esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 5 Discussion
### Impact of LoRa Research Proposals in the Smart City Initiative
#### 5.7.3. LoRa Proposals for the Generation of New Business Opportunities  
We classified the articles selected for this study as shown in Table 16, in which the contributions of LoRa's proposals are schematized from the perspectives of social, economic, organizational, and material reuse. We can say that the proposals delivered a great contribution for the improvement of particular organizational processes of each subcategory of the smart city. In the case of healthcare solutions, there was a greater focus on the social aspects, and most of the proposals did not consider the economic and material reuse aspects.  
The circular economy in the smart city approaches the reuse of resources that are limited in nature, such as food or water. The proper management of these elements allows maintaining the sustainability approach of the city. The proposals related to energy management considered this feature, especially those focused on renewable energy and smart grids. Aspects such as the prediction of equipment maintenance or the degrading conditions of a product could contribute to this aspect of reuse, since they could allow the identification of the degrading conditions of a product and its substitution before its total degradation. This may be of interest to the manufacturing industry, because a completely non-damaged part can be reused to develop new components. Manufacters can also start considering this aspect of reuse to design their products.  
The circular economy drives new business models and interactions in the city. By using the data generated by IoT solutions, it is possible to generate direct benefits for the industry and for the local government. Manufacturers can, based on the user usage patterns of their products, find specific needs at a given time. They can consider in this way the creation of components that allow their reuse or the creation of new products from these. At the local government level, the determination of these patterns allows validating a service or understanding when it can be used by another citizen to optimize resources.  
The sale of services is a new business model that has had a remarkable growth in recent years, which allows the reuse of existing infrastructures of companies and local governments significantly improving what is offered to the users or citizens. This is another context that could be strongly exploited by research and industry. Of the analyzed works, less than 1% focused on generating service sales models of LoRa IoT implementations.  
Figure 22: Three-phase cycle to generate intelligence for data decision.
## 1 Introduction  
The \((n\times m)\)-puzzle is defined as follows. Given \(nm-1\) numbered tiles arranged in row-major order in an \(n\times m\) grid leaving a blank space in the last position where one tile is missing, the aim is to scramble the puzzle and return it to the initial configuration by repeatedly sliding an adjacent tile into the blank location. When \(n=m\) the puzzle is also called the \((n^{2}-1)\)-puzzle. For example, Figure 1 shows an example for the 15-puzzle of the solved configuration (left) and a random configuration (right).  
Figure 1: The solved 15-puzzle (**left**), and a random configuration (**right**).  
The 15-puzzle has a long and interesting history (see, for example, Hordern [1]) that is said to date back to the 1870s. More recently, the 15-puzzle has appeared in the form of various apps on mobile devices and as minigames inside larger games. For example, the 15-puzzle can be found in the original _Final Fantasy_(Square Enix, 1987) and _The Legend of Zelda: The Windowaker_(Nintendo 2003), and the 8-puzzle can be found in _Machinarium_(Amanita Design, 2009).  
[PERSON] and [PERSON] [2] have proved that the problem of finding the minimum number of moves for the \((n^{2}-1)\)-puzzle is NP-hard, and they demonstrate that a polynomial time approximation algorithm exists. [PERSON], [PERSON], and [PERSON] [3] show an \(O(n^{3})\) time algorithm for the \((n^{2}-1)\)-puzzle, which therefore uses \(O(n^{3})\) moves in the worst case.  
[PERSON] [4] gave worst case upper and lower bounds of \(5n^{3}\) and \(n^{3}\), respectively, on the number of moves required to solve the \((n^{2}-1)\)-puzzle using a greedy algorithm. Whilst upper bounds are certainly interesting, a hypothetical player faced with solving a random configuration of the puzzle is likely be more concerned about the expected number of moves than the worst case. [PERSON] [4] also gave lower bounds of at least \(2n^{3}/3\) for the expected number of moves, and at least \(0.264n^{3}\) moves for a random configuration with probability one.  
We extend this work by showing both theoretically and experimentally that the greedy algorithm solves the \((n^{2}-1)\)-puzzle in expected number of moves \(\frac{8}{3}n^{3}+O(n^{2})\). The main body of this paper is divided into three sections. Section 2 contains a brief description of the greedy algorithm. Section 3 contains the average-case analysis of the number of moves required. Section 4 contains an experimental verification of this analysis.
## 3 Methodology
### Logistic Regression(LR) Model  
The LR model is useful for predicting whether groundwater exists in a particular location, based on the predictor variables. The primary reason for using the LR model is to explain the relationship between dependent variables and independent variables [63]. The advantage of the LR model is that variables need not have normal distribution, regardless of whether they are continuous, discrete, or a combination of both types [26]. In this study, dependent variables indicate the presence of groundwater using a binary variable. The following show the relationship between groundwater presence and the dependency of a variable:  
\[P=\frac{e^{Z}}{1+e^{Z}} \tag{5}\]  
\[Z=a+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}+\cdots+b_{m}x_{m} \tag{6}\]  
\[Z=log_{c}\bigg{[}\frac{p}{1-p}\bigg{]}=logit(p) \tag{7}\]  
where \(a\) is the intercept of the LR model, \(x_{1},x_{2},\cdots,x_{m}\) are regression coefficient of the logistic regression model, and \(Z\) is a linear combination function of the coefficient representing a linear relationship. The parameters \(b_{1},b_{2},\cdots,b_{m}\) are the independent variable. The probability (p) represents to the estimated probability of potential groundwater. The value of Z is denoted in the binary form, where \(Z=1\) implies more than a specific amount of groundwater (T \(\geq\) 2.61 m\({}^{2}\)/day or SPC \(\geq\) 4.88 m\({}^{3}\)/day/m), and \(Z=0\) indicates either less than that specific amount or no groundwater. Function Z is represented as logit (p) is a likelihood ratio that the dependent variable Z is 1. The LR model coefficient is a value that represents the percentage (%) of the variance of the dependent that variable is explained by the independent variable, and has a value between 0.00 and 1.00. A value closer to 1.00 means closer to a perfect relationship, which is almost the same as the square of the multiple correlation coefficient in a linear regression.
# Device Modeling of Quantum Dot-Organic Light Emitting Diodes for High Green Color Purity
## 3 Results and Discussion
### Emission Spectrum with Different G-QD Thicknesses  
Figure 5 shows the emission spectrum according to the thickness of the G-QD layer when the G-QDs density in the layer is 10%. As shown in Figure 5, when the thickness of the layer is 2 \(\upmu\)m, the emission spectrum is strong in both the blue and green wavelength bands. This happens because the thickness is too thin; thus, the blue incident light cannot be adequately scattered in the layer. In other words, it can be seen that a large portion of the blue incident light passes through the layer and generates blue light leakage, and some of the light is scattered in this layer, contributing to the intensity of the green emission spectrum. When the thickness is increased to 4 \(\upmu\)m, the emission spectrum in the blue wavelength band tends to decrease rapidly. However, the FWHM in the green region is 67.63 nm, which is still large, and the peak is broad. This tendency shows an improved behavior when the thickness increases to 6 \(\upmu\)m or more and almost disappears at the 10 \(\upmu\)m thickness, resulting in sharp peaks (FWHM 46.59 nm) in the green region. However, even at a thickness of 10 \(\upmu\)m, an emission spectrum due to blue light leakage is still observed in the blue wavelength band. In other words, it would be difficult to completely block blue light leakage by simply increasing the thickness of the layer. Therefore, it seems necessary to supplement the device structure to increase the green color quality.  
Figure 6 shows the CIE1931 color space chromaticity diagram when the thickness of the G-QD layer is 10 \(\upmu\)m. As shown in Figure 6, the color coordinates of Gx and Gy  
Figure 4: The emission spectrum of blue OLED with different G-QD densities.  
Figure 5: The emission spectrum with different G-QD thicknesses.  
are 0.364 and 0.592, respectively. It shows a value that is less than the standard color coordinates, Gx 0.170 and Gy 0.797 for deep green in Rec. 2020 or BT2020 being used for ultra-high-definition television and a wide color gamut [16; 17]. It can be seen that to increase the green color purity, the G-QD layer should not be used alone, but the color purity should be increased by reinforcing an additional layer above the G-QD layer.
## 3 Explosion Test Program
### Damaged HFPC Panels
#### 3.2.1 Control Specim  
First, the control panels made of plain concrete, PP FRC, PVA FRC and Steel FRC were tested with contact detonation. The fiber reinforcement effect as well as fiber type effects were analyzed.  
As depicted in Figure 6, the plain concrete panel was totally torn apart because of the explosion effect. Due to the britteness, the plain concrete panels could not sustain dynamic loading by contact detonation. By contrast, fiber-reinforced specimen S2 to S4 behaved much better than the plain concrete.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**No.** & **Front Crater Diameter** & **Front Crater Depth** & **Rear Crater Diameter** \\ \hline S1-1 & - & - & - \\ S1-2 & - & - & - \\ S2-1 & 200/200 & - & 247/320 \\ S2-2 & 177/170 & - & 372/372 \\ S3-1 & 245/320 & - & 336/390 \\ S3-2 & 234/289 & - & 207/273 \\ S4-1 & 139/156 & 20 & 230/290 \\ S4-2 & 152/204 & 33 & 241/280 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Blast damage for plain and fiber-reinforced concrete panels (in mm).  
Figure 5: Detonation test setup.  
For specimen number S2-1 and S2-2, the front view of the damaged panels showed 4 main broken blocks with 13 and 16 radial cracks. Meanwhile, 19 and 20 radial cracks occurred by the rear view of the damaged panels.  
The panel of No. S3-1 was broken into 3 main blocks and 9 smaller pieces, front and rear view showed 28 and 21 cracks. The S3-2 specimen was featured with 4 main blocks and 5 smaller pieces and 19/15 main cracks were observed.  
Steel FRC panels had the least damage, with the whole structure remaining intact. Only craters, with rear surface larger, and finer radial cracks, and about 20 cracks on the front and 26 cracks on the rear, occurred.  
The front crater diameter and depth were plotted in Figure 7a, and the rear crater diameter was shown in Figure 7b. It was clear that plain concrete performed worst in blast resistance, and PP FRC and PVA FRC panels were torn up. The Steel FRC exhibited good resistance against blast loading only leaving small craters on both the front and rear surfaces. It is worth noting the rear crater dimensions were much larger than the front crater. The reason for this phenomenon might lie in the fact that rear surface reflected tensile stress wave causes severer damage to the concrete panel since its tensile behavior is much weaker compared to the compressive property.  
Figure 6: Damage mode of concrete panels without/with fiber reinforcement.
## 2 Materials and Methods
### Vis/NIR Spectroscopy  
Vis/NIR spectroscopy was performed using a PS-100 model spectrometer (Apogee Instruments, Inc., Logan, UT, USA). Equipped with a CCD detector, the spectrometer offered a resolution of 1 nm across a spectral range of 350-1150 nm. A halogen-tungsten light source was employed. Spectra were acquired via a USB connection to a computer and processed using Spectra Wiz software V6.32. Prior to sample analysis, dark and white reference spectra were established. The dark spectrum was recorded with the light source off, while a standard Teflon disk served as the white reference under illumination. Spectral data were collected from four distinct points on each sample. These individual spectra were averaged to generate an absorption spectrum for each sample [22]. Ultimately, 90 spectral data points were extracted for subsequent analysis.
## 3 Diagnostic and Prognostic Framework Modelling
### Battery Capacity Fade Model  
The Coulombic Efficiency factor (\(\eta_{C}\)) models the effect of battery capacitor fade with aging and cycling. This factor represents the amount of charge that can be extracted from a battery given the charged capacity. The reduction of the battery capacitor with aging is modelled as:  
\[C_{k}=(\eta_{C})^{k}\cdot C_{0}, \tag{2}\]  
where \(k\) represents the cycle and \(C_{0}\) is the initial capacitance of the battery. Through experimental testing, a typical value for \(\eta_{C}\) = 0.9987. However, the capacity usually increases when the battery is allowed to rest between cycles. Therefore, a battery'self-recharge during rest' factor was introduced by [PERSON] and [PERSON] [14]. The battery EOL model is now expressed as:  
\[C_{k}=(\eta_{C})^{k}\cdot C_{0}+\beta_{1}\cdot\exp\left(\frac{-\beta_{2}}{ \Delta t_{k}}\right), \tag{3}\]  
where \(\Delta t_{k}\) is the resting time between \(k\) and \(k-1\) cycles and \(\beta_{1}\) and \(\beta_{2}\) are the model parameters to be determined and tracked using the particle filter algorithm.
## 2 Analysis of the Proposed Design Strategy
\[R_{\rm opt}=\frac{V_{\rm dc}}{I_{\rm max}/2}=\frac{V_{\rm dc}}{I_{\rm dc}},P_{ \rm opt}=\frac{1}{2}\cdot\frac{V_{\rm dc}}{I_{\rm dc}} \tag{7}\]  
When the load impedance decreases to \(R_{\rm opt}/p\) (\(p\geq 1\)), defined as \(R_{\rm LO}\), the voltage swing must reduce to prevent the current swing from exceeding \(I_{\rm max}\). Conversely, when the load impedance increases to \(pR_{\rm opt}\), defined as \(R_{\rm HI}\), the current swing must decrease to ensure that the voltage swing remains within \(2V_{\rm dc}\). The corresponding output powers, \(P_{\rm LO}\) and \(P_{\rm HI}\), are given in Equation (8). \[R_{\rm LO}=\frac{R_{\rm opt}}{p},\;R_{\rm HI}=\text{p}\cdot R_{\rm opt}\,,\;\; \;P_{\rm LO}=P_{\rm HI}=\frac{P_{\rm opt}}{p} \tag{8}\]  
To analyze the impact of the reactance on power, the series reactance  
Based on the above analysis, by plotting the impedance space on the Smith chart, a closed power contour curve can be obtained, as shown in Figure 3a. By combining the load-line power contours with the theoretical impedance space of CCBJ, a more realistic representation of power variations within the CCBJ-mode impedance space can be obtained. As shown in Figure 3b, by plotting the CCBJ-mode impedance space alongside the 0.3 dB and 0.5 dB power contours on the same Smith chart, the impedance regions where the power fluctuations are less than 0.3 dB and 0.5 dB within the CCBJ-type impedance space can be identified. As a result, designing a power amplifier within the overlapping impedance space leads to a highly efficient class-J power amplifier with excellent in-band saturation power flatness.
## 4 Discussion
PLMN plays a key role in fibrinolysis, and its activation to plasmin allows fibrin matrix degradation and remodeling to achieve wound healing [27]. In addition, TENT, which enhances plasminogen activation and is related to the wound tissue healing process [28], also shows increased affinity for anodized surfaces. These results aresupported by the Panther analysis, in which proteins differentially more adsorbed onto anodized surfaces were related to blood coagulation and plasminogen activating cascades. In addition, an interaction network including molecules with functions in fibrinolysis and blood coagulation was identified among the UP-differential proteins by the STRING analysis. As has been demonstrated in previous works, the differential adsorption of blood coagulation-related proteins onto a biomaterial can condition clot formation around that surface [13]. The fibrin clot serves as a matrix for cytokines and growth factors that \"jump start\" wound healing, and provides a scaffold for the chemoattraction of inflammatory cells, the initiation of cell movements and connective tissue contraction [29]. Regarding the effect of anodizing treatments on the abutment immune response, the levels of TNF-a secreted by the THP-1 cells, related to the M1 proinflammatory macrophage phenotype, showed a significant decrease for anodized surfaces (especially PA) when compared to MC. At the same time, TGF-\(\beta\), which exerts strong anti-inflammatory action, increased its secretion in those cells cultured on anodized surfaces. This cytokine is associated with M2 or tissue reparative macrophage phenotype polarization, being a key regulator of myofibblast differentiation, wound contraction and remodeling [29]. Of note in the proteomics results was the great adsorption of SAMP, IC1 and C4 BPB on PA. IC1 and C4 BPB are known regulators/inhibitors of the complement system pathway and are associated with anti-inflammatory functions [30]. On the other hand, the role of penetraxin SAMP on the regenerative process around biomaterials is still unclear, but it has been associated with inflammation, tissue remodeling and coagulation [31]. [PERSON] and [PERSON] [32] found that SAMP reduced the accumulation of inflammatory macrophages and prevented fibrosis in vivo. This, together with the lower adsorption of proinflammatory proteins on the anodized surfaces, correlated with the in vitro findings. Moreover, insulin growth factor (IGF)-1 activity is regulated by the IGF binding proteins; here, IBP4, which inhibits the activity of IGF-1 and is related to antifibrotic activity [33; 34], showed a greater adherence onto PA versus MC (5.4-fold higher). [PERSON] et al. [20] demonstrated that IGF-I is a monocyte/macrophage activating factor that enhances production, so its IBP4-related inhibition might explain the significant decrease in the in vitro TNF-\(\alpha\) release observed for PA. This favoring of anodized surfaces towards M2 macrophage phenotype polarization ensures the resolution of inflammation and the optimal transition to the proliferative phase of wound healing [29].
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies  
Hydrogen production technologies can be divided into four groups, namely thermochemical processes, biological processes, electrolytic processes, and photolytic processes [23; 24], as represented in Figure 2.  
(i) Thermochemical processes are those where heat and chemical reactions are used to generate syngas from organic materials. (ii) Biological processes are those where microorganisms produce hydrogen through biological processes. (iii) Electrolytic processes are those where water is split into hydrogen and oxygen through one electrical current. (iv) Photolytic processes are those where solar irradiation is used to conduct the hydrolysis process.  
Depending on the required feedstock, the production technology used, and the associated GHG emissions, the produced hydrogen can be classified into different categories [25; 26]. Green hydrogen is produced through the electrolysis of water powered by renewable energy or through biomass gasification, resulting in no GHG emissions. Purple hydrogen is also produced through electrolysis but is powered by nuclear energy, leading to near-zero emissions. Turquoise hydrogen is produced from natural gas through pyrolysis, a process that produces low emissions and no direct GHG emissions from hydrogen itself. Blue hydrogen comes from fossil fuels but includes carbon capture and storage (CCS) to significantly reduce emissions. Gray hydrogen is also derived from fossil fuels but without any CCS, leading to high levels of GHG emissions. [25; 27]. The main hydrogen production technologies are described below.
## 2 Materials and Methods
### Function Approximation Using IT2 Fuzzy Logic Systems
: Motivated by [18], (27) is satisfied because \(\varsigma_{s,i,j_{i}}>1\) and the following results are satisfied regardless of the magnitude of \(\big{|}s_{i,j_{i}}\big{|}\)  
\[\begin{array}{ll}\left|\sum_{k=1}^{\infty}\frac{\Gamma(1+ \overline{\gamma}_{i,j_{i},1})}{\Gamma(1-k+\overline{\gamma}_{i,j_{i},1})\Gamma (1+k)}D^{k}s_{i,j_{i}}D^{\overline{\gamma}_{i,i,1}-k}s_{i,j_{i}}\right|\leq \beta_  
where \(\delta_{i,n_{i}}=\left|\rho_{i,n_{i}}\right|+\left|\varepsilon_{i,n_{i}}\right|\). We select the final control input as  
\[u_{i}=\delta_{0,n_{i}}^{-1}\Big{[}-\Theta_{i,n_{i}}^{T}\Xi_{i,n_{i}}-k_{i,n_{i},1}s_{i,n_{i}}-\phi_{i,n_{i}}+\dot{x}_{i,n_{i}}-s_{i,n_{i}-1}^{T}\delta_{0,n_{ i}-1}(e_{i,n_{i}}+\eta_{i,n_{i}})-k_{i,n_{i},2}sig_{i}^{1/2}(s_{i,n_{i}})-k_{i,n_{i},3}s_{i,n_{i}}(\left|s_{i,n_{i}}\right|+v_{i,n_{i}})^{-1}\Big{]}, \tag{43}\]  
where \(k_{i,n_{i},l}>0,\ l=1,2,3\), and \(v_{i,n_{i}}>0\) are constants. This completed the proof. \(\square\)  
**Remark 1**. : _In (31), (37), and (43), the control term\(-k_{i,j_{1},1}s_{i,j_{1}}\) attracts the state away from the sliding surface to the sliding surface. \(-k_{i,j_{1},3}s_{i,j_{1}}(\left|s_{i,j_{1}}\right|+v_{i,j_{1}})^{-1}\) is modified control term from the switching control \(-k_{i,j_{1},3}sign(s_{i,j_{1}})\) to attenuate chattering due to the sign function, which limits the states to stay within the sliding surface._  
Considering (43), (42) is written as  
\[D^{\overline{\gamma}_{i},n_{i},1}V_{i,n_{i}}\leq-\sum_{k=1}^{n_{i}}\left(k_{i, k,2}-\beta_{si,k}\right)\left|s_{i,k}\right|^{3/2}-\left|s_{i,n_{i}}\right| \Big{(}k_{i,n_{i},3}\left|s_{i,n_{i}}\right|\big{(}\left|s_{i,n_{i}}\right|+v_ {i,n_{i}}\big{)}^{-1}-\delta_{i,n_{i}}\Big{)}, \tag{44}\]  
where \(\delta_{i,n_{i}}=\left|\rho_{i,n_{i}}\right|+\left|\varepsilon_{i,n_{i}}\right|\). If the gains in (44) are selected to satisfy the condition  
\[k_{i,n_{i},3}\big{|}s_{i,n_{i}}\big{|}\big{(}\left|s_{i,n_{i}}\right|+v_{i,n_ {i}}\big{)}^{-1}>\delta_{i,n_{i}}, \tag{45}\]  
then  
\[D^{\overline{\gamma}_{i},n_{i},1}V_{i,n_{i}}\leq-\sum_{k=1}^{n_{i}}\left(k_{ i,k,2}-\beta_{si,k}\right)\left|s_{i,k}\right|^{3/2}. \tag{46}\]  
**Remark 2**. : _The robustness to uncertainties of the proposed control system is guaranteed provided that the conditions of (33), (39), and (45) are satisfied. Thus, the gain parameters \(k_{i,j_{i},3}\), \(j_{i}=1,\ldots,n_{i}\) should be selected sufficiently by large value such that these conditions are satisfied._  
**Remark 3**. : _In this backstepping control, the command filtering errors \(\eta_{i,j_{i}}\) of (22) are conveniently compensated as presented in the controllers (31), (37), and (43) by adding them to each controller._