esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The ATLAS MAC Protocol
### Traffic Load-Status Differentiation
Conversely, for a \(L_{i}\) value less than 0.74, the queue length stays below \(q_{i}^{l}\). Therefore, we select the former and latter observations for the _over-load_ and _low-load_ states, respectively; whereas, for the _moderate-load_ and _high-load_ states we evenly divide the range between 0.74 and 0.92. Furthermore, in selecting each load-status, a queue length constraint is also associated with the selected load index (\(L_{i}\)) values. Table 1 shows the categories of the loads along with their differentiation conditions with respect to the estimated \(L_{i}\) for \(Ch_{i}\) and an expected queue length constraint. The ATLAS assumes that if a cluster-head has \(L_{i}\leq 0.74\) or its queue length satisfies \(q_{i}\leq q_{i}^{l}\), then it is in a low-load state; whereas either for \(L_{i}>0.92\) or \(q_{i}\geq q_{i}^{u}\) the cluster-head is in an over-load state. The moderate-load and high-load are evenly categorized within the range between minimum and maximum bounds of \(L_{i}\) for low-load and over-load, respectively, and also should satisfy a common expected queue length constraint, such that \(q_{i}^{l}<q_{i}<q_{i}^{u}\). Upon detecting the load, each cluster-head attaches two load-status notification (\(LN\)) bits at every transmitted data and beacon packet. Thus, by obtaining the \(LN\) of the cluster-head, the gateway and sensor nodes become aware of the overall traffic load condition of the surrounding environment and similarly share the information with other sensors in their vicinity. Consequently, the traffic load-aware and receiver-driven communications are controlled either by the gateway node or the cluster-heads.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
###### Abstract  
This paper presents the design of decentralized robust controllers for a network of inverter-based distributed generation systems with LC filters in the scale of a nanogrid. Using overlapping decomposition, the network of inverters is clustered into several subnetworks such that all inverters within a subnetwork are strongly coupled and there is no or a weak coupling effect between any two inverters from different subnetworks. For the inverters within the same subnetwork, decentralized robust controllers are designed sequentially in the \(\mu\)-synthesis framework. In addition, all controllers are designed to be robust against \(\pm\)10% variations in the LC filter parameters. To assess the performance of the proposed sequentially-designed controllers and compare it to that of the benchmark independently-designed ones, the distances between two neighboring inverters from the same and different subnetworks are considered to be 200 (m) and 800 (m), respectively. In this case, time-response and robustness analysis results illustrate the superiority of the proposed sequentially-designed controllers in the overlapping decomposition framework over the benchmark independently-designed ones. Moreover, transient overload and nonlinear load analyses demonstrate that the proposed sequentially-designed decentralized controllers are able to keep the load voltage within \(\pm\)10% of the nominal value and the harmonic voltage distortions to less than 4%.  
networked system; overlapping decomposition; distributed generation; \(\mu\)-synthesis; robust controller; sequential design; inverter; uncertainty
# EPO Extension of Dispatching Rules to Minimize Effects of Time Uncertainty in Production Scheduling
## 4 Case Study  
The EPO extension was tested on data coming from a company operating in the printing industry. The company is a leading communication services provider in Europe with deep expertise in print and print-related areas. It has several cooperating printing plants located in Poland, which are equipped with technologies enabling the production of printed materials of different sizes, volumes, and complexity. The range of company products comprises magazines, catalogs, brochures, retail leaflets, directories, manuals, and many more. Production planning in such a company is a very complex process due to the large variety of product types, variability in the page counts and production runs, as well as the variety of machinery (sheet-fed and web presses, folders, cutting and binding machines) and transport between plants.
# Sol-Gel Silica Nanoparticles in Medicine: A Natural Choice. Design, Synthesis and Products
## 3 Silica NP Design
### NPs Characterization  
NPs size and morphology, chemical and surface composition, along with crystallinity (or lack of) were used to explain limitations in clinical translation, NPs clearance and inflammatory processes [81; 82; 83; 84; 88].  
As far as size is concerned, NPs may exhibit different size profiles and different shell thicknesses (in core-shell structures), showing in all cases an outstanding surface-to-volume ratio, responsible for an extremely active/reactive surface performance. Size determines in vivo distribution, intracellular uptake, toxicity, and targeting ability, influencing drug loading, drug release, and in vivo and in vitro stability. Smaller particles have a great risk of aggregation during storage and incubation in vitro, but have higher mobility and longer circulation half-life in vivo. To run an effective and reproducible biomedical NPs system, monosized distribution is required, usually between 10 and less than 200 nm.  
As to shape, NPs may exhibit an extensive range of geometries--from spherical to tubular, through centric, eccentric and star like. While spherical NPs are good candidates for drug delivery, anisotropic structures can sometimes provide higher efficiencies in drug deliver (due to a more favorable configuration with the cell), although the sharp edges and corners may induce injuries to blood vessels. NPs may be hollow, dense, nanostructured, or in core-shell (eventually with multiple cores) structures, enhancing the NP load capacity and specific targeting ability.  
NPs may differ in surface chemical composition, a critical parameter in determining their drug-loading efficiency, releasing profile, circulation half-life, tumor targeting and clearance from the body. A hydrophilic surface makes the NPs more resistant to the plasma proteins adsorption (preventing the formation of corona) and thus avoiding their recognition and uptake by the MPS. Coating the NP surface with a hydrophilic polymer (like PEG) or directly synthesizing NPs with hydrophilic surface compounds (in situ synthesis) are two strategies to overcome the challenge. Rapid opsonization and clearance is observed for NPs with excess positive surface charges [90].  
As regards the renal system, neutral surface charge gives the highest chance to pass through renal filtration (and being excreted in urine), while both positively and negatively charged NPs adsorbed more serum proteins, increasing their hydrodynamic diameter and thus reducing their ability to be eliminated [91]. Unlike the long time clearance process taken by bile (or other MPS organs), the renal system removes the NPs from the body through the urine, with minimal side effects [88].
## 3 Methods
### Emissivity Estimation  
To estimate LST using mono- or split-window algorithms, emissivity must be known a priori. For the comparison with MODIS data, the emissivity was estimated using the _Vegetation cover method_ (VCM) from [30]. The VCM method was proposed for AATSR data. Due to the spectral similarity of the thermal bands of AVHRR and AATSR, the method could be employed for this study.  
\[\varepsilon_{k}=\varepsilon_{kv}FVC+\varepsilon_{kg}(1-FVC)+4\langle d\varepsilon _{k}\rangle FVC\ (1-FVC) \tag{16}\]  
\(\varepsilon_{k}\) with \(k=4\), 5 is the emissivity of band 4 resp. 5, \(\varepsilon_{kv}\) is the emissivity of vegetation, \(\varepsilon_{kg}\) is the emissivity of the ground below the vegetation, \(\langle d\varepsilon_{k}\rangle\) is the maximum cavity term, and FVC is the fractional vegetation cover. The coefficients \(\varepsilon_{kg}\), \(\varepsilon_{kv}\) and \(\langle d\varepsilon_{k}\rangle\) are dependent on land surface cover and the spectral band. Additionally, to the estimation of band emissivity, the method of [30] offers the possibility to estimate the related uncertainty of the emissivity. It should be noted that this expression does not account for the anisotropy of emissivity. As pointed out by [37] on the example of inorganic soils, this can lead to systematic errors in LST ranging between 0.4 K and 1.3 K for dry atmospheres.
# PSI Analysis of Adversarial-Attacked DCNN Models
## 2 Materials and Methods
### Pretrained DCNN Models  
Pre-trained AlexNet [22] and VGG11 [23] models on the ILSVRC2012 dataset, as shown in Table 2, were downloaded from the PyTorch model, ZOO [24]. Both DCNN architectures, illustrated in Figures 1 and 2, respectively, are purely feedforward in nature, where each layer's input solely relies on the output of the preceding layer. AlexNet is composed of 5 convolutional layers (Conv1 to Conv5) that produce feature maps using linear spatial filters, along with 3 fully connected layers (FC1 to FC3). Following each convolutional and fully connected layer, a rectifying nonlinear unit (ReLU) of the form max (x,0) is applied to all units.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Training** & **Validation** & **Testing** & **Categories** \\ \hline ImageNet & 1,281,167 & 50,000 & 100,000 & 1000 \\ \hline \multirow{3}{*}{Caltech256} & Total data & \multicolumn{2}{c}{Data/Category} & Categories \\ \cline{2-5}  & \multirow{2}{*}{30,607} & Min. & Med. & Avg. & Max. \\  & & 80 & 100 & 119 & 827 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of ImageNet and Caltech256 datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **No. of Layer** & **VGG11** & **AlexNet** \\ \hline \multirow{8}{*}{Convolution layer} & 1 & Conv3-64 & Conv11-96 \\ \cline{2-4}  & 2 & Conv3-128 & Conv5-256 \\ \cline{2-4}  & 3 & Conv3-256 & Conv-3-384 \\ \cline{2-4}  & 4 & Conv-3-256 & Conv-3-384 \\ \cline{2-4}  & 5 & Conv-3-512 & Conv-3-256 \\ \cline{2-4}  & 6 & Conv-3-512 & \\ \cline{2-4}  & 7 & Conv-3-512 & \\ \cline{2-4}  & 8 & Conv-3-512 & \\ \hline \multirow{2}{*}{FC layer} & 1 & FC-4096 & FC-4096 \\ \cline{2-4}  & 2 & FC-4096 & FC-4096 \\ \hline Output layer & & FC-1000 & FC-1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configurations of VGG11 and AlexNet architectures.  
In specific convolutional layers, ReLU is followed by a subsequent max pooling sub-layer. VGG11 shares architectural similarities with AlexNet but differs in two aspects. Firstly, VGG11 employs smaller receptive fields (3 by 3 with a stride of 1) compared to AlexNet's (11 by 11 with a stride of 4). Secondly, VGG11 possesses more layers, specifically 8 convolutional layers, compared to AlexNet.  
The designation \"Conv#\" signifies the results of the ReLU sublayer within the convolutional layer numbered #, whereas \"FC#\" represents the outcomes of the #th fully connected layer following ReLU activation. To extract DCNN activations, the DNNBrain toolbox [25] was utilized. The activation map for every unit or channel was averaged to yield per-unit or per-channel activations for each instance. The activation of a unit for a specific object category was determined by averaging the per-unit responses across all instances belonging to that category.
## 4 Conclusions  
In this paper, the influence of lithium carbonate and sodium carbonate in CSA-based mortars manufactured with SCMs replacing OPC was investigated in terms of fresh properties, mechanical strength, water absorption, free shrinkage and natural carbonation. The following conclusions were made according to the results of the present paper:  
Figure 7: Carbonation of mortar (**a**) TM, (**b**) SM1 and (**c**) SM2 with different admixtures.  
* SC and LC act as accelerator admixtures in CSA-based mortars independent of the nature of the binder employed, promoting a remarkable pore structure refinement and an improvement in elasto-mechanical properties at early age, especially by using SC;
* LC strongly reduces the workability and the pot life of mortars, compromising the possible utilization in the construction site. On the contrary, SC ensures a workability and a pot life similar to mortars manufactured without hardening accelerating admixtures;
* The SC-based mortars were characterized by a less porous binder matrix and a lower water absorption coefficient (\(-25\%\)) with respect to those of LC-based or no-admixed mortars, regardless of the binder used;
* The use of LC, alone or with SC, increased the free shrinkage of both TM and SMs up to 100% while the addition of SC reduced the shrinkage up to 50%;
* A 60% reduction in carbonation depth can be observed in mortars containing SC respect to mortar without admixtures while the addition of LC or blends SC-LC led to lower benefits, ranging from 30% to 40%;
* The SC is the most suitable additive because increased the compressive strength, reduced the shrinkage, reduced the water adsorption coefficient and reduced the carbonation depth of CSA-based mortars.  
All the authors contributed in equal part to conceptualization of the research, carrying out of tests and writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to acknowledge [PERSON] (University Parthenope of Naples) for the support in the experimental tests to determine the porosity of the mortar and Eng. [PERSON] for carrying out the elasto-mechanical tests and carbonation measurements.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Adsorption Experiments
#### 3.6.3 Adsorption Kinetics  
According to the method of 2.2.5, the ceramsite with a certain mass was added to a beaker containing Cr(VI) solution with a pH of 5 and a concentration of 10 mg/L according to the ratio of solid to liquid of 1:40. After sealing, it was put into a water bath at 25 \({}^{\circ}\)C for shock reaction. At certain intervals, 1.5 mL solution was taken out by a syringe, and then filtered with 0.45 \(\upmu\)m filter membrane. The concentration of Cr in filtrate was determined. Thus, the change of Cr removal rate with time is calculated, and the pseudo-second-order kinetic equation is used for fitting:  
\[\frac{\mathrm{t}}{\mathrm{q_{t}}}=\frac{1}{\mathrm{k_{1}q_{e}^{2}}}+\frac{1}{ \mathrm{q_{e}}}\mathrm{t} \tag{5}\]  
where, k\({}_{1}\) is a pseudo-second-order adsorption rate constant, and q\({}_{t}\) is the adsorption capacity of the adsorbent on metal ions at time t (mg/g).  
As can be seen from Figure 5, the kinetics of adsorption reaction are relatively fast in the early stage, and then tend to reach equilibrium. The entire process fits well with the pseudo-second-order dynamics equation, the correlation coefficient is 0.9937. The adsorption of biochar ceramsite to Cr (VI) belongs to the chemical adsorption. The adsorption rate is related to the active sites on the surface of the adsorbent. At the initial stage of the reaction, there are abundant active sites on the surface of the adsorbent and the adsorption rate is  
Figure 4: The fit of Langmuir and Freundlich adsorption isotherm model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **[PERSON]** & \multicolumn{4}{c}{**[PERSON]**} \\ \hline
**b** & **Qmax** & **R2** & **k** & **n** & **R2** \\ \hline
0.0866 & 14.45 & 0.9832 & 2.20 & 2.16 & 0.9881 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adsorption parameters of Langmuir and Freundlich adsorption isotherms.  
fast. As the active sites were occupied by Cr(VI), the rate gradually decreases until the adsorption equilibrium is reached [32].
## 3 Results and Discussion  
### Parameters of the Model  
The parameters of the model used in simulation are listed in Table 1. The ribs are uniform beam elements with rectangular cross section. The geometrical sizes of the two ribs are the same to simplify the model. The primary excitation is the plane wave with amplitude \(P_{0}=1\) Pa, incident at \(\theta=\frac{\pi}{4}\) and \(\alpha=\frac{\pi}{4}\). The structure damping is ignored in the modeling, thus a constant internal loss factor \(\eta=0.01\) is introduced into the complex Young's modulus as \(E(1+i\eta)\) to make the simulation results more practical [14]. The properties of the inertial actuators used in this research are given in Table 2.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 2 Extraction of Silica  
The extraction of silica from various agro-wastes has been acknowledged by many researchers across the globe. The agricultural wastes have been identified as the potential raw material for the preparation of silicon composites. The sugarcane bagasse ash is an abundant by-product of agro-food waste materials. These waste materials pose a threat to the environment and thus require proper utilization to get valuable products. It is thus the cheapest source of silica production, and the chemical compositions of a typical sugarcane bagasse ash with geographical locations are shown in Table 1. Moreover, the benefits include: Easy to handle and affordable. It is noteworthy to realize the potential reasons to have several methods to produce silica from various agro-wastes. These include, low material consumption, thus reduced costs, the high silica content in the agro-wastes, relative silica quality, nature of the material (amorphous), energy content, and eventually, low energy consumption [12; 13]. Figure 2: Images of a typical sugarcane plantation, stalks, and bagasse from the South African sugar industry [8]. _SiO\({}_{2}\)_ The table above shows the chemical make-up in the extracted silica from sugarcane bagasse ash from various countries, and it is quite evident that it is composed mostly of _SiO\({}_{2}\)_. In South Africa, the bagasse constitutes approximately 84% of _SiO\({}_{2}\)_, the third-highest after India and Brazil with 87.59% and 96.2%, respectively. This makes sugarcane bagasse an ideal source of _SiO\({}_{2}\)_ to investigate and explore for further studies [14; 16; 26]. The calculation below was carried out to find the percentage yield from the chemical treatment reported in [27]. The results from XRF, XRD, and SEM showed the metal oxides present and key findings on silica, which were calculated using the approximation calculation method [27]. 100 g of SBA introduces approximately 76.34 g of _SiO\({}_{2}\)_  
Therefore, unknown (X) amount of SCBA will introduce 73.21 g of _SiO\({}_{2}\)_  
The aforementioned implies, = \(\frac{100\times\ 73}{76.34}=\frac{7300}{76.34}=95.625\) g of SCBA (3) For, _Al\({}_{2}\)O\({}_{3}\)_ since 100 g SCBA introduces 6.7 g of _Al\({}_{2}\)O\({}_{3}\)_, then: 95.625 g of SCBA (4) will introduce = \(\frac{95.625\times\ 6.7}{100}=\frac{640.688}{100}=6.407\) g of _Al\({}_{2}\)O\({}_{3}\)_  
\(Fe_{2}\)O\({}_{3}\): Since 100 g SCBA introduces 6.3 g of _Fe\({}_{2}\)O\({}_{3}\)_, then: 95.625 g of SCBA will introduce \(\frac{95.625\times\ 6.3}{100}=\frac{642.438}{100}=6.024\) g of _Fe\({}_{2}\)O\({}_{3}\)_
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 4 Results and Discussions
### Control Effects on Flow Structures and Pressure Fields  
#### 4.2.1 Vortical Structures  
Figure 15 shows the predicted vortical structures with and without control as iso-surfaces of the second invariant \(q\equiv\mid\mid\Omega\mid\mid^{2}-\mid\mid\mid\mathbb{S}\mid\mid^{2}\), where \(\Omega\) and \(\mathbb{S}\) are the anti-symmetric parts and symmetric parts, respectively, of the velocity gradient tensor for the phase-averaged flow fields at the phase where the vortex collides with the downstream edge of the cavity. The iso-surfaces are color-coded by the values of the streamwise vorticity.  
Figure 14: (**a**) The variation of the sound-reduction level, in decibels, at the fundamental frequency of 1500 Hz as a function of the applied voltage; and (**b**) the variation of the sound-reduction level for different lower-electrode widths as a function of the freestream velocity \(U_{0}\).  
Figure 15: Iso-surfaces of the second invariant of \(q/(U_{0}/L)^{2}=0.45\) color-coded by values of the streamwise vorticity for phase-averaged flow fields at \(U_{0}=30\) m/s at the phase where the vortex collides with the downstream edge of the cavity: (**a**) baseline flow; and (**b**) controlled flow.  
As shown in Figure 15a, two-dimensional large-scale vortices are generated in the baseline cavity flow. Low-pressure regions are generated in the large-scale vortices, and an expansion wave is radiated due to the collision of the vortices with the downstream edge of the cavity [16]. Figure 15b shows that streamwise vortices are induced by the actuators in the incoming boundary layer and form rib structures between the large-scale vortices.  
Figure 16 shows the iso-surfaces of the component of the second invariant \(q_{z}\), which was computed from Equation (6):  
\[q_{z}\equiv|\omega_{z}|^{2}-\left\|\left(\begin{array}{cc}S_{xx}&S_{yx}\\ S_{yy}&S_{yy}\end{array}\right)\right\|^{2}q_{z}\equiv|\omega_{z}|^{2}-\left\| \left(\begin{array}{cc}S_{xx}&S_{yx}\\ S_{xy}&S_{yy}\end{array}\right)\right\|^{2}, \tag{6}\]  
where \(\omega_{z}\) is the spanwise vorticity and \(S_{xx}\), \(S_{xy}\), and \(S_{yy}\) are the elements of the symmetric parts of the velocity gradient tensor. This quantity is related to vortices with spanwise axes and is color-coded by the spanwise vorticity in the figure. Figure 16 also shows the contours of streamwise vorticity in the shear layer (\(y/\theta=2.9\)).  
As shown in Figure 16a, vortical structures with spanwise axes are apparent in the baseline flow. These vortices correspond approximately to the above-mentioned large-scale vortices. The periodic collision of these vortices with the downstream edge of the cavity produces the intense cavity tone. Figure 16b shows that these vortices are distorted by the streamwise vortices in the controlled flow.
# Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools
## 5 System Verification
### Displacement Measurement
#### 5.2.2 Translation Error Identification  
The laser interferometer can detect only one error at a time. Therefore, as shown in Figure 10, the measurement experiment of the displacement must be carried out twice, so that the laser interferometer can monitor the positioning error and the straightness error.  
According to the identification method in Section 4.3, Table 3 shows the coefficients of the fitting polynomials of the positioning error and straightness error of the X, Y axis, respectively.  
The discrete datum for the positioning errors at each sampling point can be obtained by inputting the feedback value of MT into the polynomial. Figure 12 shows the residual errors of the translation errors, including positioning errors \(\delta_{x}(x)\) and \(\delta_{y}(y)\) and the straightness errors \(\delta_{y}(x)\) and \(\delta_{x}(y)\) at each sampling point. It can be seen that the identification error of the translation errors is within \(\pm 2\)\(\mu\)m.  
Figure 11: Residual errors of the proposed system: (a) Residual error of \(\varepsilon_{y}(x)\); (b) Residual error of \(\varepsilon_{x}(y)\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**(Arsee/MM)**} & \multicolumn{1}{c}{**The First Order**} & \multicolumn{1}{c}{**The Second Order**} & \multicolumn{1}{c}{**The Third Order**} \\ \hline \(\varepsilon_{y}(x)\) & \(-1.01\times 10^{-2}\) & \(2.59\times 10^{-5}\) & \(-6.50\times 10^{-8}\) \\ \(\varepsilon_{x}(y)\) & \(-3.29\times 10^{-3}\) & \(1.44\times 10^{-4}\) & \(-4.53\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of \(\varepsilon_{y}(x)\) and \(\varepsilon_{x}(y)\).