esa-sceva/satcom-chunk-collection huggingface dataset:
# Compaction Characteristics and Minimum Void Ratio Prediction Model for Gap-Graded Soil-Rock Mixture
## 2 Vibrating Compaction Test
### Results and Discussion
#### 2.2.3 Influence of mass content in fine-grained group (\(f_{c}\))  
In engineering practice, the gap intervals and the gradations of the fine-grained group and the coarse-grained group of SRMs are known, and they are dependent upon the available materials. It is  
Figure 7: Particle breakage of SRMs with fractal dimension \(D\) at different gap intervals.  
necessary to study the mass content \(f_{c}\) of the fine-grained group in an SRM to achieve the minimum void ratio of the SRM. Figure 8 presents the relationship between the void ratios \(e\) of the SRMs with different basic gradations and the mass contents \(f_{c}\) at the gap interval of 5-20 mm.  
The relationship is approximately bilinear, and the turning point corresponds to the minimum void ratio of the SRM, which can be explained with the help of Figure 9. Figure 9 illustrates coarse-grained particles enclosing a void space in the case of \(f_{c}=0\). When \(f_{c}\) increases gradually, the fine-grained particles enter into the voids as filling components, leading to the decrease of the void ratio of the SRM (Figure 9). Figure 9 shows the state when all voids are fully occupied by the fine-grained particles and the minimum void ratio of the SRM is achieved. The corresponding \(f_{c}\) is called the optimal mass contents of the fine-grained group. If \(f_{c}\) continues to increase, coarse-grained particles will be separated by the fine-grained particles and \"float\" in the matrix of fine-grained particles, as shown in Figure 9,e, resulting in the increase of the void ratio of the SRM. As \(f_{c}\) increases up to 1.0, the coarse-grained particles disappear and only fine-grained particles exist in the mixture (Figure 9), where the maximum void ratio is reached.  
The optimal mass content \(f_{c}\) and the minimum void ratio of gap-graded SRMs are important parameters in engineering practice. In the next part, we attempt to build a model for predicting the optimal mass content \(f_{c}\) and minimum void ratio of gap-graded SRMs.  
Figure 8: Changes of void ratios of the SRMs with \(f_{c}\) under different basic gradations.  
Figure 9: Illustration of the mixture of coarse-grained and fine-grained particles.
# Development Cycle Modeling: Resource Estimation
## 4 Results
### SAbMDE-COCOMO Comparison  
The successful comparison of SAbMDE resource utilization and COCOMO effort estimates requires a slight adjustment of the independent variable that indexes the SAbMDE resource utilization values. For SAbMDE, skill index = 0 indicates that an agent makes random composition decisions; whereas skill index = 10 indicates perfect decisions. However, for COCOMO, skill index = 0 indicates a minimal (non-random) level of skill whereas skill index = 10 indicates a maximum skill level that is less than perfect. This was described by [PERSON] [38] (p. 31), e.g., \"_Analyst teams that fall in the fifteenth percentile are rated very low and those that fall in the ninetieth percentile are rated as very high.\"_ To resolve these scaling differences, SAbMDE skill indices 0 and 10 were removed. SAbMDE skill indices 1 and 9 were matched to COCOMO skill indices 0 and 10; and the remaining SAbMDE skill index intervals were stretched by 10/8 to fit the new end points. The result of this adjustment is shown in Table 8. The new 9-point scale now has the same meaning for both SAbMDE and COCOMO estimate values. Table 8 also shows the corresponding Scaled \(EM\) values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Standard**} & \multicolumn{2}{c}{**Truncated**} & \multicolumn{2}{c}{**Adjusted**} \\ \hline \multicolumn{1}{c}{**COCOMO**} & \multicolumn{2}{c}{**SAbME**} & \multicolumn{2}{c}{**SAbME**} & \multicolumn{2}{c}{**COCOMO**} \\ \hline
**Skill Index** & **Scaled EM** & **Skill Index** & **Skill Index** & **Scaled EM** \\ \hline
0.00 & 4.28 & 1.00 & 0.00 & 4.28 \\
1.00 & 3.85 & 2.00 & 1.25 & 3.74 \\
2.00 & 3.43 & 3.00 & 2.50 & 3.21 \\
3.00 & 3.00 & 4.00 & 3.75 & 2.68  
When the Figure 7 COCOMO and SAbMDE estimates are recalculated with their corresponding adjusted skill indices, the estimates can be compared confidently. The comparison, COCOMO with SAbMDE estimates overlaid, is shown graphically in Figure 8.  
The SAbMDE and COCOMO estimates, \(S_{i}\) and \(C_{i}\), were closely matched by setting initial composable element prices, \(g_{cp}(1,1)\) from (10), and then incrementally increasing those prices until the estimates' mean sum of differences (MSD) were minimized using (16). The minimization was performed with a simple brute force technique. The minimization target on the right-hand side of (17) is calculated by applying a minimization criterion, \(\varepsilon\), to the mean sum of the COCOMO estimate for a given KLOC value. For example, \(\varepsilon=0.1\).  
\[msd=\frac{1}{N}\sum_{i=0}^{N}|C_{i}-S_{i}| \tag{16}\]  
\[msd\leq\frac{\varepsilon}{N}\sum_{i=0}^{N}|C_{i}| \tag{17}\]  
The SAbMDE and COCOMO estimates compare favorably over their common skill index ranges. They compare even more favorably over a central range, skill index from 3 to 7; and it was this central range that was used for the MSD matching.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## Appendix B Figures  
Figure 11 shows the monthly O-B/B statistics of FY3D used in experiment one, the relative difference between ECMWF forecast and FY3D is less than 2%. Since the RO sounding occurs randomly on the earth's surface, the standard deviation is relatively huge, less than 10%, which is an acceptable range. The FY3D in September with such quality established a good condition for experiment one.  
Figure 12 shows level intervals of 137 ECMWF 4 dvar, 31 levels of FNL, and 37 levels of ERA5. Their interval generally increases as the altitude increases. EC4 Dvar has the smallest intervals, which are less than 500 m below the height of 25 km. The level intervals of FNL and ERA5 are similar, but between 2-4 km, 12-15 km, ERA5 has a smaller interval than FNL.  
To ensure the reliability of experiment one, we also did the comparison using 541 MetOp RO profiles on 1 March 2018. The results were shown in Figure A4, and FY3D's results are in Figure 3. These two figure were set with the same coordinate, axes, and ticks. The results of MetOp are consistent with that of FY3D. Firstly, the difference (\(\rm E-T\)) of both direct and exp algorithm are in the same order of magnitude, which is \(1\times 10^{-5}\) in the range of 5-30 km and \(1\times 10^{-6}\) in the range of 30-60 km, as shown in Figure A4a,b and Figure 3a,b. The results of FY3D have less error because it has passed 4 QC. The experiment based on MetOp used the same collocation method as experiment 1. But we did not use 4 QC. Secondly, they have the same conclusion on which algorithm is more accurate, that if the exp. Thirdly, the log-linear interpolation also causes more undulation to (\(\rm E_{dir}-E_{exp}\)) than which is caused by log-cubic interpolation. Moreover, the error induced by interpolation for exp is larger than that for direct. Therefore, the results of experiment one are reliable.  
The integral accuracy is subject to the order of magnitude of refractivity (input), which is shown in Figure A5. Intriguingly, when the order of magnitude is \(1\times 10^{-2}\) above (below) the height of 60 km, the direct algorithm's result is less (larger) than the true value.  
Figure 14 is the same as Figure 7 but based on the log-linear interpolation. Below the height of 35 km, the RD and difference of EC4 DVar become \(\pm\)1%, \(\pm\)1 \(\times\) 10\({}^{-5}\) from 0.5% and \(\pm\)0.5\(\times\) 10\({}^{-5}\) in Figure 7. Between 35 to 50 km, those values are the same, less than 5%. In terms of FNL and ERA5, more RD values reach \(\pm\)4% below 35 km, and more of those are over \(\pm\)5% above 35 km. There is more undulation for results based on linear interpolation.
## 1 Introduction
The adaptive robust control strategy of a multidimensional Taylor network proposed in this paper consists of a parameter adaptation part, a multidimensional Taylor network compensating item as well as a robust feedback section. The model-driven parameter adaptation section is composited according to the true dynamic modeling in accordance with parameter change; the multidimensional Taylor network equalizer, which is driven by data, is used for compensating and estimating the deviation, which is being modeled, and the complex interference, which is unmodeled, and the remaining refactoring errors in the first two parts are caused by the ordinary robust feedback. In comparison with other neural network control methods and traditional multidimensional Taylor network control algorithms, the adaptive robust control strategy of a multidimensional Taylor network is a new incorporation of model-based controlment and data-driven controlment. Because of its good nonlinear approximation capability, a multidimensional Taylor network with the regulative weight can establish nonlinear projection mappings between input and output. In essence, the weight adjustment process can be regarded as a learning mechanism, using the running data of the linear motor to build an ideal structure using the indicating route as well as the nonlinear recompensation. At same time, the multidimensional Taylor network has excellent nonlinear mapping ability. Thus, it is of great practical significance to combine the two together. A multidimensional Taylor network is used to improve the performance of adaptive robust control, and adaptive robust control is used to ensure the stability of the system. To sum up, the main contribution of this paper is to propose an adaptive robust control strategy based on multidimensional Taylor networks, which has excellent trajectory tracking performance and anti-interference ability. In this control strategy, the input of the multidimensional Taylor networks is determined only by referable trajectories, and it does not need any model information. In addition, the multidimensional Taylor network designed can accurately refer to the relationships between state variables (position and velocity) and any complex disturbance, which is the basis of disturbance suppression. The rest of the paper is arranged as follows: The dynamic model and problem description are offered in Section 2. The proposed multidimensional Taylor network adaptive robust control strategy is depicted in Section 3. The laboratory findings are shown in Section 4. The final section, Section 5, comes to a conclusion. In years to come, the devised adaptive robust control strategy based on a multidimensional Taylor network will be further studied in MIMO systems. Enhanced accommodative controlling approaches are to be utilized. An adaptive robust control strategy for a multidimensional Taylor network will be proposed to enhance its model compensation function.
# Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
### Work and Organization of This Paper  
This paper intends to design a hybrid checkpointing strategy based on a multi-frequency hierarchy ad hoc network. In this strategy, the MH\({}_{\text{s}}\) in the same cluster use the synchronous checkpointing strategy, while the MH\({}_{\text{s}}\) in different clusters make use of the asynchronous checkpointing strategy. The main contributions of this paper include the following:  
1. It proposes the hybrid checkpointing model combined the coordinated and uncoordinated checkpointing after considering the communication features and kinds of resource restraints of mobile ad hoc networks;
2. It discusses the checkpoint correctness criteria for a hybrid checkpointing model including the checkpoint taking criteria, rollback criteria and indelibility criteria.
3. It discusses the triggering occasions of checkpoints for processes in the same cluster and different clusters according to the characteristics and requirements of different types of checkpoints.
4. It designs the intra-cluster and inter-cluster checkpoint dependence graph according to the dependence relations among checkpoints. Then it discusses the elimination rules of checkpoints in the same cluster and different clusters based on these graphs. What is more, it provides the algorithms for checkpoints in the same cluster and different clusters and rollback recovery algorithm. At the same time, it proves the correctness of the algorithms by theory.
5. By testing the performances of the proposed strategy, this paper verifies that the hybrid checkpointing strategy is a good trade off method and it has advantages such as less of dependence on cluster heads, low overhead and shorter recovery times.  
The rest of the paper is organized as follows: Section 2 introduces the ad hoc network topological structure and the model of hybrid checkpointing. The correctness criteria for hybrid checkpointing model and the triggering occasions of kinds of checkpoints are discussed in Sections 3 and 4. In Sections 5 and 6, we propose the elimination strategy of checkpoints and related algorithms. Then we discuss the processing method for handoffs in Section 7. In Section 8, we present the testing of performance and analysis. The last part gives the conclusions of this paper and proposes future work.
## 2 Materials and Methods
### Data Collection  
Data collection was performed at admission, discharge and 3 months after hospital stay by the same investigators previously trained. Patients completed an initial assessment that included anthropometric and sociodemographic data (age, sex, body mass index, and comorbidity index (Charlson index) [13]). Data collected from the medical history included the length of hospital stay and the CAP severity, which was evaluated with the CURB-65 score [14] and FINE scale [15].  
The primary study outcomes included respiratory symptoms (dyspnea and fatigue), disability levels, and health-related quality of life.  
Dyspnea was measured by the Borg modified scale 0-10 (no dyspnea-maximum dyspnea) [16]. This scale has been used in previous studies to evaluate dyspnea in hospitalized patients with respiratory diseases [17; 18]. Fatigue was assessed by a Numeric Rating Scale (NRS) from 0 (no fatigue) to 10 (maximum fatigue) [19].  
World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) was used to assess the functionality of CAP patients. This tool measures the health and functionality of patients, with the levels of difficulties that present for performing different activities. It consists of 36 items, collected in 6 domains, which are scored from 1 (slight) to 5 (unable to do). The total score ranges from 36 to 180, where greater values represent greater disability [20].  
Health-related quality of life was evaluated using the Euroqol 5 Dimension-5 Levels (Eq-5D5L). This scale presents two sections. The first measure of 5 dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) scored as no, slight, moderate, severe problems, or unable to perform it. The second section presents a Visual Analogue Scale where patient evaluated their health status from 0 (the worst imaginable health) to100 (the best imaginable health). The Spanish version of Eq-5D has established reliability and was validated [21]. This scale has been used in previous studies that have evaluated respiratory pathologies [22,23].
# Optimal Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
## 2 Optimization Model of Disintegration Strategy in Multiplex Networks under Layer Node-Based Attack
Denote by \(n=\left|\widehat{V}\right|\) the disintegration strength parameter and \(q=n/(N\times M)\) is the removed ratio of the nodes. We define a disintegration strategy as  
\[\widehat{X}= \begin{bmatrix}x_{11}&x_{12}&\cdots&x_{1N}\\ x_{21}&x_{22}&\cdots&x_{2N}\\ \vdots&\vdots&\ddots&\vdots\\ x_{M1}&x_{M2}&\cdots&x_{MN}\end{bmatrix}  \tag{4}\]  
where \(x_{ai}=1\) if layer node \(v_{i}^{\alpha}\in\widehat{V}\), otherwise \(x_{ai}=0\). It is easy to obtain that  
\[n=\sum_{\alpha=1}^{M}\sum_{i=1}^{N}x_{ai} \tag{5}\]  
The goal of the optimization model is to identify the optimal solution \(\widehat{X}^{*}\) that could maximize the disintegration effect. The optimization model for the disintegration strategy in a multiplex network under layer node-based attack can be described as follows:\[s.t. \begin{cases}\max\Phi(\widehat{X})\\ \\ x_{ai}=0\text{ or }1,\alpha=1,...,M;i=1,2,...,N\end{cases}  \tag{6}\]  
Considering the complementary relationship of different layers in the multiplex network, we use the size of the giant component \(C\) in the aggregated network as the measure function of the network performance. The \(\Phi(\widehat{X})=C(G)-C(\widehat{G})\) is used to measure the disintegration effect of strategy \(\widehat{X}\). For a network with \(N\) multiplex nodes and \(M\) layers, there are \(C_{NM}^{n}\) ways to pick \(n\) nodes for removal, which increases sharply with \(N\) and \(M\). Therefore, we consider solving this problem using some meta-heuristic algorithms. In particular, the tabu search algorithm has been proved to be an effective method for solving similar problems in single-layer networks [17]. In this article, we similarly use the tabu search (TS) algorithm to seek the optimal solution of the above optimization model. The details of the TS algorithm could be found in references [17; 29; 30]. In the experiments of this article, we set the maximum of iterations \(T_{max}=1000\), the number of candidates \(n_{can}=100\) and the length of the tabu list \(L=50\). We could obtain the approximate optimal disintegration strategy by solving the optimization model in Formula (6) through the TS algorithm.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods
### Processing Chain
#### 2.3.6 Calibration and Validation of Results  
To calibrate and assess the effectiveness of the processing chain, the flood-detection protocol was applied to the outskirts of Antananarivo (Figure 3). Results of the processing chain are compared to results from optical Sentinel 2 images presented on data subsection. After comparing results, we chose the best filter and polarisations as well as the NDR threshold for flood detection.  
Figure 2: Processing chain overview. GEE: Google Earth Engine, HAND: height above nearest drainage, NDR: normalised difference ratio.  
To achieve a more impartial validation process, a set of 100 random points was generated for each predicted class, distinguishing between areas affected by flooding and those unaffected (flooded and non-flooded, respectively). Subsequently, a comprehensive analysis of optical and radar images, along with water index, was conducted using a visual interpretation approach. This method allowed for the actual ground-truth state of each point within the study area. This interpretation process was conducted with consideration for the available information regarding historical occurrences of flooding within the study area, ensuring a robust and reliable validation process.  
During Batsirai, partial flood maps were produced by the FAO [10] for the island's eastern seabard and the Rapid Mapping component of the Copernicus Emergency Management Service (CEMS) program for certain zones. The CEMS Rapid Mapping product is a mapping solution tailored for rapid disaster response [8]. It leverages advanced Earth observation satellites (such as TerraSar-X data) to deliver near-real-time, high-resolution satellite imagery and geospatial data during crucial early phases of emergencies like earthquakes, floods, and wildfires. CEMS Rapid Mapping empowers authorities and first responders to quickly assess damage, identify affected areas, and coordinate targeted relief efforts. This rapid access to precise mapping data enhances situational awareness and accelerates aid delivery, bolstering community resilience and safety. Those products were compared to the results obtained by our processing chain for the same zones.  
Figure 3: Map of floods detected around Antananarivo generated from Sentinel-1 and Sentinel-2. Optical images acquired 13 March 2018 and 18 September 2017 (reference). Radar images acquired 12 March 2018 and 18 September 2017 (reference).  
For further validation, the results of the processing chain were compared to the results of the S1 Chain developed by [PERSON] et al. [52]. This processing chain downloads and extracts Sentinel-1 imagery within the Sentinel-2 tile footprints, incorporates multitemporal filtering, calculates the normalised difference ratio, and generates a mask delineating flooded regions. The data used were processed following the passage of Cyclone Batsirai in 2022, using the same Sentinel-1 data that we processed for this cyclone event. This makes it possible to compare the different chain outputs.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 4 Construction of UPF Model
### Registration and Encryption
#### 4.2.2. EnC Construction  
* Input: pk,m.
* Output: \(C=\left(C_{1},\ C_{2}\right)\).  
The first encryption for message m is processed, where an ElGamal algorithm is used as an encryption function. When we assume that the healthcare provider (H) encrypts their information m, the public key is \(g^{x_{H}}(=y)\) and the ciphertext is \(C_{1}=g^{r},\ C_{2}=(y^{r}\cdot m)\).
## 3 Problem Formulation
### Offloading Model
#### 3.3.1 Execution Time of Task Offloading  
Since this model is an optimization model based on latency and energy consumption balancing, we inevitably need to establish a model to measure latency. We know that propagation delay is negligible compared to the total latency, so we only need to consider three components: transmission delay, computational processing delay, and queuing delay arising from the real-time arrival of tasks.  
Regarding the computational processing delay, we can represent it based on the ratio of the computational resource required by the task \(c_{j}^{k}\) to total computation frequency of server cores \(p_{i}*core\) :  
\[t_{com}=c_{j}^{k}/(p_{i}*core) \tag{3}\]  
In this context, \"core\" represents the number of server cores.  
Considering the transmission delay of each task between different servers and users, we can express it by the ratio of the amount of data required for transmission, \(d_{j}^{k}\), to the transmission rate \(R_{j}\) or \(R_{j,i}\), where \(R_{j}\) represents the link transmission rate for tasks offloaded to the server closest to the user, and \(R_{j,i}\) represents the link transmission rate for tasks offloaded to other servers. Both rates can be calculated using the Shannon formula:  
\[R_{j}=B*log_{2}(1+P*h_{i,j}/N_{0}) \tag{4}\]  
After determining the transmission rate, we next discuss the transmission delay and obtain the total delay by adding it to the computational processing delay and queuing delay \(t^{\prime}\) :  
1. When the user offloads the task to the local server, i.e., \(i=y\), the total delay for a single task is calculated as follows: \[t_{j,k}^{i,1}=c_{j}^{k}/(p_{i}*core)+d_{j}^{k}/R_{j}+t^{\prime}\] (5)
2. When the local server closest to the user is busy and the task needs to be offloaded to other edge servers, i.e., \(i\
eq y\), the total delay for a single task is calculated as follows: \[t_{j,k}^{i,2}=t_{j,k}^{i,1}+d_{j}^{k}/R_{y,i}\] (6) In summary, the total delay for a single task can be summarized as follows: \[t_{j,k}^{i}=\begin{cases}t_{j,k}^{i,1}&\text{if }i=y\\ t_{j,k}^{i,2}&\text{if }i\
eq y\end{cases}\] (7) From this, we can write the total delay for all tasks of a single user: \[t_{j,k}=\sum_{i=1}^{m}(t_{j,k}^{i}*X_{j,k}^{i})\] (8)However, since this paper discusses the offloading and processing of multiple batches of tasks on multiple edge servers, we can further take the maximum value of the offloading and processing delays of all single-user tasks as the total delay for all users and all tasks:  
\[t_{j}=max\Big{\{}t_{j,k}\Big{\}} \tag{9}\]