esa-sceva/satcom-chunk-collection huggingface dataset:
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 3 Mathematical Statement and Solution Methods of the Optimization Problem
### The Static Optimization Problem
#### 3.1.2 Intertemporal Static Optimization  
In Equations (1)-(4), time is not involved and consequently, operation optimization can be performed at a single operating point, e.g., the nominal loading condition (design point). However, as stated in Section 2.2, in reality, intertemporal optimization is needed. This can be achieved, while keeping the mathematical statement of Equation (4), with the following approach.  
It is considered that the operating profile of the system for the time period that the optimization is to be performed (e.g., day, week, season, the whole life cycle of the ship) is known, i.e., all forms of power needed on board (mechanical, electric, and thermal) are known functions of time and of variables related to the operation of the ship. A discretization of the profile is performed: the whole time period is divided in \(N\) time intervals of duration \(\Delta T_{n},\;n=1,\;2,\;\ldots,\;N\) in such a way that a steady-state operation can be considered in each interval, i.e., the loads remain constant at a value in good approximation to the real profile. The transients are ignored under the assumption that their duration is negligible in comparison to the whole operating period. Of course, larger \(N\) and shorter \(\Delta T_{n}\) lead to better approximation of the real with the discretized profile.  
Next, a time decomposition is assumed, i.e., it is considered that the operation in a time interval does not affect and it is not affected by the operation in other intervals. Then, the solution procedure described after Equation (5) is followed, with the operation optimization being performed for one interval after the other. Each interval is characterized by its own set of operation-independent variables, \(\mathbf{v}_{n}\). Applications of this solution method appear in several publications, such as Refs. [5; 6; 18; 19].  
However, the conditions for time decomposition are not always satisfied. If, e.g., the duration of the trip from port A to port B is fixed, the speed of the ship in a time interval affects the speed in other time intervals. If time decomposition is not applicable, the operation optimization problem cannot be solved for one interval after the other, as described in the preceding, but there is need to handle all the operation-independent variables \(\mathbf{v}_{n},\;\;n=1,\;2,\;\ldots,\;N\) at a single step [7; 8]. The dimensionality of the problem (number of independent variables) increases significantly and the solution may be inefficient or impossible. In such a case, the best way out is the application of dynamic optimization, which is the subject of next section.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 4 Sinkhorn-Knopp with Successive Overrelaxation
### Overrelaxed Projections  
We recall that \(P_{\mathcal{C}_{k}}\) are operators realizing the Bregman projection of matrices \(\gamma\in\mathbb{R}^{n_{1}n_{2}}\) onto the affine sets \(\mathcal{C}_{k}\), \(k=1,2\), as defined in (4). For \(\omega\geq 0\), we now define the \(\omega\)-relaxed projection operator \(P_{\mathcal{C}_{k}}^{\omega}\) as:  
\[\log P_{\mathcal{C}_{k}}^{\omega}(\gamma)=(1-\omega)\log\gamma+\omega\log P_{ \mathcal{C}_{k}}(\gamma), \tag{20}\]  
where the logarithm is taken coordinate-wise. Note that \(P_{\mathcal{C}_{k}}^{0}\) is the identity, \(P_{\mathcal{C}_{k}}^{1}=P_{\mathcal{C}_{k}}\) is the standard Bregman projection, and \(P_{\mathcal{C}_{k}}^{2}\) is an involution (in particular because \(\mathcal{C}_{k}\) is an affine subspace). In the following, we will consider overrelaxations corresponding to \(\omega\in[1;2)\). A naive algorithm would then consist of iteratively applying \(P_{\mathcal{C}_{2}}^{\omega}\circ P_{\mathcal{C}_{1}}^{\omega}\) for some choice of \(\omega\). While it often behaves well in practice, this algorithm may sometimes not converge even for reasonable values of \(\omega\). Our goal in this section is to make this algorithm robust and to guarantee its global convergence.  
**Remark 2**.: _Duality gives another point of view on the iterative overrelaxed Bregman projections: they indeed correspond to a successive overrelaxation (SOR) algorithm on the dual objective \(E\) given in (6). This is a procedure that, starting from \((\alpha^{0},\beta^{0})=(\mathbf{0},\mathbf{0})\), defines for \(\ell\in\mathbb{N}^{*}\),_  
\[\alpha^{\ell+1} =(1-\omega)\alpha^{\ell}+\omega\operatorname*{argmax}_{\alpha}E (\alpha,\beta^{\ell}) \tag{21}\] \[\beta^{\ell+1} =(1-\omega)\beta^{\ell}+\omega\operatorname*{argmax}_{\beta}E( \alpha^{\ell+1},\beta), \tag{22}\]  
_From the definition of the projections in (4) and using again the relationships \(u_{i}=e^{\alpha_{i}/\varepsilon}\), \(v_{j}=e^{\beta_{j}/\varepsilon}\) and \(\gamma_{i,j}^{0}=e^{-c_{ij}/\varepsilon}\), Expressions (21) and (22) correspond to the overrelaxed projections (20)._
_Article_  
**Examination of Indoor Mobile Mapping Systems**  
**in a Diversified Internal/External Test Field**  
**[PERSON] 1,*, [PERSON] 2 \({}^{\copyright}\), [PERSON] 1 and [PERSON] 1**  
1 GECO Laboratory, Department of Civil and Environmental Engineering, University of Florence, via [PERSON] 8, I-50121 Florence, Italy; [EMAIL_ADDRESS] (V.B.); [EMAIL_ADDRESS] (E.I.P.)  
2 Polytechnic Department of Engineering and Architecture, University of Udine, via delle Scienze 206, I-33100 Udine, Italy; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS] Tel.: +39-055-2756587  
Received: 17 November 2017; Accepted: 6 March 2018; Published: 9 March 2018  
**Abstract: Nowadays, simultaneous localization and mapping (SLAM) algorithms support several commercial sensors which have recently been introduced to the market, and, like the more common mobile mapping systems (MMSs), are designed to acquire three-dimensional and high-resolution point clouds. The new systems are said to work both in external and internal environments, and completely avoid the use of targets and control points. The possibility of increasing productivity in three-dimensional digitization projects is fascinating, but data quality needs to be carefully evaluated to define appropriate fields of application. The paper presents the analytical measurement principle of these indoor mobile mapping systems (IMMSs) and the results of some tests performed on three commercial systems. A common test field was defined in order to acquire comparable data. By taking the already available terrestrial laser scan survey as the ground truth, the datasets under examination were compared with the reference and some assessments are presented which consider both quantitative and qualitative aspects. Geometric deformation in the final models was computed using the so-called Multiscale Model to Model Cloud Comparison (M3C2) algorithm. Cross sections and cloud to mesh (C2M) distances were also employed for a more detailed analysis. The real usability assessment is based on the features of recognizability, double surface evidence, and visualization effectiveness. For these evaluations, comparative images and tables are presented.**  
**Keywords: indoor mobile mapping systems (IMMSs); simultaneous localization and mapping (SLAM); TLS; GNSS; INS; 3D model comparison; point models**
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## 2 Nanoporous Singe-Layer Graphene
### Challenges  
The greatest advantage of nanoporous single-layer graphene lies in the monoatomic thickness, which conceptually exerts the lowest membrane resistance and possesses the greatest potential for achieving high CO2 permeability [10]. However, freestanding nanoporous single-layer graphene is not capable of serving as a functional membrane owing to its insufficient mechanical properties. Apart from that, nanoporous single-layer graphene membranes are not readily scalable. Single-layer graphene is normally produced by a chemical vapor deposition (CVD) method with a surface area that is too small [11]. Furthermore, pore generation using the aforementioned techniques remain difficult to control, making uniform pore size generation and narrow pore size distribution not easily realizable. To enable nanoporous single-layer graphene as a functional membrane, the most feasible approach is to adopt a composite membrane design where the single-layer graphene layer first undergoes treatment to form nanopores before transferring to the top of a porous polymer substrate as the selective layer (Figure 2a). In this regard, large-scale and defect-free transfer of the nanoporous single-layer graphene onto the polymer substrate is yet another challenge to consider.
## 2 Materials and Methods
### Determination of Temperature and Time (t.v.) of Vulcanization of NR Composites  
The effect of adding organic peroxides and fillers such as CB and SWCNTs on the vulcanization of NR was evaluated. As previously mentioned, three organic peroxides: T29, DCP, and T145, were selected as cross-linking promoters; for comparison, sulfur was also considered as a cross-linking agent. Thermal decomposition of organic peroxides has been observed when they are exposed to heat energy through the vulcanization process; organic peroxides generate free radicals resulting in the cross-linking of unsaturated bonds during the vulcanization of NR. The thermal decomposition process of most organic peroxides follows first-order kinetics with a degradation rate constant \(k_{d}\)[30]. The activation energy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **NR (phr)** & **CB (phr)** & **SWCNTs (phr)** \\ \hline NR & 100 & 100 & 100 \\ Zinc oxide & 5 & 5 & 5 \\ Stearic Acid & 1 & 1 & 1 \\ Sulfur & 2.5 & 2.5 & 2.5 \\ MBTS & 1 & 1 & 1 \\ PBN & 1 & 1 & 1 \\ CB & - & 0.7 & - \\ SWCNTs & - & - & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formulations used for sulfur vulcanized NR composites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **NR (phr)** & **CB (phr)** & **SWCNTs (phr)** \\ \hline NR & 100 & 100 & 100 \\ Organic peroxide 1 & 2.5 & 2.5 & 2.5 \\ CB & - & 0.7 & - \\ SWCNTs & - & - & 0.7 \\ \hline \hline \end{tabular} \({}^{1}\)Organic peroxides used were T29, DCP, or T145.  
\end{table}
Table 3: Formulations used for organic peroxide vulcanized NR composites.  
associated with the thermal decomposition together with the free radical half-life (\(t_{1/2}\)) were calculated according to the following equations:  
\[k_{d}=Ae^{-\frac{E_{d}}{Rd}} \tag{1}\]  
\[t_{1/2}=\frac{\ln 2}{k_{d}} \tag{2}\]  
where \(k_{d}\) is the decomposition rate constant, \(A\) is the Arrhenius pre-exponential factor, \(E_{a}\) corresponds to the activation energy of thermal decomposition of the organic peroxide, \(R\) is the universal gas constant, and \(T\) is the temperature at which the thermal decomposition occurs, i.e., the vulcanization temperature.  
The vulcanization temperatures were selected according to the processing conditions suggested by the organic peroxide supplier, being 129, 160, and 173 \({}^{\circ}\)C for T29, DCP, and T145, respectively. The theoretical vulcanization times for each organic peroxide were calculated according to the reported decomposition rate constant [30] at the selected temperature and using Equation (2). We assumed that the vulcanization times (_t.v._) were close to the peroxide half-life (\(t_{1/2}\)) since the lifetimes of the free radicals were in the order of milliseconds. To compare the theoretical vulcanization times, the experimental vulcanization times (_t.v._) of the NR composites were evaluated by oscillatory rheometric measurements using an oscillatory rheometer, Model MCR 301 ([PERSON], Ashland, VA, USA).
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 6 Architecture of WSN vs. Architecture of IDS
### Naive or Flat WSN Architecture for Agent-Based IDS  
Agent-based IDS involves installing the detection model in a selected subset of sensor nodes, which are called Monitor Nodes (MNs), to reduce the detection overhead faced by the stand-alone and distributed approaches. In its approach, selected nodes perform detection in addition to their normal sensing, communication, and routing activities in the case of flat WSN architecture. Agents' tasks are relocated to another predefined subset of nodes after a certain period of time or when performing a specific mission, which improves IDS detection efficiency and increases network lifetime. Agent-based IDS is especially suitable for WSNs, as nodes near the BS can be excluded from communicating all of their samples when developing the reference ML model at the BS because they do not contribute much to the determination of hypersphere of the developed ML model. Agent-based IDS is typically preferred over centralized IDS architecture, especially for networks with geographically dispersed nodes, as in a centralized approach the nodes consume more power when transmitting their data to the central location.
## 1 Introduction
### Motivations and Contributions
This increases the likelihood of high collisions and massive energy consumption, and hence, a self-organized FDCP-MAC protocol for multi-hop ad hoc UWAN network has been developed [39]. With the aid of a fixed time HS and a time stamped conflict resolution mechanism, this proposed protocol can more effectively use the channel and prevent massive collisions at high network loads. Additionally, this protocol increases throughput while decreasing collision rates. Our contributions are summarized as follows:  
* We reviewed various FD systems and protocols for underwater communication and classified them based on the Physical and MAC layers. The impact of various features on the FD systems and protocols discussed is thoroughly explained. This survey looks at various methodologies for improving the quality and efficiency of these reliable FD systems and protocols, with the aim of further expanding the implementation of these systems and protocols in the future. * We contributed by comparing different FD-physical layer-based UWAC Systems in terms of different metrics including spectral efficiency, BER, transmission range, and data rate, as well as implementing these protocols in different environments. The advantages and disadvantages of these protocols are also discussed. * We contributed by comparing various FD MAC protocols based on throughput, delay efficiency, energy efficiency, transmission rate, and data rate. These protocols' advantages and disadvantages are discussed in detail. * We contributed by laying out a number of significant challenges that affect the performance of FD-UWAC systems and MAC layered protocols, all of which must be carefully addressed as these systems are built. * To the best of our knowledge, some new open issues and potential research directions are listed, which will aid in the advancement of the aforementioned FD systems and MAC protocols.
## 2 Analysis of the Working Principle of MOTs
### Atomic Force and Motion Analysis  
The movement of a neutral atom is affected by gravity, the magnetostatic force, and the scattering force at the same time. The scattering force is approximately 1000 times greater than the combined forces of gravity and the magnetostatic force. Hence, when considering forces on atoms, only the scattering forces are typically taken into account. In the case of one dimension, the total scattering force acting on the atom can be calculated as follows [14].  
\[\overset{\rightarrow}{F}=\overset{\rightarrow}{F}_{+}+\overset{ \rightarrow}{F}_{-} \tag{1}\]  
\[\overset{\rightarrow}{F}_{\pm}=\hbar\overset{\rightarrow}{k}\frac{\Gamma}{ 2}\frac{\Omega^{2}/2}{\delta_{\pm}^{2}+\Omega^{2}/2+\Gamma^{2}/4} \tag{2}\]  
where \(\hbar=1.0545887\times 10^{-34}\J\cdot\J\) is the Planck constant, \(\overset{\rightarrow}{k}\) is the laser wave vector, \(\Gamma\) is the natural line width, and \(\Omega\) is the Rabi frequency. The detuning \(\delta_{\pm}\) of the resonance transition spectrum of the laser to atoms shifts with changes in the atomic velocity and magnetic field.  
\[\delta_{\pm}=\delta\mp\overset{\rightarrow}{k}\cdot\overset{\rightarrow}{v} \pm\mu^{\prime}Ax/\hbar \tag{3}\]  
where \(\delta=|\omega-\omega_{0}|\) represents the laser frequency detuning, \(\overset{\rightarrow}{v}\) represents the atomic velocity, \(\mu^{\prime}\) represents the effective magnetic moment, \(A\) represents the axial magnetic field gradient, and \(x\) represents the coordinates of the atom on the axis.  
The equation describing the motion of atoms can be formulated as follows.  
\[\begin{array}{c}mx^{\prime\prime}=\hbar k\frac{\Gamma}{2}(\frac{\Omega^{2}/ 2}{\delta_{+}^{2}+\Omega^{2}/2+\Gamma^{2}/4}\\ -\frac{\Omega^{2}/2}{\delta_{-}^{2}+\Omega^{2}/2+\Gamma^{2}/4})\\ \delta_{\pm}=\delta\mp\overset{\rightarrow}{k}\cdot\overset{\rightarrow}{x ^{\prime}}\pm\mu^{\prime}Ax/\hbar\\ x(0)=x_{0}\\ x^{\prime}(0)=v_{0}\end{array} \tag{4}\]  
where \(x_{0}\) represents the initial position of the atom and where \(v_{0}\) represents the initial velocity of the atom. The motion of atoms in the MOT can be simulated by solving the atomic motion equation via the Runge-Kutta method, given the initial velocity and position of the atoms.  
Taking the \({}^{87}\)Rb atom as an example, we consider the laser intensity \(I=6\) mW/cm\({}^{2}\), laser radius \(r=1\)cm, magnetic field gradient \(A=-400\) G/m, and laser detuning \(\delta=-17\) MHz in the MOT. Assuming that atoms start from the edge of the trap at different speeds, the motion velocity of each atom in the MOT varies with position, as shown in Figure 3.  
In Figure 3, the area between the two black dashed lines indicates the region in which the trap has an effect. Various colored and shaped lines represent the paths of atoms with different initial velocities. Figure 3 shows that atoms starting from the edge of the trap with initial velocities of 5 m/s and 13 m/s can slow down within the trap and become trapped in the center, whereas atoms with an initial velocity of 21 m/s escape from the trap.
## 1 Introduction
Other methods include Simple Linear Regression (SLR), Classical Seasonal Decomposition (CSD), Holt-Winters exponential smoothing (HW) [17], as well as X12 ARIMA [15]. All of the extraction methods give better or worse estimates of the 'trend' between them. However, SLR was found to be the least accurate method for c-Si and thin-film because it is sensitive to seasonality and outliers in the time-series, especially when long term data are not available [17]. In this work, we use STL decomposition as a statistical method for computing PLR. STL uses locally weighted regression and thereby provides robust estimates of the trend and seasonal components that are not distorted by outliers and missing values compared to other methods [18,19]. STL handles any type of seasonality, not only monthly or quarterly data. The seasonal component is allowed to change over time. The user can control the rate of change and the smoothness of the trend-cycle. However, STL does not handle trading day or calendar variation automatically, and it only provides facilities for additive decompositions. A complete description of STL procedure is given in [18]. In this work, we also evaluate the calculation of PLR using the year-on-year (YoY) approach which follows a different concept. The idea behind the YoY approach is explained in Section 3.4. The performance of PV systems has been extensively evaluated worldwide [20,21] mainly in Europe [5] and the US. In particular, among the comprehensive study of the performance of PV systems in China, Japan, the US, Europe, and Australia have been reported in respectively [22, 23, 24, 25]. In South-East Asia, PR of PV system operations has been reported such as in [26, 27, 28]. Recent studies about the PLR are available among others in [1, 5, 6, 7, 8, 9, 10, 14, 15, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36]. However, only two studies were based on PV systems in South-East Asia, and no study was found in Indonesia. Therefore, in this work, we include PV systems in Indonesia to fill the gap of PLR study of PV systems located in the tropical rainforest climate in South-East Asia. We present the performance of fifteen grid-connected PV systems using six different module types operating under the real outdoor conditions in three climates in Europe, Australia, and Indonesia. The quality of the PV systems will be evaluated using the performance indicators previously discussed: PR and PLR. The available time-series gathered by outdoor PV system monitoring and meteorological data used in this work ranged from two to nine years depending on the PV systems.
###### Abstract  
(1) Background: Recycling of water and non-converted nutrients is considered to be a necessity for an economically viable production of microalgal biomass as a renewable feedstock. However, medium recycling might also have a negative impact on algal growth and productivity due to the accumulation of growth-inhibiting substances. (2) Methods: Consecutive batch processes with repeated water recycling after harvesting of algal biomass were performed with the saline microalga _Microchloropsis salina_ in open thin-layer cascade photobioreactors operated at a physically simulated Mediterranean summer climate. The impact of water recycling on culture performance was studied and the composition of the recycled water was analyzed. (3) Results: Water recycling had no adverse effect on microalgal growth and biomass productivity (14.9-21.3 g m\({}^{-2}\) d\({}^{-1}\)) if all necessary nutrients were regularly replenished and KNO\({}_{3}\) was replaced by urea as the nitrogen source to prevent the accumulation of K\({}^{+}\) ions. Dissolved organic carbon accumulated in recycled water, probably promoting mixotrophic growth. (4) Conclusion: This study shows that repeated recycling of water is feasible even in high-density cultivation processes with _M. salina_ of more than 30 g L\({}^{-1}\) cell dry weight, increasing culture performance while reducing nutrient consumption and circumventing wastewater production.  
microalgae; _Microchloropsis salina_; open photobioreactor; thin-layer cascade; water recycling