esa-sceva/satcom-chunk-collection huggingface dataset:
# Antibacterial Effect of Topically Administered Transcamic Acid in Large Joint Arthroplasty
## 2 Materials and Methods
### Kinetic Growth Assay  
Kinetic growth assays were performed to determine the effect of TXA on bacterial growth. Two-times dilutions of TXA in BHI broth were prepared in clear 96-well microtiter plates (sterile, F-Bottom) from Greiner Bio-One (Frickenhausen, Germany). To minimize evaporation from wells during incubation, the outer wells of the plates were filled with 200 \(\upmu\)L of distilled water. Plate covers were pretreated with 0.05% Triton X-100 (Sigma-Aldrich, St. Louis, MO, USA) in 20% ethanol to make them hydrophilic and avoid condense formation during incubation [31]. Briefly, 6 mL of the solution was pipetted onto the cover and allowed to coat the surface for 30 s. The solution was then pipetted off and the cover was left to air-dry.  
Bacterial suspensions were prepared as described earlier for MIC determination so that the final bacterial concentrations in the assay were 5 \(\times\) 10\({}^{5}\) cfu/mL. Final tested TXA concentrations in the kinetic growth assay ranged from 100 mg/mL to 0.78 mg/mL. Bacterial suspensions without TXA were assessed along with each tested strain as positive growth (reference) controls. BHI alone and BHI with all tested TXA concentrations (without bacteria) were also assessed for background subtraction as negative growth controls (blanks).  
The 96-well microtiter plate was incubated in a Tecan Infinite 200 Pro MPlex plate reader at 37 \({}^{\circ}\)C for growth and monitoring of turbidity. Applied assay conditions were as follows: wavelength, 600 nm; mode, kinetic cycle; kinetic cycle duration, 24 h; read interval, 15 min; shaking, orbital; shaking speed, 244 rpm; shaking duration, 120 s; temperature, 37 \({}^{\circ}\)C. Plate shaking was set to occur twice per cycle, at the beginning and end of the 15 min incubation interval, to prevent sedimentation of bacteria. Obtained measurements were used to generate growth curves of bacteria at different TXA concentrations by plotting OD 600 nm against time. The maximum specific growth rate (\(\upmu\)) and the lag phase duration (\(\uplambda\)) were obtained by applying the Gompertz model [32]:  
\[\text{y}=\text{a}\times\text{exp}\ (-\text{exp}(-(\text{x}-\text{x}0)/\text{b})). \tag{1}\]  
The equation was fitted to bacterial growth curves using SigmaPlot 12.0 (Systat Software, Chicago, IL, USA) and all model parameter estimates (a, b, and x0) were so obtained. The maximum specific growth rate was then calculated as:  
\[\upmu=\text{a}/(\text{b}\times\text{e}), \tag{2}\]  
and the lag phase duration as:  
\[\uplambda=\text{x}0-\text{b}. \tag{3}\]
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 3 Results and Discussion
### Practical Implications for Landscape Sustainability
#### 3.4.4 User Comfort and Space Planning  
Seasonal and diurnal variations in thermal comfort indicate the need for space-use planning. For example, relocating open-air campus activities to morning or evening hours during summer can significantly improve comfort. Moreover, integrating green areas into social and recreational zones can encourage longer and healthier outdoor engagement [56; 66].
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 3 Related Works
### AI Approach
There are other factors that could affect the user's connectivity, such as fading and shadowing effects on the channel conditions and the interferences from other transmitting users. The fading and shadowing effects are propagation environment-dependent, e.g., built-up vs. open-space, while the interferences are dependent on the presence of other concurrent transmissions in the same frequency band, particularly at the cell edges. These critical factors were not considered in the above work.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### Select High-Quality Nodes for Participation in Consensus
[102] introduced APBFT, an adaptive version of the PBFT protocol that enhances the efficiency of the consensus process by adjusting the number of nodes involved based on their response times and reliability. This method minimizes communication overhead and decreases response latency, all the while preserving the algorithm's asynchronous characteristics, thereby increasing its resilience to Denial-of-Service (DoS) attacks. [PERSON] and his team Ref. [103] proposed XG-PBFT, an improved PBFT algorithm based on XGBoost-based grouping. XG-PBFT constructs a dataset by training on key parameters that influence node performance, which serves as a classification index for nodes. The XGBoost algorithm is then used to train the dataset, and new nodes joining the system are grouped accordingly based on the trained model. Nodes with higher parameter scores are assigned to the consensus group to participate in consensus, while the remaining nodes are placed in a general group to assist in consensus verification. To minimize system resource consumption, XG-PBFT optimizes the PBFT consensus protocol by addressing its high communication complexity. Table 11 provides a comparative overview of the mechanisms for rewards and penalties utilized by various consensus algorithms. This table offers an in-depth summary of these mechanisms and elucidates their impact on the number of nodes engaged in the consensus process. Here, N signifies the overall number of nodes within the network, whereas d  
Figure 11: A diagram of selecting high-quality nodes for consensus participation. indicates the threshold for nodes that participate in consensus for the T-PBFT algorithm. In scenarios involving large-scale networks, the implementation of these algorithms can greatly diminish the number of nodes engaged in the consensus process. Consequently, this results in a notable decrease in both communication overhead and latency, while enhancing transaction throughput. Nevertheless, a significant challenge lies in the fact that these algorithms may introduce centralization risks and increase the overhead of dynamic assessments. They are suitable for scenarios where node behavior is relatively stable and reputation evaluation data is complete (e.g., enterprise-level consortium blockchains). However, they are vulnerable to reputation falsification attacks in open and dynamic environments, such as public IoT networks.
## 4 Conclusions  
This research aims to examine the acoustic and vibrational characteristics of 3D-printed components in relation to the size of internal flaws localized at the neutral line. The objective is to determine the contribution of defect size, position, and modal responses, including peak frequency, damping, and amplitude. Therefore, in flexion mode, this study evaluates parts with defects ranging from 3 mm to 10 mm. The results indicate that these defects have a modest effect on the modal parameters. For both detectors, increasing defects above 3 mm has little effect on peak frequency. Furthermore, in the absence of faults, both sensors exhibit a weak frequency peak and an elevated amplitude. According to the acoustic detector, small flaws of 3 to 5 mm improve damping more than parts with no defects. Despite delamination, this sensor showed reduced damping, amplitude, and elevated peak frequencies with 7 and 10 mm defects. At the 7 mm and 10 mm defects, on the other hand, the vibration sensor clearly identifies the delamination with a minor increase in damping. In addition, the neutral line location causes damping to decrease as the defect size rises by up to 5 mm. Small flaws (3 mm) with local hardening at the edge cause elevated peak frequency. Therefore, this study highlights the importance of performing further experiments with other materials and different kinds of defects, such as fractures and porosity, to have a clearer understanding of the detectors' detection sensitivity and variance. In this context, vibration sensors have the ability to detect delamination and local hardening along the neutral axis compared to acoustic detectors. However, both sensors struggle to detect frequency changes in defects larger than 3 mm. Additionally, the sensors only examine one specific closed internal defect shape and concentrate on defects that are very small compared to the overall shape of the part. In this regard, the overall size of the samples and number of samples are two additional factors that may significantly affect the modal characteristics. Accordingly, coupled finite element modeling (FEM) would shed more light on the propagation of stress waves and the localized impacts induced by the type and location of defects. In this approach, future study may employ FEM analysis to validate and support experimental results.  
Conceptualization, A.D. and F.-E.J.; methodology, A.D., F.-E.J. and I.O.; validation, A.D.; formal analysis, A.D. and F.-E.J.; investigation, F.-E.J.; data curation, F.-E.J.; writing--original draft preparation, F.-E.J.; writing--review and editing, A.D., R.E.A. and F.D.; supervision, A.D.; project administration, A.D.; funding acquisition, A.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank the Wallonie Aerotraining Network for supporting this work.  
The authors declare no conflicts of interest.
###### Abstract
developed a series of dendritic oxygen probes to improve the quantum yields of oxygen probes and give them some additional characteristics [25; 26]. These oxygen probes have hydrophobic shells with platinum-porphyrin as the core. For these dendrimers, phosphorescent metalloporphyrins were encapsulated inside hydrophobic dendrimers, which form disclosure shells by enveloping the chromophores to control oxygen diffusion to the excited triplet states. Polyethylene glycol (PEG)ylation or the carboxylation of dendritic molecules can not only ensure the high water solubility of sensors, but also prevent direct interactions between probes and biomacromolecules. Therefore, the quantum yields of these porphyrin-containing dendritic molecules were raised to between 0.017 and 0.073 [26]. Since 2012, in order to enable the application of hydrophobic probes in biological research, we have started to use micelles formed by amorphous block copolymers to encapsulate highly efficient hydrophobic oxygen probes. At present, we mainly use amphiphilic polymers, including grafted polymers [27; 28] and simple block copolymers [29]. The quantum yields of platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorophenyl)-porphyrin (PtTFPP) in these micelles depend on whether there is energy transfer in the micelles [28] and the structures of the polymers [29], which are approximately between 0.107 and 0.231. Other research groups have reported the use of conjugated polymers or metalloporphyrins nanoparticles to achieve a high quantum yield of 0.18 for intracellular oxygen sensing [30; 31; 32]. Considering the abundance of polymer architectures, herein we used multi-arm block copolymers as carriers for achieving high quantum yields of PtTFPP in water. In recent years, multi-arm block polymers have gradually attracted considerable interest. It has been reported that micelles composed of multi-armed polymers may exhibit various morphologies and assemblies than linear polymers [33; 34]. Also, multi-arm star polymers have higher terminal groups' concentrations than conventional linear polymers [35], which enables further modification or the functionalization of the multi-arm polymers. Herein, we used a multi-arm poly(ethylene glycol) as the initiator to initialize the polymerization of \(\varepsilon\)-caprolactone to obtain multi-arm block copolymers (Scheme 1). The micelles formed from these
# An Empirical Recommendation Framework to Support Location-Based Services +
## 3 Preliminaries
### Point of Interest (POI)  
Because of the availability of GPS technology, it is easy to get the location of a user. Point of Interest (POI), defines the points which are interested to a user. Geographical Information System (GIS) can also provide a list of POIs on a certain location which includes information about the name of the location, latitude, longitude, and rating of that place [5]. Table 2 shows a sample of POI of a user which contains the name of the place, co-ordinates, type, and rating of that place.
## 2 Related Work
### Traditional Characterization Methods  
Traditional characterization models in entity linking aim to encode sentences into computable vector forms, and earlier utilized the bag-of-words model to characterize context. The bag-of-words model is utilized by many researchers [7; 8; 9] to represent the context of mention and the descriptive text of candidate entities in vector form, and then calculated the similarity between the candidate entities and the mentions using the cosine similarity. However, the bag-of-words model usually adopts one-hot encoding that is extremely sparse and ignores syntax and word order, which leads to poor semantic representation. Later, [PERSON] et al. [10] utilize Latent Semantic Analysis (LSA) [11] to characterize documents, which utilizes Singular Value Decomposition (SVD) to perform dimensionality reduction to identify potential semantic relationships between texts. The LSA method can remove the irrelevant information from the original vector space and make the semantic relationship more explicit; however, the computational complexity of the SVD solution is large. Subsequently, [PERSON] et al. [12] proposed the Word2 Vec method to represent words as dense vectors, which had the advantage of making the vectors between semantically similar words closer and allowed the operation of vectors to reflect the relations between certain words. In addition, the Word2 Vec method is optimized efficiently and simpler to implement compared to the LSA method. Later researchers further combine Word2 Vec with other methods to enhance the performance of entity linking. [PERSON] et al. [13] proposed an entity-linking method combining word vector and graph model, which employed Word2 Vec to construct word vectors and adopted a random walk algorithm to calculate the similarity of keywords. Additionally, the classic Word2 Vec model was modified by [PERSON] et al. [14] based on extensive entity word co-occurrence data, which improved the semantic representation of the word vector model and enhanced the effectiveness of subsequent links. Nevertheless, the Word2 Vec model does not factor in context, and each word in a vocabulary exists in one place within the vector space. [PERSON] et al. [15] proposed the GloVe model, which integrates the advantages of Word2 Vec and LSA models and can capture global corpus. Furthermore, [PERSON] et al. [16] proposed the CoVe model that can consider contextual semantic information, which is based on the GloVe model and a machine translation model to obtain contextual word embedding. Summarizing, many proposed word-embedding techniques have not considered contextual semantic information until CoVe was proposed; however, this method still primarily utilizes the model performance of downstream tasks.
## 2 Materials and Methods
### Influence of Contact Time on Diazep Sorption  
#### 2.3.1 Influence of Initial Drug Concentrations  
Fifty milligrams of sorbent were contacted with 50 mL of diaazepam SIF solutions in glass vessels immersed in a thermostatic water bath, at 37.0 \(\pm\) 0.1 \({}^{\circ}\)C. Small volumes of 10\({}^{-2}\) M HNO\({}_{3}\) or 10\({}^{-2}\) M NaOH solutions were added, in order to maintain the suspensions pH at 6.8 \(\pm\) 0.05. The pH was measured by Agilent 3200 pH meter. We have calibrated the pH electrode with buffers (Merk, titrisol) at 37 \({}^{\circ}\)C. Rotating magnetic bar ensured a good stirring of the suspension. Then, 5 mL samples were withdrawn from the reaction vessel at given times and immediately centrifuged.  
For activated charcoal, the initial concentration of diaazepam SIF solutions were 0.3, 0.5 and 0.7 g/L and for natural Na-montmorillonite was 6, 12 and 18 mg/L.  
A high-performance liquid chromatography (HPLC) system was used to determine the diazepam concentrations, both before and after adding activated charcoal or Na-montmorillonite. The HPLC system included: Integrator Model CR501, liquid pump Model LC-10 AS, variable wavelength UV-VIS detector Model SPD-10A, system controller Model SCL-10A and auto-injector Model SIL-10A.  
A Hypersil reversed phase C-18 column with 250 by 4.6 mm i.d. and 5 um particle size was used. The mobile phase has contained acetonitrile methanol, 1% phosphate buffer (pH--3) in ratio of 18:58:24 (v/v/v). The flow rate was 1 mL/min. The eluent was monitored by UV detection at 232 nm. By plotting average peak area against concentration calibration curve was created.  
We have calculated the adsorbed diazepam amount by means of the difference between the initial and the final concentration of the solution.
## 3 Proposed Approach
### Metric Motion in Ortho-Rectified GPI  
As shown in Figure 12, the median motion vector \(m\) obtained from the ortho-rectified GPI matches with the robot motion with an unknown scale because vector \(m\) is obtained in the pixel space. Thus, we need the scale factor between the pixel space to the metric space to measure the actual robot motion. To find the scale of the motion vector, some assumptions are defined. Assuming that an ortho-rectified GPI is obtained using the virtual camera that is perfectly perpendicular to the flat ground, the scale of the motion vector is calculated using the height z from the ground to the camera's lens center and the focal length of the camera f. The scale factor is expressed in Equation (23).  
\[\text{scale}=\frac{z}{\overline{f}}, \tag{23}\]  
\[\overline{m}=scale*m. \tag{24}\]  
By using the scale factor, the robot's actual motion vector \(\overline{m}\), as shown in Figure 13, is calculated by multiplying the scale factor by the pixel motion \(m\).  
Figure 12: The motion vector on the ground plane image.  
Figure 13: Relation of the motion vector in ground plane image and actual motion vector.
# A Single-Launch Deployment Strategy for Lunar Constellations
## Appendix A Canonical Transformation for the Elliptic Restricted 4-Body Problem
\begin{array}{l}\tilde{H}^{(\epsilon)}=\frac{1}{2}\big{(}P_{1}^{2}+P_{2}^{2 }+P_{3}^{2}\big{)}+P_{1}Q_{2}-P_{2}Q_{1}-\frac{\kappa}{2}\big{(}2Q_{1}^{2}-Q_ {2}^{2}-Q_{3}^{2}\big{)}+\\ +E_{1}(\theta_{M},\theta_{E})Q_{1}+E2(\theta_{M},\theta_{E})Q_{2}+E_{3}(\theta _{M},\theta_{E})Q_{3}+E_{4}(\theta_{M},\theta_{E})\end{array}  \tag{16}\]  
For the transformation to be canonical, the following identity must be verified  
\[\tilde{H}^{(\epsilon)}(\mathbf{Q},\mathbf{P})=H(\mathbf{q},\mathbf{p},\theta _{b},\theta)+\frac{\partial S}{\partial\beta}\frac{\partial\beta}{\partial \theta}+\frac{\partial S}{\partial\theta}=H(\mathbf{Q},\mathbf{P}) \tag{17}\]  
The reader may have noticed that the derivative \(\partial S/\partial t\) is here replaced by a convective one, which results from the selection of rotating-pulsating coordinates used for expressing ER4 BP dynamics. Expanding Equation (17) leads to  
\[ \begin{cases}-f_{3}+2\Bigg{(}\frac{\mu}{|x_{L_{1}}+\mu|^{3}}+\frac{1-\mu}{|x_ {L_{1}}+\mu-1|^{3}}\Bigg{)}f_{4}+f_{1}^{\prime}+E_{1}=0\\ -f_{1}-\Bigg{(}\frac{\mu}{|x_{L_{1}}+\mu|^{3}}+\frac{1-\mu}{|x_{L_{1}}+\mu-1|^{ 3}}\Bigg{)}f_{2}+f_{3}^{\prime}+E_{2}=0\\ -\Bigg{(}\frac{\mu}{|x_{L_{1}}+\mu|^{3}}+\frac{1-\mu}{|x_{L_{1}}+\mu-1|^{ 3}}\Bigg{)}f_{6}+f_{5}^{\prime}+E_{3}=0\\ f_{1}-f_{2}+f_{4}^{\prime}=0\\ f_{3}+f_{4}+f_{2}^{\prime}=0\\ f_{5}+f_{6}^{\prime}=0\end{cases}  \tag{18}\]Finally, Equation (A15) can be solved (i.e., using symbolic algebra), determining the value of the \(b_{j}^{i}\) coefficients  
\[\left\{ \begin{array}{l}b_{1}^{1}=-E_{2}\cos\theta_{E}+\frac{\mu}{|x_{L_{1}}+\mu |^{3}}\cos\theta_{E}+\frac{1-\mu}{|x_{L_{1}}+\mu-1|^{3}}\cos\theta_{E}+E_{1} \sin\theta_{E}\\ b_{2}^{1}=\left(\frac{\mu}{|x_{L_{1}}+\mu|^{3}}\cos\theta_{E}+\frac{1-\mu}{|x_ {L_{1}}+\mu-1|^{3}}\cos\theta_{E}+E_{1}\sin\theta_{E}-E_{2}\cos\theta_{E} \right)\cot\theta_{E}\\ b_{1}^{2}=-\cos\theta_{E}\Bigg{(}\frac{\mu}{|x_{L_{1}}+\mu|^{3}}+\frac{1-\mu} {|x_{L_{1}}+\mu-1|^{3}}-E_{2}\Bigg{)}\\ b_{2}^{2}=-\cos^{2}\theta_{E}\csc\theta_{E}\Bigg{(}\frac{\mu}{|x_{L_{1}}+\mu|^{ 3}}+\frac{1-\mu}{|x_{L_{1}}+\mu-1|^{3}}-E_{2}\Bigg{)}\\ b_{1}^{3}=-\Bigg{(}\frac{\mu}{|x_{L_{1}}+\mu|^{3}}+\frac{1-\mu}{|x_{L_{1}}+\mu- 1|^{3}}-E_{2}\Bigg{)}\csc\theta_{E}\\ b_{1}^{5}=E_{3}\sin\theta_{E}\\ b_{2}^{5}=-E_{3}\cos\theta_{E}\end{array} \right.\] (A16)  
All the \(b_{j}^{i}\) not reported are null.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 8 Behavioral Anomaly and Change Detection
### Long-Term Behavior Change Detection  
A point anomaly may not necessarily have to trigger a health-related alarm. It could simply be a one-time change in behavior due to special events. However, consecutive abnormal days could indicate a long-term behavioral change. Thus, we trigger a long-term behavior change alert once \(N\) consecutive abnormal days are detected. Specifically, when five consecutive days are detected as abnormal, we report a long-term change and notify the caregivers, so they can investigate whether the anomaly is of medical relevance by assessing the patient's data or contacting the patient directly. Detected long-term behavior changes are depicted as red bars in Figure 14. We can see that six and five long-term changes were detected with sequence prediction and Isolation Forest-based algorithms, respectively. Among them there are sleep absence; as well as sleep quality and quantity triggered changes.