esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 5 Discussion
### Take-Off Time Interval and Deployment Strategies  
We considered how the take-off time interval and the different strategies used in the operation of the behaviors presented affected the results.  
In the LTID strategy, the longer the take-off time interval is, the longer the deployment. However, for short take-off time intervals, the number of collisions between individuals increases considerably because the density of vehicles in a given area is not limited and therefore increases the probability of collision in the most active areas.  
In general, the greater the number of active agents (shorter take-off time interval), the more energy is required. However, in some cases, a longer take-off time interval can increase, rather than decrease, the energy consumed by the swarm.  
This result could be seen in _wall_ maps with SLABE behavior because in some areas of the environment, there is not sufficient information about the beacons that provide robots with navigation information. Therefore, active robots develop a more random search behavior, increasing the flight time for some maps. The results indicate that for the LTID strategy, the best take-off time interval for the unstructured maps is between 12 and 18 s.  
The FGID strategy, limits the maximum number of active vehicles that can circulate in the environment. The time required for deployment increases for low values of the maximum number of vehicles (2 or 5 vehicles maximum). However, for more than 10 vehicles, no substantial improvements and a greater probability of collision are observed, regardless of the behavior used. This probability stabilizes the results obtained for the LTID strategy, which has no limit on the number of active vehicles. This evidence suggests that between 10 and 20 active UAVs in parallel is optimal for large maps of the _hard_ typology and between 5 and 10 active UAVs is optimal for small maps of the _easy_ typology.  
In general, it is convenient to limit the number of active individuals; therefore, the FGID strategy produces better results than those of the LTID strategy, at the cost of requiring mesh-type point-to-point communication (which allows the number of active individuals available at a given time to be known) and requiring to determine for a certain environment the maximum number of active agents.
## 4 Fragility Analysis Based on IDA Method  
### Ground Motion Records  
The selection of ground motion records is a critical factor influencing the analysis results of the IDA method. To mitigate the impact of ground motion variability on structural seismic response and enhance computational efficiency, this study adopts a method that aligns with the design response spectrum. Specifically, it ensures that the response spectrum values near the fundamental period of the structure closely match those of the design response spectrum at corresponding periods. Furthermore, the US FEMA P695 [40] report outlines eight criteria for selecting ground motion records: (1) the earthquake magnitude should be no less than 6.5; (2) the fault mechanism must be either strike-slip or thrust; (3) site conditions should be hard soil or rock; (4) the epicentral distance must be at least 10 km; (5) no more than two records can come from the same earthquake; (6) the peak acceleration should not be less than 0.2 g, and the peak velocity should not be less than 15 cm/s; (7) the effective duration of the ground motion should be at least 4 s; (8) strong-motion instruments should be placed in free fields or on the ground floor of small buildings. Research indicates that using 10-20 ground motion records can reduce variability and ensure the accurate evaluation of structural seismic capacity [37,41]. FEMA P695 recommends 22 sets of ground motion records. In conjunction with GB/T50011-2010 [42], this paper selects 10 sets of ground motion records from the PEER strong-motion database for IDA model calculations. Details of the selected records are provided in Table 3, and the comparison between the selected seismic waves and the target RG1.60 [43] response spectrum is shown in Figure 10.  
Figure 9: Amplification factor of floor acceleration.
###### Abstract  
A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG). Considering that the major driving force for changes in TWS is precipitation, we tested our hypothesis that coarse resolution, i.e., 1\({}^{\circ}\), GRACE TWSA can be effectively downscaled to 0.1\({}^{\circ}\) using GPM IMERG data. The algorithm for the downscaling process comprises the development of a regression equation at the coarse resolution between the GRACE and GPM IMERG data, which is then applied at the finer resolution with a subsequent residual correction procedure. An ensemble of GRACE data from three processing centers, i.e., GFZ, JPL and CSR, was used for the time period from June 2018 until March 2021. To verify our downscaling methodology, we applied it with GRACE data from 2005 to 2015, and we compared it against modeled TWSA from two independent datasets in the Thrace and Thessaly regions in Greece for the same period and found a high performance in all examined metrics. Our research indicates that the downscaled GRACE observations are comparable to the TWSA estimated with hydrological modeling, thus highlighting the potential of GRACE data to contribute to the improvement of hydrological model performance, especially in ungauged basins.  
d 2021 13 5149 1
## 1 Introduction  
Amniotic membrane (AM), or annion, is the deepest of the three layers that constitute fetal membranes. It is a semi-transparent membrane that consists of a single layer of annion epithelial cells fixed to a large basement membrane, and an avascular stromal matrix [1]. It has been demonstrated that the human amniotic membrane contains collagen types IV and VII, such as with the Bowman membrane cornea, in addition to integrin, fibronectin, and laminin [2, 3]. It was employed for the first time for therapeutic use in 1910 by [PERSON] for skin transplantations [4], while its first application in ophthalmology was described by [PERSON], who used the fetal membrane (both amniotic membrane and chorion) to treat epithelial conjunctival defects in patients with symblepharon [5]. Several studies have demonstrated the different properties of amniotic membrane, such as pain reduction, anti-inflammatory activity, and antiachisev and antiangiogenic effects [6, 7, 8, 9, 10, 11]. In the last two decades, the amniotic membrane has progressively been used to treat different ophthalmic conditions, such as chemical burns, persistent corneal epithelial defects, reconstruction of ocular surfaces, ocular pemphbigoid, Stevens-Johnson syndrome, and bullous keratopathy [1, 12, 13, 14, 15]. Depending on the underlying pathology, three main surgical techniques can be used.  
The first one is the inlay or graft technique and is used for stromal defects. The AM is put with the epithelial side up. The graft is fixed with 10-0 nylon sutures at the periphery of the corneal defect. The epithelium is expected to grow above the AM [16]. The second one is the overlay or patch technique, used for non-healing epithelial defects with little or no stromal involvement (i.e., chemical burns or recurrent corneal erosions). The AM can be used with either the epithelial side or the stromal side up and is sutured beyond the epithelial defects and the central stromal defect [16]. The epithelium is expected to grow under the AM. The third one is the sandwich technique, used for deep stromal defects with large non-healing epithelial defects [16]. With this technique, two or more layers of AM are used; the inner one acting as a graft and the outer one acting as a patch. The epithelium is expected to grow between the two layers [16]. What all these techniques have in common is that they require the use of sutures, which can cause several complications. It is for this reason that in recent years surgical techniques that do not involve the use of sutures have been developed, such as cyanoacrylate and fibrin glue, which are the most widely used.  
In this case series, we used a sutureless amniotic membrane transplantation (SAMT) in patients with corneal perforation secondary to ocular surface inflammatory diseases. The technique consists in applying a double layer of AM and a therapeutic contact lens on the corneal surface of the patient, followed by the eyelid closure with Steri-strip(tm). It can be done with relative ease also by general ophthalmologists who are not necessarily corneal specialists.
## 3 Results and Discussion
### Analysis of Fatigue Cumulative Damage Mechanism of CNFPUC Predicted by Fatigue Resistivity  
The CNF in the seepage zone overlapped with each other, as shown in Figure 13a. On one hand, it played a bridging role at the crack tip, which could consume energy by drawing out the fibers and sharing a large part of the energy used to develop the main crack and the microcrack in the matrix. On the other hand, the pull out of CNF destroyed the overlapping connection between the fibers, and the resistivity of CNFPUC increased gradually with the propagation of the main crack and the microcrack.  
In the initial stage, due to the inevitable existence of some initial microcracks and micropores in the CNFPUC matrix, the deformation and the resistivity of CNFPUC increased rapidly owing to the existence of these initial defects. At the initial stage of bending fatigue, the main macrocrack had not developed yet because of the restriction of CNF; the main fatigue crack and the microcrack could not develop rapidly, and the growth rate of strain and resistivity was changed from fast to slow at the macro level.  
On one hand, with the increase of the amplitude of the fatigue stress and the number of fatigue cycles, microcracks were formed by matrix cracking and shearing failure at the interface between the matrix and the fibers, resulting in further crack propagation. On the other hand, when the interface between the CNF and the polyurethane was degummed, some fibers were drawn out. The crack tip extended to the interface of the matrix fiber, and if the stress at the tip had been large enough, fracture of the fiber may have occurred. The overlapping conductive network of CNF was further destroyed, resulting in a gradual increase in the CNFPUC resistivity. Macroscopically, the resistivity growth rate of CNFPUC increased with the increase of stress level.  
When the main crack developed to the critical state of unstable propagation, and the CNF had been pulled out and could no longer consume energy, the fatigue damage entered the third stage of rapid growth, as shown in Figure 13b. At this time, the damage accumulation in the material was serious. At the macro level, the bending deformation of the specimen increased rapidly, the surface of the specimen showed obvious cracks, the fiber lap joint condition worsened, and the resistivity of the specimen increased sharply. Finally, the development of cracks in the internal weakness of CNFPUC led to fracture failure of the specimen.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 4 Transmission Grid Model in Quality of Protection Modeling Language
### Security Metrics  
The system behavior, which is formally described by the cryptographic protocol, can be modeled by the proposed QoP-ML. One of the main aims of this language is to abstract the quality of protection of a particular version of the analysed cryptographic protocol. In the QoP-ML, the influence of the system protection is represented by the means of functions. During the function declaration, the quality of protection parameters are defined and details about this function are described. These factors do not influence the flow of the protocol, but they are crucial for the quality of protection analysis. During that analysis, the function's quality of protection (QoP) parameters are combined with the next structure of QoP-ML, the security metrics. In this structure, one can abstract the functions' time performance, their influence on the security attributes required for the cryptographic protocol or other important factors during the QoP analysis.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 5 Computational Validation
### Results for the Second Test Feeder  
Table 5 shows the numerical results in the 69-bus grid when the proposed and comparative metaheuristics were implemented.  
The results in Table 5 allow observing the following:  
* The proposed GNDO approach achieves the best reduction with respect to the benchmark case, with a value of USD/year 1,032,408.85, i.e., 27.044%. The DCVSA reached an annual reduction of USD/year 1,031,881.8, corresponding to an improvement of 27.0309%, and the CBGA reduced the expected annual operating costs by about USD/year 1,031,821.52, i.e., 27.0293% with respect of the benchmark case.
* The nodes to locate PV generators for the proposed GNDO and the CBGA are the same, i.e., nodes 19, 61, and 64. However, the sizes assigned to the PV sources in these nodes differ between them. This behavior is explained by using the Gaussian distribution functions and decreasing the radius in the GNDO approach, which allows shifting from exploring the solution space to exploiting it.
* The improvement obtained with the GNDO with respect to the CBGA was USD 587.33 per year of operation, whereas, for the DCVSA, this improvement was about USD/year 527.05. These results demonstrate that the proposed master-slave optimizer is the best current solution reported for the 69-bus DC network with a monopolar configuration, i.e., the GNDO represents the reference point for future research in this area. It is worth mentioning that the proposed GNDO approach takes about 376.88 s to solve the problem regarding the optimal placement and sizing of PV sources in monopolar DC  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Site (Node)/Size (kW)** & \(\boldsymbol{z_{\text{cost}}}\) (USS/Year)** \\ \hline Bench. case & – & 3,817,420.38 \\ \hline CBGA & \{19(790.80),\ 61(1789.05),\ 64(1147.42)\} & 2,785,598.86 \\ \hline DCVSA & \{23(772.01),\ 62(2340.27)\}\(63(618.53)\) & 2,785,538.58 \\ \hline GNDO & \{19(497.00),\ 61(2399.99),\ 64(847.04)\} & 2,785,011.53 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Application of the GNDO approach to the location and sizing of PV generation in the 69-bus grid and comparison of results with literature reports.  
distribution networks, i.e., it takes less than 6.5 min to solve a highly complex optimization problem with excellent numerical results.
## 1 Introduction
In fact, due to the complexity of multivariable nonlinear systems, as well as the lack of constructive/systematic strategies for ensuring the fixed-time convergence, a fundamental problem on how to organize a controller that renders multivariable nonlinear systems fixed-time stable remains largely open. Being aware of the above obstacles, a new approach is subtly developed in this paper. Compared with the existing works [24; 26; 27; 28; 29; 30; 31; 32; 26; 33], the main contributions of this paper can be summarized from two aspects: (i) This paper is focused on the problem of fixed-time stabilization for time-varying second-order multivariable nonlinear systems; thus, compared with the existing results concerning scalar systems or single input systems (e.g., [24; 26; 27; 28; 29; 30]), we offer a novel insight on how to tackle the problem of fixed-time stabilization for a more general class of nonlinear systems. (ii) By introducing extra manipulations in the feedback domination, the technique of adding a power integrator [34] is skillfully revamped to develop a distinctive approach to the synthesis of a fixed-time stabilizer together with a Lyapunov function which is significantly important for verifying fixed-time convergence and stability. NotationsAll notations utilized throughout this paper are highlighted as follows. \(\mathbb{R}\) is the set of real numbers, \(\mathbb{R}^{+}\) is the set of nonnegative real numbers, and \(\mathbb{R}^{n}\) denotes the \(n\)-dimensional Euclidean space. Furthermore, \(\mathbb{R}^{n\times m}\) is the set of \(n\times m\) real matrices, \(I_{n}\) denotes the identity matrix of dimension \(n\), \((\cdot)^{T}\) represents the transpose of a vector or a matrix, and \((\cdot)^{+}\) is the Moore-Penrose pseudoinverse of a matrix. Given a constant \(p\in\{p\in\mathbb{R}\mid p=\ p_{1}/p_{2}\ \text{with}\ p_{1}\ \text{being}\ \text{positive integer}\ \text{and}\ p_{2}\ \text{being}\ \text{a positive odd integer}\}\ \subset\mathbb{R}\), a vector \(\mathbf{y}=(y_{1},\ldots,y_{n})^{T}\in\mathbb{R}^{n}\), and a diagonal matrix \(A=\text{diag}(a_{1},\ldots,a_{n})\in\mathbb{R}^{n\times n}\), for simplicity of notation we denote \(\mathbf{y}^{p}=(y_{1}^{p},\ldots,y_{n}^{p})^{T}\in\mathbb{R}^{n}\), \(A^{p}=\text{diag}(a_{1}^{p},\ldots,a_{n}^{p})\in\mathbb{R}^{n\times n}\), and \(\mathbf{sign}(\mathbf{y})=(\text{sign}(y_{1}),\ldots,\text{sign}(y_{n}))^{T} \in\mathbb{R}^{n}\) where \(\text{sign}(\cdot)\) is the standard sign function satisfying \(\text{sign}(y)=1\) if \(y>0\), \(\text{sign}(y)=0\) if \(y=0\), and \(\text{sign}(y)=-1\) if \(y<0\).
# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 3 Methodology
### Model Overview  
We propose a novel end-to-end metric learning model named dual-branch multi-scale relation networks with tutoring learning to address the challenge of few-shot classification problems. Our method is based on the concept of multiple layers and multi-scale analysis while incorporating relation network metrics, and the model framework is illustrated in Figure 2.  
As shown in Figure 2, our model has a dual-branch structure, which is mainly composed of four modules: a feature extractor \(F_{\theta}\), a multi-scale feature generator \(G_{\phi}\), a relation network module \(R_{\varphi}\), and a tutoring learning module \(T\). The samples are fed into the feature extractor to obtain their respective feature maps. Subsequently, these feature maps serve as input for the multi-scale feature generator, which generates feature maps at five different scales. Next, the features of the query image and support images from various categories are concatenated in the channel dimension at each scale to form support-query feature pairs. These pairs are then input into the relation network module. In this branch, multi-scale feature pairs use a shared relation network for similarity metrics, resulting in separate relation scores and losses at each scale. The relation score is a five-dimensional vector indicating the probability that the query image belongs to one of five categories. In Branch 2, we extract two groups of feature maps from the middle and last layers of the feature extractor, as well as an additional group of deeper feature maps from the fifth subbranch of the multi-scale feature generator. Subsequently, these three groups of feature maps are concatenated in the channel dimension to obtain support-query feature pairs, which are then fed into the relation network module in Branch 2. Relation scores and losses at hierarchy layers are obtained by training three weight-unshared relation networks. Additionally, to better utilize the relation networks in measuring shallow feature pair information and contribute to overall performance, we propose a tutoring learning module \(T\). In this module, the knowledge summarization of Branch 1 and Branch 2 is obtained  
Figure 2: Illustration of the DbMRNT framework, showing a 5-way 1-shot episode setting as an example.  
by aggregating their own relation scores. Subsequently, the richer knowledge acquired by the relation network in Branch 1 is transferred to Branch 2 by constructing soft labels, facilitating the tutoring process from Branch 1 to Branch 2.
# A Self-Calibration of Capacitor Mismatch Error for Pipeline ADCs
## 4 Measurement Results  
A 30 MS/s 12-bit pipeline ADC that employed the proposed self-calibrating technique was implemented in 0.18 \(\upmu\)m CMOS technology and the ADC occupied a die area of 0.35 mm\({}^{2}\). The die micrograph and the printed circuit board for the chip measurement are shown in Figures 9 and 10 shows the measurement setup of the ADC. A single-ended sinusoidal signal is generated using a function generator and band-pass filtered for the purpose of anti-aliasing. The filtered single-ended signal is then converted to differential signals by using a transformer chip before it is applied to the ADC. The ADC output is captured by using a field programmable gate array (FPGA) board and transferred to a computer for performance evaluation.  
Figure 9: Chip micrograph (**a**) and measurement board (**b**).  
The measured static performances of the ADC before and after calibration are shown in Figure 11. Before the calibration, the measured DNL and INL were less than \(+0.82/-0.75\) and \(+1.12/-1.79\) LSB, respectively. After calibration, the DNL and INL improved to \(+0.45/-0.41\) and \(+0.47/-0.91\) LSB, respectively. The measured power spectra for input frequencies of 2.09 MHz and 14.1 MHz are also shown in Figure 12, respectively. At an input frequency of 2.09 MHz, the measured SNDR and SFDR improved from 63.9 dB and 69.3 dB to 68.9 dB and 84.1 dB, respectively, and the resulting ENOB, calculated as (SNDR\(-1.76\))/6.02, was 11.1 bit after calibration. After calibration, the measured dynamic performances versus the input frequency are shown in Figure 13. The measured SNDR and SFDR varied from 67.9 to 69.1 dB, 82.4 to 84.2 dB, respectively, for input frequencies up to the Nyquist frequency. The total ADC power consumption was 35 mW, including the reference generation and all peripheral circuits. The FOM, which is calculated as power/(2 ENOB-sampling frequency), was 512 fJ/conversion. The ADC performance is summarized in Table 1.  
Figure 11: Measured static performances before and after calibration: (**a**) Before calibration. (**b**) After calibration.  
Figure 10: Measurement setup.  
Figure 12: Measured dynamic performances for input frequencies of 2.09 MHz and 14.1 MHz before and after calibration: (**a**) Before calibration with 2.09 MHz input (**b**) After calibration with 2.09 MHz input (**c**) Before calibration with 14.1 MHz input (**d**) After calibration with 14.1 MHz input.  
Figure 13: Measured dynamic performances versus input frequency.