esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Prokaryotic Expression and Purification of DcTPSb1 Protein  
Following the optimization of codons, the _DcTPSb1_ gene was synthesized and ligated into the pDEST15 plasmid, which contained a glutathione S-transferase (GST) tag of 26 kDa. The recombinant plasmid, designated pDEST15-DcTPSb1, was then transformed into E. coli BL21 (DE3) competent cells. Positive clones were identified and incubated at 37 \({}^{\circ}\)C in the presence of ampicillin at OD600 nm of 0.6. The mixed cultures were obtained by 20 min centrifugation after a culture mixture was mixed with the inducing compound, Isopropyl-\(\beta\)-d-thiogalactopyranoside (IPTG), to reach a final concentration of 0.2 mM. Induction of protein expression was carried out by shaking the cultures at 180 rpm for 18 h at a temperature of 20 degrees Celsius. Once the fusion protein with expression and good water solubility was identified using SDS-PAGE, the purification process commenced. The purification began with high-pressure or ultrasonic crushing of the mixture, resulting in the obtaining of supernatant through centrifugation and subsequent filtration to remove impurities. The supernatant containing the target protein was gradually introduced into a purification column, allowing for slow dripping and collection, with this operation repeated approximately 3 times to maximize protein adsorption onto the beads. The column was rinsed around 6 times with a rinsing solution consisting of 50 mM Tris, pH 7.6. Subsequently, the elution buffer (50 mM Tris, 50 mM NaCl, 20 mM reduced glutathione, pH 8.0) was employed to elute the precipitate for further purification. The purified GST-DcTPSb1 protein was then subjected to additional analysis to verify its size and location.
# Modern MultiPort Converter Technologies: A Systematic Review
## 3 Common Multiport Topologies  
Figure 7, displays one of the earliest non-isolated multiport converter topologies recorded. Two typical boost converters are connected in series to form the topology [70]. At that time, creating a new multiport topology by connecting two or more converters in series or parallel was typical practice. Numerous topologies of this kind were employed for the creation of independent power.  
The following equation can be used to express the voltage on the output port that is linked to the load:  
\[V_{OUT}=\frac{V_{PV}}{1-\delta_{1}}+\frac{V_{FC}}{1-\delta_{2}}, \tag{2}\]  
where \(\delta_{1}\), \(\delta_{2}\) are duty cycles for \(Q_{1}\), \(Q_{2}\) are respectively.  
Figure 8, shows the partially isolated multiport converter, which combines direct and magnetic coupling. The converter was created specifically for application in combination with fuel cells [71].  
The six semiconductor switching devices used in the multiport converter (\(Q_{1}\)-\(Q_{6}\)). The application of a high-frequency transformer helps to isolate the output port from the input. While \(Q_{5}\) and \(Q_{6}\) form a bidirectional half-bridge for the output side, \(Q_{1}\) and \(Q_{2}\) together with \(Q_{3}\) and \(Q_{4}\) make two boost half-bridges.  
The following equation represents the power flow to the converter's load side:  
Figure 8: Topology of 3-port, 2-input boost multiport converter.  
Figure 7: Topology of 3-port, 2-input boost multiport converter.  
\[P=\frac{V_{IN}V_{OUT}\phi}{n\omega L_{X}}\left(1-\frac{\phi}{\pi}\right), \tag{3}\]  
where  
\(P\)--is the power flow from input to output side, W  
\(V_{IN}\)--is the input side DC voltage, V  
\(V_{OUT}\)--is the output side DC voltage, V  
\(\omega\)--is the angular frequency, rad/s  
\(n\)--is the number of turns of the high-frequency transformer,  
\(\phi\)--is the phase shift angle between control signals, rad  
\(L_{X}\)--is the leakage inductance, H.  
According to Equation (3), at a particular switching frequency, the phase shift angle and leakage inductance have a significant impact on the output power. This architecture was created for the integration of fuel cells.  
Figure 9, presents the topology of a fully isolated multiport converter. This topology is suggested in [72; 73], and it is created as a modular system with three ports for each module.  
The purpose of diode \(D_{0}\) in Figure 9 is to protect the fuel cell by preventing the flow of reverse current. Current ripples are filtered and softened by choke \(L_{1}\). Power flow in the system is regulated by the phase shift of the gate control signals for the active bridge that connects the fuel cell port and output ports.
## 5 Discussion
### Objective Function
#### 5.3.1 Weighted Sum  
* Advantages: The weighted sum approach is appreciated for its simplicity due to its straightforward implementation and understanding. It allows the adjustment of weights according to the significance of each objective, providing a clear way to prioritize among different goals. It simplifies optimization by transforming multiple objectives into a single composite objective using weights. This consolidation renders the multi-objective problem compatible with traditional optimization techniques and tools, which makes the solution process manageable.
* Disadvantages: The effectiveness of the weighted sum approach significantly relies on the selection of weights for each objective. Different sets of weights lead to vastly different solutions; thus, identifying the optimal weights to balance the importance of each objective is critical and challenging. Moreover, when objectives are on substantially different scales, assigning weights that accurately reflect each objective's relative importance becomes complicated. It often necessitates the normalization of objectives to a standard scale, which introduces additional complexity to the optimization process.
## 3 Results and Discussion  
### Surface Roughness of the Substrate  
The surface of the aluminium was analysed with the goal of determining the surface roughness of our substrate base material before the formation of the PMHM joint. The raw data of the surface roughness profiles obtained for the different surface conditions are displayed on Figure 4.  
Figure 4: Optical profilometry’s 3D profiles of the aluminium: (**a**) without any surface treatment, (**b**) after been polished by SP, and (**c**) after the GB process.  
The raw data presented in Figure 4 allow us to make a comparison of surfaces exposed to different surface treatments. The optical profilometry results revealed a higher surface roughness in the SP and GB surfaces when compared with the aluminium surface without any surface treatment. The SP and GB techniques lead to the homogenisation of roughness and the increased roughness of the aluminium surface. The small sharp peaks present on the surface of untreated aluminium (Figure 4a) were converted into a more uniform roughness distribution for both SP and GB surfaces. The higher degree of abrasion was found in the GB surfaces, where the rounded shapes of its valleys, resulting from the impact of the projected particles during the GB process, were noticeable. The respective values of the Ra and Rz obtained are displayed in Table 3.  
The surface roughness obtained for the aluminium substrate without any treatment (Ra = 0.16 \(\upmu\)m and Rz = 0.58 \(\upmu\)m) is in accordance with the values reported on other studies (Ra = 0.18 \(\upmu\)m and Rz = 0.8 \(\upmu\)m) in which the surface roughness of aluminium substrates was estimated, before the formation of PMHM joints [22; 23; 83]. For the treated surface conditions, the direct comparison of the surface roughness values with previous works becomes more difficult, as these values are dependent on the process parameters. However, it is worthy of note that the values obtained for SP aluminium (Ra = 0.32 \(\upmu\)m and Rz = 1.12 \(\upmu\)m) are in the range of the surface roughness values reported by [PERSON] et al. (Ra = 0.3 to 3 \(\upmu\)m) [84], but well below the range (0.7 \(<\) Ra \(<\) 3.15 \(\upmu\)m) reported in similar works where the SP was applied on the aluminium of single-lap joints [85; 86; 87]. The surface roughness values of aluminium after the GB treatment (Ra = 1.65 \(\upmu\)m and Rz = 6.20 \(\upmu\)m) are comparable with the values (Ra = 1.9 \(\upmu\)m) reported by [PERSON] et al. [83], but well below the values reported by [PERSON] et al. (Ra = 5.2 \(\upmu\)m) [85], or by [PERSON] et al. (Rz = 89 \(\upmu\)m) [22; 23]. In addition to SP and GB surface treatments, there are other methods to increase the surface roughness of the aluminium used in PMHM single-lap joints. Some authors report the use of techniques such as shot penning (Ra = 2 and 6 \(\upmu\)m) [88], chemical etching (Ra = 1.7\(-\)1.92 \(\upmu\)m) [86; 89], or anodization (Ra = 2.13\(-\)4.77 \(\upmu\)m) [89].
## 4 Response Reconstruction for an Actual Continuous Bridge
### Data Preparation and ANN Configuration  
Figure 9 presents a sample of the typical monitoring data covering several hours. The primary cause of strain variation was temperature-induced strain, as shown in Figure 9a. The local peaks observed in the strain time-history curve were induced by the passage of vehicles. It is crucial to highlight that these local peaks represent the effect of both low-frequency and high-frequency bridge strain. Figure 9b depicts the high-pass filtered bridge strain, which is a zero-mean time series. Considering the bridge's natural frequencies were above 1 Hz, the cutoff frequency was set at 1 Hz. Figure 9c offers an enlarged view of the filtered data presented in Figure 9b.  
It should be noted that this study did not record either traffic conditions or environmental effects. However, traffic conditions (with/without a vehicle, single/multiple vehicles) could be inferred from the amplitude and duration of the high-frequency bridge responses. For instance, the data in Figure 9c were categorized into \"Vehicle passing\" and \"Without vehicle\". The high-frequency responses caused by vehicles exhibited a significantly higher amplitude. Additionally, the duration of these high-amplitude responses aided in distinguishing between the effects of a single vehicle and those of multiple vehicles. The average velocity for vehicles on this bridge was between 40 and 70 km/h, translating to a passage time of roughly 4 to 9 s per vehicle. If the high amplitude persisted beyond this typical time frame, this suggested that several vehicles were crossing the bridge at the same time. During the validation process, it was verified that both the training and test data encompassed scenarios involving both single and multiple vehicles.  
The dynamic responses at one cross-section were predicted using monitoring data from two other cross-sections. For each cross-section, a single strain gauge was chosen, yielding two input features for the BP-ANN and one output feature. The BP-ANN architecture included two hidden layers: the first with 128 neurons and the second with 32 neurons. The learning rate was configured at 0.001, with a batch size of 100 and a total of 500 epochs. The rectified linear unit (ReLU) function is used as the activation function. Since the theoretical derivation showed that the relationship was time-invariant, during training, data from various time points were randomly sampled in batches.  
Figure 8: Layout of the Fuchang Bridge health monitoring system with multiple strain gauges [45].
## 1 Introduction
We add an additional decoder module at the end of the single shot multibox detector (SSD) [16] architecture (encoder module) to form an encoder-decoder module, in which a new skip connection block is incorporated into each layer of the decoder to integrate the feature maps from the encoder and decoder modules. The proposed encoder-decoder modules effectively enrich the feature maps via integrating the high-level semantically strong features with low-resolution and low-level detailed features with high-resolution. This method is effective to extract discriminative features even from low-resolution and noisy IR images. * Second, we propose an attention module that re-weights the multi-scale features generated from the encoder-decoder module. By adding the attention mechanism, the network selectively emphasizes useful information and suppresses ineffective information while re-weighting the features from the encoder-decoder modules. The attention module significantly eliminates background interference while highlighting pedestrians so that there is a boost in the detection performance of the IR pedestrian detector, even when the brightness is similar among the pedestrians and backgrounds. * Finally, experimental results on two challenging datasets demonstrate that our AED-CNN shows the best performance. Our approach outperforms in detection precision by 5.1% and 23.78% on the Keimpung University (KMU) [21] (the KMU pedestrian detection database [21] is downloaded from: [[https://cvpr.kmu.ac.kr/](https://cvpr.kmu.ac.kr/)]([https://cvpr.kmu.ac.kr/](https://cvpr.kmu.ac.kr/)) for academic use) and Computer Vision Center (CVC)-09 [22] (the CVC-09 far infrared (FIR) sequence pedestrian dataset [22] (available online, 28 April 2016) is download from: [[http://adas.cvc.uab.es/elektra/enigma-portfolio/item-1/](http://adas.cvc.uab.es/elektra/enigma-portfolio/item-1/)]([http://adas.cvc.uab.es/elektra/enigma-portfolio/item-1/](http://adas.cvc.uab.es/elektra/enigma-portfolio/item-1/))) pedestrian datasets, respectively, when compared with the state-of-the-art oriented center-symmetric local binary (OCS LBP) + cascade random forest (CaRF) [21] method. The remaining part of this paper is arranged in the following manners. Section 2 briefly introduces the previous related works. Section 3 explains the proposed AED-CNN in detail. Experimentalresults and analysis are presented in Section 4. Finally, Section 5 summarizes our work and describes future works.
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences  
Based on statistical results obtained in the previous Section 2, we can confirm that chaotic maps cannot be used alone as a secure PRNG-CS. The question is how to combine some of them to build PRNGs-CS secure against statistical and cryptanalytic attacks, and efficient, in terms of hardware cost, throughput, and efficiency. A compromise between security and effectiveness is usually made, and it is first about the degree of security required for a given application.  
In this section, we created three secure pseudorandom number generators (PRNGs), known as LSP-PRNG, LST-PRNG, and LSPT-PRNG, by combining different mathematical maps such as the Logistic map, Skew-Tent map, PWLCM map, and 3D-Chebyshev map. We also examined the stream cipher systems associated with each PRNG.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## Appendix A
### Experimental Results After Applying Harmonic Noise  
Experiments with harmonic noise were conducted across four levels (Levels 1-4) with varying parameters: Level 1 (amplitude 0.03, fundamental frequency 20, harmonic set [2,3]), Level 2 (amplitude 0.1, fundamental frequency 40, harmonic set [2, 3, 4]), Level 3 (amplitude 0.2, fundamental frequency 40, harmonic set [2, 3, 4]), and Level 4 (amplitude 0.5, fundamental frequency 60, harmonic set [2, 3, 4]). The experimental results, as illustrated in Figures 12 and 12, and Table 14, show a significant decrease in model performance as noise intensity increases. In the MFPT and PU datasets, there was no substantial difference in performance among the models. However, in the CWRU dataset, the TSSequencer model performed the worst, with performance degradation becoming more pronounced as noise intensity increased. Additionally, the FCN model showed the poorest performance for the MFPT and PU datasets. At Level 3 on the CWRU dataset, the XCM model achieved the best performance, while the MultiPatchTST model consistently outperformed others at Levels 1, 2, and 4 across all metrics for all three datasets. Sequential models such as LSTM and GRU were significantly affected by harmonic noise, particularly at Levels 3 and 4, showing considerable performance degradation. While GRUAttention and LSTMAttention, enhanced with self-attention mechanisms, performed better than standard sequential models, they remained more sensitive to noise compared to Transformer-based models. The PatchTST and MultiPatchTST models, leveraging patch-based learning and Transformer architectures, demonstrated superior accuracy and stability, particularly at higher noise levels (Levels 3 and 4). In Table 14, the noise level is expressed as a signal-to-noise ratio (SNR). Figure A1: Experimental results by harmonic noise level for the CWRU dataset. Figure A2: Experimental results by harmonic noise level for the MFPT dataset.
## 3 Results and Discussion  
### Study Design  
A systematic search of three library databases enabled the accumulation of previous publications that have incorporated DOX-loaded graphene nanocarriers for research into the response of breast and ovarian cancer cell lines. As illustrated by the search and inclusion flowchart (Figure 3) from the initial search, the study identified a total of 175 studies, 47 from PubMed(r), 91 from Web of ScienceTM, and 34 from Ovid Medline(r); a further three were identified through referencing. Through combining the search engine's results, twenty-four publications were immediately excluded through duplication. Seventy records were then removed simply because their title did not adhere to the inclusion criteria; for example, those that were based on GQD nanohybrids, those that loaded more than one chemotherapeutic in addition to DOX, or those of an unrelated topic. A further thirty studies were removed through screening of the abstract, and thirty-four were removed through screening the full text. Commonly the excluded papers used an inappropriate cell line, incorporated multiple drugs, or provided insufficient drug release data.  
Table S1 reports the title, first author, journal, date of publication, and DOI for the seventeen publications [44; 64; 65; 66; 67; 68; 69]. Those included were published in sixteen scientific journals ranging from 2008 to 2021. Of these, 65% were published within the last five years (since 2016), highlighting the advancement of research and research interest in the area of graphene nanomaterials for drug delivery.  
The inclusion criteria excluded the use of publications that were based on GQDs and of those that incorporated the use of hydrogels. By limiting the studies to those based on 2D graphene and GO nanosheets, more comparisons can be drawn and evaluated. It should be highlighted that studies were excluded due to the inconsistent use of cancer cell lines and biological assays. Several studies incorporated the use of multiple cell lines but did not consistently represent them in all in vitro assays undertaken. These publications were therefore excluded due to inconsistencies in their experimental protocols and published results.
# Remedial Modelling of Steel Bridges through Application of Analytical Hierarchy Process (AHP)
## 2 Deterioration Mechanism
### Corrosion  
The corrosion of steel is not only limited to steel bridges but extends to all types of steel structures. According to [PERSON] (2008) the annual cost of corrosion for countries such as the United States, the United Kingdom, Australia, Japan, Kuwait, Germany, Sweden, Finland, China and India is somewhere between one and five per cent of the nation's GNP [14].  
The continual shift of anodic and cathodic sites provides the necessary environment for uniform corrosion to prosper. This mechanism works on reducing the thickness and thus, the load carrying capacity of steel members [15]. However, this type of attack is of less concern than more invasive modes such as crevice and pitting corrosion, as it is relatively easy to detect and manage [14].  
Galvanic corrosion occurs between dissimilar metals that have varying potential differences when situated in corrosive environments. According to the galvanic series, the metal with the greatest tendency to corrode will become the anode, while the less reactive metal will become the cathode. In the presence of an electrolyte, the anode will corrode as electrons flow from anodic to cathodic regions [10]. The rate of deteriorative loss is dependent on the presence of electrolytes, the potential difference, and the size ratio between cathodic and anodic sites [14].  
Crevice corrosion manifests itself in gaps that exist between metals, and metals to non-metals in steel structures, whereby the crevice behaves as the anode and the exterior the cathode. This localised form of attack requires conditions that allow for stagnant pooling of liquids for extended periods of time (e.g., small holes, welds, joints, and around washers and bolts). In some circumstances, crevice corrosion may require over 12 months to initiate, however, deterioration will occur at an ever-increasing rate once the mechanism is developed.  
Pitting corrosion is a major cause of steel bridge failure, due to difficulties associated with the detection of pits that are typically covered with corrosive products [14]. The accelerated growth of this deterioration mechanism can be explained by the autocatalytic nature of this process, with surface anomalies generally the origin for deteriorative loss. Temperature is also a limiting factor, as corrosion of some materials will not proceed within a certain temperature range.
## 2 Methodology
### Multizone Airflow Simulation Tool
#### 2.3.1 Weather Input  
The minimum requirements for ventilation modelling are side wind velocity (speed and direction) and external temperature. There are many sources of weather information available, but some may misrepresent the real situation due to errors in data collection. For this study, three different weather datasets (IPMA-2020 GCD, EnergyPlus weather data and SNIRH) were analysed in a preliminary stage of the study:  
* IPMA-2020 GCD--Weather Station was obtained directly from IPMA and is a raw set of the wind intensities and directions for the year 2020. These data were collected at the weather station near Lisbon airport, 6.4 km from the Sao Cristovao church.
* EnergyPlus weather data. The Lisbon dataset is based on the spatial interpolation of public weather data published by Instituto de Meteorologia 1951-1980 and obtained from the EnergyPlus database. The data in this file are a representation of the average values collected across 30 years [44].
* SNIRH makes all the data collected available on its website. In the case of Lisbon, three weather stations are available for data retrieval, but unfortunately, there are significant gaps in the analysed data due to equipment malfunctions and lost information.  
From the analysis of the weather files, it can be said that the SNIRH file showed several deficiencies in recording the wind direction, so the SNIRH weather file was discarded due to the lack of wind data. In a first stage of modelling, different simulations were carried out to assess the impact of using the IPMA and EnergyPlus weather datasets, in order to evaluate the consequence of using either dataset on the ACH values. From the preliminary tests, it was concluded that although the IPMA database provides a wider range of wind directions, the data it provides are for a single year (2020), and therefore, using these values as a reference in the simulation may not guarantee the best representation of climate trends over time. Weather data in building ventilation performance and IAQ is a complex issue that can lead to incorrect assumptions, as reported by [44; 45; 46]. Thus, the weather file used in the simulations is the one obtained from the EnergyPlus database [47].