esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bacteriophages
After their discovery, [PERSON] used phage preparations to treat bacterial dysentery [88]. Owing to its success in treating bacterial infection, the use phage therapy escalated in countries such as Tbilisi, Georgia, and Poland to treat conditions such as typhoid, fever, dysentery, surgical wound infections, peritonitis urinary tract infection, and septicaemia [89]. However, the use of phage therapy was halted by the discovery of the first antibiotic, penicillin, in 1940. In addition, lack of knowledge of phage therapy decreased the use of phage therapy. Despite this, Soviet countries continued to use phages to treat bacterial infections in humans. Bacteriophages are ubiquitously found in the vicinity of their host bacteria. The phage replication cycle encompasses the processes of adsorption, penetration, nucleic acid replication, and virion assembly. Consequently, the cellular membrane undergoes lysis, leading to the release of mature virions. While it is true that certain phages can infect multiple bacterial species or strains, most phages exhibit specificity towards a particular bacterial species or strain. The determination of specificity is regulated by the existence of receptors located on the outer surface of bacterial cells, such as lipopolysaccharideacharide (LPS), flagella, and/or other surface proteins [90]. Bacteriophages can exhibit either a lytic or lysogenic life cycle, depending on the specific life cycle they adopt after infecting a host cell. Lytic phages undergo a lytic cycle, which starts when the bacteriophage binds to specific receptors on the surface of the bacterial cell. The phage then injects its DNA into the host cell and assumes control of the cellular machinery. While using the host cell's transcription and translation mechanisms, phage-specific proteins are synthesized, and the phage's DNA is replicated. The components then assemble into new phage particles, and they accumulate and exert pressure on the cell's internal environment, causing the bacterial cell to lyse or rupture. In the lytic lifecycle of a bacteriophage, the lysis process is a crucial phase. It enables the newly produced phage particles to disseminate and infect other susceptible bacterial cells, ultimately resulting in the phage population increasing. Temperate phages, on the other hand, undergo the lysogenic cycle, wherein their genetic material integrates and coexists with the host genome in a stable manner, forming a prophage. This prophage replicates along with the host cell during replication. According to [PERSON] et al. [80], temperate phages have the ability to transition into the lytic cycle when they encounter cellular stress.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.15. Multifunctional Display (MFD)  
The IMO defines the Multifunctional Display (MFD) as \"_A single visual display unit that can present, either simultaneously or through a series of selectable pages, information from more than a single function of an INS_\" [6]. A task station in the bridge consists of the MFD with dedicated controls to display and operate any navigational task [6], so that individual components (e.g., RADAR and ECDIS) can be combined in one unit [73]. The OOW can switch over between the displays of the components connected in the MFD. The MFD is an essential part of the INS. However, it does not have to be used as part of the INS; it may be also deployed as dedicated equipment in conventional bridges.
## 4 Evaluation and Results  
The application of the resources and techniques presented in Section 3 allows for the exploratory and critical analysis to be divided into a descriptive statistical analysis (Section 4.1) that presents the general situation of Spanish STPs and prepares the information for the second key stage of the analysis: a data analysis based on machine-learning tools (Section 4.2).  
To analyse the current state of the Spanish STPs, the most recent data range was selected for each STP and feature from the period 2015-2019.
## 3 Results and Discussion
### Ensemble Model  
To increase the efficiency of predictions, an ensemble classifier was built from three independent models: SVM, ANN, and BDT. Similarly, to random forests, an ensemble is a meta-classifier that internally uses several, preferably strongly independent models, respectively aggregating their predictions to generate the result [42]. The choice of algorithms in that case cannot be accidental. For example, the logistic regression method would be a bad choice because the selected parameters of heat meters are linearly independent (Figure 2). The assumption about the differentiation of models is important because similar classifiers make the same mistakes, so the final group model would only duplicate them. Sufficient differentiation can be achieved for the same algorithms by the appropriate division of the training set into subsets (bagging). It also happens that subsets of data attributes are used instead of data subsets. In this paper, the authors decided on a different approach, namely the use of already trained (and optimized) models and building of a collective classifier using the most popular voting algorithms with weights. The results of the obtained ensemble classifier can be seen in Table 4.  
For the most important metrics (_MCC_ and _AUC_), the ensemble classifier was better than the average of the optimized models by more than one percent, which gave 22% and 29%, respectively, of the total possible improvement. The increase in detectability of failures by more than 1.5% for the \(Precision_{1}\), \(Recall_{1}\), and \(f1_{1}\) metrics was also important.
## 4 Architecture of Software Receiver
### Software Architecture  
The software is developed using Microsoft Visual Studio under 64-bit version of Microsoft Windows 7. Most source codes are programmed using C++. The functionalities with high computational complexity, such as correlation operation and covariance matrix calculation, are programmed by assembly. The component list of the software is in Table 1, and detailed explanations of the components are given in the following subsections.  
\begin{table}
\begin{tabular}{l l l} \hline
**Component** & **Description** & **Frequency** \\ \hline Automatic gain control & Equalize noise power of all elements of & Call right after reading \\  & antenna array to full 16-bit representation. & every 1 ms data from \\  & Hand-coded assembly. & solid state drive. \\ \hline Software correlator & Perform 16-bit complex data correlation by & every 1 ms \\  & software approach. & every 1 ms \\  & Hand-coded assembly. & \\ \hline Acquisition/Tracking & Signal processing and positioning function for & every 1 ms for \\ and Positioning & GPS/WAAS satellites. & acquisition/tracking, \\  & every 100 ms for & every 100 ms for \\  & positioning & \\ \hline \end{tabular}
\end{table}
Table 1: Component list of the software architecture including short descriptions and how often each component is called.  
Figure 4: Beamforming architecture for steering 12 beams simultaneously using the MVDR algorithm.
## 2 Photonic Crystal Cavities for Opto-Mechanical Interaction
### Experimental Opto-Mechanical PhC Cavities  
Leveraging nanofabrication technologies, optical and mechanical degrees of freedom can be coupled in the nanoscale opto-mechanical system. For the purposes of sensing applications, the on-chip opto-mechanical system is designed to convert variations of sensing factors to the nano-mechanical motion. Furthermore, the nano-mechanical motion can be optically detected via the induced opto-mechanical interaction, thereby probing the target sensing signal. Based on interaction effects between optical cavity and mechanical resonator, opto-mechanical cavities can be divided into two types: mechanically tunable cavities and radiation pressure-driven cavities. Before elaborating the working mechanism of opto-mechanical sensors, we introduce the several typical configurations of optical cavity first.
## 3 Results and Discussion
### The Relationship between N, P, and Water Quality
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Overlying Water**} & \multicolumn{3}{c}{**Sediment**} \\ \cline{2-7}  & **NH\({}_{3}\)-N** & **TN** & **TP** & **NH\({}_{3}\)-N** & **TN** & **TP** \\ \hline pH & \(-0.162\) & \(0.02\) & \(0.286\) ** & \(0.078\) & \(0.149\) & \(-0.028\) \\ COD & \(0.444\) ** & \(0.471\) ** & \(0.695\) ** & \(-0.004\) & \(0.132\) & \(0.132\) \\ DO & \(-0.133\) & \(-0.365\)** & \(-0.259\) * & \(-0.115\) & \(-0.167\) & \(-0.179\) \\ Eh & \(0.003\) & \(0.026\) & \(0.124\) & \(0.07\) & \(-0.013\) & \(-0.266\) * \\ \hline \hline \end{tabular}  
* Significant correlation at 0.01 level (both sides);
* Significant correlation at 0.05 level (both sides).  
\end{table} 
Table 2: Correlation between N, P, and other water quality parameters in overlying water and sediment. NH\({}_{3}\)-N. In this article, the measured values of TN, TP, and NH\({}_{3}\)-N of the overlying water in Table S3 were set as predictive variables, the measured values of COD (Table S4) at corresponding points were set as response values, the calculation of the PLS regression equation was realized by invoking the \"plsregress\" function in MATLAB software, and the results are displayed in Figure 7.  
Most of the data were distributed on both sides of the trend line, and only the eight data (9.5% of the total data) with predicted values of 100-300 in the lower left corner of Figure 7 deviated significantly from the trend line. It was preliminarily judged that the COD values in the regional water bodies can be simply estimated from the measured values of TN, TP, and NH\({}_{3}\)-N in the overlying water using this regression equation. However, COD was actually influenced by complex factors in the water; it was impossible to highly accurately estimate the COD value through simple linear regression, which should be deeply studied in the future. Although, there were some errors in the regression prediction results, which also confirmed the complex relationship between N, P, and the water quality parameters.
## 3 Results and Discussion
### Effect of Adsorption Conditions
#### 3.3.6 Effect of CPP200 Dosage  
The identifiable sites and active surface areas could be promoted by increasing the amounts of adsorbent, which greatly affects the adsorption process. Herein, the effect of adsorbent dosage on adsorption was investigated in order to evaluate the influence of this key factor (Figure 7D). With the amount of CPP200 increased from 5.0 mg to 30.0 mg, the MB removal rate increased from 27.53% to 97.43%, respectively. MB removal was expected to increase at higher adsorbent dosages due to increased numbers of active binding sites as well as increased effective surface area [48]. However, the removal rate of MB was not significantly affected by further increasing adsorbent dosages (for example, \(\geq\)25.0 mg), indicating that the relatively lower residual MB in the solution would be less likely to interact with CPP200 because of the competitive adsorption between water and MB. Although the removal rate of MB increased significantly, the adsorption capacity of CPP200 decreased from 55.51 mg/g to 32.74 mg/g. We propose that insufficient utilization of the adsorbent occurred, and the active sites were not fully used. Considering the relationship between adsorbent consumption and removal efficiency, 5 mg of adsorbent was used for the following adsorption experiments.
# Privacy and Security in Federated Learning: A Survey
## 4 Privacy in Federated Learning
### Privacy Attacks in FL
#### 4.1.2 Malicious Server  
In a centralized federated learning setting, the central server is one of the most critical parts of the architecture. If the server is malicious or is accessed by unauthorized persons, then all local models sent by clients can be intercepted and studied in order to reconstruct a part of the original client data. The malicious server can then analyze the model shared by the clients in a discontinuous way (passive mode) or follow the chronological evolution of the model shared by the victim (active mode) [43].
## 5 Computational Analysis
### Response Analysis Without Nonlinear Terms  
At the outset, an analysis was conducted for the nonlinear term-free scenario (i.e., setting \(\mathbf{F}_{\text{sfd}}\) and \(\mathbf{F}_{\text{bnl}}\) in Equation (58) to zero). The investigation focused on three support positions, primarily examining the frequency response characteristics, shaft centerline trajectory, and time-domain response of the system. Specifically, when analyzing the shaft centerline trajectory and time-domain response, the study concentrated on the vicinity of the rotor critical speeds.  
As determined in Section 2, the first three critical speeds of the rotor lie within the range of 2000 to 5000 rpm; hence, this range was selected for solving the amplitude-frequency characteristics. Analysis of the rotor's modal shape diagram (Figure 4) indicates that applying an unbalanced force at the sixth concentrated mass can excite the third-order critical speed response.  
The first three critical speeds of the rotor, as calculated from the Campbell diagram, are presented in Table 1.  
After solving the model over a speed range of 2000 to 5000 rpm using the Newmark Beta method, the resulting amplitude-frequency response is depicted in Figures 5-7 below:  
Figure 4: Modal shape diagram.  
Figure 5: Amplitude–frequency curve for bearing 1.  
third. At the second bearing location, the amplitude of the third critical speed is the highest, with the second and first being approximately equal, and the second being slightly higher than the first. At the third bearing location, the amplitude of the second critical speed is the highest, with the third and second being approximately equal, and the third being slightly lower than the second, while the first has the lowest amplitude.
## 3 Modeling and Analysis of Attitude Control of Missile
### Pitch Control Model Considering Wind Disturbance  
The model of the missile in this paper has an axis-symmetric and cruciform shape, based on the general assumption [26], the model in the ascent phase that considers gust disturbances is shown in Figure 16.  
Figure 15: Estimation of equivalent disturbance by ADRC under different set points.  
Assuming that the missile is not disturbed by the wind at the initial state, by this time, the flight state of the rocket is: the ground speed is \(\mathbf{V}\), the angle of attack is \(\alpha_{v}\), the track angle is \(\theta\), the pitch angle is \(\theta\), and the roll angle is \(\gamma\). Then assume wind \(\mathbf{W}\) act on the missile in the direction as shown in Figure 16, so that the wind deflects the missile by \(\alpha_{w}\) with respect to the direction of air velocity. That is, the increment of the angle of attack under wind disturbance is defined as \(\alpha_{w}\). At this time, the angle of attack changed into \(\alpha\), for the sake of research, wind speed can be decomposed into vertical speed \(V_{y}\) and horizontal speed \(V_{x}\), consider \(\mathbf{V}_{a}\) as air speed, then \(\mathbf{V}_{a}\), \(\mathbf{V}\) and \(\mathbf{W}\) fulfill the relationship \(\mathbf{V}=\mathbf{W}+\mathbf{V}_{a}\).  
In this paper, the wind is considered to be vertical wind, and at this point, the vehicle will generate an additional angle of attack [27].  
As in Figure 16, _xoy_ refers to the longitudinal plane of the ground coordinate system, and \(x_{1}\) to the axis of the center of the projectile body, \(P\) is the thrust of the rocket engine, \(X\) is the resistance, Y is lift and \(G\) is gravity.  
As reference [28], make the wind a disturbance factor, and the non-linear mathematical model of the missile pitching channel under the vertical wind disturbance is obtained as follows  
\[\dot{\theta}=\frac{P\sin\alpha_{v}+Y}{mV}-\frac{g\cos\theta}{V} \tag{22}\]  
\[\dot{\vartheta}=\omega_{y}\sin\gamma+\omega_{z}\cos\gamma \tag{23}\]  
\[\dot{\omega}_{z}=\frac{M_{z}}{J_{z}}+\frac{J_{x}-J_{y}}{J_{z}}\omega_{x} \omega_{y} \tag{24}\]  
\[\alpha_{v}=\alpha-\alpha_{w} \tag{25}\]  
\[\vartheta=\alpha_{v}+\theta \tag{26}\]  
\[\alpha_{w}=\arctan\frac{W_{y}}{V} \tag{27}\]  
\(J_{x}\), \(J_{y}\), \(J_{z}\) are the moments of inertia of roll, yaw and pitch axes.
# The Additive Manufacturing of Glass: A Critical Review
### General AM Overview and Examples
#### 1.3.1 Relevance to Art and Design  
It has been noted that recent research in the domain of glass art has included the creative development of digital manufacturing techniques [17]. This includes so-called vocabularies of form which have arisen from incorporating the following approaches (for example): (i) waterjet cutting into stained glass practice, (ii) computer-based image-making into 2D and 2.5D screen-printed and sandblasted imagery in kiln-formed glass; and (iii) 3D printing in cast glass to create previously unobtainable forms.  
The application of glass AM is not yet widespread in artistic practice, and therefore, what have not been replaced--to date--include traditional analogue techniques. However, glass AM has the potential to remove the associated constraints presently required to digitally translate a derived form into a resulting object in glass. At present, to turn a 3D model into cast glass, a polymeric 3D print is usually created and realised through gravity-based lost-wax kil casting--a process largely unchanged since its invention many centuries ago. Intermediary materials such as silicone, wax, plaster, and other refractories are used in a sequence of reversals between positive and negative, each process with a loss of fidelity to the original form [17]. In the glass art sector, a gap in knowledge exists, and there is a demand for makers who can realise digitally derived designs and machine-fabricated forming processes. Benefits of the direct production of 3D forms via the additive manufacturing of glass include the following:  
* Rapid prototyping, allowing the translation of an idea into a form within a short period of time (i.e., hours);
* The ability to realise shapes and structures, from direct manufacturing in 3D, that are otherwise unobtainable from conventional glass processing methods;
* The ability to realise detail in 'internal' structures that is not possible through methods analogous to the lost-wax approach;
* The ready combination of colour transitions, and diverse features.  
Some examples are provided in Figures 4 and 5.
## 5 Conclusions and Future Perspectives
* MASS self-organizing networks and data sharing: Research will focus on meeting the shared needs of MASS autonomous navigation and the construction of maritime spatial information. A highly efficient MASS self-organizing network architecture will be established to enable real-time sharing and collaborative processing of perception data, supporting comprehensive, multi-angle maritime environment awareness and laying the foundation for building smarter, more collaborative maritime traffic systems. * Overcoming deployment and implementation challenges: Through model compression and hardware acceleration technologies, the computational demands will be reduced, and inference efficiency will be improved to ensure real-time performance on low-power embedded platforms. At the same time, efforts will be made to resolve compatibility issues between perception models and existing ship automation systems, navigation software, and international maritime regulations. The development of open interfaces compliant with IMO standards will facilitate seamless integration of technology with existing navigation systems, ensuring the alignment of performance, regulatory compliance, and safety, thus promoting stable and reliable deployment. S.C.: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing--original draft, Writing--review and editing. M.G. : Conceptualization, Data curation, Funding acquisition, Methodology, Writing--review and editing. P.S. : Visualization, Validation, Formal analysis. X.Z. : Data curation, Writing--review and editing, Visualization, Formal analysis. A.Z. : Supervision, Funding acquisition, Data curation. All authors have read and agreed to the published version of the manuscript. This project was supported partially by the National Natural Science Foundation of China (52201414), Young Elite Scientists Sponsorship Program of the China Institute of Navigation (No. YESSCIN2023002), National R&D Program of China (2022 YFC3006000, 2022 YFB3207400), Natural Science Foundation of Tianjin (22)CQNJC00290) and Fundamental Research Funds for the Central Public Welfare Research Institutes (TKS20240305) and Tianjin Transportation Technology Development Plan Project (2023-47). Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest.
## 3 Results
### Changes in Muscle and Local Carotid Stiffness in Response to Different Exercise Intensity  
The intensity at 80%1 RM elicited a greater magnitude strain rate (\(p\) = 0.01, d = 0.61) and AUC reductions (\(p\) < 0.001, d = 0.93) than those of 40%1 RM, respectively (Figure 3). Both _u/_PWV-sys (\(p\) = 0.01, d = 0.78) and _u/_PWV-dia (\(p\) = 0.02, d = 0.63) significantly increased after an acute bout of exercise of intensity at 40 and 80% 1 RM (Figure 4). There was no interaction between time and exercise intensity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**40\%1 RM**} & \multicolumn{2}{c}{**80\%1 RM**} \\ \hline  & **Pre** & **Post** & **Pre** & **Post** \\ \hline cSBP, mmHg & 108 \(\pm\) 5 & \begin{tabular}{c} 111 \(\pm\) 4 \\ NS \\ \end{tabular} & 109 \(\pm\) 5 & \begin{tabular}{c} 112 \(\pm\) 4 \\ NS \\ \end{tabular} \\ \hline cDBP, mmHg & 68 \(\pm\) 2 & \begin{tabular}{c} 67 \(\pm\) 1 \\ NS \\ \end{tabular} & 68 \(\pm\) 3 & \begin{tabular}{c} 66 \(\pm\) 2 \\ NS \\ \end{tabular} \\ \hline cPP, mmHg & 41 \(\pm\) 3 & \begin{tabular}{c} 44 \(\pm\) 3 \\ NS \\ \end{tabular} & 41 \(\pm\) 2 & \begin{tabular}{c} 46 \(\pm\) 2 * \\ (\(p\) = 0.04) \\ \end{tabular} \\ \hline cFPWV, cm/s & 775 \(\pm\) 23 & \begin{tabular}{c} 809 \(\pm\) 29 \\ NS \\ \end{tabular} & 762 \(\pm\) 28 & \begin{tabular}{c} 807 \(\pm\) 24 \\ NS \\ \end{tabular} \\ \hline baPWV, cm/s & 1195 \(\pm\) 52 & \begin{tabular}{c} 1247 \(\pm\) 51 \\ (\(p\) = 0.07) \\ \end{tabular} & 1198 \(\pm\) 58 & \begin{tabular}{c} 1302 \(\pm\) 72* \\ (\(p\) < 0.0001) \\ \end{tabular} \\ \hline Muscle strain & 0.128 \(\pm\) 0.009 & \begin{tabular}{c} 0.118 \(\pm\) 0.010 \\ NS \\ \end{tabular} & 0.128 \(\pm\) 0.006 & \begin{tabular}{c} 0.102 \(\pm\) 0.009 * \\ (\(p\) = 0.009) \\ \end{tabular} \\ \hline Muscle strain & 327 \(\pm\) 33 & \
\begin{tabular}{c} 307 \(\pm\) 25 \\ NS \\ \end{tabular} & 357 \(\pm\) 23 &
\begin{tabular}{c} 239 \(\pm\) 19 * \\ (\(p\) < 0.0001) \\ \end{tabular} \\ \hline Value = mean \(\pm\) SEM; 1 RM= 1 repetition maximum; cSBP = carotid systolic blood pressure; cDBP = carotid diastolic blood pressure; cPP = carotid pulse pressure; cFPWV = carotid-femoral pulse wave velocity; balPWV= brachial-ank pulse wave velocity. AUC = area under curve. * = vs. Pre. NS = no significance vs. Pre. & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Central blood pressure, pulse wave velocity, and muscle strain changes pre- and post-exercise.  
Figure 3: The changes of strain rate (**A**) and AUC (**B**) after 40 and 80%1 RM. * = vs. Pre.
## 5 Parameter Analysis
### Effect of Capsule Expansion Radius  
The effect of the capsule expansion radius \(R_{\rm c}\) on the lateral displacement and bending moment of the pile is described in Figure 9. The radii of the capsule are 0.10 m, 0.15 m, 0.20 m, 0.25 m, and 0.30 m, respectively. It can be noticed that the lateral displacement of the pile clearly increases with the increase in the capsule expansion radius, with the maximum lateral displacement occurring at the center of the capsule (10 m). When the radius