esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Effect of a Rate-Dependent Work of Adhesion in the Detachment of a Dimpled Surface
## 3 Results
### Pull-Off Detachment Stress  
We have investigated the effect of the unloading rate \(\hat{\hat{\sigma}}_{A}\) and of the exponent \(n\) on the detachment curves of a dimple from an elastic substrate with rate-dependent work of adhesion. Here we give a closer look at the dependence of the pull-off stress \(\widehat{\sigma}_{A,pull}\) on the unloading rate \(\hat{\hat{\sigma}}_{A}\) by varying \(\alpha_{d}\) and \(n\). Figure 6a shows the pull-off stress \(\widehat{\sigma}_{A,pull}\) as a function of the dimensionless unloading rate \(\hat{\hat{\sigma}}_{A}\) for \(\alpha_{d}=[0.2,0.4,0.6,0.8]\) and \(n=0.5\). Panel (a) shows that the the pull-off stress \(\widehat{\sigma}_{A,pull}\) approaches the elastic value (obtained from Equation (7)) only at very low unloading rate \(\hat{\hat{\sigma}}_{A}\approx 10^{-4}\), while it increases considerably when \(\hat{\hat{\sigma}}_{A}\) is increased, or by increasing \(\alpha_{d}\). In the regime of high unloading rate, the pull-off stress dependence on \(\hat{\hat{\sigma}}_{A}\) is well captured by a power law behavior, while that on \(\alpha_{d}\) is linear. For \(\hat{\hat{\sigma}}_{A}\gtrsim 10^{-2}\) and \(n=0.5\), a very good fit of the pull-off stress is given by the power law  
\[\widehat{\sigma}_{A,pull}=(1.32\alpha_{d}+0.065)\hat{\hat{\sigma}}_{A} \tag{12}\]  
whose predictions are given by dot-dashed black lines (almost indistinguishable from the solid curves obtained numerically). Panel (b) shows that for a fixed \(\alpha_{d}=0.5\), the pull-off stress increases with the exponent \(n\). At low \(\hat{\hat{\sigma}}_{A}\), all the curves start from the same elastic solution (black dashed lines) as the pull-off stress depends only on \(\alpha_{d}\) and then increases for larger unloading rates with a power law behavior whose slope is very well fitted by a power law \(\widehat{\sigma}_{A,pull}\propto\widehat{\sigma}_{A}^{\ \ \ \ \ 0.3n+0.05}\). (For \(\dot{\widehat{\sigma}}_{A}\gtrsim 10^{-2}\), an estimate of the pull-off stress as a function of \(\alpha_{d}\) and \(n\) can be obtained by \(\widehat{\sigma}_{A,pull}=2n(1.32\alpha_{d}+0.065)\widehat{\sigma}_{A}^{\ \ \ \ 0.3n+0.05}\), where we found this to be more accurate close to \(n\approx 0.5\) and \(\alpha_{d}=0.5\).) Notice that typically \(0<n<1\); hence, the increase of the pull-off stress with the unloading rate is sublinear.
## 4 Results
### Environmental and Economic Damages of the Fire  
Finally, burned trees and woody biomass loss maps were created from the burn severity, stand density, and DBH maps (Figure 8). As expected from the stand density map (Figure 5 and Table 5), the mean woody biomass loss was greater in the UR domain than in the WLD domain (6.89 ton ha\({}^{-1}\) compared to 2.81 ton ha\({}^{-1}\)). The total number of burned trees was comparable in both domains, with 8120 vs. 8700 burned trees in UR and WLD, respectively (203 and 102 trees ha\({}^{-1}\), respectively). Results reflecting estimated uncertainty of stand density and consumed biomass are provided as maps of low and high estimates in Figures 10 and 11.  
The spatially distribution economic loss caused by the fire of 2016 in Haifa, and calculated with the _i-Tree_ model, is shown in Figure 9, while lower and higher estimates from the uncertainty analysis are shown in Figure 12.  
Figure 9 shows that the greater economic loss is in UR. This is in spite of the large quantity of burned trees in the eastern part of the WLD domain (Figure 8a), and an equal amount of burned trees in the area of the two domains. The damage in the UR was estimated at 840 \(\pm\) 210 K USD per hectare, with a total loss of 33,600 \(\pm\) 8400 K USD for the entire UR burnt area, while in WLD it was one order of magnitude smaller, estimated at 85.6 \(\pm\) 21.4 K USD per hectare, with a total damage of more than 7300 \(\pm\) 1800 K USD for the entire WLD burnt area. The total economic damage related to environmental damage, of the fire of 2016 in the WUI of Haifa was estimated at ca. 41 \(\pm\) 10 M USD.  
Figure 7: Burn severity maps derived from \(\Delta\)NDVI using (**a**) 3 m Planet and (**b**) 10 m Sentinel-2A images. The plot-based classification of the burn severity levels is also shown (circles).
## 4 Current Approaches to Measure Resilience in Smart ICI
### Quantitative (Holistic) Approach
#### 4.2.2 Multi-Phase Resilience Trapezoid (\(\phi\Lambda\)E\(\Pi\)) or (FLEP)  
An additional innovative resilience quantification framework to the GR metric is the Multi-Phase Resilience Trapezoid (MPRT) [43]. The FLEP approach focuses on developing a resilience trapezoid (\(\phi\Lambda\)E\(\Pi\)) that delineates various infrastructure timeline phases for smart ICIs. Figure 7 illustrates the multi-phase resilience trapezoid. This approach utilizes distinct indicators for each phase and aggregates them to represent the overall resilience for any infrastructure. By applying this approach, a precise estimation of resilience is provided, facilitating decision making for system functionality improvement.  
The primary assumption of this model is that all the different indicators for the _pre-disturbance resilient state_ (i.e., \(R_{0o}\), and \(R_{0i}\), respectively) are 100% before the event occurs at \(t_{o}\), e.g., 100% of the demand for each user, and the transmission lines between them are online sequentially. The main four key resilience metrics are presented in Table 2, specifically addressing how active (\(\phi\)) and how deep (\(\Lambda\)) the resilience declines in _Phase I_, the magnitude (\(E\)) of the post-event functionality loss situation in _Phase II_, and the speed (\(\Pi\)) at which the system recovers to its primary resilient state in _Phase III_. This collection, which includes four methods, is described as the resilience metric system (\(\phi\Lambda\)E\(\Pi\)) and is referred to as (FLEP).
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 2 Molecular Mechanisms Underlying the Development of \(\beta\)-Thalassemia  
Adult hemoglobin (Hb) consists of globin and heme, with the globin portion made up of a tetramer composed of two \(\alpha\)-globin chains and two \(\beta\)-globin chains (\(\alpha_{2}\beta_{2}\)) [6]. Normal hemoglobin consists of Hb Portland (\(\zeta_{2}\gamma_{2}\)), Hb Gower 1 (\(\zeta_{2}\epsilon_{2}\)), Hb Gower 2 (\(\alpha_{2}\epsilon_{2}\)), HbF (\(\alpha_{2}\gamma_{2}\)), HbA1 (\(\alpha_{2}\beta_{2}\)), and HbA2 (\(\alpha_{2}\delta_{2}\)). The human \(\beta\)-globin gene cluster is situated on the short arm of chromosome 11 at 11p15.4, and this \(\beta\)-globin gene cluster shows specific expression and repression during various stages of individual development. There are two transitions that occur during development: from embryo to fetus and then to adult, specifically from \(\varepsilon\) to \(\gamma\) and from \(\gamma\) to \(\beta\) (\(\delta\)) [7]. The transition from HBE to HBG1/HBG2 takes place during the process of primitive erythropoiesis that culminates in definitive erythropoiesis; the transition from HBG1/HBG2 to HBB occurs during definitive erythropoiesis in the perinatal period [7].  
\(\beta\)-thalssemia is primarily caused by genetic defects in the \(\beta\)-globin gene and its regulatory sequences, which leads to the absence or diminished synthesis of this protein in erythroid progenitor cells. Consequently, a reduction or absence of \(\beta\)-globin synthesis results in an imbalance in the \(\alpha/\beta\) chain synthesis ratio, primarily observed in the homozygous form. This imbalance leads to the accumulation of free \(\alpha\)-globin chains, which aggregate into highly toxic forms that deposit within red blood cells. As a result, this accumulation causes cellular destruction and impairs erythropoiesis [8; 9].
## 2 Numerical Analysis
### Modeling and Material Properties
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Variable** & **Acronym** & **Unit** & **Range** \\ \hline
1 & Relative density & EG & kPa & \(20\times 10^{-3}\), \(40\times 10^{-3}\), \(50\times 10^{-3}\) \\
2 & Horizontal offset pile–tunnel & \(\mathrm{O_{H}}\) & m & \(0.0\mathrm{T_{D}}\), \(2.0\mathrm{T_{D}}\), \(5.0\mathrm{T_{D}}\)\({}^{(1)}\) \\
3 & Vertical offset pile–tunnel & \(\mathrm{O_{V}}\) & m & \(0.0\mathrm{T_{D}}\), \(1.5\mathrm{T_{D}}\), \(5.0\mathrm{T_{D}}\)\({}^{(1)}\) \\
4 & Pile diameter & \(\mathrm{P_{D}}\) & m & \(0.5\), \(0.8\), \(1.0\) \\
5 & Pile length & \(\mathrm{P_{L}}\) & m & \(6.5\), \(10.0\), \(12.5\), \(20.0\), \(22.0\), \(25.0\), \(35.0\), \(44.0\) \\
6 & Raft width & \(\mathrm{B_{R}}\) & m & \(12.0\), \(20.0\), \(25.0\) \\
7 & Pile number & \(\mathrm{P_{N}}\) & ea & \(9\), \(25\) \\
8 & Pile spacing & \(\mathrm{P_{CTC}}\) & m & \(1.25\mathrm{P_{D}}\), \(2.5\mathrm{P_{D}}\), \(5.0\mathrm{P_{D}}\)\({}^{(2)}\) \\
9 & Offset raft–tunnel face & \(\mathrm{T_{F}}\) & m & \(10.0\mathrm{B_{R}}\), \(4.0\mathrm{B_{R}}\), \(1.0\mathrm{B_{R}}\), \(0.0\mathrm{B_{R}}\)\({}^{(3)}\) \\ \hline \end{tabular}  
* TD: Tunnel diameter; \({}^{2}\) PD: Pile diameter, \({}^{3}\) BR: raft width.  
\end{table} 
Table 1: Variables for numerical analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Acronym**} & \multirow{2}{*}{**Unit**} & \multicolumn{4}{c}{**Value**} \\ \cline{4-7}  & & & **Loose Sand** & **Medium Sand** & **Dense Sand** & **Pile and Raft** \\ \hline Unit weight & \(\gamma\) & \(\mathrm{kN/m^{3}}\) & \(15\) & \(16\) & \(17\) & \\ Young’s modulus & \(E\) & kPa & \(20\times 10^{-3}\) & \(40\times 10^{-3}\) & \(50\times 10^{-3}\) & \(3.92\times 10^{-7}\) \\ Poisson’s ratio & \(v\) & - & \(0.3\) & \(0.3\) & \(0.3\) & \(0.15\) \\ Cohesion & \(c\) & kPa & \(0\) & \(0\) & \(0\) & - \\ Shear resistance angle & \(\varphi\) & \({}^{\circ}\) (deg) & \(35\) & \(40\) & \(45\) & - \\ Dilatancy angle & \(\psi\) & \({}^{\circ}\) (deg) & \(5\) & \(10\) & \(15\) & - \\ \(\mathrm{R_{inter}}\) & - & - & \(0.8\) & \(0.6\) & \(0.5\) & - \\ \hline \end{tabular}
\end{table} 
Table 2: Constitutive model and material properties for numerical analysis.
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation
### Experimental Results  
#### 3.2.1 MEMS-Laser Vibrometry Cross-Validation  
To validate the MEMS sensing's efficacy in monitoring unstable rock mass vibrations under constant micro-motion conditions, a comparative analysis was conducted between high-precision LDV measurements and MEMS-acquired data. Both systems were aligned along identical measurement axes, with synchronized sampling at 1000 Hz over a 60 s acquisition period. Prior to analysis, sensitivity calibration and 5 th-order Butterworth low-pass filtering (cutoff: 500 Hz) were applied to mitigate device-specific discrepancies.  
As illustrated in Figure 7a, the MEMS sensor demonstrates amplitude fidelity to LDV measurements, exhibiting merely 3.4% standard deviation error with reduced amplitude variance. Figure 7b reveals that, while environmental vibrations introduce sub-100 Hz discrepancies in LDV data, both modalities show a strong spectral coherence in the range of 100-500 Hz. These results confirm MEMS technology's capability to reliably capture micro-motion vibration signatures critical for rock mass stability assessment.
# A New Bloom Filter Architecture
## 4 Bloom Filter Analysis  
Since Bloom filters can produce false positives, and even though the false positive rate can be controlled by increasing the size of the Bloom filter, Bloom filter-based algorithms should carefully handle false positives. Functional Bloom filters produce indeterminable results, and hence functional Bloom filter-based algorithms should handle the indeterminable cases as well.  
The _indeterminable_ and _false positive_ of the _l_-BF are resolved in our proposed algorithm by the _p_-BF. Hence, here we only consider the _indeterminable_ and _false positive_ of the _p_-BF. In our proposed algorithm, if the _p_-BF produces _indeterminable_, the search procedure should access the off-chip hash table to obtain the output face. If the _p_-BF produces false positives, the search procedure returns an incorrect output face. Therefore, we present a theoretical analysis of the probabilities of _indeterminable_ and _false face return_ for the _p_-BF.  
Assume that a _p_-BF has \(m\) cells, \(N\) elements, \(k\) hash functions, and \(L\) different faces. Assuming that the name prefixes are equally distributed to each face, the number of elements for each face set, \(n^{\prime}\) is equal to \(\frac{N}{L}\).  
The probability that a specific cell has conflict value **X** can be calculated as follows. In programming, let \(p_{a}\) be the probability that at least one of the hash indexes for \(n^{\prime}\) elements included in a specific face set points to a cell. Then, \(p_{a}\) can be calculated as  
\[p_{a}=\Big{(}1-(1-\frac{1}{m})^{kn^{\prime}}\Big{)}. \tag{3}\]  
Since the elements included in a face set have the same output face, even though multiple hash indexes indicate the same specific cell, the cell has the output face value and not the conflict value.  
Let \(p_{b}\) be the summation of the probabilities that at least one of the hash indexes for elements not included in the specific set indicates the same cell:  
\[p_{b}=\sum_{i=1}^{L-1}\Big{(}1-(1-\frac{1}{m})^{k(N-in^{\prime})}\Big{)}. \tag{4}\]In calculating \(p_{b}\), \(i\) is increased in order to avoid duplicate calculations. Then, the probability that a specific cell has conflict value **X** is \(p_{a}\) multiplied by \(p_{b}\).
## 3 Three-Dimensional Finite Element Model
### Residual Stress Calculation  
For uniform in-depth residual stress distributions, the principal residual stresses \(\sigma_{x}\) and \(\sigma_{y}\) can be calculated from the calibration coefficients a and b, the measured relaxed strains \(\epsilon_{1}\) and \(\epsilon_{3}\). [PERSON]'s ratio v, and the Young's modulus E of the material [11]. The following relationships apply:  
\[\sigma_{x}=-\frac{E(\epsilon_{1}+\epsilon_{3})}{2a(1+\
u)}-\frac{E(\epsilon_ {1}-\epsilon_{3})}{2b} \tag{3}\]  
\[\sigma_{y}=-\frac{E(\epsilon_{1}+\epsilon_{3})}{2a(1+\
u)}+\frac{E(\epsilon_ {1}-\epsilon_{3})}{2b} \tag{4}\]  
The calibration coefficients are dependent on the hole depth, type of strain gauge rosettes, and hole diameter of the drilled hole. A known uniform stress distribution is applied on the sides where the hole is not situated and the relaxed strains are evaluated to calculate these calibration coefficients [9; 10].  
Figure 8: Mesh sensitivity study.  
Figure 7: Finite element model with drilled hole.  
For non-uniform residual stress distributions, the relation between residual stresses and relaxed strains is more difficult compared to uniform distributions. The standard errors in the combination strain vectors should also be considered to determine the principal residual stresses. For non-uniform residual stress fields, the calculated relaxed strains of the 3D model will be compared with the theoretical determined relaxed strains according ASTM E837-13a of an orthotropic bridge deck. These strains are used to determine a residual stress distribution of the orthotropic bridge deck.
## 3 Model Establishment and Parameter Determination  
### Model Establishment  
To reasonably simulate the surface deformation characteristics during mining and their impact on surface stability, the study thoroughly analyzed geological data and the range of the surface displacement belt. The spatial distribution of each stratum and its positional relationship with the ore body were comprehensively considered. The model assumes that the rock and ore bodies are quasi-homogeneous blocks/sections, thus conveying that they occupy approximately the same area and can be defined with the same parameters. The surrounding rock is simplified into granite porphyry (hanging wall) and crystal tuff (footwall), while the ore body consists of silver-lead-zinc ore. Using Rhino software, a mine model was established, covering three mining areas with an east-west length of 2500 m, a north-south width of 3000 m, and a vertical height ranging from \(-\)610 m elevation to the surface. To simulate a more realistic mining scheme, the model includes the adjacent BJ and YH mining areas. To reduce the number of grids, the ore body grid size was set to 5 m, and the surrounding rock grid size was set to 25 m. Figure 4 shows the ore body model, with a total of 9,105,363 nodes and 55,090,665 elements.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 2 Experimental Methodologies
### Metrics  
We use three basic metrics for evaluating the performance of the low-power links in forests: RSSI, LQI and packet reception ratio (PRR). These three metrics are often used in empirical studies and networking protocol designs.  
* PRR: If node \(A\) sends \(n\) packets to node \(B\) and \(B\) correctly receives \(m(m\leq n)\) packets, then the PRR of the link from \(A\) to \(B\) is equal to \(\frac{m}{n}\). Calculated at the receiver side, PRR is often used as a benchmark metric for link reliability in wireless protocol design and operation, especially in routing protocols.
* RSSI: This is a reading calculated by a receiver's radio chip, which generally is the average of the signal strength of eight-symbol periods. For the TelosB node, which integrates the CC2420 radio chip, the returned RSSI value ranges from \(-100\) dBm to \(0\) dBm. The RSSI involves not only the received signal, but the background noise, and generally, the received signal is hard to discern from noise when the returned RSSI is lower than \(-90\) dBm.
* LQI: The receiver can measure the strength quality of a successfully received packet by calculating the average correlation of the first eight symbols of this packet. LQI is often used to approximate the chip error rate. The TelosB node produces an LQI value of at most 110 and at least 50.  
In this paper, we first conduct experiments under the two in-city forest environments with only a single low-power wireless link formed by two nodes; second, we deploy a small-scale wireless sensor network of ten nodes in a mountain forest environment and investigate the performance of the wireless in-network links.
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 3 Field Experiment  
The system was deployed on the CSIRO Dark-water Inland Observatory Network pontoon at Googong reservoir, New South Wales, Australia. Figure 6 shows a snapshot and basic measurements of the system. The vertical measurement will be projected to an absolute water height using the ultrasonic sensor measurement. The GNSS collection session was set from 16:00:12 p.m. to 21:59:57 p.m. Coordinated Universal Time (UTC) at 15 s intervals. One device (id: F043) is installed on a fence on the dam embankment as the primary base station, assumed stable, with a 0.5 km distance to the other device (id: F044) mounted on the pontoon, as seen in Figure 7. The coordinates of F043 are processed by AUSPOS [37]. Data used in this study were collected between 1 February 2024 00:00:00 a.m. and 30 May 2024 23:50:00 p.m., covering approximately four months.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Approximate Value (m)** \\ \hline \(\sigma_{apc}\) & GNSS kinematic & 0.015 \\  & positioning error & \\ \(\sigma_{uls}\) & Ultrasonic sensor & 0.020 \\  & measurement error & \\ \(\sigma_{tilt}\) & Tilting error & 0.005 \\ \(\sigma_{noise}\) & Other unknown error & 0.010 \\ \(\sigma_{wl_{engis}}\) & WSE estimation error & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 3: WSE estimation error budget.
## 4 Propagation Model Evaluation  
In this section, we will analyze the behavior of different propagation models when simulating an underwater wireless sensor network deployed in a specific network scenario location. We will study both performance results and sensibility under network scenario parameters. For that purpose we will describe the characteristics and parameters associated to the network scenario, the MAC protocol we will employ, and the traffic load characterization.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 3 The Proposed Attention-Based Full-Scale Fusion Network
### Roof Discrimination  
Considering the high cost of annotating semantic segmentation data and the fact that satellite images without buildings can be easily selected (weakly labeled images), we add a roof discrimination (RD) module to predict whether the input image has a roof or not. The roof discrimination module consists of a dropout layer, a convolutional layer with kernel of \(1\times 1\), a max-pooling layer, and a sigmoid layer. Through this extra classification task, weakly labeled images can be further utilized to enhance the network's representation ability. Note that the roof discrimination module is applied to the output of the last encoder.  
We manually select weakly labeled data. The weakly labeled data used in this study belong to the same batch of data captured by the same satellite as the labeled data, ensuring consistency in image quality and a lack of environmental differences. It should be noted that the cost of the selection is much lower than that of the annotation of semantic segmentation datasets.
## 3 Results
### Adaptability Assessment Tools and Models
#### 3.1.3 IconCUR, 2012  
IconCUR [43] is a conceptual framework to support decision-making regarding the adaptive reuse of built assets using a methodological approach combining Adaptive Management (AM) and Multiple Criteria Decision Analysis (MCDA). The model uses primary criteria of condition, utilization, and reward to create a 3D modeling of a space where the property status can be mapped using three coordinates (\(x\),\(y\),\(z\)) corresponding to the three initial criteria condition, utilization, and reward, respectively. The decision strength (\(z\)) is based on the type of relationship existing between the property's physical condition (\(x\)-axis) and the current level of utilization (\(y\)-axis). The following relationship patterns are expected:  
* Low condition and low utilization--reconstruct or dispose
* High condition and high utilization--retain or extend
* Low condition and high utilization--renovate or preserve
* High condition and low utilization--reuse or adapt  
Condition and utilization are measured on a scale of 0 (low) to 5 (high). Reward (z-axis) quantifies the intervention value based on its financial, social, and environmental impacts. It is also measured on a scale of 0 (low) to 5 (high). However, the difference between reward values before and after the intervention determines the value of the decision.  
The framework relies on a weighted matrix to objectify and justify the measurements. Each of the model's criteria (condition, utilization) and sub-criteria (collective utility, stakeholder interest) embraces a number of key attributes provided in columns and weighted according to their impact with a total equal to 100%. In turn, each of the key attributes consists of key elements provided in rows with a total also equal to 100%. Performance is measured at the intersection of each attribute and element using a scale of 0 (weak) to 5 (strong). The score for each criterion and sub-criterion can subsequently be calculated. To overcome the subjectivity of the assessments, a risk sensitivity analysis must be carried out to ensure the stability of the key variables. The criterion of reward is then calculated by multiplying the score of the two sub-criteria, collective utility and stakeholder interest, and divided by 5.