esa-sceva/satcom-chunk-collection huggingface dataset:
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 3 System Model and Framework Overview
### Dynamic Spatial Meshing  
\"Dynamic spatial meshing\" refers to a technique utilized across various fields that improves simulation accuracy and efficiency by dynamically adjusting grid density to accommodate areas with complex or rapidly changing phenomena. The dynamic adaptive  
Figure 1: Mobile acquisition task allocation process based on differential privacy protection for mobile crowdsensing.  
grid index planning used in this paper was established in Reference [35]. As illustrated in Figure 2, it involves fixed first- and second-level privacy budget allocation strategies. The strategy calculates the granularity of the first-level grid based on the number of workers. It combines the privacy budget of the first-level grid with the Laplace mechanism to introduce noise into the nodes of the first-level grid. Subsequently, the query data of each grid in the first level are utilized to calculate the granularity of the second-level grid. This process involves establishing the second-level grid and adding noise twice in the second-level grid.
## 2 Materials and Methods
### Flow System  
A flow cell system (Figure 1) containing a recirculating tank (planktonic cells) and a flow cell composed of removable coupons with polyvinyl chloride (PVC) slides for biofilm formation was operated as described by [PERSON] et al. [9]. Briefly, _E. coli_ cells were recirculated at 30 \({}^{\circ}\)C for 10 days under turbulent flow conditions (Reynolds number = 4600) [9], which are the hydrodynamic conditions previously used by the group [9; 10; 22; 33] and completely characterized by computational fluid dynamics [34]. The tank was aerated and continuously fed with fresh medium at a flow rate of 0.025 L h\({}^{-1}\)[9].  
To determine the effect of culture medium on biofilm formation and eGFP expression, two different media were tested: LB-Miller (Sigma, St. Louis, MO, USA) and M9 ZB medium. According to the supplier, LB-Miller is made of 10 g L\({}^{-1}\) tryptone, 5 g L\({}^{-1}\) yeast extract, and 10 g L\({}^{-1}\) NaCl. The M9 ZB medium was prepared as indicated by the Novagen pET System manual and [PERSON] et al. [30; 35]. The mixture of 10 g L\({}^{-1}\) N-Z-amine A and 5 g L\({}^{-1}\) NaCl was first prepared. Then, 100 mL of 10\(\times\) M9 salts solution (10 g L\({}^{-1}\) NH\({}_{4}\)Cl, 30 g L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\), 60 g L\({}^{-1}\) Na\({}_{2}\)HPO\({}_{4}\) 7\(\upmu\)G\({}_{2}\)O), 1 mL of 1 M MgSO\({}_{4}\) solution, and 10 mL of 400 g L\({}^{-1}\) glucose solution were added. All solutions were prepared and autoclaved separately. All compounds were purchased from Merck, Alges, Portugal. The comparison of carbon and nitrogen sources and amounts between media are presented in Table S1 in Supplementary Material. Both liquid media were supplemented with 20 \(\upmu\)g mL\({}^{-1}\) kanamycincin.  
Figure 1: Schematic representation of the flow cell system (adapted from [3]).
## 3 Results and Discussion
### TPC and TFC in CSC  
Several studies reported that the inclusion of an abiotic or biotic elicitor to the growth medium notably elevated the synthesis of bioactive compounds by activating a defense system [29].  
Figure 3: Effect of CuO NPs on bioactive compounds in CSC of _G. sylvestre_. (**a**) Gymnemic acid II (GA II), (**b**) Total phenolic content (TPC), (**c**) Total flavonoid content (TFC). Different letters indicate a significant difference at \(p\leq 0.05\).  
We investigated the impact of CuO NPs on the accumulation of TPC and TFC in CSC of _G. sylvestre_. CuO NPs at the concentration of 3 mg/L led to the maximum production of TPC (245.10 mg/g DCM GAE) and TFC (4.57 mg/g DCM QE) compared to control CSC (95.75 GAE and 2.55 QE; Figure 3b,c). However, the use of 5 mg/L of CuO NPs in CSC decreased the amount of TPC (190.15 GAE) and TFC (3.85 QE). TPC and TFC were improved by the elicitation of CuO NPs in hairy roots of Chinese cabbage [29] and AgNPs in CSC of bitter gourd [25]. Elicitation with silicon dioxide NPs elevates the PC accumulation in hairy roots of _Dracocephalum kotschyi_[45]. The mechanism of an elicitor's impact on cells, organs and plants is not well-documented. However, biotic or abiotic elicitors easily attack the cell membrane and stimulate defensive signals like electrolyte leakage, oxidative burst, production of reactive oxygen species (ROS) and protein phosphorylation and dephosphorylation. Phytoalexins like PC are increased by secondary messenger molecules including H\({}_{2}\)O\({}_{2}\). The over-expression of _PAL_ (a defense gene) leads to the enhanced production of PC [46]. Several studies have demonstrated that treatments of plants with cadmium oxide NPs or CuO NPs produce an increase in PC, which could act as an antioxidant to scavenge ROS [47; 48]. ROS may act as a normal signal for adaptation to Cu stress and could induce the accumulation of TPC in lentil roots [49]. Elicitation of magnetite NPs and static magnetic field elicitation can increase the content of phenolic compounds and polyphenol oxidase in the CSC of _Dracocephalum polychaetum_[9]. TPC and TFC can also be determined by both the surface area and size of the NPs used. Smaller NPs will induce more stress and, in response, more TPC and TFC will be biosynthesized. Our results suggest that treatment with CuO NPs improved the amount of TPC and TFC in CSC of _G. sylvestre_.
# Economic Feasibility of Retrofitting an Ageing Ship to Improve the Environmental Footprint
## 3 Gas Emissions
### \(Co_{2}\) Emissions  
The European Union presented its _\"Fit for 55\"_ package of energy and climate laws, aimed at reducing carbon emissions by 55% compared to the levels from 1990 to 2030 and achieving carbon neutrality by 2050. To achieve the latest EU target, transport emissions need to be reduced by 90%.  
This package of directives includes the introduction of the Fuel EU Maritime (FEM) Initiative and the Carbon Border Adjustment Mechanism (CBAM) and amendments to the Renewable Energy Directive (RED) and the Energy Efficiency Directive (EED). Being part of this \"Fit for 55\" policy package, the European Commission announced the update of Directive 2003/87/EU, which is based on the EU's MRV system [52] (monitoring, reporting and verification of carbon dioxide emissions from maritime transport) and includes the maritime industry in ETS (Emissions Trading System). The EU states that their actions are due to the slow and insufficient progress made by the IMO.  
The ETS is a mandatory cap-and-trade system that sets a general limit on greenhouse gas emissions over a specified period.  
The Allowances, known as European Union Allowances (EUA), are auctioned between the participants and the number of allowances available compared to the total number of emissions creates a carbon price; the available allowances will decrease linearly about the set emission reduction targets.  
The MRV system will continue to apply to ships over 5000 GT regarding the CO\({}_{2}\) emissions, being released during voyages to and from an EU port to a non-EU port, to an EU port from a non-EU port, between EU ports and during the ship is moored in an EU port.  
Emission factors (\(EF\)) for marine fuels and LNG will remain constant between MRV and ETS:  
* Diesel/gas oil: 3206 tCO\({}_{2}\) per tonne of fuel consumed;
* Liquid fuel: 3151 tCO\({}_{2}\) per tonne of fuel consumed;
* Heavy fuel: 3141 tCO\({}_{2}\) per tonne of fuel consumed;
* LNG: 2750 tCO\({}_{2}\) per tonne of fuel consumed.
# Exposure and Health Effects of Bacteria in Healthcare Units: An Overview
## 3 Results
### Characteristics of the Selected Studies
#### 3.1.1 Reference Values for Bacteria  
Regarding the reference values for bacteria, the median level of colony forming units (CFU) in the wards of hospital 1 was 129.87 (87.46-268.97) CFU/m\({}^{3}\) and, for hospital 2, was 297.97 (217.66-431.85) CFU/m\({}^{3}\)[32], both part of a teaching hospital in Tehran. A study conducted in a Brazilian hospital reported median bacterial loads of 345.25 CFU/m\({}^{3}\) in the post-surgical restroom and 566 CFU/m\({}^{3}\) in operating theatres [33]. Another study in a hospital from Poland reported a median bacterial load of 347.4 CFU/m\({}^{3}\) (257.1-436.3 CFU/m\({}^{3}\)) [34]. In a university hospital from Greece, the highest mean total microbial load (689 CFU/m\({}^{3}\)) and the highest Gram-negative bacteria load (4.16 CFU/m\({}^{3}\)) were observed in the intensive medicine ward [35]. A study dedicated to hospitals in the USA observed a mean bacterial load of 720 CFU/m\({}^{3}\)[36]. Some of the analyzed articles did not quantify the bacterial contamination found (5 out of 13).
## 3 Experimental Results and Analysis
### Sensitivity of the Parameters  
In this part, we focus on the \(D_{m}\) in NAD, \(D_{l}\) in LSH and the number of neighborhood scales (\(\mu+\
u\)). Here, we select the parameters shown in Table 4; the data of scene 1 is used to compare the influence of different parameters on the proposed method. In order to evaluate the classification effect of the three kinds of objects as a whole, we average the \(F_{1}\)-score values of the three object classification results to obtain the mean value \(\text{mF}_{1}\), which is used as the overall classification effect evaluation metric. As shown in Table 4, considering the results of \(\text{mF}_{1}\) and the accuracy in combination, in comparing the parameters of the first three rows one can see that when \(D_{m}\) is 15, the classification effect is better; however, the value of \(D_{m}\) is not sensitive to the classification effect. According to the results of rows 2, 4 and 5, the classification effect of \(D_{l}\) is obviously improved at 15. However, when the value of \(D_{l}\) is too large, the classification effect is reduced. Therefore, the value of \(D_{l}\) is relatively sensitive to the classification result. According to the results of rows 4, 6, 7 and 8-10, the classification effect is improved when the value of the scale \(\mu+\
u\) is increased. However, when the scale exceeds 4, the classification effect will be reduced to some extent. Therefore, the value of the scale is sensitive to the results of the classification and needs to be within a reasonable range. One can see from Table 4 that the tree and building classes are relatively less affected by the changes of the \(D_{m}\) and \(D_{l}\), and that the results are susceptible to the size of the scale. The increasing values of \(D_{m}\) and \(D_{l}\) would likely cause a change in the car classification effect. Considering the overall effect of the classification and the factors of accuracy and feature dimension, we select \(D_{m}=15\), \(D_{l}=15\) and (\(\mu+\
u\)) = 3 as the optimal parameter values.  
Figure 5: Scene 2 classification results. (**a**) ground truth, (**b**) proposed method, (**c**) method 1, (**d**) method 2, (**e**) method 3, (**f**) method 4, (**g**) method 5, (**h**) method 6, and (**i**) method 7. ((**f**-**i**) are taken from Ref [23]. The red points are cars, green points are buildings and blue points are trees.)
## 4 Power Consumption Models
### WiFi Power Consumption Models  
The power consumption model for LTE and WiFi 802.11g air interfaces has linear dependency on the data rate in measurements done in [19], as shown in the following. Power consumption (mW) when receiving data is estimated as  
\[P_{\text{rx}}=\alpha_{\text{rx}}R_{\text{rx}}+\beta \tag{5}\]  
The power consumption (mW) when transmitting data is estimated as  
\[P_{\text{tx}}=\alpha_{\text{tx}}R_{\text{tx}}+\beta \tag{6}\]  
The parameters \(\alpha_{\text{rx}}\) and \(\alpha_{\text{tx}}\) are linear scaling factors for reception and transmission, \(R_{\text{rx}}\) is the received data rate, \(R_{\text{tx}}\) is the transmitted data rate and \(\beta\) is the basic power consumption in the active mode. Based on several references, parameters for these models are given in Table 2. It can be seen that the older air interfaces behave according to Equations (5) and (6), including the LTE device model in [19] and the 802.11g model in the same paper. The more recent 802.11n model that was defined in [30] based on measurements reported in [20] is quite flat.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Air Interface** & **ff\({}_{\text{rx}}\) (mW/Mbps)** & **ff\({}_{\text{tx}}\) (mW/Mbps)** & **\(\beta\) (mW)** \\ \hline
[19] & LTE & 51.97 & 438.39 & 1288.04 \\  & WiFi, 802.11g & 137.01 & 283.17 & 132.86 \\
[20;30] & WiFi, 802.11n & 6 & 4 & \(\beta_{\text{rx}}\) = 450, \(\beta_{\text{tx}}\) = 980 \\
[32] & 802.11 ac & \(\sim\)2100 mW * & \(\sim\)2500 mW * & 287 \\
[32] & 802.11 ad & \(\sim\)2100 mW * & \(\sim\)2000 mW * & 1938 \\ \hline \hline \end{tabular}
* * Over a large bit rate range the power consumption is quite flat in recent 802.11 ac and ad interfaces.  
\end{table}
Table 2: Power consumption parameters of different long term evolution (LTE) and WiFi models; \(\alpha_{\text{rx}}\): linear scaling factor for reception; \(\alpha_{\text{tx}}\): linear scaling factor for transmission; \(\beta\): basic power consumption in the active mode.  
The most recent 802.11 ac and 802.11 ad measurements given in [32] show that both receiver power consumption and transmitter power consumption are almost flat, regardless of the bit rate. The basic power consumption is much lower in 802.11 ac, but the 802.11 ad interface always consumes a lot of energy when it is active. There is no big difference when receiving or transmitting data compared to the basic power consumption according to [32]. However, the results indicate that with the latest models, the best option for energy saving is to send the data with the best possible data rate in order to be able spend more time in the basic power consumption mode.
## 2 Proposed Method
### Feature Points Matching Strategy  
After the feature point descriptors are constructed, the feature vector sets of SAR and optical images are obtained, respectively. It is necessary to establish a correct match based on the similarity measurement criterion between feature vector descriptors and the matching relationship. This chapter will first use the two-way matching algorithm to establish the initial matching relationship, and then use the random sampling consensus algorithm to remove the wrong matching point pairs, and calculate the parameters of the transformation model by retaining the correct matching point pairs. The main process is as follows:
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Strontium Nanoparticles  
Strontium is a trace metal that mimics the properties of calcium and magnesium, with an important role in human tissues. Due to its properties, strontium is an important element in dentistry and bone regeneration. Strontium stimulates bone growth by enhancing collagen and other bone matrix protein synthesis. It facilitates osteoblast differentiation and simultaneously reduces bone resorption by osteoclasts differentiation [170]. The plasma protein binding capacity for divalent strontium cation is similar to calcium cation. The transport of Sr\({}^{2+}\) ions is controlled by the same carriers as Ca\({}^{2+}\) ions.  
Strontium nanoparticles (Sr-NPs) are spherical or flake metal particles with a specific surface area of 30-60 m\({}^{2}\)/g in a size range of 10-200 nm. Sr-NPs are used either in bone regeneration to improve the mechanical and biological properties of diverse polymers and metal alloys [171; 172] or encapsulated as inorganic nanocarriers in drug delivery and biomedical imaging [173; 174].  
The bioactive microspheres enriched with monodispersed Sr-NPs regulate the phenotype of macrophages and promote early angiogenesis. New vessels at the center of scaffolds one week after implantation in a rat's bone were observed, which suggested enhanced early vascularization and subsequent support of new bone formation [175]. The nanoparticles of bioglass doped with 10% strontium immersed in SBF created an apatite layer on the surface. The stem cells increased in the presence of strontium in the nanoparticles prefer the expression of osteogenic genes and proteins, even without osteogenic supplementation [176]. Synthesized nanoparticles of strontium oxide with a diameter of 32.6 nm showed good antibacterial properties against _Bacillus subtilis_[177].  
[PERSON] et al. prepared Sr-NPs by the green synthesis method in the presence of carboxymethyl cellulose as a green biological stabilizer. According to the reaction conditions, nanoparticles of different sizes and distributions were created. Synthetized Sr-NPs are suitable for preclinical imaging and ideal for noninvasive disease progression monitoring, cell trafficking, and gene expression patterns in experimental animals. Sr-NPs were applied successfully as an efficient agent for in vivo imaging after injection into the neck area and the peritoneum of male BALB/c mice. The use of Sr-NPs is a good alternative to eliminate toxic and carcinogenic compounds in in vivo imaging [178].
# Study of the Mechanism of a Stable Deposited Height During GMAW-Based Additive Manufacturing
## 2 Experimental Details  
### Experimental Conditions  
The objective of the present research is to develop a system by combing a welding station and an industrial robot which provides processing freedom. As shown in Figure 1, the industrial robot is a COMAU SMART 5 NJ60-2.2, which can realize multiple degrees of freedom during manufacturing and can transfer the component from one position to another. The welding system is a KEMPPI A7 MIG welder. A temperature measuring device is mounted under the substrate to ensure that the temperature of the substrate is held constant at the beginning of every experiment. The software control process is running on a Beckhoff industrial computer, which communicates between the robot and the welding system.  
During the experiments, the torch is stationary, and the robot moves the substrate near the torch. The KEMPPI A7 MIG welder provides a \"wise thin+\" procedure in which the output welding voltage and current are based on the wire feeding speed. The temperature of the substrate remains the same as the ambient temperature during the experiments.  
[PERSON] et al. [9] found that when the cooling time is set appropriately, the deposition shape increases in height and decreases in thickness. If the welding direction is reversed for each layer, the variance of the deposition height becomes small; if the welding direction does not change for each layer, the height becomes uneven, and the starting point is higher than the ending point. The experiments build thin walls comprising 100 layers with different parameters, and the direction is changed after each layer is finished to keep the height of the thin wall uniform. The wire feed speed is 3.0 m/min, travel speed is 0.011 m/s, welding voltage and current are set by a wise thin+ procedure based on the wire feed speed, interval time between two layers is 5 s, and the initial dry extension of the electrode is 12 mm.
## 5 Results and Discussion  
Spatial and statistical analyses were performed to calculate the level of coverage of the provision of SITEUR with respect to the social needs of transport in the Guadalajara Metropolitan Area (GMA). The use of geographic information systems (GIS) highlighted not only the geographic dimension of SITEUR, but the social transport needs (STN) and their spatial patterns. The indexes calculated were the Index of Mass Transport Provision (IMTP), the Index of Social Transport Needs (ISTN) and the Index of Social Transport Needs Covered (ISNTC). All indexes were mapped with five graduated symbols calculated using quintiles of the geographic sections (GS) to create cut-off index levels: very low, low, medium, high, and very high.
# Object Detection Network Based on Feature Fusion and Attention Mechanism
## 2 Related Works
### Visual Attention Mechanism  
Recently, neuroscience and cognitive learning theory have developed rapidly. Evidence from the human perception process shows the importance of the attention mechanism. [PERSON] [22] proposed a neurobiology framework that laid the foundation for the visual attention model. The visual attention mechanism is divided into two processing methods: Bottom-up model and top-down model. The bottom-up visual attention model is driven by data from the low-level features of the image. However, the top-down visual attention model is more complicated than the bottom-up visual attention model. The top-down visual attention model is driven by tasks, so it requires tasks to provide relevant prior knowledge.  
Various visual attention models have been proposed. [PERSON] et al. [23] proposed the Itti attention model, which is based on feature integration. [PERSON] et al. [24] put forward the ATM attention model by training with image sub-blocks. [PERSON] et al. [25] combined bottom-up and top-down models. Recently, tentative efforts have been made towards applying attention to the deep neural network. The attention model has good results in the fields of image caption, machine translation, speech recognition, etc. [PERSON] et al. [11] proposed a single-layer attention model to solve source language alignment problems of different lengths in machine translation. [PERSON] [26] applied a single attention model to news recommendation and screening filed. Muti-attention mechanisms (hierarchical attention, dual attention) can accomplish tasks more accurately. [PERSON] [27] proposed the hierarchical attention model to complete the abstract extraction of the article. [PERSON] et al. [28] used the dual attention model for the recommendation system.  
In object detection, there are several methods based on attention mechanisms that have been proposed. [PERSON] [29] used self-attention to learn the correlation between features on the feature map and obtain new features. [PERSON] [12] proposed an object relation module based on self-attention. The visual attention mechanism can be roughly divided into two types: Learning weight distribution and task focus. We added the attention module in the classification network to the learn weight. The attention module is a mask branch to enhance the interest feature and weak background interference.