esa-sceva/satcom-chunk-collection huggingface dataset:
# Percolation Threshold of Red-Bed Soft Rock during Damage and Destruction
## 3 Results and Discussion
### Percolation Test Results of Compression Damage of Red-Bed Soft Rock
#### 3.2.2 Percolation Threshold for Uniaxial Compression Test of Red-Bed Soft Rock  
Through the method outlined in Section 2.3.2, the curve of the stress-strain relationship can be obtained from the damage constitutive model and compared with the stress-strain relationship curve obtained from the test, as shown in Figure 8. Several characteristics of the curve predicted by the damage constitutive model can be seen in this figure:  
1. Based on the AE phenomenon, the stress-strain relationship calculated by the damage constitutive model accurately simulates the stress-strain relationship of rock samples under uniaxial compression.
2. The curves based on laboratory test of rocks generally show a compaction stage during which there is not much AE signal. As the constitutive model of percolation damage is based on AE data, it cannot accurately simulate compaction.
3. In the laboratory test curve, the stress may fall, rise, and then fall again in the yield stage and strain softening stage, however, the calculated curve from the constitutive model of percolation damage cannot simulate this fluctuation in stress completely and accurately.  
Using the established damage constitutive model and data from the AE test, the process of damage and percolation threshold in red-bed soft rock or silty mudstone can be obtained as shown in Table 2.  
According to percolation theory, porosity can be used to roughly describe the initial degree of damage to the cement inside soft rock. Therefore, initial damage \(D_{\alpha}\) of the rock is characterized by porosity. For the same batch of samples, the porosity is similar. Therefore, the same initial damage value can be used, that is, the measured porosity value of 0.084 for soft rock. The amount of damage in the process (\(D_{b}\)) was 0.25 minimum, 0.35 maximum and 0.3067 on average; the percolation threshold was 0.334 minimum, 0.434 maximum and 0.3907 on average. In Section 3.1, the percolation threshold of red-bed soft rock or silty mudstone calculated according to the theory of renormalization group was 0.3636, with an error of 6.9% compared with an error of 0.3907 here. The small error verifies the rationality of the theoretical percolation threshold and indicates that the renormalization group model is in good agreement with the experimental results.
# Healing Effects of Photobiomodulation on Diabetic Wounds
###### Abstract  
Diabetic patients frequently develop chronic ulcers of the lower extremities, which are a frequent cause for hospitalization and amputation, placing strain on patients, their families, and healthcare systems. Present therapies remain a challenge, with high recurrence rates. Photobiomodulation (PBM), which is the non-invasive application of light at specific wavelengths, has been shown to speed up healing of chronic wounds, including diabetic foot ulcers (DFUs). PBM produces photophysical and photochemical changes within cells without eliciting thermal damage. It has been shown to promote tissue regeneration and speed up wound repair by reducing inflammation and oxidative stress, accelerating cell migration and proliferation, and promoting extracellular matrix production and release of essential growth factors. The shortage of rigorous, well-designed clinical trials makes it challenging to assess the scientific impact of PBM on DFUs, and lack of understanding of the underlying mechanisms also hinders the conventional use of this therapy. This review gives a glimpse into diabetic wound healing and PBM, and the effects of PBM on diabetic wound healing.  
photobiomodulation; laser therapy; diabetes mellitus; diabetes; wound healing; chronic ulcers +
Footnote †: journal: Applied Sciences
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 7 Experimental Results
### Overhead Analysis  
To evaluate the runtime overhead of the DRL-based scheduling executor, we analyze the overhead in Section 7.4, where \(\theta=4\) represents a highly skewed, high-contention scenario. Our overhead analysis focuses on high-contention workloads, where both the benefits and costs of DCoS are most evident. Figure 13 presents the execution profile across different phases of transaction processing. Note that for IC3, which uses an active waiting mechanism under high contention, we include its associated costs within the analysis phase for comparison. DCoS spends the largest percentage of execution time in the execute phase. Although DCoS introduces an additional analysis phase responsible for hot data item identification and DRL-based scheduling decisions, the abort-related overhead stays below 20%, which is significantly lower than the abort-related overhead observed in NoWait (40%) and Silo (45%). We also observe that IC3, which fully decomposes transactions, incurs even higher analysis overhead while achieving lower execution efficiency. This comparison illustrates that DCoS strikes a more favorable balance between coordination cost and execution throughput.
## 3 Interpretation of Velocity Signals from Impact Echo Method
### Identification and Classification of Defects from Feature Parameters  
Based on the feature parameters being determined, an identification and classification scheme was being designed to be able to distinguish the type of defect, if any, that would be present at the pile. Two main classifications were being made: first, based on comparisons of the measured pile tip reflection time \(T\) with the reference time \(T_{r}\); and second, based on the number of peak reflections \(N\). A perfectly intact and uniform pile with no geometric and material defect would have a total reflection time \(T\) almost equal to the reference time \(T_{r}\) for the normal quality condition of the concrete material, and the number of reflection peaks was just two. When the measured tip reflection time  
Figure 3: Intact and uniform pile.  
\(T<T_{r}\), it signified that the quality of the concrete material was higher than expected, and when \(T>T_{r}\), it signified the presence of any low-quality material defect.  
The presence of intermediate peaks greater than or equal to two signified a geometric or material defect. Geometric defects, such as the presence of neck, notch, crack, or voids, are all generally categorized as a reduction in the area of the pile cross-section, while bulge defects are generally categorized as enlargement in area. Pile defects due to reduction in the area or having material defects had a downward direction or negative sign of the first reflected wave and then an upward or positive sign of the next reflected wave, thus having the sign (\(-/+\)), as shown in Figure 4. On the other hand, the presence of a geometric pile defect with an enlarged section had an upward direction or positive sign of the first reflected wave and then a downward or negative sign of the next reflected wave, thus having the sign (\(+/-\)), as shown in Figure 5.  
The proposed algorithm for the pile defect identification scheme was implemented in a pseudo-code, as shown in Figure 6, which was also graphically presented in the flowchart diagrams shown in Figure 7a,b. The pseudo-code in Figure 6 and the flowchart in Figure 7 had been used as reference guidelines for the interpretation, classification, and diagnosis of the type of pile defects. These had been implemented in program code using Excel VBA (Visual Basic for Applications).  
Figure 4: Pile with neck defect.  
Figure 5: Pile with the bulge.  
Based on visual inspection of the amplified and WPT decomposed signals, the basic input parameters (e.g., total travel time \(t\), reference time \(T_{\text{r}}\), and the number of significant peaks \(n\)) were entered, and the interpreted diagnosis of probable pile defects was presented in the output. A parametric study was performed in the following section to verify and test the proposed identification scheme through numerical simulation of impact-echo tests of bored piles with the application of WPT and to verify and compare its performance with the typical evaluation method by amplification of the original velocity signals without WPT applied.  
Figure 6: Pseudo-code for pile defect diagnosis.
## 3 Laboratory Studies  
Firstly, the size of the obtained rock blocks was reduced using an industrial rock saw. After resizing the rock blocks, cubical rock samples with 70 \(\times\) 70 \(\times\) 70 mm were cut and trimmed for each rock type. The laboratory tests were performed under over-dried conditions. Selected rock properties, (i.e., \(\rho_{\mathrm{d}}\), \(w_{\mathrm{a}}\), \(V_{\mathrm{p}}\)) were determined following the recommendations of the International Society for Rock Mechanics [30]. These properties are  
Figure 1: Sampling location map.  
Figure 2: Experimental flowchart for laboratory studies.  
based on non-destructive testing methods, and they may be declared as crucial indicators of a rock's suitability as a dimension stone, reflecting fundamental aspects of its physical and mechanical behavior. Specifically, the \(\rho_{\text{d}}\) is used to measure the rock's compactness, \(w_{\text{a}}\) indicates a relative measure of rock porosity and resistance to rock weathering processes, and \(V_{\text{p}}\) characterizes the internal integrity and a relative measure of rock strength.  
In addition to assessing non-destructive rock properties, Bohme abrasion tests were also performed in accordance with the TS EN 14157 [31] standard. This test is widely used in industry to measure the wear resistance of natural stones. In this context, the Bohme Abrasion Value (BAV) is considered an important factor in determining the suitability of natural stones, particularly for flooring and high-traffic applications. BAV also provides a comparative measure of abrasion resistance, indicating that selected dimension stones meet the durability standards required in construction and architectural applications. Some of the laboratory studies are illustrated in Figure 3.  
Soft computing analyses were conducted based on a comprehensive database that included \(\rho_{\text{d}}\), \(w_{\text{a}}\), \(V_{\text{p}}\), and BAV values. Descriptive statistics of the database are presented in Table 3, providing an overview of data distribution and clarifying the range of these rock properties.  
The laboratory analyses revealed significant variability in these properties. More profoundly, the \(\rho_{\text{d}}\) values ranged from 1.47 to 2.99 g/cm\({}^{3}\), and \(w_{\text{a}}\) varied widely from 0.09% to 24.23%, reflecting differences in porosity and their potential durability against drying-w  
Figure 3: Laboratory studies. (**a**) Determination of \(\rho_{\text{d}}\) and \(w_{\text{a}}\); (**b**) pulse wave velocity measurements; (**c**) BAV test.  
The V\({}_{\text{p}}\) and BAV ranged from 1.23 to 7.36 km/s and from 5.28 to 60.07 cm\({}^{3}\)/50 cm\({}^{2}\), respectively. These results provide a comprehensive dataset for further soft computing analyses.
## 6 Conclusions  
This paper has proposed a leakage location method using the modified generalized cross-correlation (GCC) location method in combination with the attenuation-based location method using multilayer perceptron neural networks (MLPNN). The GCC location method can compensate for the weakening effect of the different propagation paths on the leakage-induced signals, can increase the degree of the correlation between two measured signals and improve the accuracy of the time delay estimation. Unlike the cross-correlation function (CCF) location method, the MLPNN locator locates a leak not only by the time delay estimation between two measured signals but also by the energy ratios of two leakage-induced signals measured by two sensors in the frequency bands with greater power spectral density. In addition, the wave speed was determined more accurately by using the peak frequency in combination with the group speed dispersive curve of the fundamental flexural mode. The MLPNN locator was experimentally verified by using a pressurized piping system and a signal acquisition system. The average of the relative location errors obtained by the MLPNN locator was reduced by 14% compared to that using the CCF location method. Hence, the MLPNN locator is more suitable for locating a leak in a gas pipe than the CCF location method is.  
**Data Availability:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Author Contributions:** Conceptualization, C.-M.L.; methodology, Q.W. and C.-M.L.; software, Q.W.; validation, Q.W.; formal analysis, Q.W.; investigation, Q.W.; resources, C.-M.L.; data curation, X.X.; writing--original draft preparation, Q.W.; writing--review and editing, Q.W.; visualization, Q.W.; supervision, C.-M.L.; project administration, C.-M.L.; funding acquisition, C.-M.L.  
**Funding:** This research was funded by a 2017 grant from the Russian Science Foundation (Project No. 17-19-01389).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Contrails and Their Dependence on Meteorological Situations
## 4 Discussion
This also makes it very difficult to predict the occurrence of contrails, as such a prediction would require meteorological data with much higher accuracy and latency than is provided today. The imbalanced representation of short-lived and long-lived contrails at different humidities in the dataset made it difficult to perform the classification. From the measured data, it was found that the contrail lifetime is linearly dependent on the water vapor content of the air mass, and the duration begins to increase more noticeably at relative humidity values with respect to ice above 20%. The evaluation of the recorded contrails yielded the finding that persistent contrails were present even in an environment that was not supersaturated respective to the ice, according to sounding measurements. No correlation could be found in the data to explain this. A possible explanation for the occurrence of long-lived contrails in an unsaturated environment could be the fact, reported in some studies [19; 32] dealing with the accuracy of humidity measurements with the Vaisala RS41 SG probe, that the probe tends to underestimate the relative humidity level relative to water in units of percent at higher levels, which could indicate a shift into the region of supersaturation respective to ice. Accurate routine measurements of humidity values at tropopause levels are demanding. The revised MOZAIC dataset [33] may be used to test previous estimates on ice supersaturation occurrence. Relative humidity depends on saturation conditions; a 0.5 K temperature bias causes a 7% change in RHi at temperatures of \(-60\)\({}^{\circ}\)C. Aircraft humidity measurements have to be corrected for adiabatic heating by air compression in inlets, by 30 K for 240 m s\({}^{-1}\)speed, in addition to sensor-deicing heating [34]. An RHi accuracy of 10% is not easy to reach, therefore. During research measurements with the German Aerospace Center Falcon, the sensors often indicated a slightly subsaturated ambient humidity (RHi = 95%) while the contrail was measured in actual RHi 115% [35].
## 3 Review of Previous Work
### Maximum Ratio Combiner Fusion Statistic  
For the communication channel with low SNRs, it has been shown that the LRT fusion statistic for identical local detection performance indices can be approximated in [18] by:  
\[\Lambda_{\text{MRC}}=\frac{1}{N}\sum_{i=1}^{N}h_{i}z_{i} \tag{3}\]  
Note that the fusion statistic \(\Lambda_{\text{MRC}}\) depends on the channel fading envelope \(h_{i}\).
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 4 Scheme Model
### Security Model  
The challenger generates a DBDH challenge. \(\mathfrak{B}\) is a polynomial-time adversary attacking the DBDH problem, \(\mathfrak{A}\) is a polynomial-time adversary attacking this scheme, \(\mathfrak{B}\) uses \(\mathfrak{A}\) to attack the DBDH problem, and \(\mathfrak{B}\) is viewed as \(\mathfrak{A}\)'s challenger. We define the security concepts to show the security of the VO-PH-MAABE scheme. This scheme uses the concept of ciphertext indistinguishable security under adaptive selection plaintext attack (INDS-CPA) in the constructed proof. Including adversary \(\mathfrak{A}\) and challenger \(\mathfrak{B}\), the specific game is as follows.  
\(\mathbf{Initialization}\): Adversary \(\mathfrak{A}\) submits a challenge access policy \((M^{*},\rho^{*})\) to Challenger \(\mathfrak{B}\).  
\(\mathbf{Setup}\): Challenger \(\mathfrak{B}\) chooses a sufficiently safe parameter \(\lambda\) to run the \(Setup(1^{\lambda})\) algorithm to generate the public parameter \(GP\). Challenger \(\mathfrak{B}\) sends \(GP\) to adversary \(\mathfrak{A}\).  
\(\mathbf{Query\ Phase}\) **1**: The adversary adaptively queries the following Oracles.  
\(O_{A\mathbf{s}}\): Adversary \(\mathfrak{A}\) specifies a set of corrupted permissions, and for each attribute \(\omega\) belonging to the uncorrupted permissions, \(\mathfrak{B}\) runs \(AASetup(GP,\omega)\) to generate the public attribute key \((e(g,g)^{\omega_{\omega}},g^{\beta_{\omega}})\) and returns it to \(\mathfrak{A}\).  
\(O_{U\mathbf{l}AK}\): Adversary \(\mathfrak{A}\) submits an identity and its corresponding attribute set \((uid,S^{*})\) to \(\mathfrak{B}\). When the attribute set is insufficient for the challenge access strategy, adversary \(\mathfrak{B}\) runs \(UAKGen(uid,(M^{*},\rho^{*}),S^{*},GP)\) to generate the attribute key \(UAK_{\omega,uid}\), and returns it to \(\mathfrak{A}\).  
\(\mathbf{Challenge}\): \(\mathfrak{A}\) submits two equal-length symmetric keys \(KEY_{0}\) or \(KEY_{1}\) to \(\mathfrak{B}\). \(\mathfrak{B}\) rolls a coin to determine the value of \(\gamma\in\{0,1\}\) and executes \(Encrypt(KEY_{\gamma},M_{data},(M^{*},\rho^{*}),GP,e(g,g)^{\kappa_{\omega}},g^{ \beta_{\omega}})\to CT^{*}\) and sends it to \(\mathfrak{A}\).  
\(\mathbf{Query\ Phase}\) **2**: Adversary \(\mathfrak{A}\) receives the challenge ciphertext \(CT^{*}\) and continues to follow \(Query\phase\)1 adaptive querying.  
\(\mathbf{Guess}\): Adversary \(\mathfrak{A}\) guesses \(\gamma^{\prime}\in\{0,1\}\) for \(\gamma\). If \(\gamma=\gamma^{\prime}\) then it is concluded that adversary \(\mathfrak{A}\) wins. The advantage of adversary \(\mathfrak{A}\) in winning this game is defined as \(Adv^{INDS-CPA}_{VO-PH-MAABE}(\mathfrak{A})=|Pr[\gamma=\gamma^{\prime}]-1/2|\).  
If no adversary can break the above INDS-CPA game by a non-negligible margin in polynomial time, then the VOPH-MAABE scheme is considered INDS-CPA safe.
## 4 Related Work
This ensures that an in-fly packet is forwarded based on rules with a timestamp at least equal to the timestamps of rules where it has been processed in upstream switches. To do so, a controller module operation and switch module operation are designed to manage and maintain the required timestamps. Regarding the proposed update algorithm, there are four important steps: the first is the backward closure step whose role is to include the new forwarding rules that precede those already included. This step permits propagating the installation of new rules backward along routing paths. The same technique is applied to include the new rules that follow those already included, ensuring the second step named forward closure. The third step is tagging timestamps, which sets the new added rule timestamps that have not been set in the preceding steps. The send-back rules is the fourth step, in which a new and temporary rule is added if a packet takes its old path and encounters a switch at which the rule that would have matched in the old configuration has been already deleted. In this case, this temporary rule allows forwarding the packet in the direction of the new path from where it can continue traveling to its destination. Works like [22; 23; 24; 25; 26; 27] proposed studying optimization problems on consistent updates in SDNs. The authors of [22] had as an objective to update as many switches as possible at a time, in what is known as node-based, in a loop-free manner. The authors of [23] showed that the node-based optimization problem is NP-hard. In the same context, the authors of [24] demonstrated that scheduling consistent updates for two destinations is NP-hard for a sublinear number of update rounds. In [25], the authors presented an approach that aims to minimize the number of messages needed between the controller and the switches to update networks, known as round-based, in a loop-free manner. In the same context, the authors in [26] distinguished between two loop-free network updates, the strong loop-freedom and the relaxed loop-freedom. The strong loop-freedom requires that the entries (i.e., forwarding rules) stored at the switches be loop-free at all points of time, whereas the relaxed loop-freedom requires entries stored by switches along a forwarding path to be loop-free. The problem was shown to be difficult in the strong loop-freedom case. In the worst case, \(\Omega(n)\) rounds may be required, where \(n\) is the DSdefinition.On the other hand, \(O(log~{}n)\) rounds always exist in the relaxed loop-freedom case. In [27], the authors studied local checkability properties like s-treachability (i.e., whether the network is acyclic or contains a cycle), introduced in [28]. This work studies the applicability of such theory in the context of SDNs by considering the case of updating spanning trees in a loop-free manner. Unlike previous works, the scope of this work is to determine a round-based update with a constant number of rounds, in such a way that every switch should be able to locally decide if it can change its forwarding behavior.
## 1 Introduction  
The objective of this paper is to describe the motivation, design, and prototype testing for an autonomous system of hyperspectral radiometers suitable for validation of satellite-derived water reflectance.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Input Feature Extraction Based on Trajectory Features Correlation
#### 4.2.1 Construction of a Composite Indicator of Feature Correlation
This study constructs a composite indicator that comprehensively evaluates the correlation of features through a weighted summation method. The specific calculation Equation (8) is as follows:  
\[\rho=\alpha\times\left|PCC_{(X,Y)}\right|+\beta\times\left|SRCC_{(X,Y)}\right| +\gamma\times\left|NMIE_{(X,Y)}\right| \tag{8}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) represent the weights of each indicator, respectively. To balance the differences and contributions between different indicators and ensure the accuracy and reliability of the composite indicators, this study adopts the equal-weighttreatment, letting the weight of each indicator take the value of \(1/3\). To determine the correlation between the variables more precisely, this study set the following criteria for determining the correlation of the variables. \[ \begin{cases}\rho\geq 0.8,&\text{Extremely high correlation.}\\ 0.6\leq\rho<0.8,&\text{High correlation.}\\ 0.4\leq\rho<0.6,&\text{Moderate correlation.}\\ 0.2\leq\rho<0.4,&\text{Low correlation.}\\ \rho<0.2,&\text{Extremely low correlation.}\end{cases}  \tag{9}\]  
Finally, based on the computational results, this study selects the features with higher correlation as inputs to the trajectory restoration model.