}^{-1}\) (Ag mode), 617 cm\({}^{-1}\) (Bg mode), 671 cm\({}^{-1}\) (Ag mode), and 821 cm\({}^{-1}\) (Bg mode).  
The most intense peak corresponds to the 301 cm\({}^{-1}\) vibration related to Hf-Hf bonds and the formation of oxygen vacancies [5; 9]. A defined peak at 133 cm\({}^{-1}\) arises in the  
Figure 3: Raman spectra of HfO\({}_{2}\) samples S1–S4 and p-Si substrate. Raman spectra were acquired with a 532 nm laser.  
Figure 2: Grazing incidence X-ray diffractograms of the p-Si substrate and the S1–S4 HfO\({}_{2}\) samples compared with the monoclinic powder diffraction file, PDF 36-0104.  
spectrum of the S2 sample in accordance with the higher crystallinity of the sample. Nevertheless, there are only slight differences in the peak intensities between the films; therefore, not much information can be derived from the effect of the post-deposition treatment on the local film structure.
## 5 Conclusions  
We have designed and implemented a GNSS-R instrument called SPIR with the objective of providing a way to demonstrate the synoptic capabilities of the PARIS interferometric technique. Therefore, we have designed and built an instrument capable of acquiring GNSS signals from two antenna arrays of eight elements each and recording them onto an array of hard disks. The signals of each antenna element are individually down-converted coherently to IF (in-phase and quadrature) with an RF bandwidth of about 35 MHz, sampled at an 80 MHz rate, and quantized using one bit per sample. Data is precisely time-tagged and recorded at a sustained rate of 320 MBytes/s (2.56 Gbits/s) during a period of up to three hours. The instrument can operate at L1, L2, and L5 GNSS bands, and its operation can be easily expanded to higher frequency bands by the coherent heterodyne down-conversion to L-band. Signal processing and data-analysis is done off-line, using a specially designed signal processor that performs digital beamforming and signal correlation. This signal processor currently implements the clean-replica and interferometric GNSS-R techniques, which allows for an intercomparison of both techniques using exactly the same data.  
We have demonstrated the performance of the instrument by presenting a variety of ancillary and monitoring data collected by the instrument itself in laboratory and airborne tests. Furthermore, we have also presented the clean replica and interferometric waveforms obtained in an airborne campaign over the Baltic Sea and processed using our signal processor.  
The SPIR instrument has provided significant results in support of GNSS-R remote sensing, and in particular, the development of future space-borne GNSS-R missions such as GEROS-ISS by validating waveform models and altimetry retrieval techniques.  
This work has been carried out with the financial support of the following research projects and contracts: Spanish research projects: AYA2011-29183-C02-02 (Ministerio de Economia, Industria y Competitividad) and ESF2015-70014-C2-2-R (MINECO/FEDER), ESA Contract: RFQ/3-12747/09/NL/JD-CCN4, and the European FP7 project E-GEM (UE 607126). We would like to thank Aalto University's Skyvan team for their contribution to the success of the airborne campaigns and to [PERSON] for his help in the campaigns.  
The instrument was designed and built by [PERSON], [PERSON] and [PERSON]. The signal processor and data-analysis studies were done by [PERSON], [PERSON], [PERSON] and [PERSON]. The experimental campaigns were planned and carried out by [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 4 Experimental Results and Analysis
### Performance Evaluation on the KOMPSAT-3 Dataset
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Algorithm** & \(F_{1}\)**-Score** & **KC (\%)** \\ \hline \multirow{4}{*}{Test area 1} & JF + UNET & 0.86 & 86.17 \\  & JF + ATTUNET & 0.85 & 84.09 \\  & Modified-UNet++ & 0.85 & 84.76 \\  & Proposed & 0.88 & 87.11 \\ \hline \multirow{4}{*}{Test area 2} & JF + UNET & 0.60 & 60.14 \\  & JF + ATTUNET & 0.69 & 68.62 \\  & Modified-UNet++ & 0.77 & 77.06 \\  & Proposed & 0.83 & 82.8 \\ \hline \multirow{4}{*}{Test area 3} & JF + UNET & 0.42 & 39.61 \\  & JF + ATTUNET & 0.58 & 56.49 \\  & Modified-UNet++ & 0.35 & 32.57 \\  & Proposed & 0.59 & 58.04 \\ \hline \multirow{4}{*}{Test area 4} & JF + UNET & 0.30 & 28.91 \\  & JF + ATTUNET & 0.35 & 34.03 \\  & Modified-UNet++ & 0.50 & 49.92 \\  & Proposed & 0.40 & 39.78 \\ \hline \multirow{4}{*}{Avg. \(\pm\) Std.} & JF + UNET & 0.54 \(\pm\) 0.21 & 53.71 \(\pm\) 21.85 \\  & JF + ATTUNET & 0.62 \(\pm\) 0.18 & 60.81 \(\pm\) 18.29 \\ \cline{1-1}  & Modified-UNet++ & 0.62 \(\pm\) 0.20 & 61.08 \(\pm\) 20.94 \\ \cline{1-1}  & Proposed & 0.68 \(\pm\) 0.19 & 66.93 \(\pm\) 19.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(F_{1}\)-score and KC with data-augmented training pair images for the KOMPSAT-3 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Algorithm** & \(F_{1}\)**-Score** & **KC (\%)** \\ \hline \multirow{4}{*}{Test area 4} & DI + UNET & 0.29 & 28.3 \\  & DI + ATTUNET & 0.32 & 31.37 \\  & JF + UNET & 0.15 & 14.22 \\  & JF + ATTUNET & 0.14 & 12.52 \\  & Modified-UNet++ & 0.09 & 7.82 \\  & Proposed & 0.4 & 39.78 \\ \hline \multirow{4}{*}{Avg. \(\pm\) Std.} & DI + UNET & 0.52 \(\pm\) 0.17 & 50.71 \(\pm\) 0.17 \\  & DI + ATTUNET & 0.57 \(\pm\) 0.19 & 56.12 \(\pm\) 0.19 \\  & JF + UNET & 0.26 \(\pm\) 0.13 & 24.25 \(\pm\) 0.13 \\ \cline{1-1}  & JF + ATTUNET & 0.33 \(\pm\) 0.13 & 31.33 \(\pm\) 0.13 \\ \cline{1-1}  & Modified-UNet++ & 0.35 \(\pm\) 0.24 & 33.52 \(\pm\) 0.24 \\ \cline{1-1}  & Proposed & 0.68 \(\pm\) 0.19 & 67.19 \(\pm\) 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Moreover, the proposed architecture can achieve efficiency with no increase in learning time such as data augmentation.
## 4 Software-Defined Network (SDN)
### SDN Architecture  
An SDN consists of the three-layer system presented in Figure 7.  
1. Data plane: This contains packet-forwarding devices and is linked to the control plane via an appropriate interface. The interface is a communication channel that links the controller and application plane or the control and data planes. A south-bound interface (SBI) is an interface to or from the controller and network devices, such as Open-Flow [38]. A northbound interface (NBI) is a channel to or from a controller and upstream SDN-aware applications. The east/westbound interface is the communication protocol used to connect the distributed SDN controllers [39].
2. Control plane: This contains the network operating system (NOS), which orchestrates the operations and the computations. The SDN controller is the network brain, which  
Figure 7: The general structure of SDN technology [40].  
engages the network application and services, control plane containers, and database in which information is gathered. The SDN controller handles the routing, manages the interference, and allocates the security functions when finding weird attitudes [41]. It is the powerful plane of SDN network architecture, which manages and configures network traffic. The main requirement of the SDN controller is the flexibility to configure remotely [41]. It is responsible for getting useful information from devices and communicating with the network's abstracting view to SDN applications. Many routing schemes are commonly used with the control plane, including open shortest path first (OSPF), routing information protocol (RIP), and enhanced interior gateway routing protocol (EIGRP), which are managed with IPV4 and IPV6 using the control plane [35]. When the switch receives a packet and checks the matching on the entry table, it acts when there is a corresponding opening in the table. Otherwise, the switch forwards the packet to the SDN controller, which makes the forwarding decision and sends the switch the corresponding decision [41].
3. Application layer: This is above the control plane and is responsible for providing the admin with the network status, accessing the collected data, and offering many other SDN benefits. It is used in business platforms to manage network operations based on the business point of view [35].
###### Abstract  
Sensor nodes are small, low-cost electronic devices that can self-organize into low-power networks and are susceptible to data packet loss, having computational and energy limitations. These devices expand the possibilities in many areas, like agriculture and urban spaces. In this work, we consider an IoT environment for monitoring a coffee plantation in precision agriculture. We investigate the energy consumption under low-power and lossy networks considering three different network topologies and an Internet Engineering Task Force (IETF) standardized Low-power and Lossy Network (LLN) routing protocol, the Routing Protocol for LLNs (RPL). For RPL, each secondary node selects a better parent according to some Objective Functions (OFs). We conducted simulations using Contiki Cooja 3.0, where we considered the Expected Transmission Count (ETX) and hop-count metric (HOP) metrics to evaluate energy consumption for three distinct topologies: tree, circular, and grid. The simulation results show that the circular topology had the best (lowest) energy consumption, being 15% better than the grid topology and 30% against the tree topology. The results help the need to improve the evolution of RPL metrics and motivate the network management of the topology.  
Keywords:internet of things; energy consumption; routing protocol for low-power and lossy networks; precision agriculture; mesh networks +
Footnote †: journal: Sensors
# Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
## 1 Introduction
These scholars demonstrated the increasing maturity and sophistication of nonlinear guided wave theory, as well as the continuous expansion and deepening of its application scope, in aspects such as the selection of excitation modes, the clarification of conditions for generating second harmonics in nonlinear guided waves, and the application of nonlinear guided wave technology in the field of small defects identification in single-layers. In the field of composite structures, [PERSON] (2018) employed Lamb wave dispersion curves and mode expansion analysis of guided wave excitation to characterize the properties of adhesive layers in composite plates. [PERSON] (2018) provided a theoretical foundation for assessing bond quality and selected an appropriate excitation mode for Lamb waves. [PERSON] et al. [PERSON] et al. (2018) proposed an improved normal mode expansion method which yields accurate elastodynamic solutions since these solutions satisfy all the governing equations and boundary conditions for forced Lamb waves in plates. Further validation of the effectiveness of the improved normal mode expansion method was carried out through comparisons with finite element solutions. Therefore, research on nonlinear ultrasonic guided wave detection techniques for composite structures primarily remains at the theoretical discussion stage, with limited studies and experimental evaluations conducted. This paper focuses on applying nonlinear guided waves to detect weakened adhesive layers in composite structures. The wave equation and the transfer matrix method were utilized to establish a mathematical dispersion equation for the transfer matrix of composite structures. Calculations were then performedto obtain the dispersion curves of guided waves within the composite structure, leading to the selection of the A1-S1 mode. A finite element was employed to simulate the detection of localized and overall weakening in the adhesive layer of the composite structure. The degree of weakening and its approximate location can be ascertained by comparing and evaluating the variations in the amplitude of the second harmonic between weakened and non-weakened regions of the adhesive layer within the composite. Experimental analysis was conducted to investigate the propagation characteristics of nonlinear ultrasonic guided waves in the composite structure. This experimentation confirmed the feasibility of nonlinear guided wave technology for inspecting multilayer composite structures. The proposal of this detection method has laid a robust foundation for assessing adhesive weakness in composite structures using nonlinear guided waves.
## 5 Research Methodology and Empirical Findings
### AI and Simulation System  
With existing data and architecture, introducing AI models and performing predictive analysis models can effectively help companies solve service problems and accelerate innovation. Figure 5 shows the modeling process of service quality and reliability prediction in this framework. In the reliability analyzing process, lack of data and omissions will cause analysis difficulties. However, in addition to searching for relevant and possibly usable data, the interpretability of identification data is crucial. Removing the outliers and adding the integrated sections can make the presentation of the observation more objective. The data scarcity issue still cannot be solved after using the Kaplan-Meier methodology. [PERSON] et al. proposed and compared the structure for predicting reliability by various machine-learning models [45]. A recent study showed that in reliability prediction, the MLP model has relatively better predictive capabilities [45]. To obtain valuable referenced forecasting, we will use the MLP model because of its performance. After obtaining the prediction results, the simulation structure can perform a series of analyses and adjustments following the current service delivery status for the reference of decision makers.
## 2 Materials and Methods
### RQA-Based Method for AerT Automatic Detection  
RQA is a powerful method used to analyze nonlinear dynamical systems. It involves reconstructing time series data into a phase space using delay embedding techniques, where vectors are created based on an embedding dimension \(m\) and time delay \(\tau\). A matrix \(R\) is then formed by comparing points in this phase space using a predefined threshold distance \(\epsilon\). The resulting matrix is graphically represented as a Recurrence Plot (RP), showing patterns of recurrence, such as diagonal lines indicating repeating behaviors. Key parameters like \(\epsilon\), \(\tau\), and \(m\) are chosen carefully to suit the characteristics of the data and study goals. Measures derived from the recurrence plot, such as Determinism (DET) and Laminarity (LAM), quantify the predictability and structure of the system based on the lengths of diagonal and vertical (or horizontal) lines in the plot, respectively. For a more detailed overview of this nonlinear analysis, see Appendix C.  
RQA has been used to provide a straightforward framework for describing the coupling of cardio-respiratory physiology under increasing physical exertion in terms of both complexity and dynamical transitions. This RQA-based methodology has been validated using DET and LAM to identify ventilatory thresholds, as outlined in references [6; 7].  
In particular, the assessment of the AerT is based on identifying a minimum in DET (DET\({}_{\text{min}}\)), which is corroborated by a corresponding minimum in LAM (LAM\({}_{\text{min}}\)).
# Analysis of the Guide Vane Jet-Vortex Flow and the Induced Noise in a Prototype Pump-Turbine
## 2 Pump-Turbine Unit  
### Parameters of Pump-Turbine  
Figure 1 is the schematic map of the pump-turbine (fluid domain in prototype scale) which consists of the draft tube, runner, guide vane, stay vane, and volute. Fluid, as indicated by the vector, flows from the draft tube side to volute side in pump mode for water storage. The runner rotates in the clockwise direction in this view by \(n_{d}\) = 430 r/min. As shown in the meridional view, the radius at runner high pressure side is \(R_{hi}\) = 2.08 m. The radii at runner low pressure side \(R_{lovs}\) (at shroud) and \(R_{lovth}\) (at hub) are 1.12 m and 0.64 m, respectively. The width at runner outlet \(b_{hi}\) is 0.4 m. The blade number of the runner, guide vane, and stay vane are 9, 20, and 20, respectively. Based on the turbomachinery similarity, the specific speed of runner \(n_{q}\) can be calculated by:  
\[n_{q}\ =\ \frac{n_{d}\sqrt{Q_{d}}}{H_{d}^{3/4}} \tag{1}\]  
where \(Q_{d}\) is the best efficiency point (BEP) flow rate and \(H_{d}\) is the BEP head. In this case, the specific speed \(n_{q}\) is equal to 31.5.
## 3 Experimental Set-Up
### Capturing and Processing Data
Using the RMS value of the detected frame and the received data, we determine the distance and a specific position from each transmitter detected. To evaluate the effectiveness of the decoding code, we focus exclusively on the performance of the top photodiode, excluding measurements from the side photodiodes. This is because, due to the tilt of the side photodiodes, they are often oriented away from the lights in many areas, making them difficult to detect. Consequently, their measurements would not be representative of assessing their performance.