esa-sceva/satcom-chunk-collection huggingface dataset:
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 5 Experiments
### Evaluation
#### 5.2.2 Edge Depth Estimation  
In Figure 11, a comparison of the edge-matching operators is presented. The results show that both the Census and SC operators are capable of producing accurate edge depth maps. However, using a single operator can result in depth instability for some edge points and be discarded. By combining the two operators, more edge points on the map can obtain depth values and have greater support in the subsequent depth propagation process.
## 4 Discussion
### Energy Dissipation  
There are three key parameters used to evaluate the energy dissipation performance of a joint: the total dissipated energy (\(W_{\rm total}\)), the equivalent viscous damping coefficient (\(\xi_{\rm e}\)), and the energy dissipation coefficient (\(E_{\rm e}\)). \(W_{\rm total}\) is the cumulative dissipated energy (\(W\)) of the joint at each drift level of the loading process, where \(W\) is the area enclosed by the hysteresis curve at a certain cycle time, \(E_{\rm e}\) is the ratio of the total energy in a hysteresis loop to the elastic energy of the component, and \(\xi_{\rm e}=E_{\rm e}\) /2\(\pi\). The idealized load versus deflection relationship is presented in Figure 21, and \(E\) can be defined as follows:  
\[E_{\rm e}=\frac{S_{\rm ABC}+S_{\rm CDA}}{S_{\rm OBE}+S_{\rm ODF}} \tag{8}\]  
where \(S_{\rm ABC}\) and \(S_{\rm CDA}\) represent the areas enclosed by the upper half and lower half of the hysteresis loop, respectively, and \(S_{\rm OBE}\) and \(S_{\rm ODF}\) represent the areas of the corresponding triangle, respectively.  
Table 5 compares the values of \(W_{\rm total}\), \(\xi_{\rm e}\), and \(E_{\rm e}\) in the limit state, and Figure 22 shows the variation in \(W_{\rm total}\) and \(\xi_{\rm e}\) as a function of the drift ratio. Through comparison, the following conclusions can be drawn:  
(1) The \(\xi_{\rm e}\) values of specimens SJ-7 and SJ-8 are the largest among all the specimens, indicating that beam buckling deformation is the most effective method for dissipating energy in such joints. According to the American standard ANSI/AISC 341-10 [30], they also satisfy the seismic requirement for intermediate moment frames.  
(2) The \(\xi_{\rm e}\) values of specimens SJ-4 and SJ-6 are the smallest among all the specimens. This was due to that dissipating energy in such joints were mainly realized by the deformation of the bolt.  
(3) The \(\xi_{\rm e}\) values of specimens SJ-1, SJ-2, SJ-3 and SJ-5 are in the middle. For specimens SJ-1 and SJ-2, the joint dissipates energy mainly through the crushing of concrete and the deformation of the steel tube wall. For specimens SJ-3 and SJ-4, the energy dissipation is mainly achieved through the end-plate deformation. Those indicate that the energy dissipation of these two deformation modes is in the medium level.  
(4) The use of stiffeners enhances the energy dissipation capacity of the joint; although a certain deformation of the end-plate is also found, the joint mainly dissipates energy through the local buckling deformation of the weak beam.  
(5) When the joint exhibits failure mode I, increasing the end-plate thickness has little effect on the energy dissipation capacity of the joint. When the joint exhibits failure mode II, increasing the end-plate thickness significantly reduces the energy dissipation capacity of the joint.  
Figure 21: Definition of a hysteresis loop.
## 2 Materials and Methods
### Plate-Based Growth Experiments  
Growths of the _Spirulina_ strain across different salinity treatments were assessed through plate-based growth experiments using sterile 24-well microplates following [PERSON] et al. [18] with modifications. Each well is inoculated with 0.2 mL of diluted culture solution and added to 1.8 mL of salinity-adjusted Zarrouk medium. The microplates were covered with gas-permeable polyvinylidene chloride membranes for gas exchange while preventing contamination through aseptic membrane replacement at each measurement. The experiments were conducted with four replicates per salinity treatment.  
Growth rates were estimated by monitoring changes in biomass, which were inferred from optical density (OD) measurements [19]. OD measurements were taken every two days over a 20-day incubation period, specifically between 16:00 and 17:00. OD was measured at 450 nm using a BioTek microplate reader with Gen5 2.09 Software (BioTek Instruments; Winusky, Vermont, USA) configured with an area scan protocol.  
The maximum specific growth rate (\(r\), d\({}^{-1}\)) for each treatment was estimated by measuring optical density (OD) values at predetermined time intervals (days) and plotting them against time. The data was then fitted to a logistic growth model (Equation (1)), which has an initial lag phase followed by a rapid exponential phase and ends with a plateau corresponding to the stationary phase using non-linear regression techniques.  
\[N(t)=\frac{K}{1+\left(\frac{K-N_{0}}{N_{0}}\right)e^{-rt}} \tag{1}\]where \(N(t)\) is the OD at time \(t\) in days, \(K\) is the carrying capacity, \(N_{0}\) is the initial OD, \(r\) is the specific growth rate, and \(e\) is the base of the natural logarithm.  
The specific growth rate was determined by finding the steepest slope of the fitted curve, which corresponded to the maximum first-derivative value during the exponential growth phase. This parameter indicates the intrinsic division rate under each treatment condition and provides important information about the culture growth dynamics.
## 7 Risk Analysis
### Emergency Response Service
#### 7.3.4 Spoofing  
1. **Component**  
_Security risk_: Unauthorized persons can manipulate ERS components to intentionally trigger false crash notifications. This can lead to unnecessary deployment of emergency services, wasting resources, and causing confusion.  
_Security measure_: A possible mitigation may based on appropriate authentication mechanisms for the cloud.
## 1 Introduction
Interestingly, electrospun mats can be subjected to a thermal post-treatment above the glass transition (T\({}_{g}\)) and below the melting temperature (T\({}_{m}\)) of the polymer, also termed annealing, in order to remove or minimize their porosity and produce continuous and homogenous films [51; 52; 53; 54; 55]. Until now, this technology has been successfully applied to different polyester-type biopolymers with different potential applications in the food packaging field. For instance, electrospun poly(3-hydroxybutyrate) (PHB) films showed better optical properties, similar barrier performance, and higher elongation at break and toughness in comparison with equivalent films obtained by compression molding [51]. Electrospun films of PHB, PVOH, and also polylactide (PLA) were also developed by electrospinning and originally applied as coating materials on a paper-based packaging material to develop multilayers with improved barrier properties against water and limonene vapors [52]. In another study carried out by [PERSON] et al. [53], a similar strategy was followed to coat cellulose nanopapers by PHB and poly(3-hydroxybutyrate-\(co\)-3-hydroxyvalerate) (PHBV) electrospun layers. Similarly, electrospun ultrathin fibers of bio-waste derived PHBV were subjected to annealing by [PERSON] et al. [54] to successfully produce continuous biopolymer films with similar barrier performance than petroleum-based polyethylene terephthalate (PET) films. Other recent studies have been focused on the incorporation of antimicrobial or antioxidant ingredients in the electrospun fibers, which can be thereafter integrated as active layers in packaging structures. For example, [PERSON] et al. [56] prepared electrospun active films of PHBV with antimicrobial and antioxidant properties by the incorporation into the fibers of different essential oils (EOs) and natural extracts (NEs). Also, [PERSON] et al. [57] recently developed multilayer bioactive films with controlled release capacity of the natural antioxidant gallic acid (GA) by the incorporation of electrospun PLA interlayers into cast-extruded PLA films. Lastly, [PERSON] et al. [58] recently prepared antimicrobial PLA films containing _Allium usinum_ L. extract by electrospinningning. The aim of this study was to obtain, for the first time, electrospun pectin-based films as potential candidates for food packaging applications. First, various water-based pectin solutions containing different amounts of PEO and/or in combination with the addition of two different types of plasticizers were processed by electrospinningning to determine the best system to produce a film. Thereafter, the selected electrospun mats were characterized and the most promising fibers were selected and subjected to annealing to produce pectin-based films. The morphology, chemical, and thermal properties of the films were reported. The optimal electrospun film was, finally, applied as an interlayer in a multilayer structure based on PHBV and the barrier properties of the resultant multilayers were analyzed and compared to an equivalent multilayer of a cast-film pectin interlayer.
## 2 Integrated Navigation System of SINS/DVL/MCP/PS  
The integrated navigation system of SINS/DVL/MCP/PS shown in Figure 1 is adopted, which is similar to the navigation scheme presented in [5; 6]. Considering the difficulty of obtaining the aided level position in underwater conditions, the position reference system in [5; 6] is abandoned.  
In Figure 1, the SINS constitutes the basic navigation system providing the attitude, velocity and position of AUV through integration of the angular rate and acceleration measured by gyroscopes and accelerometers, whereas DVL, MCP and PS provide velocity, yaw, and height for data fusion with a federated Kalman filter [5; 6]. In the data fusion process, subsystems constructed by SINS and aided systems, including SINS/DVL, SINS/MCP, and SINS/PS, are first built with standard Kalman liking filters (such as Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter) used for data fusion; then, a federated Kalman filter is used to fuse the information from all subsystems to obtain the estimation [5; 6].  
The navigation solutions of the SINS and data fusion are two independent and parallel processes. As shown in Figure 1, these two processes are connected with a closed feedback mode. The estimations from the federated Kalman filter are used to reset the navigation parameters and to compensate for sensor errors. Error propagation models are the basis of data fusion. In the following subsections, error models for each subsystem will be introduced.
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 5 The DETO Algorithm in the Ultra-Dense Network
The details of distributed DETO are described below:  
1. Initialize step: Each device executes all its subtasks locally to obtain the completion time. The edge servers will broadcast their communication rate and computation capability to all the users. 2. Iterative steps:Each user calls the DETO algorithm discussed in Section 4, assuming that if it uploads its task to each server based on the previous information broadcasted by the server. After that, each user will inform the maximum completion time reduction result and corresponding offloading strategy to the edge network. After the edge network receives all the information transferred by the user, they will accept the offloading request of only one device with the greatest gain, and then only the selected user can update its offloading and duplication strategy. Finally, the related server will broadcast the updated information about the transmitting rate and its scheduling result to the network, which then facilitate the users to make further choices in the next iteration. 3. Convergence step: After a finite step, when all the user cannot further reduce its own delay by changing strategy unilaterally, the distributed algorithm is terminated. Although the distributed DETO algorithm may achieve a higher completion time, it has much higher computation efficiency and provides a workable solution in the ultra-dense network. In the distributed DETO algorithm, each IoT device adjusts its own offloading strategies rely solely on the offloading scheme of other users in the lasted international step. Thus, the computational complexity of distributed DETO is \(O(n+2e)\), where \(n\) and \(e\) denote the number of subtasks and edges in DAG. A possible drawback is that the convergence time may add further overhead to the offloading system. We argue that this process incurs only an initial delay as normally the task DAGs do not change drastically for a given IoT system. Once the process is finished, each IoT device can perform its offloading decisions to reduce the overall task delay.In the next section, we prove the convergence of the distributed DETO by simulation.
## 2 Background and Related Work
### Key Challenges in Urban Traffic Management
However, implementing these advancements faces challenges,including ensuring seamless integration of traffic lights, sensors, and vehicles, and requires substantial investments in infrastructure, hardware, and software.
## 3 Results and Discussion
### Discussion of the Results
An economic feasibility analysis of the reduction was tried as a final exercise. To this end, some prerequisites needed to be examined and determined. First, the period for the analysis had to be set. It was determined as five years, beginning from 2019 when the survey was implemented and when the reduction begins in 2023. Second, a social discount rate should be set. Concerning this, the government-run Korea Development Institute announced the suggested use of 4.5% as a social discount rate. This study adopted the value. Third, the time of \"present\" as a baseline for calculating the present value had to be set. In this study, this was set to be 2019, the time when the survey was conducted. The next important information that was needed was benefits and costs arising from the reduction. As presented earlier, the economic benefits ensuing from the reduction would occur annually for 10 years, from 2019 to 2023. The costs largely relate to collection and treatment of SMD and were taken from \"The Third Marine Debris Master Plan (2019-2023)\" contained in Korea Ministry of Oceans and Fisheries [48]. The costs amounted to about KRW 6.69 billion (USD 0.60 million), KRW 10.20 billion (USD 0.91 million), KRW 11.22 billion (USD 1.00 million), KRW 12.35 billion (USD 1.10 million), and KRW 12.66 billion (USD 1.13 million) over the period 2019-2023, respectively. All benefits and costs are expressed in a 2019 constant price. The ratio of benefit over cost (B/C), which is one of the indicators for cost-benefit analysis (CBA), can be calculated from the constant values of benefits and costs. It is a simplification of the real CBA. The present value of the benefits arising from the reduction was computed as KRW 1103.02 billion (USD 98.24 million) and that of the costs arising from the reduction became KRW 48.16 billion (USD 4.29 million). Thus, the net present value of the reduction became KRW 863.89 billion (USD 76.94 million), which is larger than zero and thus implies that the reduction passed the economic feasibility analysis. Furthermore, the ratio of benefit over cost was computed to be 18.93, which is larger than one, confirming the finding that the reduction secures economic feasibility. In conclusion, the reduction of SMD in South Korea is socially beneficial. Therefore, a continuous and stable reduction must be conducted.
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 3 Results
### How Does Spatial Resolution Affect Satellite Estimates of Windthrow Tree-Mortality?
AIC = Akaike Information Criteria; S\({}_{\text{yx}}\) = standard error of the estimates; RMSE = root mean square error; Sigma = residual standard deviation; R\({}^{2}\)KL = Kullback-Leibler coefficient of determination. Figure 4: Distribution of windthrow tree-mortality (%) estimated from satellite data with varying spatial resolution (i.e., from 2 m to 30 m pixel). (**a**) Windthrow tree-mortality in field subplots; (**b**) Windthrow tree-mortality in virtual subplots. Hollow black circles are the outliers. Figure 3: GLM models describing the relationship between windthrow tree-mortality measured in the field and estimated using remote sensing data. (**a**) Functions describing the relationship between NPV and windthrow tree-mortality (%) measured in the field; (**b**) Linear models describing the relationship between windthrow tree-mortality measured in the field and estimated using remote sensing. The black dotted and red lines are the 1:1 line (perfect linear relationship) and the actual relationship between observed and estimated values, respectively; (**c**) Symmetry of the distribution of residuals in relation to windthrow tree-mortality values estimated using remote sensing. Which Sensor Produces the Most Reliable Estimates of Windthrow Tree-Mortality across an Extent Gradient of Windthrow Severity? All satellites produced consistent estimates of windthrow tree-mortality from moderate to high disturbance severity (i.e., 25% to 42% of windthrow tree-mortality). Remote estimates overestimated field measurements by 5-10% from old-growth forest to moderate windthrow severity. From moderate to extreme windthrow severity, we found the opposite pattern--i.e., 10-15% underestimation (Figure 5a). Remote estimates of tree mortality for our virtual subplots covering a wider disturbance gradient were similar from low to moderate windthrow tree-mortality. From moderate to extreme windthrow tree-mortality, Landsat 8 and Sentinel 2 yielded comparable values, which were also different from those obtained from WorldView 2 (Figure 5b). Although with greater associated uncertainties at specific ranges of tree mortality (low to moderate and high to extreme severity), Landsat 8 and Sentinel 2 captured a relatively higher proportion of the existing variation of windthrow tree mortality over a wider disturbance gradient.
###### Abstract  
Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, we propose in this paper a cross-layer routing protocol which supports mobility for large-scale wireless sensor networks, which we name CLR-MSPH. We adapt CLR-MSPH for the hierarchical architecture of the network, and it performs on cluster-based wireless sensor networks where the network is organized in clusters. Our proposed protocol deals with the problem of handover data after the mobile sink leaves the radio range of cluster head without sending all data stored in the cluster head's buffer. We also introduce a mobility model for the mobile sink for a better data collection process. CLR-MSPH is considered as an extending implementation of BMAC protocol with handover mechanism (BMAC-H). In order to prove the efficiency of the proposed protocol, we compare CLR-MSPH to BMAC-H, where we adapted BMAC-H to perform in cluster-based wireless sensor networks. The simulation results show that CLR-MSPH performs better than BMAC-H in terms of packets reception rate, energy, and latency.  
Keywords:cross-layer; MAC layer; network layer; mobility model; cluster; mobile sink; handover; prediction; mobility management +
Footnote †: journal:
# Energy Consumption Characteristics for Design Parameters of Permanent Magnet-Based Al Billet Heater
###### Abstract  
This study analyzed design factors to maximize energy efficiency, via numerical analysis, through an examination of the characteristics of a heating system that uses permanent magnets and is employed for preheating in the aluminum cladding extrusion process. The design parameters of the billet heater using permanent magnets are the magnetic flux direction, the number of magnets, clearance, and eccentricity. The magnetic flux density, current density, power loss, temperature, and energy consumption characteristics were examined using the results of the parameter variations. Numerical analysis for the base model was conducted, and it was experimentally verified that the aluminum billet reached 450 \({}^{\circ}\)C in about 260 s, and the temperature error at that time was about 2%. The analysis results show that the optimal factor conditions vary significantly depending on the magnetic force direction of the permanent magnet, that is, the circumferential (tangential) and centrifugal (normal) directions. Furthermore, eccentricity has an effect on efficiency in general, and the narrower the clearance was between the magnet and billet, the higher the efficiency achieved. That is, it was confirmed that the power loss increased by about 1.79% in the four permanent magnets to the tangential model, and increased by about 10.51% in the 12 permanent magnets to the tangential model when an eccentricity of 2 mm was applied at a clearance of 2.5 mm. In addition, the optimal design parameters of a system that heats aluminum billets with a diameter of 60 mm and a length of 70 mm were proposed, and the importance of the design parameters was revealed. In this study, it was found that 12 magnets were the most effective when the magnetic flux pole direction was tangential.  
magnetic flux; billet heater; permanent magnet; energy consumption; electromagnetic-thermal analysis +
Footnote †: journal: Applied Sciences
## 2 The Site and Microgrid Description
### Description of Microgrid Structure
A Sunny Island 5048 inverter was used in the hybrid system whereas Microcare L8/5000/48 and Victron energy Multiplus 48/5000 inverters were respectively used in the grid-assisted and off-grid systems. Down the PV system, the component balance chain is the battery bank. As shown in Figure 3, each system has its battery bank. To this effect, a set of eight 6 V M-Solar 3 MIL 255 batteries connected in series was used in the off-grid system. Over 100 h discharge period, each battery delivers 900 Ah with cell voltage above 1.85 V and a constant temperature of 25 \({}^{\circ}\)C. Trojan L16 RE-2V and Hoppecke 6 OPzS solar power batteries respectively made-up the hybrid and grid-assisted system. Both batteries are 2 V, as such, 24 sets of batteries were connected in series in both systems. The Trojan battery is rated to deliver 1235 Ah over 100 h while maintaining a voltage above 1.75 V/cell at 25 \({}^{\circ}\)C. At the same temperature and a constant cell voltage above 1.80 V, the Hoppecke battery will deliver 910 Ah over 100 h. These imply that in an open circuit and at full charge, the off-grid, hybrid and grid-assisted batteries can supply 43.20, 59.28, and 43.68 kWh, respectively in 100 h.
## 2 Theoretical Background
### Artificial Neural Network (ANN)  
ANN has been utilized in the science and engineering area for more than 80 years, and its performance in creating a high-level and non-linear relationship for the selected variables have been verified in many cases [46; 47; 48; 49; 50; 51; 52]. ANN is inspired by the neural structure of the human brain, and various network architecture, learning algorithms and transfer functions were proposed after long-term development [53; 54]. Among ANN, the multi-layer perceptron (MLP) was considered to be the best ANN architecture and contains five parts, including the input layer, the hidden layer, the output layer, neurons in the layer and weight among these neurons. The back-propagation (BP) algorithm is the most popular MLP, and the knowledge form the training datasets will be forwarded, while the errors will be passed back to guide the updating of weights value [55; 56; 57]. During BP training, the weight updated according to the cost function for achieving better training performance, and that process will be ended when the cost function equal to or less than the setting level [58].