esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulations and Discussion
### Parameter Values
#### 3.1.1 Effects of Change in Rainfall Intensity in Botswana
Figure 6: Plant–livestock population dynamics in Kgagodi when rainfall intensity is (**a**) decreased by 10%, (**b**) increased by 10%, and (**c**) at baseline. both forage availability and livestock productivity. Additionally, vegetation thrives under increased rainfall intensity, supporting the livestock population and providing farmers with ample opportunities for sustainable harvesting. Despite the challenges associated with being situated in a designated red zone area, as noted by [8], farmers still keep livestock in high numbers. The combination of a favorable rainy season and the inflow of water into the Okavango Delta from Angola plays a critical role in maintaining elevated livestock populations in this region, as illustrated in Figure 7, as compared with other areas in the country. Despite the effects of wild animals and floods that usually come around June and August, the land recovers or re-germinates quickly up to its carrying capacity. In Mabuttsane, livestock population dynamics are strongly influenced by rainfall intensity (Figure 8). When rainfall intensity decreases, as shown in Figure 8a, livestock numbers predominantly remain below 2000 units. This highlights the region's vulnerability to reduced rainfall, as lower precipitation directly impacts vegetation growth, thereby limiting the forage available to sustain larger herds. Conversely, as illustrated in Figure 8b, when rainfall intensity is increased by 10%, the livestock population grows significantly, reaching over 4000 units in the later years of the 15-year study period. This increase demonstrates how enhanced rainfall positively impacts forage availability, supporting larger livestock numbers over time. The gradual rise in livestock numbers with increased rainfall also indicates that the ecosystem in Mabuttsane has a strong capacity to adapt and expand its carrying capacity under favorable conditions. Figure 7: Plant–livestock population dynamics in Sehitlwa when rainfall intensity is **(a)** decreased by 10%, **(b)** increased by 10%, and **(c)** at baseline.
## 3 Results
### Correlation Analysis between the CatWalk XT(r) Parameters and Other Measurements
#### 3.4.1 Correlation between the CatWalk XT(r) Parameters and the BBB Open Field Scores  
Since the timepoint N1 was not achievable in the CatWalk XT(r) gait analysis, we calculated Spearman correlation coefficients for the BBB open field scores of timepoints N2 to N6 with all studied CatWalk XT(r) parameters of the hindlimbs (the forelimbs are not assessed in the BBB open field score and were therefore excluded) in all animals of the SCI group. As shown in Figure 8, the CatWalk XT(r) parameter max contact area showed the highest correlation with the BBB open field scores over the course of the experiment (\(r\) = 0.88), followed by the swing speed (\(r\) = 0.73), print area (\(r\) = 0.73), and regularity index (\(r\) = 0.7). In contrast, the intensity parameters max contact mean intensity (\(r\) = 0.17) and mean intensity (\(r\) = 0.375) were only weakly correlated with the BBB open field scores. Overall, a very strong correlation between the BBB open field score and the CatWalk XT(r) gait analysis was found in 1 out of 16 (6.3%) and a strong correlation in 8 out of 16 (50%) assessed parameters. These findings suggested a comparable ability of the CatWalk XT(r) gait analysis and the BBB open field score to describe spontaneous recovery after severe cervical SCI.  
Figure 7: (**A**) GFAP-stained (green) spinal cord cross-section of an animal in the SCI group six weeks after the injury. The intramedullary cyst is framed with the ROI measurement function in Image J (red line). (**B**) Axial distribution of preserved spinal cord tissue percentage on 11 spinal cord cross-sections with an intersection distance of 240 \(\upmu\)m.
## 3 Results
### Ultra-Resolution Maps  
Since the SoftVoting classifier showed slightly better performance than the Random Forests and XGBoost classifiers employed in the research, the SoftVoting classifier was chosen for the mapping of land cover and vegetation types. Furthermore, due to superior performance of the ultra-resolution suite over the very high-resolution suite using bi-seasonal images, the land cover and community-level vegetation maps were produced by utilizing the ultra-resolution suite using bi-temporal images. The ultra-resolution (0.5 m) land cover and community-level vegetation maps produced in this research are shown in Figures 16-18 for the Hakkoda, Zao, and Shiranuka sites, respectively. These ultra-resolution maps show the detailed view and distribution of land cover and community-level vegetation types.  
Figure 15: Overall performance summary.  
Figure 16: Ultra-resolution map with 16 classes produced for the Hakkoda site (**top**). The red polygon area has been zoomed in to show finer details (**bottom left**) along with the true-color composite image (**bottom right**).  
Figure 17: Ultra-resolution map with 23 classes produced for the Zao site (**top**). The red polygon area has been zoomed in to show finer details (**bottom left**) along with the true-color composite image (**bottom right**).  
In Figure 16, the zoomed-in map (bottom left) shows the distribution of Abies ECF (olive color) among the Sasa Shrub (lime color) with some barren lands (pink red color). This distribution is well matched with the true-color composite image (bottom right). The dark green trees are Abies ECF in the true-color composite image whereas the light green areas are Sasa Shrub.  
In Figure 17, the zoomed-in map (bottom left) shows the distribution of Abies ECF (olive color) and Pinus Shrub (dark green) among the Sasa Shrub (lime color) with built-up areas (red color). This distribution is well matched with the true-color composite image (bottom right). The dark green trees are Abies ECF and Pinus Shrub in the true-color composite image, whereas the light green shrubs are Sasa Shrub.  
In Figure 18, the zoomed-in map (bottom left) shows the distribution of Abies ECF (olive color)) among the Quercus DBF (green color) with a few scattered barren pixels (pink red color). This distribution is well matched with the true-color composite image (bottom right). The dark green trees are Abies ECF in the true-color composite image, whereas the light green trees are Quercus DBF.
## 2 ROV Description  
The deep sea ROV \"Sea Dragon\" is a large-scale working class ROV developed and built by the Underwater Engineering Research Institute of Shanghai Jiao Tong University (Figure 1). The results presented in this paper are based on the Sea Dragon ROV. The main physical data of the ROV are reported in Table 1. The ROV is equipped with seven thrusters (three vertical and four horizontal). The position of the thrusters on the vehicle is shown in Figure 2.  
Figure 1: The Sea Dragon remotely operated vehicle (ROV) at sea.  
The sensor set available for the working class ROV includes:  
* The global positioning system (GPS);
* Ultra-short baseline (USBL);
* Altimeter: 200 kHz transmit frequency standard, depth rating 6000 m;
* Depth sensor: 6000 m rating, 0.01% accuracy;
* The inertial measurement unit (IMU);
* Doppler Velocity Log (DVL): working frequency 600 kHz, 0.3% full scale accuracy.  
An Ordinary Compact PCI industrial control computer is adopted as the upper computer and PC104 is used for the lower computer.
# Containerised Application Profiling and Classification Using Benchmarks
## 2 Related Work and Research
### Application Resource Management and Load Balancing in the Cloud  
Because benchmarking and profiling are used as bootstraps to efficiently utilise the provisioned cloud services, a more cost-aware tool [14] tries to automate the process of deploying, configuring and profiling in the cloud. Fine-grained statistics are collected from executions on different cloud instances and are used in combination with corresponding costs to choose the optimal execution environment. In the same way, DocLite [15] is a containerised tool that tries to rank VMs through benchmarking in near real time. It showed that using small containers in combination with lightweight benchmarks produces very similar results to large containers. Close to lightweight benchmarks, microbenchmarks have been proposed as an acceptable type of benchmark that can be used to predict the performance of a given application on different cloud instances [16]. The problem that arises is the selection of the most relevant microbenchmarks for a given application. In our case, benchmarking of available cloud configurations is supposed to have been completed, and the produced application profiles are used to match a used benchmark so as to predict the applications performance.  
RUBiS [17] is a profiling-as-a-service Web application that tackles the problem of cloud application management through scaling and profiling at the same time. The user provides some performance and especially cost constraints, which are used to guide the scaling process while producing generic, but detailed, profiles for as many nodes that comprise the application as possible. In [18], autoscaling is accomplished through offline benchmark profiling. Simplified models of the application workload that mimic typical users' behaviour are used to feed the application in order to get insights about its performance, enabling the administrator to devise a scalability strategy considering QoS and cost.  
Taking into account the background workload of shared VMs, the authors in [19] perform a canonical correlation analysis in order to infer the system factors that affect performance the most. Resource usage data gathered through online profiling enables the prediction of resource requirements for a particular service level of the application. An extremely similar case ([20]) makes use of the hardware performer counters to produce the requested profiles. We did not incorporate this parameter in our analysis because it demands that the user has deep knowledge of the way the application uses shared resources, and we also examine everything in containerised environments, but actually devise a profiling strategy for any kind of applications.
## 4 Discussion of Inversion Results and Accuracy Evaluation
### The Influence of Regularization Constraint on Accuracy  
The use of appropriate constraints in atmospheric inversion problems is a crucial part of the inversion problem. Many researchers use the optimal estimation method in data analysis, where the constraint matrix is the inverse of the a priori covariance matrix. Due to the lack of data, a complete matrix is often unavailable, so a priori covariance matrix is constructed in a special way. Tikhonov regularization is a special constraint, which has been applied to many atmospheric inversion problems. Based on Band 2, the observed vertical profile is constructed to study the influence of Tikhonov regularization constraints on the accuracy of the retrieval of temperature. In the retrieved process, the optimal estimated solution of temperature in MLT is shown in Figure 17, with Tikhonov regularization constraint and without regularization constraint. The red and blue profiles represent the inversion results with Tikhonov regularization constraints and without constraints.  
Figure 18 evaluates the influence of the Tikhonov regularization constraint of a priori covariance matrix on inversion accuracy from the point of view of the bias between retrieved temperature and actual temperature. It can be seen that the construction of an a priori covariance matrix through regularization constraints can slightly improve the accuracy of retrieved atmospheric temperature. Compared with the traditional optimal estimation method, the mean absolute bias in the vertical field of view is reduced by about 0.4 K.  
Figure 16: The mean absolute bias between retrieved temperature and actual temperature through different bands.
## 3 Master-Slave Optimization Framework  
To evaluate the effectiveness and robustness of the proposed PMVO-based scheduling strategy, a comparative analysis was conducted using two additional metaheuristic algorithms: the Parallel Vortex Search Algorithm (PVSA) and the Chu & Beasley Genetic Algorithm (CBGA). All algorithms were integrated with the exact power flow solver and tested under identical conditions. This multi-algorithm comparison forms the basis of the experimental framework described in Section 4 and supports the performance benchmarking presented in Section 5.
# Investigation of 2 DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation
## 4 Hardware Test  
To design the proposed controller, sinusoidal signals of different amplitudes have been considered as references. The sinusoidal signals are designed accordingly, with three amplitudes, such as 0.1, 0.2 and 0.3 amp, and a frequency of 4\(\pi\) Hz. In this system, torque is considered to be the system input and the current is considered to be an output, since the current and voltage are responsible for the motor rotation with certain amount of torques.  
From the experiment, it is noticed that the amplitudes of 0.1 and 0.2 amp do not yield much effort to the patient, while the hardware moves very smoothly at 0.3 amp and this amplitude provides enough effort to the patient for elbow flexion and extension, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Controller Parameters** & **Description** & **Value** \\ \hline \(P\) & Proportional gain & 0.5 \\ \(I\) & Integral gain & 400 \\ \(D\) & Derivative gain & 9.5494 \(\times\) 10\({}^{-5}\) \\ \(N\) & Filter coefficient & 23.3961 \\ \(b\) & Set point weight for proportional part & 1 \\ \(c\) & Set point weight for derivative part & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Controller parameters.  
Figure 7: The performance of the controller on the estimated system (**a**) step response (**b**) sine wave.  
Figure 8: Current vs. time when amplitudes are considered as (**a**) 0.1 amp (**b**) 0.2 amp and (**c**) 0.3 amp.  
In order to evaluate the tracking performance of the controller, the root mean square error (_RMSE_) method has been considered. RMSE can be expressed by the following Equation [40].  
\[RMSE=\left|\sqrt{\left(\frac{\sum_{i=1}^{N}\left(y_{s_{i}}-y_{e_{i}}\right)^{2}} {N_{y}}\right)}\right| \tag{8}\]  
where, \(y_{s_{i}}=\) Actual values of \(y\), \(y_{e_{i}}=\) Desired values of \(y\) and \(N_{y}=\) Total number of \(y\).  
From Table 4, it can be seen that the controller offers RMSE less than 5%. Hence, for tracking, this controller is suitable for this system.
## 4 CO2 Emissions Analysis and Indicators in Sub-Projects
### Reinforcement Concrete  
For tunnel reinforcement sub-projects, CO2 emissions are not related to the shield tunnel length. The main factor, namely the amount of reinforced CO2 emissions of the shield tunnel, is inappropriate. Furthermore, the accounting data indicate significant variations in CO2 emissions based on the reinforcement method used, requiring a separate analysis for each project.  
As indicated in Table 3, except for Projects 2 and 3, the variation in CO2 emissions for tunnel reinforcement projects is relatively minor. For Tunnel 2, the volume of reinforcement is 2.5 times higher than that of the other projects. This increase is primarily due to the challenging reinforcement conditions in Project 2, which necessitated additional secondary reinforcement at several crossing points, substantially raising the required amount of tunnel reinforcement.  
Figure 10 analyzes three reinforcement methods individually. The average CO2 emissions per unit volume for Cement Soil Mixing Pile, Jet Grouting Pile, and Secondary Grouting are 317.4 kgCO2e/m3, 523.4 kgCO2e/m3, and 493.3 kgCO2e/m3, respectively. The maximum deviation between the average and actual values is less than 3%, indicating that these values are reliable as CO2 emissions prediction indicators.  
Figure 9: CO2 emission index of unit of investment in shield tunnel.  
Therefore, to reduce CO2 emissions during tunnel reinforcement, the optimal strategy is to increase tunnel length and decrease the number of working shafts. Eliminating one working shaft can reduce the reinforcement volume, resulting in a decrease of approximately 4000 \(\mathrm{tCO_{2e}}\). Furthermore, minimizing crossings through challenging terrain and reducing secondary grouting can contribute to further emission reductions. It is also recommended to adopt reinforcement methods with lower \(\mathrm{CO_{2}}\) emissions following a thorough comparison.
## 2 Mobile Networking Background
The MIB and SIB messages are publicly broadcasted and advertised as the base station is a public entity for serving the cellular connectivity, often serving a large number of mobile users freely entering and exiting the cell. Receiving and decoding the MIB and SIB enable the mobile user to attach to the base station and set up a communication channel, resulting in the user-dedicated cell radio network temporary identifier (C-RNTI). This wireless channel setup is followed by digital control including the registration and authentication verification as well as the security setup between the user and the core network. From the user and (universal) subscriber identity module (SIM or USIM) registration (which occur in advance before the user activation to receive the connectivity service and not drawn in Figure 2), the core network derives the mobile subscriber identification number (MSIN) and the more temporary ID of the temporary mobile subscription identifier (TMSI) and shares that with the user and the base station. The MSIN is not communicated in plain-text and rather is processed by an encryption function \(E\), thus exchanging \(E\)(MSIN). Afterward, the user can use the established RF and digital channel for data communications and networking applications.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 5 Engineering of PLA-Based Electrospun Structures for Biomedical Applications
### PLA-Based Electrospun Yarns
Firstly, the core part in the form of continuous yarn was separately electrospun using an apparatus  
Figure 8: (**a**) Double-nozzle electrospinningine set-up to produce continuous twisted yarns, Reproduced with permission from [45] (License number: 5235871036609), (**b**) SEM images of the PLA twisted yarn prepared by double-nozzle electrospinningine set-up Reproduced with permission from [45] (License number: 5235871036609), (**c**) cross-section image of electrospun PLA yarn with core–shell structure, reproduced from an open access paper [17] distributed under the terms of the Creative Commons CC BY license, (**d**) SEM image of the cross-section of hollow PLA nanofiber yarn reproduced from [56], distributed under the terms of the Creative Commons CC BY license, (**e**) electrospinningine set-up composed of a rotating collector having a plurality of point electrodes to produce, adapted from [16], reprinted with permission from [16] Copyright ©2022 American Chemical Society. (**f**) Dual plied PLA suture yarn, reprinted (adapted) with permission from [16] Copyright ©2022 American Chemical Society. made from a rotating collector, and an infusion pump equipped with a syringe (Figure 8e). The fibrous bundle was twisted to form a yarn and drawn using a guidewire and further wound on a rotating bobbin. To produce yarn with core-shell structure, PLGA/drug solution electrospun upon the pre-spun PLLA core yarn (Figure 8f). The double-nozzle electrospinning set-up also has been applied by [PERSON] et al. [48; 56] to collect PLA-based nanofibrous yarns with hollow structures as vascular scaffolds (Figure 8d). For this purpose, firstly, a three-layered nanofibrous structure composed of PVA multifilament, PVA nanofibers, and electrospun PLA fibers, as the core, middle, and shell layers respectively, were fabricated. Afterward, in order to form a hollow PLA nanofibrous structure, the middle layer was eliminated in water, and the core part was also extracted. In this study, besides the mechanical properties, cell attachment, biocompatibility, and blood compatibility of PLA hollow yarn were considered to evaluate its potential as a vascular graft.
## 3 Case Study  
### The Case Study  
The case study selected is a bridge with a straight deck having a total length of 200 m with four equal spans of 50 m (Figure 2). The case study has been adopted in many previous works published by many authors since the 1990s [21], and also in a recent work in which the IMPA\(\beta\)[18] has been presented. The bridge is located in Reggio Calabria, Italy, but since in this work we used the strong ground motion records of 2016 Central Italy earthquakes, the actual location of the bridge can be considered Norcia, Italy. The deck of the bridge consists of 14-m-wide pre-cast concrete box girder supported by piers through bearings locked in the transverse direction and, at the abutments, through elastomeric bearings (movement in the longitudinal direction is allowed at the abutments, but transverse displacements are restrained).  
The three circular piers (P1, P2 and P3), have different heights and a diameter of 2.5 m. The bridge was designed, according to Eurocode 8, using concrete C20/25 and steel B450C; C20/25 has a characteristic compressive cylindric strength of 20 MPa and the B450 a characteristic yield strength of 450 MPa. The design of the bridge was done considering a seismic action with a design peak ground acceleration of 0.35 g and a behavior factor q = 3.0. The design loads adopted for dimensioning the bridge structural elements are listed in Table 1.  
The response of the bridge model was estimated through the employment of nonlinear static and dynamic analyses. The dynamic analyses were performed adopting a set of ground motions (GM) selected according to the criteria described in SS3.2; from the set of ground motions, a median response spectra (RSm) was defined. The GMs were used to perform the NRHAs as well as their mean spectrum (RSm) were used for the purpose of evaluating the pushover-based procedure (the performance point is evaluated, for each intensity level, adopting RSm for the seismic demand).  
In this paper, (SS4) analysis results are presented in terms of deformed shape of the bridge in transverse direction and bridge capacity curve (i.e., monitoring point displacement versus seismic intensity).  
Figure 1: (Top) Degrees of freedom of the bridge: performing a pushover analysis the displacement u\({}_{\rm r}\) of the control point (u\({}_{\rm r}\) is the transversal displacement). (Bottom) Procedure for the definition of the capacity curve of the incremental modal pushover analysis for bridges (IMPA\(\beta\)) as an envelope of uniform pushover analysis (UPA) and modal pushover analysis (MPA) at each intensity level.