esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results
### Comparisons with MDS-MAP  
The MDS-MAP algorithm was chosen because it can also be used in range-based localization algorithms. In addition, thus far it has been used to obtain much higher localization accuracy.  
Figure 7(c) shows the position error versus the connectivity in uniform networks. The connectivity of network means the average neighbors each node has. The numbers of anchor nodes and total nodes are 10 and 200 in a \(10r\times 10r\) network. \(R\) goes from 1.25 to 2.5, in increments of 0.25 [23]. A randomly  
Figure 6: The communication cost and position error versus the number of nodes. (a) average calculation steps versus the number of nodes in LASM(B). (b) average calculation steps in LASM(P). (c) the position error versus the number of nodes in LASM(B). (d) position error in LASM(P).  
generated example is shown in Figure 7(a). When the connectivity is small, the accuracy of LASM is worse than MDS-MAP. When the connectivity is large, e.g., it is 30, the accuracy of LASM and MDS-MAP is slightly similar. In the experiment, just a single reinitializing is used in LASM(P). The accuracy of LASM(P) will get better with more reinitializations. However, this will increase communication cost. The accuracy of localization is decreased in order to obtain a lower communication cost.  
Figure 7(d) shows the position error versus the connectivity in irregular networks. The numbers of anchor nodes and total nodes are 10 and 160 in a 10\(r\times\)10\(r\) C-shaped network. \(R\) goes from 1.25 to 2.5, in increments of 0.25 [23]. The average connectivity levels are 8.1975, 11.2931, 14.6406, 18.8562, 22.1138, and 27.6275. A randomly generated example is shown in Figure 7(b). The accuracy of LASM is a little similar with MDS-MAP(P) as shown in [23]. From Figure 7(d) it can be found that the accuracy of LASM is better than MDS-MAP(C) whenever the connectivity is small or large. This is because the MDS-MAP(C) uses the shortest paths between all pairs of nodes replacing their actual Euclidean distances. The complexity of LASM and MDS-MAP is shown in Table 1. It can be found that the computational complexity of LASM is smaller than that of MDS-MAP(C), and the communication complexity of LASM is smaller than that of MDS-MAP(P). The low complexity of the proposed LASM algorithm is more suitable for the large scale network on the moon.  
Figure 7: Comparisons with MDS-MAP. (a) a random example in uniform networks. (b) a random example in C-shaped networks. (c) the position error versus connectivity in uniform networks. (d) the position error versus connectivity in C-shaped networks.
## 5 Attributes and Characteristics of FL
### Non-IID Data in FL
#### 5.8.5 Synchronous vs. Asynchronous Updates  
Synchronous and asynchronous mechanisms are central to federated learning's model updates. In synchronous FL, a central server waits for updates from all nodes beforeaggregating the model. This approach faces issues such as \"stale updates\" when some devices lag, due to device differences and network reliability [81].
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 1 Introduction  
One of the current greatest global public health problems is the pathogenesis associated with the emergence of generalized infectious bacteria [1]. _Pseudomonas aeruginosa_ has the ability to infect not only humans, but also animals and plants. Given its role as an important human pathogen, several virulence factors have been studied, as well as their regulatory systems [2]. This pathogen is armed with a wide set of virulence factors, allowing it to harm host cells and modulate human adaptive immune mechanisms, thus causing the appearance of new infections [3]. The T3 SS system is one of its main virulence factors, and a very complex matrix of environmental signals is integrated to tightly control the injection of proteins into host cells, where they are directed to specific compartments and manipulate a number of cell processes [4]. However, the pathogenesis of this bacterium is not exclusively focused on the emergence of new infectious diseases, but also on biofilm formation and on the appearance of new antibiotic-resistant strains, or on the increase in prevalence of already known strains [5]. The acquisition of resistance to two or more classes of antibiotics by pathogenic bacteria is denominated as multidrug-resistance (MDR) [6]. _P. aeruginosa_ is one of the bacterial species that have developed an alarming number of multidrug-resistant strains. These strains are associated with significant morbidity and mortality and they are responsible for 10% of nosocomial infections [7]. This review aims to provide an overview of the metabolic pathways underlying the pathogenicity of _P. aeruginosa_ and to describe minutely mechanisms such as its different virulence factors and their multiple regulatory systems, as well as the biofilm formation and resistance to different antibiotics. The purpose of this review is also to provide a better understanding of these mechanisms and to support further research into alternatives to combat this pathogen.
## 2 Direct Operator Method for Solving Linear and Nonlinear VFIE
### Nonlinear VFIE  
Consider now the nonlinear VFIE in the Hammerstein form (1) where the Fredholm type functionals \(\Phi_{j}(y)\) are nonlinear and are given in (6).  
**Theorem 2** ( Nonlinear VFIE).: _Let the nonlinear Volterra-Fredholm integral operator of the second kind \(\mathcal{I}:X\to X\) be defined by_  
\[\mathcal{I}y=\mathcal{J}y-g\Phi(y),\quad y=y(x)\in X, \tag{18}\]  
_where the Volterra-type integral operator of the second kind \(\mathcal{J}:X\to X\) is defined as_  
\[\mathcal{J}y=y-\mathcal{K}y,\quad y=y(x)\in X, \tag{19}\]  
_and the operator \(\mathcal{K}\) as in (3), and the vector of functions \(g\) and the vector of nonlinear functionals \(\Phi\) as in (5) and (6), respectively. If the integral operator \(\mathcal{J}\) is bijective on \(X\), and its inverse is denoted by \(\mathcal{J}^{-1}\), then the exact solution of the nonlinear VFIE._\[\mathcal{I}y=f,\quad f=f(x)\in X, \tag{20}\]  
_is given by_  
\[y=\mathcal{J}^{-1}f+\mathcal{J}^{-1}g\mathbf{a}^{*}, \tag{21}\]  
_for every vector \(\mathbf{a}^{*}=\Phi(y)\), which is a solution of the nonlinear algebraic (transcendental) system of the m equations_  
\[\mathbf{a}=\Phi\Big{(}\mathcal{J}^{-1}f+\mathcal{J}^{-1}g\mathbf{a}\Big{)}.\]  
**Proof.** Applying the inverse operator \(\mathcal{J}^{-1}\) to the nonlinear VFIE in (20), we get  
\[y=\mathcal{J}^{-1}f+\mathcal{J}^{-1}g\Phi(y). \tag{22}\]  
Acting then by the vector \(\Phi\) on both sides, we have  
\[\Phi(y)=\Phi\Big{(}\mathcal{J}^{-1}f+\mathcal{J}^{-1}g\Phi(y)\Big{)},\]  
where it is reminded that the elements of the vector \(\Phi\) are nonlinear functionals, and no linear operations are allowed. By defining \(\mathbf{a}=\Phi(y)\), we obtain the nonlinear algebraic (transcendental) system  
\[\mathbf{a}=\Phi\Big{(}\mathcal{J}^{-1}f+\mathcal{J}^{-1}g\mathbf{a}\Big{)}.\]  
Let \(\mathbf{a}^{*}\) be a solution of this system. Substitution then of \(\mathbf{a}^{*}\) into (22) yields (21). The solution \(y(x)\) satisfying \(\mathbf{a}^{*}=\Phi(y)\) is a solution of the nonlinear VFIE (21). \(\square\)
# Assessing Seismic Vulnerability and Pollution Risks of Wastewater Treatment Plants
###### Abstract  
Empirical studies are valuable for assessing soil and water pollution, as they can reduce costs and save time. The present study discusses previous research results using a questionnaire to gather experts' judgments on technical issues and potential pollution related to the vulnerability of Wastewater Treatment Plants (WWTPs) in Greece. The questionnaire included 44 closed-type questions based on the Likert Scale. It was distributed to a representative sample of 116 operators over seven (7) months (April-November 2021). Geographical Information Systems (GISs) were employed to visualize the spatial distribution of the seismic vulnerability of WWTPs. The study outputs include eight (8) maps depicting the spatial distribution of seismic vulnerability, both with and considering soil-water pollution, by calculating the existence of seismic hazards and identifying potentially affected regions. Additionally, eight (8) tables support this analysis. The survey findings highlight the most vulnerable regions and WWTPs in the country. The results suggest that after excluding Zone III, the WWTPs of Zone II of the national Seismic Hazard Map (SHM) are estimated to be the most vulnerable. This study spatially visualizes the indicator of seismic vulnerability (ISV) and the seismic vulnerability index concerning potential soil-water pollution (ISV-REF), according to the SHM and regions. Most WWTPs have low ISV-REF, while maps illustrate the exceedance of that parameter, identifying the safest units and indicating that Zone I has the safest units according to the exceedance percentages. Integrating data on regions, ISV, ISV-REF, and their exceedance in GIS could lead to authorities' and technicians' decisions to implement quick measures. Researchers should also focus their studies more precisely, mitigating the seismic vulnerability of critical infrastructure, such as WWTPs.  
seismic vulnerability; WWTPs; GIS; soil-water pollution; seismic hazard map; spatial distribution; indicator of seismic vulnerability +
Footnote †: journal: Applied Sciences  
1
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 4 Results and Discussion
### Simulation Results and Analyze  
#### 4.2.1 Simulation 1  
In the first simulation, we aimed to analyze how the mobility of the jammers and local maintenance designed for sensor nodes would impact the WSN's reliability and performance. We designed four parameter combinations for this experiment: fixed jammer with no local maintenance (FF), fixed jammer with local maintenance (FT), movable jammer with no local maintenance (TF), and movable jammer with local maintenance (TT). For the first two cases, as the jammer does not move, it only takes one tick to observe the impact of the parameter design on the experiment. For the latter two cases, since the jammer is movable, it takes a longer time, and its influence range is much larger than before. Therefore, we designed a running time of 5 ticks for the dynamic situation.  
The results in Figure 6a indicate that the continuous movement of the jammer affects a wider range of sensor nodes. The use of local maintenance significantly improves the total MCS performance compared to not using it (FT vs. FF, TT vs. TF), resulting in better reliability. In the case of local maintenance, TT outperformed FT, mainly because the jammer moved farther away from the base station in the fifth tick, resulting in fewer affected sensor nodes.  
In Figure 6b, the APL performances of FF, FT, and TT are similar, while only TF's performance is exceptionally good. This may be due to the fact that in this simulation, the jammer was mostly moving around the periphery of the screen, resulting in a large number of peripheral nodes with a larger number of hops being disconnected from the base station, leaving only the inner peripheral nodes with a smaller number of hops. However, it should be noted that MCS is a more critical indicator than APL because the establishment  
Figure 6: (**a**) Comparison of MCS with different parameter settings; (**b**) Comparison of APL with different parameter settings.  
of a connection between sensor nodes and the base station is a prerequisite for discussing transmission delay.
# Enhancing the Accuracy of Advanced High Temperature Mechanical Testing through Thermography
## 3 Results
### Infra-Red Considerations
#### 3.1.1 External Influences  
Thermal radiation reflections emitted from reflective metallic surfaces can cause significant complications with temperature accuracy and as such, any reflective surface within a furnace must be covered with a high emissivity black coating. An example of thermal radiation reflections within a horizontal bulb configuration radiant lamp furnace are given in Figure 7a,b. Coating the internal reflective surfaces to avoid complications and inaccuracies with respect to radiation from nearby heat sources proved effective as seen in Figure 7c,d, where there is a clear reduction in thermal radiance providing a more accurate temperature distribution across the specimen.  
Thermocouples attached to a specimen's surface have been the primary method of temperature calibration outlined by the governing standards for mechanical tests involving dynamic temperature such as TMF. However, shown in Figure 8, are clear examples of insulation and thermal distortion caused by thermocouples attached to the surface of a test specimen. In Figure 8a when imaging a pre-oxidied metallic surface, the vast difference in emissivity between the TC sheathing and specimen surface, causes overexposure and image distortion. However, once thermocouples are removed as seen in Figure 8b, the non-uniformity of the pre-oxidised metallic surface still does not provide an accurate base for measurement, highlighting the need for stable and uniform surface preparation prior to thermography control upon un-coated metallic surfaces.  
Clear hot spots exist beneath the point of contact of the tie-on TC, the insulating effect is magnified by spot welded thermocouples aligned along the specimen's surface, Figure 8c. The effect exists upon both metallic and non-metallic materials, Figure 8d,e respectively. Specimens imaged without attached thermocouples show no distortion or hot spots and a well distributed, uniform thermal path exists, Figure 8f-h.  
Figure 7: A 12 kW Infra-red radiant vertically mounted quartz lamp furnace (**a**) uncoated reflective internal furnace surfaces; (**b**) Thermography view of specimen through uncoated furnace shown in (**a**); (**c**) Internal furnace surface coated with HE23 thermal paint; (**d**) Thermography view of specimen through HE23 coated furnace shown in (**c**).
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
###### Abstract  
The degree and amount of sand production in hydrate reservoirs is related to the selection of stable production processes, but there is currently a lack of quantitative sand production prediction research using real logging data and formation samples from hydrate reservoirs. To reveal the dynamic change characteristics of in-situ reservoirs during hydrate decomposition, and explore quantitative prediction methods for guiding production practice, it is conducted a series of numerical simulations and quantitative prediction experiments. The numerical simulations are carried out using different sand-out prediction methods by using hydrate logging data during drilling, while quantitative prediction experiments of water production and sand-out are carried out based on in-situ reservoir samples. Our experiments indicate that hydrate mining is facing a serious risk of sand-out. The particle transport in the reservoir changes from \"large-channel seepage\" to \"umbrella seepage\" and then to \"uniform fine flow\" as the replacement flow rate decreases. A quantitative prediction model for water and sand production is also established. As a result, our study can provide support for the advancement of technology for long-term stable production and sand control of hydrates, laying the groundwork for developing a stable production plan for natural gas hydrates in offshore areas and determining the optimal depressurisation method.  
gas hydrate; logging data; in-situ reservoir samples; produced water out-sand; quantitative out-sand prediction +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Dataset Preprocessing
```
Input: client_id Output:\(X_{\text{client}}\), \(y_{\text{client}}\)
Create non-IID splits based on client_id;  Set random seed using client_id;  Generate array of indices \([0,1,...,\text{len}(X_{\text{train}})-1]\);  Shuffle the indices; # Choose a fraction of the data for this client;  Set fraction = 0.04;  Calculate client data size = int(0.04 \(\times\) len(\(X_{\text{train}})\));  Select client indices from shuffled indices based on client data size; \(X_{\text{client}}=X_{\text{train}}[\text{client\_indices}]\); \(y_{\text{client}}=y_{\text{train}}.\text{iloc}[\text{client\_indices}]\); return\(X_{client}\), \(y_{client}\);
```  
**Algorithm 1**Load and Distribute CICIoT2023 Data Train  
A crucial aspect of FL is the allocation of data fractions to individual clients. Here, the function selects a predetermined fraction of the data (commonly represented as 'fraction', set to 0.04 in the provided code) to be assigned to each client. The size of this allocation, termed 'client_data_size', is determined based on the specified fraction and the total size of the training data. Client-specific indices are then selected from the shuffled array, corresponding to the subset of data allocated to each client. These indices are used to extract the relevant data ('X_client' and 'y_client') from the training dataset ('X_train' and 'y_train'). Finally, the function returns the client-specific data ('X_client' and 'y_client') for training data in each edge device. In FL, the test dataset is typically shared among all clients to evaluate the performance of the global model.
## 1 Introduction  
The increased use of renewable energy systems worldwide during recent decades has caused saturation in some local electrical grids. The primary cause of this saturation is the intermittent nature of the electrical power produced by these technologies and the subsequent stability issues that the grid operators are faced with, along with the ever-increasing demand for the application of green technologies mainly by public sectors and private local and international investors.  
An efficient method of energy storage is achieved by transferring quantities of water from a lower body of water to a higher elevation by converting it to potential energy and by using it later to regenerate electricity. Energy transformations require the use of pumps and hydraulic turbines to complete the cycle. Renewable energy systems come into play by providing their electrical output to the pumping stations instead of feeding them directly to the grid, thus avoiding the negative effects on network stability.
# Mitigation of Gaseous Emissions from Swine Manure with the Surficial Application of Biochars
## 2 Materials and Methods
### Gas Concentration Measurements
#### 2.5.3 Volatile Organic Compounds  
Gas samples were also composited for the VOC analyses greatly reducing the variability inherent with gaseous emissions from manure. A four-port manifold was connected to a block of four (identical) treatment containers. A clean glass bulb with a volume of 1 L was connected to the manifold and on the other side to two pocket pumps with a suction rate of 175 mL\(\cdot\)min\({}^{-1}\) to flush air through the glass bulb. After 15 min of flushing, connections ports were closed, and the bulb was detached from the manifold and the pumps. Solid-phase microextraction (SPME) fiber assembly divinylbenzene/Carboxen/polydimethylsiloxane (DVB/Car/PDMS) with a length of 1 cm was inserted into the bulb through septa on it. After 10 min of passive sample collection, the SPME was injected into a thermal desorption-multidimensional GC-MS/olfactometry system (TD-MDGC-MS/O) by Microanalytics (Round Rock, TX, USA) for separation and analysis of VOCs. The details of the instrument are described in [PERSON] et al. [44]. Targeted compounds such as isovaleric acid, phenol, \(p\)-cresol, indole, and skatole were scanned with selected ion monitoring (SIM) mode (Figure S4). We initially tracked dimethyl disulfide, dimethyl trisulfide, propanoic acid, isobutyric acid, butyric acid,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Trial**} & **RO Biochar Dose** & **HAP Biochar Dose** & **Control** & **Manure Source** & **Trial Length (d)** \\  & **(kg\(\cdot\)m\({}^{-2}\))** & **(kg\(\cdot\)m\({}^{-2}\))** & **Control** & **Manure Source** & **Trial Length (d)** \\ \hline \(1\) & 1.65 & 2.0 & 0 & AG 450 & 30 \\ \(2\) & 1.65 & 2.0 & 0 & ISF & 30 \\ \(3\) & 1.65 & 2.0 & 0 & PF & 30 \\ \hline \hline \end{tabular} Note: mass of RO per reactor = 40.1 g: mass of HAP per reactor = 48.6 g.  
\end{table}
Table 2: Matrix of the experiment.  
and valeric acid. However, they were not consistently detected in all three trials and consequently are not reported here.
## 4 Starlink as Case Study
### Stage 4: Final Mass Budget Estimation  
As outlined in Section 4.3, both the total dry mass and the propellant mass are known. Table 5 below represents the dry mass partitioning and propellant mass, which shows the final Starlink version 1 mass budget distribution. The mass budget is partitioned according to ESA's LCI database and the type of case study. For instance, Starlink uses an electric propulsion system. Hence, appropriate thruster, propellant, and tank are taken into account instead of taking the LCI of a generic propulsion subsystem for accuracy. This table could be applied to any LEO constellation mission for finding the mass distribution for LCA. However, an LCI of a generic propulsion system could be used if the type of propulsion is unknown.
###### Abstract  
The aim of this study was to determine the basic chemical composition, the content of bioactive compounds and antioxidant activity in currant leaves. The leaves of black, red and white currant shrubs were collected in May, and in the beginning of June, July and August, for two years between 2018 and 2019. The proximate analysis, including dry matter, protein, fat, ash and total carbohydrates, was determined. In addition, the content of the polyphenols and the total antioxidant activity using ABTS, DPPH and FRAP assays were conducted. The highest concentration of protein was detected in the whitecurrant leaves harvested in May in both years, while the highest content of crude fat was found in the blackcurrant leaves harvested in both years, with the exception of the August harvest. Extracts from the blackcurrant leaves collected in June/July 2019 had the highest antioxidant activity that was measured by the ABTS method (about 7000 umol Trolox/g DM) and confirmed by other methods, while extracts from the whitecurrant leaves produced from the August 2018 collection had the lowest antioxidant activity (1884 umol Trolox/g DM). Currant leaves are a rich source of bioactive compounds and contain higher amounts of polyphenols as compared with currant fruits. These compounds may play a very important role in the risk reduction and even prevention of the most chronic non-communicable diseases. Therefore, further research is needed to identify currant leaves as a source of bioactives for functional foods and natural health products. The highest antioxidant activity was in the redcurrant leaves from all the harvest times in 2018 as measured by the ABTS and FRAP methods. On the contrary, blackcurrant leaves from all the harvest times in 2019 had the highest antioxidant activity.  
blackcurrant; redcurrant; whitecurrant; leaves; antioxidant activity; basic chemical composition +
Footnote †: journal: Applied Sciences