esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Experiments
### Korea Network
#### 4.2.2 Assigned Flow Comparison  
To illustrate the assigned flow difference and effect of travel time alleviation when CAVs drove on different roadway classes, Figure 9 illustrates the link flow differences between scenario 1 and other scenarios on a Geographic Information System (GIS) map. It should be noted that the scenarios were based on the specifications presented in Figure 7c. Links were categorized by thickness and color to emphasize the degree of the difference. The green color showed that the assigned flow in scenario 1 was higher than the assigned flows in other scenarios, and the red color indicated the reverse. The results in Figure 9 clearly show that the multi-class TAP considering mixed modes had a significant impact on flow allocations. The majority of the differences occurred on the freeway, where the freeway generally had a larger capacity; hence capacity was increased more when CAVs traveled more on the freeway compared to other roadways. Using vehicle kilometers traveled (VKT) and vehicle hours traveled (VHT) to evaluate the effect of allowing different node classes for CAVs, VKT was not significantly affected by CAV driving (the values changed by 0.4%, 0.2%, and \(-\)0.5% in scenarios 2, 3, and 4, respectively, compared with scenario 1, whereas VHT was significantly decreased when CAVs drove on more roadways (the values changed by \(-\)2.3%, \(-\)4.1%, and \(-\)8.3% in scenarios 2, 3, and 4, respectively, compared with scenario 1). The reasons for the results were that more CAVs driving on the roadways could promote an increase in the overall network capacity, and this increased capacity influenced travel time reduction. Specially, when CAVs traveled more on the freeway, capacity on the freeway was increased compared to other roadways and travelers changed their route passing through the freeway to minimize their travel time, despite the travel distance increase. As a result, VKT was slightly increased in the comparison between scenario 1 and scenario 2 and between scenario 1 and scenario 3.  
Figure 9: _Cont._
## 3 Key Building Blocks  
The key building blocks: autonomic M2M manager, M2M service capabilities, M2M messaging system, M2M gateways towards energy constrained M2M asset devices and creation of trust to enable end-to-end security for M2M applications are described in this chapter.
## 1 Introduction
In this paper, we propose a novel Hybrid Model (HM) that unifies the HW exponential smoothing technique with LSTM to address the limitations of the HW method in predicting long-range trends. Our study aimed to contribute to this field by incorporating different predictors. By merging the HW method with LSTM, we anticipate the achievement of highly accurate power data predictions that can be utilized to optimize the performance of PV plants. The proposed HM method for implementing a PVPF system is expected to provide a robust solution that surpasses the performance of the individual methods. To address the challenging task of optimizing LSTM hyperparameters, which significantly affects the accuracy and prediction of the network [12], we employed a Bayesian Optimization (BO) algorithm. The BO technique is a global optimization heuristic that has gained considerable popularity for hyperparameter tuning. Using the BO algorithm, we can efficiently explore the hyperparameter space and identify the ideal set of hyperparameters that corresponds to the best model performance. This study makes the following contributions to the field of PVPF methods. First, we propose an LSTM architecture that incorporates the BO algorithm for hyperparameter tuning to improve the model performance. Second, we introduce an HM method for time-series prediction that combines LSTM cells and HW time-series modeling to enhance the accuracy of the predictor. Third, we present experiments to forecast rooftop PV power production in a real-world case study, where meteorological variable measurements, such as solar irradiance and temperature, are not available at the plant site. The remainder of this paper is organized as follows. Section 2 provides a comprehensive overview of PVPF methods, along with a review of the pertinent literature. Section 3 delves into the proposed framework, which employs exponential smoothing methods for time-series forecasting integrated with the LSTM and BO algorithms. In Section 4, the PV datasets utilized are presented and the outcomes of implementing the proposed approach are highlighted. Section 5 examines and discusses the findings and provides a supplemental analysis. Finally, Section 6 concludes this paper.
## 3 Weighted Local-World Evolution Model
### Edge-weight evolution mechanism
#### 3.2.2. Evolution Model Construction Algorithm  
* **(1): Initial setting**. At the beginning of the network, there are \(m_{0}\) nodes and \(e_{0}\) edges, and they form a fully coupled network in which weights \(w_{0}\) are assigned to each edge.
* **(2): Growth**. Each time a new node \(v_{n}\) is added and this node is connected to the previous \(m\) nodes.
* **(3): Local-world priority connection**. Randomly select \(M\) (\(M\geq m\)) nodes from the existing nodes of the network as the newly joined node's local-world. Newly added nodes according to the probability (\(\prod_{Local}(s_{i})\)) of preferential connection connect with \(m\) nodes in the local-world, and the probability of strength selection is: \[\prod_{Local}(s_{i})=\prod_{i\in Local^{\prime}}\frac{s_{i}}{\sum_{j\in Local }s_{j}}\] (14) where, \(\prod i\in Local^{\prime}\) indicates the probability of randomly selecting nodes in existing nodes, and the choice of nodes is based on the priority of weight selection, that is, the probability that an old node \(v_{i}\) is selected is: \[\prod_{i\in Local^{\prime}}=\frac{M}{N(t)}\] (15) where, \(N(t)\) is the total number of nodes in the network at time \(t\). When the time interval for joining the nodes is unchanged, there is \(N(t)=N+t\). As shown above, this means that there is a greater probability that a node with a greater weight is selected. Obviously, at time (\(m\leq M\leq N+t\)), and at each moment, the newly added node selects \(m\) nodes from the local-world according to the preferential connection mechanism to establish a connection, and therefore, \(m\) edges are newly added each time.  
Figure 3. Edge-weight evolution mechanism: (**a**) Network topology when implementing function 1 at time \(t_{1}\); (**b**) Network topology when implementing function 2 at time \(t_{1}\); (**c**) Network topology when implementing function 3 at time \(t_{1}\); (**d**) Overall network topology at time \(t_{2}\).  
* **(4): Dynamic evolution of edge weights.** Each time the newly added edge \(e_{i}\) is given a weight \(\omega_{0}\). For the sake of simplicity, it is considered that the newly added edge only partially causes the weights of the edge of the connection node \(v_{i}\) and its neighbor node \(j\in\tau(i)\) to be readjusted, and the adjustment method is implemented according to the content of rule 4.
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 2 Tunnel Lighting Controls: Strategies and Technologies
### Comparison of Hardware Cost and Energy-Saving for Four Control Strategies
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Category**} & \multicolumn{2}{c}{**Item**} & \multicolumn{2}{c}{**Qty.**} & \multicolumn{2}{c}{**Cost Per Unit/5**} \\ \hline \multirow{8}{*}{**ICS**} & \multirow{8}{*}{**TSCS**} & & 30 W & 391 & 41.78 \\  & & & 50 W & 59 & 47.97 \\  & & & LED lamps & 80 W & 16 & 69.64 \\  & & & 140 W & 62 & 95.95 \\  & & & 240 W & 21 & 123.8 \\ \cline{2-6}  & \multirow{2}{*}{**TSCS**} & & Local optical transceiver & 1 & 1121.87 \\  & & & Remote optical transceiver & 8 & 1060.08 \\ \cline{2-6}  & & & Server & 1 & 3961.74 \\  & & & Local dimming controller & 1 & 1315.42 \\  & & & Remote dimming & 8 & 1005.91 \\  & & & controller & & \\ \cline{2-6}  & & & Luminance meter & 1 & 1857.07 \\ \cline{2-6}  & & & Infrared sensor & 2 & 79.7 \\  & & & Microwave detector & 2 & 162.03 \\  & & & Surveillance camera & 12 & 558.2 \\ \hline Total & \$60,795 & \$53,613 & \$51,756 & \$38,432 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of initial installation costs for various control strategies in Madang tunnel.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s SA-Based Proposal  
This work [37] extends their previous research on using nature-inspired algorithms for addressing the IR problem [38]. In this case, authors focused on dealing with complex 3D face recognition instances using the FRGC v2 database simulating both verification and identification systems. They used a simulated annealing (SA) algorithm for tackling optimization tasks present in face recognition. Moreover, as in [38] they newly made use of the Surface Interpenetration Measure (SIM) for the corresponding assessment of the quality of the matching of pairs of range images. From the conducted experiments, their method achieved outstanding results, at a False Acceptance Rate (FAR) of 0.1% and, regarding identification scenarios, they obtained a rank-one accuracy of 99.5%.
## 3 Microalgal/Cyanobacterial Metabolites and Phytohormones with Potential Interest for Agriculture
### Carotenoids  
Carotenoids are accessory pigments used by algae during photoautotrophic growth. These compounds are obtained through polymerisation of five-carbon isoprene units [51; 59]. Although they are essential for photosynthetic organisms, production of these compounds has also been described for bacteria and fungi [48; 60]. Their role in algae is associated with light harvesting, protein assembly in photosystems and protection against photo-induced free radicals exposure [49]. The main carotenoids obtained from microalgae include alpha- and beta-carotene, lutein, lycopene, astaxanthin and zeaxanthin. _Spirulina_ and _Dunaliella_ species are the major sources of alpha- and beta-carotene (up to 10% DW), respectively. Lutein is typically obtained from _Chlorella protothecoides_, _Scevedesmus almeriensis_ and _Murielopsis_ species, which can accumulate between 4.3 and 5.4 mg g\({}^{-1}\) DW. Lycopene, in turn, can be found in several green algae, such as _Chlorella marina_, _Chlorella regularis_ and _Dunaliella salina_. Similarly, astaxanthin is mainly produced by green algae, such as _Haematococcus pluvialis_ and _Chlorella zafingiensis_, being _H. pluvialis_ the major source of this compound, accumulating up to 3% DW. Finally, zeaxanthin is obtained from red and green microalgae, such as _P. cruentum_ and _Chlorella ellipsoidea_. According to several studies, zeaxanthin in _P. cruentum_ accounts for 97.4% of the total carotenoids content of this microalgae. On the other hand, _C. ellipsoidea_ can accumulate up to 4.26 mg g\({}^{-1}\) DW of zeaxanthin [47; 49; 51; 52]. Carotenoids from microalgae have been extensively used in food, feed, cosmetics, nutraceuticalil and pharmaceutical applications, due to the antioxidant, anti-inflammatory and anticancer properties exhibited by these compounds [47; 48; 49; 52]. Regarding agricultural practices, carotenoids have been applied in soils' bioremediation and also as antioxidants, fertilisers and biopesticides, with major impacts on soil improvement and crops' protection.  
They are also applied to increase the availability of provitamin A, contributing to the development of biofortified crops [50; 53].
## 2 Materials and Methods
### Aroma Compound Analysis  
To extract volatile compounds, 1 g of homogenized loengo pulp was placed in a Teflon-stoppered vial and extracted via Headspace Solid-Phase Micro Extraction (HS-SPME). The aromas were analyzed using an Agilent 6890 series GC coupled with an Agilent 5973 MS (Agilent, USA) and with a Gerstel MPS-2 sampler. Trapping was carried out under orbital agitation for 45 min with a 65 \(\upmu\)m DVB (divinylbenzene), CAR (carboxen), and PDMS (polydimethylsiloxane) fiber (Supelco, Bellefonte, PA, USA) at 50 \({}^{\circ}\)C after 10 min incubation. A DB-WAX capillary polar column of 60 m, 0.25 mm, and phase film thickness of 0.25 \(\upmu\)m (J&W Scientific, Folsom, CA, USA) was used with H\({}_{2}\) as the carrier gas, set at a flow rate of 1.5 mL-min\({}^{-1}\). The fiber was desorbed at 250 \({}^{\circ}\)C and the compounds were separated using the following temperature program: the initial oven temperature was 40 \({}^{\circ}\)C (5 min isotherm), then increased by 2 \({}^{\circ}\)C-min\({}^{-1}\) to 140 \({}^{\circ}\)C, then by 10 \({}^{\circ}\)C-min\({}^{-1}\) to 250 \({}^{\circ}\)C, and maintained for 10 min. The analyzer and source temperatures were 150 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. The data were analyzed with MassHunter version B.06.00 (Agilent Technologies, USA). The NIST 2011 (National Institute of Standard Technology) database was chosen to tentatively identify peaks through comparison with their mass spectra [24].  
To identify the aromatic compounds, the retention time and Kovats retention index of each molecule were compared with the library mentioned above, considering the match factor equal to or greater than 80%. The quantified aroma compounds selected for this study were the peaks presenting more than 0.5% of the total relative area. The results were expressed in \(\upmu\)g eq. 3-heptanol internal standard\(\cdot\)kg\({}^{-1}\) of Fresh Matter (FM). The limit of detection and quantification (LOD and LOQ) were calculated using the signal/noise ratio method and were estimated at 0.87 \(\upmu\)kg\({}^{-1}\) and 2.90 \(\upmu\)kg\({}^{-1}\) for LOD and LOQ, respectively.
## 2 AUV Dynamics and Control Architecture  
### AUV Dynamic Model for Controlling  
The six motions of the AUV are defined as sway, surge, roll, heave, yaw, and pitch by the Society of Naval Architects and Marine Engineers (SNAME [72] (Table 1).  
According to the guidance, navigation, and control of underwater vehicles [9; 73; 74; 75; 76; 77], the kinematic model in the inertial frame and the dynamic model in the main frame of AUVs can be written as Equations (1) and (2), respectively.  
\[\eta\)=\(J(\eta)\
u, \tag{1}\]  
\[M\dot{\
u}+C(\
u)\
u+D(\
u)\
u+g(\eta)=\tau(\
u,\ u), \tag{2}\]  
where \(\eta=[\eta_{1}{}^{T},\eta_{2}{}^{T}]^{T}\) consists of the position \(\eta_{1}=[x,y,z]^{T}\) and the orientation \(\eta_{2}=[\phi,\theta,\psi]^{T}\) of the vehicle expressed in the inertial frame, while \(\
u=[\
u_{1}{}^{T},\
u_{2}{}^{T}]^{T}\) includes the linear \(\
u_{1}\)= \([u,v,w]^{T}\) and the angular \(\
u_{2}=[p,q,r]^{T}\) velocities of the vehicle expressed in the body frame. The model matrices \(M\), \(C(\
u)\), and \(D(\
u)\) denote inertia, Coriolis, and damping, respectively, while \(g(\eta)\) is a vector of gravity and buoyancy forces. On the right-hand side of Equation (2), \(\tau(\
u,u)\) is the vector of resultant forces and moments acting on the AUV, and \(\mathbf{u}\) represents the control inputs.  
A model of state-space discreteness can be used to model the control evolution of an AUV that is used to estimate the states of an AUV by using the EKF or UKF [78; 79; 80; 81; 82] methodologies; the motion of the control system can be described as shown in Equation (3).  
\[\left\{\begin{array}{c}\mathbf{x_{k}}=\mathbf{f_{k-1}}(\mathbf{x_{k-1}},\ \mathbf{u_{k-1}})+\ \mathbf{w_{k-1}}\\ \mathbf{y_{k}}=\mathbf{h_{k}}(\mathbf{x_{k}})+\ \mathbf{v_{k}}\end{array}\right. \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Degree of** & **Motions** & **Force and Moment** & \
\begin{tabular}{c} **Linear and** \\ **Angular Velocity** \\ \end{tabular} &
\begin{tabular}{c} **Position and Euler** \\ **Angles** \\ \end{tabular} \\ \hline
1 & Surge & \(X\) & \(u\) & \(x\) \\
2 & Sway & \(Y\) & \(v\) & \(y\) \\
3 & Heave & \(Z\) & \(w\) & \(z\) \\
4 & Roll & \(K\) & \(p\) & \(\phi\) \\
5 & Pitch & \(M\) & \(q\) & \(\theta\) \\
6 & Yaw & \(N\) & \(r\) & \(\psi\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Society of Naval Architects and Marine Engineers (SNAME) notations for underwater vehicles (data from [64]).  
where \(\mathbf{x}=\left[\begin{array}{c}\eta\\ \
u\end{array}\right]\), \(\mathbf{x_{k}}\) is the state variable vector at the \(k\)-th instant of \(\mathbf{x}\), \(\mathbf{u_{k}}\) and \(\mathbf{y_{k}}\) are the system's inputs and outputs, respectively, and \(\mathbf{h_{k}}\), \(\mathbf{w_{k}}\), and \(\mathbf{v_{k}}\) are the measurement function, additive process, and measurement noise, respectively.
## 4 Results
### Density, Carbonate Content, Durability and Rock Strength  
The dry density shows a decreasing trend from top to bottom, where the clay levels predominate and where the lower values were obtained as 1.65 Mg/cm\({}^{3}\). On the rocky levels, the values range from 2.00 to 2.31 Mg/m\({}^{3}\), with the highest values on level 1, feldspathic arenite, and the lowest in level 3. In addition, the porosity ranged from 13 to 24%, displaying an inverse correlation with the density (Table 3).  
From the carbonate content obtained from the Bernard calcimeter tests, it was clear that the highest values were located at the top, decreasing towards the bottom of the column (Table 3).  
Figure 5: Scanning electron micrographs of the clay levels. (**A**) General view of the massive and laminar texture of the clayey levels, with coarse quartz grains (Q), some dolomitic rhombic crystals (Do), and palygorskite fibers. (**B**) Stacked clays with domains defined by overlapping face-to-face contacts. (**C**) Triple point structures originated by the orientation of clay minerals normal to the bedding plane. (**D**) Detail of the triple point structure.  
Two groups were formed using the results obtained: the levels that collapsed very little or not at all (collapsed mass \(\leq\)10%), and those that completely collapsed (Table 3).  
Levels 1, 2, 3, and 5 behaved as rocks since they were scarcely or not at all affected when submerged in water. Frequently, insignificant amounts of material were lost from the surface of the samples, but did not exceed 2%, except for level 2, which was less competent than the other rocky levels according to the field observations, and lost 9%.  
The samples from levels 4, 6, 7, and 8 completely collapsed and, therefore, they were considered as soils. Nevertheless, there were some differences between the levels described as cohesive and granular: in the former, the process of collapse was gradual, but in the granular level, level 7, it happened immediately after being submerged. This indicated that the cohesive forces between the particles were controlled by suction, which was reduced as soon as immersion took place.  
According to the point load test results, the rocks within the DC Formation were soft, and although the differences were not very significant, feldspathic arenites appeared to be more resistant than the lutites.
## 2 Characteristics of Surface Potential Distribution for DC Grounding Electrode
### Characteristics of Surface Potential Distribution for DC Grounding Electrodes  
#### 2.3.1 Breakage in the Straight Region  
According to Figure 7, the polar ring of the DC grounding electrode is mainly divided into a straight part and a ring part, so it is necessary to study the breaking of these two areas separately. Firstly, in the straight part, the straight part is 250 m long, and a filter well is set every 50 m. The six filter wells numbered 1-6 divide the straight part of one side into five units. The unit length between any two adjacent filter wells is the same, and the number of buried grounding electrodes is basically the same, while the upper and lower sides are symmetrical. Therefore, all the straight parts are regarded as 10 identical units. Considering that the whole field of the grounding electrode is large, when a small number  
Figure 7: DC grounding electrode simulation diagram. (**a**) Simulation calculation model; (**b**) DC grounding electrode top view.  
of feeder rods are broken in the local area, the influence range of the potential change in the whole field is small.  
Therefore, we choose the unit between filter wells 3 and 4 for specific analysis, and set different numbers of broken feeder rods in this unit [27]. The potential change results are shown in Figure 8. The difference between the surface potential values under normal conditions and those after a certain number of feeder rods were broken was calculated to determine the range of surface potential variations influenced by fractures in localized areas of the grounding electrode. The results are shown in Figure 9.  
As shown in Figure 9 and Table 2, when one grounding electrode in the straight-line section of the DC grounding system fails, it leads to a potential change greater than 1 V within a range of approximately 12 m. When three grounding electrodes fail consecutively, it results in a potential change greater than 1 V within a nearby range of 28 m. The greater the number of failures, the larger the change in surface potential values in the adjacent area and the wider the impact range.  
Figure 8: Surface potential distribution after breakage between filter wells 3 and 4 in the straight region. (**a**) Under normal operating conditions; (**b**) One feeder rod broken; (**c**) Three feeder rods broken; (**d**) Five feeder rods broken; (**e**) Ten feeder rods broken.  
Figure 9: The change in surface potential after breakage between filter wells 3 and 4. (**a**) One feeder rod broken; (**b**) Three feeder rods broken; (**c**) Five feeder rods broken; (**d**) Ten feeder rods broken.
# Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things
## 3 Problem Statement and Research Goals
### End-To-End Security in LLNs
While DTLS can avoid the TCP handshake, it still has to perform its own handshaking mechanism in order to negotiate key exchange and authentication methods. The overhead of this handshake in terms of delay or amount of network traffic can be problematic for some types of constrained nodes and networks. Specifically, previous research has shown that in duty-cycled multi-hop networks, the delay introduced by the DTLS handshake can run up to fifty seconds [8] for four wireless hops. The authors also correctly conclude that the memory for storing the DTLS session state on constrained nodes is typically limited to a handful of nodes for Class 1 devices. Additionally, other research [22] has shown that ephemeral DTLS sessions with constrained devices should be avoided as their energy expenditure is up to 60% higher when compared to a single DTLS session with a long lifetime. Therefore, one goal of this work is to limit the impact of the DTLS handshake on delay and energy expenditure, while supporting more than just a handful of simultaneous DTLS sessions per constrained device. The third group of issues stems from naively deploying end-to-end security in (multi-hop) Low-power and Lossy Networks (LLNs) and from allowing unmonitored access to LLNs to malicious users. In these networks, resources are sparse (see above), and care should be taken in order to avoid unwanted depletion of these resources by Denial-of-Service (DoS) attacks. For example, by repeatedly opening and closing DTLS sessions, a malicious user can significantly reduce the lifetime of a battery-powered device. A malicious user could also send large datagrams to the LLN, which will trigger fragmentation that can exhaust the allocated network buffers in the LLNs. Most of these resource-depletion threats can be mitigated by monitoring and restricting access to the LLN at the edge of the network, where an unconstrained firewall or gateway system resides. However, end-to-end security encumbers such systems from authenticating parties (as constrained devices cannot support strong authentication) and therefore restricting access to authorized parties. Here, this work will study how end-to-end security can be reconciled with the need for traffic filtering at the edge of the network and the need for strong authentication.